Abteilung Systemanalyse, Integrated Assessment und Modellierung

Renewable energy is the key to reducing greenhouse gas emissions, and is one of the most concerning issues worldwide. China has the largest hydropower potential in the world. Yet, how China’s hydropower potential will change under 1.5 °C and 2.0 °C global warming and beyond remains unknown. Here, we find that China’s hydropower will increase greatly because of global warming. Gross hydropower potential (GHP) will increase by about one-half compared to the baseline period (1986-2015) under 1.5 °C and 2.0 °C warming, and about two-thirds under 4.5 °C warming. The spatial and temporal changes in GHP will vary largely. GHP will increase relatively more in summer than in winter, and more in Southwest China than in other regions. Compared to GHP, increases in per-capita GHP will be relatively less under 1.5 °C (5%) and 2.0 °C (7%) warming, but of a similar magnitude under 4.5 °C warming (71%). This study provides important information on China’s hydropower potential changes under global warming.

Study region: Middle and lower Hanjiang River (MLHR) basin, China.
Study focus: Changes in streamflow are often due to intertwined factors like climatic variability, land use change, and hydraulic constructions, whose relative impact is however poorly understood. In this study, we disentangled these effects by comparing real data and modelled scenarios of catchment behaviour. Modelled scenarios employ the soil and water assessment tool (SWAT), whereas the scenario comparison uses the Indicators of Hydrologic Alteration (IHA).
New hydrological insights for the region: Our analyses show that (1) watershed inlet was the major factor altered the streamflow regime at the watershed outlet, which pointed us to consider only the relative catchment contribution in further analyses. (2) Climate variability was the main driver of the net changes in natural hydrological regime, which downplayed the effect of land use change on streamflow variability. (3) The streamflow regulation associated to the progressive increase in reservoirs and their operation significantly altered the flow pattern, causing a general decrease in average streamflow, an attenuation of extreme events indicators, and an alteration of the pulse pattern with more frequent but shorter pulses. (4) The average water withdrawals at MLHR were estimated, which were 8.03 × 10⁹ m³/year. Overall, this research provides a path to assess the hydrologic impact of cascading reservoirs at the basin level under climate variability and land use change elsewhere.

Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning models are currently used to automate classification of organisms into taxonomic units. However, imprecision in these classifiers introduces a measurement noise that is difficult to control and can significantly hinder the analysis and interpretation of data. We overcome this limitation through ensembles of Data-efficient image Transformers (DeiTs), which not only are easy to train and implement, but also significantly outperform the previous state of the art (SOTA). We validate our results on ten ecological imaging datasets of diverse origin, ranging from plankton to birds. On all the datasets, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 29.35% to 100.00%, and often achieving performances very close to perfect classification. Ensembles of DeiTs perform better not because of superior single-model performances but rather due to smaller overlaps in the predictions by independent models and lower top-1 probabilities. This increases the benefit of ensembling, especially when using geometric averages to combine individual learners. While we only test our approach on biodiversity image datasets, our approach is generic and can be applied to any kind of images.

Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They are usually estimated by calibrating the model to observations. However, at regional scales in which different cultivars of a crop species may be grown, not accounting for inherent differences in phenological development between cultivars in the model and the presence of model deficits lead to inaccurate parameter estimates. To account for inherent differences between cultivars and to identify model deficits, we used a Bayesian multi-level approach to calibrate a phenology model (SPASS) to observations of silage maize grown across Germany between 2009 and 2017. We evaluated four multi-level models of increasing complexity, where we accounted for different combinations of ecological, weather, and year effects, as well as the hierarchical classification of cultivars nested within ripening groups of the maize species. We compared the calibration quality from this approach to the commonly used pooled approach in which none of these factors are considered. The pooled model led to over-confident process model parameter estimates and comparatively poor calibration quality. The mean value of the unexplained residual error standard deviation reduced from 5.5 BBCH (phenological development units) in the pooled model case (BM-0) to 5.3 BBCH when eco-region and year effects (BMM-1) were considered. Additionally accounting for weather effects (BMM-2a) resulted in a mean value of 5.2 BBCH. Calibration quality especially improved when the hierarchical classification of cultivars within ripening groups of maize was incorporated. Including the hierarchical classification with eco-region and year effects (BMM-2b) led to a mean residual error of 4.4 BBCH while additionally considering weather effects in the full model case (BMM-3) resulted in a value of 4.3 BBCH. Our findings have implications for regional model calibration and data-gathering studies, since it emphasizes that ripening group and cultivar information is essential. Furthermore, we found that if this information is not available, at least weather, eco-region and year effects should be taken into account. Accounting for only the eco-region and year effects led to parameter-compensation of the missing weather effects. Our results can facilitate model improvement studies since we identified possible model limitations related to temperature effects in the reproductive (post-flowering) phase and to soil-moisture. We demonstrate that Bayesian multi-level calibration of a phenology model facilitates the incorporation of hierarchical dependencies and the identification of model limitations. Our approach can be extended to full crop models at different spatial scales.

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics package, MITgcm, into a single framework, SPUX-MITgcm. To mitigate uncertainty in the atmospheric forcing, we use a smoothed particle Markov chain Monte Carlo method, where the intermediate model state posteriors are resampled in accordance with their respective observational likelihoods. To improve the uncertainty quantification in the particle filter, we develop a bi-directional long short-term memory (BiLSTM) neural network to estimate lake skin temperature from a history of hydrodynamic bulk temperature predictions and atmospheric data. This study analyzes the benefit and costs of such a state-of-the-art computationally expensive calibration and assimilation method for lakes.

Magnetic Resonance Spectroscopy (MRS) and Spectroscopic Imaging (MRSI) are non-invasive techniques to map tissue contents of many metabolites in situ in humans. Quantification is traditionally done via model fitting (MF), and Cramer Rao Lower Bounds (CRLBs) are used as a measure of fitting uncertainties. Signal-to-noise is limited due to clinical time constraints and MF can be very time-consuming in MRSI with thousands of spectra. Deep Learning (DL) has introduced the possibility to speed up quantitation while reportedly preserving accuracy and precision. However, questions arise about how to access quantification uncertainties in the case of DL. In this work, an optimal-performance DL architecture that uses spectrograms as input and maps absolute concentrations of metabolites referenced to water content as output was taken to investigate this in detail. Distributions of predictions and Monte-Carlo dropout were used to investigate data and model-related uncertainties, exploiting ground truth knowledge in a synthetic setup mimicking realistic brain spectra with metabolic composition that uniformly varies from healthy to pathological cases. Bias and CRLBs from MF are then compared to DL-related uncertainties. It is confirmed that DL is a dataset-biased technique where accuracy and precision of predictions scale with metabolite SNR but hint towards bias and increased uncertainty at the edges of the explored parameter space (i.e., for very high and very low concentrations), even at infinite SNR (noiseless training and testing). Moreover, training with uniform datasets or if augmented with critical cases showed to be insufficient to prevent biases. This is dangerous in a clinical context that requires the algorithm to be unbiased also for concentrations far from the norm, which may well be the focus of the investigation since these correspond to pathology, the target of the diagnostic investigation.

Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating such deep learning models internally are being made, but the traceability of model states and processes and their interrelations to model input and output is not yet fully understood. Direct interpretability of mechanistic processes has always been considered an asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic neural ordinary differential equation (ODE) models that perform as well as state-of-the-art deep learning methods in stream flow prediction while maintaining the ease of interpretability of conceptual hydrologic models. In neural ODEs, internal processes that are represented in differential equations, are substituted by neural networks. Therefore, neural ODE models enable the fusion of deep learning with mechanistic modelling. We demonstrate the basin-specific predictive performance for 569 catchments of the continental United States. For exemplary basins, we analyse the dynamics of states and processes learned by the model-internal neural networks. Finally, we discuss the potential of neural ODE models in hydrology.

Success stories are rare in conservation science, hindered also by the research-implementation gap, where scientific insights rarely inform practice and practical implementation is rarely evaluated scientifically. Amphibian population declines, driven by multiple stressors, are emblematic of the freshwater biodiversity crisis. Habitat creation is a straightforward conservation action that has been shown to locally benefit amphibians, as well as other taxa, but does it benefit entire amphibian communities at large spatial scales? Here, we evaluate a landscape-scale pond-construction program by fitting dynamic occupancy models to 20 y of monitoring data for 12 pond-breeding amphibian species in the Swiss state Aargau, a densely populated area of the Swiss lowlands with intensive land use. After decades of population declines, the number of occupied ponds increased statewide for 10 out of 12 species, while one species remained stable and one species further declined between 1999 and 2019. Despite regional differences, in 77% of all 43 regional metapopulations, the colonization and subsequent occupation of new ponds stabilized (14%) or increased (63%) metapopulation size. Likely mechanisms include increased habitat availability, restoration of habitat dynamics, and increased connectivity between ponds. Colonization probabilities reflected species-specific preferences for characteristics of ponds and their surroundings, which provides evidence-based information for future pond construction targeting specific species. The relatively simple but landscape-scale and persistent conservation action of constructing hundreds of new ponds halted declines and stabilized or increased the state-wide population size of all but one species, despite ongoing pressures from other stressors in a human-dominated landscape.

Daten zur zeitlichen Entwicklung der Vielfalt wirbelloser Kleinlebewesen in Fliessgewässern verschiedener Kantone werden den nationalen Datensätzen BDM und NAWA gegenübergestellt. Bis auf wenige Ausnahmen zeigt sich eine auf kantonaler und nationaler Ebene einheitliche Entwicklung: Vor allem die Anzahl pestizidtoleranter Familien und wärmeliebender Arten pro Probenahmestandort nimmt in den letzten Jahrzehnten zu. Mögliche Ursachen dieses Trends sind der Klimawandel sowie eine verbesserte Gewässerqualität.

Effluents of wastewater treatment plants can impact microbial communities in the receiving streams. However, little is known about the role of microorganisms in wastewater as opposed to other wastewater constituents, such as nutrients and micropollutants. We aimed therefore at determining the impact of wastewater microorganisms on the microbial diversity and function of periphyton, key microbial communities in streams. We used a flow-through channel system to grow periphyton upon exposure to a mixture of stream water and unfiltered or ultra-filtered wastewater. Impacts were assessed on periphyton biomass, activities and tolerance to micropollutants, as well as on microbial diversity. Our results showed that wastewater microorganisms colonized periphyton and modified its community composition, resulting for instance in an increased abundance of Chloroflexi and a decreased abundance of diatoms and green algae. This led to shifts towards heterotrophy, as suggested by the changes in nutrient stoichiometry and the increased mineralization potential of carbon substrates. An increased tolerance towards micropollutants was only found for periphyton exposed to unfiltered wastewater but not to ultra-filtered wastewater, suggesting that wastewater microorganisms were responsible for this increased tolerance. Overall, our results highlight the need to consider the role of wastewater microorganisms when studying potential impacts of wastewater on the receiving water body.

Aim: Biological invasions are a major threat to biodiversity in aquatic and terrestrial habitats. Insects represent an important group of species in freshwater and terrestrial habitats, and they constitute a large proportion of non-native species. However, while many non-native insects are known from terrestrial ecosystems, they appear to be less represented in freshwater habitats. Comparisons between freshwater and terrestrial habitats of invader richness relative to native species richness are scarce, which hinders syntheses of invasion processes. Here, we used data from three regions on different continents to determine whether non-native insects are indeed under-represented in freshwater compared with terrestrial assemblages.
Location: Europe, North America, New Zealand.
Methods: We compiled a comprehensive inventory of native and non-native insect species established in freshwater and terrestrial habitats of the three study regions. We then contrasted the richness of non-native and native species among freshwater and terrestrial insects for all insect orders in each region. Using binomial regression, we analysed the proportions of non-native species in freshwater and terrestrial habitats. Marine insect species were excluded from our analysis, and insects in low-salinity brackish water were considered as freshwater insects.
Results: In most insect orders living in freshwater, non-native species were under-represented, while they were over-represented in a number of terrestrial orders. This pattern occurred in purely aquatic orders and in orders with both freshwater and terrestrial species. Overall, the proportion of non-native species was significantly lower in freshwater than in terrestrial species.
Main conclusions: Despite the numerical and ecological importance of insects among all non-native species, non-native insect species are surprisingly rare in freshwater habitats. This is consistent across the three investigated regions. We review hypotheses concerning species traits and invasion pathways that are most likely to explain these patterns. Our findings contribute to a growing appreciation of drivers and impacts of biological invasions.

Increased attention directed at frozen soil hydrology has been prompted by climate change. In spite of an increasing number of field measurements and modeling studies, the impact of frozen soil on hydrological processes at the catchment scale is still unclear. However, frozen soil hydrology models have mostly been developed based on a bottom-up approach, i.e., by aggregating prior knowledge at the pixel scale, which is an approach notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore the impact of frozen soil at the catchment scale, following a top-down approach, implying the following sequence: expert-driven data analysis → qualitative perceptual model → quantitative conceptual model → testing of model realism. The complex mountainous Hulu catchment, northeast of the Qinghai-Tibet Plateau (QTP), was selected as the study site. First, we diagnosed the impact of frozen soil on catchment hydrology, based on multi-source field observations, model discrepancy, and our expert knowledge. The following two new typical hydrograph properties were identified: The low runoff in the early thawing season (LRET) and the discontinuous baseflow recession (DBR). Second, we developed a perceptual frozen soil hydrological model to explain the LRET and DBR properties. Third, based on the perceptual model and a landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the FLEX-Topo-FS model can represent the effect of soil freeze-Thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, including the LRET and DBR events. Furthermore, its realism was confirmed by alternative multi-source and multi-scale observations, particularly the freezing and thawing front in the soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best of our knowledge, this study is the first report of LRET and DBR processes in a mountainous frozen soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen soil hydrological model which represents these complex processes and has the potential for wider use in the vast QTP and other cold mountainous regions.

For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use the inner dimension of deep neural network based Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models.

In simplified models of glasses we clarify the existence of two different kinds of coexisting activated dynamics, with one of the two dominating over the other. One is the energy barrier hopping that is typically used to understand activation, and the other, which we call entropic activation, is driven by the scarcity of convenient directions in phase space. When entropic activation dominates, the height of the energy barriers is no longer the primary factor governing the system's slowdown. In our analysis, dominance of one mechanism over the other depends on temperature and the shape of the density of states. We also find that at low temperatures a phase transition between the two kinds of activation can occur. Our observations are used to provide a scenario that can harmonize the facilitation and thermodynamic pictures of the slowdown of glasses into a single description.

The interaction of multiple hazards across various spatial and temporal scales typically causes compound climate and extreme weather events. Compound concurrent hot day and night (CCHDNs) extremes that combine daytime and nighttime heat are of greater concern for health than individual hot days (HDs) or hot nights (HNs), even though their frequency is lower. We utilize a bottom-up exploratory approach to investigate how adaptation options and various unfolding future scenarios alleviate the impacts of the heatwaves and affect the frequency and intensity of CCHDNs. We use climate observations (1981-2020) and Switzerland's future climate model scenarios (CH2018) to analyze historical and future trends of the individual hot day followed by a hot night (HDNs, first metric), and the length and frequency of CCHDNs (second and third metrics) in the near-future (2020-2050) and far-future (2070-2100). Results show more frequent and lengthier HDNs in cities under all emission scenarios, notably significant under high emissions scenarios. The highest increase of HDNs occur in i) Lugano with 65.8 days (decade^-1) in the historical period and 110 (371) days (decade^-1) in near-future (far-future), ii) Geneva with historical 48 days (decade^-1) to 108 (362) (decade^-1), iii) Basel with 48-74 (217) days in the future, followed by iv) Bern with 15-44 (213) days and v) Zürich with 14-50 (217) days (decade^-1) in the near-future and far-future, respectively. We consistently project that the CCHDNs in April-October become more likely and intense in all cities under all emission scenarios, with higher increases under the RCP8.5 scenario and after the 2050s. The frequency of compound extreme heatwaves (exceeding both historical thresholds of night and day temperatures) may increase by 3.5-7.8-fold and become 3.3-5.3-fold lengthier in all cities of Switzerland in the far-future. We find that the adaptation options targeting higher tolerance to increased minimum temperatures contribute more to reducing compound extreme events' frequency and intensity than adaptation options that address the maximum daily temperature.

Lakes are often described as sentinels of global change. Phenomena like lake eutrophication, algal blooms, or reorganization in community composition belong to the most studied ecosystem regime shifts. However, although regime shifts have been well documented in several lakes, a global assessment of the prevalence of regime shifts is still missing, and, more in general, of the factors altering stability in lake status, is missing. Here, we provide a first global assessment of regime shifts and stability in the productivity of 1,015 lakes worldwide using trophic state index (TSI) time series derived from satellite imagery. We find that 12.8% of the lakes studied show regime shifts whose signatures are compatible with tipping points, while the number of detected regime shifts from low to high TSI has increased over time. Although our results suggest an overall stable picture for global lake dynamics, the limited instability signatures do not mean that lakes are insensitive to global change. Modeling the interaction between lake climatic, geophysical, and socioeconomic features and their stability properties, we find that the probability of a lake experiencing a tipping point increases with human population density in its catchment, while it decreases as the gross domestic product of that population increases. Our results show how quantifying lake productivity dynamics at a global scale highlights socioeconomic inequalities in conserving natural environments.

Identification of critical source areas (CSAs) for non-point source (NPS) pollution is of great significance for environment governance and prevention. However, the CSAs are generally characterized as great spatial dispersion, and spatially heterogeneous precipitation has a great influence on the spatial distribution of nutrient yields. Therefore, we identify the CSAs for nutrient yields in an agricultural watershed of Northeast China at hydrological response units (HRUs) scale based on the Soil and Water Assessment Tool (SWAT), assess the impacts of spatially heterogeneity of precipitation on the identification of the CSAs, analyze the sensitivity of nutrient yields to precipitation by scenarios analysis method, and further identify priority management areas (PMAs) that have poor ability to retain nutrients. The results showed that the CSAs for nutrient yields identified by uniform precipitation showed greater fluctuation range and coverage area than actual precipitation; the major prevention areas of total nitrogen (TN) yield were mainly distributed in regions nearby main stem of lower reaches, while that of total phosphorus (TP) yield were mostly located in urban area nearby outlet of the watershed; the identification of the PMAs significantly decreased the CSAs for TN yield, whereas that for TP yield was no significant difference with the CSAs. This study could provide scientific guidance for the NPS pollution governance and prevention.

There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.

Identifying sublethal pesticide effects on aquatic organisms is a challenge for environmental risk assessment. Long-term population experiments can help assessing chronic toxicity. However, population experiments are subject to stochasticity (demographic, environmental, and genetic). Therefore, identifying sublethal chronic effects from “noisy” data can be difficult. Model-based analysis can support this process.
We use stochastic, age-structured population models applied to data from long-term population experiments with Daphnia galeata in 1L aquaria with and without chronic pesticide treatments (diazinon and diuron) at sublethal concentrations. Posterior analysis following Bayesian inference of model parameters and states helped choosing an adequate description of life-history characteristics under the specific experimental conditions (a zero-inflated negative binomial distribution for reproduction and mortality without density dependence). For the insecticide treatments, the inferred marginal posterior parameter distributions indicated the need for a mortality rate that increases with time, indicating cumulative chronic toxic effects of diazinon on Daphnia populations. With this study, we demonstrate how stochastic models can be used to infer mechanisms from population data to help identifying sublethal pesticide effects.

BACKGROUND: The effective reproductive number, R_e, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, R_e estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.
OBJECTIVES: We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate R_e in near real time, independent of clinical data and without the associated biases.
METHODS: We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based R_e from this incidence.
RESULTS: The method to estimate R_e from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the R_e estimates from case report data as R_e estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer R_e.
DISCUSSION: To our knowledge, this is the first time R_e has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens.

The traditional description of a hydrological system with a deterministic, conceptual model and a lumped output error term does not explicitly consider the main mechanisms of uncertainty generation due to approximate process representation, unobserved variability in processes and influence factors, and input uncertainty. In this study, we test the description of such intrinsic uncertainty in conceptual models by making process rates stochastic through stochastic, time-dependent rate parameters. We analyze the advantages and challenges of this approach by using Bayesian inference to jointly estimate model parameters, parameters of the stochastic processes, and the time series of the stochastic parameters. Numerically, we use a particle filter to infer the stochastic time series and to approximate the marginal likelihood for Markov chain sampling of the constant parameters. Compared to the lumped error formulation, we achieve a more realistic description of uncertainty, as we obtain larger errors in intrinsic states, larger uncertainty during prediction than calibration periods (a feature missing for simple lumped error models), and autocorrelated model outputs. However, the additional degrees of freedom introduced with stochastic parameters can lead to an unintentional compensation for model deficits or input errors. This problem is symptomatic of model structure inadequacy or poor selection of the parameters that are made stochastic, and can be diagnosed through cross-validation and careful posterior analysis. The proposed approach is computationally more demanding and its implementation more challenging than the traditional description of hydrological systems using a deterministic model with a lumped error term. However, its advantages both in providing a more realistic representation of uncertainties and in diagnosing model deficiencies suggest its adoption and further development in future studies.

In der Schweiz gibt es verschiedene Fisch-Monitoring-Programme. Nun wurden sie erstmals gemeinsam ausgewertet, mit dem Ziel, statistische Zusammenhänge zwischen vom Menschen beeinflussten Umweltfaktoren (Temperatur, landwirtschaftliche Landnutzung, Hydromorphologie, Wanderhindernisse) und dem Vorkommen von Fischen bzw. deren Dichte zu identifizieren. Die Analyse weist darauf hin, dass alle häufigeren Fischgattungen durch diese Umweltfaktoren in ihrem Vorkommen beeinflusst werden, und zwar auf unterschiedliche Weise.

 

Mechanistic and process-oriented crop models are important tools to quantify the potential impacts of climate change on crop production and yields and to evaluate the efficacy of management strategies, policies, or actions developed by the stakeholders. This review focusses on the epistemic uncertainty associated with the use of crop models. It firstly identifies the main sources of uncertainties from the perspectives of crop model inputs, methods for estimating crop parameters, crop model structure/complexity/process scale, and the underpinning experimental datasets. Pathways for managing those uncertainties are identified and future research directions are discussed. The conclusion is that strengthening experimental studies on the effects of extreme temperatures including their interaction with enhanced atmospheric CO₂ concentration on crop production and further improvement, evaluation, and inter-comparison of crop models based on new experimental datasets will contribute to the reduction of uncertainties in projected climate change impacts and evaluated adaptation options. It is envisaged that crop models will continue to serve as an important research tool in addressing climate change in the agricultural sector specifically and in general with respect to global food security. Therefore, this review will provide the agroclimate impact modelling community with information on the sources of uncertainties and the ways forward to tackle this critical issue.

Climate change was projected to have negative effects on water availability and consequently a serious constraint to food production in many areas of the world. However, such effects have not been well understood, particularly over rainfed croplands, partly because of the poor representation of green water in associated assessments. In this study, we develop an integrated agricultural water scarcity index (WSI_ag) that incorporates blue and green water components to examine the agricultural water scarcity in the baseline (1981-2005) and future (2026-2050) periods under the Representative Concentration Pathway (RCP)2.6 and RCP6.0 scenarios. Results show that ∼3.8 million km² (∼39% of total) croplands experienced water scarcity in the baseline period and it would expand by more than 3% in the future. Under the two scenarios, WSI_ag projections are similar overall and are higher than that under the baseline in 83%-84% of global total croplands. Differences are found between the scenarios in Amazonia, Southern Africa, and South Asia. The increases in future WSI_ag are dominated by the decreased water availability in ∼60% of total croplands and ∼24% of which is dominated by the increased crop water requirement. Changes in green water availability have a significant contribution to changes in WSI_ag in 16% of global croplands, mostly in arid/semiarid regions (e.g., the South edge of the Sahel, Southern Africa, Northeast China, and Central America). This implies the important role of green water management for agriculture in these regions. The integrated assessment can help develop effective strategies for agricultural water management under climate change.

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall-runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (I_A) and baseflow index (I_B). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (I_A < 0.9) with low baseflow (I_B < 0.4) were the easiest to model, with a single-reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall-runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (I_B < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (I_A < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.

Hydrological modeling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrological sciences. A major challenge is to identify a model structure that reflects the hydrological processes relevant to the catchment of interest. This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest. We extend a method previously introduced for mechanism identification in gauged basins, by formulating the inference equations in the space of (regionalized) flow indices and by accounting for posterior parameter uncertainty. A flexible hydrological model is used to generate candidate mechanisms and model structures, followed by statistical hypothesis testing to identify "dominant" (more a posterior probable) model mechanisms. The proposed method is illustrated using real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments are treated as ungauged. 624 hydrological model structures from the flexible framework FUSE are employed. In real data experiments, the method identifies a dominant mechanism in 27% of 112 trials (processes and catchments). The most identifiable process is routing, whereas the least identifiable processes are percolation and unsaturated zone processes. In synthetic experiments, where "true" mechanisms are known, the reliability of method varies from 60% to 95% depending on the combined regionalization and hydrological error; the probability of making an identification remains stable at around 25%. More broadly, the study contributes perspectives on hydrological mechanism identification under data-scarce conditions; limitations and opportunities for improvement are outlined.

On the basis of a tensor representation of protein shape, obtained by an affine decomposition of residue velocity, we show how to identify actions at continuum scale for both single proteins and their complexes in terms of power equivalence. The approach constructs and justifies a continuum modeling of protein complexes, which avoids a direct, atomistic-based, simulation of the whole complex, rather it focuses (in a statistical sense) on a single protein and its interactions with the neighbors. In the resulting setting we also prove the existence of equilibrium configurations (native states) under large strains.

Understanding concurrent drought events in global main croplands is crucially important for food security, effective adaptation to climate change and human well-being. Yet, there is a lack of comprehensive studies on concurrent drought probability changes among main crop production countries on a global scale, especially for the future. Here, we studied concurrent drought among 26 main crop production countries for the time period 1861-2099. During the historical period, probability of concurrent moderate and severe drought among the countries was relatively low, and the maximum concurrent moderate and severe drought probability will double and triple under RCP8.5 extreme climate, respectively. Concurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under RCP8.5, respectively, compared with zero in the historical period. Limiting RCPs to RCP2.6 can decrease the concurrent probability of severe drought at least 2% (at least 3% for RCP8.5).

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the classification performance. We quantified the gain obtained throught the introduction of taxonomic and experimental information, compared to classification based on chemical information alone. We used our approach with standard machine learning models (K-nearest neighbors, random forests and deep neural networks), as well as the recently proposed Read-Across Structure Activity Relationship (RASAR) models, which were very successful in predicting chemical hazards to mammals based on chemical similarity. We were able to obtain accuracies of over 93% on datasets where, due to noise in the data, the maximum achievable accuracy was expected to be below 96%. The best performances were obtained by random forests and RASAR models. We analyzed metrics to compare our results with animal test reproducibility, and despite most of our models "outperform animal test reproducibility" as measured through recently proposed metrics, we showed that the comparison between machine learning performance and animal test reproducibility should be addressed with particular care. While we focused on fish mortality, our approach, provided that the right data is available, is valid for any combination of chemicals, effects and taxa.

Flooding in the Yangtze River Basin could severely damage socio-economic development, river ecosystems, food security, hydropower production and transportation in China. The Yangtze River Basin accounts for approximately 30% of China's gross domestic product (GDP) and is an engine for the country's rapid economic growth. One commonly held belief is that climate change has intensified extreme flood events, leading to increasing economic damage in the Yangtze River. Here, we quantitatively attributed economic exposure to climate change (i.e. climate-induced changes in weather-related events) and GDP growth, and assessed benefits, i.e. the reduction in economic exposure, from flood defence dikes of varying heights. To do this, we developed a framework by combing a large scale hydrological model, a hydraulic model, and long-term GDP data. We find that climate-induced changes in flood inundation area and resulted economic exposure were decreasing overall, whereas GDP growth drove the increases of potential economic exposure to floods. We also reveal that the basin average flood defence dikes should be at least approximately 3.5 m high to achieve an about ten-year average flood occurrence. Our results have significant policy and socioeconomic development implications.

Recently, a plethora of studies reporting insect declines has been published. Even though the common theme is decreasing insect richness, positive trends have also been documented. Here, we analysed nationwide, systematic monitoring data on aquatic insect richness collected at 438 sites in Switzerland from 2010 to 2019. In addition to taxonomic richness, we grouped taxa in accordance with their ecological preferences and functional traits to gain a better understanding of trends and possible underlying mechanisms. We found that in general, richness of aquatic insects remained stable or increased with time. Warm-adapted taxa, common feeding guilds and pesticide-tolerant taxa showed increasing patterns while cold-adapted, rarer feeding guilds and pesticide-sensitive taxa displayed stable trends. Both climate and land-use-related factors were the most important explanatory variables for the patterns of aquatic insect richness. Although our data cover the last decade only, our results suggest that recent developments in insect richness are context-dependent and affect functional groups differently. However, longer investigations and a good understanding of the baseline are important to reveal if the increase in temperature- and pesticide-tolerant species will lead to a decrease in specialized species and a homogenization of biotic communities in the long term.

Over the last 40 years, a growing number of restoration projects have been implemented to improve the ecological conditions of highly degraded rivers and their floodplains. Despite considerable investment in these projects, information is still limited about the effectiveness and the success of such river restoration measures, mainly due to a lack of standardised and interdisciplinary assessment approaches. During the project 'Wilde Mulde—Restoration of a dynamic riverine landscape in Central Germany', we implemented hydromorphological restoration measures (installation of large wood, removal of rip-rap, reconnection of a former river side-arm) along a lowland river in Central Germany. We carried out intensive scientific monitoring of biodiversity, hydromorphology, ecosystem functions and services, as well as socio-economic aspects. A Before/After-Control/Impact (BACI) design was used to identify the spatial and temporal effects of the restoration measures and to distinguish them from changes caused by background variation. For this, we used a comprehensive set of indicators, including abiotic (flow velocity, diversity of riverbed topography, and flow resistance), biological (ecosystem respiration, macroinvertebrates, fish, carabids, vegetation, and birds) and socio-economic (acceptance and public awareness) indicators as well as the ecosystem service indicator aesthetic quality of the landscape. To meet the inherent challenges of such a large-scale field experiment, like unpredictable environmental conditions, we used an experimental approach that allowed us to demonstrate a measurable success of the implemented restoration measures. The majority of the abiotic and some of the biological and socio-economic indicators at the restored sites approached values of a natural reference site while already deviating from values of a nonnatural reference site two years after restoration. In addition to the applied interdisciplinary approach, multiple scales of field investigations and data analyses are essential as key components for evaluating successful river and floodplain restoration projects.

The sustainability of agriculture in the Mediterranean climate is challenged by high irrigation water demands and nitrogen fertilizer losses to the environment, causing significant pressure on groundwater resources and groundwater-dependent ecosystems. Advanced irrigation technologies and improved fertilizer management have been promoted as key solutions to reduce the agricultural impact on aquatic systems. However, it remains unclear how different irrigation-fertilizer practices perform on the long-term under a highly variable climate, such as the Mediterranean one. Here, we conduct hydrological simulations over a fifty-year period to quantify the magnitude and dynamics of groundwater recharge and nitrogen leaching under five real-case irrigation-fertilizer practices observed in Valencia (eastern Spain). The Valencian Region is the largest citrus-producing region of Europe and current irrigation-fertilizer practices reflect the ongoing transformation of irrigation systems from flood to drip irrigation. Our simulations highlight three major implications of the irrigation transformation for groundwater resources. First, the transformation from flood to drip irrigation reduces the recharge fraction (19% vs. 16%) and especially the nitrogen leaching fraction (33% vs. 18%) on the long term. Second, the long-term performance of the two irrigation practices is subject to substantial inter-annual differences controlled by precipitation variability. The sensitivity of recharge and nitrogen leaching to annual meteorological conditions is stronger in drip irrigation, which eventually leads to a similar performance of flood and drip irrigation in wet years if fertilizer inputs are similar. Third, we identify a pronounced year-to-year nitrogen memory in the soil, whereby an enhanced (decreased) nitrogen leaching is observed after anomalously dry (wet) years, affecting the performance of irrigation-fertilizer practices. Overall, the study demonstrates the highly variable nature of the performance of irrigation-fertilizer practices, and the major findings can guide future efforts in designing sustainable water management strategies for agricultural areas with a Mediterranean climate.

GLDAS2.0 provides long-term fine resolution gridded hydrometeorological data sets, which are necessary for water-related studies, particularly in some transboundary rivers that are partially without observation. Yet, GLDAS2.0 has only been validated at limited locations, and few studies have been conducted to develop approaches to correct the GLDAS2.0 data for transboundary rivers. This work assessed the GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau and Northeast China (NC). To achieve these goals, observational data from 1982 to 2010 and a water and energy budget-based distributed hydrological model including biosphere after calibration and validation were employed. We find that the GLDAS2.0 data (except for wind speed) can reasonably replicate observed seasonal variations. However, its specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summer. In NC, the trends of its precipitation, air temperature, downward longwave radiation, and wind speed are consistent with the observations. In the Yarlung Tsangpo, Lancang, and Nu Rivers, the trends of all GLDAS2.0 data reproduce the observation very well, that is, wetting, warming, and dimming trends. Validations show that the corrections are effective and the corrected forcing data can be successfully used in hydrological simulation with improved performance than the raw GLDAS2.0 data, which demonstrates the usefulness of the methodology and corrected forcing data to hydrometeorological studies in transboundary rivers in China as well as in other nearby regions/countries.

Over the past decades, water scarcity has become prevalent in many regions of the world. Previous water scarcity assessments largely considered “blue water” (surface and groundwater) and overlooked “green water” (soil moisture), despite its key role in supporting crop production. Moreover, the long-term evolution of water scarcity remains unclear. Here, we propose an agricultural water scarcity index (WSI_AW) that explicitly combines green and blue water. The WSI_AW measures the degree to which both green and blue water availabilities can satisfy the agricultural water requirement. The WSI_AW can directly indicate water scarcity in both rainfed and irrigated areas, which cannot be captured by the blue water scarcity index alone. Using the WSI_AW, we assessed the evolution of water scarcity across regions of the world during 1971–2010 at a spatial resolution of 0.5 arc degree. We found that 22% of the 228 river basins with a WSI_AW of >1 presented a much broader geographical coverage compared with estimates of water scarcity based on blue water only (8% of total basins). The WSI_AW also exhibited a clear trend of increasing severity over the study period, particularly in major agricultural regions. Expanding cropland and increasing/competing water withdrawals by other sectors were the main reasons for the intensification of water scarcity, which was pronounced in some Asian and African river basins. The proposed WSI_AW can be applied to different regions with various scales and is important for addressing global water scarcity issues.

Regional scale distributed conceptual models are typically developed with a bottom-up approach, which is process-inclusive but prone to over-parameterization. Here we demonstrate a proof of concept top-down approach for distributed conceptual model development, intended to emphasize dominant streamflow generating processes and to fulfill the principle of model parsimony. A key challenge in applying the top-down approach to distributed model development is devising a model comparison experiment that is both informative and limited to a few model alternatives. Here, we show how such model comparisons can be informed by a perceptual model of key processes that control streamflow response variability at the regional scale. We demonstrate our approach for the 27,100 km² Moselle catchment, using the perceptual model developed in Part 1 of this two-part paper. We develop 5 distributed model structures for simulating daily streamflow at 26 subcatchments, and validate them on subcatchments that are not used during the calibration process. Our model comparisons illustrate how the spatial distribution of precipitation, lithology and topography affect the simulation of key signatures of streamflow response variability in the Moselle catchment, providing a basis to justify model decisions. Our analyses show how a minimally parameterized distributed model, with 12 calibration parameters, matches signatures of streamflow average (r = 0.96), baseflow index (r = 0.86), and hydrograph lag time (correct at 22 out of 26 subcatchments). Our proposed top-down approach contributes to improving distributed model development strategies, and can be used to develop parsimonious process based regional models elsewhere.

Excessive groundwater pumping exacerbates aquifer depletion and poses a major threat in regions all around the world that already suffer from overuse or climate change. In this situation, accurate and reliable predictions of long-term aquifer water balances are key prerequisites to manage groundwater sustainably. Compared to spatially explicit numerical models, lumped hydrological models are computationally fast, lean on data requirement and more accessable for quantifying uncertainty. However, lumped hydrological models are mainly designed to simulate river discharge only, not aquifer storage. Consequently, calibration only includes stream flow data. In this study, we hypothesize that we can extend a lumped hydrological models (here HBV) towards a lumped geohydrological model (LGhM) by additional, designated terms for water budget and groundwater storage. The model building is inspired by the geometry and hydrogeological large-scale properties of the catchment's aquifers. Underground flow routing resembles major groundwater flow paths. The model is calibrated and evaluated on both groundwater storage data and surface discharge data. We apply our LGhM to a MODFLOW-based virtual reality describing the unconfined Wairau Plain Aquifer, New Zealand. We consider and discuss specifically river-groundwater exchange processes, long-term forecast of aquifer storage dynamics, and groundwater depletion in a hypothetical, persistent drought. Our model evaluation shows very plausible predictive capabilities in 40-year forecasts with synthetic weather time series and several years of groundwater depletion in the extreme drought case.

As an important agriculture production area in the world and a flood prone area, future hydro-climate changes in winter and spring in Northeast China could have remarkable influence on spring water resources and flood. Yet, studies on future hydro-climate variations with special considerations of snow influences remain limited so far in the region. Here, we studied future winter and spring hydro-climate changes to the end of the century with special considerations of snow variations under the RCP2.6, RCP6.0 and RCP8.5 scenarios in Northeast China. A water and energy budget-based distributed biosphere hydrological model with improved snow physics was implemented. We find that winter and spring are warming 0.08 °C and 0.06 °C annually under 30 years moving average in RCP8.5. Air temperature increasing rate in winter is approximately two times higher than that in spring in 2030–2059. In this period, snow melt contribution to spring average runoff and maximum runoff decrease by at least 39% and 23%, respectively, and spring soil moisture decreases by 7%. The spring snow melt runoff peak will move from April to March under the warmest climate condition (i.e., 2070–2099 in RCP8.5). The earlier snow melt renders the snow melt contribution to total runoff decrease to almost zero in May, which could increase drought severity. This study sheds some lights on changes in hydrological regimes under climate change with a focus on snow melt and the influences in the entire Northeast China for the first time, and is helpful for adaption to future climate change.

Adaptive radiations are known for rapid niche diversification in response to ecological opportunity. While most resources usually exist prior to adaptive radiation, novel niches associated with novel resources can be created as a clade diversifies. For example, in African lake cichlid radiations some species prey upon other species of the clade (intraclade consumers). Using a trait-based eco-evolutionary model, we investigate the evolution of intraclade consumers in adaptive radiations and the effect of this novel trophic interaction on the diversification process of the radiating clade. We find that the evolutionary emergence of intraclade consumers halts the diversification processes of other ecomorphs as a result of increased top-down control of density. Because high productivity enables earlier evolution of intraclade consumers, highly productive environments come to harbour less species-rich radiations than comparable radiations in less productive environments. Our results reveal how macroevolutionary and community patterns can emerge from ecological and microevolutionary processes.

Perceptual catchment models are a key first step towards hydrologic synthesis and provide the process foundation of predictive models. However, their development is usually limited to the headwater catchment scale where field work can be done. Here we develop a perceptual model approach for a regional scale catchment. We use a systematic "top-down" approach based on an expert-driven interpretation of the streamflow responses of nested subcatchments. This approach complements the traditional "bottom-up" perceptual model development based on fieldwork observations, which is impractical at the regional scale due to measurement limitations. As an example to illustrate our approach and demonstrate its feasibility, we use the 27,100 km² Moselle catchment, with 26 gauged subcatchments. Our perceptual model of the Moselle catchment presents a description of dominant processes that we consider a-posteriori reasonable, but not a-priori obvious: where precipitation, much more than evaporation or groundwater exchange controls the spatial variability of average streamflow, lithology influences the partitioning between baseflow and quickflow, and topography and land use control hydrograph lag times. A striking feature of our analysis is the rather minor role that vegetation and soil appear to play in streamflow spatial variability at the scale of our inquiry. We use our perceptual model to build a distributed yet parsimonious hydrological model in Part 2 of this paper series. Although the perceptual model is specific for the Moselle, we hope that the perceptual model approach used here can be helpful to others working in large catchments in different hydroclimates, where other factors will dominate.

Our study uses regional-scale maps to quantify carbon storage and sequestration from different land use types to evaluate the effects of future land use scenarios. We developed an integrated modeling approach to assess the spatiotemporal impacts of land use/cover change (LUCC) on the provision and value of the carbon storage and sequestration during the historical period (2000-2019) and predicted scenarios (2019-2046) in the Jiroft plain, Iran. We integrated several analytic tools for our analysis, which was comprised of Google Earth Engine (GEE), Cellular Automata Markov Chain (CA-MC) model, Intensity Analysis (IAA), and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Our results demonstrate that: (1) agriculture and urban expansion led to a considerable decrease in carbon storage, mainly due to rapid deforestation from 2000-2019; (2) if the historical trend continues under the business as usual (BAU) scenario, it will lead to considerable social costs due to the loss of stored carbon in the plain (2,624,113 Mg) with an annual average sequestration loss of -475,547 Mg; (3) the downward carbon sequestration trend could potentially be reversed by employing the environmentally sound planning (ESP) scenario that is estimated to save 3,705,491 Mg in carbon storage, with annual average sequestration gain of + 605,830 Mg. The design scenarios provide a useful guide for policymakers and local governments to help understand the potential outcomes of the various development strategies, which will ultimately lead to more effective ecosystem management.

Using the parameters associated with the best-fit simulation (i.e., the simulation with the highest objective function value) to represent a calibrated hydrological model is inadequate. The reason is that the calibrated model's best objective function value is usually not significantly different from the next best value or the values after that. This non-uniqueness of the objective function values causes a problem because the best solution's parameters are often significantly different from the next best set of parameters. Therefore, only using the best simulation parameters as the calibrated model's sole parameters to interpret the watershed processes or perform further modeling analyses could produce misleading results. Furthermore, the lack of pristine watersheds makes the task of watershed-scale calibration increasingly challenging. Subjective thresholds of acceptable performance criteria suggested by some researchers, based on comparing the measured and the best solution signals, are often not achievable. Hence, to obtain a satisfactory fit, researchers and practitioners are often forced to compromise the science behind their work. This article discusses the fallacy in using the best-fit solution in hydrologic modeling. A two-factor statistic to assess the goodness of calibration/validation is discussed, considering model output uncertainty.

The spatial and temporal distribution pattern is an outstanding feature of the relationship among ecosystem services (ESs) that explains links between human activities and disturbed chemical composition of ecosystems. This study investigated the spatiotemporal variation of land use/cover changes (LUCC) and quantifies the change in four essential ecosystem services with an emphasis on soil (nutrient delivery ratio, carbon storage, crop production, and water yield) and their relationships in the Jiroft plain, Iran, during 1996-2016 through analytical tools including Land Change Modeler, and the Integrated Valuation of Ecosystem Services and Tradeoff. During the 20-year concentrate period, there was a considerable overall gain in cropland (5396 km²) and urban (1787 km²), loss of unused land (5692 km²), water (2088 km²), and forest (1083 km²). As a result of LUCC, while crop production and nutrient delivery ratio showed a rising trend, overall carbon storage and water yield decreased. The spatiotemporal trade-off between carbon storage and crop production, the temporal trade-off between crop production and water yield, and synergy between water yield and crop production were widespread in Jiroft plain. These results showed that the interaction among ESs mutates over time and can be changed under planning and policies. This study will enrich the research of the geographical distribution of ESs interaction in dryland ecosystems to provide practical ecosystem management under local conditions.

The amplitude of the 11 yr solar cycle is well known to be subject to long-term modulation, including sustained periods of very low activity known as Grand Minima. Stable long-period cycles found in proxies of solar activity have given new momentum to the debate about a possible influence of the tiny planetary tidal forcing. Here, we study the solar cycle by means of a simple zero-dimensional dynamo model, which includes a delay caused by meridional circulation as well as a quenching of the α-effect at toroidal magnetic fields exceeding an upper threshold. Fitting this model to the sunspot record, we find a set of parameters close to the bifurcation point at which two stable oscillatory modes emerge. One mode is a limit cycle resembling normal solar activity including a characteristic kink in the decaying limb of the cycle. The other mode is a weak sub-threshold cycle that could be interpreted as Grand Minimum activity. Adding noise to the model, we show that it exhibits Stochastic Resonance, which means that a weak external modulation can toss the dynamo back and forth between these two modes, whereby the periodicities of the modulation get strongly amplified.

Small streams in catchments with agricultural land use are at high risk of diffuse pollution by herbicides. Fast transport processes can cause concentration peaks that exceed regulatory requirements. These processes have a high spatio-temporal variability and data characterizing their occurrence is often sparse. For this reason, such systems show a stochastic behavior at the resolution we observe them (same input and initial conditions lead to different output). Realistic model representations should acknowledge this pronounced apparent intrinsic stochasticity. However, a deterministic description of the physical and chemical processes at the catchment scale is state of the art in research and practice. We explore the potential of stochastic process formulations in combination with the Bayesian learning paradigm to (a) improve the quantification of the uncertainty of conceptual catchment-scale pesticide transport models and (b) gain new mechanistic insights about the system by interpreting the temporal evolution of the stochastic processes. This is done with the help of a framework for time-varying stochastic parameters. Thereby, we find that (a) the stochastic process formulation can lead to a more realistic characterization of the uncertainty of internal states and model output compared to the deterministic one, and that (b) the temporal dynamics of parameters resulting from the inference can highlight model deficits (and inspire improvements) such as a better sustained baseflow in dry periods. We also identify two key challenges: numerical difficulties in sampling the posterior and the question of where to introduce and how to constrain the additional degrees of freedom such that they are not misused.

Climate change threatens winter tourism in the Alps severely, and ski resorts are struggling to cope under uncertain climate change. We aim to identify under what conditions physical and economic tipping points for ski resorts may occur under changing climate in six Swiss ski resorts representing low, medium, and high elevation in the Alps. We use exploratory modeling (EMA) to assess climate change impacts on ski resorts under a range of futures adaptation options: (1) snowmaking and (2) diversifying the ski resorts' activities throughout the year. High-resolution climate projections (CH2018) were used to represent climate uncertainty. To improve the coverage of the uncertainty space and account for the climate models' intra-annual variability, we produced new climate realizations using resampling techniques. We demonstrate the importance of five factors, namely climate scenarios (RCPs), intra-annual climate variability, snow processes model, and two adaptation options, in ski resorts survival under a wide range of future scenarios. In six ski resorts, strong but highly variable decreases in the future number of days with good snow conditions for skiing (GSD) are projected. However, despite the different characteristics of the resorts, responses are similar and a shrunk of up to 31, 50, and 62 days in skiing season (Dec-April) is projected for the near-future (2020–2050), mid-future (2050–2080), and far-future (2070–2100), respectively. Similarly, in all cases, the number of days with good conditions for snowmaking (GDSM) will reduce up to 30, 50, and 74 days in the skiing season in the near-, mid-, and far-future horizons, respectively. We indicate that all ski resorts will face a reduction of up to 13%, 33%, and 51% of their reference period (1981–2010) revenue from winter skiing activities in the near-, mid-, and far-future horizons. Based on the outcomes of the EMA, we identify Dynamic Adaptive Policy Pathways (DAPP) and determine the adaptation options that ski resorts could implement to avoid tipping points in the future. We highlight the advantages of adaptive planning in a first of its kind application of DMDU techniques to winter tourism. We specify the possible adaptation options ranging from "low revenue diversification and moderate snowmaking" to "high revenue diversification and large snowmaking" and demonstrate when an adaptation action fails and a change to a new plan is needed. By the end of the century, we show that only ski resorts with ski lines above 1800–2000 m elevation will survive regardless of the climate scenarios. Our approach to decision-making is highly flexible and can easily be extended to other ski resorts and account for additional adaptation options.

We propose and compare through simulations different ways of linking a continuum descriptiond of protein dynamics and translocation through tissue pores with the low-scale protein discrete structure and its inner interactions. At first we consider a model emerging from a version of the Cauchy-Born rule, written in terms of velocities. Then, we explore its variants, which account for the rate at which amino acids move relatively to the protein mass center. In representing the molecular morphology, such variants allow us to overcome the limits associated with the assumption of homogeneous deformation sustaining the Cauchy-Born rule, which appears to be appropriate essentially in confined environment, where fluctuations are bounded. The suggested variants in the discrete-to-continuum identification furnish a significant overall picture of protein mechanics. Our analysis paves the way to a continuum description of flowing molecular clusters.

In this work, we revisit the description of dynamics based on the concepts of metabasins and activation in mildly supercooled liquids via the analysis of the dynamics of a paradigmatic glass former between its onset temperature T_o and mode-coupling temperature T_c. First, we provide measures that demonstrate that the onset of glassiness is indeed connected to the landscape, and that metabasin waiting time distributions are so broad that the system can remain stuck in a metabasin for times that exceed τ_α by orders of magnitude. We then reanalyze the transitions between metabasins, providing several indications that the standard picture of activated dynamics in terms of traps does not hold in this regime. Instead, we propose that here activation is principally driven by entropic instead of energetic barriers. In particular, we illustrate that activation is not controlled by the hopping of high energetic barriers and should more properly be interpreted as the entropic selection of nearly barrierless but rare pathways connecting metabasins on the landscape.

Experiments featuring non-equilibrium glassy dynamics under temperature changes still await interpretation. There is a widespread feeling that temperature chaos (an extreme sensitivity of the glass to temperature changes) should play a major role but, up to now, this phenomenon has been investigated solely under equilibrium conditions. In fact, the very existence of a chaotic effect in the non-equilibrium dynamics is yet to be established. In this article, we tackle this problem through a large simulation of the 3D Edwards-Anderson model, carried out on the Janus II supercomputer. We find a dynamic effect that closely parallels equilibrium temperature chaos. This dynamic temperature-chaos effect is spatially heterogeneous to a large degree and turns out to be controlled by the spin-glass coherence length ξ. Indeed, an emerging length-scale ξ^* rules the crossover from weak (at ξ ≪ ξ^*) to strong chaos (ξ ≫ ξ^*). Extrapolations of ξ^* to relevant experimental conditions are provided.

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

Statistical species distribution models (SDMs) are widely used to quantify how taxa respond to environmental conditions and to predict their distribution. However, the application of SDMs to freshwater fish taxa is complicated by the active dispersal of fish taxa through river networks, and the species- and habitat-dependent observation process (i.e., the sampling method and effort) required to accurately sample their distributions. Many studies have applied presence-absence models (PAMs) to fish taxa, while more recent studies have proposed zero-inflated models (ZIMs) to account for count observations with many zeroes. However, relatively few studies have incorporated the observation process into the model structure, which would facilitate the combination of data from various monitoring programs that differ in their observation process. In this study, we use conceptual models to identify potentially dominant natural and anthropogenic environmental conditions with a direct, mechanistic effect on the distributions of freshwater fish taxa in Switzerland, a region with a large range of environmental conditions, from alpine streams that are mainly affected by hydromorphological alterations to lowland streams in densely populated areas with intensive agricultural land use. Moreover, numerous barriers impede fish migration along the entire river network. Using combined data from two fish monitoring programs in Switzerland, we applied an exhaustive cross-validation procedure to select a set of environmental variables with the highest (out-of-sample) predictive performance for the PAM and ZIM for fish density (individuals/m²) of the seven most prevalent fish taxa (Salmo spp., Cottus spp., Squalius spp., Barbatula spp., Barbus spp., Phoxinus spp., Gobio spp.). We used these variables to develop a PAM and ZIM for each taxon that accounts for differences in sampling methods and sampling effort. We quantified the quality of fit during calibration using all samples and predictive performance during 5-fold cross-validation of each model.
Results show that stream temperature and stream morphology within the accessible habitat commonly appear among the best predictive presence-absence models for multiple taxa. Spatial variables that account for migration barriers and quantify morphological conditions within the accessible habitat were selected for 6 out of 7 taxa. The selected PAMs performed well for all taxa with an intermediate prevalence (10–40%), with an explanatory power () of between 0.32 - 0.37 during calibration using all samples and only minor decreases in explanatory power during cross-validation (= 0.34 – 0.44). As expected, the PAM for the highly prevalent Salmo spp. (91%) failed to predict the few absence data points. By contrast, the ZIM model performed best for Salmo spp., with a standardized likelihood ratio of 1.56. For all other taxa besides Barbus spp. the ZIM models also had likelihood ratios above one, indicating a better predictive performance than the null model. We hope this study stimulates the development and application of fish species distribution models based on prior knowledge of causally linked environmental variables and incorporating observation errors to improve their predictive performance. This can facilitate learning from biomonitoring data to support management.

Wastewater treatment plants (WWTPs) play an important role in retaining organic matter and nutrients but to a lesser extent micropollutants. Therefore, treated wastewater is recognized as a major source of multiple stressors, including complex mixtures of micropollutants. These can potentially affect microbial communities in the receiving water bodies and the ecological functions they provide. In this study, we evaluated in flow-through channels the consequences of an exposure to a mixture of stream water and different percentages of urban WWTP effluent, ranging from 0% to 80%, on the microbial diversity and function of periphyton communities. Assuming that micropollutants exert a selective pressure for tolerant microorganisms within communities, we further examined the periphyton sensitivity to a micropollutant mixture extracted from passive samplers that were immersed in the wastewater effluent. As well, micropollutants in water and in periphyton were comprehensively quantified. Our results show that micropollutants detected in periphyton differed from those found in water, both in term of concentration and composition. Especially photosystem II inhibitors accumulated in periphyton more than other pesticides. Although effects of other substances cannot be excluded, this accumulation may have contributed to the observed higher tolerance of phototrophic communities to micropollutants upon exposure to 30% and 80% of wastewater. On the contrary, no difference in tolerance was observed for heterotrophic communities. Exposure to the gradient of wastewater led to structural differences in both prokaryotic and eukaryotic communities. For instance, the relative abundance of cyanobacteria was higher with increasing percentage of wastewater effluent, whereas the opposite was observed for diatoms. Such results could indicate that differences in community structure do not necessarily lead to higher tolerance. This highlights the need to consider other wastewater constituents such as nutrients and wastewater-derived microorganisms that can modulate community structure and tolerance. By using engineered flow-through channels that mimic to some extent the required field conditions for the development of tolerance in periphyton, our study constitutes a base to investigate the mechanisms underlying the increased tolerance, such as the potential role of microorganisms originating from wastewater effluents, and different treatment options to reduce the micropollutant load in effluents.

There is growing concern about the dire socioeco-logical consequences of abrupt transitions between alternative ecosystem states in response to environmental changes. At the same time, environmental change can trigger evolutionary responses that could stabilize or destabilize ecosystem dynamics. However, we know little about how coupled ecological and evolutionary processes affect the risk of transition between alternative ecosystem states. Using shallow lakes as a model ecosystem, we investigate how trait evolution of a key species affects ecosystem resilience under environmental stress. We find that adaptive evolution of macrophytes can increase ecosystem resilience by shifting the critical threshold, which marks the transition from a clear-water state to a turbid-water state to a higher level of environmental stress. However, following the transition, adaptation to the turbid-water state can delay the ecosystem recovery back to the clear-water state. This implies that restoration could be more effective when implemented early enough after a transition occurs and before organisms adapt to the alternative state. Our findings provide new insights into how to prevent and mitigate the occurrence of regime shifts in ecosystems and highlight the need to understand ecosystem responses to environmental change in the context of coupled ecological and evolutionary processes.

1. Migratory fish populations, like salmon, have dramatically declined for decades. Because of their extensive and energetically costly breeding migration, anadromous fish are sensitive to a variety of environmental stressors, in particular infrastructure building in freshwater streams that increases the energetic requirements of the breeding migration and food declines in the ocean.
2. While the effects of these stressors separately are well documented, the cumulative and interactive impacts of them are poorly understood. 3.
3. Here, we use a bioenergetics model recently developed for fish life history to investigate the individual life history and population responses to these stressors combined.
4. We find that food decline in the ocean can mitigate rather than exacerbate the negative effect of elevated migration costs imposed by infrastructure building in streams. This counterintuitive effect results from the highly nonlinear manner in which these stressors interact and affect the individual energetics. In particular, this effect arises from the fact that individuals growing in the ocean under higher food conditions reach larger sizes with concomitant larger migration costs but are leaner. As a consequence of their lower energy densities, they spend most of their energy reserves to transport their body upstream when migration costs are high, and little is left for reproduction, resulting in lower individual fitness.
5. Our results highlight the need of a mechanistic understanding integrating individual energetics, life history and population dynamics to accurately assess biological consequences of environmental change. A free Plain Language Summary can be found within the Supporting Information of this article.

Bedrock rivers represent a hydrogeological environment in which surface water flows along an exposed bedrock surface. Studies of hyporheic exchange have exclusively involved rivers composed of unconsolidated fluvial sediments, leaving a critical knowledge gap. This study evaluates the conditions that could support bedform-scale hyporheic exchange within a fractured sedimentary bedrock river based on field data collected near Guelph, Canada. Hyporheic exchange at the bedform-scale was evaluated by numerically modeling the migration of a conservative solute tracer through a bedrock riverbed within a two-dimensional vertical cross-section along the flow direction. A stochastic discrete fracture-matrix framework was developed to represent measured subsurface fractured bedrock properties, producing a probabilistic distribution of potential hyporheic exchange pathways. Flow and transport model results indicate that: (a) bedform-scale hyporheic exchange within a bedrock river exists with high fluid flow velocities in fractures, yet long solute residence times due to diffusion across the fracture-matrix boundary; (b) the coincidence of fractures and hydrodynamic head gradients across the riverbed controls the spatial extent of bedform-scale hyporheic exchange; and (c) the potential variability in hyporheic exchange residence time is large (i.e., tens of years) due to the inherent variability in facture network properties. Our field-based numerical study indicates that the average (median) residence time may not be a good proxy for the potential natural attenuation capacity of a fractured sedimentary bedrock riverbed and that hyporheic exchange has the potential to emplace surface water contaminants within the fractured porous rock matrix that could become a long-term source of trace contaminants.

Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes and to support operational water resource management. Conceptual models, which approximate catchment dynamics using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands, and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g., a reservoir) to spatially distributed configurations (e.g., nested sub-catchments), with the ability to customize all individual model components. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, discusses the software design and implementation choices, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. Comprehensive documentation is available online and provided as a Supplement to this paper. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations for model comparison and for operational work.

Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2–4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate infection incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.

The exposure of ecologically critical invertebrate species to biologically active pharmaceuticals poses a serious risk to the aquatic ecosystem. Yet, the fate and toxic effects of pharmaceuticals on these nontarget aquatic invertebrates and the underlying mechanisms are poorly studied. Herein, we investigated the toxicokinetic (TK) processes (i.e., uptake, biotransformation, and elimination) of the pharmaceutical diclofenac and its biotransformation in the freshwater invertebrate Hyalella azteca. We further employed mass spectrometry-based metabolomics to assess the toxic effects of diclofenac on the metabolic functions of H. azteca exposed to environmentally relevant concentrations (10 and 100 μg/L). The TK results showed a quick uptake of diclofenac by H. azteca (maximum internal concentration of 1.9 μmol/kg) and rapid formation of the conjugate diclofenac taurine (maximum internal concentration of 80.6 μmol/kg), indicating over 40 times higher accumulation of diclofenac taurine than that of diclofenac in H. azteca. Depuration kinetics demonstrated that the elimination of diclofenac taurine was 64 times slower than diclofenac in H. azteca. Metabolomics results suggested that diclofenac inhibited prostaglandin synthesis and affected the carnitine shuttle pathway at environmentally relevant concentrations. These findings shed light on the significance of the TK process of diclofenac, especially the formation of diclofenac taurine, as well as the sublethal effects of diclofenac on the bulk metabolome of H. azteca. Combining the TK processes and metabolomics provides complementary insights and thus a better mechanistic understanding of the effects of diclofenac in aquatic invertebrates.

Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of "fidelius" process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of "minimally restrictive process parameterization equations—MR-PPEs," which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over "parameter spaces" to a search over "function spaces."

Water and energy circulate between provinces and sectors through products and services. The multi-scale input–output method can quantify the resources embodied in direct consumption, domestic trade and foreign trade and the resources in intermediate input and final consumption. In this study, this method is used to calculate the embodied water intensity and embodied energy intensity at the provincial level in China. The impacts of interprovincial water and energy transfers on provincial water stress were analyzed with reference to their water stress index. The results show that direct consumption is the main component of the embodied water intensity, with an average proportion of 52.21%. However, differences among provinces are significant, ranging from 14.82 to 80.11%. The embodied energy intensity is mainly reflected in domestic imports, with a national average of 61.01%. Domestic exports (3.69 × 10¹¹ m³), urban household consumption (2.01 × 10¹¹ m³), and gross fixed capital formation (1.49 × 10¹¹ m³) share the largest proportion of final consumption, and they are also the main components in the final consumption of energy. Provincial water and energy transfers do not play major roles in relieving water stress in the net inflow areas but increase water stress in the net outflow areas. Provinces with high embodied water intensity and energy-induced water intensity tend to transfer more water to other provinces, while provinces with low intensity receive more supplies from other areas. Therefore, it is important to focus on improving the water use efficiency in provinces with active trade and high water and energy intensities, pay more attention to the demand side and avoid the continuous expansion of indirect consumption due to excessive restrictions on direct consumption.

In this study, contributions of climate change and human activity to streamflow changes were estimated to enable decision makers to develop adaptation strategies for management of regional water resources. Flow trends and climate variables were analysed with the Mann-Kendall method. The year 1986 was selected to perform the Pettitt test to identify the change point of the runoff time series. Then, three methods were used for impact differentiation: climatic elasticity, least-squares support-vector machine (LS-SVM), and the soil and water assessment tool (SWAT). The results showed that climate change (38–67%) and human activities (33–62%) influence runoff reduction. Thus three management scenarios are introduced to reduce the effects of climate change and human activity: (1) adjusting wheat and barley cultivation levels; (2) maintaining wheat and barley cultivation levels and replacing other crops with potatoes; (3) increasing irrigation efficiency. All scenarios showed an increase in runoff, but the first scenario had the most impact.

Models that mimic an original model might have a different model structure than the original model, that affects model output. This study assesses model structure differences and their impact on output by comparing 7 model implementations that carry the name HBV. We explain and quantify output differences with individual model structure components at both the numerical (e.g., explicit/implicit scheme) and mathematical level (e.g., lineair/power outflow). It was found that none of the numerical and mathematical formulations of the mimicking models were (originally) the same as the benchmark, HBV-light. This led to small but distinct output differences in simulated streamflow for different numerical implementations (KGE difference up to 0.15), and major output differences due to mathematical differences (KGE median loss of 0.27). These differences decreased after calibrating the individual models to the simulated streamflow of the benchmark model. We argue that the lack of systematic model naming has led to a diverging concept of the HBV-model, diminishing the concept of model mimicry. Development of a systematic model naming framework, open accessible model code and more elaborate model descriptions are suggested to enhance model mimicry and model development.

Conservation of natural resources is vital for sustainable management, especially in fragile semi-arid ecosystems. Forest plantations can provide a wide range of ecosystem services and deliver a measure of protection for soil and water resources. This study proposes a novel framework (Optimum Land Suitability Score, OLSS) to prioritize the most suitable areas with high priority for restoring degraded lands and protecting erosion-prone areas. We applied OLSS to the Latian watershed located in Tehran, Iran. The Latian watershed was divided into 56 sub-basins, where we studied the importance of each sub-basin for afforestation. We used a multicriteria analysis using the Fuzzy Analytical Network Process to bridge the gap between previous studies for determining suitable areas for afforestation in which 21 factors of environmental variables, morphometric characteristics, and topographical indices were considered. Finally, sub-basins were divided into four classes based on the fuzzy theory. The evaluated result indicated that 9 sub-basins showed the highest priority for afforestation. The identified sub-basins were mostly located in areas of depleted plant coverage due to overgrazing and human interventions. We proposed afforestation with proper species adapted to the environmental characteristics of prioritized sub-basins as ecological management. The measure should decrease erosion and flood risk and sustain the Latian reservoir storage capacity. OLSS offers valuable information for watershed managers and decision-makers to invest in soil conservations.

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

River networks are one of the main routes by which the public could be exposed to environmental sources of antibiotic resistance, that may be introduced e.g. via treated wastewater. In this study, we applied a comprehensive integrated analysis encompassing mass-flow concepts, chemistry, bacterial plate counts, resistance gene quantification and shotgun metagenomics to track the fate of the resistome (collective antibiotic resistance genes (ARGs) in a microbial community) of treated wastewater in two Swiss rivers at the kilometer scale. The levels of certain ARGs and the class 1 integron integrase gene (intI1) commonly associated with anthropogenic sources of ARGs decreased quickly over short distances (2-2.5 km) downstream of wastewater discharge points. Mass-flow analysis based on conservative tracers suggested this decrease was attributable mainly to dilution but ARG loadings frequently also decreased (e.g., 55.0-98.5 % for ermB and tetW) over the longest studied distances (6.8 and 13.7 km downstream). Metagenomic analysis confirmed that ARG of wastewater-origin did not persist in rivers after 5 ∼ 6.8 km downstream distance. sul1 and intI1 levels and loadings were more variable and even increased sharply at 5 ∼ 6.8 km downstream distance on one occasion. While input from agriculture and in-situ positive selection pressure for organisms carrying ARGs cannot be excluded, in-system growth of biomass is a more probable explanation. The potential for direct human exposure to the resistome of wastewater-origin thus appeared to typically abate rapidly in the studied rivers. However, the riverine aquatic resistome was also dynamic, as evidenced by the increase of certain gene markers downstream, without obvious sources of anthropogenic contamination. This study provides new insight into drivers of riverine resistomes and pinpoints key monitoring targets indicative of where human sources and exposures are likely to be most acute.

Landscape heterogeneity is a comprehensive reflection of spatial patch and gradient, which is an important feature of landscape pattern. The China land use/cover dataset (CLUD) with 1 km spatial resolution was used in this study to investigate the dynamic changes of land use/cover in the Yarlung Tsangpo River basin (YTRB) of China from 1980 to 2015 through the land use transition matrix and dynamic degrees. The semi-variogram model of the study area was constructed at different scales to determine the landscape characteristic scale of the YTRB. The traditional landscape index method and moving window method were used to investigate the watershed landscape pattern at the class and landscape levels, respectively. The results showed that the overall change in land use over the YTRB was not significant during the period 1980–2015, whereas the relative change in each type of land use was significant. Permanent glacier-snow significantly degraded by 22.72%, while built-up areas increased by 106.38%. The degree of the landscape fragmentation and diversity gradually increased from upstream to downstream. The unused land has the highest degree of fragmentation, and the agglomeration degree of urban land patches increased. The result of land use dynamic degree indicated that natural factors were the main causes of changes in land use during the first sub-period 1980–2000, while human activities were the major drivers of changes in land use during the second sub-period 2000–2015. The results can provide important information on the impact of regional development and environmental governance policies on the changes in land use/cover and landscape in Tibet.

Protected areas (PAs) are essential for biodiversity conservation and for the delivery of ecosystem services (ESs). However, little is known about their effectiveness in providing ESs and contribution to species richness, especially in arid regions. Effectiveness evaluation is fundamental to understanding the extent of management enhancement required to fulfill conservation targets. In this study, we analyzed the supply of six ESs (water yield, nutrient retention, soil retention, sand fixation, carbon storage, and biodiversity richness) by landscapes in China's arid region of Xinjiang Uygur Autonomous Region (hereafter Xinjiang). The aim was to identify distribution of ESs hotspots and the extent of hotspots located within or outside national PAs. The results showed significant spatial heterogeneity and coverage differences in six types of ESs hotspots. Hotspots coverage of six ESs on average accounted for 10.45 % of the total area, distributed mainly in mountains and oases covered by vegetation and wetlands. Among these ESs hotspots, over 50 % fell within PAs. This suggested that although PAs delivered moderately well outcomes in preserving ESs and biodiversity in Xinjiang, conservation gaps needed to be addressed. Our study also revealed substantial differences in ESs supplied by different PAs, and serious deficiency existed in some PAs in protecting either biodiversity or key ESs outlined in their conservation objectives. Our study illustrated the priority areas for future conservation expansion and stressed the urgent shift toward broadening the goals of PAs from a dominant focus to ones that encompass multiple ESs for human well-being.

The inclusion of environmental flow requirements (EFRs) in global water scarcity assessments is essential to obtain a reasonable representation of the water scarcity status. However, at a global scale, the quantification of EFRs is subject to large uncertainties resulting from various methods. So far, it is unclear to what extent the uncertainties in EFRs affect global water scarcity assessments. In this study, we examined the differences between EFR estimation methods and quantified their effects on spatially explicit water scarcity assessments, based on reconstructed global water withdrawal data and naturalized streamflow simulations. The global mean EFRs estimated by different methods ranged from 129 m³ s^-1 to 572 m³ s^-1. Consequently, with the fulfillment of the EFRs, the area under water scarcity ranged between 8% and 52% of the total global land area, and the affected population ranged between 28% and 60% of the total population. In India and Northern China, 44%-66% and 22%-58% of the country's land area, respectively, is affected by water scarcity; this percentage is higher than that found in other countries. The effects of different EFRs on water scarcity assessment are large in many regions, but relatively small in regions that experience intensive water use due to anthropological activities (such as Northern China and India). Through this study, we have put forth the need for the reconciliation of the estimates of EFRs to produce more reasonable and consistent water scarcity assessments.

Approximately 70% of the available water in the entire Tibetan Plateau is stored in glaciers. Understanding the impact of climate change on the glacier mass balance is crucial given that the Plateau is the "water tower" of East and Southeast Asia. However, the historical records of the glacier mass balance for the Tibetan Plateau are scattered and incomplete. In this study, we reconstructed the long-term glacier mass balance from 1975 to 2013 (the data can be downloaded at https://doi.org/10.11888/Glacio.tpdc.270382) using the field observations for seven major glaciers and corresponding meteorological data extracted from the GLDAS CLSM (Global Land Data Assimilation Systems based on the Catchment Land Surface Model) data set. The effects of refrozen water and snow depth on the glacier mass balance was examined. In addition, the response of glacier mass balance to climate change was investigated. The results indicate that most of the glaciers experienced a mass loss during the study period. Large mass loss occurred in glaciers in the southeastern part of the plateau. The glacier in the northwestern part of the plateau (the Muztagh No.15 Glacier) shows a small mass gain. Regarding the mass components of the glacier mass balance, most mass balances are dominated by meltwater, while the Muztagh No. 15 has a component offset. Further analysis manifests that mass balances in the western glaciers heavily change to the change of moisture factor (precipitation), while for the eastern glaciers, mass balance changes respond strongly to the changes of heat factors (air temperature and net radiation). The differences in the mass balance changes are closely related to the westerlies and Indian summer monsoon.

Biological diversity depends on multiple, cooccurring ecological interactions. However, most studies focus on one interaction type at a time, leaving community ecologists unsure of how positive and negative associations among species combine to influence biodiversity patterns. Using surveys of plant populations in alpine communities worldwide, we explore patterns of positive and negative associations among triads of species (modules) and their relationship to local biodiversity. Three modules, each incorporating both positive and negative associations, were overrepresented, thus acting as "network motifs." Furthermore, the overrepresentation of these network motifs is positively linked to species diversity globally. A theoretical model illustrates that these network motifs, based on competition between facilitated species or facilitation between inferior competitors, increase local persistence. Our findings suggest that the interplay of competition and facilitation is crucial for maintaining biodiversity.

Dam construction is mostly aimed for multiple functions, including irrigation water provision, hydropower, and some others that bring substantial social benefits. However, global warming impacts on the interaction of the positive outcomes of damming remain little known, particularly in terms of the sustainability of their co-benefits, whereby investigating the different impacts of global warming scenarios of 1.5 °C and 2 °C has been a hotspot in water resources and energy research worldwide. This study used an integrative analysis based on a hydrological, techno-economic and agricultural modeling framework to evaluate the effects of global warming scenarios of 1.5 °C and 2 °C on the co-benefits between hydropower and irrigation in the Mekong River basin. The results show the declined hydropower generation and irrigation water supply in the Mekong River basin under 1.5 °C and 2 °C warming scenarios. The co-benefits between the hydropower and the irrigation is more undermined by the global warming of 2 °C relative to 1.5 °C in the Mekong River basin. Moreover, the changes of co-benefits are sensitive to the consideration of the protected areas in the basin. With the consideration of the protected areas, the co-benefits would be enhanced by 2 °C global warming compared to 1.5 °C global warming. Therefore, it is critical for decision-makers to consider the tradeoffs between the environment and dam construction for ensuring energy and food security under global warming scenarios.

The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 μm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.

The synergy between experiment, theory, and simulations enables a microscopic analysis of spin-glass dynamics in a magnetic field in the vicinity of and below the spin-glass transition temperature T_g. The spin-glass correlation length, ξ(t, t_w; T), is analysed both in experiments and in simulations in terms of the waiting time t_w after the spin glass has been cooled down to a stabilised measuring temperature T < T_g and of the time t after the magnetic field is changed. This correlation length is extracted experimentally for a CuMn 6 at. % single crystal, as well as for simulations on the Janus II special-purpose supercomputer, the latter with time and length scales comparable to experiment. The non-linear magnetic susceptibility is reported from experiment and simulations, using ξ(t, t_w; T) as the scaling variable. Previous experiments are reanalysed, and disagreements about the nature of the Zeeman energy are resolved. The growth of the spin-glass magnetisation in zero-field magnetisation experiments, M_ZFC(t, t_w; T), is measured from simulations, verifying the scaling relationships in the dynamical or non-equilibrium regime. Our preliminary search for the de Almeida–Thouless line in D = 3 is discussed.

Agricultural irrigation is the major water consumer in the Mediterranean region. In response to the growing pressure on freshwater resources, more efficient irrigation technologies have been widely promoted. In this study, we assess the impact of the ongoing transition from flood to drip irrigation on future hydroclimatic regimes under various climate change scenarios, with a particular focus on actual evapotranspiration and groundwater recharge in the Mediterranean region of Valencia, Spain. Hydroclimatic predictions for the near‐term future (2020‐2049) and the mid‐term future (2045‐2074) were made under two emission scenarios (RCP 4.5 and RCP 8.5) using a hydrological model that was forced with data from five GCM‐RCM combinations and field‐based irrigation volume and frequency observations. Our findings suggest that climate change could lead to statistically significant changes in the regional hydroclimatic regime despite projection uncertainties. Major changes include a statistically significant decrease in mean groundwater recharge of up to ‐6.6% under flood irrigation and ‐9.3% under drip irrigation and contrasting changes in mean actual evapotranspiration for flood and drip irrigation in the order of +1% and ‐2.1%, respectively. Since sustainably available water resources in the Valencia region are entirely allocated, the expected changes and associated uncertainties create a challenging context for future water management. Our simulations further indicate that, rather than climate change, the choice of irrigation technique may have a greater impact on actual evapotranspiration and groundwater recharge. Our findings therefore highlight the importance of considering both climate change and irrigation technique when assessing future water resources in irrigated Mediterranean agriculture.

Hydrological models have traditionally been used for the prediction in ungauged basins despite the related challenge of model parameterization. Short measurement campaigns could be a way to obtain some basic information that is needed to support model calibration in these catchments. This study explores the potential of such field campaigns by i) testing the relative value of continuous water-level time series and point discharge observations for model calibration, and by ii) evaluating the value of point discharge observations collected using expert knowledge and active learning to guide when to measure streamflow. The study was based on 100 gauged catchments across the contiguous United States for which we pretended to have only limited hydrological observations, i.e., continuous daily water levels and ten daily point discharge observations from different hypothetical field trips conducted within one hydrological year. Water level data were used as a single source of information, as well as in addition to point discharge observations, for calibrating the HBV model. Calibration against point discharge observations was conducted iteratively by continually adding new observations from one of the ten field measurements. Our results suggested that the information contained in point discharge observations was especially valuable for constraining the annual water balance and streamflow response at the event scale, improving predictions based solely on water levels by up to 50% after ten field observations. In contrast, water levels were valuable to increase the accuracy of simulated daily streamflow dynamics. Informative discharge sampling dates were similar when selected with either active learning or expert knowledge and typically clustered during seasons with high streamflow.

The replacement of flood-irrigation systems by drip-irrigation technology has been widely promoted with the aim of a more sustainable use of freshwater resources in irrigated agriculture. However, evidence for an irrigation efficiency paradox emphasizes the need to improve our understanding of the impacts of irrigation transformations on water resources. Here, we developed a distributed hydrological modeling approach to investigate the spatiotemporal effect of flood and drip irrigation on groundwater recharge. The approach recognizes differences in the water balance resulting from the localized application of water in surface drip-irrigated fields and the more extensive application of water in flood irrigation. The approach was applied to the semi-arid Mediterranean region of Valencia (Spain) and calibrated using a multi-objective framework. Multiple process scales were addressed within the framework by considering the annual evaporative index, monthly groundwater level dynamics, and daily soil moisture dynamics. Daily simulations from 1994 to 2015 suggested that, in our hydroclimatic conditions, (a) annual recharge is strongly related to annual rainfall, which had a four times higher impact on recharge than the type of irrigation practice, (b) flood-irrigated recharge tends to exceed drip-irrigated recharge by 10% at annual time scales, (c) however, recharge response to a particular precipitation event is smaller in flood irrigation than in drip irrigation, and (d) 8–18 rainfall events could generate more than half of the annual recharge in drip and flood irrigation, respectively. Our results highlight the importance of understanding the hydrological dynamics under different irrigation practices for supporting irrigation infrastructure policies.

Understanding the mixing between surface water and groundwater as well as groundwater travel times in vulnerable aquifers is crucial to sustaining a safe water supply. Age dating tracers used to infer apparent travel times typically refer to the entire groundwater sample. A groundwater sample, however, consists of a mixture of waters with a distribution of travel times. Age dating tracers only reflect the proportion of the water that is under the dating range of the used tracer, thus their interpretation is typically biased. Additionally, end-member mixing models are subject to various sources of uncertainties, which are typically neglected. In this study, we introduce a new framework that untangles groundwater mixing ratios and travel times using a novel combination of in-situ noble gas analyses. We applied this approach during a groundwater pumping test carried out in a pre-alpine Swiss valley. First, we calculated transient mixing ratios between recently infiltrated river water and regional groundwater present in a wellfield, using helium-4 concentrations combined with a Bayesian end-member mixing model. Having identified the groundwater fraction of recently infiltrated river water (F_rw) consequently allowed us to infer the travel times from the river to the wellfield, estimated based on radon-222 activities of F_rw. Furthermore, we compared tracer-based estimates of F_rw with results from a calibrated numerical model. We demonstrate (i) that partitioning of major water sources enables a meaningful interpretation of an age dating tracer of the water fraction of interest and (ii) that the streambed has a major control on the estimated travel times.

In hydrological modeling, the identification of model mechanisms best suited for representing individual hydrological (physical) processes is of major scientific and operational interest. We present a statistical hypothesis-testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a “dominant” mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms. The uncertainty in the test statistic is approximated using bootstrap sampling from the model ensemble. Synthetic experiments (with varying error magnitude and multiple replicates) and real data experiments are conducted using the hydrological modeling system FUSE (7 processes and 2–4 mechanisms per process yielding 624 feasible model structures) and data from the Leizarán catchment in northern Spain. The mechanism identification method is reliable: it identifies the correct mechanism as dominant in all synthetic trials where an identification is made. As data/model errors increase, statistical power (identifiability) decreases, manifesting as trials where no mechanism is identified as dominant. The real data case study results are broadly consistent with the synthetic analysis, with dominant mechanisms identified for 4 of 7 processes. Insights on which processes are most/least identifiable are also reported. The mechanism identification method is expected to contribute to broader community efforts on improving model identification and process representation in hydrology.

Study region: Northeast China
Study focus: Northeast China is an important region for industry and agriculture in China. In this region, investigations are lacking on the spatial distribution of snow melt contributions to the spring maximum runoff/discharge, and no studies have compared the spring and summer potential flood risks. Here, for the first time in Northeast China, we investigated the spatial distribution of snow melt contributions to spring maximum runoff/discharge and compared the spring and summer potential flood risks in terms of their spatial distributions, crop production and economic exposures.
New hydrological insights for the region: We find that snow contributes approximately three-fourths of spring maximum floods from 1982 to 2011. On average, potential economic exposures to the summer and spring floods represent 3.9% and 0.4% of total GDP, respectively. Potentially exposed production of maize, rice and soybean to summer floods accounts for approximately 2.8% of the total, and potentially exposed wheat production to the spring floods accounts for 0.3% of the total. GDP growth amplifies increasing trends of potential economic exposure, while changes in potential crop production exposure are dominated by flood variations. This study is unique in that snow melt contributions to the spring maximum floods are quantified and that potential GDP and crop production exposure risks to spring and summer floods are quantified and compared for the first time in Northeast China.

Study region: Northeast China
Study focus: Northeast China is one of the largest agricultural regions in the world and a strategically important granary for China. Information on hydrological extremes is crucially important for water management. Yet, comprehensive studies on hydrological extremes, both extreme flood and drought events, based on hydrological gauge comprehensive and large coverage observations are still lacking in Northeast China. This study investigated observed hydrological extremes with 124 hydrological gauges in the region ranging from the 1950s to present for the first time.
New hydrological insights for the region: We find that flood extremes, mean discharge and standardized discharge are decreasing in over 80% of the gauges, and no gauges show significantly increasing flood extremes. The findings suggest that sustainable water management strategies should be employed to meet increasing water demand for long-term development. In addition, we find that flood peak discharge and flood volume are significantly correlated in all the gauges, motivating flood risk studies considering multiple flood variables together. Further, we quantify probability changes of severe and extreme hydrological droughts, and find that the probability of drought events is decreasing in most of the gauges, suggesting that the number of drought events is reducing.
Data availability: Discharge data are from the hydrology bureau, and can be found by contacting Songliao River Water Resources Commission, Ministry of Water Resources, the People’s Republic of China (http://www.mwr.gov.cn/english/).

A wide variety of environmental contaminants has been shown to disrupt immune functions of fish and may compromise their defense capability against pathogens. Immunotoxic effects, however, are rarely considered in ecotoxicological testing strategies. The aim of this study was to systematically evaluate the suitability of an in vitro immuno-assay using selected fish immune parameters to screen for chemicals with known immunotoxic potential and to differentiate them from non-immunotoxicants. Non-stimulated and lipopolysaccharide-stimulated head kidney leukocytes of rainbow trout (Oncorhynchus mykiss) were exposed for 3 h or 19 h to chemicals with different modes of action. As immune parameters, phagocytosis activity, oxidative burst activity and cytokine transcription (IL-1β, TNFα, IL-10) were examined, accompanied by in silico modelling. The immunotoxicants dexamethasone, benzo(a)pyrene, ethinylestradiol and bisphenol A significantly altered the immune parameters at non-cytotoxic concentrations whereas diclofenac had only weak effects. However, the two baseline chemicals with no known immunotoxic potential, butanol and ethylene glycol, caused significant effects, too. From our results it appears that the in vitro fish leukocyte assay as performed in the present study has only a limited capacity for discriminating between immunotoxicants and non-immunotoxicants.

Stochastic hydrological process models have two conceptual advantages over deterministic models. First, even though water flow in a well‐defined environment is governed by deterministic differential equations, a hydrological system, at the level we can observe it, does not behave deterministically. Reasons for this behavior are unobserved spatial heterogeneity and fluctuations of input, unobserved influence factors, heterogeneity and variability in soil and aquifer properties, and an imprecisely known initial state. A stochastic model provides thus a more realistic description of the system than a deterministic model. Second, hydrological models simplify real processes. The resulting structural deficits can better be accounted for by stochastic than by deterministic models because they, even for given parameters and input, allow for a probability distribution of different system evolutions rather than a single trajectory. On the other hand, stochastic process models are more susceptible to identifiability problems and Bayesian inference is computationally much more demanding. In this paper, we review the use of stochastic, time‐dependent parameters to make deterministic models stochastic, discuss options for the numerical implementation of Bayesian inference, and investigate the potential and challenges of this approach with a case study. We demonstrate how model deficits can be identified and reduced, and how the suggested approach leads to a more realistic description of the uncertainty of internal and output variables of the model compared to a deterministic model. In addition, multiple stochastic parameters with different correlation times could explain the variability in the time scale of output error fluctuations identified in an earlier study.

Of all economic activities in the North China Plain, agriculture accounts for the highest consumption of water resources. As water saving by using technologies to alleviate groundwater depletion has its limitations, adjusting the cultivation patterns of water-intensive crops becomes an unavoidable measure to ensure water sustainability. Different cropping patterns (including fallowing) and water supply scenarios were set up, using the DSSAT crop model and a regression model, in order to predict the effects of cropping adjustments on groundwater sustainability and food production in the piedmont region of the plain. The results indicate that: (1) irrigation water requirement needs to be limited to a maximum level of 187 mm/a to sustain groundwater at the present level. Above this level, every 100 mm of irrigation pumping leads to a decline of 0.49 m in the groundwater level; (2) under the current water supply conditions, and taking groundwater sustainability, food supply, water use efficiency and soil fertility recovery into consideration, leaving 34% of the cropland fallow or leaving it fallow every 3 years seems to be the best option; (3) If an extra amount of water of 0.2 or 0.5 billion m³ is supplied as a result of the SNWT Project, the percentage of cropland which should be left fallow can decrease to 25% or 11% respectively. The comprehensive information on the water and food nexus derived from this study can provide a reference framework for policies concerning sustainable water use and food security in the region.

Soybean import accounts for 90% of China's total domestic soybean supply. Such import has a substantial impact on how the country's resources are used as well as on its environment. In this study, we performed a national-scale assessment of the impact of soybean import on domestic cropland conversion, crop production, water use and nitrogen (N) fertilizer application. Results show that soybean production in China decreased by 26% (4.46 million tons) and sown areas were reduced by 25% (2.39 million ha) from the peak of 2004 to 2016. Of the areas taken out of the soybean production, 70% were converted to maize, 20% to rice, 3% to vegetables and 7% to fruits during this period. As a result of the cropland conversion, the production of maize, rice, vegetables and fruits increased by 10.42, 3.34, 2.49 and 3.26 million tons respectively. However, irrigation water use in the areas that were converted to the cultivation of the four types of crops increased by 96.42% (3.05 km³), with much of it coming from northern provinces where water is generally scarce. The application of N fertilizer increased by 256.65 thousand tons (almost 5 times) on the converted areas, partly due to the loss of the N-fixing soybean cultivation. Although a large quantity of virtual water and land were imported through soybean trade, the water use and N application were increased in reality. The analysis of the land-water-food-environment nexus in the context of soybean import provides comprehensive and useful information about the benefits and trade-offs associated with China's international soybean trade.

The grey water footprint (GWF) is defined as freshwater requirements for diluting pollutants in receiving water bodies. It is widely used to measure the impact of pollutant loads on water resources. GWF can be transferred from one area to another through trade. Although pollution flow has previously been investigated at the national level, there has been no explicit study on the extent to which crop trade affects GWF across regions and the associated changes in grey water stress (GWS). This study analyzes pollution flow associated with interprovincial crop trade based on nitrogen (N) and phosphorus (P) loss intensity of three major crops, namely, maize, rice and wheat, which is simulated by a grid-based crop model for the period 2008–2012, and evaluates the spatial patterns of GWS across China. The results indicate that the integrated national GWF for N and P was 1271 billion m³ yr⁻¹, with maize, rice, and wheat contributing 39%, 37%, and 24%, respectively. Through interprovincial crop trade, southern China outsourced substantial N and P losses to the north, leading to a 30% GWS increase in northern China and 66% GWS mitigation in southern China. Specifically, Jilin, Henan, and Heilongjiang Provinces in the northern China showed increases in GWS by 161%, 114%, and 55%, respectively, while Fujian, Shanghai, and Zhejiang in the south had GWS reductions of 83%, 85%, and 80%, respectively. It was found that the interprovincial crop trade led to reduced national GWF and GWS. Insights into GWF and GWS can form the basis for policy developments on N and P pollution mitigation across regions in China.

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

The impact of climate change on water availability has become a significant cause for concern in the Zayandeh-Roud Reservoir in Iran and similar reservoirs in arid regions. This study investigates the climate change impact on water supply and availability in the Zayandeh-Roud River Basin. For better management, the Soil & Water Assessment Tool (SWAT) was used to develop a hydrologic model of the basin. The model was then calibrated and validated for two upstream stations using the Sequential Uncertainty Fitting (SUFI-2) algorithm in the SWAT-CUP software. The impact of climate change was modeled by using data derived from five Inter-Sectoral Impact Model Intercomparison Project general circulation models under four Representative Concentration Pathways (RCPs). For calibration (1991–2008), the Nash–Sutcliffe efficiency (NSE) values of 0.75 and 0.61 at the Ghaleshahrokh and Eskandari stations were obtained, respectively. For validation (2009–2015), the NSE values were 0.80 and 0.82, respectively. The reservoir inflow would probably reduce by 40–50% during the period of 2020–2045 relative to the base period of 1981–2006. To evaluate the reservoir's future performance, a nonlinear optimization model was used to minimize water deficits. The highest annual water deficit would likely be around 847 MCM. The lowest reservoir reliability and the highest vulnerability occurred under the extreme RCP8.5 pathway.

Various ecosystem functions provided by floodplains depend on a natural river activity and floodplain morphology. Therefore, anthropogenic alterations of rivers modify their flooding regimes and may affect the provisioning of numerous ecosystem functions. Restoration projects, which aim at reestablishing natural processes of floodplains, require a better understanding of the ecosystem's ability to simultaneously provide multiple functions (multifunctionality) and how this relates to the environmental template.
Here we investigate the relationship between environmental drivers and ecosystem multifunctionality. We focus on 24 ecosystem functions, representing five ecosystem services provided by floodplains of the Mulde River: plant productivity, biodiversity provisioning, retention of sediments, nutrients and pollutants. These functions were measured on 74 plots located on three well preserved floodplain sites of the Mulde River. We described synergies and trade-offs between single functions using correlations and calculated quantitative measures of ecosystem multifunctionality, quantified as the number of functions provided above either 50% of maximal functioning, or 75% of maximal functioning. We then explored relations of multifunctionality with two environmental factors, which also affect the probability of flooding i.e., the hydrological distance and the distance to the water table.
Although numerous functions related to sedimentation processes were positively correlated to each other, they traded off with functions related to biodiversity provisioning. This advocates the application of a holistic measure of ecosystem functioning. Multifunctionality indices decreased with an increase of both distance to the water table and hydrological distance, with effects of the distance to the water table being most strongly negative. These findings imply that ecosystem multifunctionality is highest at sites which are flooded regularly. We conclude that restoration attempts which shorten hydrological distance and distance to the water table, like removal of artificial embankments or reconstruction of side channels, may have a positive effect not only on single functions, but also on overall ecosystem multifunctionality. We also advocate the application of a multifunctionality measure to facilitate management and restoration of floodplains.

To prioritise sustainable sanitation systems in strategic sanitation planning, indicators such as local appropriateness or resource recovery have to be known at the pre-planning phase. The quantification of resource recovery remains a challenge because existing substance flow models require large amounts of input data and can therefore only be applied for a few options at a time for which implementation examples exist. This paper aims to answer two questions: How can we predict resource recovery and losses of sanitation systems ex-ante at the pre-planning phase? And how can we do this efficiently to consider the entire sanitation system option space? The approach builds on an existing model to create all valid sanitation systems from a set of conventional and emerging technologies and to evaluate their appropriateness for a given application case. It complements the previous model with a Substance Flow Model (SFM) and with transfer coefficients from a technology library to quantify nutrients (phosphorus and nitrogen), total solids (as an indicator for energy and organics), and water flows in sanitation systems ex ante. The transfer coefficients are based on literature data and expert judgement. Uncertainties resulting from the variability of literature data or ignorance of experts are explicitly considered, allowing to assess the robustness of the model output. Any (future) technologies or additional products can easily be added to the library. The model is illustrated with a small didactic example showing how 12 valid system configurations are generated from a few technologies, and how substance flows, recovery ratios, and losses to soil, air, and water are quantified considering uncertainties. The recovery ratios vary between 0 and 28% for phosphorus, 0–10% for nitrogen, 0–26% for total solids, and 0–12% for water. The uncertainties reflect the high variability of the literature data but are comparable to those obtained in studies using a conventional post-ante material flow analysis (generally about 30% variability at the scale of a an urban area). Because the model is fully automated and based on literature data, it can be applied ex-ante to a large and diverse set of possible sanitation systems as shown with a real application case. From the 41 technologies available in the library, 101,548 systems are generated and substance flows are modelled. The resulting recovery ratios range from nothing to almost 100%. The two examples also show that recovery depend on technology interactions and has therefore to be assessed for all possible system configurations and not at the single technology level only. The examples also show that there exist trade-offs among different types of reuse (e.g. energy versus nutrients) or different sustainability indicators (e.g. local appropriateness versus resource recovery). These results show that there is a need for such an automated and generic approach that provides recovery data for all system configurations already at the pre-planning phase. The approach presented enables to integrate transparently the best available knowledge for a growing number of sanitation technologies into a planning process. The resulting resource recovery and loss ratios can be used to prioritise resource efficient systems in sanitation planning, either for the pre-selection or the detailed evaluation of options using e.g. MCDA. The results can also be used to guide future development of technology and system innovations. As resource recovery becomes more relevant and novel sanitation technologies and system options emerge, the approach presents itself as a useful tool for strategic sanitation planning in line with the Sustainable Development Goals (SDGs).

Landuse change and climate change are the main drivers of hydrological processes. The purpose of this study was to analyse the separate and combined future effects of climate and landuse changes on water balance components on different spatial and temporal scales using the integrated hydrological Soil and Water Assessment Tool model. The study focused on the changes and relationship between water yield (WYLD) and sediment yield (SYLD) in the heterogeneous Taleghan Catchment in Iran. For future climate scenarios, RCP 4.5 and RCP 8.5 of GFDL-ESM2M GCM were used for 2020-2040. A Markov chain model was used to predict landuse change in the catchment. The results indicated an increase in precipitation and evapotranspiration. The findings also showed that the relationship between WYLD and SYLD is direct and synergic. Climate change has a stronger effect on WYLD than landuse change, whereas landuse change has a stronger effect on SYLD. The conversion of rangelands to barren land is the most critical landuse change that could increase SYLD. The highest increase in WYLD and SYLD in scenario RCP4.5 resulted from the combined effects of climate and landuse change. We estimated WYLD of about 295 mm and SYLD of around 17 t/ha. The proposed methodology is universal and can be applied to similar settings to identify the most vulnerable regions. This can help prioritize management strategies to improve water and soil management in watersheds.

Freshwater biodiversity loss is a major concern, and global warming is already playing a significant role in species extinctions. Our main goal was to predict climate change impacts on aquatic insect species distribution and richness in Swiss running waters according to two climate change scenarios (RCP2.6 and RCP8.5), using different modeling approaches, that is, species distribution models (SDMs), stacked-SDMs (S-SDMs) and a macroecological model (MEM). We analyzed 10,808 reaches, used as spatial units for model predictions, for a total river network length of 20,610 km. Results were assessed at both the countrywide and the biogeographic regional scales. We used incidence data of 41 species of Ephemeroptera, Plecoptera and Trichoptera (EPT) from 259 sites distributed across Switzerland. We integrated a coupled model for hydrology and glacier retreat to simulate monthly time-step discharge from which we derived hydrological variables. These, along with thermal, land-cover, topographic and spatially explicit data, served as predictors for our ecological models. Predictions of occurrence probabilities and EPT richness were compared among the different regions, periods and scenarios. A Shiny web application was developed to interactively explore all the models’ details, to ensure transparency and promote the sharing of information. MEM and S-SDMs approaches consistently showed that overall, climate change is likely to reduce EPT richness. Decrease could be around 10% in the least conservative scenario, depending on the region. Global warming was shown to represent a threat to species from high elevation, but in terms of species richness, running waters from lowlands and medium elevation seemed more vulnerable. Finally, our results suggested that the effects of anthropogenic activities could overweight natural factors in shaping the future of river biodiversity.

A wide knowledge base regarding the ecological preferences of benthic macroinvertebrates is synthesized in public databases. This knowledge can assist in disentangling the influence of multiple environmental factors on the probability of occurrence of macroinvertebrates and in identifying anthropogenic impacts on the macroinvertebrate assemblage. We aimed to examine and extend current knowledge on ecological preferences by confronting it with independent biomonitoring datasets and to assess how the taxonomic resolution of datasets and the prevalence of taxa affects our ability to do so. We used a habitat suitability-based multi-species distribution model (HS-MSDM) and applied Bayesian inference to confront current knowledge (formalized as prior probability distributions) against independent biomonitoring data across rivers in Switzerland. Shifts in the resulting posterior probability distributions relative to the priors indicate a disagreement with the current knowledge of ecological preferences. Ecological preferences for temperature and organic matter had the highest influence on the predicted occurrence of macroinvertebrates in the model, followed by flow velocity, insecticide pollution, and substratum. Three-fold cross-validation tests demonstrated that the HS-MSDM predicted the distribution of taxa with a relative frequency of occurrence between 0.2 and 0.8 considerably better than a model without consideration of environmental factors. However, it was less able to predict the distribution of taxa with a frequency of occurrence <0.1 or >0.9. Nine taxa with a frequency of occurrence between 0.4 and 0.8 were identified as potentially useful bioindicators, given their strong association with the environmental factors in the model. We also identified 29 taxa for which part of the ecological preference data, particularly temperature and flow-velocity preferences, should be re-examined. For river morphology, 18 sensitive and 10 insensitive taxa were identified, although direct and uniquely linked prior knowledge regarding morphology was lacking for all taxa. Phylogenetically derived information on ecological preferences could be integrated and updated to fill gaps in ecological preference databases. However, the taxonomic resolution of the biomonitoring and ecological preference data plays an important role, as we show by identifying families comprising species that respond differently to environmental factors. These results demonstrate the value of conducting biomonitoring at the most detailed taxonomic level possible.

The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH₄⁺-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R² from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH₄⁺-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required.

The formation of world-class megalopolises has been a goal of urban development agencies around the world owing to their economic advantages. On their bids of becoming a world-class megalopolis, water availability is a factor that requires consideration. China has set an ambitious goal of developing a world-class megalopolis in the water-scarce Beijing-Tianjin-Hebei (BTH) region. This study investigates the water challenge the BTH region faces and the effects of main water conservation measures in the region towards the goal. An inter-city input–output model was constructed for identifying the water gap in the region and analyzing the effectiveness of main water conservation measures under various scenarios. The results indicate a significant gap between the water required to achieve the goal of becoming a world-class megalopolis and the region’s available water resources. Although proposed water conservation measures of improving water use efficiency and reducing agricultural water use provide a modest improvement, the amount of water required for urban development still exceeds the availability. The study emphasizes the significance of agricultural water use reduction in Hebei through crop system replacement from water-intensive winter wheat to water-saving crops. The study also proposes an alternative option of adjusting the development plan through redefining the boundary of the BTH megalopolis by excluding part of cities in Hebei. The results of this study contribute to a better understanding of the effect of water scarcity on urban development and thus provide references for other water-scarce regions with ambitious urban development goals.

Socioeconomic development has led to increased consumption of both blue and green water. Consequently, China is facing serious water scarcity issue. However, few studies have investigated interactions of blue and green water footprints, as well as driving forces underlying the changes in water footprints across provinces and sectors. To fill in this knowledge gap, we quantified the spatial-temporal dynamics of the blue and green water footprint (BWF and GWF, respectively), and analyzed the key factors that drive the provincial-level changes in BWF and GWF from 2002 to 2012. The analysis is facilitated by the approaches of multi-region input-output analysis and structural decomposition analysis, and we developed one decoupling index to quantify the water-economy relation and substitution between green and blue water. The results show that China's BWF averaged at 161 billion m³/yr, about one-third the size of the GWF. In addition, water scarce provinces in Northern China were moving towards decoupling between economic growth and blue water consumption, with GWF playing an increasingly important role. The changes in the WFs were mainly influenced by changes in affluence (final demand per capita), technological improvements (decreased direct water consumption intensity), and consumption pattern (composition of the final demand) rather than changes in the population and export. Technology improvement, consumption pattern shift and industrial structure adjustment contribute to WF reductions, thus help improve water security and sustainability in China. This study provides a new approach to analyze water-economy relations for water scarce countries.

Vegetation is the most important component of terrestrial ecosystem, reflecting the quality of ecological environment. In this study, non-parametric Mann-Kendall test and Sen's slope were applied to analyze the spatiotemporal variabilities of vegetation coverage, precipitation, temperature and drought during the period 1981-2015 in the Yarlung Zangbo River basin (YZRB), based on GIMMS NDVI3g, CHIRPSv2.0, China Meteorological Forcing Dataset (CMFD) and CRU TS 4.03 (scPDSI) datasets. The Pearson Correlation Analysis and geostatistical methods were adopted to investigate the correlations between climatic variables and vegetation coverage as well as the time-lag effects for different vegetation types. The results showed that the spatial distribution of vegetation coverage is generally characterized as increasing from upper to lower reaches. The northwestern region of lower reaches showed a greening trend in vegetation, while degradation of vegetation was detected in southeastern region. The vegetation in the upper and middle reaches was significantly positively correlated with precipitation and drought, while a significantly negative correlation was detected between vegetation and temperature in the middle reaches and southeast region of the lower reaches, as well as vegetation and precipitation in the lower reaches. One-month lag effect of precipitation and temperature on vegetation appeared in the upper reaches, while no lag for precipitation and one-month lag for temperature were detected in the middle reaches, and both precipitation and temperature showed no lag effect on vegetation in southeast region of the lower reaches. The vegetation types in the YZRB were mainly consisted of meadow, alpine vegetation, shrubs and grassland, in which the growth of shrubs was relatively better than other vegetation types. All the four vegetation types showed a slightly greening trend, which were positively correlated with precipitation and drought, while showed a negative correlation with temperature. The findings help to better understand the mechanism of vegetation dynamics and provide references for ecosystem monitoring and assessment in the YZRB.

Herbicide pollution in headwater streams due to agricultural practices is a major environmental concern and is characterized by episodic peak concentrations from fast transport paths. We rely on previous experimental studies in a small (1.2km²) agricultural catchment in the Swiss Plateau, to model dynamic diffuse herbicide pollution with emphasis on fast transport paths in a conceptual modelling framework at the catchment scale. We show how experimentalists’ understanding of the fate of herbicides (perceptual model) can be translated into conceptual models considering sorption, degradation, and fast transport of water and chemicals facilitated by impervious surfaces, tile drains and artificial shortcuts. Different types of experimental data (streamflow, high-frequency concentration measurements, and soil–water distribution coefficients) are used in a joint Bayesian inference of model parameters. We assess the ability of different spatial configurations of hydrological response units in explaining observed heterogeneity in transport behaviour of two corn herbicides. Thereby, we find that (1) relatively simple conceptual models can provide a realistic description of herbicide fate in small agricultural catchments, (2) accounting for spray drift onto hard surfaces is necessary to avoid a severe model bias during the first rainfall event after application, and (3) including catchment-specific experimentalist knowledge about important elements like artificial shortcuts and tile drains leads to a reduction in uncertainty of 30% compared to the more conventional conception of the proximity to the stream as the dominant risk factor.

The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

Key decisions in the design of biomonitoring programs include taxonomic resolution, geographic extent, and site selection, each of which can affect our ability to infer human impacts on biodiversity from biomonitoring data. These decisions are constrained by monitoring goals and budget limitations, which may require trade-offs between them. In this study, we use species distribution models (SDMs) to assess the effects of key decisions in biomonitoring design on our ability to infer the effects of natural and anthropogenic environmental conditions on the occurrence of benthic macroinvertebrates in streams. We compared 4 datasets that differ in their site-selection strategy, geographic extent, and taxonomic resolution using data from Swiss federal and cantonal biomonitoring programs. We used individual SDMs with 3-fold cross validation to identify the environmental variables that best predict the probability of taxa occurrence across the datasets. We then used a hierarchical multi-species distribution model (hmSDM) to identify how key aspects of biomonitoring design influence the relative importance of the selected explanatory (predictor) variables in the model as well as the model’s predictive performance. The relative importance of the explanatory variables in the hmSDMs was lowest for the dataset with a grid-based site-selection approach and family-level resolution. An increase in predictive performance was achieved by either using a species-level taxonomic resolution for Ephemeroptera, Plecoptera, and Trichoptera or by combining different biomonitoring programs at the family level to increase the number of sites and improve the coverage of environmental conditions. Selecting monitoring sites to provide a good coverage of environmental conditions, while also targeting sites with rare combinations of environmental conditions, could further improve biomonitoring program data. Models based on finer taxonomic resolution revealed that widespread families consist of species and genera with different and stronger responses to environmental conditions. However, many families include species that are too rare to allow inference of significant responses to environmental conditions. We show that hmSDMs of stream invertebrates can contribute to the selection of specific taxa for identification at finer taxonomic resolution. This strategy could facilitate the standardization and combination of multiple biomonitoring datasets and improve the identifiability of stream invertebrate responses to environmental conditions in biomonitoring programs.

We use a simple model to extend network models for activated dynamics to a continuous landscape with a well-defined notion of distance and a direct connection to many-body systems. The model consists of a tracer in a high-dimensional funnel landscape with no disorder. We find a nonequilibrium low-temperature phase with aging dynamics that is effectively equivalent to that of models with built-in disorder, such as the trap model, step model and random energy model. Finally, we compare entropy with energy-driven activation, and we remark that the former is more robust to the choice of the dynamics since it does not depend on whether one uses local or global updates.

Many animal species across different taxa change their habitat during their development. An ontogenetic habitat shift enables the development of early vulnerable-to-predation stages in a safe "nursery" habitat with reduced predation mortality, whereas less vulnerable stages can exploit a more risky, rich feeding habitat. Therefore, the timing of the habitat shift is crucial for individual fitness. We investigate the effect that size selectivity in mortality in the rich feeding habitat has on the optimal body size at which to shift between habitats using a population model that incorporates density dependence. We show that when mortality risk is more size dependent, it is optimal to switch to the risky habitat at a smaller rather than larger body size, despite that individuals can avoid mortality by staying longer in the nursery habitat and growing to safety in size. When size selectivity in mortality is high, large reproducing individuals are abundant and produce numerous offspring that strongly compete in the nursery habitat. A smaller body size at habitat shift is therefore favored because strong competition reduces growth potential. Our results reveal the interdependence among population structure, density dependence, and life history traits, and highlight the need for integrating ecological feedbacks in the study of life history evolution.

Regime shifts have been documented in a variety of natural and social systems. These abrupt transitions produce dramatic shifts in the composition and functioning of socioecological systems. Existing theory on ecosystem resilience has only considered regime shifts to be caused by changes in external conditions beyond a tipping point and therefore lacks an evolutionary perspective. In this study, we show how a change in external conditions has little ecological effect and does not push the system beyond a tipping point. The change therefore does not cause an immediate regime shift but instead triggers an evolutionary process that drives a phenotypic trait beyond a tipping point, thereby resulting (after a substantial delay) in a selection-induced regime shift. Our finding draws attention to the fact that regime shifts observed in the present may result from changes in the distant past, and highlights the need for integrating evolutionary dynamics into the theoretical foundation for ecosystem resilience.

Forests play an important role in the Earth’s system. Understanding the states and changes in global forests is vital for ecological assessments and forest policy guidance. However, there is no consensus on how global forests have changed based on current datasets. In this study, five global land cover datasets and Global Forest Resources Assessments (FRA) were assessed to reveal uncertainties in the global forest changes in the early 21^st century. These datasets displayed substantial divergences in total area, spatial distribution, latitudinal profile, and annual area change from 2001 to 2012. These datasets also display completely divergent conclusions on forest area changes for different countries. Among the datasets, total forest area changes range from an increase of 1.7 × 10⁶ km² to a decrease of 1.6 × 10⁶ km². All the datasets show deforestation in the tropics. The accuracies of the datasets in detecting forest cover changes were evaluated by a global land cover validation dataset. The spatial patterns of accuracies are inconsistent among the datasets. This study calls for the development of a more accurate database to support forest policies and to contribute to global actions against climate change.

Urban flooding poses an increasing threat to citizens and property and has become a global challenge. The primary cause of urban flooding is rapid urbanization, which is outpacing infrastructure development and drainage system improvements. This paper utilizes a verified flood model that couples hydrological and hydrodynamic processes to investigate the flood response under rainfall events with different patterns. A sponge city pilot area in China is taken as a case study. Drainage system performance, surface inundation patterns, and relation between them are investigated by carrying out statistical analysis and Pearson correlation analysis, respectively. Results show that with increases in the rainfall recurrence interval (RRI) and peak position ratio (PPR), drainage system performance indicators (DSPIs) generally become worse, and the total inundation area (TIA) and surface inundation areas of larger depths become larger. The response time exhibits varied patterns but generally decreases with increasing recurrence intervals and peak ratios. For surface inundation, the recurrence interval presents an increasingly significant role compared with the peak ratio as rainfall becomes heavier. Compared with other DSPIs, the volume of overflowed manholes (VOMs) shows a delayed response to rainfall and has a stronger relationship with the inundation areas with greater water depths, which indicates a possible solution of utilizing manhole storage for water management and flood control. Analyzing the response of drainage systems and flood patterns to different rainfall events using a model coupling hydrological and hydrodynamic processes comprehensively reveals the specific problems of the study area and possible innovative solutions. The analytical method and knowledge gained can help support urban flood management in China and around the world.

Aquatic pesticide pollution from both agricultural and urban pest control is a concern in many parts of the world. Making an accurate assessment of pesticide exposure is the starting point to protecting aquatic ecosystems. This in turn requires the design of an effective monitoring program. Monitoring is also essential to evaluate the efficacy of mitigation measures aimed to curb pesticide pollution. However, empirical evidence for their efficacy can be confounded by additional influencing factors, most prominently variable weather conditions. This review summarizes the experiences gained from long-term (>5 years) pesticide monitoring studies for detecting trends and provides recommendations for their improvement. We reviewed articles published in the scientific literature, with a few complements from selected grey literature, for a total of 20 studies which fulfill our search criteria. Overall, temporal trends of pesticide use and hydrological conditions were the two most common factors influencing aquatic pesticide pollution. Eighteen studies demonstrated observable effects to surface water concentrations from changes in pesticide application rates (e.g., use restriction) and sixteen studies from interannual variability in hydrological conditions during the application period. Accounting for seasonal- and streamflow-related variability in trend analysis is important because the two factors can obscure trends caused by changes in pesticide use or management practices. Other mitigation measures (e.g., buffer strips) were only detectable in four studies where concentrations or loads were reduced by > 45%. Collecting additional agricultural (e.g., pesticide use, mitigation measures) and environmental (e.g., precipitation, stream flow) data, as well as establishing a baseline before the implementation of mitigation measures have been consistently reported as prerequisites to interpret water quality trends from long-term monitoring studies, but have rarely been implemented in the past.

This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km²), an alpine and pre-alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability and build a model that reflects them, we follow a two-stage approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgement to identify the most plausible cause–effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow-related processes, and landscape features such as geology produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology, and geology maps) are available in numerous regions around the globe.

The importance of faecal sludge management is gaining recognition. However, methods are still lacking to reasonably estimate the quantities and qualities (Q&Q) that need to be managed, which makes the planning for and implementing of management solutions quite difficult. The objective of this study was to collect and analyse Q&Q of faecal sludge at a citywide scale, and to evaluate whether "SPA-DET" data (=> spatially analysable - demographic, environmental and technical) could then be used as predictors of Q&Q of faecal sludge. 60 field samples and questionnaires from Hanoi and 180 from Kampala were analysed. Software tools were used in an iterative process to predict total solids (TS) and emptying frequency in both Hanoi, Vietnam and Kampala, Uganda. City-specific data could be predicted with types of "SPA-DET" data as input variables, and model performance was improved by analysing septic tanks and pit latrines separately. Individual models were built for TS concentrations and emptying frequency. In addition, a model was built across both cities for emptying frequency of septic tanks based on number of users and containment volume, indicating predictive models can be relevant for multiple cities. Number of users, containment volume, truck volume and income level were identified as the most common variables for the correction function. Results confirm the high intrinsic variability of faecal sludge characteristics, and illustrate the importance of moving beyond simple reporting of city-wide average values for estimations of Q&Q. The collected data and developed scripts have been made available for replication in future studies.

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO₂) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO₂, temperature, water supply, and applied nitrogen ("CTWN") for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

Black soldier fly larvae treatment is an emerging technology for the conversion of biowaste into potentially more sustainable and marketable high-value products, according to circular economy principles. Unknown or variable performance for different biowastes is currently one challenge that prohibits the global technology up-scaling. This study describes simulated midgut digestion for black soldier fly larvae to estimate biowaste conversion performance. Before simulation, the unknown biowaste residence time in the three midgut regions was determined on three diets varying in protein and non-fiber carbohydrate content. For the static in vitro model, diet residence times of 15 min, 45 min, and 90 min were used for the anterior, middle, and posterior midgut region, respectively. The model was validated by comparing the ranking of diets based on in vitro digestion products to the ranking found in in vivo feeding experiments. Four artificial diets and five biowastes were digested using the model, and diet digestibility and supernatant nutrient contents were determined. This approach was able to distinguish broadly the worst and best performing rearing diets. However, for some of the diets, the performance estimated based on in vitro results did not match with the results of the feeding experiments. Future studies should try to establish a stronger correlation by considering fly larvae nutrient requirements, hemicellulose digestion, and the diet/gut microbiota. In vitro digestion models could be a powerful tool for academia and industry to increase conversion performance of biowastes with black soldier fly larvae.

One third of the global carbon emissions are emitted by the building sector. Over the last decades, space heating loads have decreased in modern buildings, and domestic hot water (DHW) is now oftentimes the largest energy consumer in the household. We developed the WaterHub modeling framework to assess the potential of technologies or measures targeting DHW energy demand. The framework combines process-based technological models and stochastic water demand modeling in a modular way to allow for holistic simulations of complex DHW systems. In two rigorous tests of the modeling framework, we demonstrated the importance of water consumption dynamics in the modeling of DHW systems, showing that static modeling leads to underestimated heat losses and wrong energy consumption predictions. In an exemplary case study, we identified and quantified the synergistic interactions between water boiler temperatures and a drain water heat recovery device, demonstrating the strength of this methodology for optimizing strategies targeting DHW systems. With its modular structure, this open-source modeling framework can be extended to include any DHW-related technology, providing a useful common platform for collaboration between technology developers and water experts.

Key traits of unicellular species, such as cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, linking such empirical scaling laws to the critical regime of realistic individual-based model classes is difficult. Here, we reveal new empirical scaling evidence associated with a transition in the population and the chlorophyll dynamics of phytoplankton. We offer a possible explanation for these observations by deriving scaling laws in the vicinity of the critical point of a new universality class of non-local cell growth and division models. This "criticality hypothesis" can be tested through new scaling predictions derived for our model class, for the response of chlorophyll distributions to perturbations. The derived scaling laws may also be generalized to other cellular traits and environmental drivers relevant to phytoplankton ecology.

The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydrometeorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average.

Predictive models such as species distribution models (SDMs) are increasingly applied to inform conservation efforts and management decisions. A thoughtful predictor choice, and hence the variable selection process, is a challenge when modeling large communities. Often, variable choice is made for an entire community and not for specific species, resulting in less appropriate predictors for at least some species and affecting model performance and predicted distributions. Using 2 German river catchments as a model system, we investigated (1) application of boosted regression trees (BRTs) as a variable selection procedure to choose the optimal set of environmental predictors and (2) whether model performance is increased by applying custom-made predictor sets to individual species. From a community of 67 benthic macroinvertebrate species, 10 increased in accuracy with the customized predictor set and 10 species decreased in accuracy. Notably, current preference, stream notation, and functional group differed between these species’ groups, which correspond to varied environmental conditions in their known occurrence sites. The species that increased in accuracy showed a preference toward lowland conditions and were far less widespread than the species that decreased in accuracy. We conclude that BRTs are a useful tool for selecting variables for SDMs on large communities. Also, for specialist, rare, or invasive species, determining a species-specific custom-made predictor set may be preferable because species’ preferences may not be representative of the entire study area. Our study describes a structured variable selection approach that can be readily implemented to predict species distributions informative for river management decisions.

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.

Urbanization plays an important role in a global change, but there are few studies that combine land use with topography and precipitation. The urbanization in Jinan, a typical city of North China, between the 1980s and 2005 was analyzed by transition matrix analysis, and the topographic effects on land use changes were explored considering altitude, slope, and aspect. The temporal trends and abrupt changes of annual and flood season precipitation for the last 60 years were detected by multiple nonparametric detection methods, and the precipitation indices were adopted to characterize the frequency and intensity of precipitation. The relationship between urbanization and precipitation was finally investigated by grey correlation analysis. The results showed that land use structure in Jinan experienced a dramatic change during recent decades due to the urbanization process, the conversion of cropland to forest, and the protection of spring in Jinan. The land use types that closely related to mankind's activities and living behaviors were more concentrated in areas with lower altitude and slope, and detected more significant changes. The significant abrupt changes of precipitation generally occurred in 1989 and concentrated in urban areas and southern mountainous areas with high increment. The increase of flood season precipitation was more significant in the central urban region with a maximum increasing rate of 39.52%, and the increase in the number of storm and precipitation days was also obvious. The precipitation indices in the flood season were more closely related to changes in farmland and settlements affected by urbanization with a maximum correlation coefficient of 0.75. These findings illustrate the impact of urbanization on the variability of precipitation and support that the changes in the urban underlying surface have a certain effect on the surface energy balance.

We describe the co-design process to create a co-adapted, integrated and sustainable FANFAR flood forecast and alert system in West Africa. It is based on a structured Multi-Criteria Decision Analysis (MCDA) framework that guides the participants of four co-design workshops and the FANFAR consortium through problem structuring phases, including stakeholder analysis, agreeing on objectives and options (FANFAR system configurations), eliciting expert predictions and stakeholder preferences and assessing the performance of system options based on these input data. We also introduce interactions with participants in and between the co-design workshops (e.g. rainy season surveys) concerning their experiences with floods in West Africa and with using the FANFAR flood forecast and alert system. This includes feedback on the FANFAR technical system components from hands-on practical sessions in the FANFAR co-design workshops. In this report, we focus strongly on the methods, and also present preliminary results. [...]

The U.S.‐China trade conflict has already considerably reshaped China's food imports, and should the conflict continue, it might have substantial impacts on global food supply dynamics as well as China's food supply sources. We address these implications by analyzing recent trends in China's food imports and associated use of land and water resources. We show that China's limited land and water availability will make it challenging to replace soybean imports from the United States with its own production, but switching to new trading partners by investment and cooperation could secure China's food supply while avoiding much negative environmental impacts on exporting countries.

Water footprint (WF) measures human appropriation of water resources for consumptive use of surface and ground water (blue WF) and soil water (green WF) and for assimilating polluted water (grey WF). Questions have been often asked about the exact meaning behind the numbers from WF accounting. However, to date environmental sustainability of WF has never been assessed at the sub-national level over time. This study evaluated the environmental sustainability of blue, green and grey WF for China's 31 mainland provinces in 2002, 2007 and 2012, and identified the unsustainable hotspots. Overall, the total WF increased by 30% between 2002 and 2012. The growth can be attributed to the increase of grey WF because the green and blue WF showed only a slight rise. Among all provinces investigated in 2012, eleven showed unsustainable blue WF (sustainability index SI<0), which were mainly located in the North China Plain. There were 12 provinces that displayed unsustainable green WF, and they were distributed in China's southern and southeastern areas. The grey WF was not sustainable in approximately two third of provinces (19), which were mainly located in China's middle and northern regions and Guangdong province. More than half of China's provinces showed trends of improved SI of green and blue WF from 2002 to 2012. However, the SI of grey WF decreased in almost two third of provinces. Poor levels of WF sustainability were due to water scarcity and pollution, which intensify the degradation of local rivers and ecosystems and make restoration more difficult. The results shed light on the policy making needed to improve sustainable water management, and ecological restoration of hotspot regions.

Phosphorus (P) losses from fertilized croplands to inland water bodies cause serious environmental problems. During wet years, high precipitation disproportionately contributes to P losses. We combine simulations of a gridded crop model and outputs from a number of hydrological and climate models to assess global impacts of changes in precipitation regimes on P losses during the 21st century. Under the baseline climate during 1991-2010, median P losses are 2.7 ± 0.5 kg P ha^-1 year^-1 over global croplands of four major crops, while during wet years, P losses are 3.6 ± 0.7 kg P ha^-1 year^–1. By the end of this century, P losses in wet years would reach 4.2 ± 1.0 (RCP2.6) and 4.7 ± 1.3 (RCP8.5) kg P ha^-1 year^-1 due to increases in high annual precipitation alone. The increases in P losses are the highest (up to 200%) in the arid regions of Middle East, Central Asia, and northern Africa. Consequently, in three quarters of the world’s river basins, representing about 40% of total global runoff and home up to 7 billion people, P dilution capacity of freshwater could be exceeded due to P losses from croplands by the end of this century.

Increasing temperature trends are expected to impact yields of major field crops by affecting various plant processes, such as phenology, growth, and evapotranspiration. However, future projections typically do not consider the effects of agronomic adaptation in farming practices. We use an ensemble of seven Global Gridded Crop Models to quantify the impacts and adaptation potential of field crops under increasing temperature up to 6 K, accounting for model uncertainty. We find that without adaptation, the dominant effect of temperature increase is to shorten the growing period and to reduce grain yields and production. We then test the potential of two agronomic measures to combat warming‐induced yield reduction: (i) use of cultivars with adjusted phenology to regain the reference growing period duration and (ii) conversion of rainfed systems to irrigated ones in order to alleviate the negative temperature effects that are mediated by crop evapotranspiration. We find that cultivar adaptation can fully compensate global production losses up to 2 K of temperature increase, with larger potentials in continental and temperate regions. Irrigation could also compensate production losses, but its potential is highest in arid regions, where irrigation expansion would be constrained by water scarcity. Moreover, we discuss that irrigation is not a true adaptation measure but rather an intensification strategy, as it equally increases production under any temperature level. In the tropics, even when introducing both adapted cultivars and irrigation, crop production declines already at moderate warming, making adaptation particularly challenging in these areas.

Gauge observed runoff can reflect influences of both natural hydrological cycle and human intervention. The Global Land Data Assimilation System (GLDAS) 2.0 and 2.1 provide abundant runoff which are useful for water resources assessment in ungauged/poorly gauged regions. However, GLDAS2.0 and GLDAS2.1 runoff have only been validated and inter-compared in very limited regions. In this study, they are evaluated and inter-compared utilizing gauge observation in 11 large river basins in China. Results show their runoff have large uncertainties: absolute values of relative bias (|RB|) being above 39% and Nash-Sutcliffe efficiency lower than 0.15 on average, but GLDAS2.1 is better. Both of them have large uncertainty in the Tibetan Plateau:|RB|are higher than 40%. The gap between GLDAS runoff and observations could attribute to both GLDAS system uncertainty and the fact that GLDAS does not consider human intervention. Therefore, cautions should be taken when using them in coupled human-natural systems.

As one of the major agricultural regions in the world, water scarcity problems in Northeast China have drawn much attention recently. Because of cold and long winter period, snow is an important component in the hydrological system. Yet few studies have been conducted to systematically assess its role. This study quantified the effects of snow on runoff and soil moisture in the entire region in a 30-year time period (1982-2011) for the first time. A water and energy budget-based distributed biosphere hydrological model with improved snow physics after calibration and validation is employed. A Standardized Snow depth Index (SSdI) is also proposed to quantify snow variations. Result shows that snow contributes 11% to runoff annually on average and 66% and 33% in April and May (main months for crop planting). Soil moisture could decrease by at least 20% in March-May if there would be no snow, and the major agriculture area suffers more than other regions. We also found that SSdI is indicative of standardized soil moisture index and standardized runoff index in spring, particularly useful for supporting water management in agriculture. These results indicate that snow performs like an important reservoir: redistribute water resources among months. This study provides unique insights into the importance of snow in the entire region. The results improve the awareness of the importance of snow to water resources management and indicate that it is worth paying attention to snow in water resources management in this region.

Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.

Active species reintroduction is an important conservation tool when aiming for the restoration of biological communities and ecosystems. The effective monitoring of reintroduction success is a crucial factor in this process. Here, we used a combination of environmental DNA (eDNA) techniques and species distribution models (SDMs) to evaluate the success of recent reintroductions of the freshwater fish Alburnoides bipunctatus in central Germany. We built SDMs without and with eDNA presence data to locate further suitable reintroduction sites and potentially overlooked populations of the species. We successfully detected eDNA of A. bipunctatus at all reintroduction sites, as well as several adjacent sites mostly in downstream direction, which supports the success of reintroduction efforts. eDNA-based species detection considerably improved SDMs for A. bipunctatus, which allowed to identify species presence in previously unknown localities. Our results confirm the usefulness of eDNA techniques as standard tool to monitor reintroduced fish populations. We propose that combining eDNA with SDMs is a highly effective approach for long-term monitoring of reintroduction success in aquatic species.

We present a single computational model for both quartz-enhanced photoacoustic spectroscopy and resonant optothermoacoustic detection trace gas sensors. These sensors employ a quartz tuning fork to detect the acoustic pressure and thermal waves generated when a laser excites a trace gas. The model is based on a coupled system of equations developed by Morse and Ingard for pressure and temperature in a fluid. The pressure and temperature solutions drive the resonant vibration of the tuning fork, which is modeled using the equations of linear elasticity. At high ambient pressure, excellent agreement is obtained with laboratory experiments. This result provides the first quantitative match between a fully computational simulation and experiments for a QEPAS sensor. Such a model could ultimately facilitate sensor design optimization. At low ambient pressure (less than 60 Torr), quantitative agreement is obtained after reweighting the contributions from the pressure and thermal components of the signal. While this result is a substantial improvement over previous results in which a scaling factor was required to obtain agreement at any ambient pressure, at low pressures, it appears that a more accurate physical model may be required to match experimental data.

Surface water quality management requires foresighted decision making regarding long-term investments. It should consider multiple objectives (e.g. related to different pollutants and costs), integrate multiple sources of pollution (point and diffuse sources), and external conditions that change over time (climate, population and land-use changes). Multi-attribute value theory can support such decisions, especially the development of an assessment method. Integrated surface water quality assessment methods including micropollutants are currently lacking or in development in many countries. Important steps for the development of such an immission oriented and integrated surface water quality assessment method are discussed in this paper and exemplified for organic micropollutants. The proposed assessment method goes beyond simple pass-fail criteria for single substances. It provides a continuous assessment on a scale from zero to one based on five color-coded water quality classes and suggestions for the visualization of assessment results. It takes into account the toxicity of the micropollutants and their mixture to aquatic organisms by comparing measured concentrations to environmental quality standards (EQS). The focus of this paper is on aggregation over multiple substances and time. Advantages and disadvantages of different aggregation methods are discussed as well as their implications for practice. The consequences of different aggregation methods are illustrated with didactical examples and by an application of the proposed water quality assessment method to pesticide monitoring data from Switzerland. Recommendations are provided that account for the purpose of the assessment. Furthermore, the paper illustrates how the proposed method can facilitate dealing with uncertainty and a transparent communication of monitoring results to support water quality management decisions.

Resource recovery and emissions from sanitation systems are critical sustainability indicators for strategic urban sanitation planning. In this context, sanitation systems are the most often structured using technology-driven templates rather than performance-based sustainability indicators. In this work, we answer two questions: Firstly, can we estimate generic resource recovery and loss potentials and their uncertainties for a diverse and large set of sanitation systems? And secondly, can we identify technological aspects of sanitation systems that indicate a better overall resource recovery performance? The aim is to obtain information that can be used as an input into any strategic planning process and to help shape technology development and system design for resource recovery in the future.
Starting from 41 technologies, which include novel and conventional options, we build 101,548 valid sanitation systems. For each system we quantify phosphorus, nitrogen, total solids, and water flows and use that to calculate recovery potentials and losses to the environment, i.e. the soil, air, or surface water. The four substances cover different properties and serve as a proxy for nutrient, organics, energy, and water resources. For modelling the flows ex-ante, we use a novel approach to consider a large range of international literature and expert data considering uncertainties. Thus all results are generic and can therefore be used as input into any strategic planning process or to help guide future technology development.
A detailed analysis of the results allows us to identify factors that influence recovery and losses. These factors include the type of source, the length of systems, and the level of containment in storage and treatment. The factors influencing recovery are related to interactions of different technologies in a system which shows the relevance of an modelling approach that allows to look at all possible system configurations systematically. Based on our analysis, we developed five recommendations for the optimization of resource recovery: (i) prioritize short systems that close the loop at the lowest possible level; (ii) separate waste streams as much as possible, because this allows for higher recovery potentials; (iii) use storage and treatment technologies that contain the products as much as possible, avoid leaching technologies (e.g. single pits) and technologies with high risk of volatilization (e.g. drying beds); (iv) design sinks to optimise recovery and avoid disposal sinks; and (v) combine various reuse options for different side streams (e.g. urine diversion systems that combine reuse of urine and production of biofuel from faeces).

Species distribution models (SDMs) are often criticised for lacking explicit linkage to ecological concepts. We aim to improve the ecological basis of SDMs by integrating prior knowledge about ecological preferences of organisms. Additionally, we aim to support a systematic, data-driven review of such prior knowledge by confronting it with independent monitoring data using Bayesian inference. We developed a series of multi-species distribution models (MSDMs) with increasing complexity to predict the probability of occurrence of taxa at sampling sites based on habitat suitability functions that are parameterized with prior ecological knowledge. We subsequently assessed the models` predictive performance with 3-fold cross-validation. So far, if ecological preferences or functional traits have been used in SDMs, they were mainly used as fixed inputs without considering their uncertainty. We take the additional step of considering uncertainty about preference parameters by including them as uncertain prior information that is subsequently updated with Bayesian inference. We apply the series of models in a case study on macroinvertebrates in Swiss streams. We analyse differences in the quality of fit, changes in predictive performance, and the potential to learn about the parameters from the data. We consider ecological preferences for natural and human modified environmental factors including temperature, flow velocity, organic matter concentration, insecticide pollution, and substratum. Results indicate that updating prior knowledge on ecological preferences with Bayesian inference, rather than using it as fixed input, improves model fit and predictive performance. For example, the predictive performance measured by the deviance for validation data improves by 17 % and the explanatory power increases 3.8 times from a model that treats ecological preferences as fixed scores to a model that treats them as uncertain parameters. The spatial distribution of many taxa, including rare taxa with frequencies of occurrence down to about 5 %, which are difficult to model with SDMs that do not consider prior information, can be captured by the new models. Integrating prior knowledge as uncertain parameters in a Bayesian framework establishes ecological interpretable links between taxa and their environment and supports a systematic revision and complementation of databases on ecological preferences, even in case of poor or missing prior knowledge. Model outputs need to be carefully interpreted by modellers and experts on ecological preferences. Increased exchange between these research fields will benefit further integration of ecological preferences into SDMs.

1. Recent advances in molecular methods foster the documentation of small spatial scale biological diversity over large geographical areas. These advances allow to correctly record α-diversity, but also enable biomonitoring that describes intraspecific molecular diversity, providing valuable insights into the contemporary history of species. Such information is essential for the accurate monitoring of freshwater communities and provides a promising tool to identify conservation priorities at various spatial scales.
2. Here, we combined morphological species determinations with genetic characterisation via DNA barcoding and species distribution modelling. We aimed to investigate whether closely related amphipod species occupying overlapping ecological niches and occurring in partial sympatry, demonstrate similar spatial patterns of intraspecific genetic diversity and share comparable population histories. Therefore, we characterised the amphipod fauna within the Kinzig catchment (1,058 km², Hesse, Central Germany) that is a tributary of the Main River and part of the long-term ecological research network using genetics.
3. Our genetic analysis revealed two more taxonomic entities than previously known. The most common species was Gammarus fossarum clade 11 (or type B), followed by Gammarus roeselii clade C, Gammarus pulex clade D, G. pulex clade B and a very rare previously unknown lineage within the G. fossarum-species complex, which we refer to as G. fossarum clade RMO. These five taxa differed in their intraspecific genetic diversity, with G. fossarum clade 11 demonstrating the highest diversity and having a prominent small-scale pattern with endemic haplotypes in headwater regions. Distributions were predicted for the three most abundant molecularly identified species.
4. The upstream reaches maintained high intraspecific α- and β-diversity, pointing towards a more complex population structure of G. fossarum clade 11. This highlights the importance of considering intraspecific diversity for the conservation of individual species. DNA-based species distribution models shed light on species-specific habitat preferences, and showed spatial distribution patterns that supported ecological inference and conservation management. Barcoding specimens prior to modelling can increase robustness and performance of distribution models as juveniles can be incorporated, and cryptic species complexes disentangled.
5. Our integrative study contributes to the further development of science-informed and holistically considered effective conservation measures. Some poorly dispersing hololimnic species may serve as representatives for our understanding of the natural history of the local communities in headwater regions - and their protection. Intraspecific genetic diversity should be considered in conservation management decisions as it can provide valuable information on past and present population demography, connectivity, and recovery processes of species—information that rarely can be achieved by traditional monitoring approaches.

Synchrotron hard X-ray spectroscopy with focussing optics allows recording X-ray fluorescence (XRF) maps at energies around element specific X-ray absorption edges. Stacking multiple XRF maps along the energy axis yields chemical images that contain spatially resolved information on the speciation of the absorber in the sample matrix at the micrometre scale. Short dwell times needed to keep measurement time and radiation-induced sample changes within acceptable limits result in spectral noise that affects the uncertainty in data analysis. In this study, we develop a model to quantify the uncertainty associated with the processing of XRF image stacks using Bayesian inference. To demonstrate the potential of our approach, the model is applied to stacks of XRF maps collected around the copper (Cu) K-edge (pixel size: 3 × 3 μm², map sizes: 500 × 500 μm²). The investigated samples include digested sewage sludge spiked with either CuO nanoparticles (NP) or dissolved CuSO₄ and their corresponding ashes obtained through incineration. The chemical imaging data reveal differences in species distribution between sludge spiked with CuO NP or dissolved Cu. These differences disappear during the incineration process and the resulting ashes exhibit almost identical Cu species distribution. The uncertainty analysis approach developed in this study can be used for data interpretation, but can also be used for the planning of chemical imaging experiments at synchrotron beamlines.

Climate change can affect the provision of ecosystem services in various ways. In this study, we provide an integrated assessment of climate change impacts on ecosystem services, considering uncertainties in both climate projection and model parameterization. The SWAT model was used to evaluate the impacts on water regulation, freshwater, food, and erosion regulation services for the Broye catchment in Western Switzerland. Downscaled EURO-CORDEX projections were used for three periods of thirty years: base climate (1986–2015), near future (2028-2057), and far future (2070-2099). Results reveal that in the far future, low flow is likely to decrease in summer by 77% and increase in winter by 65%, while peak flow may decrease in summer by 19% and increase in winter by 26%. Reduction in summer precipitation reduces nitrate leaching by 25%; however, nitrate concentrations are projected to increase by 14% due to reduced dilution. An increase in winter precipitation increases nitrate leaching by 44%, leading to an increase of nitrate concentration by 11% despite increasing discharge and dilution. Yields of maize and winter wheat are projected to increase in the near future but decrease in the far future because of increasing water and nutrient stress. Average grassland productivity is projected to benefit from climate change in both future periods due to the extended growing season. This increase in productivity benefits erosion regulation as better soil cover helps to decrease soil loss in winter by 5% in the far future. We conclude that water regulation, freshwater and food services will be negatively affected by climate change. Hence, agricultural management needs to be adapted to reduce negative impacts of climate change on ecosystem services and to utilize emerging production potentials. Our findings highlight the need for further studies of potentials to improve nutrient and water management under future climate conditions.

The correlation length ξ, a key quantity in glassy dynamics, can now be precisely measured for spin glasses both in experiments and in simulations. However, known analysis methods lead to discrepancies either for large external fields or close to the glass temperature. We solve this problem by introducing a scaling law that takes into account both the magnetic field and the time-dependent spin-glass correlation length. The scaling law is successfully tested against experimental measurements in a CuMn single crystal and against large-scale simulations on the Janus II dedicated computer.

Heavy metal (HM) pollution is a serious and urgent issue in integrated watershed management in China and worldwide. Determining effective management strategies for pollution control requires quantification of input and output metal fluxes and the inherent uncertainties. Process-based metal models can simulate the metal movement on a watershed scale providing essential information for metal budget. However, there has been little effort to quantify the uncertainties in metal flux simulations. In this work, we modified the previously developed SWAT-HM (Soil and Water Assessment Tool - Heavy Metal) model by adding two external model inputs (atmospheric deposition and agricultural-source input) and one process-based module (plant uptake). We then linked the modified model with the SWAT Calibration and Uncertainty Programs SWAT-CUP for stochastic calibration and uncertainty analysis. The modified SWAT-HM was used to model the fluxes of primary inputs and outputs of zinc (Zn) in both uplands (soil) and channel (bed sediment) in the upper Liuyang River watershed in south-central China. To calibrate the model with uncertainty analysis, we used six-years of daily streamflow, daily sediment load, and daily Zn load at monthly frequency at the watershed outlet. In the upland phase, we identified Zn input from atmospheric deposition and Zn output through soil erosion as the most significant fluxes. In the channel phase, bed sediment was the critical Zn sink receiving 5,100 to 42,000 kg yr^-1 Zn. The method used in SWAT-HM calibration and uncertainty analysis is general with potential application to similar settings in the world.

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT's database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

The widespread application of deterministic hydrological models in research and practice calls for suitable methods to describe their uncertainty. The errors of those models are often heteroscedastic, non-Gaussian and correlated due to the memory effect of errors in state variables. Still, residual error models are usually highly simplified, often neglecting some of the mentioned characteristics. This is partly because general approaches to account for all of those characteristics are lacking, and partly because the benefits of more complex error models in terms of achieving better predictions are unclear. For example, the joint inference of autocorrelation of errors and hydrological model parameters has been shown to lead to poor predictions. This study presents a framework for likelihood functions for deterministic hydrological models that considers correlated errors and allows for an arbitrary probability distribution of observed streamflow. The choice of this distribution reflects prior knowledge about non-normality of the errors. The framework was used to evaluate increasingly complex error models with data of varying temporal resolution (daily to hourly) in two catchments. We found that (1) the joint inference of hydrological and error model parameters leads to poor predictions when conventional error models with stationary correlation are used, which confirms previous studies; (2) the quality of these predictions worsens with higher temporal resolution of the data; (3) accounting for a non-stationary autocorrelation of the errors, i.e. allowing it to vary between wet and dry periods, largely alleviates the observed problems; and (4) accounting for autocorrelation leads to more realistic model output, as shown by signatures such as the flashiness index. Overall, this study contributes to a better description of residual errors of deterministic hydrological models.

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity of the loss landscape and of the dynamics within it, and (2) to what extent DNNs share similarities with glassy systems. Our findings, obtained for different architectures and datasets, suggest that during the training process the dynamics slows down because of an increasingly large number of flat directions. At large times, when the loss is approaching zero, the system diffuses at the bottom of the landscape. Despite some similarities with the dynamics of mean-field glassy systems, in particular, the absence of barrier crossing, we find distinctive dynamical behaviors in the two cases, showing that the statistical properties of the corresponding loss and energy landscapes are different. In contrast, when the network is under-parametrized we observe a typical glassy behavior, thus suggesting the existence of different phases depending on whether the network is under-parametrized or over-parametrized.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a “pull” of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Aquatic ecosystems are under severe pressure. Human activities introduce an array of pressures that impact ecosystems and their components. In this study we focus on the aquatic domains of fresh, coastal and marine waters, including rivers, lakes and riparian habitats to transitional, coastal as well as shelf and oceanic habitats. In an environmental risk assessment approach, we identified impact chains that link 45 human activities through 31 pressures to 82 ecosystem components. In this linkage framework >22,000 activity-pressure-ecosystem component interactions were found across seven European case studies. We identified the environmental impact risk posed by each impact chain by first categorically weighting the interactions according to five criteria: spatial extent, dispersal potential, frequency of interaction, persistence of pressure and severity of the interaction, where extent, dispersal, frequency and persistence account for the exposure to risk (spatial and temporal), and the severity accounts for the consequence of the risk. After assigning a numerical score to each risk criterion, we came up with an overall environmental impact risk score for each impact chain. This risk score was analysed in terms of (1) the activities and pressures that introduce the greatest risk to European aquatic domains, and (2) the aquatic ecosystem components and realms that are at greatest risk from human activities. Activities related to energy production were relevant across the aquatic domains. Fishing was highly relevant in marine and environmental engineering in fresh waters. Chemical and physical pressures introduced the greatest risk to the aquatic realms. Ecosystem components that can be seen as ecotones between different ecosystems had high impact risk. We show how this information can be used in informing management on trade-offs in freshwater, coastal and marine resource use and aid decision-making.

Aim: Species distribution models (SDMs) are widely used to study geographic distributions of taxa in response to natural and anthropogenic environmental conditions. For a community, common approaches include fitting individual SDMs (iSDMs) to all taxa or directly modelling community properties such as richness. However, the parameters of iSDMs are difficult to identify for rare taxa, and community properties do not reveal taxon‐specific responses. Individual models can be combined into a hierarchical multispecies distribution model (mSDM) that constrains taxon‐specific parameters according to overarching community parameters, or a joint model (jSDM) in which interdependencies between taxa are jointly inferred. We compare how individual, hierarchical multispecies and joint SDMs differ in quality of fit, explanatory power and predictive performance, and analyse how these properties depend on the prevalence of taxa.
Taxa: Presence–absence observations of 245 benthic macroinvertebrate taxa identified at a mixed taxonomic resolution.
Location: Four hundred and ninety‐two sites in rivers throughout Switzerland.
Methods: Individual, hierarchical and joint hierarchical generalized linear models (GLM) were developed for all taxa. Parameters were estimated using maximum likelihood estimation or Bayesian inference with Hamiltonian Markov chain Monte Carlo simulations. Predictive performance was assessed with cross‐validation. In addition, the predicted family and species richness of the models was compared with a GLM for richness.
Results: Individual models show a slightly higher quality of fit largely due to overfitting for rare taxa. The mSDM achieves a similar quality of fit and explanatory power, mitigates overfitting for rare taxa and considerably improves predictive performance over the whole community. The joint models further improve the quality of fit, but decrease predictive performance and increase predictive uncertainty.
Main conclusions: We show that even a relatively simple mSDM combines many of the analytical capabilities of iSDMs and improves predictive performance. Increasingly complex mSDMs and jSDMs provide additional analytical possibilities, but depending on the data and research questions, different levels of complexity may be appropriate.

The capacity of ecosystems to supply ecosystem services is decreasing. Sustaining this supply requires an understanding of the links between the impacts of pressures introduced by human activities and how this can lead to changes in the supply of services. Here, we apply a novel approach, assessing ‘risk to ecosystem service supply’ (RESS), across a range of aquatic ecosystems in seven case studies. We link aggregate impact risk from human activities on ecosystem components, with a relative score of their potential to supply services. The greatest RESS is found where an ecosystem component with a high potential to supply services is subject to high impact risk. In this context, we explore variability in RESS across 99 types of aquatic ecosystem component from 11 realms, ranging from oceanic to wetlands. We explore some causes of variability in the RESS observed, including assessment area, Gross Domestic Product (GDP) and population density. We found that Lakes, Rivers, Inlets and Coastal realms had some of the highest RESS, though this was highly dependent on location. We found a positive relationship between impact risk and service supply potential, indicating the ecosystem components we rely on most for services, are also those most at risk. However, variability in this relationship indicates that protecting the supply of ecosystem services alone will not protect all parts of the ecosystem at high risk. Broad socio-economic factors explained some of the variability found in RESS. For example, RESS was positively associated with GDP and artificial and agricultural land use in most realms, highlighting the need to achieve balance between increasing GDP and sustaining ecosystem health and human wellbeing more broadly. This approach can be used for sustainable management of ecosystem service use, to highlight the ecosystem components most critical to supplying services, and those most at risk.

In this study, we investigate whether commercial microwave links (CML) can bridge the gap between rain gauge and radar. We performed a rainfall-runoff monitoring experiment with rain gauges, CMLs and weather radar in a small urbanized catchment during several months. The results of comparing rainfall data and resulting sewer flows suggest that quantitative precipitation estimates from CMLs require adjustment to local rain gauges to produce satisfactory results in a similar fashion as weather radar. In our study, CML with baseline removal and wet-antenna correction, but without adjustment, lead to substantial bias and underestimate rainfall-runoff ratios by more than 48% (Radar: ca. 40%). In contrast, adjusted CML produced results very similar to those from a dense rain gauge network and underestimated rainfall-runoff by about 11%. Further work is needed to better understand influential factors on the data quality of QPEs from CML. In addition, incentives are needed to motivate both telecommunication companies to provide CML data and weather service companies to use them in their products, i.e. commercializing information from opportunistic sensors.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Good decision-making requires the joint consideration of two aspects: knowledge about consequences of decision alternatives and knowledge about values, which can be expressed as decision objectives and preferences. Obtaining information on potential consequences and on stakeholder values, structuring this information, and integrating it in a meaningful way presents challenges for environmental decisions. Traditionally, natural and engineering sciences are concerned with improving the prediction of potential consequences. In this dissertation, the focus is on values, with the aim of a better integration of both aspects: How can we improve the inclusion of subjective values, expressed as objectives and preferences, in decision-making? [...]

Um gute Entscheidungen zu treffen, müssen zwei Aspekte betrachtet werden: Zum einen braucht es Wissen über die Konsequenzen der Entscheidungsalternativen, zum anderen müssen Werte, die in Form von Zielen und Präferenzen ausgedrückt werden können, einbezogen werden. Informationen zu den möglichen Konsequenzen und zu den Werten von Akteuren zu sammeln, sie zu strukturieren und schlussendlich zusammenzuführen, sind Herausforderungen für die Entscheidungsfindung im Umweltbereich. Natur- und Ingenieurswissenschaften bemühen sich traditionellerweise vor- wiegend darum, die Abschätzung von Konsequenzen zu verbessern. In dieser Dissertation liegt der Fokus auf den Werten, mit dem Ziel einer besseren Integration dieser beiden Aspekte für die Entscheidungsfindung: Wie können wir den Einbezug subjektiver Werte - Ziele und Präferenzen - in Entscheidungen verbessern? [...]

Multi-criteria decision analysis (MCDA) requires an accurate representation of the preferences of decision-makers, for instance in the form of a multi-attribute value function. Typically, additivity or other stringent assumptions about the preferences are made to facilitate elicitation by assuming a simple parametric form. When relaxing such assumptions, parameters cannot be elicited easily with standard methods. We present a novel approach for identifying multi-attribute value functions which can have any shape. As preference information indifference statements are used that can be elicited by trade-off questions. Instead of asking one indifference statement for each pair of attributes, we ask for multiple trade-offs at different points in the attribute space. This allows inferring parameters of complex value functions despite the simplicity of the preference statements. Parameters are estimated by taking into account preference and elicitation uncertainty with a probabilistic model. Statistical inference supports identifying the most adequate preference model out of several candidate models through quantifying the uncertainty and assessing the need for non-additivity. The approach is elaborated for determining value functions by hierarchical aggregation. We apply it to an assessment of the ecological state of rivers, which is used to support environmental management decisions in Switzerland. Preference models of four experts were quantified, confirming the feasibility of the approach and the relevance of considering non-additive functions. The method suggests a promising direction for improving the representation of preferences.

Decision-making in environmental management requires eliciting preferences of stakeholders and predicting outcomes of decision alternatives. Usually, preferences and predictions are both uncertain. Uncertainty of predictions can be tackled by multi-attribute utility theory, but the uncertainty of preferences remains a challenge. We demonstrate an approach for including both uncertainties in a multi-criteria decision analysis (MCDA), using utility theory and the concept of expected expected utility. For a decision regarding a regional merger of wastewater infrastructure in Switzerland, we constructed preference models for four stakeholders. These models also allowed for non-additive interactions between objectives. We evaluated the performance of eleven decision alternatives for which we predicted potential outcomes. Even though uncertainties were high, we could draw conclusions based on the expected expected utility of alternatives. Building a pipeline to discharge treated wastewater to a larger river emerged as a potential consensus alternative to mitigate the problem of micropollutants in a small stream. We investigated the robustness of the findings with sensitivity analysis regarding the preference parameters and the included objectives. In their actual decision, the stakeholders partly preferred other alternatives than those proposed by the model. Their choices could be explained by reduced decision models in which only few objectives were included. This may indicate the use of simplified choice heuristics by the stakeholders. The presented approach is feasible for supporting other difficult environmental or engineering decisions in practice, for which we give a number of recommendations.

Domestic Hot Water (DHW) systems are large energy consumers in newly-built residential buildings. Mitigation measures involve more efficient hot water appliances and distribution systems, waste heat recovery systems, or changes in consumer habits. However, the implementation of these measures must be investigated carefully, as combinations may lead to unforeseen systemic interactions limiting their potential. In this article, we present tools to identify and optimize these interactions. The WaterHub modules were developed for Material and Energy Flow Analyses (MEFA) of domestic hot water systems. Two modules are available: (i) the WaterHub Modelica library includes models for MEFA system definition, and (ii) The HydroGen Python module provides methods for the stochastic generation of appliance-specific hydrographs, used as input data for the simulation of the system energy and water flows. First, we describe the technical aspects of these modules. Second, we provide an example of how they may be used in a didactic scenario analysis of a heat recovery device.

Drought events have significant impacts on agricultural production in Sub-Saharan Africa (SSA), as agricultural production in most of the countries relies on precipitation. Socio-economic factors have a tremendous influence on whether a farmer or a nation can adapt to these climate stressors. This study aims to examine the extent to which these factors affect maize vulnerability to drought in SSA. To differentiate sensitive regions from resilient ones, we defined a crop drought vulnerability index (CDVI) calculated by comparing recorded yield with expected yield simulated by the Environmental Policy Integrated Climate (EPIC) model during 1990–2012. We then assessed the relationship between CDVI and potential socio-economic variables using regression techniques and identified the influencing variables. The results show that the level of fertilizer use is a highly influential factor on vulnerability. Additionally, countries with higher food production index and better infrastructure are more resilient to drought. The role of the government effectiveness variable was less apparent across the SSA countries due to being generally stationary. Improving adaptations to drought through investing in infrastructure, improving fertilizer distribution, and fostering economic development would contribute to drought resilience.

The majority of hydrological and environmental models contain parameters that must be specified before the model can be used. Parameter estimation is hence a very common problem in environmental sciences and has received tremendous amount of research and industry attention. This chapter reviews some of the key principles of parameter estimation, with a focus on calibration approaches and uncertainty quantification. The distinct approaches of manual calibration, optimization, multi-objective optimization, and probabilistic approaches are described in terms of key theory and representative applications. Advantages and limitations of these strategies are listed and discussed, with a focus on their ability to represent parametric and predictive uncertainties. The role of posterior diagnostics to check calibration and model assumptions that impact on parameter estimation is emphasized. Auxiliary tricks and techniques are described to simplify the process of parameter estimation in practical applications. The chapter concludes with an outline of directions for ongoing and future research. It is hoped that this chapter will help hydrologists and environmental modellers get to the current state of research and practice in model calibration, parameter estimation, and uncertainty quantification.

Shower systems, related hot-water systems and pipe losses dominate residential water-related energy. However, many time-based technological, behavioural and environmental factors influence water use and related energy consumption. This has meant that, to date, the performance of these systems has been relatively poorly understood. Here we build on earlier stationary modelling and analysis to develop and apply a Dynamic Water-Related Energy Material Flow Analysis Model in Households (DYNWAREHO). We use this to evaluate a highly monitored household to answer "What are the key energy pathways, their relative importance, and the key factors of influence, in household shower and hot-water supply systems? We explore how significantly hot-water losses can be influenced by showering behaviour and/or technological changes such as pipe insulation. We show that in the household studied, over 85% of the energy used for water heating flows as waste down the drain. Losses are the second largest energy user and account for more than 10% in the studied household. Energy use for actual "showering" accounts for less than 5% of the total. New technologies could influence this significantly. This result clarifies the significant wastage of energy inherent in the design and use of current shower systems. The analysis shows various options designed to improve design and management; it was made possible by the creation of the DYWAREHO model, applicable to any household, and calibrated on a specific Australian household. If acted upon through policy decisions and an appropriate design of shower and wastewater systems, the research can substantially improve the management of water-related energy.

Freshwater ecosystems are increasingly under threat as they are confronted with multiple anthropogenic impairments. This calls for comprehensive management strategies to counteract, or even prevent, long-term impacts on habitats and their biodiversity, as well as on their ecological functions and services. The basis for the efficient management and effective conservation of any ecosystem is sufficient knowledge on the state of the system and its response to external influence factors. In freshwater ecosystems, state information is currently drawn from ecological assessments at the reach or site scale. While these assessments are essential, they are not sufficient to assess the expected outcome of different river restoration strategies, because they do not account for important characteristics of the whole river network, such as habitat connectivity or headwater reachability. This is of particular importance for the spatial prioritization of restoration measures. River restoration could be supported best by integrative catchment-scale ecological assessments that are sensitive to the spatial arrangement of river reaches and barriers. Assessments at this scale are of increasing interest to environmental managers and conservation practitioners to prioritize restoration measures or to locate areas worth protecting. We present an approach based on decision support methods that integrates abiotic and biotic ecological assessments at the reach-scale and aggregates them spatially to describe the ecological state of entire catchments. This aggregation is based on spatial criteria that represent important ecological catchment properties, such as fish migration potential, resilience, fragmentation and habitat diversity in a spatially explicit way.We identify the most promising assessment criteria from different alternatives based on theoretical considerations and a comparison with biological indicators. Potential applications are discussed, particularly for supporting the strategic, long-term planning and spatial prioritization of restoration measures.

Der Schweizer Makrozoobenthos-Index IBCH geniesst für die Zustandsbewertung von Fliessgewässern bei den Kantonen eine hohe Akzeptanz. In den letzten zehn Jahren zeigte sich aber, dass nicht nur anthropogene Faktoren die Bewertung beeinflussen, sondern auch natürliche wie die Höhenlage oder die biogeografische Region. Basierend auf einer Analyse der bisherigen Ergebnisse wurde der IBCH_2019 entwickelt, der diese Mängel korrigiert und eine plausiblere Bewertung der Gewässer erlaubt.

The AQUACROSS project was an unprecedented effort to unify policy concepts, knowledge, and management of freshwater, coastal, and marine ecosystems to support the cost-effective achievement of the targets set by the EU Biodiversity Strategy to 2020. AQUACROSS aimed to support EU efforts to enhance the resilience and stop the loss of biodiversity of aquatic ecosystems as well as to ensure the ongoing and future provision of aquatic ecosystem services. The project focused on advancing the knowledge base and application of Ecosystem-Based Management. Through elaboration of eight diverse case studies in freshwater and marine and estuarine aquatic ecosystem across Europe covering a range of environmental management problems including, eutrophication, sustainable fisheries as well as invasive alien species AQUACROSS demonstrated the application of a common framework to establish cost-effective measures and integrated Ecosystem-Based Management practices. AQUACROSS analysed the EU policy framework (i.e. goals, concepts, time frames) for aquatic ecosystems and built on knowledge stemming from different sources (i.e. WISE, BISE, Member State reporting within different policy processes, modelling) to develop innovative management tools, concepts, and business models (i.e. indicators, maps, ecosystem assessments, participatory approaches, mechanisms for promoting the delivery of ecosystem services) for aquatic ecosystems at various scales of space and time and relevant to different ecosystem types.

1. Freshwater ecosystems are under a constant risk of being irreversibly damaged by human pressures that threaten their biodiversity, the sustainability of ecosystem services (ESs), and human well‐being. Despite the implementation of various environmental regulations, the challenges of safeguarding freshwater assets have so far not been tackled successfully.
2. A promising way forward to stop the loss of freshwater biodiversity and to sustain freshwater‐based ESs is by implementing ecosystem‐based management (EBM), an environmental planning and adaptive management approach that jointly considers social and ecological needs. Responsible for considerable recent success in sustainably managing and conserving marine ecosystems, EBM has not yet been championed for fresh waters.
3. A major reason for the delayed uptake of EBM in fresh waters is likely to be its complexity, requiring planners to be familiar with the latest developments in a range of different research areas. EBM would therefore benefit from becoming more tangible to receive attention on the ground.
4. To facilitate uptake, eight core research areas for EBM and their innovations are introduced, and the way in which they feed into the workflow that guides the EBM planning stage is explained.
5. The workflow links biodiversity distributions with ES supply‐and‐demand modelling and SMART (specific, measurable, attainable, relevant, and timely) target planning, including scenario‐ and cross‐realm perspectives, the prioritization of management alternatives, spatial prioritization of biodiversity conservation and ES areas, and the quantification of uncertainties. Given the extensive resources, time, and technical capacity required to implement the full workflow, a light and an ultralight version of the workflow are also provided.
6. Applied in concert, the eight well‐known research areas allow for better planning and operationalizing, and eventually for implementing EBM in freshwater ecosystems. EBM has great potential to increase public acceptance by introducing the consideration of human needs and aspirations into typically biodiversity‐driven conservation and management approaches. This will ultimately improve the integrity of freshwater ecosystems.

The objective of the SWATCH21 project is to improve our understanding of eco-hydrologic services at the catchment level, and biodiversity at the river scale. Six research questions are proposed: (i) How can we improve the access to input data for hydrological and ecological modeling? (ii) What is the role of glacier and snow in modifying the hydrological services? (iii) How can we best assess hydrologic services supplies and demands with the available data and tools? (iv) What will be the impact of the main hydrologic changes on species diversity in rivers? (v) Can we meet the targets of multi-sectorial river-related policies under different climate and landuse forecasting scenarios? (vi) How detailed do ES data and models need to be to answer relevant policy questions? The above questions are tackled through an integrated framework to access, share, process, model, and deliberate on hydrologic ecosystems services. State-of-the-art models have been selected, and will be compared and improved to model different ecosystems and their services. Initial results from a first SWAT model of Switzerland and Species Distribution Models are presented. Expected outputs from various climate and land use change scenarios include rivers' hydrology, predicted biodiversity, and the assessment of ecosystem services in terms of provisioning services (e.g. water resources), regulating services (e.g. nutrient, sediment and flood water retention), and cultural services (e.g. biodiversity, recreation). The expected outcome of the project is to improve integrated evidence-based water policy in the future through the analysis of tradeoffs and synergies between services.

In this study, we examine the spatial and temporal characteristics of water stress in China for the historical (1971–2010) and the future (2021–2050) periods using a multimodel simulation approach. Three water stress indices (WSIs), that is, the ratios of water withdrawals to locally generated runoff (WSI_R), to natural streamflow (WSI_Q), and to natural streamflow minus upstream consumptive water withdrawals (WSI_C), are used for the assessment. At the basin level, WSI_R estimates generally match the reported data and indicate severe water stress in most northern basins. At the grid cell level, the WSIs show distinct spatial patterns of water stress wherein WSI_R (WSI_Q) estimates higher (lower) water stress compared to WSI_C. Based on the WSI_C estimates, 368 million people (nearly one third of the total population) are affected by severe water stress annually during the historical period, while WSI_R and WSI_Q suggest 595 and 340 million, respectively. Future projections of WSI_C indicate that more than 600 million people (43% of the total) might be affected by severe water stress, and half of China's land area would be exposed to stress. The found aggravating water stress conditions could be partly attributed to the elevated future water withdrawals. This study emphasizes the necessity of considering explicit upstream and downstream relations with respect to both water availability and water use in water stress assessment and calls for more attention to increasing levels of water stress in China in the coming decades.

Human activities, as well as climate change, have had increasing impacts on natural hydrological systems, particularly streamflow. However, quantitative assessments of these impacts are lacking on large scales. In this study, we use the simulations from six global hydrological models driven by three meteorological forcings to investigate direct human impact (DHI) and climate change impact on streamflow in China. Results show that, in the sub-periods of 1971–1990 and 1991–2010, one-fifth to one-third of mean annual streamflow (MAF) reduced due to DHI in northern basins and much smaller (< 4 %) MAF reduced in southern basins. From 1971–1990 to 1991–2010, total MAF changes range from −13 % to 10 % across basins, wherein the relative contributions of DHI change and climate change show distinct spatial patterns. DHI change caused decreases in MAF in 70 % of river segments, but climate change dominated the total MAF changes in 88 % of river segments of China. In most northern basins, climate change results in changes of −9 % to 18 % of MAF, while DHI change results in decreases of 2 % to 8 % in MAF. In contrast with the impacts of climate change that may increase or decrease streamflow, DHI change almost always contributes to decreases in MAF over time, wherein water withdrawals are supposed to be the major impact on streamflow. This quantitative assessment can be a reference for attribution of streamflow changes at large scales despite uncertainty remains. We highlight the significant DHI in northern basins and the necessity to modulate DHI through improved water management towards a better adaptation to future climate change.

Climate change alters hydrological processes and results in more extreme hydrological events, e.g. flooding and drought, which threaten human livelihoods. In this study, the large-scale distributed variable infiltration capacity (VIC) model was used to simulate future hydrological processes in the Yarlung Zangbo River basin (YZRB), China, with a combination of the CMIP5 (Coupled Model Intercomparison Project, fifth phase) and MIROC5 (Model for Interdisciplinary Research on Climate, fifth version) datasets. The results indicate that the performance of the VIC model is suitable for the case study, and the variation in runoff is remarkably consistent with that of precipitation, which exhibits a decreasing trend for the period 2046–2060 and an increasing trend for 2086–2100. The seasonality of runoff is evident, and substantial increases are projected for spring runoff, which might result from the increase in precipitation as well as the increase in the warming-induced melting of snow, glaciers and frozen soil. Moreover, evapotranspiration exhibits an increase between 2006–2020 and 2046–2060 over the entire basin, and soil moisture decreases in upstream areas and increases in midstream and downstream areas. For 2086–2100, both evapotranspiration and soil moisture increase slightly in the upstream and midstream areas and decrease slightly in the downstream area. The findings of this study could provide references for runoff forecasting and ecological protection for similar studies in the future.

International food trade entails virtual water flows across trading partners. It has been proposed to attenuate regional water scarcity by importing water‐intensive commodities from water‐abundant regions. In addition to alleviating water scarcity in virtual water importing countries, existing studies have reported that food trade also generates global water savings. However, little is known how these global water savings may alleviate water scarcity, which is more relevant to the sustainable use of water resources than only assessing the savings. In this paper, we conducted a comprehensive review on studies of water savings and losses associated with food trade on different spatial scales. We found that the concept of global water savings is built on the disparities in water productivity across countries, whereas the regional water savings measure the inflows of virtual water trade. The significance of water savings is dimmed by the fact that the savings are often not driven by water scarcity. Meanwhile, lacking policy relevance impairs the usefulness of water saving accounting. Future studies should link water savings to alleviating water scarcity at various levels. The water saving accounting needs to go to finer scale, for example, to subnational and river basin scales, to support real water resource management. In the meantime, interdisciplinary efforts are necessary to enhance the water savings as a holistic measure for addressing water scarcity on regional and global scales.

In addition to high temperature, high humidity can have significant consequences on thermal comfort of human beings. The co-occurrence of high temperature and high humidity (so-called 'oppressive hot days') often results in heat stress events, but the extent to which it is affected by preceding surface moisture has not been fully understood to date. In this study, we examine the relations between preceding 3-month standardized precipitation index (SPI) and the number of hot days indicated by the surface air temperature (NHD-Tx) and the wet-bulb globe temperature (NHD-Wx) that combines both temperature and humidity in the hottest month in low latitudes. Results show that, in contrast with the negative correlations between SPI and NHD-Tx, which are associated with the previously reported precipitation deficit-temperature feedback, significant positive correlations between SPI and NHD-Wx are found in some low latitude areas. The probability of above-average NHD-Wx could be ~30% higher after wet conditions than that after dry conditions in areas like southern South America, some parts of Africa, and West Asia. Hotspot analyses further show that abundant preceding rainfall has an asymmetric impact on oppressive hot days by favoring more above-average NHD-Wx. Our analyses imply that a local feedback may exist between surface moisture and oppressive hot extremes, via which the unbearable heat stress over some parts of the tropics is modulated, controlled, and/or caused by changes in the preceding near-surface humidity/soil moisture. The spatially heterogeneous patterns of the relations between preceding rainfall and heat stress confirm the precipitation deficit-temperature feedback in many areas and reveal the coexistence of surface moisture-oppressive heat stress in several low latitude areas. We emphasize the necessity of considering both feedbacks for a better understanding of the distinct roles of preceding rainfall in the consequent development of heat stress in low latitudes.

The water–energy nexus (WEN) is dynamic and complicated in megacities, most of which are challenged by water scarcity and the mandate to reduce energy consumption. A salient feature of water and energy services in megacities is that they are supported by a web of regional infrastructure, extending far beyond the geographic boundaries of the cities, resulting in a strong dependence on resources imported from outside. Understanding the WEN of megacities has implications not only for more efficient resource utilization but also for synergistic regional development and corporation. This study provides a quantitative assessment of the WEN of Beijing within and beyond its geographic boundaries. Results show that water for local internal energy production and transformation accounts for 220 million m³/year, or 5.6% of its total freshwater use in 2016, and the energy for local water abstraction, supply, and treatment is 3.06 billion kWh, accounting for 1.1% of its total energy consumption for the same year. The external water for "imported" energy is 290 million m³/year, 1.3 times of Beijing's freshwater use for internal energy. This means that more water for energy is consumed outside Beijing than that within Beijing. The energy for external water is negligible because the bulk of the water transfer into Beijing relies on gravity and because the energy for construction of the transfer infrastructure is not included. Analysis of the WEN revealed the contradiction between the two independently conceived policies of Beijing: to meet the "three-red-line" target in the water sector, recycled water and transferred water use will rise, making it more difficult to meet the carbon emission control targets. Therefore, adopting low energy intensity, nature-based water recycling is a wise policy choice.

Optimizing water–food–energy (WFE) relations has been widely discussed in recent years as an effective approach for formulating pathways toward sustainable agricultural production and energy supply. However, knowledge regarding the WFE nexus is still largely lacking, particularly beyond the conceptual description. In this study, we combined a grid-based crop model (Python-based Environmental Policy Integrated Climate—PEPIC) with a hydropower scheme based on the Distributed Biosphere Hydrological (DBH) model to investigate the WFE interplays in China concerning irrigated agricultural production and hydropower potential. The PEPIC model was used to estimate crop yields and irrigation water requirements under various irrigated cropland scenarios, while the DBH model was applied to simulate hydrological processes and associated hydropower potential. Four major crops, i.e., maize, rice, soybean, and wheat, were included for the analyses. Results show that irrigation water requirements present high values (average about 400 mm yr⁻¹) in many regions of northern China, where crop yields are much higher on irrigated land than on rainfed land. However, agricultural irrigation has largely reduced hydropower potential up to 50% in some regions due to the substantial withdrawal of water from streams. The Yellow River basin, the Hai River basin, and the Liao River basin were identified as the hotspot regions concerning the WFE interactions and tradeoffs. Further expansion the irrigated cropland would increase the tradeoffs between supporting sustainable food production and conserving hydropower potential in many parts of China. The results provide some insights into the WFE nexus and the information derived is useful for supporting sustainable water management, food production while conserving the potential for hydropower generation in China.

Niederschlags- und Mischabwasserentlastungen aus Siedlungen tragen zu Mikroverunreinigungen in Gewässern bei. Mit Passivsammlern wurden erstmals Konzentrationsbereiche an 20 Standorten für insgesamt 95 Ereignisse bestimmt, was die Analyse von Unterschieden zwischen Standorten sowie die Identifikation relevanter Eintragspfade unterstützt. An 13 Standorten waren bei mindestens einer Substanz die Konzentrationen im Mischabwasser höher als akute Qualitätskriterien.

Contaminants in sewer overflows can contribute to exceedances of environmental quality standards, thus the quantification of contaminants during rainfall events is of relevance. However, monitoring is challenged by i) high spatiotemporal variability of contaminants in events of hard-to-predict durations, and ii) a large number of remote sites, which would imply enormous efforts with traditional sampling equipment. Therefore, we evaluate the applicability of passive samplers (Empore styrene-divinylbenzene reverse phase sulfonated (SDB-RPS)) to monitor a set of 13 polar organic contaminants. We present calibration experiments at high temporal resolution to assess the rate limiting accumulation mechanisms for short events (<36 h), report parameters for typical sewer conditions and compare passive samplers with composite water samples in a field study (three locations, total 10 events). With sampling rates of 0.35–3.5 L/d for 1 h reference time, our calibration results indicate a high sensitivity of passive samplers to sample short, highly variable sewer overflows. The contaminant uptake kinetic shows a fast initial accumulation, which is not well represented with the typical first-order model. Our results indicate that mass transfer to passive samplers is either controlled by the water boundary layer and the sorbent, or by the sorbent alone. Overall, passive sampler concentration estimates are within a factor 0.4 to 3.1 in comparison to composite water samples in the field study. We conclude that passive samplers are a promising approach to monitor a large number of discharge sites although it cannot replace traditional stormwater quality sampling in some cases (e.g. exact load estimates, high temporal resolution). Passive samplers facilitate identifying and prioritizing locations that may require more detailed investigations.

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

We present a novel stochastic model for quantifying gross solids (GS) physical disintegration under varying turbulent flow conditions and used a unique experimental setup for model calibration and validation. The stochastic deterioration model predicts faeces size evolution over time. It conceptually entails the two main processes of solid fragmentation, namely breakage and erosion. Model parameters were calibrated on synthetic faeces and validated with real human ones. A cylindrical reactor was used, where turbulent flow was forced by an array of water jets and the physical disintegration of the faeces was monitored using a high speed camera. Image analysis of breakage experiments obtained under backlight illumination allowed determination of the evolution of the solids’ size over time. The flow field in the reactor was characterised by particle image velocimetry (PIV) using tracer particles seeded into the water. We found different disintegration behaviours depending on turbulence intensity and water content of the solid. In conditions of low shear stress, dense solids hardly disintegrated. Generally, the model predictions mirrored the broad range in the solids disintegration rate imparted by the high variability in flow conditions and in solids characteristics. It is expected that, similar to our experiments, also in real sewer systems both flow conditions and solid characteristics are highly variable and the stochastic model can be tailored to capture this variability. We thus anticipate that the model can be integrated into existing sewer models predicting sewer flows and solids’ movement. From these, shear stress, flow velocities and transport of individual solids can be inferred. The integration of the present solids disintegration model may provide better predictions of hot-spots for solids accumulation and blockages in sewers.

Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems' intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a data set of 461 empirical ecological time series. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically grounded basis for a model-free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.

Messungen von Oberflächenabflüssen werden selten im urbanen Raum durchgeführt, weil traditionelle Messgeräte zu teuer sind oder direkt über oder innerhalb des Fliessgewässers installiert werden müssen. Deswegen sind Daten über Überschwemmungen im urbanen Raum nur selten vorhanden, was die Kalibrierung und Validierung von Überflutungsmodellen erschwert. In dieser Studie wird ein Ansatz vorgestellt, bei dem marktübliche Überwachungskameras zur Durchflussmessung eingesetzt werden.

Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end‐members, and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end‐members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set, which includes helium (⁴He) analyzed on site to determine mixing ratios in groundwater. Thereby, we identify an unknown end‐member that contributes up to 84 ± 9% to the water mixture observed at our study site. For the ⁴He analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE‐MIMS), operated in the field. To test the reliability of on‐site ⁴He analysis, we compare results obtained with the GE‐MIMS to the conventional lab‐based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer‐aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied, and (ii) on‐site ⁴He analysis with the GE‐MIMS system is an accurate and reliable alternative to the lab‐based analysis.

Over the past decades, advances in data collection and machine learning have paved the way for the development of autonomous simulation frameworks. Among these, many are capable not only of assimilating real‐time data to correct their predictive shortcomings but also of improving their future performance through self‐optimization. In hydrogeology, such techniques harbor great potential for informing sustainable management practices. Simulating the intricacies of groundwater flow requires an adequate representation of unknown, often highly heterogeneous geology. Unfortunately, it is difficult to reconcile the structural complexity demanded by realistic geology with the simplifying assumptions introduced in many calibration methods. The particle filter framework would provide the necessary versatility to retain such complex information but suffers from the curse of dimensionality, a fundamental limitation discouraging its use in systems with many unknowns. Due to the prevalence of such systems in hydrogeology, the particle filter has received little attention in groundwater modeling so far. In this study, we explore the combined use of dimension‐reducing techniques and artificial parameter dynamics to enable a particle filter framework for a groundwater model. Exploiting freedom in the design of the dimension‐reduction approach, we ensure consistency with a predefined geological pattern. The performance of the resulting optimizer is demonstrated in a synthetic test case for three such geological configurations and compared to two Ensemble Kalman Filter setups. Favorable results even for deliberately misspecified settings make us hopeful that nested particle filters may constitute a useful tool for geologically consistent real‐time parameter optimization.

Despite the large literature about non-additive value aggregation techniques, in the large majority of applied decision support processes, additive value aggregation functions are used. The main reasons for this may be the simplicity of the approach, minimum elicitation requirements, software availability, and the appeal of the underlying preference independence concepts that may be strengthened by an adequate choice of sub-objectives and attributes. However, in an applied decision support process, the decision maker(s) or the stakeholders decide on the sub-objectives and attributes to characterize the state of a system and they have to provide information that allows the decision analyst to express their preferences as a value function of these attributes. It is the task of the decision analyst to find the parameterization and parameter values of a value function that fits best the expressed preferences. We describe a value function elicitation process for the ideal morphological state of a lake shore, performed with stakeholders from federal and cantonal authorities and from environmental consulting companies in Switzerland. This process led to the elicitation of strongly non-additive and partly even non-concave value aggregation functions. The objective of this paper is to raise the awareness about the importance of carefully testing the assumptions underlying parameterized (often additive) value aggregation techniques during the preferences elicitation process and to be flexible regarding evaluating value functions that deviate from the often used additive aggregation scheme. This can lead to a higher confidence that additive aggregation is suitable for the specific decision problem or to the selection of alternative aggregation techniques that better represent the decision maker’s preferences in case additivity is violated.

Mit einem statistischen Modell wurden schweizweite Monitoringdaten über die Zusammensetzung von Makrozoobenthosgemeinschaften in Schweizer Fliessgewässern ausgewertet. Temperatur, Landwirtschaft und Hydromorphologie wurden dabei als wichtige Einflussfaktoren im Modell identifiziert. Arten aus der gleichen Familie reagieren in vielen Fällen unterschiedlich auf natürliche und menschliche Umwelteinflüsse. Neben der taxonomischen Auflösung wirkt sich auch die Stellenauswahl auf die Aussagekraft der Resultate aus.

Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.

Global initiatives have been increasingly focusing on mainstreaming the values of biodiversity and ecosystem services into decision-making at all levels. Due to the accelerated rate at which biodiversity is declining and its consequences for the functioning of ecosystems and subsequently, the services they provide, there is need to develop comprehensive assessments of the services and the benefits nature delivers to society. Based on expert evaluation, we identified relevant flow linkages in the supply-side of the socio-ecological system, i.e. from biodiversity to ecosystem services supply for eight case studies across European aquatic ecosystems covering freshwater, transitional, coastal and marine waters realms. Biological mediated services were considered, as well as those reliant on purely physical aspects of the ecosystem, i.e. abiotic outputs, since both have implications for spatial planning, management and decision-making. Due to the multidimensional nature of ecosystems and their biodiversity, our approach used ecosystem components such as habitats and biota as proxies for biodiversity and as the focal point for linkage identification. Statistical analysis revealed the importance of considering mobile biota in the spatial assessment of habitats. Contrary to literature evidences so far, our results showed significantly different and complementary ecosystem services supply patterns across the continuum of aquatic realms. The implemented score of ecosystem services supply has a high potential for integrated aquatic ecosystem service supply assessments in the context of ecosystem-based management.

Iran is experiencing unprecedented climate-related problems such as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts, and floods. Here, we use the ensemble of five high-resolution climate models to project maximum and minimum temperatures and rainfall distribution, calculate occurrences of extreme temperatures (temperatures above and below the historical 95th and 5th percentiles, respectively), analyze compound of precipitation and temperature extremes, and determine flooding frequencies across the country. We found that compared to the period of 1980–2004, in the period of 2025–2049, Iran is likely to experience more extended periods of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 days: precipitation <2 mm, Tmax ≥30 °C) as well as wet (for ≤3 days: total precipitation ≥110 mm) conditions, and higher frequency of floods. Overall, the combination of these results projects a climate of extended dry periods interrupted by intermittent heavy rainfalls, which is a recipe for increasing the chances of floods. Without thoughtful adaptability measures, some parts of the country may face limited habitability in the future.

Inaccurate description of uncertainty in the error models can cause biases in parameter estimation. When the parameters of the deterministic model and the error model are inferred jointly from the observations, the posterior converges to regions that reflect the processes in both high and low flows. If the nature of errors in low and high flows is different to the extent that the same error description cannot be used for both, biases in inference are introduced. In such cases, the parameter posterior will adjust to the region of the hydrograph with longer proportionate presence in the calibration time series. In this paper we demonstrate that the autoregressive order 1 (AR1) description of errors can lead to sub-optimally performing predictive models if the calibration period has substantial sections of inadequately modelled flows. Inference is performed within the Bayesian framework. We show this for a synthetic example as well as a case study. We also see that the predictive uncertainty bands that we get using the AR1 description can be overconfident and also admit negative values. To mitigate this, we analyze an alternative to additive error models. We use a distribution with a non-negative support, gamma in this study, reflecting the uncertainty in the system response at every time step. The gamma distribution is conditioned on the deterministic model output, which determines its mode and standard deviation. We capture autocorrelation in time using copulas. Given that copulas can capture dependence between different marginals, we use different specifications of the marginal distribution for high and low flows. The results show that 1) biases in parameter estimation can be reduced if a representative error description is attained using the flexibility of a copula-based likelihood. 2) The non-negative support allows to make more realistic uncertainty intervals for low flows. 3) However, the autocorrelation parameter in copulas severely interacts with the model and heteroscedasticity parameters. 4) While the formulation, in principle, should be of added value for parameter inference, in case of less informative priors, the flexibility of this description can produce non-robust inference.

Physical water consumption (PW) in economic sectors are linked with virtual water consumption (VW) embodied in the supply chain of commodities. It is important to quantitatively assess PW and VW in economic sectors and understand interconnections between them for supporting water resources management. Here, we applied an IO framework to analyse how the PW is linked to the VW from both production and consumption perspectives. A water scarce region consisting of Ganzhou, Linze, and Gaotai counties (GLG) in the Heihe river basin in China is used as a case study. The agricultural sector is broken down into seven sub-sectors represented by individual major crops. From production perspective, the results show that the agricultural sector accounted for 98.1% of total PW, which can be divided into 79.0% of final demand driven water consumption and 21% of intermediate demand driven water consumption. From consumption perspective, the VW of all the sub-sectors in the agricultural sector was largely attributed to the direct VW. The sector “Food and tobacco processing” (the downstream of the agricultural sector) has the largest indirect VW among all the sectors. Most PW in GLG was used to produce the low value added primary agricultural products to fulfil the final demand (VW) outside GLG. The results suggest that the water resources management in arid regions should guide the restructuring of economy from increasing agricultural production to increasing the economic value of the embodied virtual water, and reducing the export of the virtual water from the low economic value products.

The increased number and capacity of municipal wastewater treatment plants (WWTPs) in China has driven the emission of methane (CH₄). Few studies have focused on quantification of CH₄ emissions from municipal WWTPs of different cities and analysis of socioeconomic factors influencing the quantity of emissions. Here we estimated CH₄ emissions from WWTPs in China for 229 prefectural‐level cities, based on data from 2,019 working municipal WWTPs. The results show the total CH₄ emissions to be 1,169.8 thousand tons (29.2 MtCO₂e) in 2014, which is over three times that of the municipal WWTPs in the United States in 2016. Large cities along the east coast regions had larger CH₄ emissions in absolute and per capita terms. Correlation analysis shows that cities with higher gross domestic product, household food consumption expenditure, or household consumption expenditure produced more degradable organics in wastewater, thus more CH₄ emissions. Measures to control the sources of degradable organics and regulate WWTP processes with less emission factor are key to mitigate CH₄ emissions. In addition to aerobic or anaerobic wastewater treatment systems, factors such as wastewater temperature, length of sewer, and the addition of nitrate that influencing emission factor are suggested to be involved in CH₄ emission modeling.

Weather generators (WGs) are tools that create synthetic weather data, which are statistically similar to the observed data. Considering the limitations of most rainfall generators to preserve satisfactorily the fundamental statistical properties (e.g., spatial and temporal coherence) simultaneously, we propose a new framework for multisite rainfall generation. The framework consists of three main components: (1) a spatiotemporal rainfall field, described as spatial modes and their corresponding temporal evolution based on empirical orthogonal function analysis (EOFA). (2) The time series of these spatial modes, decomposed into intrinsic mode functions (IMFs) with characteristic frequencies (periods) using Hilbert-Huang transform (HHT). (3) Stochastic simulation (SS), achieved by assigning random phases for the specific IMFs. The current model, EHS (EOFA + HHT + SS), is compared with two other typical multi-site rainfall generators, MulGETS (parametric model) and KNN (non-parametric model) for a network of 12 stations in Xiang River basin, China. These three models are assessed based on their ability to simulate sequences with statistical attributes that are similar to those observed. We compare the basic statistics (mean, standard deviation, skewness), extreme value characteristics (95th percentile and maximum), spatial dependence (spatial correlation and spatial continuity ratio), and temporal dependence statistic (autocorrelation, wet/dry spells, and low-frequency variability). The results show that EHS rainfall generator has a similar capacity as KNN model in reproducing the spatial structure of the original rainfall field, and has a greater ability than MulGETS and KNN model to preserve the historical temporal statistics, especially the autocorrelation at various time scales and low-frequency variability. Overall, EHS is a useful model for generating realistic multi-site rainfall field and can be expected to generate plausible scenarios for impact studies.

Trade in commodities implies trade in virtual water (VW), which refers to the water that was used to produce the traded goods. Various studies have quantified international or inter-provincial virtual water (VW) flows related to the trade in crops and animal products. Until date, however, no effort has been undertaken to understand how the water embodied in traded feed crops (trade stage TS1) will be transferred further because of trade in animal products (trade stage TS2). This is the first study showing this mechanism, in a case study in China for maize (the major pig feed) and pork (the dominant meat), considering the period 2000–2013. We estimate the annual green and blue water footprints in maize production and then quantify the inter-provincial VW flows related to trade in maize (TS1) and trade in maize embodied in pork (TS2). Results show that in TS1, maize-related VW flowed from the water-scarce North to the water-rich South, with an increase of 40% over the study period (from 43 to 61 billion m³ y^-1). In TS2, about 10% of the water embodied in maize exports from North to South China returns in the form of pork, with an increase in the absolute amount of 25% (from 4.8 to 6.1 billion m³ y^-1). Considering blue VW flows specifically, we find that North-to-South blue VW flows decreased by 5% in TS1, while South-to-North blue VW flows increased by 23% in TS2.

Most research on drought assessment adopted historical in-situ observations, however, there has been increased data availability from remote sensing during the recent years. This study utilizes the two sources of data in drought assessment. Using the historical in-situ observations, the spatiotemporal variations of meteorological drought were firstly investigated by calculating the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1, 3, 6-month time scales in Northeast China. Using remote sensing data, the combined deficit index (CDI) for agricultural drought assessment was computed based on tri-monthly sum of deficit in antecedent rainfall and deficit in monthly NDVI at land cover type and sub-type levels in the same region. In the end, the agricultural drought calculated by the CDI was evaluated against the deficit in, as well as deficit in Land Surface Temperature (LST) and Evapotranspiration (ET), in order to verify the applicability of the CDI for agricultural drought assessment in the study region. The results showed that the CDI has better correlations with the SPEI (R² = 0.48) than the SPI (R² = 0.05) at 3-month scales with weight factor a = 0.5 in dry farming areas. The spatial pattern of the CDI showed that the area of agricultural drought increased from July to October. In addition, a significant linear correlation was found between the CDI and anomaly in annual agricultural yield (R² = 0.55), and anomaly in monthly land surface temperature (R² = 0.42). The results prove that the CDI calculated by remote sensing data is not only a reliable indicator for agricultural drought assessment in Northeast China, but also provides useful information for agricultural drought disaster prevention and mitigation, and water management improvement.

Application of integrated hydrological models to manage a watershed's water resources are increasingly finding their way into the decision-making processes. The Soil andWater Assessment Tool (SWAT) is a multi-process model integrating hydrology, ecology, agriculture, and water quality. SWAT is a continuation of nearly 40 years of modeling efforts conducted by the United States Department of Agriculture (USDA) Agricultural Research Service (ARS). A large number of SWAT-related papers have appeared in ISI journals, building a world-wide consensus around the model's stability and usefulness. The current issue is a collection of the latest research using SWAT as the modeling tool. Most models must undergo calibration/validation and uncertainty analysis. Unfortunately, these sciences are not formal subjects of teaching in most universities and the students are often left to their own resources to calibrate their model. In this paper, we focus on calibration and uncertainty analysis highlighting some serious issues in the calibration of distributed models. A protocol for calibration is also highlighted to guide the users to obtain better modeling results. Finally, a summary of the papers published in this special issue is provided in the Appendix.

Blossoming visions foresee an ubiquitous availability of data enabled through emerging sensors, novel transmission technologies and revisited data analysis techniques. It is often expected that this likewise applies to urban water management (Kerkez et al., 2016; Eggimann et al., 2017). Contrary to the envisioned easiness to collect data, an in-creasing number of studies report about a declining effort to conduct experimental field re-search in catchment and urban hydrology, implying the risk of scientific advancement to be misdirected (Blume et al., 2017; van Emmerik et al., 2018). In this paper, we present re-sults from a unique sensor network that collects, transmits, and organizes very high resolu-tion status information from an urban drainage catchment. Our findings are based on a two-year operation at full-scale with more than 50 sensor nodes connected to a low-power wireless sensor network (LPWSN). In essence we i) illustrate the scalability of LPWSNs, ii) reveal telemetry limitations of the LoRaWAN standard (LoRa®Alliance 2015), iii) suggest a novel technique (LoRaMesh) to master this constraint, and iv) discuss challenges associ-ated with managing the amount of data collected from diverse sources. We find: 1. the proposed transmission technique considerably improves telemetry performance of the en-tire network without introducing additional gateway stations (total packet loss 2.2 %). 2. Dynamic and flexible data management solutions come at a price. 3. It is not the pure amount of data that is most challenging but the diversity of different signals. 4. Full metada-ta integration in the data warehouse is laborious but clearly consolidates data interpretation in the long-term run.

We thank the authors, Varis and Keskinen, and Nauditt, for their constructive contributions. We endorse their key comments, further referring to recent literature and events, including the UN 2018 High Level Political Forum on sustainable development. Here, we elaborate on the epistemological perspective of the water–energy–food nexus conceptualization, assessment, discourse and operationalization.

This study investigates Bayesian signature-domain inference of hydrological models using Approximate Bayesian Computation (ABC) algorithms, and compares it to ‘‘traditional’’ time-domain inference. Our focus is on the quantification of predictive uncertainty in the streamflow time series and on understanding the information content of particular combinations of signatures. A combination of synthetic and real data experiments using conceptual rainfall-runoff models is employed. Synthetic experiments demonstrate: (i) the general consistency of signature and time-domain inferences, (ii) the ability to estimate streamflow error model parameters (reliably quantify streamflow uncertainty) even when calibrating in the signature domain, and (iii) the potential robustness of signature-domain inference when the (probabilistic) hydrological model is misspecified (e.g., by unaccounted timing errors). The experiments also suggest limitations of the signature-domain approach in terms of information loss when general (nonsufficient) statistics are used, and increased computational costs incurred by the ABC implementation. Real data experiments confirm the viability of Bayesian signature-domain inference and its general consistency with time-domain inference in terms of predictive uncertainty quantification. In addition, we demonstrate the utility of the flashiness index for the estimation of streamflow error parameters, and show that signatures based on the Flow Duration Curve alone are insufficient to calibrate parameters controlling streamflow dynamics. Overall, the study further establishes signature-domain inference (implemented using ABC) as a promising method for comparing the information content of hydrological signatures, for prediction under data-scarce conditions, and, under certain circumstances, for mitigating the impact of deficiencies in the formulation of the predictive model.

One open question in environmental sciences is whether effective management of natural resources depends on the fit between the bio-physical and the governance system. To address this question, we investigate water quality in transboundary rivers and ask to what extent a fit between the area covered by the physical extent of pollution and the area in which this pollution is addressed through management and policy regulation can be observed. We adopt a spatial approach and argue that the visualization of social-ecological overlap and misfit supports science and practice when taking decisions about how best to explain or address ineffectiveness and cause-effect mismatches in transboundary river management.
We focus on drinking water supply in the international river basin of the Rhine focusing on micropollutants. These persistent trace compounds have potential toxic effects on humans and ecosystems, which makes them a relevant type of pollution that needs to be taken into consideration. Based on a combination of mass flow and social network analysis, we can conclude that the Rhine River is characterized by large social-ecological overlap, but that some parts of the catchment area still lack integration.

Hydrological variables are among the most influential when analyzing or modeling stream ecosystems. However, available hydrological data are often limited in their spatiotemporal scale and resolution for use in ecological applications such as predictive modeling of species distributions. To overcome this limitation, a regression model was applied to a 1 km gridded stream network of Germany to obtain estimated daily stream flow data (m³ s⁻¹) spanning 64 years (1950–2013). The data are used as input to calculate hydrological indices characterizing stream flow regimes. Both temporal and spatial validations were performed. In addition, GLMs using both the calculated and observed hydrological indices were compared, suggesting that the predicted flow data are adequate for use in predictive ecological models. Accordingly, we provide estimated stream flow as well as a set of 53 hydrological metrics at 1 km grid for the stream network of Germany. In addition, we provide an R script where the presented methodology is implemented, that uses globally available data and can be directly applied to any other geographical region.

Long-term observations on riverine benthic invertebrate communities enable assessments of the potential impacts of global change on stream ecosystems. Besides increasing average temperatures, many studies predict greater temperature extremes and intense precipitation events as a consequence of climate change. In this study we examined long-term observation data (10–32 years) of 26 streams and rivers from four ecoregions in the European Long-Term Ecological Research (LTER) network, to investigate invertebrate community responses to changing climatic conditions. We used functional trait and multi-taxonomic analyses and combined examinations of general long-term changes in communities with detailed analyses of the impact of different climatic drivers (i.e., various temperature and precipitation variables) by focusing on the response of communities to climatic conditions of the previous year. Taxa and ecoregions differed substantially in their response to climate change conditions. We did not observe any trend of changes in total taxonomic richness or overall abundance over time or with increasing temperatures, which reflects a compensatory turnover in the composition of communities; sensitive Plecoptera decreased in response to warmer years and Ephemeroptera increased in northern regions. Invasive species increased with an increasing number of extreme days which also caused an apparent upstream community movement. The observed changes in functional feeding group diversity indicate that climate change may be associated with changes in trophic interactions within aquatic food webs. These findings highlight the vulnerability of riverine ecosystems to climate change and emphasize the need to further explore the interactive effects of climate change variables with other local stressors to develop appropriate conservation measures.

Drought is a slow-onset phenomenon with devastating effects on many aspects of human livelihood. Drought events around the world have had significant impacts on agricultural production. Sub-Saharan Africa (SSA) is at the core of this threat because rainfed subsistence farming dominates the food production and where socio-economic infrastructure are often inadequately prepared to cope with disasters. The recurrence of droughts in the past decades has triggered many famines in SSA and is projected to increase as a consequence of climate change. There is therefore a need to develop a research framework, which quantifies the crop drought vulnerability and identifies influencing physical and socio-economic factors. This study applies a biophysical crop model to map out hotspots of maize drought vulnerability across SSA and investigates the physical and socio-economic drivers. The derived knowledge from linking vulnerability to socio-economic factors support strategies to mitigate the negative impacts of drought on agricultural production. [...]

Dürren zeichnen sich durch einen schleichenden Beginn und verheerende Auswirkungen auf den Menschen und seine Umwelt aus. In verschiedenen Regionen der Welt hatten Dürren bereits grosse Beeinträchtigungen der landwirtschaftlichen Produktion zur Folge. Eine der gefährdetsten Regionen befindet sich in Afrika südlich der Sahara (ASS), wo Selbstversorgung ohne anthropogene Bewässerung weit verbreitet ist und wo die sozioökonomische Infrastruktur besonders verwundbar ist in Bezug auf Naturkatastrophen. Dürren verursachten viele Hungersnöte in ASS während den vergangenen Jahrzehnten und bedingt durch die Klimaänderung werden diese zunehmen. Der Einfluss von Dürren auf die landwirtschaftliche Produktion, inklusive der zugrundeliegenden sozioökonomischen Faktoren bedarf deshalb weiterer Froschung. In der vorliegenden Arbeit wird ein bio-physikalisches Erntemodell angewendet, um besonders dürreanfällige Maisanbaugebiete in ASS zu identifizieren und die physikalischen und sozioökonomischen Einflussfaktoren zu untersuchen. Das so erarbeitete Wissen kann verwendet werden, um die Auswirkungen von Dürren auf die landwirtschaftliche Produktion zu minimieren. [...]

Drought as a slow-onset phenomenon inflicts important losses to agriculture where the degree of vulnerability depends not only on physical variables such as precipitation and temperature, but also on societal preparedness. While the scopes of physical and social vulnerability are very different in nature, studies distinguishing these two aspects have been lacking. In this study we address the physical and social aspects of drought vulnerability of maize (CDVI_phy and CDVI_soc) in Sub-Saharan Africa (SSA). To quantify vulnerability, we applied a probabilistic framework combining a Drought Exposure Index (DEI) with a physical or social Crop Failure Index, CFI_phy or CFI_soc, respectively. DEI was derived from the exceedance probability of precipitation. Maize yields, simulated using the Environmental Policy Integrated Climate (EPIC) model, were used to build CFI_phy, whereas the residual of simulated and FAO recorded yields were used to construct CFI_soc. The results showed that southern and partially central Africa are more vulnerable to physical drought as compared to other regions. Central and western Africa, however, are socially highly vulnerable. Comparison of CDVI_phy and CDVI_soc revealed that societal factors cause more vulnerability than physical variables in almost all SSA countries except Nigeria and South Africa. We conclude that quantification of both drought vulnerabilities help a better characterization of droughts and identify regions where more investments in drought preparedness are required.

Crop yields exhibit known responses to droughts. However, quantifying crop drought vulnerability is often not straightforward, because components of vulnerability are not defined in a standardized and spatially comparable quantity in most cases and it must be defined on a fine spatial resolution. This study aims to develop a physical crop drought vulnerability index through linking the Drought Exposure Index (DEI) with the Crop Sensitivity Index (CSI) in Sub-Saharan Africa. Two different DEIs were compared. One was derived from the cumulative distribution functions fitted to precipitation and the other from the difference between precipitation and potential evapotranspiration. DEIs were calculated for one, three, six, nine, and twelve-month time scales. Similarly, CSI was calculated by fitting a cumulative distribution function to maize yield simulated using the Environmental Policy Integrated Climate (EPIC) model. Using a power function, curves were fitted to CSI and DEI relations resulting in different shapes explaining the severity of vulnerability. The results indicated that the highest correlation was found between CSI and DEI obtained from the difference between precipitation and potential evapotranspiration in one, three, and six-month time scales. Our findings show that Southern African countries and some regions of Sahelian strip are highly vulnerable to drought due to experiencing more water stress, whereas vulnerability in Central African countries pertains to temperature stresses. The proposed methodology provides complementary information on quantifying different degrees of vulnerabilities and the underlying reasons. The methodology can be applied to different regions and spatial scales.

Process-based crop models are increasingly used to assess the effects of different agricultural management practices on crop yield. However, calibration of historic crop yield is a challenging and time-consuming task due to data limitation and lack of adaptive auto-calibration tools compatible with the model to be calibrated on different spatial and temporal scales. In this study we linked the general auto-calibration procedure SUFI-2 (Sequential Uncertainty Fitting Procedure) to the crop model EPIC (Environmental Policy Integrated Climate) to calibrate maize yield in Sub-Saharan African (SSA) countries. This resulted in the creation of a user-friendly software, EPIC⁺, for crop model calibration at spatial levels of grid to continent. EPIC⁺ greatly speeds up the calibration process with quantification of parameter ranges and prediction uncertainty. In the SSA application, we calibrated three sets of parameters referred to as Planting Date (PD), Operation (e.g., fertilizer application, planting density), and Model parameters (e.g., Harvest index, biomass-energy ratio, water stress harvest index, SCS curve number) in three steps to avoid parameter interaction and identifiability problems. In the first step, by adjusting PD parameters, the simulated yield results improved in Western and Central African countries. In the next step, Operation parameters were calibrated for individual countries resulting in a better model performance by more than 40% in many countries. In the third step, Model parameters were calibrated with significant improvements in all countries by an average of 50%. We also found that countries with less socio-political volatility benefited most from the calibration. For countries where agricultural production had trends, we suggest improving the calibration results by applying linear de-trending transformations, which we will explore in more detail in a subsequent study.

This study considers Bayesian calibration of hydrological models using streamflow signatures and its implementation using Approximate Bayesian Computation (ABC). If the modeling objective is to predict streamflow time series and associated uncertainty, a probabilistic model of streamflow must be specified but the inference equations must be developed in the signature domain. However, even starting from simple probabilistic models of streamflow time series, working in the signature domain makes the likelihood function difficult or impractical to evaluate (in particular, as it is unavailable in closed form). This challenge can be tackled using ABC, a general class of numerical algorithms for sampling from conditional distributions, such as (but not limited to) Bayesian posteriors given any calibration data. Using ABC does not avoid the requirement of Bayesian inference to specify a probability model of the data, but rather exchanges the requirement to evaluate the pdf of this model (needed to evaluate the likelihood function) by the requirement to sample model output realizations. For this reason ABC is attractive for inference in the signature domain. We clarify poorly understood aspects of ABC in the hydrological literature, including similarities and differences between ABC and GLUE, and comment on previous applications of ABC in hydrology. An error analysis of ABC approximation errors and their dependence on the tolerance is presented. An empirical case study is used to illustrate the impact of omitting the specification of a probabilistic model (and instead using a deterministic model within the ABC algorithm), and the impact of a coarse ABC tolerance.

Im Rahmen des Modul-Stufen-Konzepts (MSK) werden Methoden zur Untersuchung und Bewertung der Fliessgewässer für die Bereiche Hydrodynamik und Morphologie, Chemie und Ökotoxikologie sowie Biologie erarbeitet. Es sind Methoden auf zwei Stufen vorgesehen, die sich in ihrer Bearbeitungsintensität und ihrem räumlichen Bezug unterscheiden. Der vorliegende Bericht beschreibt, wie die aquatische Vegetation der Fliessgewässer flächendeckend in einem grösseren Gebiet (Stufe F) oder systembezogen, mittels eines höher aufgelösten Messstellennetzes, in einem Einzugsgebiet (Stufe S) erfasst und beurteilt werden kann. Bei der Kartierung von Gewässerabschnitten mit einheitlichen Standortverhältnissen werden alle Gefässpflanzen, Armleuchterlagen, Moose und grün gefärbten Fadenalgen sowie polsterbildenden Algen auf der Gewässersohle erfasst. Von der Ufervegetation werden nur Arten berücksichtigt, die ihre Hauptverbreitung im regelmässig überfluteten Uferbereich haben. Die Gewässerabschnitte werden aufgrund von abiotischen Standortfaktoren (Beschattung, Gefälle, Abfluss, Wassertiefe, Substratzusammensetzung) einem von sechs verschiedenen Vegetations-Flusstypen unterschiedlicher Abflussklassen zugewiesen. Die Vegetation wird anschliessend durch Vergleich des Istzustandes mit einer möglichst naturnahen Referenz für den jeweiligen Vegetations-Flusstyp typspezifisch beurteilt. Die Bewertung, die sich an den ökologischen Zielen in Anhang 1 der Gewässerschutzverordnung (GSchV) orientiert, erfolgt mittels typ-spezifischer Zielhierarchien und Wertefunktionen in fünf Beurteilungsklassen. Bewertet werden die Bereiche Diversität (Anzahl typgerechte Arten und Wuchsformen), Zusammensetzung (Anteile typgerechter Wuchsformen und Neophyten sowie Dominanzstruktur) und Biomasse (absolute Deckung höherer Makrophyten und Algen) im Abschnitt. Ergänzend zu dieser gewässerökologischen Bewertung erfolgt eine Bewertung aus Sicht Naturschutz, anhand der nationalen Prioritätseinstufung der vorhandenen Arten sowie ihrem Beitrag zur Biodiversität, anhand von Leitwerten. Im Anschluss an die Typisierung und Bewertung, die durch ein elektronisches Auswertungs-Tool erfolgt, prüfen die Bearbeitenden die Resultate auf Plausibilität. Als Endprodukte werden einerseits Datentabellen mit allen Informationen zu den kartierten Untersuchungsabschnitten generiert, andererseits erfolgt eine Zusammenstellung der wichtigsten Informationen pro Untersuchungsabschnitt.

Knowing the impact of land-use and land-cover (LULC) changes on the distribution of water yield (WY) is essential for water resource management. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we investigated the spatial-temporal variations of WY from 1990 to 2015 in China's northern semi-arid region of Beijing-Tianjin-Hebei (Jing-Jin-Ji). We quantified the combined effects of LULC dynamics and climatic variation on WY. Furthermore, we identified the relative contribution of main LULC types to WY. For our study region, the built-up area increased by 35.66% (5380 km²) during the study period. In the meantime, cropland, grassland, and wetland decreased continuously. The expansion of built-up area and decline of vegetated land led to an increase of 1047 million m³ (5.1%) in total WY. The impacts of LULC changes on WY were mainly determined by the biophysical characteristics of LULC composition. Vegetated land has relatively lower WY coefficients due to higher rates of evapotranspiration and water infiltration. Built-up areas and bare land have higher WY coefficients as a result of their impermeable surface. The spatial-temporal analysis of WY with specification of WY coefficients by LULC types can facilitate integrated land-use planning and water resource management.

In China, the regional development policy has been shifting from solely economic orientation to ecologically sound economic growth. Using the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region as a case study, we evaluated the temporal variations in ecosystem service values (ESVs) associated with land use changes from 1990 to 2015. We analyzed the dynamic relations between ESVs and the economy (indicated by the gross domestic product, GDP) by introducing the elasticity indicator (EI), which reflects the growth synchronism between the two, and the ecosystem service load (ESL), which reflects the ecological efficiency of economic growth. The results showed that the land use changes in Jing-Jin-Ji have been characterized by decreases in water areas, cropland, and grassland and increases in woodland and built-up areas. The ESVs of woodland and water areas contributed to 80% of the total ESV of the region, and the total ESV increased by 13.87% as a result of an area increase in woodland (26.87%). The average EI of Jing-Jin-Ji improved from 0.028 to 0.293 over the study period, indicating that the growth of ESVs was being balanced with the growth in the GDP. The average ESL decreased by 1.24, suggesting a significant improvement in ecological efficiency per unit GDP. Within the Jing-Jin-Ji region, large disparities in EI and ESL were shown to exist among Beijing, Tianjin, and Hebei owing to their differences in ecological resources, GDP compositions, and development levels. The study highlights the needs to reinforce woodland and water conservation, adjust economic structures, and balance the intraregional development to achieve the ecological-economic integrity of the region.

Increasing demand for food is driving a worldwide trend of agricultural input intensification. However, there is no comprehensive knowledge about the interrelations between potential yield gains and environmental trade-offs that would enable the identification of regions where input-driven intensification could achieve higher yields, yet with minimal environmental impacts. We explore ways of enhancing global yields, while avoiding significant nitrogen (N) emissions (N_e) by exploring a range of N and irrigation management scenarios. The simulated responses of yields and N_e to increased N inputs (N_in) and irrigation show high spatial variations due to differences in current agricultural inputs and agro-climatic conditions. Nitrogen use efficiency (NUE) of yield gains is negatively correlated with incremental N_e due to N_in additions. Avoiding further intensification in regions where high fractions of climatic yield potentials, ≥ 80%, are already achieved is key to maintain good NUE. Depending on the intensification scenarios, relative increases in N_e could be reduced by 0.3−29.6% of the baseline N_e with this intensification strategy as compared to indiscriminate further intensification, at the cost of a loss of yield increases by 0.2−16.7% of the baseline yields. In addition, irrigation water requirements and N_in would dramatically decrease by considering this intensification strategy.

China's "Developing a Water-saving Society" policy is an ambitious water conservation strategy. However, there has been little evidence of the policy's effectiveness in terms of saving water at a river basin level. In this paper, we used a basin-scale hydroeconomic input-output model to calculate the water consumption (WC) by producers and consumers (WC_p and WC_c), respectively in the first pilot area for implementation of this policy, the Heihe River Basin, which belongs to an arid area in inland China. The results show that, after implementation of the policy, WC_c decreased sharply but WC_p only decreased slightly. A structural decomposition analysis revealed that decreased water consumption intensity played an important role in reducing water consumption. However, this effect was largely offset by the increasing exports of virtual water in goods and services. Our results suggest that in arid river basins, measures in saving water must be accompanied with a cap on total water consumption which is set in accordance with the local water endowments and consideration of environmental water requirement.

Fertilization, crop uptake followed by plant harvest, runoff and erosion, and transformations of phosphorus (P) in soil are the major factors influencing the P balance of croplands. It is important to integrate plant-soil-management interactions into consistent modeling systems to determine the effect of P fertilization conditions on yields and to quantify P losses. Previous assessment of P losses on large scales did not consider the interactions among these factors. Here we applied a grid-based crop model to estimate global P losses from three most produced crops: maize, rice, and wheat. The model was forced by detailed P input data sets over the period 1998–2002. According to our simulations, global P losses from the three crops reached 1.2 Tg P/year, and about 44% of it was due to soil erosion. The global total P losses were dominated by contributions from a few hot spot regions. Reducing P fertilizer in regions experiencing excessive P uses and hence losses, especially in China and India, could achieve the same yields as today and save about two thirds of global total P inputs, with the cobenefits of declining global total P losses by 41% and downstream water quality improvement. Reducing soil erosion and retaining more crop residues on croplands could further save P inputs and alleviate P losses. This study is of significance to determine the major factors influencing P balance across regions of the world and help policy makers to propose efficient strategies for tackling P-driven environmental problems.

Global food trade entails virtual flows of agricultural resources and pollution across countries. Here we performed a global-scale assessment of impacts of international food trade on blue water use, total water use, and nitrogen (N) inputs and on N losses in maize, rice, and wheat production. We simulated baseline conditions for the year 2000 and explored the impacts of an agricultural intensification scenario, in which low-input countries increase N and irrigation inputs to a greater extent than high-input countries. We combined a crop model with the Global Trade Analysis Project model. Results show that food exports generally occurred from regions with lower water and N use intensities, defined here as water and N uses in relation to crop yields, to regions with higher resources use intensities. Globally, food trade thus conserved a large amount of water resources and N applications, and also substantially reduced N losses. The trade-related conservation in blue water use reached 85 km3 y−1, accounting for more than half of total blue water use for producing the three crops. Food exported from the USA contributed the largest proportion of global water and N conservation as well as N loss reduction, but also led to substantial export-associated N losses in the country itself. Under the intensification scenario, the converging water and N use intensities across countries result in a more balanced world; crop trade will generally decrease, and global water resources conservation and N pollution reduction associated with the trade will reduce accordingly. The study provides useful information to understand the implications of agricultural intensification for international crop trade, crop water use and N pollution patterns in the world.

As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set.

In Maputo, the capital of Mozambique, nitrate concentrations above 250 mg L^–1 in groundwater have been reported. This happens due to the widespread use of latrines and septic tanks that allow for constant infiltration of its content into the soil and eventually to groundwater sources, a situation that is widespread in the Global South and represents a serious threat for human health and for the environment. This is a reflection of limited access to safe and adequate sanitation services, which the local authorities have set to improve in the forthcoming decades with a recently commissioned city-wide sanitation masterplan serving as a basis for the works. In this article, we aimed at understanding whether the infrastructure projected in the masterplan would lead to a reduction of nitrogen reaching groundwater. Currently, according to our calculations, almost 500 tonnes of nitrogen reach the city's groundwater sources each year, with the masterplan potentially resulting in a 14% reduction, a small reduction due to its reliance on maintaining and expanding fecal sludge services, without considering investments to improve domestic systems (e.g., construction of contained systems). An alternative, not presented in the Masterplan and put forward by the authors, could be the construction of simplified sewers in two of the city's most densely populated neighborhoods, with a potential 29% reduction in nitrogen reaching groundwater.

Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which require large sampling and analytical efforts to obtain representative data on the actual water quality.
Models can also be used to predict to what degree streams are affected by these pollutants. However, spatially distributed modelling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed.
To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. For larger catchments, routing was explicitly considered by coupling the iWaQa to the AQUASIM model.
The model was calibrated with datasets from three different small catchments (0.5–24.6km²) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for herbicides and biocides in Switzerland for different years on 12 catchments of much larger size (31–35899km²) and for herbicides for the entire Rhine basin upstream of the Dutch–German border (160000km2) without any modification. For most compound–catchment combinations, the model predictions revealed a satisfactory correlation (median r²: 0.5) with the observations. The peak concentrations were mostly predicted within a factor of 2 to 4 (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated; the predictions of the actual timing of peak concentrations, however, was generally poor.
Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinize model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of background concentrations to land use intensity.
Despite these limitations the findings indicate that key drivers and processes are reasonably well approximated by the model and that such a simple model that includes land use as a proxy for compound use, weather data for the timing of herbicide applications and discharge or precipitation as drivers for transport is sufficient to predict the timing and level of peak concentrations within a factor of 2 to 3 in a spatially distributed manner at the scale of large river basins.

Knowledge of spatial variation in pollutant profiles among sea turtle nesting locations is limited. This poses challenges in identifying processes shaping this variability and sets constraints to the conservation management of sea turtles and their use as biomonitoring tools for environmental pollutants. We aimed to increase understanding of the spatial variation in polycyclic aromatic hydrocarbon (PAH), organochlorine pesticide (OCP) and polychlorinated biphenyl (PCB) compounds among nesting beaches. We link the spatial variation to turtle migration patterns and the persistence of these pollutants. Specifically, using gas chromatography, we confirmed maternal transfer of a large number of compounds (n = 68 out of 69) among 104 eggs collected from 21 nests across three nesting beaches within the Yucatán Peninsula, one of the world's most important rookeries for hawksbill turtles (Eretmochelys imbricata). High variation in PAH profiles was observed among beaches, using multivariate correspondence analysis and univariate Peto-Prentice tests, reflecting local acquisition during recent migration movements. Diagnostic PAH ratios reflected petrogenic origins in Celestún, the beach closest to petroleum industries in the Gulf of Mexico. By contrast, pollution profiles of OCPs and PCBs showed high similarity among beaches, reflecting the long-term accumulation of these pollutants at regional scales. Therefore, spatial planning of protected areas and the use of turtle eggs in biomonitoring needs to account for the spatial variation in pollution profiles among nesting beaches.

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

Considering the unstable condition of water resources in Iran and many other countries in arid and semi-arid regions, groundwater studies are very important. Therefore, the aim of this study is to model groundwater potential by qanat locations as indicators and ten advanced and soft computing models applied to the Beheshtabad Watershed, Iran. Qanat is a man-made underground construction which gathers groundwater from higher altitudes and transmits it to low land areas where it can be used for different purposes. For this purpose, at first, the location of the qanats was detected using extensive field surveys. These qanats were classified into two datasets including training (70%) and validation (30%). Then, 14 influence factors depicting the region’s physical, morphological, lithological, and hydrological features were identified to model groundwater potential. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), boosted regression tree (BRT), random forest (RF), artificial neural network (ANN), K-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and support vector machine (SVM) models were applied in R scripts to produce groundwater potential maps. For evaluation of the performance accuracies of the developed models, ROC curve and kappa index were implemented. According to the results, RF had the best performance, followed by SVM and BRT models. Our results showed that qanat locations could be used as a good indicator for groundwater potential. Furthermore, altitude, slope, plan curvature, and profile curvature were found to be the most important influence factors. On the other hand, lithology, land use, and slope aspect were the least significant factors. The methodology in the current study could be used by land use and terrestrial planners and water resource managers to reduce the costs of groundwater resource discovery.

In order to apply hydrological models in the water resources investigation successfully, careful calibration and uncertainty analysis are required. Although many automatic calibration methods were developed, the time consumed for running the hydrologic model is still a problem for hydrologic modelers. To reduce the computational complexity and increase performance of the calibration procedure, a software package (Fast Automated Calibration Tool, FACT) that works on Soil and Water Assessment Tool (SWAT) was developed. Sequential Uncertainty Fitting Algorithm (SUFI-2) was chosen to build the software package on, because SUFI-2 combines optimization with uncertainty analysis and can handle a large number of parameters. SUFI-2 implemented in SWAT-CUP (a software program that was developed for SWAT) is very useful in an interactive manner, however; it has some drawbacks which are the time consumed, user interaction requirement and update problems of SWAT model files. In this study, the calibration procedure was implemented in a MATLAB script, which completes the full calibration in one single run. The developed tool was applied on Sarısu-Eylikler Basin SWAT model that had r² = 0.41, NSE = 0.11 between 1992 and 2010 without calibration. When the model was calibrated using SUFI-2 in SWAT-CUP, the model was improved to r² = 0.57, NSE = 0.44, P-factor = 0.69 and R-factor = 1.00 at the end of 42 iterations using 100 simulation counts in each iteration. However; when the developed calibration tool was applied with 250 simulation counts without any user interaction for each iteration, the model was improved to r² = 0.59, NSE = 0.57, P-factor = 0.72, and R-factor = 1.32 at the end of three iterations. Thus, the developed calibration procedure took a shorter time (unlike SUFI2 in SWAT-CUP, FACT takes several hours) compared to SWAT-CUP with minimum user involvement.

Aridity-the ratio of atmospheric water supply (precipitation;P) to demand (potential evapotranspiration; PET) - is projected to decrease (that is, areas will become drier) as a consequence of anthropogenic climate change, exacerbating land degradation and desertification^1-6. However, the timing of significant aridification relative to natural variability - defined here as the time of emergence for aridification (ToEA) - is unknown, despite its importance in designing and implementing mitigation policies ^7-10. Here we estimate ToEA from projections of 27 global climate models (GCMs) under representative concentration pathways (RCPs) RCP4.5 and RCP8.5, and in doing so, identify where emergence occurs before global mean warming reaches 1.5 °C and 2 °C above the pre-industrial level. On the basis of the ensemble median ToEA for each grid cell, aridification emerges over 32% (RCP4.5) and 24% (RCP8.5) of the total land surface before the ensemble median of global mean temperature change reaches 2 °C in each scenario. Moreover, ToEA is avoided in about two-thirds of the above regions if the maximum global warming level is limited to 1.5 °C. Early action for accomplishing the 1.5 °C temperature goal can therefore markedly reduce the likelihood that large regions will face substantial aridification and related impacts.

Losses and gains in species diversity affect ecological stability^1–7 and the sustainability of ecosystem functions and services^8–13. Experiments and models have revealed positive, negative and no effects of diversity on individual components of stability, such as temporal variability, resistance and resilience^{2,3,6,11,12,14}. How these stability components covary remains poorly understood¹⁵. Similarly, the effects of diversity on overall ecosystem stability¹⁶, which is conceptually akin to ecosystem multifunctionality^17,18, remain unknown. Here we studied communities of aquatic ciliates to understand how temporal variability, resistance and overall ecosystem stability responded to diversity (that is, species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12,939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components covaried negatively along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of the negative effects of diversity on individual stability components³. Integrating our findings with the ecosystem multifunctionality concept revealed hump- and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a U-shaped relationship. The effects of diversity on ecosystem multifunctionality would also be hump- or U-shaped if diversity had positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform the perceived effects of diversity on ecological stability and may help to translate this science into policy-relevant information.

Global precipitation products are very important datasets in flow simulations, especially in poorly gauged regions. Uncertainties resulting from precipitation products, hydrological models and their combinations vary with time and data magnitude, and undermine their application to flow simulations. However, previous studies have not quantified these uncertainties individually and explicitly. This study developed an ensemble-based dynamic Bayesian averaging approach (e-Bay) for deterministic discharge simulations using multiple global precipitation products and hydrological models. In this approach, the joint probability of precipitation products and hydrological models being correct is quantified based on uncertainties in maximum and mean estimation, posterior probability is quantified as functions of the magnitude and timing of discharges, and the law of total probability is implemented to calculate expected discharges. Six global fine-resolution precipitation products and two hydrological models of different complexities are included in an illustrative application. e-Bay can effectively quantify uncertainties and therefore generate better deterministic discharges than traditional approaches (weighted average methods with equal and varying weights and maximum likelihood approach). The mean Nash-Sutcliffe Efficiency values of e-Bay are up to 0.97 and 0.85 in training and validation periods respectively, which are at least 0.06 and 0.13 higher than traditional approaches. In addition, with increased training data, assessment criteria values of e-Bay show smaller fluctuations than traditional approaches and its performance becomes outstanding. The proposed e-Bay approach bridges the gap between global precipitation products and their pragmatic applications to discharge simulations, and is beneficial to water resources management in ungauged or poorly gauged regions across the world.

Performance of four satellite precipitation products, namely, the China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing technique (CMORPH), as well as 3B42 calibrated and 3B42-RT dataset, which are derived from the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA), were evaluated from daily to annual temporal scales over Beijing, using observations from 36 ground meteorological stations. Five statistical properties and three categorical metrics were used to test the results. The assessment showed that all four satellite precipitation products captured the temporal variability of precipitation. Although four satellite precipitation products captured the trend of more precipitation in the northeastern regions, all four products showed different distribution from the observations for 2001–2015 over Beijing. All precipitation products tended to overestimate moderate precipitation events and underestimate heavy precipitation events over Beijing, except for 3B42RT, which tended to overestimate most precipitation events. By comparison, the CMFD performed better than the CMORPH, 3B42 calibrated, and 3B42-RT datasets, having the higher correlation coefficient and low root mean squared difference, and mean absolute difference at all temporal scales. The average correlation coefficient of the CMFD, CMORPH, 3B42 calibrated, and 3B42-RT products for all 36 stations were 0.70, 0.60, 0.59, and 0.54 for daily precipitation and 0.78, 0.32, 0.74, and 0.44 for monthly precipitation. Overall, the CMFD was the most reliable for the Beijing region.

Subsurface flow is often recognized as a dominant runoff generation process. However, observing subsurface properties, and understanding how they control flow pathways, remains challenging. This paper investigates how surface slope and bedrock cleavage control subsurface flow pathways in a slate bedrock headwater catchment in Luxembourg, characterised by a double-peak streamflow response. We use a range of experimental techniques, including field observations of soil and bedrock characteristics, and a sprinkling experiment at a site located 40 m upslope from the stream channel. The sprinkling experiment uses Br⁻ as a tracer, which is measured at a well downslope from the plot and at various locations along the stream, together with well and stream hydrometric responses. The sprinkling experiment is used to estimate velocities and celerities, which in turn are used to infer flow pathways. Our results indicate that the single or first peak of double-peak events is rainfall-driven (controlled by rainfall) while the second peak is storage-driven (controlled by storage). The comparison between velocity and celerity estimates suggests a fast flowpath component connecting the hillslope to the stream, but velocity information was too scarce to fully support such a hypothesis. In addition, different estimates of celerities suggest a seasonal influence of both rainfall intensity rate and residual water storage on the celerity responses at the hillslope scale. At the catchment outlet, the estimated of the total mass of Br⁻ recovered in the stream was about 2.5% of the application. Further downstream, the estimate mass of Br⁻ was about 4.0% of the application. This demonstrates that flowpaths do not appear to align with the slope gradient. In contrast, they appear to follow the strike of the bedrock cleavage. Our results have expanded our understanding of the importance of the subsurface, in particular the underlying bedrock systems, and the importance of cleavage orientation, as well as topography, in controlling subsurface flow direction in this catchment.

Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5°C and 2°C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO₂ fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5°C and 2°C warming above pre-industrial levels, as well as at different CO₂ levels determined by similar probabilities to lead to 1.5°C and 2°C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO₂ forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO₂ concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5°C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.

Key global indicators of biodiversity decline, such as the IUCN Red List Index and the Living Planet Index, have relatively long assessment intervals. This means they, due to their inherent structure, function as late-warning indicators that are retrospective, rather than prospective. These indicators are unquestionably important in providing information for biodiversity conservation, but the detection of early-warning signs of critical biodiversity change is also needed so that proactive management responses can be enacted promptly where required. Generally, biodiversity conservation has dealt poorly with the scattered distribution of necessary detailed information, and needs to find a solution to assemble, harmonize and standardize the data. The prospect of monitoring essential biodiversity variables (EBVs) has been suggested in response to this challenge. The concept has generated much attention, but the EBVs themselves are still in development due to the complexity of the task, the limited resources available, and a lack of long-term commitment to maintain EBV data sets. As a first step, the scientific community and the policy sphere should agree on a set of priority candidate EBVs to be developed within the coming years to advance both large-scale ecological research as well as global and regional biodiversity conservation. Critical ecological transitions are of high importance from both a scientific as well as from a conservation policy point of view, as they can lead to long-lasting biodiversity change with a high potential for deleterious effects on whole ecosystems and therefore also on human well-being. We evaluated candidate EBVs using six criteria: relevance, sensitivity to change, generalizability, scalability, feasibility, and data availability and provide a literature-based review for eight EBVs with high sensitivity to change. The proposed suite of EBVs comprises abundance, allelic diversity, body mass index, ecosystem heterogeneity, phenology, range dynamics, size at first reproduction, and survival rates. The eight candidate EBVs provide for the early detection of critical and potentially long-lasting biodiversity change and should be operationalized as a priority. Only with such an approach can science predict the future status of global biodiversity with high certainty and set up the appropriate conservation measures early and efficiently. Importantly, the selected EBVs would address a large range of conservation issues and contribute to a total of 15 of the 20 Aichi targets and are, hence, of high biological relevance.

In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty.
This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management.
We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality.

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.

In agroecosystem management, conflicts between various services such as food provision and nutrient regulation are common. This study examined the trade-offs between selected ecosystem services such as food provision, water quantity and quality, erosion and climate regulations in an agricultural catchment in Western Switzerland. The aim was to explore the existing land use conflicts by a shift in land use and management strategy following two stakeholder-defined scenarios based on either land sparing or land sharing concepts. The Soil and Water Assessment Tool (SWAT) was used to build an agro-hydrologic model of the region, which was calibrated and validated based on daily river discharge, monthly nitrate and annual crop yield, considering uncertainties associated with land management set up and model parameterization. The results show that land sparing scenario has the highest agricultural benefit, while also the highest nitrate concentration and GHG emissions. The land sharing scenario improves water quality and climate regulation services and reduces food provision. The management changes considered in the two land use scenarios did not seem to reduce the conflict but only led to a shift in trade-offs. Water quantity and erosion regulation remain unaffected by the two scenarios.

Trade of commodities can lead to virtual water flows between trading partners. When commodities flow from regions of high water productivity to regions of low water productivity, the trade has the potential to generate water saving. However, this accounting of water saving does not account for the water scarcity status in different regions. It could be that the water saving generated from this trade occurs at the expense of the intensified water scarcity in the exporting region, and exerts limited effect on water stress alleviation in importing regions. In this paper, we propose an approach to measure the scarce water saving associated with virtual water trade (measuring in water withdrawal/use). The scarce water is quantified by multiplying the water use in production with the water stress index (WSI). We assessed the scarce water saving/loss through interprovincial trade within China using a multi-region input-output table from 2010. The results show that interprovincial trade resulted in 14.2 km³ of water loss without considering water stress, but only 0.4 km³ scarce water loss using the scarce water concept. Among the 435 total connections of virtual water flows, 254 connections contributed to 20.2 km³ of scarce water saving. Most of these connections are virtual water flows from provinces with lower WSI to that with higher WSI. Conversely, 175 connections contributed to 20.6 km³ of scarce water loss. The virtual water flow connections between Xinjiang and other provinces stood out as the biggest contributors, accounting for 66% of total scarce water loss. The results show the importance of assessing water savings generated from trade with consideration of both water scarcity status and water productivity across regions. Identifying key connections of scarce water saving is useful in guiding interregional economic restructuring towards water stress alleviation, a major goal of China’s sustainable development strategy.

We quantify uncertainties in the location and magnitude of extreme pressure spots revealed from large scale multiphase flow simulations of cloud cavitation collapse. We examine clouds containing 500 cavities and quantify uncertainties related to their initial spatial arrangement. The resulting 2,000-dimensional space is sampled using a nonintrusive and computationally efficient multilevel Monte Carlo (MLMC) methodology. We introduce novel empirically optimal control variate coefficients to enhance the variance reduction in MLMC. The proposed multilevel control variates Monte Carlo leads to more than two orders of magnitude speedup when compared to standard Monte Carlo methods. We identify large uncertainties in the location and magnitude of the peak pressure pulse and present its statistical correlations and joint probability density functions with the geometrical characteristics of the cloud. Characteristic properties of spatial cloud structure are identified as potential causes of significant uncertainties in exerted collapse pressures.

Calibration of individual based models (IBMs), successful in modeling complex ecological dynamical systems, is often performed only ad-hoc. Bayesian inference can be used for both parameter estimation and uncertainty quantification, but its successful application to realistic scenarios has been hindered by the complex stochastic nature of IBMs. Computationally expensive techniques such as Particle Filter (PF) provide marginal likelihood estimates, where multiple model simulations (particles) are required to get a sample from the state distribution conditional on the observed data. Particle ensembles are re-sampled at each data observation time, requiring particle destruction and replication, which lead to an increase in algorithmic complexity. We present SPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo (PMCMC) algorithm, which mitigates high computational costs by distributing particles over multiple computational units. Adaptive load re-balancing techniques are used to mitigate computational work imbalances introduced by re-sampling. Framework performance is investigated and significant speed-ups are observed for a simple predator-prey IBM model.

The development of approaches to tackle the European Union (EU) water-related challenges and shift towards sustainable water management and use is one of the main objectives of Horizon 2020, the EU strategy to lead a smart, sustainable and inclusive growth. The EU is an increasingly water challenged area and is a major agricultural trader. As agricultural trade entails an exchange of water embodied in goods as a factor of production, this study investigates the region's water-food-trade nexus by analysing intra-regional virtual water trade (VWT) in agricultural products. The analysed period (1993-2011) comprises the enactment of the Water Framework Directive (WFD) in the year 2000. Aspects of the VWT that are relevant for the WFD are explored. The EU is a net importer of virtual water (VW) from the rest of the world, but intra-regional VWT represents 46% of total imports and 75% of total exports. Five countries account for 60% of total VW imports (Germany, France, Italy, The Netherlands, Belgium) and 65% of total VW exports (The Netherlands, France, Germany, Belgium and Spain). Intra-EU VWT more than doubled over the period considered, while trade with extra-EU countries did not show such a marked trend. In the same period, blue VWT increased significantly within the region and net import from the rest of the world slightly decreased. Water scarce countries, such as Spain and Italy, are major exporters of blue water in the region. The traded volumes of VW have been increasing almost monotonically over the years, and with a substantial increase after 2000. The overall trend in changes in VWT does not seem to be in accordance with the WFD goals. This study demonstrated that VWT analyses can help evaluate intertwining effects of water, agriculture and trade policies which are often made separately in respective sectors.

Food security, and in particular food availability, depends on environmental resources, climatic conditions and agricultural practices, having water resources as a common denominator. There is major concern about food security in countries having limited water resources, due to the large volumes of water that are required to produce food commodities. Water-deficit countries tend to rely on international trade to close the gap between water (and thus food) demand and supply. Against this backdrop, the purpose of this study is twofold. First, to analyse water resources in the Middle East and North African region also considering the political economy trends and dynamics, which drive the region’s demand for water. Secondly, the study aims to increase understanding on the role that trade of agricultural commodities has played in meeting the requirements of the MENA populations, in terms of food and associated water ‘embedded’ as a factor of production. The study argues that virtual water trade, that is, the virtual transfer of the water used for agricultural production from producing to consuming countries, has provided the region’s economies with water and food security over the past 25 years. The study shows that virtual water imports have more than doubled and the increase has been more than proportional to population growth in the area. Food products account by far for the largest share of virtual water flows, while crops and high value foods are the main categories of agricultural products associated with virtual water imports. The largest share of the MENA imports originate from outside the region, thus determining a marked dependency on water resources available elsewhere, but not always from water-secure countries.

This paper describes the development of a Climate Change Toolkit (CCT) to perform tasks needed in a climate change study plus projection of extreme weather conditions by analyzing historical weather patterns. CCT consists of Data Extraction, Global Climate Data Management, Bias Correction and Statistical Downscaling, Spatial Interpolation, and Critical Consecutive Day Analyzer (CCDA). CCDA uses a customized data mining approach to recognize spatial and temporal patterns of extreme events. CCT is linked to an archive of 0.5° historical global daily dataset (CRU, 1970–2005), and GCM data (1960–2099) for five models and four carbon scenarios. Application of CCT in California using ensemble results of scenario RCP8.5 showed a probable increase in the frequency of dry periods in the southern part of the region, while decreasing in the north. The frequency of wet periods may suggest higher risks of flooding in the north and coastal strips. We further found that every county in northern California may experience flooding conditions of 1986 at least once between 2020 and 2050.

Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a ~810 km² Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil andWater Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.g., from ~15 km² down to ~1 km² ) when delineating sub-basins in SWAT. We also showed that both the MARINE and SWAT models were equally able to reproduce peak discharge, flood timing and volume, and that they were both limited by rainfall and soil data. Hence, the SWAT model appears to be a reliable modelling tool to predict discharge over long periods of time in large flash-flood-prone basins.

System identification, sensitivity analysis, optimization and control, require a large number of model evaluations. Accurate simulators are too slow for these applications. Fast emulators provide a solution to this efficiency demand, sacrificing unneeded accuracy for speed. There are many strategies for developing emulators but selecting one remains subjective. Herein we compare the performance of two kinds of emulators: mechanistic emulators that use knowledge of the simulator's equations, and purely data-driven emulators using matrix factorization. We borrow simulators from urban water management, because more stringent performance criteria on water utilities have made emulation a crucial tool within this field. Results suggest that naive data-driven emulation outperforms mechanistic emulation. We discuss scenarios in which mechanistic emulation seems favorable for extrapolation in time and dealing with sparse and unevenly sampled data. We also point to advances in Machine Learning that have not permeated yet into the environmental science community.

Uncertainties due to climate change and population growth have created a critical situation for many megacities. Investigating spatio-temporal variability of water resources is, therefore, a critical initial step for water-resource management. This paper is a first study on the evaluation of water-budget components of water resources in Istanbul using a high-resolution hydrological model. In this work, the water resources of Istanbul and surrounding watersheds were modeled using the Soil andWater Assessment Tool (SWAT), which is a continuous-time, semi-distributed, process-based model. The SWAT-CUP program was used for calibration/validation of the model with uncertainty analysis using the SUFI-2 algorithm over the period 1977–2013 at 25 gauge stations. The results reveal that the annual blue-water potential of Istanbul is 3.5 billion m³, whereas the green-water flow and storage are 2.9 billion m³ and 0.7 billion m³, respectively. Watersheds located on the Asian side of the Istanbul megacity yield more blue-water resources compared to the European side, and constitute 75% of the total potential water resources. The model highlights the water potential of the city under current circumstances and gives an insight into its spatial distribution over the region. This study provides a strong basis for forthcoming studies concerning better water-resources management practices, climate change and water-quality studies, as well as other socio-economic scenario analyses in the region.

Understanding the drivers of stream macroinvertebrate distribution patterns - the most diverse animal group in freshwater ecosystems - is a major goal in freshwater biogeography. Climate and topography have been shown to explain species' distributions at continental scales, but the contribution of natural and anthropogenically altered streamflow is often omitted in large-scale analyses due to the lack of appropriate data. We test how macroinvertebrate occurrences can be linked to streamflow observations and evaluate the relative importance of streamflow regimes and water use for macroinvertebrate occurrences from 19 orders across Europe. We first paired species sampling locations with hydrological gauging stations considering 5 combinations of the geographic distance and difference in flow accumulation (upstream contributing area). We then used Generalized Linear Models to assess the influence of the streamflow regime, simulated water use, and climate and topography on the occurrence of macroinvertebrates. The pairing method that assigned species records to the closest gauging station in terms of both distance and flow accumulation performed best. Most of the species studied occurred preferentially in river habitats with low mean annual streamflow and streamflow variability, high winter streamflow, and low levels of water withdrawals for irrigation or manufacturing. We conclude that flow accumulation is a useful proxy to evaluate the proximity of species records to gauging stations, omitting species records that do not belong to a given stream reach. The strong contribution of streamflow and water use indicators on macroinvertebrate occurrences underline their importance for yielding robust occurrence estimates.

Study region: Alberta, Canada.
Study focus: The security of freshwater supplies is a growing concern worldwide. Understanding dynamics of water supply and demand is the key for sustainable planning and management of watersheds. Here we analyzed the uncertainties in water supply of Alberta by building an agro-hydrological model, which accounts for major hydrological features, geo-spatial heterogeneity, and conflicts over water-food-energy resources. We examined the cumulative effects of natural features (e.g., potholes, glaciers, climate, soil, vegetation), anthropogenic factors (e.g., dams, irrigation, industrial development), environmental flow requirements (EFR), and calibration schemes on water scarcity in the dynamics of blue and green water resources, and groundwater recharge.
New hydrological insights for the region: Natural hydrologic features of the region create a unique hydrological system, which must be accurately represented in the model for reliable estimates of water supply at high spatial and temporal resolution. Accounting for EFR, increases the number of months of water scarcity and the population exposed. Severe blue water scarcity in spring and summer months was found to be due to irrigated agriculture, while in winter months it was mostly due to the demands of petroleum or other industries. We found over exploitation of the groundwater in southern subbasins and concluded that more detailed analysis on groundwater flow and connectivity is required. Our study provides a general and unified approach for similar analyses in other jurisdictions around the world.

1. Species distribution models (SDM) are widely used to predict occupancy patterns at fine resolution over wide extents. However, SDMs generally ignore the effect of biotic interactions and tend to overpredict the number of species that can coexist at a given location and time (hereafter, the alpha-capacity). We developed an extension of SDMs that integrates species-level and community-level modelling to account for the above drivers.
2. The alpha-adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a community and the site's alpha-capacity and adjusts the PoO, such that: (i) their sum will equal the alpha-capacity as predicted by probability theory; and (ii) the adjusted PoO are dependent upon the relative suitability of each species for that site. The new method was tested using community data comprising 87 freshwater invertebrate species in an LTER watershed in Germany. We explored the ability of the method to predict alpha and beta-diversity patterns. We further focused on the effect on model performance at the species-level of the error associated with modelling alpha-capacity, of differences in gamma diversity (the size of the community) and of the type of community (random or guild-based).
3. The models that predicted alpha-capacity contained considerable error, and thus adjusting the PoO according to the modelled alpha-capacity resulted with decreased performance at the species level. However, when using the observed alpha-capacity to mimic a good alpha-capacity model, the alpha-adjusted SDMs usually resulted in increased performance. We further found that the alpha-adjusted SDM was better than the original SDM at predicting beta-diversity patterns, especially when using similarity indices that are sensitive to double absences.
4. Using the alpha-adjusted SDM approach may increase the predictive performance at the species and community levels if alpha-capacity can be assessed or modelled with sufficient accuracy, especially in relatively small communities of closely interacting species. With better models to predict alpha-capacity being developed, alpha-adjusted SDM has considerable potential to provide more realistic predictions of species-distribution patterns.

Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior.

The recent explosion of high dimensional, high resolution ‘big-data’ from automated bioinformatics measurement techniques demands new methods for unsupervised data processing. An essential analysis step is the identification of groups of similar data, or ‘clusters’, in noisy high-dimensional data spaces, as this permits to perform some analysis steps at the group level. Popular clustering algorithms introduce an undesired cluster shape bias, require prior knowledge of the number of clusters, and are unable to properly deal with noise. Manual data gating, often used to assist these methods, is based on low-dimensional projection techniques, which is prone to obscure the underlying data structure. While Hebbian Learning Clustering successfully overcomes all of these limitations (by using only local similarities to infer global structure), previous implementations were unsuited to deal with big data sets. Here, we present a novel implementation based on realistic neuronal dynamics that removes also this obstacle. By a performance that scales favourably compared to all standard clustering algorithms, unbiased large data analysis becomes feasible on standard desktop hardware.

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual "iWaQa" model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

The economic concerns of low-income farmers are barriers to nutrient abatement policies for eutrophication control in surface waters. This study brings up a perspective that focuses on integrating multiple-pollutant discharge permit markets with farm management practices. This aims to identify a more economically motivated waste load allocation (WLA) for non-point sources (NPS). For this purpose, we chose the small basin of Zrebar Lake in western Iran and used the soil and water assessment tool (SWAT) for modeling. The export coefficients (ECs), effectiveness of best management practices (BMPs), and crop yields were calculated by using this software. These variables show that low-income farmers can hardly afford to invest in BMPs in a typical WLA. Conversely, a discharge permit market presents a more cost-effective solution. This method saves 64% in total abatement costs and motivates farmers by offering economic benefits. A market analysis revealed that nitrogen permits mostly cover the trades with the optimal price ranging from $6 to $30 per kilogram. However, phosphorous permits are limited for trading, and their price exceeds $60 per kilogram. This approach also emphasizes the establishment of a regional institution for market monitoring, dynamic pricing, fair fund reallocation, giving information to participants, and ensuring their income. By these sets of strategies, a WLA on the brink of failure can turn into a cost-effective and sustainable policy for eutrophication control in small basins.

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized SoilWater Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil andWater Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.

Parameter estimation for agent-based and individual-based models (ABMs/IBMs) is often performed by manual tuning and model uncertainty assessment is often ignored. Bayesian inference can jointly address these issues. However, due to high computational requirements of these models and technical difficulties in applying Bayesian inference to stochastic models, the exploration of its application to ABMs/IBMs has just started. We demonstrate the feasibility of Bayesian inference for ABMs/IBMs with a Particle Markov Chain Monte Carlo (PMCMC) algorithm developed for state-space models. The algorithm profits from the model's hidden Markov structure by jointly estimating system states and the marginal likelihood of the parameters using time-series observations. The PMCMC algorithm performed well when tested on a simple predator-prey IBM using artificial observation data. Hence, it offers the possibility for Bayesian inference for ABMs/IBMs. This can yield additional insights into model behaviour and uncertainty and extend the usefulness of ABMs/IBMs in ecological and environmental research.

The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R², bR², NSE, MNS, RSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are "conditioned" on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.

The Black Sea catchment (BSC) is facing important demographic, climatic and landuse changes that may increase pollution, vulnerability and scarcity of water resources, as well as beach erosion through sea level rise. Limited access to reliable time-series monitoring data from environmental, statistical, and socio-economical sources is a major barrier to policy development and decision-making. To address these issues, a web-based platform was developed to enable discovery and access to key environmental information for the region. This platform covers: landuse, climate, and demographic scenarios; hydrology and related water vulnerability and scarcity; as well as beach erosion. Each data set has been obtained with state-of-the-art modelling tools from available monitoring data using appropriate validation methods. These analyses were conducted using global and regional data sets. The data sets are intended for national to regional assessments, for instance for prioritizing environmental protection projects and investments. Together they form a unique set of information, which lay out future plausible change scenarios for the BSC, both for scientific and policy purposes.

Concerns about the water–energy–food (WEF) nexus have motivated many discussions regarding new approaches for managing water, energy and food resources. Despite the progress in recent years, there remain many challenges in scientific research on the WEF nexus, while implementation as a management tool is just beginning. The scientific challenges are primarily related to data, information and knowledge gaps in our understanding of the WEF inter-linkages. Our ability to untangle the WEF nexus is also limited by the lack of systematic tools that could address all the trade-offs involved in the nexus. Future research needs to strengthen the pool of information. It is also important to develop integrated software platforms and tools for systematic analysis of the WEF nexus. The experience made in integrated water resources management in the hydrological community, especially in the framework of Panta Rhei, is particularly well suited to take a lead in these advances.

Modern agriculture is facing a trilemma in the context of population growth and social–economic development: increasing food supply and nutritional quality, reducing resource requirements, and minimizing negative environmental consequences. Current research tackling challenges in agriculture often focuses on one or two aspects of the trilemma and/or is limited to providing a general picture of the challenges. Therefore, there is a need to develop a systematic research framework for investigating trade-offs in the trilemma and to develop pathways to sustain the increasing food supply while mitigating negative impacts of agricultural production.

Grid-based large-scale crop models have been developed as a useful tool to address a variety of agricultural issues, e.g. simulating crop–water–nutrient relations, projecting crop yields under certain agronomic conditions, and quantifying climate change impacts. However, large-scale crop models are rarely used to investigate the water–nutrient–food–environment nexus. Meanwhile, few studies have addressed the role of global food trade in redistributing agricultural resources and environmental impacts in the context of the nexus. Against this background, the present study aimed to develop a large-scale crop model and enhance its capabilities in modelling the global water–food–environment–trade nexus under several agricultural intensification scenarios. Knowledge derived from the study on the nexus is useful in supporting the development of global strategies for addressing the agricultural trilemma.

For this purpose, a grid-based Environmental Policy Integrated Climate was programmed under a Python environment: PEPIC. The PEPIC model was first used to investigate uncertainties derived from an important resource, i.e. the choice of different potential evapotranspiration (PET) estimation methods, on simulating the crop–water relations of maize. The simulations demonstrated substantial differences in PET by using different estimation methods, even in the same climate zone, leading to large uncertainties in simulating crop–water relations in terms of crop water productivity. However, the uncertainties showed varied patterns for different variables, i.e. more significant on water-related variables while less on crop yields. Water availability played an important role in the uncertainties. This information highlights the significance of considering the impacts of different PET methods in crop model application. By using the Penman–Monteith estimation method, the PEPIC model provided the best crop yield estimates and relatively reasonable simulations of evapotranspiration in different climate zones. Therefore, the Penman–Monteith method was recommended for global crop modelling in the situation of a whole set of required climate variables being available. It was also used in the following sections of this study.

The grey water footprint refers to the volume of water that is required to assimilate polluted water. It reflects the intensity of water pollution caused by water use for human activities. This study aims to address some major shortcomings associated with grey water footprint accounting in the literature and discuss several ways towards its improvement. Global maize production is used for illustration. The study specifically tackles three issues: the appropriate water quality standards for grey water footprint assessment; grey water footprint for multiple pollutants; and the influence of spatial resolution of the assessment on the level of grey water stress. A biophysical crop model is applied to quantify nitrogen and phosphorus losses to water in maize production on a global scale with a 0.5-degree spatial resolution. The study shows that the grey water footprint calculation is highly sensitive to the water standards applied. The results also suggest that the grey water footprint relating to nitrogen and phosphorus pollution caused by maize production alone has already exceeded their local water availability in many parts of the world. Grey water stress shows a more critical situation at the grid level than at the watershed level for maize cultivation because the former represents the local concentration whereas the latter gives the average situation of the whole watershed. This study highlights the need for standardizing the setting of water quality standards for a consistent grey water footprint assessment taking into consideration the diverse aquatic ecosystems and ambient water quality requirements across regions, as well as the presence of multiple pollutants in water bodies.

Water scarcity has become a major constraint to socio-economic development and a threat to livelihood in increasing parts of the world. Since the late 1980s, water scarcity research has attracted much political and public attention. We here review a variety of indicators that have been developed to capture different characteristics of water scarcity. Population, water availability, and water use are the key elements of these indicators. Most of the progress made in the last few decades has been on the quantification of water availability and use by applying spatially explicit models. However, challenges remain on appropriate incorporation of green water (soil moisture), water quality, environmental flow requirements, globalization, and virtual water trade in water scarcity assessment. Meanwhile, inter-and intra-annual variability of water availability and use also calls for assessing the temporal dimension of water scarcity. It requires concerted efforts of hydrologists, economists, social scientists, and environmental scientists to develop integrated approaches to capture the multi-faceted nature of water scarcity.

Scientific investigations in medicine and beyond increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing. This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Die Makrozoobenthos-Erhebungsmethode des Modul-Stufen-Konzepts (MSK) wird breit angewandt. Wie eine Umfrage zeigt, entspricht sie überwiegend den Erwartungen der Anwender und Anwenderinnen. Um die Verlässlichkeit zu steigern, bedarf es jedoch einer Weiterentwicklung der Methode. So soll die Methode künftig den natürlichen Hintergrund miteinbeziehen, weitergehende Indices sollen auf Schweizer Daten getestet und die Ergebnisse publiziert werden.

Predicting the composition and dynamics of communities is a challenging but useful task to efficiently support ecosystem management. Community ecology has developed a number of promising theories, including food webs, metabolic theory, ecological stoichiometry and environmental filtering. Their joint implementation in a mechanistic modeling framework should help us to bring community ecology to a new level by improving its predictive abilities. One of the challenges lies in the proper consideration of model uncertainty.
In this paper we contribute to this challenging task by modeling the temporal dynamics of macroinvertebrate communities in a stream subjected to hydropeaking in Switzerland. To this end, we extended the mechanistic model Streambugs regarding flood-induced drift-processes and the use of trait information to define performance filters.
Model predictions without any calibration were in the right order of magnitude but did not reflect the dynamics of most of the invertebrate taxa well. Bayesian inference drastically improved the model fit. It revealed that a large share of total model output uncertainty can be attributed to observation errors, which exceeded model parameter uncertainty. Observed and simulated community-aggregated traits helped to identify and understand model deficits. The combination of different ecological theories and trait information in a single mechanistic modeling framework combined with Bayesian inference can thus help to predict responses of communities to environmental changes, which can support ecosystem management.

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

Hydrological models have been widely used for water resources management. Successful application of hydrological models depends on careful calibration and uncertainty analysis. Spatial unit of water balance calculations may differ widely in different models from grids to hydrological response units (HRU). The Soil and Water Assessment Tool (SWAT) software uses HRU as the spatial unit. SWAT simulates hydrological processes at sub-basin level by deriving HRUs by thresholding areas of soil type, land use, and slope combinations. This may ignore some important areas, which may have great impact on hydrological processes in the watershed. In this study, a hierarchical HRU approach was developed in order to increase model performance and reduce computational complexity simultaneously. For hierarchical optimization, HRUs are first divided into two-HRU types and are optimized with respect to some relevant influence parameters. Then, each HRU is further divided into two. Each child HRU inherits the optimum parameter values of the parent HRU as its initial value. This approach decreases the total calibration time while obtaining a better result. The performance of the hierarchical methodology is demonstrated on two basins, namely Sarisu-Eylikler and Namazgah Dam Lake Basins in Turkey. In Sarisu-Eylikler, we obtained good results by a combination of curve number (CN2), soil hydraulic conductivity, and slope for generating HRUs, while in Namazgah use of only CN2 gave better results.

While the number of river restoration projects is increasing, studies on their success or failure relative to expectations are still rare. Only a few decision support methodologies and integrative methods for evaluating the ecological status of rivers are used in river restoration projects, thereby limiting informed management decisions in restoration planning as well as success control. Moreover, studies quantifying river restoration effects are often based on the assessment of a single organism group, and the effects on terrestrial communities are often neglected. In addition, potential effects of water quality or hydrological degradation are often not considered for the evaluation of restoration projects.
We used multi-attribute value theory to re-formulate an existing river assessment protocol and extend it to a more comprehensive, integrated ecological assessment program. We considered habitat conditions, water quality regarding nutrients, micropollutants and heavy metals, and five instream and terrestrial organism groups (fish, benthic invertebrates, aquatic vegetation, ground beetles and riparian vegetation). The physical, chemical and biological states of the rivers were assessed separately and combined to value the overall ecological state.
The assessment procedure was then applied to restored and unrestored sites at two Swiss rivers to test its feasibility in quantifying the effect of river restoration. Uncertainty in observations was taken into account and propagated through the assessment framework to evaluate the significance of differences between the ecological states of restored and unrestored reaches. In the restored sites, we measured a higher width variability of the river, as well as a higher width of the riparian zone and a higher richness of organism groups. According to the ecological assessment, the river morphology and the biological states were significantly better at the restored sites, with the largest differences detected for ground beetles and fish communities, followed by benthic invertebrates and riparian vegetation. The state of the aquatic vegetation was slightly lower at the restored sites. According to our assessment, the presence of invasive plant species counteracted the potential ecological gain. Water quality could be a causal factor contributing to the absence of larger improvements.
Overall, we found significantly better biological and physical states, and integrated ecological states at the restored sites. Even in the absence of comprehensive before-after data, based on the similarity of the reaches before restoration and mechanistic biological knowledge, this can be safely interpreted as a causal consequence of restoration. An integrative perspective across aquatic and riparian organism groups was important to assess the biological effects, because organism groups responded differently to restoration. In addition, the potential deteriorating effect of water quality demonstrates the importance of integrated planning for the reduction of morphological, water quality and hydrological degradation.

1. Habitat destruction, biological invasions and water quality deterioration are serious threats to native communities and can lead to modifications in community composition and structure, and in ecosystem function.
2. To predict the consequences of river restoration, biological invasions and water quality change in the taxonomic composition of macroinvertebrate communities in rivers, we extended the mechanistic model Streambugs, which describes growth, respiration and death of interacting taxa under given environmental conditions.
3. The model parameterised with prior knowledge on macroinvertebrate traits had already a reasonable explanatory power for data from restored and unrestored sites in two Swiss rivers. As anticipated, the explanatory power of the model was increased further by incorporation of learning from observations through Bayesian inference. The predictive application of the model to changes in water quality and invasions by alien species indicated the importance of integrative planning of management measures: although invasive species are predicted to be able to colonise restored and unrestored sites and replace native species, both invasion of alien species and subsequent exclusion of native taxa is predicted to be reduced when water quality improves.
4. The unavoidable simplification in the mathematical description of complex interactions in the macroinvertebrate community results in predictions that are uncertain. Our modelling techniques make the best attempt to quantify this uncertainty. Despite the uncertainty that always calls for more research, this study makes a considerable step towards improving our understanding of community structure and supporting river management through mechanistic, predictive modelling of macroinvertebrate communities in rivers under changing environmental conditions.

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.
The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.
Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).
For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

As water resources become further stressed due to increasing levels of societal demand, understanding the effect of climate and landuse change on various components of the water cycle is of strategic importance. In this study we used a previously developed hydrologic model of the Black Sea Catchment (BSC) to assess the impact of potential climate and landuse changes on the fresh water availability. The BSC model was built, calibrated, and validated against observed daily river discharge for the period of 1973-2006 using the Soil andWater Assessment Tool (SWAT) as the modeling tool. We employed the A2 and B2 scenarios of 2017-2050 generated by the Danish Regional Climate Model (HIRHAM), and four potential future landuse scenarios based on the Intergovernmental Panel of Climate Change (IPCC)'s special report on emissions scenarios (SRES) storylines, to analyze the impact of climate change and landuse change on the water resources of the BSC. The detailed modeling and the ensemble of the scenarios showed that a substantial part of the catchment will likely experience a decrease in freshwater resources by 30 to 50%.

The relationship between tracer velocities and wave or wetting front celerities is essential to understand water flowing from hillslopes to the stream. The connection between maximum velocity and celerities estimated by means of experimental techniques has not been explored. To assess the pattern of infiltrating water front and dominant flow direction, we performed sprinkling experiments at a trenched plot in the Weierbach catchment in Luxembourg. Maximum velocities and wetting front celerities were inferred at different depths using artificial tracers, soil moisture measurements (TDR), and geophysical techniques. The flow direction was predominantly vertical within the observed plot, with almost no lateral flow observed until depths of 2–3 m; shallow trench flow was intermittent and associated with preferential flow. Average celerity estimates using TDR and geophysical techniques were equal to 707 ± 234 mm h^-1 and 971 ± 625 mm h^-1, respectively. Vertical maximum velocity estimates were tracer-dependent and had very variable ranges: 109.3 ± 89.3 mm h^-1 (Cl^-), 177.8 ± 199.1 mm h^-1 (Br^-), and 604.1 ± 610.7 mm h¹ (Li⁺). Preferential flow processes were inferred from maximum velocities apparently greater than celerities and scattered trench flow with highly variable tracer concentrations. The high variability between maximum velocities of different tracers indicated a complex pattern of tracer movement through the soil, not captured by celerity values alone. Our study demonstrated the importance to assess both velocities and celerities to understand flow dynamics in response to sprinkling while information on the wetting front alone would have missed important preferential flow processes.

The majority of residents in low- and middle-income countries are served by onsite sanitation. Equitable access to sanitation, including emptying, collection, and transport services for the accumulation of faecal sludge remains a major challenge. Comprehensive information on service coverage by mechanical faecal sludge emptying service providers is lacking. The purpose of this study is to analyse the spatial distribution of service coverage and identify areas without faecal sludge emptying services in Kampala, Uganda. The study uses GIS (geographic information systems) as a tool to analyse real-time data of service providers based on GPS (global positioning system) units that were installed in a representative number of trucks. Of the total recorded 5653 emptying events, 27% were located outside Kampala city boundaries. Of those within Kampala city boundaries, 37% were classified as non-household customers. Areas without service provision accounted for 13% of the total area. Service provision normalised by population density revealed much greater service provision in medium- and high-income areas than low- and very low-income areas. The employed method provides a powerful tool to optimise faecal sludge management on a city-wide scale by increasing sustainability of the planning and decision-making process, increasing access to service provision and reducing faecal sludge transport times and costs.

This study contributes to a better understanding of climate change adaptation by investigating different farming systems and by including cognitive factors as explanatory variables. We compared a food crop and a horticultural farming system, regarding applied adaptation measures and factors influencing adaptation. The data were based on a field survey of 267 smallholder farmers in Laikipia County of Kenya. A binary logistic regression was conducted against individual adaptation measures to identify determinants of adaptation. Adaptation measures employed by food crop farmers were mainly risk-reducing, such as mixed- and inter-cropping, planting early-maturing crop varieties and early planting. In contrast, horticultural farmers tended to focus more on intensifying crop production and applied crop rotation, irrigation and application of agro-chemicals, artificial fertilizer and manure. Factors positively influencing adaptation included access to extension services and risk perception among horticultural farmers, and access to workforce and farmers groups among food crop farmers. Furthermore, food crop farmers with access to less risk-prone income sources than agriculture seemed to have less motivation to adapt. The study showed that as climate change progresses, social differences between horticultural and food crop farmers are likely to increase, hence leading to inequalities in adaptation at local levels. Adaptation planners need to address these differences if sustainable adaptation is to be achieved.

This study examines the water productivity of irrigated wheat and maize yields in Karkheh River Basin (KRB) in the semi-arid region of Iran using a coupled modeling approach consisting of the hydrological model (SWAT) and the river basin water allocation model (MODSIM). Dynamic irrigation requirements instead of constant time series of demand were considered. As the cereal production of KRB plays a major role in supplying the food market of Iran, it is necessary to understand the crop yield-water relations for irrigated wheat and maize in the lower part of KRB (LKRB) where most of the irrigated agricultural plains are located. Irrigated wheat and maize yields (Y) and consumptive water use (AET) were modeled with uncertainty analysis at a subbasin level for 1990–2010. Simulated Y and AET were used to calculate crop water productivity (CWP). The coupled SWAT–MODSIM approach improved the accuracy of SWAT outputs by considering the water allocation derived from MODSIM. The results indicated that the highest CWP across this region was 1.31 kg·m⁻³ and 1.13 kg·m⁻³ for wheat and maize, respectively; and the lowest was less than 0.62 kg·m⁻³ and 0.58 kg·m⁻³. A close linear relationship was found for CWP and yield. The results showed a continuing increase for AET over the years while CWP peaks and then declines. This is evidence of the existence of a plateau in CWP as AET continues to increase and evidence of the fact that higher AET does not necessarily result in a higher yield.

Thermometers and sphygmomanometers pose a potentially large source of mercury emissions to the environment due to their high elemental mercury content. Many countries, e.g. most European countries, US have banned their uses already and many more strictly limited their use. However, in Thailand these mercury-based devices are still used, accumulating large stocks in the use phase as well as in landfills. To understand the development of mercury stocks and flows from thermometers and sphygmomanometers, a time-dependent mathematical material flow model is used in this study. The flows of mercury through these two products were calculated based on data between the years of 1962 and 2013. The simulation showed that the stock of mercury in thermometers is about 20 times smaller than the stock in sphygmomanometers. However the sum of waste flows and emissions to air and water from thermometers is 3 times larger than from sphygmomanometers. The reason is the lifetime of thermometers which is about 70 times shorter than the lifetime of sphygmomanometers. The calculated emission to air from mercury thermometers in hospitals can explain the higher mercury level measured in urine of health care staff. In order to reduce the mercury flows to the environment mercury thermometers should be replaced by alternative products as soon as possible.

Few studies of extreme precipitation have been conducted in Northeast China, particularly at multitimescales. We aim to enhance the understanding of changes and variability in extreme precipitation over the past 54 years (1961–2014) in this region. We have investigated the potential relationship among extreme precipitation, climate and agricultural drought focusing on several timescales in this region. Thirteen extreme precipitation indices at seasonal, annual, and growing-period scales were estimated on the basis of daily precipitation data from 70 meteorological stations. The results indicate that all extreme precipitation indices that reflect the features of extreme wet events showed increasing trends in spring, and the trends of these indices were almost negative for the other timescales. Spatially, the frequency, duration and intensity of extreme wet events decreased gradually from south to north. The range of influence and the duration of extreme dry events increased continuously in Northeast China. In Northeast China, extreme precipitation was more easily influenced by the polar climate than the monsoon. Furthermore, correlation between the extreme precipitation indices and comprehensive crop failure ratios of agricultural drought disasters (C index) confirmed that agricultural drought was heavily influenced by precipitation anomalies in this area.

The North China Plain is the scarcest water region in China. Its water security is closely related to interregional water movement, which can be realized by real water transfers and/or virtual water transfers. This study investigates the roles of virtual water trade and real water transfer using the Interregional Input-Output model. The results show that the region is receiving 19.4 billion m³/year of virtual water from the interregional trade, while exporting 16.4 billion m³/year of virtual water in the international trade. In balance, the region has a net virtual water gain of 3 billion m³/year from the outside. Its virtual water inflow is dominated by agricultural products from other provinces, totalling 16.6 billion m³/year, while its virtual water export is dominated by manufacturing sectors to other countries, totalling 11.7 billion m³/year. Both virtual water imports and real water transfers from the South to North Water Diversion Project are important water supplements for the region. The results of this study provide useful scientific references for the establishment of combating strategies to deal with water scarcity in the future.

Coal is the dominant fuel for electricity generation around the world. This type of electricity generation uses large amounts of water, increasing pressure on water resources. This calls for an in-depth investigation in the water-energy nexus of coal-fired electricity generation. In China, coal-fired power plants play an important role in the energy supply. Here we assessed water consumption of coal-fired power plants (CPPs) in China using four cooling technologies: closed-cycle cooling, once-through cooling, air cooling, and seawater cooling. The results show that water consumption of CPPs was 3.5 km³, accounting for 11% of total industrial water consumption in China. Eighty-four percent of this water consumption was from plants with closed-cycle cooling. China's average water intensity of CPPs was 1.15 l/kWh, while the intensity for closed-cycle cooling was 3-10 times higher than that for other cooling technologies. About 75% of water consumption of CPPs was from regions with absolute or chronic water scarcity. The results imply that the development of CPPs needs to explicitly consider their impacts on regional water resources.

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes.We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on a variety of specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of bestperforming parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events.We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models.

The development of approaches to tackle the European Union (EU) water-related challenges and shift towards sustainable water management and use is one of the main objectives of Horizon 2020, the EU strategy to lead a smart, sustainable and inclusive growth. The EU is an increasingly water challenged area and is a major agricultural trader. As agricultural trade entails an exchange of water embodied in goods as a factor of production, this study investigates the region's water-food-trade nexus by analysing intra-regional virtual water trade (VWT) in agricultural products. The analysed period (1993-2011) comprises the enactment of the Water Framework Directive (WFD) in the year 2000. Aspects of the VWT that are relevant for the WFD are explored. The EU is a net importer of virtual water (VW) from the rest of the world, but intra-regional VWT represents 46% of total imports and 75% of total exports. Five countries account for 60% of total VW imports (Germany, France, Italy, The Netherlands, Belgium) and 65% of total VW exports (The Netherlands, France, Germany, Belgium and Spain). Intra-EU VWT more than doubled over the period considered, while trade with extra-EU countries did not show such a marked trend. In the same period, blue VWT increased significantly within the region and net import from the rest of the world slightly decreased. Water scarce countries, such as Spain and Italy, are major exporters of blue water in the region. The traded volumes of VW have been increasing almost monotonically over the years, and with a substantial increase after 2000. The overall trend in changes in VWT does not seem to be in accordance with the WFD goals. This study demonstrated that VWT analyses can help evaluate intertwining effects of water, agriculture and trade policies which are often made separately in respective sectors.

Food security, and in particular food availability, depends on environmental resources, climatic conditions and agricultural practices, having water resources as a common denominator. There is major concern about food security in countries having limited water resources, due to the large volumes of water that are required to produce food commodities. Water-deficit countries tend to rely on international trade to close the gap between water (and thus food) demand and supply. Against this backdrop, the purpose of this study is twofold. First, to analyse water resources in the Middle East and North African region also considering the political economy trends and dynamics, which drive the region’s demand for water. Secondly, the study aims to increase understanding on the role that trade of agricultural commodities has played in meeting the requirements of the MENA populations, in terms of food and associated water ‘embedded’ as a factor of production. The study argues that virtual water trade, that is, the virtual transfer of the water used for agricultural production from producing to consuming countries, has provided the region’s economies with water and food security over the past 25 years. The study shows that virtual water imports have more than doubled and the increase has been more than proportional to population growth in the area. Food products account by far for the largest share of virtual water flows, while crops and high value foods are the main categories of agricultural products associated with virtual water imports. The largest share of the MENA imports originate from outside the region, thus determining a marked dependency on water resources available elsewhere, but not always from water-secure countries.

This paper describes the development of a Climate Change Toolkit (CCT) to perform tasks needed in a climate change study plus projection of extreme weather conditions by analyzing historical weather patterns. CCT consists of Data Extraction, Global Climate Data Management, Bias Correction and Statistical Downscaling, Spatial Interpolation, and Critical Consecutive Day Analyzer (CCDA). CCDA uses a customized data mining approach to recognize spatial and temporal patterns of extreme events. CCT is linked to an archive of 0.5° historical global daily dataset (CRU, 1970–2005), and GCM data (1960–2099) for five models and four carbon scenarios. Application of CCT in California using ensemble results of scenario RCP8.5 showed a probable increase in the frequency of dry periods in the southern part of the region, while decreasing in the north. The frequency of wet periods may suggest higher risks of flooding in the north and coastal strips. We further found that every county in northern California may experience flooding conditions of 1986 at least once between 2020 and 2050.

Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a ~810 km² Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil andWater Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.g., from ~15 km² down to ~1 km² ) when delineating sub-basins in SWAT. We also showed that both the MARINE and SWAT models were equally able to reproduce peak discharge, flood timing and volume, and that they were both limited by rainfall and soil data. Hence, the SWAT model appears to be a reliable modelling tool to predict discharge over long periods of time in large flash-flood-prone basins.

System identification, sensitivity analysis, optimization and control, require a large number of model evaluations. Accurate simulators are too slow for these applications. Fast emulators provide a solution to this efficiency demand, sacrificing unneeded accuracy for speed. There are many strategies for developing emulators but selecting one remains subjective. Herein we compare the performance of two kinds of emulators: mechanistic emulators that use knowledge of the simulator's equations, and purely data-driven emulators using matrix factorization. We borrow simulators from urban water management, because more stringent performance criteria on water utilities have made emulation a crucial tool within this field. Results suggest that naive data-driven emulation outperforms mechanistic emulation. We discuss scenarios in which mechanistic emulation seems favorable for extrapolation in time and dealing with sparse and unevenly sampled data. We also point to advances in Machine Learning that have not permeated yet into the environmental science community.

Uncertainties due to climate change and population growth have created a critical situation for many megacities. Investigating spatio-temporal variability of water resources is, therefore, a critical initial step for water-resource management. This paper is a first study on the evaluation of water-budget components of water resources in Istanbul using a high-resolution hydrological model. In this work, the water resources of Istanbul and surrounding watersheds were modeled using the Soil andWater Assessment Tool (SWAT), which is a continuous-time, semi-distributed, process-based model. The SWAT-CUP program was used for calibration/validation of the model with uncertainty analysis using the SUFI-2 algorithm over the period 1977–2013 at 25 gauge stations. The results reveal that the annual blue-water potential of Istanbul is 3.5 billion m³, whereas the green-water flow and storage are 2.9 billion m³ and 0.7 billion m³, respectively. Watersheds located on the Asian side of the Istanbul megacity yield more blue-water resources compared to the European side, and constitute 75% of the total potential water resources. The model highlights the water potential of the city under current circumstances and gives an insight into its spatial distribution over the region. This study provides a strong basis for forthcoming studies concerning better water-resources management practices, climate change and water-quality studies, as well as other socio-economic scenario analyses in the region.

Understanding the drivers of stream macroinvertebrate distribution patterns - the most diverse animal group in freshwater ecosystems - is a major goal in freshwater biogeography. Climate and topography have been shown to explain species' distributions at continental scales, but the contribution of natural and anthropogenically altered streamflow is often omitted in large-scale analyses due to the lack of appropriate data. We test how macroinvertebrate occurrences can be linked to streamflow observations and evaluate the relative importance of streamflow regimes and water use for macroinvertebrate occurrences from 19 orders across Europe. We first paired species sampling locations with hydrological gauging stations considering 5 combinations of the geographic distance and difference in flow accumulation (upstream contributing area). We then used Generalized Linear Models to assess the influence of the streamflow regime, simulated water use, and climate and topography on the occurrence of macroinvertebrates. The pairing method that assigned species records to the closest gauging station in terms of both distance and flow accumulation performed best. Most of the species studied occurred preferentially in river habitats with low mean annual streamflow and streamflow variability, high winter streamflow, and low levels of water withdrawals for irrigation or manufacturing. We conclude that flow accumulation is a useful proxy to evaluate the proximity of species records to gauging stations, omitting species records that do not belong to a given stream reach. The strong contribution of streamflow and water use indicators on macroinvertebrate occurrences underline their importance for yielding robust occurrence estimates.

Study region: Alberta, Canada.
Study focus: The security of freshwater supplies is a growing concern worldwide. Understanding dynamics of water supply and demand is the key for sustainable planning and management of watersheds. Here we analyzed the uncertainties in water supply of Alberta by building an agro-hydrological model, which accounts for major hydrological features, geo-spatial heterogeneity, and conflicts over water-food-energy resources. We examined the cumulative effects of natural features (e.g., potholes, glaciers, climate, soil, vegetation), anthropogenic factors (e.g., dams, irrigation, industrial development), environmental flow requirements (EFR), and calibration schemes on water scarcity in the dynamics of blue and green water resources, and groundwater recharge.
New hydrological insights for the region: Natural hydrologic features of the region create a unique hydrological system, which must be accurately represented in the model for reliable estimates of water supply at high spatial and temporal resolution. Accounting for EFR, increases the number of months of water scarcity and the population exposed. Severe blue water scarcity in spring and summer months was found to be due to irrigated agriculture, while in winter months it was mostly due to the demands of petroleum or other industries. We found over exploitation of the groundwater in southern subbasins and concluded that more detailed analysis on groundwater flow and connectivity is required. Our study provides a general and unified approach for similar analyses in other jurisdictions around the world.

1. Species distribution models (SDM) are widely used to predict occupancy patterns at fine resolution over wide extents. However, SDMs generally ignore the effect of biotic interactions and tend to overpredict the number of species that can coexist at a given location and time (hereafter, the alpha-capacity). We developed an extension of SDMs that integrates species-level and community-level modelling to account for the above drivers.
2. The alpha-adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a community and the site's alpha-capacity and adjusts the PoO, such that: (i) their sum will equal the alpha-capacity as predicted by probability theory; and (ii) the adjusted PoO are dependent upon the relative suitability of each species for that site. The new method was tested using community data comprising 87 freshwater invertebrate species in an LTER watershed in Germany. We explored the ability of the method to predict alpha and beta-diversity patterns. We further focused on the effect on model performance at the species-level of the error associated with modelling alpha-capacity, of differences in gamma diversity (the size of the community) and of the type of community (random or guild-based).
3. The models that predicted alpha-capacity contained considerable error, and thus adjusting the PoO according to the modelled alpha-capacity resulted with decreased performance at the species level. However, when using the observed alpha-capacity to mimic a good alpha-capacity model, the alpha-adjusted SDMs usually resulted in increased performance. We further found that the alpha-adjusted SDM was better than the original SDM at predicting beta-diversity patterns, especially when using similarity indices that are sensitive to double absences.
4. Using the alpha-adjusted SDM approach may increase the predictive performance at the species and community levels if alpha-capacity can be assessed or modelled with sufficient accuracy, especially in relatively small communities of closely interacting species. With better models to predict alpha-capacity being developed, alpha-adjusted SDM has considerable potential to provide more realistic predictions of species-distribution patterns.

Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior.

The recent explosion of high dimensional, high resolution ‘big-data’ from automated bioinformatics measurement techniques demands new methods for unsupervised data processing. An essential analysis step is the identification of groups of similar data, or ‘clusters’, in noisy high-dimensional data spaces, as this permits to perform some analysis steps at the group level. Popular clustering algorithms introduce an undesired cluster shape bias, require prior knowledge of the number of clusters, and are unable to properly deal with noise. Manual data gating, often used to assist these methods, is based on low-dimensional projection techniques, which is prone to obscure the underlying data structure. While Hebbian Learning Clustering successfully overcomes all of these limitations (by using only local similarities to infer global structure), previous implementations were unsuited to deal with big data sets. Here, we present a novel implementation based on realistic neuronal dynamics that removes also this obstacle. By a performance that scales favourably compared to all standard clustering algorithms, unbiased large data analysis becomes feasible on standard desktop hardware.

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual "iWaQa" model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

The economic concerns of low-income farmers are barriers to nutrient abatement policies for eutrophication control in surface waters. This study brings up a perspective that focuses on integrating multiple-pollutant discharge permit markets with farm management practices. This aims to identify a more economically motivated waste load allocation (WLA) for non-point sources (NPS). For this purpose, we chose the small basin of Zrebar Lake in western Iran and used the soil and water assessment tool (SWAT) for modeling. The export coefficients (ECs), effectiveness of best management practices (BMPs), and crop yields were calculated by using this software. These variables show that low-income farmers can hardly afford to invest in BMPs in a typical WLA. Conversely, a discharge permit market presents a more cost-effective solution. This method saves 64% in total abatement costs and motivates farmers by offering economic benefits. A market analysis revealed that nitrogen permits mostly cover the trades with the optimal price ranging from $6 to $30 per kilogram. However, phosphorous permits are limited for trading, and their price exceeds $60 per kilogram. This approach also emphasizes the establishment of a regional institution for market monitoring, dynamic pricing, fair fund reallocation, giving information to participants, and ensuring their income. By these sets of strategies, a WLA on the brink of failure can turn into a cost-effective and sustainable policy for eutrophication control in small basins.

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized SoilWater Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil andWater Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.

Parameter estimation for agent-based and individual-based models (ABMs/IBMs) is often performed by manual tuning and model uncertainty assessment is often ignored. Bayesian inference can jointly address these issues. However, due to high computational requirements of these models and technical difficulties in applying Bayesian inference to stochastic models, the exploration of its application to ABMs/IBMs has just started. We demonstrate the feasibility of Bayesian inference for ABMs/IBMs with a Particle Markov Chain Monte Carlo (PMCMC) algorithm developed for state-space models. The algorithm profits from the model's hidden Markov structure by jointly estimating system states and the marginal likelihood of the parameters using time-series observations. The PMCMC algorithm performed well when tested on a simple predator-prey IBM using artificial observation data. Hence, it offers the possibility for Bayesian inference for ABMs/IBMs. This can yield additional insights into model behaviour and uncertainty and extend the usefulness of ABMs/IBMs in ecological and environmental research.

The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R², bR², NSE, MNS, RSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are "conditioned" on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.