Department Environmental Chemistry

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.

A proper uncertainty assessment of rainfall-runoff predictions has always been an important objective for modelers. Several sources of uncertainty have been identified, but their representation was limited to complicated mechanistic error propagation frameworks only. The typical statistical error models used in the modeling practice still build on outdated and invalidated assumptions like the independence and homoscedasticity of model residuals and thus result in wrong uncertainty estimates. The primary reason for the popularity of the traditional faulty methods is the enormous computational requirement of full Bayesian error propagation frameworks. We introduce a statistical error model that can account for the effect of various uncertainty sources present in conceptual rainfall-runoff modeling studies and at the same time has limited computational demand. We split the model residuals into three different components: a random noise term and two bias processes with different response characteristics. The effects of the input uncertainty are simulated with a stochastic linearized rainfall-runoff model. While the description of model bias with Bayesian statistics cannot directly help to improve on the model's deficiencies, it is still beneficial to get realistic estimates on the overall predictive uncertainty and to rank the importance of different uncertainty sources. This feature is particularly important if the error sources cannot be addressed individually, but it is also relevant for the description of remaining bias when input and structural errors are considered explicitly.

Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

Environmental decision support intends to use the best available scientific knowledge to help decision makers find and evaluate management alternatives. The goal of this process is to achieve the best fulfillment of societal objectives. This requires a careful analysis of (i) how scientific knowledge can be represented and quantified, (ii) how societal preferences can be described and elicited, and (iii) how these concepts can best be used to support communication with authorities, politicians, and the public in environmental management. The goal of this paper is to discuss key requirements for a conceptual framework to address these issues and to suggest how these can best be met. We argue that a combination of probability theory and scenario planning with multi-attribute utility theory fulfills these requirements, and discuss adaptations and extensions of these theories to improve their application for supporting environmental decision making. With respect to (i) we suggest the use of intersubjective probabilities, if required extended to imprecise probabilities, to describe the current state of scientific knowledge. To address (ii), we emphasize the importance of value functions, in addition to utilities, to support decisions under risk. We discuss the need for testing "non-standard" value aggregation techniques, the usefulness of flexibility of value functions regarding attribute data availability, the elicitation of value functions for sub-objectives from experts, and the consideration of uncertainty in value and utility elicitation. With respect to (iii), we outline a well-structured procedure for transparent environmental decision support that is based on a clear separation of scientific prediction and societal valuation. We illustrate aspects of the suggested methodology by its application to river management in general and with a small, didactical case study on spatial river rehabilitation prioritization.

Das per 1.1.2011 in Kraft getretene revidierte Gewässerschutzgesetz bringt den Kantonen in der Schweiz zusätzliche Verpflichtungen bezüglich der Revitalisierung morphologisch beeinträchtigter Gewässer und der Verminderung negativer Auswirkungen von Wasserkraftanlagen auf Gewässerökosysteme. Da auch neue Finanzierungsquellen erschlossen werden, sind diese neuen oder verstärkten Verpflichtungen eine grosse Chance für die ökologische Aufwertung der schweizerischen Gewässersysteme. Ähnliche Anforderungen an das Gewässermanagement wurden auch im Ausland etabliert, etwa in der Europäischen Union durch die Wasserrahmenrichtlinie (WFD, 2000). Das Ziel des Gewässermanagements muss es sein, mit den verfügbaren Mitteln einen möglichst grossen ökologischen und gesellschaftlichen Nutzen zu erzielen.

Formal methods of decision analysis can help to structure a decision making process and to communicate reasons for decisions transparently. Objectives hierarchies and associated value and utility functions are useful instruments for supporting such decision making processes by structuring and quantifying the preferences of decision makers or stakeholders. Common multi-attribute decision analysis software products support such decision making processes but they can often not represent complex preference structures and visualize uncertainty induced by uncertain predictions of the consequences of decision alternatives. To stimulate strengthening these aspects in decision support processes, we propose a set of visualization tools and provide a software package for constructing, evaluating and visualizing value and utility functions. In these tools we emphasize flexibility in value aggregation schemes and consideration and communication of prediction uncertainty. The use of these tools is demonstrated with an illustrative example of river management decision support.

We examined the influence of land-use, habitat, and water quality on the spatial distribution of aquatic macroinvertebrates in two human-dominated catchments in the Swiss Plateau (Gürbe, Mönchaltorfer Aa). Land-use in the Gürbe catchment was dominated by agriculture, whereas urban land-use was more common in the Mönchaltorfer Aa. Study sites in each catchment were characterized using measures of local habitat conditions, water quality parameters including water temperature, and organic matter resources. A strong longitudinal gradient in temperature, conductivity and nitrogen was evident among sites in the Gürbe catchment, although sites on a main tributary had a strong agricultural signature and deviated from this pattern. Percentage agricultural land-use in the Gürbe was strongly correlated with algal biomass and the water quality PCA axes associated with conductivity, nitrogen (axis-1) and temperature (axis-3). Spatial grouping of sites by water quality was less evident in the Mönchaltorfer Aa, except for a strong signal by wastewater treatment plant effluents and partial differences between upper and lower basin sites. Percentage forest and agricultural land-use in the Mönchaltorfer Aa were correlated with water quality PCA axis-2, being associated with phosphorus and temperature. Macroinvertebrate densities, taxonomic richness, and axis-1 from a non-metric multidimensional scaling analysis (NMDS) of taxonomic composition were significantly correlated with water quality PCA axis-1 in the Gürbe catchment. Here, macroinvertebrate densities and NMDS axis-1 scores based on taxon relative abundances and densities were correlated with land-use features. Spatial distances between sites also were related to site differences in macroinvertebrates, reflecting the strong longitudinal environmental gradient in the Gürbe. Taxonomic differences between water quality PCA site groups were less pronounced in the Mönchaltorfer Aa, although differences were significant for trichopterans, ephemeropterans, chironomids, gastropods and coleopterans. Here, NMDS axis-1 based on taxon relative abundances and densities was correlated with forest land-use. Spatial distances between sites were not evident in macroinvertebrate site differences, reflecting the less pronounced spatial and longitudinal patterns in environmental attributes in this catchment. Our results support the hypothesis that spatial distributions of macroinvertebrates are related to spatial relationships among environmental attributes like land-use, habitat, and water quality in human-dominated catchments that depend on river network complexity, a habitat-filtering template in line with ecological niche theory.

Wirbellose Kleinlebewesen haben sehr verschiedene Ansprüche an ihren Lebensraum. Die Eawag entwickelt ein Computermodell, um die Zusammensetzung der Lebensgemeinschaften am Flussgrund vorherzusagen. Das Modell soll in Zukunft integratives Flussmanagement unterstützen und mögliche Konsequenzen verschiedener Managementmassnahmen oder des Klimawandels vorhersagen.

Using macroinvertebrates as ecological indicators for different stressors has a long tradition. However, when applied to field data, one often observes correlations between different macroinvertebrate indices that can be attributed to both correlations of stressors and inherent correlations due to the sensitivity of taxa to different stressors. Ignoring the source of any given correlation leads to ambiguous conclusions about the impact of different stressors.
Here, we demonstrate how to distinguish the causes of correlation by means of Monte Carlo simulations. We assessed to which degree trait-based indices are stressor-specific and whether this depends on the pool of taxa and its taxonomic resolution. Therefore, we (1) analysed the frequencies of "sensitive" and "insensitive" taxa for pairwise combinations of different indices, (2) analysed the inherent correlation of indices with random samples from different taxon pools derived from field samples and from a complete species list of a whole ecoregion, and (3) compared this inherent correlation with the actual correlation of the field samples. We exemplified this approach by analysing two existing indices (SPEAR_pesticides, Saprobic Index) and new indices for temperature, flow and pH stress. We used these new indices to illustrate our approach while in-depth testing of their applicability was not the focus of our study. < BR/> We found strong correlations between several indices in our study area at the Swiss Plateau. The probability that this correlation is only due to inherent correlation in the taxa sensitivities was low (maximum of 0.34). The problem of inherent correlation between indices is more severe for the smaller taxon pool with lower taxonomic resolution.
Correlation in the sensitivity of different taxa to different stressors leads to an inherent correlation in trait-based indices, which weakens their explanatory power. Our results highlight the importance of correlation analyses when using trait-based indices to guide ecosystem-management, especially in regions with reduced biodiversity.

For the first time, we combine concepts of theoretical food web modeling, the metabolic theory of ecology, and ecological stoichiometry with the use of functional trait databases to predict the coexistence of invertebrate taxa in streams. We developed a mechanistic model that describes growth, death, and respiration of different taxa dependent on various environmental influence factors to estimate survival or extinction. Parameter and input uncertainty is propagated to model results. Such a model is needed to test our current quantitative understanding of ecosystem structure and function and to predict effects of anthropogenic impacts and restoration efforts. The model was tested using macroinvertebrate monitoring data from a catchment of the Swiss Plateau. Even without fitting model parameters, the model is able to represent key patterns of the coexistence structure of invertebrates at sites varying in external conditions (litter input, shading, water quality). This confirms the suitability of the model concept. More comprehensive testing and resulting model adaptations will further increase the predictive accuracy of the model.