Abteilung Systemanalyse, Integrated Assessment und Modellierung

This paper presents some of the reasons for studying the history of hydrology and for the formation of the International Association of Hydrological Sciences (IAHS) History of Hydrology Working Group. In particular, we consider the importance of recording the histories of hydrological data, catchments, diversity in hydrology (of both people and topics), and what can be gained from the historical literature in hydrology. We also consider why the classical concepts of catchment response to rainfall have evolved slowly in hydrology, despite identified limitations, and how the major impetus for change in the 1970s, in the form of techniques for measuring environmental tracers, effectively came from developments outside the discipline. We conclude by speculating whether the questions posed by recent machine learning studies might lead to further change and understanding.

The interplay between abiotic (resource supply, temperature) and biotic (grazing) factors determines growth and loss processes in phytoplankton through resource competition and trophic interactions, which are mediated by morphological traits like size. Here, we study the relative importance of grazers, water physics, and chemistry on the daily net accumulation rates (ARs) of individual phytoplankton from natural communities, grouped into six size classes from circa 10 to 500 μm. Using a Random Forest modelling approach and 4 years of daily data from a lake, we find that water temperature is generally a pivotal control of all phytoplankton ARs. At the same time, nutrients and light are important for the smallest and the largest classes. Mesozooplankton abundance is a key predictor of the AR for small phytoplankton, with microzooplankton being important for the middle-size range. In our data, large and small phytoplankton have different (seasonal) blooming patterns: small forms are favoured by low temperature and grazing, and high phosphorus levels. Larger forms show positive ARs at high temperatures and low phosphorus (being relatively insensitive to zooplankton grazing). These results help us understand the opportunities and limitations of using size to explain and model phytoplankton responses to biotic and abiotic environmental change.

1. Particle filters and smoothers are sequential Monte Carlo algorithms used to fit non-linear, non-Gaussian state-space models. These algorithms are well placed to fit process-oriented models to animal-tracking data, especially in receiver arrays, but to date they have received limited attention in the ecological literature.
2. We introduce a Bayesian filtering–smoothing algorithm that reconstructs individual movements and patterns of space use from animal-tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a simulation-based analysis, we compare the performance of our algorithm to the prevailing heuristic methods used to study movements and space use in passive acoustic telemetry systems and analyse algorithm sensitivity.
3. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle-based maps represent simulated movements more accurately, even in dense receiver arrays, and are better suited to analyses of home ranges, residency and habitat preferences.
4. This study sets a new state-of-the-art for movement modelling in receiver arrays. Particle algorithms provide a robust, flexible and intuitive modelling framework with potential applications in many ecological settings.

1. State-space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state-space models to animal-tracking data can be difficult and computationally expensive.
2. Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state-space models to tracking data, with a focus on data from aquatic animals in receiver arrays. patter is written in R, with a performant Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping.
3. In two examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle algorithms represent an individual's possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual's (unobserved) location every 2 min during 1 month and use particles to visualise movements, map space use and quantify residency (Example Two).
4. patter facilitates robust, flexible and efficient analyses of animal-tracking data. The methods are widely applicable and enable refined analyses of space use, home ranges and residency.

The periodic flooding of rice paddies presents significant environmental challenges, including methane emissions, fertilizer pollution, and water resource stress. This study introduces a scalable approach using high-resolution (10 m) Sentinel-1 SAR imagery and Global Surface Water Extent rasters to map rice paddies and their flooding patterns. Temporal variations in SAR backscatter are condensed into a custom multi-band image for each planting season, enabling unsupervised classification to delineate rice paddies and flooding patches. This approach achieves high detection accuracy, with overall accuracy rates for paddy detection of 83.8%–89.0% in Japan, 71.5%–83.5% in the Philippines, and 72.3% for rice flooding patches in Bali, Indonesia. Unlike most existing methods, our approach does not rely on ancillary data or context-specific information for training or labeling, making it scalable and adaptable across diverse geographies. We explore this potential by examining the method’s underlying assumptions and identifying areas where these assumptions may be challenged.

This study investigates the drought of three major terminal lakes: Great Salt Lake, Salton Sea, and Lake Urmia, driven by socio-hydrological lock-in—a phenomenon characterized by feedback loops between human activities and environmental processes. Previous research has linked this drying to socio-hydrological lock-in, where rational actions by individuals collectively lead to suboptimal outcomes, exacerbating water scarcity and ecological degradation. Despite existing studies, a critical knowledge gap remains in understanding how these feedback mechanisms operate across different socio-economic and ecological contexts. This research seeks to address this gap through an integrative analysis of historical data and case studies, highlighting the interplay between increased water demand for agriculture, population growth, and governance challenges. The resultant shrinkage of these lakes exposes saline playas, generating salt-rich dust with severe ecological and health impacts, including soil salinization and respiratory illnesses. By comparing the socio-hydrological lock-in across diverse regions, this study provides insights into preventing and mitigating these cycles. It emphasizes the necessity for holistic water management strategies that integrate both supply and demand-side measures. The key contribution of this research lies in its systematic exploration of the feedback mechanisms driving socio-hydrological lock-ins, offering a foundation for developing sustainable water resource management policies globally. This work aims to influence policy and practice by highlighting the need for integrated, long-term strategies that balance economic pursuits with environmental sustainability, ensuring resilience in water management amidst mounting environmental challenges.