Decision support for river management by combining the prediction of effects of suggested measures with quantified societal goals.
We investigate human impacts on the community composition of macroinvertebrates and fish in Swiss rivers with statistical analyses of existing monitoring data, food web analyses, and computer models.
Understanding trout meta-populations in river networks and predicting the effects of habitat restoration measures.
Complex systems theory meets big phytoplankton trait data.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
Development of unified ecological assessment procedures for river management.
Towards a better understanding and more reliable predictions of complex systems dynamics.
Restoring rivers for for effective catchment management.
Building an agro-hydrological model of the world to study water resources, soil erosion, and crop yield.
Landuse and climate change effects on soil erosion and water quality in the Kagera Transboundary Watershed in the Upper Catchment of Lake Victoria Basin.
With continuing population growth, increasing affluence and economic development, and diet change towards more meat consumption, the demand for food will also increase.
Hypothesis testing using controlled experiments to characterize diffuse pollution in small agricultural catchments
Development of a dynamical model to simulate the water and water related energy flows in function of time.
Calibration of watershed-scale models suffer from a number of conceptual and technical issues, which we believe require a more careful consideration by the scientific community.
Flexible framework for conceptual hydrological modeling.
In this project we study the impact of landuse and climate change on the water resources of Salman Dam Basin located in the Fars province, Iran.
Application of a Spatially Explicit Bio-physical Crop Model to Assess Drought Impact on Crop Yield and Crop-Drought Vulnerability in Sub-Saharan Africa.
A tool kit is being developed to perform multiple tasks for climate change purposes.
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Calibrating stochastic rainfall-runoff models to scaling laws, for improved predictions of extreme events.
Development of a semi-distributed hydrological model with a “flexible” approach. Testing and comparing of different model structures to combine modeling and experimenting into a learning process.
We test a big data workflow for understanding and predicting plankton dynamics using monitoring data.
We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We use machine learning methods to predict the effects of chemicals on aquatic species.
Deep neural networks (DNNs) have shown impressive empirical performance but they are still nevertheless a black-box function modeling data.
Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.
Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.