Department Systems Analysis, Integrated Assessment and Modelling

Systems Analysis, Integrated Assessment and Modelling

In SIAM, we develop and apply models and formal techniques in order to understand, demonstrate, and predict the behavior of natural, technical, social and economical systems that pertain to water and other natural resources. Read more

New Publications

Qi, W., Feng, L., Liu, J., & Yang, H. (2022). Growing hydropower potential in China under 1.5 °C and 2.0 °C global warming and beyond. Environmental Research Letters, 17(11), 114049 (11 pp.). doi:10.1088/1748-9326/ac9c72, Institutional Repository
Zhang, X., Yang, H., Zhang, W., Fenicia, F., Peng, H., & Xu, G. (2022). Hydrologic impacts of cascading reservoirs in the middle and lower Hanjiang River basin under climate variability and land use change. Journal of Hydrology: Regional Studies, 44, 101253 (22 pp.). doi:10.1016/j.ejrh.2022.101253, Institutional Repository
Kyathanahally, S. P., Hardeman, T., Reyes, M., Merz, E., Bulas, T., Brun, P., … Baity-Jesi, M. (2022). Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology. Scientific Reports, 12, 18590 (11 pp.). doi:10.1038/s41598-022-21910-0, Institutional Repository
Viswanathan, M., Scheidegger, A., Streck, T., Gayler, S., & Weber, T. K. D. (2022). Bayesian multi-level calibration of a process-based maize phenology model. Ecological Modelling, 474, 110154 (16 pp.). doi:10.1016/j.ecolmodel.2022.110154, Institutional Repository
Safin, A., Bouffard, D., Ozdemir, F., Ramón, C. L., Runnalls, J., Georgatos, F., … Šukys, J. (2022). A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1. Geoscientific Model Development, 15(20), 7715-7730. doi:10.5194/gmd-15-7715-2022, Institutional Repository


May 11, 2023 –

The aquatic research institute Eawag has published its latest annual report and looks back on the year 2022. Water is at the heart of the most important environmental issues.

The aquatic research institute Eawag has published its latest annual report and looks back on the year 2022. Water is at the heart of the most important environmental issues.

Read more




Bridging the gap between data science and mechanistic modelling for a better understanding of community composition.
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.

SPUX - High performance environmental data science

Mechanistic modelling of the macroinvertebrate community composition in rivers.
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.
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.
Exploring the use of machine learning techniques to uncover low-dimensional features within high-dimensional datasets, both simulated and observed