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

Liu, X., Liu, W., Tang, Q., Liu, B., Wada, Y., & Yang, H. (2022). Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change. Earth's Future, 10(4), e2021EF002567 (16 pp.). doi:10.1029/2021EF002567, Institutional Repository
David, P. C., Chaffe, P. L. B., Chagas, V. B. P., Dal Molin, M., Oliveira, D. Y., Klein, A. H. F., & Fenicia, F. (2022). Correspondence between model structures and hydrological signatures: a large-sample case study using 508 Brazilian catchments. Water Resources Research, 58(3), e2021WR030619 (20 pp.). doi:10.1029/2021WR030619, Institutional Repository
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58(3), e2021WR030705 (28 pp.). doi:10.1029/2021WR030705, Institutional Repository
Mariano, P. M., & Bacci, M. (2022). A discrete-to-continuum model of protein complexes. Biomechanics and Modeling in Mechanobiology. doi:10.1007/s10237-022-01564-7, Institutional Repository
Qi, W., Feng, L., Yang, H., & Liu, J. (2022). Increasing concurrent drought probability in global main crop production countries. Geophysical Research Letters, 49(6), e2021GL097060 (11 pp.). doi:10.1029/2021GL097060, Institutional Repository

News

April 26, 2022 –

Eawag researchers are using computer algorithms to predict the toxicity of chemicals to fish. Machine learning can help set priorities for experiments and, in the longer term, enable a tremendous reduction of animal testing.

Eawag researchers are using computer algorithms to predict the toxicity of chemicals to fish. Machine learning can help set priorities for experiments and, in the longer term, enable a tremendous reduction of animal testing.

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Events

15.09.​2022,
9.00 am
Swiss Tech Convention Center Lausanne

Eawag Info Day 2022

02.11.​2022,
9.00 am
Eawag Dübendorf

PEAK-Vertiefungskurs V55/22

Projects

Bridging the gap between data science and mechanistic modelling to gain knowledge about community assembly
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.
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.