Department Systems Analysis, Integrated Assessment and Modelling

The objective of this project is to advance the forecasting capabilities of the data-driven hydrological and ecological lake modeling algorithms using methodologies inspired by data science and accelerated by high performance computing. We aim to develop a parallel framework interfacing high resolution 3D numerical solvers for the underlying lake dynamics with modern numerical Markov Chain Monte Carlo sampling methods for Bayesian inference, with particular interest in investigating particle filtering and multi-level variance reduction methodologies. The resulting framework aims at accurate data assimilation and uncertainty quantification in both model parameters and the associated forecasts. DATALAKES project is a collaboration with the Swiss Data Science Center (SDSC), EPF Lausanne and ETH Zurich, aiming at a sensor-to-frontend data platform providing and analyzing the dynamics of lake ecosystems at high spatial and temporal resolutions. Current version of the existing framework can be found at  meteolakes.ch.