Data assimilation in lake Geneva using the SPUX framework

Lakes form an integral component of ecosystems and our communities. Aside from being a source of drinking water, lakes provide additional benefits such as recreation, heat management and fishing. At the same time, human activity can significantly disrupt the natural state of the aquatic ecology. In the past, limited understanding of the hydrological and biochemical processes in aquatic systems has led to significant eutrophication in certain cases. To mitigate further risk, monitoring programs have been implemented. Recently new instrumentation, such as in situ observation platforms, remote sensing and computational resources enable comprehensive monitoring of the temporal evolution of the environment’s spatial heterogeneity.

A major focus of the DATALAKES project is to use the multiple sources of observational measurements for data assimilation and forecasting purposes. The aim is to infer the entire state of the lake as accurately as possible using high-resolution three-dimensional hydrodynamic models. Uncertainty quantification using Bayesian inference and modern Markov Chain Monte Carlo methods is implemented using the SPUX package, with the stochasticity provided by an ensemble of weather forecasts. To obtain predictions in a reasonable time, we parallelize both the particle filtering and the hydrodynamic model on the CSCS cluster. The data assimilation component will combine multiple in-situ sources with remote sensing measurements of lake water surface temperature and incorporate the respective uncertainties in measurement into the error model. To enable the use of multi-level variance reduction schemes, we perform calibration of essential hydrodynamic model parameters for a hierarchy of discretisations. The results show that the framework is capable of inferring the state of lake Geneva from observational measurements.