Application of an Ensemble Kalman Filter to A Semi-distributed Hydrological Flood Forecasting System in Alpine Catchments

Foehn, A., Schwob, A., Pasetto, D., García Hernández, J., & De Cesare, G. (2020). Application of an Ensemble Kalman Filter to A Semi-distributed Hydrological Flood Forecasting System in Alpine Catchments. In P. Gourbesville & G. Caignaert (Éds.), Advances in Hydroinformatics (p. 319‑334). Springer. https://doi.org/10.1007/978-981-15-5436-0_24

One of the key success factors for hydrological forecasts is initial conditions that represent well the conditions of the simulated basin at the beginning of the forecast. Real-time Data Assimilation (DA) has been shown to allow improving these initial conditions. In this article, two DA approaches are compared with the reference scenario working without DA (Control). In both approaches, discharge data at gauging stations are assimilated. In the first approach, a volume-based update (VBU) compares the simulated and observed volumes over the past 24 h before the start of a forecast to compute a correction factor used to update the initial soil water saturation in the upstream part of the semi-distributed hydrological model. In the second approach, an ensemble Kalman filter (EnKF) is implemented to account for the uncertainty in initial conditions, precipitation, temperature and discharge data. The comparison is carried out over two sub-basins of the Upper Rhone River basin upstream of Lake Geneva, in Switzerland, where the MINERVE flood forecasting and management system is implemented. Results differ over the two studied basins. In one basin, the two DA simulations perform better than the Control simulation,