A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of an Ensemble Kalman Filter to A Semi-distributed Hydrological Flood Forecasting System in Alpine Catchments
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, with the EnKF simulation providing the best forecasting performance. In the second basin, where the Control simulation performs best, possible challenges with hydropower-based discharges are highlighted.
Application of an Ensemble Kalman Filter to A Semi-distributed Hydrological Flood Forecasting System in Alpine Catchments
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, with the EnKF simulation providing the best forecasting performance. In the second basin, where the Control simulation performs best, possible challenges with hydropower-based discharges are highlighted.
Application of an Ensemble Kalman Filter to A Semi-distributed Hydrological Flood Forecasting System in Alpine Catchments
Springer Water
Gourbesville, Philippe (editor) / Caignaert, Guy (editor) / Foehn, Alain (author) / Schwob, Anne (author) / Pasetto, Damiano (author) / García Hernández, Javier (author) / De Cesare, Giovanni (author)
2020-07-26
16 pages
Article/Chapter (Book)
Electronic Resource
English
Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter
| British Library Online Contents | 2010
Assimilating Observation Data into Hydrological Model with Ensemble Kalman Filter
| British Library Conference Proceedings | 2011
| British Library Conference Proceedings | 2008
| British Library Online Contents | 2008