ALBERT

Description: Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez Catchment in Southern France Hydrology and Earth System Sciences Discussions, 9, 3527-3579, 2012 Author(s): E. Harader, V. Borrell Estupina, S. Ricci, M. Coustau, O. Thual, A. Piacentini, and C. Bouvier The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. A simplified Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. The study site is the 114 km 2 Lez Catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterized by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because it depends on geographical features and cloud structures, the radar rainfall input to the model is particularily uncertain and results in significant errors in the simulated discharges. The DA analysis was applied to estimate a constant correction to each event hyetogram. The analysis was carried out for 19 events, in two different modes: re-analysis and pseudo-forecast. In both cases, it was shown that the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge. The resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using ground rainfall measurements, which are more accurate than radar but have a decreased spatial resolution. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash criteria compared to the MFB correction.