Suchergebnisse

Abstract. Rainfall-runoff models are crucial tools for the statistical prediction of flash floods and real-time forecasting. This paper focuses on a karstic basin in the South of France and proposes a distributed parsimonious event-based rainfall-runoff model, coherent with the poor knowledge of both evaporative and underground fluxes. The model combines a SCS runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The efficiency of the model is discussed not only to satisfactorily simulate floods but also to get powerful relationships between the initial condition of the model and various predictors of the initial wetness state of the basin, such as the base flow, the Hu2 index from the Meteo-France SIM model and the piezometric levels of the aquifer. The advantage of using meteorological radar rainfall in flood modelling is also assessed. Model calibration proved to be satisfactory by using an hourly time step with Nash criterion values, ranging between 0.66 and 0.94 for eighteen of the twenty-one selected events. The radar rainfall inputs significantly improved the simulations or the assessment of the initial condition of the model for 5 events at the beginning of autumn, mostly in September–October (mean improvement of Nash is 0.09; correction in the initial condition ranges from −205 to 124 mm), but were less efficient for the events at the end of autumn. In this period, the weak vertical extension of the precipitation system and the low altitude of the 0 °C isotherm could affect the efficiency of radar measurements due to the distance between the basin and the radar (~60 km). The model initial condition S is correlated with the three tested predictors (R2 > 0.6). The interpretation of the model suggests that groundwater does not affect the first peaks of the flood, but can strongly impact subsequent peaks in the case of a multi-storm event. Because this kind of model is based on a limited amount of readily available data, it should be suitable for operational applications.

Abstract. Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure.