On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

Abstract

International audienceEnsemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d'Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins

Comparing the scores of hydrological ensemble forecasts issued by two different hydrological models

“…It must be noted that the errors in the precipitation ensemble forecast are already important: mean values of RMSE equal to 3.45 and 3.60 mm/day for lead times day 1 and day 2. These values, evaluated on the basis of areal mean precipitation, are similar to those presented by Thirel et al, 2008 in their study conducted over gridded PEARP forecasts in France. Normalized RMSE values show that, in some cases, the errors in precipitation forecast are twice as great as the mean observed precipitation over the same period.…”

“…The same conclusion was drawn from the evaluation of rank (Talagrand) histograms for each catchment (not shown) and the visualization of flow hydrographs. A lack of ensemble spread in PEARP precipitation and PEARP SIM-based streamflow gridded forecasts was also reported by Thirel et al (2008). Figure 4 shows the values of POD, FAR, and BIAS for the streamflow thresholds Q50 and Q90 and for the ensemble threshold p80 (80% of ensemble members exceeding the streamflow threshold).…”

“…Then, the temperature data are corrected by using the usual mean atmospheric lapse rate gradient (−0.65 K/100 m). For the precipitation, a point-by-point bias removal was used (Thirel et al, 2008). This bias removal is calibrated by comparing precipitation observations (from the SAFRAN analysis) to the PEARP EPS precipitation obtained after the spatial interpolation on the SAFRAN zones.…”

Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

“…For this reason, recent studies have examined techniques that have potential to provide skillful predictions of seasonal runoff volume (Kennedy et al, 2009;Moradkhani and Meier, 2010;Regonda et al, 2006;Thirel et al, 2008). It has been well documented that the seasonal volume of runoff is controlled by both the initial water storage of the land surface at the beginning of the season and the future water fluxes into Correspondence to: H. Moradkhani (hamidm@cecs.pdx.edu) and out of the system (Lorenz, 1975).…”

Peut-on étendre l’échéance de prévision des crues en optimisant la prévision de pluies par recherche d’analogues ? Application au bassin de la Seine à Paris

“…Différentes méthodes de prévision de type probabiliste peuvent répondre à ces besoins, dont deux sont plus fréquemment évoquées : i) la prévision météorologique d'ensemble [1], [2] et ii) la prévision probabiliste de précipitations issues de la recherche d'analogues [3], [4]. Utilisées en entrée de modèles hydrologiques, ces méthodes permettent de disposer de prévisions probabilistes de débits et de contribuer à une meilleure anticipation du risque des crues.…”