Towards an Hydrological Qualification of the Simulated Rainfall in Mountainous Areas Eddy Yates, Sandrine Anquetin, Jean-Dominique Creutin Laboratoire d’étude des Transferts en Hydrologie et Environnement, Grenoble, France

Cévennes-Vivarais : a region prone to flash floods Objective : forecast of these flash floods. We focus here on the precipitation forecast. Introduction MethodResultsConclusions Watersheds –100 to 1000 km 2 –specific outflows of up to 5 m 3 s -1 km -2 Storms – mm in 6-12 h. over some 100s km 2 HYDRAM Water depth seen by the Nîmes radar (Météo-France) October 6, 2001 Vidourle, October 6 – 7, 2001 Q~ 100 Q mean 300 mm 9 h Hilly region between the Mediterranean sea and the Massif Central. Rainy autumns.

Precipitation forecast model We use Meso-NH (Météo-France, CNRS) :  a meso-scale non-hydrostatic model  a nested configuration. The finest grid has a 2.5 km resolution which allows an explicit resolution of the convection Introduction MethodResultsConclusions

Reference observed rain fields We use kriging :  an exact interpolator  it takes into account the statistical structure of the rain-gauge data  it gives an estimation of the reliability of the interpolation (estimation variance) Simulation and observation are observed for 1h and 11h cumulated rainfall. Introduction MethodResultsConclusions

Cases studied Two simulations with very different qualities. The point is :  “how much better” is the better simulation ?  is it better for hydrological purposes too ? Introduction MethodResultsConclusions 1995 : Gardon d’Anduze2001 : Vidourle Bad localisation Not enough precipitation simulated (maximum cumulated rainfall of 160 mm vs. 260 mm) ObservationsSimulation 2001 ObservationsSimulation Quite a good localisation Not enough precipitation simulated (maximum cumulated rainfall of 100 mm vs. 170 mm) 1995

Method Introduction MethodResultsConclusions

Method Introduction MethodResultsConclusions R²(area) Observation Forecast

Method Introduction MethodResultsConclusions estimation error limit point to point correlation limit

Evolution of the correlation with the area Introduction MethodResultsConclusions h cumulated rainfall Lower short-range accuracy for short time accumulation h cumulated rainfall

Limits of the method Introduction MethodResultsConclusions

Conclusions, perspectives The method can discriminate good forecasts from very bad forecasts We need other cases to test the method The method must be tested with distributed data too (radars) Next step : use of TOPODYN (LTHE), a hydrologic model from the TOPMODEL family. It considers several scales of the watersheds. Introduction MethodResultsConclusions

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