A. Randrianasolo (1), M.H. Ramos (1), G. Thirel (2), V. Andréassian (1), E. Martin (2) (1) Hydrology Research Group, Cemagref HBAN, Antony, France (contact: (2) CNRM-GAME, Météo-France, CNRS, GMME/MOSAYC, Toulouse, France (contact: Objective: 1 Data and methods: 2 Fig. 2b: GR3P model 2) the lumped soil-moisture-accounting type rainfall-runoff model (GR3P) developed at Cemagref (Fig.2b) Fig. 2a: SIM model Skill scores 3 Contingency table Two critical thresholds for observed events: Qref1 = 50th percentile (Q50) Qref2 = 90th percentile (Q90) Threshold for forecasted events: if p = 50% of the members are greater than Qref, the event is considered as a « forecasted event » Two hydrological models: 211 catchments in France (170 to 9390 km 2 ) (Fig. 1) Weather forecasts from the PEARP ensemble prediction system of Météo-France (March 2005-July 2006): - 11 perturbed members for a forecast range of 60 h (skill scores computed for the first two days of forecast range) Time series of observed data: daily discharge, precipitation, temperature HEPEX09 Workshop – Toulouse, June 2009 Fig. 1: Location of the catchments Impact of the use of two different hydrological models on scores of hydrological ensemble forecasts Standard deviation (or spread) Ratio-RMSE: RMSE / Mean of observed streamflows Ratio-σ: Standard Deviation / Mean of forecasted streamflow BSS: the reference used is the climatology o i observed data for the day i m i mean of the ensemble forecasts for the day i N number of days used to compute the score n number of forecast members N number of days used to compute the score x i mean of the ensemble forecasts for the day i x k,i value of the member k for the day i Root Mean Square Error Brier Skill Score Results 4 PEARP-based ensemble streamflow forecasts predicted well discharges over the studied catchments Better scores are obtained from the GR3P model with updating, while SIM results are closer to the results from GR3P model without updating (for data assimilation in SIM model, see Thirel et al., 2009) Conclusions 5 Fig. 3: POD, FAR, BIAS (Leadtime = Day 1 and Qref2 = Q90) catchment area (km 2 ) GR without updatingSIM GR with updating Fig. 4: Ratio-RMSE values (Leadtime = Day 2) to assess the quality of ensemble streamflow forecasts issued by two different modelling conceptualizations of catchment response, both driven by the same weather ensemble prediction system 1) the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU developed at Météo-France, based on a distributed catchment model (Fig. 2a) Fig. 5: Spread References: 1. Thirel, G., Rousset-Regimbeau, F., Martin, E., Habets, F. (2008) On the impact of short-range meteorological forecasts for ensemble streamflow predictions. J. Hydrometeorology (9), Tangara, M. (2005) Nouvelle méthode de prévision de crue utilisant un modèle pluie-débit global. PhD Thesis EPHE-Cemagref, Paris, 374 p. 3. Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, and F. Habets (2009) A streamflow assimilation system for ensemble streamflow forecast over France. Abstract EGU Randrianasolo, A. (2009) Evaluation de la qualité des prévisions pour l'alerte aux crues. MSc Thesis ENGREF, Cemagref (ongoing). Fig. 6: BSS N number of days used to compute the score x j = 0 (the event occurs) x j = 1 (the event does not occur) p j probability of the event to occur Q50 Q90 Day 1 Day 2 GR with updatingSIMGR without updating