ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 11: 100–107 (2010) Published online 25 February 2010 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/asl.259 Comparing the scores of hydrological ensemble forecasts issued by two different hydrological models A. Randrianasolo, 1 M. H. Ramos, 1 * G. Thirel, 2 † V. Andr´ eassian 1 and E. Martin 2 1 Cemagref, Hydrology Research Group, Antony, France 2 CNRM-GAME, M´ et´ eo-France, CNRS, GMME/MOSAYC, Toulouse, France *Correspondence to: M. H. Ramos, Cemagref Antony, UR HBAN, Parc de Tourvoie, BP 44-92163 Antony Cedex, France. E-mail: maria-helena.ramos@cemagref.fr † Present address: G. Thirel, JRC, DG Joint Research Centre, European Commission, Institute for Environment and Sustainability, Ispra, Italy. Received: 31 August 2009 Revised: 9 December 2009 Accepted: 19 January 2010 Abstract A comparative analysis is conducted to assess the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response, both driven by the same weather ensemble prediction system (PEARP M´ et´ eo-France). The two hydrological modeling approaches are the physically based and distributed hydrometeorological model SIM (M´ et´ eo-France) and the lumped soil-moisture-accounting type rainfall-runoff model GRP (Cemagref). Discharges are simulated at 211 catchments in France over 17 months. Skill scores are computed for the first 2 days of forecast range. The results suggest good performance of both hydrological models and illustrate the benefit of streamflow data assimilation for ensemble short-term forecasting. Copyright  2010 Royal Meteorological Society Keywords: streamflow forecasting; hydrological ensemble prediction; verification 1. Introduction At operational flood forecasting centers, forecasters usually have to deal with forecasts issued by different models and combine them to support their decisions and communicate flood alerts to end users (Ramos et al., 2007). However, modeling approaches or setups are usually too different to allow a straightforward intercomparison of the results, and forecast interpre- tation, especially when model results diverge, can quickly become a puzzle. The objective of this paper is to assess the impact of the use of two different hydro- logical models, with different modeling conceptualiza- tions of catchment response, on scores of ensemble streamflow forecasts. Forecast verification is a vast topic and discussions have evolved into how to define objective and user-oriented verification measures for a better guidance and decision making in hydrologic forecasting (Welles et al., 2007; Pappenberger et al., 2008). In this study, the focus is not on the devel- opment of new measures, but on the application of a selected number of well-known scores largely used in atmospheric science (Jolliffe and Stephenson, 2003) to both hydrological forecasting systems. Attention is paid to the following methodological aspects: (1) to force the hydrological models with the same ensemble weather predictions, (2) to evaluate streamflow pre- dictions against observed discharges (and not against simulated, model-dependent, discharges), (3) to apply the scores over a long time period of forecasts, (4) to conduct the analysis on a large database of catch- ments, representative of a variety of climate and phys- iographic conditions. 2. Data 2.1. The PEARP ensemble prediction system This study is based on the PEARP ensemble predic- tion system (EPS), which is the M´ et´ eo-France short- range EPS, dedicated to detect localized and severe events (Nicolau, 2002). In this study, the PEARP is a 60-h EPS with a 0.25 ◦ grid resolution, which pro- duces 11 members once a day. Singular vectors are set optimal after a 12-h period. Rainfall and tempera- ture ensemble forecasts are the variables from PEARP used to force the hydrological models. Other vari- ables necessary to run the models (pressure, radiation, wind, humidity, or evapotranspiration) are evaluated from the climatology. PEARP data are downscaled in order to better fit the observations, as well as to make the forecasts available on the grid resolution of the hydrometeorological model used by M´ et´ eo- France (8 × 8 km). The downscaling is realized in two steps: first, the data are spatially interpolated on predefined zones, which are the climatologically homogeneous areas used to define the SAFRAN mete- orological analysis system of M´ et´ eo-France (see Vidal et al., 2009 for details on SAFRAN). Then, the tem- perature data are corrected by using the usual mean atmospheric lapse rate gradient (−0.65 K/100 m). For Copyright  2010 Royal Meteorological Society