1. Streamflow assimilation for improving ensemble streamflow forecasts G. Thirel (1), E. Martin (1), J.-F. Mahfouf (1), S. Massart (2), S. Ricci (2), F. Regimbeau (3), F. Habets (4). CNRM-GAME, Météo-France, CNRS, GMME, France, CERFACS, France, Direction de la Climatologie, Météo-France, France, UMR SISYPHE, UPMC, ENSMP, CNRS, Paris, France (guillaume.thirel@meteo.fr, +33 (0) 5 61 07 97 30)

2. Introduction • 2 ensemble streamflow prediction systems (ESPS) at a short- and mid-term range at Météo-France • Based on the distributed hydrometeorological model SIM • ECMWF-based ESPS (10-day range, 1.5°, 51 members) • PEARP-based ESPS (60-h range, 0.25°, 11 members) • Need to improve the initial states by an assimilation system • First validation of the ESPSs against streamflows observations

3. The SIM hydro-meteorological model Meteorological analysis SAFRAN • Observations + NWP models • Precipitation, temperature, • humidity, wind, radiations E Surface scheme H ISBA + G Snow Physiographic data for soil and vegetation Qr Qi Nash Daily Streamflow Hydrological model Poor Weak to moderate Good MODCOU Aquifer Habets et al. (2008)

4. ENSEMBLE FORECASTS ECMWF/PEARP Ensemble forecasts 51/11 members, 11/2-day forecasts T+ Precip Spatial DESAGGREGATION ENSEMBLE FORECAST SOIL ISBA MODCOU WAT. TABLES RIVERS FINAL STATES Adjusted by BLUE Initial states of ESPS : need for improvement The SIM based ESPS ANALYSIS RUN (daily) Observations Meteor. models 10-year climatology Wind, Rad., Humidity SAFRAN SOIL WAT. TABLES RIVERS STATE SOIL WAT. TABLES RIVERS FINAL STATE ISBA MODCOU

5. Strategy • 186 stations assimilated over France • Low human influence • Good quality of observations • Not too bad results given by SIM • Aim : to use observed streamflow in order to improve streamflow simulation, by adjusting the ISBA soil moisture

6. Jacobian H : H determines the sensitivity of streamflows to soil moisture variations The BLUE equations Observed streamflows Analysed state Background state Innovation vector streamflows Hypothesis : linearity of the model -> H is computed with SIM runs initialized by perturbed soil moisture states (perturbation around 0.1%) x : control variable

7. Experiments (10 March 2005 / 30 September 2006, 186 stations) 6 experiments : 3 variable states * 2 physics of the model Daily assimilation, daily observations

8. Jacobian matrix filling 186 stations stations 0 0 3 gauging stations Q1, Q2 et Q3. w1, w2 et w3 moderated sums of soil moistures on the basins Jacobian matrix : 0 0 basins

9. Principle of the assimilation system

10. Scores for a selection of 148 stations IS2 will be retained IS2 combines the best Nash and rmse scores, and the lowest increments The Doubs at Besançon

11. The Garonne at Portet-sur-Garonne

12. An exemple of the effect on ensemble forecasts ECMWF PEARP

13. Some statistical scores Scores for a selection of 148 assimilated stations for the 10-day ECMWF-SIM

14. RMSE Scores are presented against streamflow observations

15. Brier Skill Score day 1

16. Brier Skill Score day 10

17. Ranked Probability Skill Score

18. Decomposition of Brier

19. BSS for PEARP-SIM and ECMWF-SIM PEARP is slightly better, but without the unbiasing, ECMWF wins! BSSs are unbiased with the Weigel et al. (2007) method because of the impact of the number of members

20. Conclusions and perspectives • A streamflow assimilation system has been implemented and validated for the SIM suite • Better simulation of flows and initial states for the ESPSs (Thirel et al., submitted to the Journal of Hydrology) • Significative improvement of ensemble streamflow forecasts when initialized by the assimilated SIM suite • Lower RMSE, better BSS and RPSS • Few differences between SIM-PEARP and SIM-ECMWF • It is the first time that the ensemblist SIM is compared to observations, not a reference run • Perspectives : • Optimizing computing costs and the quality of the assimilation system • Using another operator (EnKF?) • Implementing the assimilation system into the SIM-ECMWF operational suite (2012?)

21. Thank you for your attention!