1. « Improvement of ensemble streamflow predictions over France of the SAFRAN-ISBA-MODCOU model » Guillaume Thirel (CNRM-GAME/GMME/MOSAYC) PhD Director : Éric Martin Jury : President : Serge Chauzy (LA) Reviewer : Vincent Fortin (Environment Canada) Reviewer : Vazken Andréassian (CEMAGREF) Examiner : Olivier Thual (CERFACS) Examiner : Pierre Ribstein (UMR Sisyphe)

2. Context Floods = major environmental hazard Damages on infrastructures, huge costs, human beings losses Flood of the Garonne river at Toulouse in 1875 Need to better anticipate these events Organisms (SCHAPI, Services de Prévision des Crues) Hydrological models Meteorological forecasts

3. Context Ensemble meteorological forecasts Post-treatment Surface observations (snow, discharges, …) Data assimilation Hydrological model(s) Forecasted discharges Discharges calibration (from Schaake et al., 2007) Initial states Meteorological forecasts

4. ISBA Physiographic data pour the soil and the vegetation + MODCOU Qr Qi E H G Aquifer Daily discharges Surface scheme Snow SAFRAN Observations + NWP outputs Precipitation, température, humidity, wind, radiations Hydrological model Meteorological analysis The SIM hydro-meteorological model Distributed model Coherent simulation of water and energy fluxes on : Atmosphere Surface/vegetation/surface soil Surface and sub-surface hydrology Grid mesh : 8x8 km Co-operation Mines Paris Tech /SISYPHE

5. Validations and valorisation of SIM Validation of the simulations by meteorological and hydrological variables Snow River discharges and aquifer levels Main applications : Follow-up of soil hydric states, effective rainfall, snow conditions Impact of climate change Flood prediction (soil wetness, discharges) Soil Water Index on 16/11/2009 Direction de la climatologie

6. Application of SIM to ensemble streamflow predictions Since 2004, everyday : ensemble discharge forecasts based on SIM (Fabienne Rousset-Regimbeau PhD, 2007). Based on the ECMWF EPS (precipitation+temperature) On the whole France, mid-term range (10 days) Statistical analysis of precipitations and discharges Article Rousset, ECMWF newsletter spring 2007 Disaggregation of precipitations on a simple, but efficient way Discharges compared to a reference SIM simulation Study case on a few recent floods

7. Scheme of the ensemble discharge forecast system based on SIM Observations + Meteorological models SIM ANALYSIS (daily) SAFRAN 10-year Climatology Wind, Rad., Humidity SOIL AQUIFERS RIVERS ECMWF/PEARP EPSs 51/11 members, 10/2.5 days forecasts ENSEMBLE PREDICTIONS Spatial DISAGGREGATION T + Precipitations ISBA MODCOU ENSEMBLE FORECASTS SOIL AQUIFERS RIVERS ISBA MODCOU SOIL AQUIFERS RIVERS

8. The Seine at Paris, March 2001 flood (decade flood) Q90 Q50 Q10 PhD Fabienne Rousset-Regimbeau

9. Objectives To improve the ensemble discharge forecast system To explore the contribution of 2 EPSs To test an improvement of the model Qualify the chain in comparison with discharge observations How : By comparing the impact of 2 EPSs on 2-day ensemble discharge forecasts By improving the system with a past discharge assimilation system

10. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

11. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

12. The 2 used EPSs ECMWF EPS 51 members 10-day forecasts Singular vectors, –Optimisation in 48H Resolution in our operational database : 1.5º PEARP EPS 11 members 2.5-day forecasts Singular vectors –Optimisation in 12H Resolution in our operational database : 0.25° -> Objective : mid-term range-> Objectif : short-term range The comparison is done on the first 48H common to both systems

13. Precipitations disaggregation Interpolation on the SAFRAN zones according to distance, then : ECMWF EPS : altitudinal gradient PEARP EPS : correction of the mean bias point by point SAFRAN ECMWF EPS (Day 1) PEARP EPS (Day 1) Precipitation amounts 11 March 2005 / 30 September 2006 All the statistical scores were better for the PEARP EPS

14. Conclusions on the comparison The ensemble discharges forecasts based on the PEARP EPS showed an improvement on small basins and for floods –Results confirmed by a set of statistical scores (RPSS, reliability diagram, False Alarm Rate and Probability of Detection, seasonal study) –Low spread, reference used = SIM simulation –Interest for flood forecasting at a short-term range in France (SCHAPI) Details of the study in On the impacts of short-range meteorological forecasts for ensemble streamflow predictions, G. Thirel, F. Rousset-Regimbeau, E. Martin, F. Habets, Journal of Hydrometeorology, 2008.

15. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

16. Justification Choice of the observations : –Snow : concerns only a limited part of the territory and discharges are influenced –Aquifer layers : many data but only few aquifers simulated into SIM –River discharges : many data over all of France available daily Choice of the variable to modify : –River water content : efficient for the short-term range, less for the mid- term range –Soil water content : concerns the whole territory, impact until the mid- term

17. Strategy 186 stations assimilated over France –Low human influence –Good quality of observations (Banque Hydro) –Good quality of SIM simulations Principle : to use observed discharges to improve the discharges simulations, by adjusting the ISBA soil moisture

18. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

19. The BLUE (Best Linear Unbiased Estimator) Analysed state Background state Innovation vector Observed discharges Choice of the BLUE because : Low dimensions of the problem Possibility to compute the solution in its matricial form Hypothesis : unbiased errors and linear model

20. Determination of the K matrix components To estimate the observations (R) and background covariance errors (B) matrices and calibrate these two matrices between them To define the state variable : the ISBA soil moisture, but which one? To estimate the Jacobian matrix H Discharges Soil moisture

21. ISBA physics Runoff : Dunne Subgrid depending on the fraction of the mesh saturated Drainage : gravitational subgrid Improvement of the hydrological transfers in the soil (Decharme et al., 2006; Quintana Seguí et al., 2009) Discharges : coming from ISBA runoff and drainage

22. State variable 3 possible choices : Soil water content : w2+w3 (runoff + drainage) Root zone water content : w2 (runoff) 2 soil layers water contents separately : (w2,w3) (runoff and drainage) Spatial aggregation (sum of the soil water contents over each sub-basin)

23. Sensitivity of the Jacobian Perturbation of 1% : the Jacobian varies according to the sign Perturbation of 0.1% : low modification according to the sign Thus, we chose to apply a perturbation of +0.1% -> respect of the linearity Clear temporal evolution : the Jacobian will be re-calculated for each assimilation

24. Filling of the Jacobian matrix 3 gauging stations y1, y2 and y3. x1, x2 and x3 soil water contents summed on the sub-basins 0 00 0 sub-basins stations Jacobian H : discharges Soil moisture Finite differences

25. Principle of the assimilation system

26. Implementation of the assimilation system PALM coupler (CERFACS) : dynamical coupler dynamique of parallel calculation codes, many applications (data assimilation, coupling) Friendly interface, modular software Intuitive gestion of data exchanges, buffer storage -> few modifications of the ISBA and MODCOU codes Simple cluster coupling Use of the Météo-France super-computer

27. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

28. Assimilation of real observations 6 experiments : 3 state variables * 2 physics of the model Daily assimilation, daily observations Period : 10 March 2005 / 30 September 2006 186 assimilated stations

29. The Doubs river at Besançon -> experiment (modification of the layers 2 and 3 soil moistures + improved physics)

30. combines the best Nash and RMSE scores, as well as the lowest increments (soil moisture + improved physics) will be kept Scores for 148 assimilated stations Scores for 49 independent stations

31. Conclusion on the discharges assimilation system Observed discharges assimilated for the first time in SIM –Positive impact of the use of PALM : CPU time save (parallel computation on the Météo-France super-computer), modularity Validation of the assimilation system –System validated on SIM-analysis –Assimilation of real observations : several configurations tested, significative improvement of the scores, low increments Article in preparation For initializing the ensemble discharges forecasts, we will keep : State variable : mean of the soil moisture into the 2 ISBA layers The assimilated states (assimilation + improved physics) daily

32. Perspectives of improvement of the assimilation system Improvement of the background and observations errors Reduction of the number of sub-basins in a sub-basin –Less simulations needed for computing H Tests of other assimilation methods –External loop? (i.e. re-calculating the Jacobian around the analysed state until it converges) -> tests showed low improvements –EnKF?

33. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

34. Conditions of the study Studied period : 11 March 2005 – 30 September 2006 Scores on 148 assimilated stations Use of the 10-day ECMWF EPS 3 systems of ensemble discharges forecasts were compared : –The real-time system –A re-forecast initialized by the initial states (modification of the soil moisture of both layers, without the improved physics) –A re-forecast initialized by the initial states (modification of the soil moisture of both layers, with the improved physics)

35. Some statistical scores Spread

36. RMSE Scores computed in comparison with observations

37. Brier Skill Score day 1 Perfect model Clima- tology

38. Brier Skill Score day 10 Perfect model Clima- tology

39. Conclusion on the impact of the assimilation Intrinsic characteristics of the ensemble discharges few modified (spread) Significative impact of the assimilation for the first days, less important then Then, the physics improvement improves the forecast quality Use of the forecasts by the forecasts eased (False Alarm Rates, POD) Article in preparation SIM-PEARP less impacted than SIM-ECMWF, scores very close

40. Plan I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system II Past discharges assimilation –1) Justification –2) Choice of the method –3) Validation of the data assimilation system III Impact of the past discharges assimilation system on the ensemble discharges forecasts IV General conclusions and perspectives

41. General conclusions and perspectives Two ensemble discharges forecasts systems based on SIM –Impact of the PEARP EPS at a short-range, on small basins and for floods A past discharge assimilation system implemented in SIM –Validation : significative impact on SIM-analysis –Low non-linearities Impact on the ensemble discharges –Strong impact of the assimilation system at a short-range, then low impact –But the improvement of the physics allows better forecasts at a mid-term range

42. Perspectives Implementation of the assimilation system for initializing the operational SIM-ECMWF chain in real-time Adding aquifer layers in SIM, and then assimilation of aquifer levels (PhD UMR SISYPHE Alexandra Stouls) Improvement of the meteorological uncertainty taking into account (EPS disaggregation) Taking into account of uncertainties linked to hydrology : into the initialization and via a stochastic physics or a multi-model forecast Seasonal forecasts with SIM (PhD CNRM Stéphanie Singla)

43. My work here on EFAS Use of satellital snow data for improving the proxy –Particule filter and EnKF Study of its impact on the EFAS forecasts –Probabilistic statistical scores 2 nd step : to see how to use other sources of rainfall data in order to improve the proxy

44. Thank you for your attention!

45. Visualisation des sorties en temps réel Site intramet : http://intra.cnrm.meteo.fr/pedeb/ http://intra.cnrm.meteo.fr/pedeb/ Sélection denviron 100 stations - prévision de débits - tableau dalerte => Visualisation du risque + de la persistance (ou non) de la prévision Probabilité de dépassement du seuil dalerte

46. BSS hauts débits (Q90) Jour 1 Jour 2 CEPMMT : 49 stations PEARP : 338 stations CEPMMT : 19 stations PEARP : 486 stations Bleu : CEPMMT meilleur (90% de certitude selon un test de ré-échantillonnage) Rouge : PEARP meilleur (90% de certitude)

47. Distribution par taille de bassin (BSS) Q10 Jour 1 Q10 Jour 2 Q90 Jour 2 Q90 Jour 1 CEPMMT PEARP Tailles des bassins

48. Variance derreur dobservations Erreurs des mesures des stations indépendantes : matrice diagonale Tests sur des cas synthétiques : 2e méthode meilleure (Nash) et donc retenue

49. Répartition spatiale de la variance derreur débauche Moyenne pondérée des 2 couches Couche 3 uniquement Couche 2 uniquement B et R diagonales B estimée en perturbant lanalyse météorologique SAFRAN, puis comparaison de lhumidité obtenue avec lhumidité de référence R estimée selon les débits observés R et B calibrées grâce à un unique coefficient

51. Expériences jumelles Variable détat = moyenne pondérée des humidités des 2 couches Assimilation sur une période de 3 mois, tous les 5 jours, fenêtre dassimilation de 5 jours Etat initial modifié, obs = simulation de référence Convergence assez rapide malgré les non- linéarités

52. Scores avec le BLUE itéré

53. Un exemple de limpact sur les prévisions densemble des débits IS2 IS1 Sans assimilation

54. Ranked Probability Skill Score

55. RMSE par taille de bassin Jour 1 Jour 10

56. Résolution

57. Fiabilité

58. Incertitude

59. Taux de fausses alarmes

60. Taux de réussite Jour 1 Jour 10 Sans assimilation Avec EI1 Avec EI2