A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

International audienceThe use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBAMODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at M´et´eo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of M´et´eo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones

A Past Discharge Assimilation System for Ensemble Streamflow Forecasts over France - Part 2: Impact on the Ensemble Streamflow Forecasts

The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.JRC.H.7-Climate Risk Managemen

The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France

An edited version of this paper was published by AGU. Copyright (2008) American Geophysical UnionThe hydrometeorological model SIM consists in a meterological analysis system (SAFRAN), a land surface model (ISBA) and a hydrogeological model (MODCOU). It generates atmospheric forcing at an hourly time step, and it computes water and surface energy budgets, the river ow at more than 900 rivergauging stations, and the level of several aquifers. SIM was extended over all of France in order to have a homogeneous nation-wide monitoring of the water resources: it can therefore be used to forecast flood risk and to monitor drought risk over the entire nation. The hydrometeorologival model was applied over a 10-year period from 1995 to 2005. In this paper the databases used by the SIM model are presented, then the 10-year simulation is assessed by using the observations of daily stream-flow, piezometric head, and snow depth. This assessment shows that SIM is able to reproduce the spatial and temporal variabilities of the water fluxes. The efficiency is above 0.55 (reasonable results) for 66 % of the simulated rivergages, and above 0.65 (rather good results) for 36 % of them. However, the SIM system produces worse results during the driest years, which is more likely due to the fact that only few aquifers are simulated explicitly. The annual evolution of the snow depth is well reproduced, with a square correlation coeficient around 0.9 over the large altitude range in the domain. The stream ow observations were used to estimate the overall error of the simulated latent heat ux, which was estimated to be less than 4 %

Modélisation des bilans de surface et des débits sur la France, application à la prévision d'ensemble des débits

The French coupled hydrometeorological model SIM (SAFRAN : interpolates atmospheric forcing over a 8 km regular grid; ISBA : surface scheme; MODCOU : distributed hydrological model) is first applied over the Seine basin. It is evaluated over a 10-year period, with a particular attention on the underground and the streamflows. The long duration floods of the Seine at Paris are also examined.A real-time ensemble streamflow prediction system over France, based on SIM, is then described. The ISBA and MODCOU models are forced by meteorological ensemble forecasts from ECMWF, downscaled to a 8 km resolution. A statistical study of the abilities of the system, especially for low flows and floods, is produced using nearly one year of forecasts. An analysis of ensemble forecasts for recent large flood events over France is also presented.Ce travail de thèse s'appuie sur l'utilisation du modèle couplé hydro-météorologique SAFRAN-ISBA-MODCOU.D'abord, la modélisation couplée du bassin de la Seine est mise en place, en utilisant une représentation détaillée des aquifères du bassin. La capacité de SIM à simuler les différentes composantes des bilans d'eau et d'énergie, le comportement du souterrain, et donc les débits, est présentée. En particulier, SIM est évalué pour la simulation des crues lentes de la Seine à Paris.Ensuite, une chaîne temps réel de prévision d'ensemble des débits sur la France, basée sur SIM, est construite, où ISBA et MODCOU sont forcés par les prévisions d'ensemble météorologiques du CEPMMT désagrégées. Une analyse statistique de la qualité des prévisions d'ensemble de précipitations désagrégées et des prévisions d'ensemble de débit est effectuée sur près d'un an de prévision. Enfin, une étude des prévisions d'ensemble de plusieurs cas de grandes crues du passé récent est présentée

On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

International audienceEnsemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d'Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins

Short- and Medium-Range Hydrological Ensemble Forecasts over France

The Safran-Isba-Modcou (SIM) distributed hydro-meteorological modeling suite is developed at Météo-France. Two ensemble forecasts were tested as input to the SIM suite. The first one was the ECMWF EPS (10-day range, 1.5° grid, 51 members). Then, the Météo-France PEARP (60-h range, 0.25° grid, 11 members) was used. The methods for disaggregating the EPSs down to the 8-km Isba grid were described. Statistical analysis of the skills of these systems (for rainfall and streamflows) was performed against observations and showed the interests of using these EPSs. The PEARP-based streamflows showed better scores than the ECMWF ones, for low and high flows.JRC.H.7-Climate Risk Managemen

Coupling a global climatic model with insurance impact models for flood and drought: an estimation of the financial impact of climate change

CCR, a French reinsurance company mostly involved in natural disasters coverage in France, has been developing tools for the estimation of its exposure to climatic risks for many years. Both a flood and a drought models were developed and calibrated on a large policies and claims database supplied every year with insurers’ data. More recently, CCR has been developing a stochastic approach in order to evaluate its financial exposure to extreme events. A large and realistic event set has been generated by applying extreme value statistic tools to simulate hazard and to estimate, using our impact models, the average annual losses and losses related to different return periods. These event sets have been simulated separately for flood and drought, with a hypothesis of independence, consistent with recent annual damage data. The newest development presented here consists in the use of the ARPEGE–Climat model performed by Météo-France to simulate two 200-years sets of hourly atmospheric time series reflecting both the current climate and the RCP 4.5 climate conditions circa year 2050. These climatic data constitute the input data for the flood and drought impact models to detect events and simulate the associated hazard and damages. Our two main goals are (1) to simulate simultaneously flood and drought events for the same simulated years and (2) to evaluate the financial impact of climate change

Coupling a global climatic model with insurance impact models for flood and drought: an estimation of the financial impact of climate change

CCR, a French reinsurance company mostly involved in natural disasters coverage in France, has been developing tools for the estimation of its exposure to climatic risks for many years. Both a flood and a drought models were developed and calibrated on a large policies and claims database supplied every year with insurers’ data. More recently, CCR has been developing a stochastic approach in order to evaluate its financial exposure to extreme events. A large and realistic event set has been generated by applying extreme value statistic tools to simulate hazard and to estimate, using our impact models, the average annual losses and losses related to different return periods. These event sets have been simulated separately for flood and drought, with a hypothesis of independence, consistent with recent annual damage data. The newest development presented here consists in the use of the ARPEGE–Climat model performed by Météo-France to simulate two 200-years sets of hourly atmospheric time series reflecting both the current climate and the RCP 4.5 climate conditions circa year 2050. These climatic data constitute the input data for the flood and drought impact models to detect events and simulate the associated hazard and damages. Our two main goals are (1) to simulate simultaneously flood and drought events for the same simulated years and (2) to evaluate the financial impact of climate change

Vers une prévision d'ensemble des débits à l'échelle des grands bassins versants français

International audienceUn système de prévisions d'ensemble hydrologiques à 10 jours sur la France a été construit à partir de la chaîne SAFRAN-ISBA-MODCOU et des sorties du modèle de prévision du CEPMMT. Dans une première étape, les données météorologiques sont désagrégées sur la grille de calcul du modèle de surface. Ensuite, un ensemble de prévisions hydrologiques est calculé. Le comportement du modèle a été testé sur une année par comparaison avec une simulation de référence, montrant de bons résultats globaux. Des travaux de recherches sont poursuivis en vue d'améliorer le système, en parallèle de tests utilisateurs. Ces travaux ont montré que l'utilisation d'une source de prévisions météorologiques d'ensemble adaptée à la courte échéance améliore nettement les scores hydrologiques à ces échéances. Enfin, un système d'assimilation des débits passés est en cours de développement afin de permettre de fournir un état initial plus réaliste au système de prévision

Suivi en temps réel des sécheresses : de l'analyse à la prévision saisonnière

Météo-France opère en temps réel depuis 2003 la chaîne de modélisation hydro-météorologique SIM, composée du module d'analyse des conditions atmosphériques en surface (SAFRAN), d'une modélisation détaillée des interactions sol-biosphère-atmosphère (ISBA) et du modèle hydrogéologique MODCOU. Cette chaîne a connu une évolution majeure en 2016 afin d'améliorer certains de ses composants. Cette application temps-réel, complétée par une réanalyse depuis 1958 permet de caractériser la situation par rapport aux années antérieures pour plusieurs variables du cycle hydrologique (précipitations, humidité du sol, enneigement). Les épisodes de sécheresse, l'évolution du stock nival, etc. peuvent ainsi être suivis au jour le jour sur l'ensemble de la France métropolitaine. Des applications de prévisions ont également été mises en place pour anticiper l'évolution de la situation hydrologique. L'initialisation des conditions hydrologiques provient de la chaîne d'analyse temps-réel et des données météorologiques prévues sont utilisées en entrée d'ISBA-MODCOU. Ainsi une application pour les échéances allant jusqu'à 10 jours utilise comme forçage météorologique les prévisions d'ensemble du CEPMMT (Centre Européen de Prévision Météorologique à Moyen Terme). Chaque jour des prévisions sont produites sur différentes zones (départements, bassins versants, etc.), l'ensemble de prévision fournit des informations pour les différentes variables hydrologiques au pas de temps quotidien. De plus la visualisation de la dispersion des prévisions renseigne sur l'incertitude associée à chaque prévision. Pour des échéances plus lointaines (jusqu'à 6 mois), deux applications de prévisions sont opérées tous les mois. La première utilise en entrée des scénarios météorologiques issus de la climatologie, alors que la seconde utilise des données issues du modèle de prévisions saisonnières atmosphériques de Météo-France. Malgré l'incertitude des prévisions, l'exploitation complémentaire des prévisions climatologique et saisonnière est un outil pour la gestion des ressources en eau. Les débits moyens mensuels prévus pour chacune des applications comparés aux débits des années précédentes permettent en effet de caractériser la situation pour les mois à venir et l'incertitude associée