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Conférence SHF « de la prévision des crues à la gestion de crise », Avignon, FRA, 14-/11/2018 - 16/11/2018; National audience; Le programme international de recherche HyMeX (Hydrological cycle in the Mediterranean eXperiment, www.hymex.org), composante du chantier multi-organisme MISTRALS lancé pour 10 ans en 2010, a pour objectif de progresser dans la compréhension du cycle de l'eau en Méditerranée, et notamment d'améliorer les connaissances et la prévision des risques hydrométéorologiques. L'étude et la prévision des épisodes de pluies intenses et crues rapides qui affectent régulièrement le pourtour Méditerranéen sont au coeur du programme HyMeX. Après une présentation de la stratégie générale d'observations qui a combiné campagnes de mesures, renforcement d'observations pendant plusieurs automnes et collectes de données socio-hydrologiques après les évènements majeurs qu'a connu la région sur la période, une revue d'ensemble des avancées scientifiques réalisées depuis le lancement d'HyMeX est proposée tant en termes de connaissances et modélisation des épisodes méditerranéens de pluie intense et de leurs impacts, qu'en termes d'évaluation et amélioration des méthodes et systèmes de prévision, et plus particulièrement les systèmes de prévision d'ensemble hydrométéorologiques à courte échéance basés sur le modèle de prévision météorologique AROME de Météo-France.

International audience; Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to also benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France in order to provide insights into the way bias correcting seasonal precipitation forecasts can contribute to maintain skill of seasonal flow predictions at extended lead times. First, we evaluate the skill of raw (i.e. without bias correction) seasonal precipitation ensemble forecasts for streamflow forecasting in sixteen French catchments. A lumped daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. A reference prediction system based on historic observed precipitation and watershed initial conditions at the time of forecast (i.e. ESP method) is used as benchmark. In a second step, we apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, reliability is an attribute that is not significantly improved. Forecast skill is generally improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments, with, however, each method being more successful in improving specific attributes of forecast quality: the simple linear scaling of monthly values contributed mainly to increase forecast sharpness and accuracy, while the empirical distribution mapping of daily values was successful in improving forecast reliability.

International audience; Floods are responsible for a major part of the total damage caused by natural disasters. Nowcasting systems providing public alerts to flash floods are very important to prevent damages from extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy in the intensity, location and timing of future intense precipitation events. Flash flood forecasting has been studied by several authors in different affected areas. The majority of the studies combines rain gauge data with radar imagery advection to improve prediction for the next few hours. Outputs of Numerical Weather Prediction (NWP) models have also been increasingly used to predict ensembles of extreme precipitation events that might trigger flash floods. One of the challenges of the use of NWP for ensemble nowcasting is to successfully generate ensemble forecasts of precipitation in a short time calculation period to enable the production of flood forecasts with sufficient advance to issue flash flood alerts. In this study, we investigate an alternative space-time geostatistical framework to generate multiple scenarios of future rainfall for flash floods nowcasting. The approach is based on conditional simulation and an advection method applied within the Turning Bands Method (TBM). Ensemble forecasts of precipitation fields are generated based on space-time properties given by radar images and precipitation data collected from rain gauges during the development of the rainfall event. The results show that the approach developed can be an interesting alternative to capture precipitation uncertainties in location and intensity and generate ensemble forecasts of rainfall that can be useful to improve alerts for flash floods, especially in small areas.

International audience; High-resolution climate change simulations over the Lesser Antilles are performed using the ALADIN-Climate regional climate model nested within the global model ARPEGE (Météo-France). Three sets of simulations areconducted at 10 km grid spacing for reference (1971-2000) and future climate (2071-2100) under two CMIP5 scenarios (RCP4.5 and RCP8.5). With the dynamical downscaling, islands of Lesser Antilles are considered as land by the model, whereas, for the driving model, there is only sea over the domain. Temperature and precipitation change are analysed on land and on sea separately. For temperature, the warming is greater on land than on sea, especially for the minimum daily temperature (3.28C vs. 2.38C for the RCP85 scenario). For precipitation, projections are less reliable because the seasonality is not well reproduced by the model.Nevertheless, simulations exhibit the fact that projections on land differ from one island to the other and disagree with those on sea notably during the wet season. This underlines the importance of the dynamical downscaling to study the climate on small islands. Statistical downscaling has been performed on the Guadeloupe Island to study changes in extreme precipitation indices. The projections provided by the regional climate model suggest an increase in extreme rainfall events: longer dry periods, a bigger annual total precipitation, more frequent very heavy daily precipitation and a stronger 1 day maximum precipitation, whereas for the driving Global Climate Model, these trends are less intense.

High-resolution climate change simulations over the Lesser Antilles are performed using the ALADIN-Climate regional climate model nested within the global model ARPEGE (Météo-France). Three sets of simulations are conducted at 10 km grid spacing for reference (1971–2000) and future climate (2071–2100) under two CMIP5 scenarios (RCP4.5 and RCP8.5). With the dynamical downscaling, islands of Lesser Antilles are considered as land by the model, whereas, for the driving model, there is only sea over the domain. Temperature and precipitation change are analysed on land and on sea separately. For temperature, the warming is greater on land than on sea, especially for the minimum daily temperature (3.2°C vs. 2.3°C for the RCP85 scenario). For precipitation, projections are less reliable because the seasonality is not well reproduced by the model. Nevertheless, simulations exhibit the fact that projections on land differ from one island to the other and disagree with those on sea notably during the wet season. This underlines the importance of the dynamical downscaling to study the climate on small islands. Statistical downscaling has been performed on the Guadeloupe Island to study changes in extreme precipitation indices. The projections provided by the regional climate model suggest an increase in extreme rainfall events: longer dry periods, a bigger annual total precipitation, more frequent very heavy daily precipitation and a stronger 1 d maximum precipitation, whereas for the driving Global Climate Model, these trends are less intense.Keywords: climate change, regional climate model, RCP scenarios, Lesser Antilles, temperature, precipitation(Published: 21 October 2014)Citation: Tellus A 2014, 66, 24065, http://dx.doi.org/10.3402/tellusa.v66.24065

International audience; This study presents the application of a precipitation forecasting technique based on analogs to forecast discharges at the Seine river basin. Given a target meteorological situation, the analog approach aims at searching in a meteorological archive a sample of past situations that are similar to the target one. From this sample, it is possible to extract the associated precipitation amounts, and then provide a probabilistic estimation of the expected daily rainfalll. Precipitation forecasts were obtained up to eight days in advance through a selection of analogs based on two levels of analogy (method A : pressure fields and humidity) and four levels of analogy (method B : by adding temperature and vertical velocity). These forecasts were introduced in the hydrological model GR3P to derive probabilistic discharge forecasts. Performances were evaluated in a perfect prognosis context (target situation comes from a meteorological reanalysis) and in an operational forecasting context (target situation comes from weather forecasts). Results show a significant improvement of precipitation forecasts when method B is used, with reduced uncertainties and lower false alarm rates. In an operational forecasting context, the highest gain obtained by method B is observed during the two first days of lead-time. Flood cases were studied and results show that it is possible to anticipate a flood event up to one week ahead. The approach here proposed can contribute to the methods currently used by operational flood forecasting services, by providing quantitative precipitation forecasts for longer lead-times.; Cette étude présente une application de la méthode de prévision des pluies par recherche d'analogues à la prévision des crues sur le bassin de la Seine à Paris. Cette technique consiste, à partir d'une situation météorologique cible, à rechercher dans une archive les situations passées les plus similaires puis à extraire les précipitations associées. Ceci permet d'estimer de manière probabiliste les précipitations attendues pour ce jour cible. La prévision des précipitations a été réalisée, pour des échéances allant de 1 à 8 jours, à partir d'une sélection de situations analogues en deux niveaux (méthode A : champs de pression et humidité), et quatre niveaux (méthode B : en rajoutant température et vitesse verticale). Ces prévisions ont ensuite été exploitées par le modèle hydrologique GR3P pour produire des prévisions probabilistes de débits. Les performances des deux approches ont été évaluées en contexte de prévision parfaite (situations cibles extraites de réanalyses) et de prévision opérationnelle (situations prévues par un modèle météorologique). Dans le premier cas, la méthode B s'avère plus performante, en réduisant fortement les incertitudes de prévision ainsi que le taux de fausses alertes. En conditions opérationnelles, le gain de la méthode B n'est perceptible que sur les deux premières échéances (J+1 et J+2). Cette étude de cas montre qu'il est possible d'élargir les échéances de prévision des crues, et de mettre à disposition des services opérationnels des outils permettant d'anticiper un événement de crue jusqu'à une semaine à l'avance.

International audience; This study presents the application of a precipitation forecasting technique based on analogs to forecast discharges at the Seine river basin. Given a target meteorological situation, the analog approach aims at searching in a meteorological archive a sample of past situations that are similar to the target one. From this sample, it is possible to extract the associated precipitation amounts, and then provide a probabilistic estimation of the expected daily rainfalll. Precipitation forecasts were obtained up to eight days in advance through a selection of analogs based on two levels of analogy (method A : pressure fields and humidity) and four levels of analogy (method B : by adding temperature and vertical velocity). These forecasts were introduced in the hydrological model GR3P to derive probabilistic discharge forecasts. Performances were evaluated in a perfect prognosis context (target situation comes from a meteorological reanalysis) and in an operational forecasting context (target situation comes from weather forecasts). Results show a significant improvement of precipitation forecasts when method B is used, with reduced uncertainties and lower false alarm rates. In an operational forecasting context, the highest gain obtained by method B is observed during the two first days of lead-time. Flood cases were studied and results show that it is possible to anticipate a flood event up to one week ahead. The approach here proposed can contribute to the methods currently used by operational flood forecasting services, by providing quantitative precipitation forecasts for longer lead-times.; Cette étude présente une application de la méthode de prévision des pluies par recherche d'analogues à la prévision des crues sur le bassin de la Seine à Paris. Cette technique consiste, à partir d'une situation météorologique cible, à rechercher dans une archive les situations passées les plus similaires puis à extraire les précipitations associées. Ceci permet d'estimer de manière probabiliste les précipitations attendues pour ce jour cible. La prévision des précipitations a été réalisée, pour des échéances allant de 1 à 8 jours, à partir d'une sélection de situations analogues en deux niveaux (méthode A : champs de pression et humidité), et quatre niveaux (méthode B : en rajoutant température et vitesse verticale). Ces prévisions ont ensuite été exploitées par le modèle hydrologique GR3P pour produire des prévisions probabilistes de débits. Les performances des deux approches ont été évaluées en contexte de prévision parfaite (situations cibles extraites de réanalyses) et de prévision opérationnelle (situations prévues par un modèle météorologique). Dans le premier cas, la méthode B s'avère plus performante, en réduisant fortement les incertitudes de prévision ainsi que le taux de fausses alertes. En conditions opérationnelles, le gain de la méthode B n'est perceptible que sur les deux premières échéances (J+1 et J+2). Cette étude de cas montre qu'il est possible d'élargir les échéances de prévision des crues, et de mettre à disposition des services opérationnels des outils permettant d'anticiper un événement de crue jusqu'à une semaine à l'avance.

La prévision des précipitations par analogie, adaptée des sorties de modèles numériques de prévision, s'est améliorée ces dix dernières années et est actuellement implémentée opérationnellement dans diverses infrastructures françaises. Le premier objectif de cette étude est de dresser l'état de l'art de cette approche. L'application de cette méthode nécessite une base de données contenant les champs des variables qui permettent de caractériser les situations météorologiques passées. Etant donné qu'il existe deux archives de génération différente (réanalyses ERA-40 et NCEP/NCAR), la sensibilité de cette méthode de prévision au choix de l'archive a été étudiée. Les résultats révèlent une faible sensibilité, même si de manière générale les performances sont légèrement supérieures avec l'archive ERA-40, notamment pour les événements pluvieux extrêmes. Enfin, des perspectives d'amélioration de la méthode susceptibles d'être exploitées à court terme sont évoquées. / Precipitation forecasting based on an adaptation of model outputs through an analog sorting technique has been improved for around ten years. The method runs operationally in several French institutions. First, this short paper presents the state of the art of this approach and the more recent developments. Second, a sensitivity analysis to the choice of the database from which the variables that characterise the past meteorological situations are extracted is performed. Two available archives are tested (ERA-40 and NCEP/NCAR re-analyses). The results show that despite the performances obtained with the ERA-40 database are slightly better, especially for heavy rainfall events, the sensitivity is weak. Finally, further ways for improvement that could be investigated are suggested.