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Analog methods (AMs) allow predicting local meteorological variables of interest (predictand), such as the daily precipitation, based on synoptic variables (predictors). They rely on the hypothesis that similar atmospheric conditions are likely to result in similar local effects. The statistical relationship is first defined (e.g. which predictors, and how many subsampling steps) and calibrated (e.g. which spatial domain, and how many analogues) before being applied to the target period, may it be for operational forecasting or for climate impact studies. A benefit of AMs is that they are lightweight and can provide valuable results for a negligible cost. AtmoSwing is an open source software that implements different AM variants in a very flexible way, so that they can be easily configured by means of XML files. It is written in C++, is object-oriented and multi-platform. AtmoSwing provides four tools: the Optimizer to establish the relationship between the predictand and predictors, the Downscaler to apply the method for climate impact studies, the Forecaster to perform operational forecasts, and the Viewer to display the results. The Optimizer provides a semi-automatic sequential approach, as well as Monte-Carlo analyses, and a global optimization technique by means of Genetic Algorithms. It calibrates the statistical relationship that can be later applied in a forecasting or climatic context. The Downscaler takes as input the outputs of climate models, either GCMs or RCMs in order to provide a downscaled time series of the predictand of interest at a local scale. The Forecaster automatically downloads and reads operational NWP outputs to provide operational forecasting of the predictand of interest. The processing of a forecast is extremely lightweight in terms of computing resources; it can indeed run on almost any computer. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical situations in the coming days, as well as the possibility for the user to go into the details of the forecasted predictand distribution.

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics.

The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences.

International audience; The proposed methodology allows for the identification of time and space scales to be satisfied for the development of a parsimonious rainfall-runoff model. The maximum acceptable time step (MATS) is first determined so that the dynamical hydrological response of the basin can be properly represented. A characteristic time of the basin hydrological response is used therefore. The MATS allows next to estimate the maximum acceptable space scale with respect to the geostatistical properties of the precipitation cumulated over the MATS. As a result, it is possible to define if the basin can be represented for the MATS by a lumped hydrological model or, if not, the minimum number of sub-basins needed for a reasonable representation of the precipitation spatial variability.

International audience; Anticipating flash floods over quick responding catchment such as the Mediterranean ones requires an appropriate anticipation of future rainfalls. We try here to answer this demand through a probabilistic adaptation of meteorological model outputs, based on an analog search for past situations similar to the expected one. The precipitations collected during those past situations allow to derive a conditional distribution for the expected rainfall. After calibration of the adaptation algorithms using the NCEP/NCAR reanalyses, an operational prototype of this analog approach has been implemented at our laboratory, using the outputs of the American model GFS . This prototype considers concurrently two types of analogy: a first one relies only on the general synoptic circulation, while the second involves both the general synoptic circulation and more local variables from the humidity fields. The catastrophic rain event of September 8 and 9th 2002 in the Gard region illustrates the value added by this second type of analogy. In this particular case, it would have allowed to foresee the exceptional character of the rain amounts already on the morning of Septembre 7th.

International audience; This presentation aims at a state of the art on the current knowledge on flood flow generation in small to medium-sized, catchments (some 10 to a few 100km2), separating well-established concepts from present interrogations. Building on recent works, one shall address the role of topography, soil and land cover, as well as the role of rainfall distribution in space. More recently, some concerns have appeared from man-made effects (like tile drains or other artificial networks: roads, embankments, ditches) which will be briefly considered. Model limitations, as well as those of instrumentation, will also be addressed as limiting factors for our knowledge and therefore for the engineering answers that can be brought to flood forecasting and mitigation. Finally, one will consider whether there are different ways of working depending on normal or extreme conditions, and some related but open questions.

International audience; The recent widespread availability of digital terrain data has resulted in increasing use, in a variety of hydrological models, of different degrees of complexity. Previous experience suggests that neither model results nor effective parameter values are independent of the resolution of the digital terrain data used. Calibration of parameter values can compensate for lack of resolution in the digital terrain data. This paper will show, for one particular model, TOPMODEL, how an analytical link can be established between the grid size of a raster digital terrain model and the effective saturated hydraulic conductivity value used in the model. The work generalizes the results of the recent study by Franchini et al. (1996) and allows the change in effective conductivity to be estimated on the basis of keeping a realistic simulation of saturated contributing areas as the DTM grid size changes.

Une analyse de l'évolution de la Fonction de Transfert du bassin du Réal Collobrier et de ses sous-bassins, dans le Var, a été effectuée en utilisant la méthode DPFT. Curieusement cette étude met en évidence un effet de ralentissement des écoulements lorsque le coefficient d'écoulement des crues est important. La modélisation des écoulements sur les versants, avec des rugosités de surface mesurées sur les bassins, permet d'interpréter ce ralentissement qui accompagne les forts débits : ceux-ci sont associés à des apports sur les versants qui proviennent de surfaces saturées contributives s'étendant d'autant plus loin que le bassin réagit fortement aux pluies. L'évolution du transfert provient donc de l'allongement du cheminement hydraulique sur les versants. Ce résultat n'est pas extrapolable aux bassins dénudés sur lesquels les phénomènes hortoniens sont prépondérants, ni aux bassins de grande taille sur lesquels l'hydraulique des écoulements dans le réseau hydrographique domine., Grésillon Jean-Michel, Obled Charles, Neyret-Gigot J. M., Le Meillour F. 27. -Variabilité de la fonction de transfert d'un bassin-versant. Analyse des causes et essai d'interprétation. In: Crues et inondations. 23emes journées de l'hydraulique. Congrès de la Société Hydrotechnique de France. Nîmes, 14-15-16 septembre 1994. Tome 1, 1994.

Sans revenir sur le déroulement météorologique de l'épisode du 22 Septembre 1992, on montre l’intérêt, pour la compréhension du phénomène et de sa dynamique, de la cartographie continue des champs de pluies horaires en utilisant : d'abord les données, les plus complètes possibles, d’un réseau sol de plus de 300 stations ; puis les données issues du radar météorologie de Nîmes-Manduel. On en tire des enseignements sur la structure interne du système précipitant et sur la complémentarité des données sol et des données radar. On évoque également l'intérêt des informations issues du système METEORAGE de localisation des impacts de foudre et l'on compare la distribution spatiale de ces impacts avec les cartes horaires de précipitations sus-citées., Tourasse Patrick, Garçon Rémy, Obled Charles, Aubonnet V., Dufour C. 5. - Nouveaux outils pour la prévision hydrométéorologique et le suivi des systèmes précipitants. L'exemple du 22 septembre 1992. In: Crues et inondations. 23èmes journées de l'hydraulique. Congrès de la Société Hydrotechnique de France. Nimes (France), 14-15-16 septembre 1994. Tome 2, 1994.

Cet article propose, à partir de l'expérience accumulée sur une douzaine de bassin, des lignes directrices pour mener à bien une modélisation pluie-débit globale. On analyse d'abord le transfert grâce à la méthode DPFT, et l'invariance de la fonction de transfert d'un évènement à l'autre. On choisit ensuite un modèle de production, plutôt hortonien ou privilégiant le développement de zones contributives. Ses problème de calibration et d'initialisation seront posés, de même que ceux d'un mode de fonctionnement continu ou par évènement et de l'adaptation en cours d’épisode. Enfin on revient sur le découpage souhaitable en bassins et sous-bassins., Obled Charles, Rodriguez J. Y., Saulnier Georges-Marie. 11. - Une approche opérationnelle pour la modélisation des bassins versants sujets aux crues éclair. In: Crues et inondations. 23èmes journées de l'hydraulique. Congrès de la Société Hydrotechnique de France. Nimes (France), 14-15-16 septembre 1994. Tome 2, 1994.