The viscous Savart sheet

International audienceWe study the viscous version of the planar Savart sheet problem, using an impacting liquid jet up to 300 times more viscous than water. Two surprising observations are made, contrasting with the traditional case introduced by Savart where viscosity plays no role. First, if the radius of a viscous sheet is typically reduced compared to the case of water for a given jet radius and impacting velocity, the smooth-flapping transition is delayed, allowing for smooth sheet radii substantially bigger than those permitted with water at large impacting Weber number. Second, the drop size distribution is bimodal, with a substantial fraction of the drops having a very small, well-defined diameter. We understand these two facts in terms of an additional model experiment, and simple physical arguments

Apport de l’étude de la fracturation et de la karstification dans la modélisation des aquifères karstiques.

International audienceLe comportement des aquifères karstiques à long terme ne peut être estiméqu’au travers de l’utilisation de modèles qui permettent de modéliserla réponse et le stockage d’eau de ces aquifères. Une bonneconnaissance de la structure de ces aquifères fortement hétérogènes estprimordiale afin de mettre en oeuvre des modèles robustes d’exploitationdurable de la ressource. Nous proposons une approche qui associel’analyse de la fracturation à plusieurs échelles (de micro à meso) àcelle du contexte géomorphologique, afin de déterminer les axes préférentielsd’écoulements au sein d’aquifères karstiques. Ces axes préférentielsd’écoulements peuvent ensuite être intégrés à des modèles, afinde prévoir au mieux, le comportement hydrodynamique de ces aquifères.Cette approche a été appliquée au karst dit de Bourbouillet (sudArdèche, France), afin d’évaluer les possibilités de captages. Les résultats,associés à ceux issus des méthodes hydrochimiques et des tests detraçages, permettent de proposer un modèle de gestion de cet aquifère

Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity

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A conceptual semidistributed model of the Coulazou River as a tool for assessing surface water-karst groundwater interactions during flood in Mediterranean ephemeral rivers

International audienceThis study aims to assess surface water-karst groundwater interactions during floods in the Mediterranean karst watershed of the Coulazou River (southern France) using a conceptual semidistributed model at 5 min time steps. The kinematic wave routing approximation is used for the transfer of surface flow, while overflows from a linear underground reservoir account for karst flows along the riverbed. After calibration, values of parameters and simulated time series are compared to independent physical measurements. Results show that direct runoff can be neglected on the karst terrains. In addition, this study demonstrates that, in some cases, karst watersheds can be considered as relatively poor systems of regulation but strong systems of amplification or generation of floods and flash floods, depending on rainfall characteristics and also on groundwater level conditions prior to the flood event. Considering that the flood peak is the most important factor defining flash flood hazard, it is shown that the flood hazard regulation effect of the karst is relatively limited for low water table conditions prior to the flood, while the aggravating effect for high water table conditions may be higher than 80% with respect to expected values from surface runoff only. These results show that understanding groundwater-surface water interactions is crucial for describing the flash flood dynamics in karst terrains

Conceptualization and classification of groundwater–surface water hydrodynamic interactions in karst watersheds: Case of the karst watershed of the Coulazou River (Southern France)

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Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

International audienceIn southern France, flash flood episodes frequently cause fatalities and severe damage. In order to inform and warn populations, the French flood forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appuià la Prévision des Inondations) initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital Basin) project in an effort to enhance flash flood predictability. The target area for this study is the Gardon d'Anduze basin, located in the heart of the Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be very high, resulting in flash flooding. Discharge and rainfall gauges are often exposed to extreme weather conditions , which undermines measurement accuracy and continuity. Moreover, the processes governing rainfall-discharge relations are not well understood for these steeply-sloped and heterogeneous basins. In this context of inadequate information on both the forcing variables and process knowledge , neural networks are investigated due to their universal approximation and parsimony properties. We demonstrate herein that thanks to a rigorous variable and complexity selection , efficient forecasting of up to two-hour durations, without requiring rainfall forecasting as input, can be derived using the measured discharges available from a feedforward model. In the case of discharge gauge malfunction, in degraded mode, forecasting may result using a recurrent neural network model. We also observe that neural network models exhibit low sensitivity to uncertainty in rainfall measurements since producing ensemble forecasting does not significantly affect forecasting quality. In providing good results, this study suggests close consideration of our main purpose: generating forecasting on ungauged basins

Inspection des conduites d’eau potable par sonde autonome à centrale inertielle et transducteurs.

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Teaching groundwater flow processes: connecting lecture to practical and field classes

International audiencePreparing future hydrogeologists to assess local and regional hydrogeological changes and issues related to water supply is a challenging task that creates a need for effective teaching frameworks. The educational literature suggests that hydrogeology courses should consistently integrate lecture class instructions with practical and field classes. However, most teaching examples still separate these three class components. This paper presents an introductory course to groundwater flow processes taught at Université Montpel-lier 2, France. The adopted pedagogical scheme and the proposed activities are described in details. The key points of the proposed scheme for the course are: (i) iterations into the three class components to address groundwater flow processes topics, (ii) a course that is structured around a main thread (well testing) present in each class component, and (iii) a pedagogical approach that promotes active learning strategies, in particular using original practical classes and field experiments. The experience indicates that the proposed scheme improves the learning process, as compared to a classical , teacher-centered approach

Flash Floods Forecasting in a Karstic Basin Using Neural Networks: the Case of the Lez Basin (South of France)

International audienceThe present study focuses on the modeling of the Lez karstic system (France) using artificial neural networks. Two methods of variable selection were compared: cross-correlation and cross-validation. In both cases, the artificial neural network forecasts closely matched the measured discharge, giving Nash criteria higher than 0.8, which can thus provide satisfactory 2-day forecasts

Modélisation hydrodynamique d’un hydrosystème karstique par réseaux de neurones : contraindre le modèle pour améliorer ses performances.

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