Abstract
Abstract. Flash floods pose significant hazards in urbanised zones and have important implications financially and for humans alike in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to improve the models of these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (Knowledge eXtraction (KnoX) methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viols-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be 3 h. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–run-off measurements.
Highlights
Flash floods are rapid and intense floods that occur within small basins
Regarding the case study on the Lez basin, it is very different from the work made by Kong-A-Siou et al (2013), as in the present study we considered flash flooding at Lavalette having an important surface water contribution; whereas the previous work investigated daily run-off of underground water at the Lez spring
These floods occur in heterogeneous basins, which are difficult to observe and to model. For this reason this paper investigates the ability to obtain information on a complex aquifer through global systemic modelling using neural networks. For this purpose we chose as a case study flash flooding at the entrance to the great city of Montpellier where large potential losses are at stake
Summary
Flash floods are rapid (they rise in a few hours) and intense floods that occur within small basins. Over the past 20 years, flash flooding in south-eastern France has caused more than 100 fatalities and several billion euros in property damage. The event of June 2010, in the river Var (southern France) caused 27 casualties and more than one billion euros of damages. Considerable efforts have been devoted to improving our understanding and forecasting of flash flooding (Gaume et al, 2009; Marchi et al, 2010). In the literature three aspects were investigated: (i) the rain event (or other cause of rising water), (ii) run-off genesis, and (iii) surface and underground geomorphologic and geologic settings that channel the water transfer toward the outlet
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