Articles | Volume 19, issue 10
Hydrol. Earth Syst. Sci., 19, 4397–4410, 2015
Hydrol. Earth Syst. Sci., 19, 4397–4410, 2015

Research article 30 Oct 2015

Research article | 30 Oct 2015

Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)

T. Darras1,2, V. Borrell Estupina2, L. Kong-A-Siou3, B. Vayssade1, A. Johannet1, and S. Pistre2 T. Darras et al.
  • 1École des mines d'Alès, LGEI, 6 avenue de Clavières, 30319 Alès CEDEX, France
  • 2Université Montpellier – Hydrosciences Montpellier, MSE, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France
  • 3MAYANE, 173 chemin de Fescau, 34980 Montferrier-sur-Lez, France

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.

Short summary
Flash floods are important hazards in urbanised zone and constitute important human and financial stakes. This paper applies a novel methodology, KnoX, dedicated to extract knowledge from a neural network model. It was shown that KnoX method could help to better characterize processes of both surface and underground floods. A case study is chosen in France: the Lez karst hydrosystem whose river crosses the city of Montpellier (400 000 inhabitants). Results will help flood warning services.