DOI: 10.5194/hess-19-4397-2015
Scopus记录号: 2-s2.0-84946615695
论文题名: Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: Case study on the Lez basin (southern France)
作者: Darras T ; , Borrell Estupina V ; , Kong-A-Siou L ; , Vayssade B ; , Johannet A ; , Pistre S
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 10 起始页码: 4397
结束页码: 4410
语种: 英语
Scopus关键词: Aquifers
; Climate change
; Flood control
; Rain
; Critical rainfall
; Flood forecasting models
; Geographic zones
; Global climate changes
; Knowledge extraction
; Neural network model
; Neural network modelling
; Rainfall contributions
; Floods
; aquifer
; artificial neural network
; climate change
; flash flood
; flood forecasting
; geographical region
; heterogeneity
; identification method
; karst
; rainfall
; runoff
; spatiotemporal analysis
; urban area
; France
; Herault
; Languedoc-Roussillon
; Lez Basin
英文摘要: 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. © 2015 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78395
Appears in Collections: 气候变化事实与影响
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作者单位: École des mines d'Alès, LGEI, 6 avenue de Clavières, Alès Cedex, France; Université Montpellier - Hydrosciences Montpellier, MSE, 2 Place Eugène Bataillon, Montpellier Cedex 5, France; MAYANE, 173 chemin de Fescau, Montferrier-sur-Lez, France
Recommended Citation:
Darras T,, Borrell Estupina V,, Kong-A-Siou L,et al. Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: Case study on the Lez basin (southern France)[J]. Hydrology and Earth System Sciences,2015-01-01,19(10)