Research on the Pre-assessment Methodology of Flash Flood Risk for Mountain Villages
Aiming at the problems of insufficient timeliness of torrential rainfall and flash flood disaster assessment methods,low degree of refinement of the disaster-bearing body and the usual lack of hydrological data in mountainous areas,the simulation software of torrential rainfall and flash flood process was developed by integrating the cellular automata model and distributed hydrological model in the case of Xiadang village in Ningde,Fujian Province.By inputting the easy-to-access data such as rainfall and digital elevation model in the software,the dynamic data such as inundation depth and inundation flow rate can be computed with a certain degree of accuracy.In this software,the dynamic data such as water depth and inundation flow rate can be calculated with certain accuracy by inputting rainfall and digital elevation model.Based on that,the index system for assessing the risk of torrential flash floods in Xiadang Village was constructed from the aspects of torrential flash flood hazard and vulnerability of the disaster-bearing body,and a refined database of the disaster-bearing body was established based on high-resolution remote sensing images and field research.A series of maps of the torrential flash flood hazard,vulnerability,and the pre-assessment of the risk of the disaster in Xiadang Village were obtained with a high degree of accuracy.The results show that under the given scenario of a 200-year rainstorm in the district,9.85%of the area of Xiadang Village is a high-risk zone,including 20 houses and 26 road sections,which are along the river with a concentration of tourist and residential population.This methodogoly could provide a scientific basis and technical reference for the rapid assessment of the risk of torrential flash floods in moun-tainous villages,as well as further refined early warning for disaster-bearing objects.
mountain villageheavy rainfall flash floodsdisaster alertrisk pre-assessmentdigital village
NETL
NSTL
万方数据
