Flood risk simulation and disaster prevention and control evaluation of Sishui River
Flooding generated by heavy rain has a complicated disaster-causing mechanism,significant damage,poor forecasting and early warning,and usually causes disasters.In order to improve the accuracy of flood prediction and minimize the impact of flood disasters,this paper takes the Sishui River Basin in Xingyang as the study area.Based on the MIKE FLOOD module,a flood simulation model coupled with MIKE11 and MIKE21 is constructed.Three factors were used to calibrate and validate the model including the observed flow of the river channel,the height of the downstream levee,and the historical flood mark.The simulated values correspond well with the observed values.To assess the distribution features of flood inundation in the study area and divide the risk level,three return period scenarios were constructed.The findings are as following.The depth and extent of flood inundation in the study area increased as rainfall intensity increased.Under the"7·20"scenario,the greatest submerged depth is up to 9 m,and Sishui Town is the most severely flooded location in all scenarios.There is a variation in the danger of inundation between the town and its surroundings.The inundation risk of agricultural land on the outskirts of town is the greatest under the three rainy return time scenarios,and the maximum inundation area accounts for 79.8%of total inundation.The proportion of various land use types within the inundated area in descending sequence is agricultural land,construction land,road land,and other types land.Draw the flood disaster transfer roadmap of"7·20"stricken Sishui Town to provide scientific basis for the decision-making of evacuation.The measures for raising embankments and dredging are put forward under the circumstances of different flood return periods of the Sishui River,and the upgrading and renovation plans of flood prevention embankments under different scenarios are formulated.
MIKE FLOODcoupling modelSishui Riverflood inundationrisk analysis
NETL
NSTL
万方数据
