南水北调与水利科技2021，Vol.19Issue(4)：689-699,11.DOI:10.13476/j.cnki.nsbdqk.2021.0072
华中多雨人口密集型流域洪水预报
Flooding prediction for a rainy,dense-population river basin of central China
摘要
Abstract
Watershed runoff forecasting has been a research focus on hydrological model and flooding prediction.In China,Xinanjiang (XAJ) model is a widely used for flood forecasting,which is of significance to areas with boom-ing economy,dense population,and high flooding risks,such as those located in central China.The Fu River basin located in Jiangxi Province is a rainy area with abundant precipitation (average annual rainfall is 1761 mm).A lumped hydrological model,three-source XAJ model based on excess storage runoff generation,was established to simulate 18 rainstorms and floods in Fu River basin from 1981 to 1995.Muskingen's piecewise continuous algo-rithm was used to calculate river flood routing and the flow process line.The processes of the flow and flood were determined by linear superposition of all the flow processes.Parameters were calibrated by daily data and frequen-cy data.Results showed that the average deterministic coefficient of the model in the field flood simulation was 0.911,the average error of runoff depth for model calibration was 4.73 ％,and the average error of runoff depth for validation was 8.21％.Therefore,the three-source XAJ model could be used as a useful forecasting model in the flood forecasting system of Fu River basin.The useful references were provided for flood forecasting research in the rainy central China.关键词
流域水文预报/三水源新安江模型/水资源规划管理/水资源持续利用/流域蓄水容量曲线Key words
watershed hydrological forecast/three-source XAJ model/excess storage runoff/parameters of the calibra-ted分类
水利科学引用本文复制引用
李雨桐,蔡宴朋,付强,宫兴龙..华中多雨人口密集型流域洪水预报[J].南水北调与水利科技,2021,19(4):689-699,11.基金项目
Research project of National Key Research and Development Program (2016yfc0502209) （2016yfc0502209）
Beijing Municipal Natural Science Foundation(JQ18028) （JQ18028）
National Natural Science Foundation of China (51879007) （51879007）
BNU Interdisciplinary Research Foundation for the First-Year Doctoral Candidates (BNUXKJC2018) （BNUXKJC2018）
