Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo
The production and sink mechanism in karst areas is complicated, and the accuracy of flood forecasting is difficult to ensure. In order to test the simulation effect of the distributed hydrological model (HEC-HMS) on the flash flood runoff process in karst areas, the hydrological model was construct...
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Editorial Office of Pearl River
2024-08-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.08.004 |
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| author | YUAN Meng HU Haiying TIAN Tian ZHU Dantong CHENG Xiangju |
| author_facet | YUAN Meng HU Haiying TIAN Tian ZHU Dantong CHENG Xiangju |
| author_sort | YUAN Meng |
| collection | DOAJ |
| description | The production and sink mechanism in karst areas is complicated, and the accuracy of flood forecasting is difficult to ensure. In order to test the simulation effect of the distributed hydrological model (HEC-HMS) on the flash flood runoff process in karst areas, the hydrological model was constructed using hydrometeorological and subsurface data in the Jinbao River Basin of Yangshuo as an example. The parameter sensitivity of the model was analyzed based on the modified Morris screening method, and nine floods were selected for model parameter rate determination and validation. The results show that: the main sensitive parameters of the hydrological model in karst area are CN(curve number), lag time, recession constant, ratio to peak and storage constant. The sensitivity of the parameters to the objective function decreases with increasing rainfall intensity, and the sensitivity of the parameters to the peak time is less affected by rainfall intensity. The runoff depth errors of the simulated floods in Jinbao River are all less than 20%, the peak time errors are all less than 1 h, and the overall qualification rate is 88.89%, with an average Nash efficiency coefficient of 0.85, which achieves the accuracy of Class B forecasting, and the flood prediction of the Yulong River is also up to the Class B accuracy, which indicates that the constructed model has good applicability to the simulation of runoff in the Jinbao River and other small watersheds in the karst mountainous areas. The model calculates the warning time and flow rate of vulnerable points under different rainfall recurrence periods, which can provide a basis for flood prevention and early warning in this area. |
| format | Article |
| id | doaj-art-9c3f7d17c5a14747897c3684d5fc2db8 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-9c3f7d17c5a14747897c3684d5fc2db82025-01-15T03:01:20ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-08-0145293755249173Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in YangshuoYUAN MengHU HaiyingTIAN TianZHU DantongCHENG XiangjuThe production and sink mechanism in karst areas is complicated, and the accuracy of flood forecasting is difficult to ensure. In order to test the simulation effect of the distributed hydrological model (HEC-HMS) on the flash flood runoff process in karst areas, the hydrological model was constructed using hydrometeorological and subsurface data in the Jinbao River Basin of Yangshuo as an example. The parameter sensitivity of the model was analyzed based on the modified Morris screening method, and nine floods were selected for model parameter rate determination and validation. The results show that: the main sensitive parameters of the hydrological model in karst area are CN(curve number), lag time, recession constant, ratio to peak and storage constant. The sensitivity of the parameters to the objective function decreases with increasing rainfall intensity, and the sensitivity of the parameters to the peak time is less affected by rainfall intensity. The runoff depth errors of the simulated floods in Jinbao River are all less than 20%, the peak time errors are all less than 1 h, and the overall qualification rate is 88.89%, with an average Nash efficiency coefficient of 0.85, which achieves the accuracy of Class B forecasting, and the flood prediction of the Yulong River is also up to the Class B accuracy, which indicates that the constructed model has good applicability to the simulation of runoff in the Jinbao River and other small watersheds in the karst mountainous areas. The model calculates the warning time and flow rate of vulnerable points under different rainfall recurrence periods, which can provide a basis for flood prevention and early warning in this area.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.08.004karst areaflash flood forecastingHEC-HMS modelparameter sensitivityJinbao River Basin |
| spellingShingle | YUAN Meng HU Haiying TIAN Tian ZHU Dantong CHENG Xiangju Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo Renmin Zhujiang karst area flash flood forecasting HEC-HMS model parameter sensitivity Jinbao River Basin |
| title | Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo |
| title_full | Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo |
| title_fullStr | Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo |
| title_full_unstemmed | Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo |
| title_short | Flood Forecasting Simulation in Karst Areas——A Case Study of Jinbao River Basin in Yangshuo |
| title_sort | flood forecasting simulation in karst areas a case study of jinbao river basin in yangshuo |
| topic | karst area flash flood forecasting HEC-HMS model parameter sensitivity Jinbao River Basin |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.08.004 |
| work_keys_str_mv | AT yuanmeng floodforecastingsimulationinkarstareasacasestudyofjinbaoriverbasininyangshuo AT huhaiying floodforecastingsimulationinkarstareasacasestudyofjinbaoriverbasininyangshuo AT tiantian floodforecastingsimulationinkarstareasacasestudyofjinbaoriverbasininyangshuo AT zhudantong floodforecastingsimulationinkarstareasacasestudyofjinbaoriverbasininyangshuo AT chengxiangju floodforecastingsimulationinkarstareasacasestudyofjinbaoriverbasininyangshuo |