摘要
平原圩区地势低洼,下垫面类型多样,产汇流过程较为复杂,水文过程模拟和预报十分困难。为解决平原圩区洪水预报难题,建立考虑水田、旱地、林地、城市区与水域等多种下垫面类型的产汇流模型,利用MIKE 11 HD水动力模型模拟河道汇流过程,提出面向平原圩区复合下垫面条件的水文水动力耦合模型;并采用BP神经网络进行河道水位预报误差校正,以提高模型精度。选择广州市南沙蕉门河排涝片为研究区,检验耦合模型的水位预报精度,并以2023年“9·7深圳特大暴雨”为移置场景输入,模拟不同排涝措施对河道水位的影响。结果表明:模型能够较好地模拟研究区场次洪水的河道水位过程,率定期和验证期的平均Nash效率系数分别为0.86和0.91,10场洪水中有8场的最高水位模拟误差小于0.05 m;采用BP神经网络校正后所有场次洪水的Nash效率系数均大于0.9,满足洪水预报的精度要求。研究区面临“9·7深圳特大暴雨”场景时存在内涝风险,需提升圩内蓄洪排涝能力。
Low-lying plain polders,characterized by diverse underlying surfaces,present significant challenges for hydrological modeling and forecasting due to the complexity of runoff generation and concentration.To address these challenges,we developed a runoff model that accounts for multiple underlying surface types,including paddy fields,dry lands,forests,urban areas,and water surfaces.The MIKE 11 HD model was employed to simulate rive network flows,and a hydrological-hydrodynamic coupling model was proposed specifically for plain polders with complex underlying surface conditions.To further improve the model's accuracy,a BP neural network was integrated to correct forecasting errors.The model's performance was evaluated in the Nansha Jiaomen River drainage area in Guangzhou.In addition,using the“9·7 Shenzhen Rainstorm”of 2023 as the input scenario,a case study was conducted to simulate the impact of different drainage measures on river water levels.Results show that the model accurately simulates river water level dynamics during flood events,with average Nash efficiency coefficients of 0.86 and 0.91 during the calibration and validation periods,respectively.For 8 out of 10 flood events,the maximum water level simulation error was less than 0.05 m.After correction by the BP neural network,all flood events achieved a Nash efficiency coefficient greater than 0.9,meeting the required accuracy of flood forecasting.The study also highlights the risk of waterlogging under the simulated scenario,underscoring the need to enhance flood retention and drainage capacity within the polders.
作者
李彬权
陈丞
肖洋
余煌浩
许栋
LI Binquan;CHEN Cheng;XIAO Yang;YU Huanghao;XU Dong(The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China;School of Environmental Science and Engineering,Suzhou University of Science and Technology,Suzhou 215009,China;Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources,Hohai University,Nanjing 210098,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2024年第5期805-816,共12页
Advances in Water Science
基金
国家重点研发计划资助项目(2023YFC3209204)
广州市南沙区水务局科技项目(2022-263)。
作者简介
李彬权(1984-),男,江苏淮安人,副教授,博士研究生导师,主要从事水文水资源方面研究。E-mail:libinquan@hhu.edu.cn。
