河网地区洪水预报模型开发及在长江下游地区的应用

Development of flood forecasting models in river network areas and the application in the lower reaches of the Yangtze River

平原水网地区的洪水预报一直是水文学科的难点问题,尤其受人类活动影响剧烈。为了更好地模拟流域水文循环过程,解决工程实际应用中的挑战,提出了一种新颖的分布式架构水文模型。该模型具有分布式特征,能够根据流域特征和需求灵活选择最合适的水文特征单元进行组合,从而支持对不同水文特征单元精确化、理论化的研究。为了实现实时预报功能,还研发了数据库耦合技术、大型河网实时校正技术及水利工程调度技术。在此基础上,应用分布式架构思想建立了太湖流域模型,对其进行了概化处理,并选取2016年实况数据对模型参数进行了率定。通过对太湖水位1 d、2 d、3 d预见期的实时预报,计算成果基本能够反映平原水网地区太湖流域的水流运动实际状况。综合而言,该分布式架构水文模型方法不仅在科学性上得到验证,而且在实现技术上具有可行性,成功地解决了从理念到现实的应用实践问题,为平原水网地区洪水预报提供了新的思路和方法。

Flood forecasting in plain river networks has always been a challenging issue in hydrology,especially with the significant impact of human activities.In order to better simulate the hydrological cycle in the basin and address the challenges in engineering applications,this paper proposes a novel distributed architecture hydrological model.The model has distributed characteristics,allowing for flexible selection of the most suitable hydrological feature units based on basin characteristics and requirements,thereby supporting the precise and theoretical study of different hydrological feature units.To achieve real-time forecasting functionality,this paper also developed database coupling technology,large-scale river network real-time correction technology,and hydraulic engineering scheduling technology.Based on this,a distributed architecture model of the Taihu Lake basin was established,which was generalized,and the model parameters were calibrated using 2016 actual data.By conducting real-time forecasts of Taihu Lake water levels with lead times of 1 d,2 d,and 3 d,the results of this study can basically reflect the actual water flow conditions in the Taihu Lake basin of the plain river network area.In summary,the distributed architecture hydrological model method proposed in this paper has not only been verified in terms of scientificity but also has feasibility in implementation technology.It successfully solves the application problems from concept to reality and provides new ideas and methods for flood forecasting in plain river network areas.