城市洪涝过程:半分布式水箱模型与河道洪水模拟

Urban pluvial flooding process:semi-distributed tank model and river flood simulation

为解析城市上游流域降雨径流与河道洪水演进的2个子过程,采用半分布式水文模型FLOWS-Tank并结合机制驱动与数据驱动的方法,以福州八一水库与斗顶水库所在小流域及晋安河主干道为例,分析了FLOWS-Tank模型参数敏感性与河道洪水模拟效果.结果表明:1)FLOWS-Tank大部分参数具有较低敏感性;2)对纳什效率系数(Nash-Sutcliffe efficiency coefficient,NSEC)和均方根误差(root mean squared error,RMSE),模型参数侧孔高度7和汇流参数(非线性水库2)在一阶敏感度和总敏感度下呈现较强的敏感性;3)五四站水位模拟得到均方误差(mean squared error,MSE)为0.001,平均绝对误差(mean absolute error,MAE)为0.012,均方对数误差(mean squared log error,MSLE)为0.0007,RMSE为0.033.FLOWS-Tank模型对于八一水库和斗顶水库2个流域模拟效果较好,总径流量随重现期增大而逐渐增加;耦合长短期记忆神经网络与生成对抗网络(generative advevsarial nets,GANs)对河道洪水模拟具有较好的适用性.

To analyze two sub-processes(rainfall-runoff in upstream urban watersheds and river flood evolution),semi-distributed hydrological model of FLOWS-Tank combining both mechanism-driven and data-driven approaches was applied to the small watersheds of Bayi and Douding reservoirs in Fuzhou,and the main channel of Jin’an River.Sensitivity of FLOWS-Tank model parameters and effectiveness of flood simulation in the river channel were studied.Most parameters of the FLOWS-Tank model were found to exhibit low sensitivity.For the Nash-Sutcliffe efficiency coefficient(NSEC)and root mean squared error(RMSE),the model parameters of side orifice height 7 and confluence parameters(nonlinear reservoir 2)showed strong sensitivity in both first-order and total sensitivity analysis.Water level simulation at the Wusi station achieved an mean squared error(MSE)of 0.001,mean absolute error(MAE)of 0.012,mean squared log error(MSLE)of 0.0007,and RMSE of 0.033.The FLOWS-Tank model demonstrated good simulation performance for the Bayi and Douding reservoir catchments,with total runoff increasing gradually as return period increased.In addition,coupling of long short-term memory(LSTM)neural networks and generative adversarial networks(GANs)proved to be well-suited for river flood simulation.