基于SWMM模型的西安市清河流域暴雨洪峰流量模拟

Simulation of Rainstorm Flood Peak Discharge in the Qinghe River Basin in Xi’an Based on SWMM Model

为探讨暴雨洪水管理模型（storm water management model,SWMM）在暴雨条件下对山区流域洪峰流量模拟的适用性,以西安市清河流域为例,在SWMM模型中分别构建了传统的集总模式和以DEM、流域水系为子流域划分依据的半分布模式。通过2016年几次典型降雨洪峰流量验证两种模式的可靠性,结果表明：集总模式与实测拟合程度较好,相对误差为6%,确定性系数为0.871。为了进一步检验集总模式的适用性,从径流过程角度去模拟,结果也令人满意,两场降雨的相对误差均为11%,两场降雨的确定性系数和纳什系数均高于0.8,表明有较好的模拟效果。由此说明,基于SWMM模型构建的集总水文模式适合于清河流域的径流模拟。最后,模拟了不同重现期下流域的径流过程。流量峰值出现在降雨开始后约1.5 h,10年一遇以下重现期径流变化相对稳定。该模型对当地水利部门评估极端降雨事件等方面具有一定的应用价值。

In order to discuss the applicability of storm water management model for flood peak discharge simulation in mountainous area,in this paper the Qinghe River Basin in Xi＇an was taken to carry out a case study on flood peak discharge using the traditional lumped model and the semi-distribution model. The semi-distribution model was based on DEM and drainage basin system to develop the SWMM model. Two different modes of hydrological models could be validated by the peak discharge during several typical rainfall episodes in 2016. The results showed that the lumped pattern was in good fitting with the measured values,the relative error was only 6%,and the deterministic coefficient was 0. 871. In order to further test the applicability of the lumped model,the results were satisfactory from the perspective of runoff process. The relative errors of the two rainfalls were 11%,and the certainty coefficient and Nash coefficient of the two rainfalls were higher than 0. 8,which revealed that the good results were achieved in the simulation. Therefore,the lumped hydrological model based on SWMM is suitable for simulating the runoff discharge in the Qinghe River Basin. At last,the runoff processes in the drainage basin under different recurrence periods were simulated. The flood peak discharge occurred about 1. 5 hours after starting rainfall,and the change of runoff discharge will be relatively stable during the following decade. This model has a certain application value to the local water conservancy department in assessing extreme rainfall events.