无上游来水城市河道径流分割及其下垫面影响

Runoff Division of Urban River without Upstream Incoming Water and Impact Analysis of Underlying Surface

为了对无上游来水城市河道径流进行分割,同时进一步探究城市下垫面对降雨-径流关系的影响,引入多种流量过程线的自动分割方法对中牟水文站2003-2016年逐日径流进行分割,应用筛选出的最优分割方法将城市河道径流分割为年基流和年直接径流,采用年直接径流系数表征城市集水特性,结合5期土地利用数据构建年直接径流系数和各类下垫面面积的多元回归模型,并对2017和2018年的年直接径流系数进行预测,结果表明:数字滤波F1法对无上游来水城市河道径流的分割效果最佳,基流和直接径流对总径流增长的贡献率分别为83.96%和16.04%;该多元回归模型在p=0.01水平上显著且具有良好的模拟效果,NSE、R^(2)、Re值分别为0.891,0.891和6.72%;此外,模型对2017和2018年直接径流系数预测的相对误差分别为4.80%和9.75%。

In order to divide runoff of urban river without upstream incoming water and further explore the impact of urban underlying surface on rainfall-runoff relationship,several automatic division methods of flow process line are introduced to divide the daily runoff of Zhongmu Hydrological Station from 2003 to 2016,then the urban river runoff is divided into annual base flow and annual direct runoff by using the selected optimal division method.The annual direct runoff coefficient is used to express the urban water catchment characteristics,and the five-phase land use data are combined to construct a multiple linear regression model of the annual direct runoff coefficient and various underlying surface areas,the annual direct runoff coefficients for 2017 and 2018 are predicted.The results show that the digital filtering method F1 has the best division effect on urban river runoff without upstream incoming water;the contribution rates of base flow and direct runoff to the total runoff growth are 83.96%and 16.04%respectively;the multiple linear regression model is significant at 0.01 level and has good performance,where NSE,R^(2),and Re are 0.891,0.891 and 6.72%respectively.Moreover,the relative errors of annual direct runoff coefficient forecast for 2017 and 2018 are 4.80%and 9.75%respectively.