递推关系概化前期产流条件改进SCS模型

降雨径流的精准模拟和预测是开展水资源管理和水土环境质量评价的重要依据之一,但现有SCS模型不能有效表征前期降雨蓄存和消耗对产流的影响,进而限制了其径流预测精度。该文基于潜在初损和有效降雨影响系数形成日有效影响雨量的递推关系,将前期产流条件概化成前期日降雨量对降雨初损的影响函数,从而构建了改进SCS模型。其中潜在初损量明确了产流前流域的最大降雨蓄存潜力和日降雨量的有效影响阈值,而前期有效降雨影响系数则表示了在蒸发蒸腾或渗漏过程作用下前期有效日降雨量的动态消耗。在小区、田间、流域3种排水面积下的模型应用结果表明,改进SCS模型能更准确地预报产流的变化,验证期的确定系数R2和纳什系数NSE比SCS原模型分别提高了27.0%~30.9%和1.0%~78.3%。前期有效降雨影响系数的稳定性较好,两模型的曲线数的拟合值比较一致。该改进SCS模型为更准确预测蒸发蒸腾或渗漏较为剧烈地区的径流提供参考。

Conceptualizing antecedent runoff condition using recurrence relation to modify SCS model

Abstract: The accurate simulation or prediction of precipitation runoff has been considered as one of the most important bases for resource management and environmental quality assessment of water and soil. The soil conservation service-curve number (SCS) model, one of the most popular runoff prediction models, cannot effectively determine the effect of the antecedent runoff condition (ARC) on runoff amount, which limits the accuracy of the model's runoff prediction. Assuming that antecedent daily precipitation depleted by evapotranspiration and seepage was linear with watershed water storage amount, the new ARC was established based on the recurrence relation of daily rainfall amount and watershed maximum rainfall storage amount. The SCS model was improved by correlating the initial abstraction with the new parameters of the potential initial abstraction and effective rainfall influence coefficient. The potential initial abstraction determines the maximum watershed rainfall storage amount prior to runoff and the threshold of daily effective rainfall amount, and the effective rainfall influence coefficient describes the dynamic depletion of antecedent daily effective rainfall amount induced by evapotranspiration and seepage. To reduce the number of unknown parameters, the relationship between the potential initial abstraction and the curve number was established under the condition that there was no rainfall for a long time prior to runoff. The data of precipitation and runoff amount from 1997 to 2008 required to assess the original and improved SCS models were collected from the 3 drainage areas of 1600 m2, 0.06 km2 and 1.36 km2 in the northern part of the Huaihe River basin, China. As antecedent daily precipitation period was 5 d and initial abstraction coefficient equaled to 0.2, the least-squares estimation method was used to calibrate the model parameters, i.e. the effective rainfall influence coefficient and the curve number, and the percent bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) were utilized to compare and assess the performance of the original and improved SCS models. The improved SCS model predicted daily runoff amount more accurately than the original model, and the improved SCS model increased the R2 and NSE by 27.0%-30.9% and 1.0%-78.3%, respectively, compared with the original during the validation period. Both models were calibrated with the close curve number, the effective rainfall influence coefficient was relatively stable, and the coefficient variation of 25% at plot scale resulted in the runoff prediction variation of less than 5%. The improved SCS model would perform better if it is applied in the areas with high evapotranspiration and seepage.