不同土壤湿度和雨强下径流曲线模型的改进

Improvement of Soil Conservation Service Curve Number model under different antecedent moisture conditions and rainfall intensities

前期土壤湿度条件和降雨强度是影响径流曲线(SCS-CN)模型径流量预测精度的重要因素。分析不同流域下二者对SCS-CN模型性能的影响,对提高模型预测精度至关重要。基于5个半干旱半湿润和湿润流域的降雨径流资料,利用偏相关分析和K-均值聚类改进SCS-CN模型。结果表明:在重新划分前期土壤湿度条件区间后,模型预测能力大幅度提升,有效降低模型平均偏差,纳什效率系数平均提高42.8%。基于最大10 min雨强对SCS-CN模型改进后,纳什效率系数得到一定提高,且半干旱半湿润流域的提升幅度略大于湿润流域。改进的模型在研究流域都取得较好的效果,平均偏差均低于7 mm;除呈村流域非汛期外纳什效率系数均达到0.93,平均提升89%;均方根误差平均降低29.2 mm。

Antecedent moisture condition and rainfall intensity are key factors that affect the runoff prediction accuracy of the Soil Conservation Service Curve Number(SCS-CN) model. Analyzing how the model behaviors depend on the two factors for different basins plays a crucial role in model improvement. This study uses partial correlation analysis and K-means cluster analysis to improve the SCS-CN model, based on the data of rainfall and runoff from five basins in semi-arid, semi-humid and humid regions. The results show that regrouping the antecedent moisture condition intervals for these basins improves its prediction ability greatly, and reduces its average deviation significantly, increasing the Nash-Sutcliffe efficiency by 42.8% on average. This model can also be improved by introducing the maximum 10 min rainfall intensity, which increases the efficiency to a certain extent and shows an improvement for the semi-arid and semi-humid basin better than that for the humid basin. The revised models have achieved good results for the study basin with an average deviation of less than 7 mm. Except for the Chengcun basin in the non-flood period, the Nash-Sutcliffe efficiency is over 0.93 or an average increase by 89%, and the Root Mean Square error is reduced by 29.2 mm.