基于前期雨量和降雨历时的SCS-CN模型改进

径流曲线法（Soil Conservation Service Curve Number，SCS-CN）对前期产流条件（Antecedent Moisture Condition，AMC）的概化，导致径流预测出现相应的突然跳跃，同时还忽略了降雨历时作为重要组成部分对地表径流的影响，影响了模型径流预测的精度。密云水库是北京市地表饮用水的重要来源，对其上游流域进行降雨径流预测有着重要的生态意义和经济意义。该研究将SCS-CN模型与前期雨量和降雨历时结合，采用API（Antecedent-Precipitation Index，前期降雨指数）模拟土壤前期水分条件，并且提出了一种考虑次降雨事件中土壤前期雨量和土壤入渗量的静态渗透方程，对SCS-CN模型进行了改进。其中，潜在最大蓄水等于前期土壤水分和土壤潜在蓄水量的和，最大静态渗透速度是流域土壤水分达到蓄满时的静态渗透速度，静态渗透系数是与土壤结构、土地利用等相关的无量纲。利用2006-2010年及2014-2020年石匣流域径流小区的200次降雨径流事件监测结果，对新改进的模型与原SCS-CN模型以及两种改进的SCS-CN模型进行了校准、验证和性能比较。结果表明，4种径流模型中，该研究改进的模型表现最好，纳什效率系数为0.77，决定系数为0.79，均方根误差为3.21 mm，相比于SCS-CN模型纳什效率系数、决定系数和均方根误差分别提高了319%、97.5%和107.5%。参数敏感性分析表明，潜在最大蓄水和静态渗透系数是最敏感的两个参数，最大静态渗透速度的参数敏感性一般，初损率的参数敏感性最差。该研究改进模型在密云水库上游潮白河流域降雨径流模拟中具有一定的适用性，可为其他地区产流计算提供参考依据。

Improvement of SCS-CN model based on antecedent precipitation and rainfall duration

Abstract: An accurate prediction of the runoff has been one of the important steps in the water supply in recent years. However, there is a sudden jump in the runoff prediction under the general configuration of Antecedent Moisture Condition (AMC) by Soil Conservation Service Curve Number (SCS-CN). At the same time, the rainfall duration cannot be considered as an important component of the surface runoff. Therefore, it is necessary to modify the prediction model of rainfall runoff for ecological and economic significance. Taking the Miyun reservoir in Beijing of China as the research object, this study aims to propose an improved SCS-CN model using antecedent precipitation and rainfall duration. A partial correlation analysis was first made between the rainfall factors (rainfall, rainfall duration, and average rainfall intensity) and slope runoff. The rainfall and rainfall duration were then selected as the important factors affecting the slope runoff in the study area. Subsequently, an updated SCS-CN model was established to combine with the early rainfall and rainfall duration. The Antecedent Prediction Index (API) was also used to simulate the soil''s early water conditions. A static infiltration equation was considered the soil early rainfall and infiltration in a rainfall event. Among them, the potential maximum water storage was equal to the sum of the previous soil moisture and the potential soil water storage. The maximum static infiltration rate was the static infiltration rate when the watershed soil moisture reached the full storage, and the static infiltration coefficient was dimensionless related to the soil structure and land use. The monitoring data was collected from the 200 rainfall runoff events in the runoff community of the Shixia basin from 2006 to 2010 and 2014 to 2020. The newly improved model was finally verified to compare with the original and two improved SCS-CN models. The results showed that the improved model performed best among the four runoff models, where the efficiency coefficient was 0.77, the determination coefficient was 0.79, and the root mean square error was 3.21 mm. The Nash efficiency coefficient, the determination coefficient, and root mean square error increased by 319%, 97.5%, and 107.5% respectively, compared with the original SCS-CN model. Furthermore, the improved model was much better than the rest, where the four SCS runoff models had underestimated the runoff. It was found that the improved SCS-CN model was positively correlated with the yield. Nevertheless, the improved model was not suitable for the rainfall runoff events with the normal soil moisture, grassland land use type, and rainfall type I (short rainfall duration, small rainfall, high rainfall intensity, and low frequency). The parameter sensitivity analysis showed that the most sensitive parameters were the potential maximum water storage and static infiltration coefficient. Specifically, there was a general parameter sensitivity of the maximum static infiltration velocity, whereas, the initial loss rate was the worst. Consequently, the improved model presented strong applicability for the rainfall runoff of Chaobai River Basin in the upper reaches of Miyun reservoir. This finding can provide a strong reference basis for the calculation of runoff yield.