Optimized parameters for SCS-CN model in runoff prediction in ridge-furrow rainwater harvesting in semiarid regions of China

作者：Qi Wang, Xiaole Zhao, Fuchun Li, Wucheng Zhao, Ibrahim Awuku, Wen Ma, Qinglin Liu, Bing Liu, Tao Cai, Yanping Liu, Xuchun Li · 发表于：Agricultural Water Management · 年份：2025 · DOI：10.1016/j.agwat.2025.109363 · 被引用次数：14 · 研究领域：Hydrology and Watershed Management Studies、Soil Moisture and Remote Sensing、Soil erosion and sediment transport

Soil erosion and water loss are the major drivers of land degradation , ecosystem malfunction, and low crop production in water-scarce regions. The Loess Plateau in China, one of the most erosion-prone areas globally, has implemented ridge-furrow rainwater harvesting technology to address water loss and soil erosion. Numerous hydrological models have been applied for runoff and sediment prediction in small watersheds. However, the application of the SCS-CN model to runoff and sediment prediction in small-scale fields has remained uncertain. Predictive models for runoff and sediment yield in ridge-furrow rainwater harvesting remained limited. This study utilized regression analysis of precipitation and runoff data from 2015 to 2018 to determine initial abstraction. The statistical parameters, including root mean square deviation (RMSE) and Nash-Sutcliffe efficiency (NSE), were employed to optimize initial abstraction ratios and potential maximum retention values of the SCS-CN model based on rainfall-runoff data from 2015 to 2018. Validation of the SCS-CN model with optimized parameters was performed using rainfall-runoff data from 2019 to 2023, leveraging NSE and coefficients of determination (R²) as evaluation criteria. The optimized initial abstraction ratios for flat planting, open-ridging, and tied-ridging were 0.09–0.14, 0.06–0.07, and 0.04–0.05, respectively. Corresponding potential maximum retention values were 58.3–93.9, 129.5–154.2, and 188.9–237.7 mm, respectively, w...