Optimized parameters for SCS-CN model in runoff prediction in ridge-furrow rainwater harvesting in semiarid regions of China
Qi Wang, Xiaole Zhao, Fuchun Li, Wucheng Zhao, Ibrahim Awuku, Wen Ma, Qinglin Liu, Bing Liu, Tao Cai, Yanping Liu, Xuchun Li
Abstract
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, while the curve numbers (CN) were 73.0–81.3, 62.2–66.2, and 51.7–57.3, respectively. For slope gradients of 5° and 10°, the optimized initial abstraction ratios were 0.05 and 0.07, respectively, with potential maximum retention values of 181.4 and 127.0 mm, respectively. The CN values for these slopes were 58.3 and 66.7, respectively. Significantly, increased slope gradients resulted in higher optimized initial abstraction ratios and CN values, along with reduced potential maximum retention values. The study concluded that ridge-furrow rainwater harvesting technology, particularly tied-ridging, demonstrated lower optimized initial abstraction ratios and CN values, coupled with higher potential maximum retention values, compared to flat planting. The SCS-CN model, incorporating optimized parameters, is a robust tool for accurately predicting runoff in ridge-furrow rainwater harvesting systems in the Loess Plateau of China.