The impact of rainfall value on the accuracy of the SCS-CN model: selection of model parameters

ABSTRACT The soil conservation service curve number (SCS-CN) model is a widely utilized tool for estimating runoff and relies on two empirical parameters: the CN and the ratio of initial abstraction to maximum potential retention (λ). The determination of the parameters is via the empirical method or calculations based on actual data. However, few studies address the effect of rainfall on parameter selection, and collecting runoff data for model analysis is challenging. This study, taking the Nemor River Basin as the research region, investigates how the combination of CN and λ impacts the model in different rainfall conditions. Using runoff plots and reanalysis product data, the study reveals that: (1) the calculated methods outperformed the empirical method, increasing the Nash Sutcliffe efficiency coefficient from 0.34 to 0.65. (2) A higher λ value (0.2 compared to 0.02) reduces runoff and smoothes the runoff curve, which becomes less obvious with increasing CN. (3) The CN values exhibit a non-monotonic relationship with rainfall, initially decreasing before rising, highlighting the need to adjust the CN based on rainfall. Moreover, the SCS-CN model's performance with reanalysis data approximates that with actual data, confirming the viability of reanalysis datasets in this region.