Estimation of Runoff using Modified SCS Models

In recent decades, due to the importance of watershed management programs and the need for adequate information and correct estimation of rainfall and runoff, many conceptual models have been proposed. These models have parameters that must be estimated according to observational data. However, finding the optimal values for the parameters of simulation models has always faced uncertainty. One of the main methods for evaluating and predicting floods is the soil conservation methods provided by the Soil Conservation Service (SCS). This study aims to calibrate model parameters for three modified SCS models, i.e. 1- the Mishra and Singh model, 2- curve number model, and 3- modified curve number (MCN) model, in a basin in southern Chile using the evolutionary PSO algorithm. Comparison of the results was carried out using performance criteria such as Kling Gupta Efficiency (KGE), Nash-Sutcliff coefficient (NS), and mean square error (RMSE). Results revealed that simulated runoff is more efficient in MCN and Mishra and Singh models with KGE = 0.91 in rainfall-runoff simulations.