Efficiency of Fuzzy Curve Number on Monthly Runoff Simulation in Beheshtabad Watershed

The runoff curve number method is widely used to predict runoff and exists in many popular software packs for modeling. The curve number is an empirical parameter important but depends largely on the characteristics of soil hydrologic groups. Therefore, efforts to reduce this effect and extract more accurate soil information are necessary. The present study was conducted to integrate fuzzy logic for extraction runoff curve numbers. A new distribution model called CNS2 has been developed. In the first part of this research, the formulation and programming of the CNS2 model were done using the Python programming language environment, then the model was implemented in the Beheshtabad watershed. This model simulates the amount of runoff production in a watershed in the monthly time step with the fuzzy curve number and takes into account the factor of rainy days, the coefficient of management of the RUSLE-3D equation, and the soils theta coefficient. The results indicated that the model with Nash-Sutcliff 0.6 and the R2 coefficient 0.63 in the calibration set and Nash index 0.53 and R2 coefficient 0.56 in the validation set had appropriate efficiency in runoff simulation. The advantage of the model is that distributive and allows for the identification of areas with higher runoff production.