Optimal selection of cost-effective biological runoff management scenarios at watershed scale using SWAT-GA tool

Abstract

Study regionStudy region: Hablehrood River (HR) watershed in the northern part of the Iranian Central Plateau. Study focusFinding the most economical management scenarios for implementing biological best management practices (BMPs) in the HR watershed is one way to solve the runoff issue in the study area. As, the best types, quantities, and locations of arid and semi-arid rangeland biological BMPs have not yet been identified or determined using the soil and water assessment tool (SWAT) or genetic algorithm (GA) to maximize runoff reductions at the lowest possible cost in arid and semi-arid rangelands, therefore, to estimate runoff and best identify biological management scenarios at the watershed scale in the current study, the SWAT and a GA model have been used. After determining the hydrological response units (HRUs), eight biological watershed management scenarios were identified through a combination of biological management activities. To determine the best management practice with the highest runoff reduction and the lowest possible cost, optimization was performed by GA based optimization model. New hydrological insights for the regionThe simulation results of this study show that all the developed biological BMPs had a significant effect on the surface runoff rate reduction and reduced the surface runoff from 4.4% to 8.2%. The fifth scenario including seeding using machine (SUM) and seeding without machine (SWM) biological activities as optimal and the best scenario with weighted coefficients of 0.95 and 0.05 for runoff reduction and cost, respectively, was determined to be the optimum practice. The results further indicate that runoff would be decreased by 76.4 million cubic meters compared base scenario runoff volume, at a cost of 469.8 billion IR Rials. The lowest volume of surface runoff, however, was related to scenario number eight, while it had the highest cost (1114.3 billion IR Rials) among all scenarios. Using optimization methods, decision-makers and managers may pinpoint the best biological BMP scenarios in terms of kinds, locations, and amounts that might reduce runoff rates as much as possible at the lowest possible cost.