Optimization of Calibration Parameters for an Event Based Watershed Model Using Genetic Algorithm

In this study, an event based rainfall runoff model has been integrated with Single objective Genetic Algorithm (SGA) and Multi-objective Genetic Algorithm (MGA) for optimization of calibration parameters (i.e. saturated hydraulic conductivity (K (s) ), average capillary suction at the wetting front (S (av) ), initial water content (theta (i) ) and saturated water content (theta (s) )). The integrated model has been applied for Harsul watershed located in India, and Walnut Gulch experimental watershed located in Arizona, USA. Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (r) between observed and simulated runoff have been used to test the performance of runoff models. The SGA and MGA integrated runoff model performance is also compared with the performance of the Hydrologic Engineering Center- Hydrologic Modeling System (HEC_HMS) model. Range of NSE values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [-0.61 to 0.79], [-0.5 to 0.74], [-3.37 to 0.82] and [-5.78 to 0.53] respectively. Range of correlation coefficient values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [0.18 to 0.95], [-0.55 to 0.90], [-0.18 to 0.97] and [-0.12 to 0.86] respectively. From the results, it is evident that the integrated model is giving the best calibrated parameters as compared to manual calibration methods. Genetic Algorithm (GA) integrated runoff models can be used to simulate the flow parameters of data sparse watersheds.