LAUSR

Watershed is the basic unit for studying different hydrologic processes. Flow forecasting in a watershed is dependent upon the rainfall. The effect of erroneous rainfall prediction is a source of uncertainty in flow forecasting. In this study, a model is proposed to improve the flow forecasting on real-time basis. The proposed model has three components (1) Adaptive Grey Rainfall Forecasting Model, (2) Rainfall-Runoff Model and (3) Fuzzy Updating Model. The proposed forecasting model is tested for lead periods of 1 to 3 h with hourly rainfall and discharge data. In this study, four different cases using combination of three models are discussed and the results are compared. The study has been carried out on three Indian watersheds namely Banha, Harsul and Khadakohol. The performance of the model is measured using Nash Sutcliffe Efficiency (E), Correlation Coefficient (r), Error of Peak Discharge (EQpeak) and Error of Time to Peak (ETpeak). It is observed that the case with integration of all three models performed good with a forecasting efficiency of E = 0.950, 0.861, 0.564; and r = 0.991, 0.972, 0.897 for lead-1, 2, 3 respectively for Banha watershed. For Harsul watershed, E = 0.898, 0.704, 0.367; and r = 0.985, 0.949, 0.834 for lead-1, 2, 3 respectively. For Khadakohol watershed, E = 0.968, 0.932, 0.787; and r = 0.994, 0.987, 0.951 for lead-1, 2, 3 respectively. EQpeak is less than 10% for lead-1 for most of the events and increased slightly for lead-2 and lead-3. ETpeak is 0 h for all lead periods of the three watersheds. The proposed model is useful for farmers in planning and monitoring of water resources for crop management and helps in taking necessary actions during heavy rains and floods.