Forecasting monthly peak flows for the flood-prone River Jhelum in the Kashmir Valley, India

ABSTRACT Prediction of the peak discharges is of great significance for water resource management and flood mitigation strategies. In this study, the performance of the deseasonalised ARIMA modelling technique was tested to evaluate its suitability for streamflow prediction in flood-prone Kashmir Valley. Monthly peak flow modelling and forecast was performed for the following three key discharge stations of the River Jhelum: Sangam, Ram Munshi Bagh, and Asham. Based on the results, the models were found to perform reasonably well for simulation and forecasting of the monthly peak flows. The values of root mean square error (RMSE) were 75.19, 85.51, and 92.15 cumecs, and MAPE values were 31.94, 29.81, and 32.96% for Sangam, Ram Munshi Bagh, and Asham stations. Nash–Sutcliffe efficiency (NSE) values for these stations were 0.89, 0.85, and 0.86. The results showed that the models could recognise the patterns in the observed time series and recognise the basic relations. The models will contribute towards designing an efficient decision support tool for flood planning and management in the flood-prone valley.