Developing monthly hydrometeorological timeseries forecasts to reservoir operation in a transboundary river catchment

Forecasting of hydrometeorological timeseries data plays a vital role in the flood forecasting and predicting future water availability for various purposes such as irrigation, hydropower generation, industrial, domestic, etc. Therefore, present study aims to forecast data, i.e., river inflows, precipitation, evaporation improved reservoir operation transboundary Mangla catchment by using auto-regressive integrated moving average (ARIMA) model. Prior applying ARIMA model, stationarity was tested. Moreover, autocorrelation function (ACF) partial (PACF) were determined calculate “p” “q” terms The best fitted structure selected evaluating coefficient determination (R2), mean square error (MAE), root (RMSE) timeseries. seasonal (1,0,0)(2,1,2)12 found be inflow whereas structures (14,1,15) (9,1,19) considered precipitation timeseries, respectively. An shortage 14% detected these forecasted period 2016–2030. It also observed that effect inflows more pronounced compared However, variations abrupt than is believed results this may support operators managers developing efficient real-rime policies strategies based on availability.