Abstract
Forecasting of hydrometeorological timeseries data plays a vital role in the flood forecasting and predicting the future water availability for various purposes such as irrigation, hydropower generation, industrial, domestic, etc. Therefore, the present study aims to forecast the hydrometeorological timeseries data, i.e., river inflows, precipitation, and evaporation for the improved reservoir operation of a transboundary Mangla catchment by using the auto-regressive integrated moving average (ARIMA) model. Prior to applying the ARIMA model, stationarity of hydrometeorological timeseries data was tested. Moreover, autocorrelation function (ACF) and partial autocorrelation function (PACF) of timeseries were determined to calculate the “p” and “q” terms of the ARIMA model. The best fitted structure of ARIMA was selected by evaluating the coefficient of determination (R2), mean square error (MAE), and root mean square error (RMSE) to forecast the hydrometeorological timeseries. The seasonal ARIMA structure of (1,0,0)(2,1,2)12 was found to be best fitted for the inflow timeseries whereas ARIMA structures of (14,1,15) and (9,1,19) were considered for forecasting the precipitation and evaporation timeseries, respectively. An average water shortage of 14% was detected by using these forecasted hydrometeorological timeseries in the reservoir operation for the period of 2016–2030. It was also observed that the seasonal effect for the reservoir inflows was more pronounced compared to the evaporation and precipitation timeseries. However, variations in the precipitation timeseries were found more abrupt than the inflows timeseries. It is believed that the results of this study may support reservoir operators and managers for developing the efficient real-rime reservoir operation policies and strategies based on the variations in the future water availability.
Highlights
In ancient times, human civilization built the towns and cities at places where water was available to them for their survival
Mangla inflow time series is nonstationary time series. It means that its mean, variances, and covariance of time series are not constant
After log transformation the inflow time series are converted into stationary
Summary
Human civilization built the towns and cities at places where water was available to them for their survival. Due to rapid increase in population, water requirements go on increasing. To resolve this issue, artificial water conservation structures, i.e., dams and reservoirs were built to fulfil the needs of human activities. Artificial water conservation structures, i.e., dams and reservoirs were built to fulfil the needs of human activities These reservoirs can be used for different purposes such as irrigation, recreation, ground water recharging and hydropower, etc. The economy of many countries in the world has based on these reservoirs especially in arid and semi-arid regions. In arid and semi-arid regions, water availability is decreasing continuously to meet the ever-increasing agricultural demands
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