Abstract
Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model parameter) for the same SPEI period in the proposed models are analogous where “p” is the order of autoregressive lags, q is the order of moving average lags and d is the order of integration. Additionally, these parameters show the strong likeness for Moving Average (M.A) and Autoregressive (A.R) parameter values. From the various developed models for the Thar region, it has been concluded that the model (0,1,0)(1,0,2) is the best ARIMA model at 24 SPEI and could be considered as a generalized model. In the (0,1,0) model, the A.R term is 0, the difference/order of integration is 1 and the moving average is 0, and in the model (1,0,2) whose A.R has the 1st lag, the difference/order of integration is 0 and the moving average has 2 lags. Larger values for R2 greater than 0.9 and smaller values of Mean Error (ME), Mean Absolute Error (MAE), Mean Percentile Error (MPE), Mean Absolute Percentile Error (MAPE), and Mean Absolute Square Error (MASE) provide the acceptance of the generalized model. Consequently, this research suggests that drought forecasting can be effectively fulfilled by using ARIMA models, which can be assist policy planners of water resources to place safeguards keeping in view the future severity of the drought.
Highlights
With the increasing human consumption rate of water around the world, and especially in highly arid regions that have experienced water depletion, the demand for water resources has increased dramatically [1]
The advantages and disadvantages of drought indices are based upon the clarity and provisional flexibilities of their administration Standardized Precipitation Index (SPI) has been endorsed as a standard drought index by the world meteorological organization that is likely due to its simple calculations, as precipitation data is itself enough to direct and test without the demand of statistical barriers
SARIMA models, on the other hand, are better at detecting changes in the drought situation than AR(1) models. These findings suggest that Autoregressive Integrated Moving Average (ARIMA) models can better predict the type and extent of droughts, and that they can be used to predict droughts in the plain [34]
Summary
With the increasing human consumption rate of water around the world, and especially in highly arid regions that have experienced water depletion, the demand for water resources has increased dramatically [1]. The effect of prolonged droughts on natural ecosystems has highly deteriorated regional agriculture, water resources and the environment [3,4,5] In such complex situations, the unavailability of proper evaluation of drought may result in wrong decisions and actions by policy makers and monitors [6,7]. The SPI is capable of showing high performance in finding and computing drought potency [18,19] Despite of all this ease, the SPI still has some issues regarding water balance. The use of climate water balance by SPEI is the differentiation between the two indices [21,22] For this purpose, days, SPEI has been widely used as a relevant index to observe the drought in various regions worldwide [23]
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