Drought forecasting using W-ARIMA model with standardized precipitation index

Abstract

Abstract Climate change and water supply shortages are paramount global concerns. Drought, a complex and often underestimated phenomenon, profoundly affects various aspects of human life. Thus, early drought forecasting is crucial for strategic planning and water resource management. This study introduces a novel hybrid model, combining wavelet transform with the Autoregressive Integrated Moving Average (ARIMA) model, known as Wavelet ARIMA (W-ARIMA), to enhance drought prediction accuracy. We meticulously analyze monthly precipitation data from January 1970 to December 2019 in Kabul, Afghanistan, focusing on multiple time scales (SPI 3, SPI 6, SPI 9, SPI 12). Comparative assessment against the conventional ARIMA approach reveals the superior performance of our W-ARIMA model. Key statistical indicators, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), underscore the improvements achieved by the W-ARIMA model, notably in SPI 12 forecasting. Additionally, we evaluate performance using metrics like R-square, NSE, PBIAS, and KGE, consistently demonstrating the W-ARIMA model's superiority. This substantial enhancement highlights the innovative model's clear superiority in drought forecasting for Kabul, Afghanistan. Our research underscores the critical significance of this hybrid model in addressing the challenges posed by drought within the broader context of climate change and water resource management.