Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid Area

Drought is one of the most severe climate-induced phenomena; with significant impacts on agriculture, water resources, and ecosystems. Drought monitoring under climate change scenarios becomes crucial, particularly in regions vulnerable to water scarcity, such as semi-arid areas in Iran. Although Global Climate Models (GCMs) contain coarse spatial resolutions, they provide valuable insights in better assessing the variability of drought characteristics—such as duration, severity, and intensity in the future. To achieve this aim, downscaling of climate variables as triggers of droughts is required to monitor drought in local scale. Latyan region in Iran, as an important area to supply water, is a critical place based on its climate, drought event occurrences, and water demand and supply stress. This study tried to accurately downscale and bias-correct the climate variables utilizing the latest CMIP6 models (ACCESS-CM2, BCC-ESM1, CanESM5, HadGEM3-GC31-LL, and MIROC6) and AI techniques in the case study. This research employs a predictor selection technique in conjunction with a stack generalization model to improve the accuracy of the downscaling process. After careful examination of predictors, surface temperature, precipitation, and surface air pressure have been used along with annual cycles for training four machine learning models including Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest and Stack Generalization (SG) models for the sake of downscaling. Results showed that MIROC6 model is the best model according to all downscaling methods. In addition, among MLs, stacked generalization model improved the statistical metrics considerably with a Nash-Sutcliffe Efficiency (NSE) of 0.64, Mean Squared Error (MSE) of 1051.3, and Kling-Gupta Efficiency (KGE) of 0.68 for MIROC6 model. Selection of the proper GCM and downscaling method can help decision-makers take proper measures against drought to reduce drought impacts.