A Novel Statistical Framework for Assessing Future Drought Using Multiple Global Climate Model: The Weighted Multimodal Adaptive Standardized Precipitation Index
摘要
Drought is one of the major consequences of global warming. Being a complex natural hazard, its accurate assessment is challenging. Simulated data of varying climate parameters from Global Climate Models (GCMs) is a crucial source for assessing the future characteristics of climate change. The objective of this article is to improve future drought assessment based on ensemble of multiple GCMs. Consequently, this study proposes a new statistical framework to improve future drought assessment based on a multiple GCM ensemble. The proposed framework introduces a new weighting scheme for Multi-Model Ensembles (MMEs), called the Precipitation Concentration Index-Based Weighting Scheme for Multi-Model Ensembles (PCIWS-MME), and a drought index known as the Weighted Multimodal Adaptive Standardized Precipitation Index (WMASPI). The application of the proposed research is based on 22 GCMs from the Phase 6 Coupled Model Intercomparison Project (CMIP6) and covers 103 grid points in Pakistan. To assess the effectiveness of PCIWS-MME, we compared its performance with the Simple Multimodel Mean (MME) and Mutual Information (MI) using the Root Mean Square Error (RMSE) and Mean Average Error (MAE). Furthermore, we evaluated the quality of WMASPI by fitting the most appropriate models, whether univariate, mixture-based, or derived from nonparametric probability plotting position formulas. The results of probabilistic modeling indicate that mixture probability models are more appropriate than univariate alternatives. For example, on the 3-month time scale under Scenario 1, the Bayesian Information Criterion (BIC) for the best-fitting univariate distribution is