A novel early-warning standardized indicator for drought preparedness and management under multiple climate model projections - Qamar, Sadia; Kartal, Veysi; Emiroglu, Muhammet Emin; Ali, Zulfiqar; Sammen, Saad Sh.; Scholz, Miklas (Wiley, 2025)
Increasing global temperatures have triggered several environmental and ecological challenges. Recurring droughts across the globe are an adverse consequence of global warming. In this research, a new drought forecasting index-the Multimodal Forecastable Standardized Precipitation Evapotranspiration Index (MFSPEI)-has been suggested using projections from multiple climate models. The MFSPEI methodology is primarily based on the first component of the Forecastable Component Analysis (FCA) and the Standardized Precipitation Evapotranspiration Index (SPEI). For application purposes, the time series data of SPEI from 10 climatic models endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP-6) at 50 random locations over the region of the Tibetan Plateau (TP) have been considered. The outcomes show that the first component of FCA captures a sufficient amount of variation while maintaining high forecastability in all the selected grid points and the chosen prominent timescales of drought monitoring indices. To assess the predictive performance of the proposed index (MFSPEI), comparison matrices of artificial neural network (ANN) models were identified. During the training and testing phases, the forecast efficiency of the developed indicator (MFSPEI) proved superior to that of the individual SPEI. The numerical assessment indicates that the deviations and difficulties in interpreting SPEI data from individual climate models can be addressed more effectively with the proposed indicator. Therefore, MFSPEI effectively reinforces drought predictions for drought preparedness and management in the context of multiple climate model projections.
Keywords: artificial neural network; climate models; climate warming; drought forecasting