A New Ensemble Index for Extracting Predictable Drought Features from Multiple Historical Simulations of Climate

Abstract

Drought assessment and forecasting under an ensemble of multiple climate simulation models play important role in early warning drought mitigation policies. This research provides a new ensemble drought measure – the Multivariate Multi-Scaler Forecastable Standardized Drought Index (MMFSDI). At a particular georeferenced point, the MMFSDI uses time-series data of precipitation from multiple climate simulation models for the characterization of drought. The methodology of MMFSDI is mainly based on Forecastable Component Analysis (FCA) and K-Component Gaussian Mixture Distribution (K-CGMD). In application, historical simulated data of precipitation from 23 climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6) at fifty grid points scattered over the Tibet Plateau region are considered to evaluate the applicability of MMFSDI. For comparative analysis, the forecasting performance of MMFSDI is compared with Standardized Precipitation Index (SPI) using Residual Mean Square Error (RMSE) and Mean Average Error (MAE) under Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models. Outcomes associated with this research shows that 1) the first component of FCA as an ensemble of multiple climate models is more describable than simple model averaging, 2) the strong consistency between MMFSDI and SPI makes MMFSDI as an alternative multi-model drought measure, and 3) the implications of ARIMA and ANN revealed that MMFSDI has inherited feature for forecasting drought. In summary, the finding of the research argues to substitute SPI with MMFSDI as MMFSDI has inherited forecasting ability.