Contrasting CESM and ECMWF for predictive modeling of spatial heterogeneity of drought indices across Colorado and Louisiana regions of the USA

This study focuses on the hydro-climatically contrasting regions of Colorado and Louisiana in the United States. Although drought indices can be evaluated employing linear and non-linear algorithms, most contributions in the literature have not adequately quantified geospatial-temporal volatility, leading to Type II errors. This study addresses these gaps by comparing ten drought indices across the Colorado and Louisiana regions of the United States over 75 years, using ERA5-Land total precipitation data. The spatial clustering of precipitation patterns was quantified using Global Moran’s Index. We employed the Autoregressive Integrated Moving Average (ARIMA), coupled with the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, and compared five ARIMA-GARCH variants to address non-asymptotic conditional volatility and temporal persistence in precipitation. Drought indices were examined across five temporal scales and contrasted with simulated parameters derived from the Community Earth System Model (CESM2). The temporal lag relationship between meteorological and agricultural droughts was evaluated using the non-parametric Time-Varying Distance Cross-Correlation Function (TV-DCCF). The ARIMA-eGARCH(1,1) model precisely detected the non-asymptotic conditional volatility in the precipitation time series. The Standardized Precipitation Index (SPI), China Z Index (CZI), and Z-Score Index (ZSI) were the most applicable indices for drought monitoring in both regions. TV-DCCF revealed that meteorological droughts significantly influenced agricultural droughts, with a lag of up to four months. CESM2-derived drought indices were mainly within the ERA5-Land uncertainty range, except for CZI and Agricultural Standardized Precipitation Index (aSPI), attributable to CESM2’s lower spatial resolution and limited sensitivity to localized extreme events. • Eigenfunction analysis revealed spatio-temporal clustering of precipitation. • ARIMA-eGARCH detected non-asymptotic conditional volatility in precipitation. • SPI, CZI, and ZSI were the most robust drought indices. • Meteorological droughts lead agricultural droughts by up to four months. • CESM-derived drought indices were mostly within the ERA5-Land uncertainty range.