Predicting compound agricultural drought and hot events using a Cascade Modeling framework combining Bayesian Model Averaging ensemble with Vine Copula (CaMBMAViC)

Abstract

Compound Agricultural Drought and Hot Events (CADHEs) cause more disastrous impacts on water-food-energy-ecology security in comparison with individual drought or hot extremes. Reliable CADHEs predictions are therefore paramount to mitigate the potential risk associated with drought and hot events and their combinations. However, there is no prediction model with multi-predictand variables for reliably predicting CADHEs by considering the compounding influence of diverse confounders. In this study, we first employed a Cascade Modeling (CaM) framework to measure the cascading effects between agricultural droughts and hot events driven by the compounding influence of different confounders. Then, a new ensemble prediction model with multi-predictand variables using a CaM framework coupling Bayesian Model Averaging ensemble with Vine Copula, namely the CaMBMAViC model, was developed to simultaneously predict agricultural droughts, hot events, and CADHEs in the warm season with 1–3-month lead times in China, in which the three most important predictors were selected as the explanatory variables. For these selected typical years (e.g., 2006, 2013, and 2019, as the droughts and hot events in these years swept over most regions of China), the proposed CaMBMAViC model has provided reliable predictions for agricultural droughts, hot events, and CADHEs with 1–3-month lead times in the warm season over most areas of China. Two performance metrics, Kling-Gupta Efficiency (KGE) and Percent Bias (PBIAS), both demonstrated that this model yielded skillful predictions for agricultural droughts (KGE > 0.9 and PBIAS = [1.79%, 1.96%]), hot events (KGE > 0.86 and PBIAS = [2.23%, 2.72%]), and CADHEs (KGE > 0.83 and PBIAS = [–0.95%, 0.36%]) for most areas in China. These findings enhance our confidence in seasonal predictions of compound climate events and help us understand their future dynamic.