Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model

Streamflow deficit (hydrological drought) poses a large risk to water resources management, agricultural production, water supply, hydropower generation, and ecosystem services. Reliable and robust hydrological drought predictions are critical for water and food security and ecosystem health under anthropogenic warming. However, the prevalent statistical prediction methods, for example, the meta‐Gaussian (MG) model, usually do not lead to accurate drought predictions. We therefore developed a new drought prediction model utilizing the Bayesian Model Averaging coupled with Vine Copula, called Bayesian Model Averaging Ensemble Vine Copula (BMAViC) model, in which previous meteorological drought, antecedent evaporative drought, and preceding hydrological drought were selected as three predictors. The BMAViC model was applied to the Upper Yellow River basin and showed robust skills during calibration and validation periods for 1‐ to 3‐month lead hydrological drought predictions. In comparison with the MG model (reference model), the skills of the proposed model were relatively stable and superior under diverse lead times. Good performances under the 1‐ to 3‐month lead times strongly implied that the BMAViC model yielded robust and accurate hydrological drought predictions. The study results enhance our confidence in seasonal drought prediction and help us understand drought dynamics in future months. Key Points Bayesian Model Averaging Ensemble Vine Copula (BMAViC) model is developed to improve the accuracy of hydrological drought prediction Multiple model ensembles are critical for robust predictions of hydrological droughts BMAViC model yields more reliable prediction than does the meta‐Gaussian model