Probabilistic long-term hydrological drought forecast using Bayesian networks and drought propagation

Abstract

Effective drought mitigation plans that can handle severe drought conditions require reliable drought forecasts. A probabilistic hydrological drought forecasting method was developed using Bayesian networks that incorporate dynamic model predictions and a drought propagation relationship. The resulting model, Bayesian networks based drought forecasting with drought propagation (BNDF_DP), was designed using current and forecast lead time drought conditions of a multi-model ensemble. Hydrological drought conditions were represented by the Palmer Hydrological Drought Index. The ranked probability score (RPS) and receiver operating characteristic (ROC) curve analysis were employed to measure forecast proficiency. The BNDF_DP model showed good performance, with an RPS 4–50% higher than a climatological model. ROC analysis indicated that the BNDF_DP offered superior forecasting skills for long-term drought, with a 2 and 3 month lead time, compared with a model that does not consider drought propagation. The overall results indicated that the BNDF_DP model was a promising tool for probabilistic drought forecasting that can provide water managers and decision-makers with the flexibility to respond to undesirable drought risks, prepare drought mitigation action plans and regulate policies based on future uncertainties.