Development of a probabilistic agricultural drought forecasting (PADF) framework under climate change

Abstract

Drought has significant impacts on human survival and social development, particularly on crop production. Agricultural drought is the most direct consequence of drought on crops. In this study, a Probabilistic Agricultural Drought Forecasting (PADF) framework was developed to employ the Ensemble Bayesian Least Square Support Vector Machine (EBLSSVM) method for bias correction in precipitation and temperature projections from multiple Regional Climate Models (RCMs). Vine Copula-Based Projection Model (VCPM) was then developed for accurate agricultural drought projections, providing deterministic results and valuable 90 % predictive intervals. The results indicate that the EBLSSVM method can generate better climate projections than the original outputs from RCMs and bias-corrected results from other bias-correction techniques. Based on the projection results from VCPM, the study found that drought will be a significant concern in Fujian province, especially in the southeast coastal region. Drought conditions are projected to be more severe in the 2050s than in the 2080s, under both RCP4.5 and RCP8.5. The average SSI values during months with a wet trend ranged from 0.1 to 0.3, whereas months with a drought trend predominantly exhibited average SSI values exceeding -0.5. Notably, SSI values as low as -2.0 were observed during wet trend months, underscoring the urgency of addressing future drought, particularly in coastal regions. However, even during wet periods, at least one extreme drought month is expected, suggesting that extreme drought conditions will become more severe in the future. CMIP5 and CMIP6 predictions showed good consistency in temporal and spatial dimensions, with CMIP6 indicating more significant and consistent future drought changes compared to CMIP5.