Agricultural Drought Early Warning in Hunan Province Based on VPD Spatiotemporal Characteristics and BEAST Detection

In the context of global warming, agricultural drought risks are exacerbated by increasing atmospheric aridity. This study pioneers the application of the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm at a provincial scale to detect change points in vapor pressure deficit (VPD), leveraging high-density meteorological station data from Hunan Province to delineate the nuanced evolution of VPD and its implications for early drought warning. Key findings reveal the following: (1) The VPD in Hunan exhibits a spatial pattern of “higher in the south than north, higher in the east than west” and a seasonal variation of “summer > autumn > spring > winter”. (2) BEAST identified abrupt changes in VPD coinciding with critical phenological periods, such as the early rice transplanting period in early April, with spatial and temporal gradient differences (up to 25 days) that can guide irrigation resource scheduling; moreover, the months of change points have been consistently advancing during the study period. (3) The dominant factors of VPD exhibit regional and seasonal differentiation. Annually, the maximum temperature (contribution rate 57.1–60.6%) is the primary factor. (4) Extreme events with VPD > 1.5 kPa for three consecutive days covered 92 stations in 2022. Combining this with the critical growth periods of double-cropping rice, it is recommended to set VPD = 1 kPa as the drought early warning threshold for the northern and southern regions. This study provides a scientific basis for the prevention and control of agricultural drought by integrating climate diagnostics and crop physiological needs.