Drought Driving Factors as Revealed by Geographic Detector Model and Random Forest in Yunnan, China

作者：Haiqin Qin, Douglas Schaefer, Ting Shen, Junchuan Wang, Zhaorui Liu, Huafang Chen, Ping Hu, Yingmo Zhu, Jinxin Cheng, Jianping Wu, Jianchu Xu · 发表于：Forests · 年份：2025 · DOI：10.3390/f16030505 · 被引用次数：7 · 研究领域：Hydrology and Drought Analysis、Climate variability and models、Hydrology and Watershed Management Studies

Yunnan Province, as a critical ecological security barrier in China, has long been highly susceptible to drought events. Characterizing the spatiotemporal distributions of drought and identifying its driving factors is crucial. Due to the complexity of drought occurrence, linear correlation analysis alone is insufficient to quantify drought drivers and their interactions. This study used the Standardized Precipitation Evapotranspiration Index (SPEI) as a drought indicator to analyze the spatiotemporal trends of drought across Yunnan and its six major river basins. The geographic detector model (GDM) and random forest (RF) were utilized to quantify the impacts of meteorological, topographical, soil, and human activities on drought, as well as the interactions among these factors. The results showed that 63.61% of the study area exhibits a significant drying trend (p-value < 0.05), with the Jinsha River Basin (JSRB) experiencing the highest frequency of extreme drought events. Precipitation (PRE), temperature, potential evapotranspiration (PET), vapor pressure deficit (VPD), and relative humidity (RH) were identified as the primary controlling factors of drought, with factor interactions displaying nonlinear enhancement effects. PRE plays a dominant role in driving drought across Yunnan, whereas elevation primarily influenced drought severity in the JSRB, Lancang River Basin (LCRB), and Nujiang River Basin (NJRB). The RF-based SPEI prediction model demonstrated superior perf...