Spatially distinct drought patterns and influencing factors across China: a machine learning approach with a comprehensive index
Yongming Cheng, Qiang An, Liu Liu, Hao Li, Guanhua Huang
Abstract
Drought is a complex hydroclimatic phenomenon that involves various elements of land surface processes. Although extensive research has been conducted on drought in China, our understanding remains incomplete due to the reliance on single indicators and neglect of the rapid greening in China. To address these limitations, we first validated the performance of the composite indicator, the Combined Climatologic Deviation Index (CCDI), in capturing drought conditions across China and its consistency with existing drought indices. We then conducted a comprehensive assessment of drought in China and its driving factors, including vegetation greenness. The results showed that from 2002 to 2018, drought conditions intensified in the Northwestern Arid Zone and the Tibetan Plateau Zone. In contrast, the Eastern Monsoon Zone showed a slight overall alleviation, though its hotspot, the North China Plain, experienced a pronounced intensification of drought. The Eastern Monsoon Zone experienced intensified droughts in spring and summer, with alleviation in autumn and winter. Drought severity increased continuously each month in the Northwestern Arid Zone and Tibetan Plateau Zone. Soil moisture (SM) and vapor pressure deficit (VPD) were key factors in developing droughts. The impact of vegetation varied across zones. In the Northwestern Arid Zone and Tibetan Plateau Zone, increasing vegetation greenness exacerbated water deficits due to evapotranspiration (ET), with sensitivities of −0.003 and −0.597 per 0.1 NDVI, respectively. In contrast, in the Eastern Monsoon Zone, it mitigated water deficits, probably through increasing precipitation recycling ratios (with a sensitivity of 0.029). This study enhances the understanding of drought mechanisms in China, emphasizing the importance of integrating multiple hydroclimatic factors and regional vegetation feedback for improved drought monitoring and mitigation.