Bo Liu

Under the background of global change, the threshold for the propagation of meteorological drought to hydrological drought is crucial for drought early warning and water resource management. However, traditional threshold studies often adopt subjective and fixed conditional probabilities and lack the revelation of the driving mechanisms under macroscopic natural geographical differentiation. This study integrates terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) mission, the standardized precipitation evapotranspiration index (SPEI), and multi-source environmental data to construct an objective threshold identification method based on Copula joint distribution and “system resilience loss”, and combines explainable machine learning to systematically explore the critical threshold for meteorological drought, triggering a TWSA deficit and its driving mechanisms from the perspectives of three major natural regions, the Eastern Monsoon Region (EMR), the Northwestern Arid Region (NAR), and the Tibetan Plateau Region (TPR). The results show that: (1) from 2005 to 2024, the TWSA significantly decreased in nearly half of China’s regions, with significant regional differentiation; (2) the response of the TWSA to meteorological drought has a significant lag (an average of 9–12 months), and shows a spatial pattern of slower in the east and faster in the northwest; (3) the probability of a TWSA deficit and the triggering threshold both have obvious grade dependence and spatial heterogeneity, with the lowest threshold in the northwest arid region, which is the most sensitive; (4) the threshold is driven by the synergy of multiple factors, with “water dominance and energy modulation”, and the dominant factors show regional differentiation; and (5) irrigation agriculture significantly reduces the drought triggering threshold and exacerbates system vulnerability. This study provides a scientific basis for understanding the geographical differentiation laws of drought propagation and regional early warning management.

Climate warming is significantly affecting the composition and function of forest ecosystems. However, the forest responses to climate change in sub-humid and temperate areas are understudied compared with cold and semi-arid areas. Here, we investigate the radial-growth responses of two subalpine conifer species along an elevational gradient located in the Qinling Mountains, a sub-humid and temperate area situated in central China. Three sites dominated by larch (Larix chinensis Beissn.) and two other sites dominated by fir (Abies fargesii Franch.) located at different elevations were sampled. L. chinensis at a higher elevation showed more common and stronger climatic signals than A. fargesii at a lower elevation. The radial growth of L. chinensis was limited by low pre-growing season temperatures and showed an increasing growth trend in the last few years. On the other hand, A. fargesii growth was limited by summer water shortage and it was characterized by a declining trend in the most recent decade. Consequently, L. chinensis would benefit from climate warming, whereas A. fargesii could be regarded as a vulnerable tree species to warming-induced drought stress.

Little is known about the relationships between treeline elevation and climate at regional and local scales. It is compelling to fill this research gap with data from the Tibetan Plateau where some of the highest alpine treelines in the world are found. This research question partially results from the lack of in situ temperature data at treeline sites. Herein, treeline variables (e.g., elevation, topography, tree species) and temperature data were collected from published investigations performed during this decade on the Tibetan Plateau. Temperature conditions near treeline sites were estimated using global databases and these estimates were corrected by using in situ air temperature measurements. Correlation analyses and generalized linear models were used to evaluate the effects of different variables on treeline elevation including thermal (growing-season air temperatures) and non-thermal (latitude, longitude, elevation, tree species, precipitation, radiation) factors. The commonality analysis model was applied to explore how several variables (July mean temperature, elevation of mountain peak, latitude) were related to treeline elevation. July mean temperature was the most significant predictor of treeline elevation, explaining 55% of the variance in treeline elevation across the Tibetan Plateau, whereas latitude, tree species, and mountain elevation (mass-elevation effect) explained 30% of the variance in treeline elevation. After considering the multicollinearity among predictors, July mean temperature (largely due to the influence of minimum temperature) still showed the strongest association with treeline elevation. We conclude that the coupling of treeline elevation and July temperature at a regional scale is modulated by non-thermal factors probably acting at local scales. Our results contribute towards explaining the decoupling between climate warming and treeline dynamics.