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Drought poses a severe threat to terrestrial ecosystems' carbon sequestration function, which in turn affects regional sustainable development. Understanding its impact on carbon source/sink dynamics is crucial for addressing extreme climate change challenges. This study, based on the Carnegie-Ames-Stanford approach (CASA) model and soil heterotrophic respiration equation, the Net Ecosystem Productivity (NEP) of the middle reaches of the Yellow River Basin (MRYRB) from 2001 to 2019 were estimated and utilized to characterize the vegetation carbon source/sink. Additionally, four factors, namely, average surface temperature (AST), root-zone soil moisture (RM), drought frequency (DF), and drought intensity (DI), were selected and a multiscale geographically weighted regression (MGWR) model was applied to assess the dominant factors influencing vegetation carbon source/sink dynamics at the spatial scale. The results indicated that: (1) MRYRB vegetation acted as a carbon sink, showing an increasing trend. Carbon sink was primarily located in Shaanxi's central region, while carbon source was primarily located in Inner Mongolia and Shaanxi's northern part. (2) Both the extent and severity of drought in the MRYRB showed a mitigating trend, presenting a spatial distribution of high-intensity low-frequency and low-intensity high-frequency. (3) AST, RM, DF, and DI had larger effects on carbon source than carbon sink. AST dominated mainly in northern Shanxi and Shaanxi, RM in central Shaanxi and Gansu, while DI and DF were concentrated in central Shaanxi and northern Shanxi. [ABSTRACT FROM AUTHOR]

Against the background of climate change, the global carbon and water cycle has undergone significant changes, and it is of great significance to explore the interrelationships of the carbon and water cycles in different regions to cope with future climate change. In this study, based on gross primary productivity (GPP) and precipitation (PRE) data, the precipitation use efficiency (PUE) of the Loess Plateau (LP) was calculated, and the Sen trend analysis and Mann‐Kendall test were used to analyse the temporal and spatial variation characteristics of PUE, as well as the first‐order difference method was used to derive the relative contributions to quantify the impact of vegetation growth and meteorological factors on PUE. The results show that (1) from 2001 to 2018, the spatial distribution pattern of vegetation PUE exhibited an increasing trend from northwest to southeast. On the time scale, the multi‐year average value of PUE was 1.17 gC m−2 mm−1, showing an overall upward trend. Among the different land cover types, paddy fields had the highest PUE level, and sparse grassland had the lowest; (2) LAI had the highest relative contribution to vegetation PUE, followed by temperature (Temp), net radiation of the ground surface (RN), specific humidity (Shum), and wind speed (WS). LAI contributes positively to 88.9% of the area. RN positively influences high‐altitude areas, while Shum had a larger area with a negative contribution. The contribution of Temp increases from northwest to southeast, and WS has balanced positive and negative contributions; (3) LAI is the dominant factor for the spatial and temporal variation of PUE in the LP, and the size of the areas where different meteorological factors dominate the changes of PUE are as follows: WS > Shum>RN > Temp. At high altitudes, the dominant meteorological driver is WS, while at low altitudes, the dominant climate driver is Shum. This study is of guiding significance for the ecological restoration and management of the LP, and it can also provide a scientific basis for the improvement of ecosystems and the sustainable management of water resources in the context of global climate change. [ABSTRACT FROM AUTHOR]