Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

Under the joint effects resulted from different changes of climate and land-use regimes, spatial-temporal variations of hydrological processes took place in certain principles. Identifying the impact of changes in individual land-use types/climatic factors on hydrological processes is significant for water management and sustainability of watersheds. In this study, seven simulation scenarios were developed using the soil and water assessment tool (SWAT) model to distinguish the impacts of climate and land-use changes on the hydrological processes in the Amur River Basin (ARB) for four periods of 1980–1990, 1991–1999, 2000–2006, and 2007–2013, respectively. Based on the multi-period simulation scenario data, partial least squares regression and ridge regression analyses were performed to further evaluate the effects of changes in individual land-use types/climatic factors on hydrologic components. The results suggested that summer precipitation and summer average temperature were the dominant climatic factors, and crops and wetlands were the principal land-use types contributing to the hydrological responses. In addition, the drastic changes in crop and wetland areas and a clear decline in summer precipitation between the periods of 1991–1999 and 2000–2006 may account for the highest-intensity impacts of climate and land-use changes on the runoff at the outlet (−31.38% and 16.17%, respectively) during the four periods.

Extreme climates pose increasing threats to ecosystems, particularly in ecologically fragile regions such as the Yellow River Basin (YRB). Leaf area index (LAI) reflects vegetation response to climatic stressors, yet spatiotemporal dynamics of such responses under future climate scenarios remain poorly understood. This study examined LAI responses to extreme climatic factors across the YRB from 2025 to 2065, utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under three Shared Socioeconomic Pathways (SSP) scenarios. Partial least squares regression was performed using historical consistency-validated and future scenario LAI data alongside 26 extreme climate indices to identify extreme climate impacts on vegetation dynamics. Time-lag and cumulative effect analyses using Pearson correlation further quantified the potential impacts of extreme climate on future vegetation dynamics. Results indicate that the regionally averaged LAI in the YRB exhibits a consistent increasing trend under all three SSP scenarios, with linear rates of 0.0016–0.0020 yr⁻¹ and the highest values under SSP5-8.5, accompanied by clear scenario-dependent spatial differences in LAI distribution and vegetation response to extreme climates, particularly in the lag and cumulative effects that depend on local hydro-climatic conditions. Partial least squares regression results identified annual total wet-day precipitation, frost days, growing season length, summer days, and ice days as the dominant extreme climate indices regulating LAI variability. In the arid and semiarid Loess Plateau regions, relatively long lag and cumulative effects imply vegetation vulnerability to delayed or prolonged climatic stress, necessitating enhanced soil and water conservation practices. These findings support region-specific ecological conservation and climate mitigation strategies for the YRB and other ecologically vulnerable watersheds.