Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 °C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy.

Agricultural irrigation accounts for nearly 70% of global freshwater withdrawals, making sustainable water management crucial for food security and ecological stability—particularly in arid and semi-arid regions. However, dynamic water-saving thresholds at both inter-annual and intra-annual scales remain insufficiently quantified in current research. To address this gap, this study developed an integrated SWAT-MODFLOW model for the Hetao Irrigation District and quantified dynamic water-saving thresholds by simulating crop yield responses under a range of irrigation scenarios. The model was calibrated (2008–2014) and validated (2014–2016), demonstrating reliable performance (R² = 0.75, NSE = 0.74) in capturing local hydrological processes. Inter-annual scenarios assessed water-saving levels of 5%, 10%, 20%, and 30% under wet, normal, and dry years, while intra-annual scenarios adjusted seasonal irrigation volumes in spring, summer, and autumn with reduction gradients of 33%, 50%, and 100%. Results show that wet and normal years could achieve a water-saving threshold of up to 20%, whereas dry years were limited to 5%. Intra-annually, autumn irrigation offered the greatest saving potential (33–100%), followed by spring (33–50%). Spatially, crop responses varied substantially: the western part of the region proved particularly sensitive, with even the optimal district-wide strategy reducing local crop yields by 10–20%. This study quantifies dynamic water-saving thresholds and incorporates spatial heterogeneity into scenario assessment. The resulting framework is transferable and provides a basis for sustainable water management in water-limited agricultural regions.

Reduced soil moisture (SM) together with elevated vapor pressure deficit (VPD) suppresses gross primary productivity (GPP) and thus weakens the capacity of the terrestrial carbon pool. Against the backdrop of global climate change, soil and atmospheric drought exert a more profound impact on vegetation growth, and their combined impacts remain unclear. Based on multi-source remote sensing observations and reanalysis datasets, three vegetation remote sensing indices, GPP, SIF, and NDVI (collectively referred to as Vegetation Remote Sensing Indices, VSI), are employed in this study to assess the relative impacts of soil and atmospheric drought on terrestrial vegetation. First, Copula-based conditional probabilities are applied to identify which factor (reduced SM or high VPD) plays a dominant role under conditions of declining vegetation productivity and to determine their corresponding thresholds. Furthermore, the underlying driving mechanisms are elucidated by utilizing Structural Equation Modeling (SEM) for path analysis to clarify how climatic factors indirectly affect vegetation productivity by influencing SM and VPD. The results suggest that vegetation growth in China’s different climatic zones is affected by distinct factors. Specifically, SM is the primary factor influencing vegetation productivity, dominating 71.16% of the nation’s vegetated areas. Its influence is particularly pronounced in arid and semi-arid regions. In contrast, the impact of VPD is predominantly concentrated in semi-humid plain regions. Furthermore, the critical thresholds for SM in different climate zones are identified: the threshold averages approximately 0.33 m³/m³ in humid and plateau regions and 0.13 m³/m³ in arid and semi-arid regions. The SEM analysis further reveals the complex pathways by which climatic variables influence vegetation growth. In SM-dominated regions, higher SM directly promotes vegetation growth; in VPD-dominated regions, drier air imposes a stronger suppression on vegetation growth. Nonetheless, the plateau temperate semi-arid zone demonstrates distinct hydrometeorological characteristics. Attributed to the region’s unique hydrometeorological conditions, the negative effects of higher VPD are generally outweighed by the favorable conditions for photosynthesis with which it co-occurs. These findings clarify the intricate impacts of SM and VPD on vegetation productivity, providing a foundational framework for the development of tailored ecological management strategies and drought early warning systems.

The determination of ecological water requirements (EWRs) is of critical significance for maintaining watershed sustainable development and river health. However, the estimation of instream and off-stream EWRs remains uncertain due to the complicated and competitive interaction between off-stream EWR resources (mainly vegetation water requirements in low-intensity human-use basins) and instream EWR resources (runoff), especially in arid watersheds. In this study, instream and off-stream EWRs are determined by considering the interaction between vegetation variations and hydrological processes, as well as their climate impact, using a two-way ecohydrological model in a representative semi-arid basin. The increased infiltration capacity of the substrate, resulting from continuous vegetation growth without mortality, enhances deep soil water return flow, thereby boosting baseflow to streams. Lateral flow is shown to contribute up to 39.50% of the instream runoff. While downstream grassland growth is dependent on vertical water input, upstream forests experience energy-limited transpiration despite increased water storage, regardless of lateral flow distribution. Changes in precipitation (either an increase or decrease) simultaneously affect (i.e., increase or decrease) both basin instream and off-stream EWRs. In contrast, temperature increases of up to 3 °C generally enhance instream EWRs by raising evapotranspiration (ET). However, this effect may be diminished or even reversed when plants become water-stressed under higher temperatures, resulting in a reduction of off-stream EWRs. The findings of this research provide a scientific foundation for water resource management in semi-arid basins.

The parsimony of the Budyko water balance model has prompted its extensive use for analyzing climate–vegetation–water interactions. Existence of time–space symmetry in the Budyko model, governing the hydrological predictions across both temporal (short‐term annual) and spatial (long‐term mean annual) domains, has enabled its application in ungauged basins through use of space‐for‐time substitution (SFTS). However, time–space symmetry in the relationship between the Budyko shape parameter and in vegetation changes has not been well studied. The Budyko curve, which varies spatially across regions due to differences of vegetation, also varies temporally as vegetation evolves over time. Here, we investigate this problem in a representative basin with several sub‐catchments, which includes sensitivity to vegetation dynamics and climate change using two shape parameters (n₁ and n₂) and the vegetation cover, M. Interestingly, time–space symmetry was found between M/Φ and n₁/Φ or exp(−n₂)/Φ, compared to just M versus n separately (which produces asymmetry), implying a coupled ecohydrological adjustment mechanism between vegetation functioning and hydrological structure co‐regulated in response to climatic constraints counteracting the effects of spatio‐temporal heterogeneity on water balance partitioning. Our study proposes a novel and streamlined ecohydrological model within the time–space equivalence hypothesis and recognizes that the symmetry/asymmetry is crucial for accurate SFTS application.