Cun Zhan

Large-scale ecological restoration has substantially altered vegetation patterns in many environmentally sensitive regions, yet how the relative importance of climatic and non-climatic influences varies among seasons remains insufficiently understood. This study investigated long-term vegetation dynamics and their seasonal drivers in the Fen River Basin, China, using kernel Normalized Difference Vegetation Index (kNDVI) derived from the GIMMS-3G+ dataset for 1982–2022. Trend analysis, partial and multiple correlation analyses, residual trend analysis, and land-use change information were integrated to characterize vegetation changes and assess the relative contributions of climatic and residual-based anthropogenic influences. The results showed widespread vegetation greening across the basin. Growing-season kNDVI increased significantly at a rate of 0.0029 yr⁻¹, with significant increases occurring over 90.74% of the basin, while the mean growing-season kNDVI after 2000 was 23.1% higher than that before 2000. Precipitation showed the strongest overall relationship with vegetation, with positive correlations across 92.86% of the basin, whereas solar radiation exhibited relatively weak associations. Residual-based attribution further revealed pronounced seasonal differences in vegetation drivers. The climate-predicted component accounted for 55.9% of the attributed vegetation greening in spring, whereas the residual component accounted for larger relative shares in summer (60.3%) and autumn (61.7%). Over the entire growing season, the climate-predicted and residual components accounted for 41.6% and 58.4% of the attributed greening, respectively. Land-use transitions after 2000, particularly farmland-to-forest and farmland-to-grassland conversions, provided additional evidence consistent with a possible contribution of ecological restoration to vegetation recovery. Overall, the results reveal a clear seasonal shift from stronger climatic control in spring toward increasing residual-based anthropogenic influences in summer and autumn. This study demonstrates the value of integrating long-term kNDVI remote sensing, seasonal attribution, and land-use information for monitoring vegetation recovery and supporting adaptive ecosystem management in ecologically sensitive regions.

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments.

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr⁻¹ and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr⁻¹ to 0.0052 yr⁻¹ and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies.