Bin Ma

Long-term ecological water conveyance has been implemented to reconnect hyper-arid inland-river corridors, but ecological effectiveness cannot be inferred from delivered water volume alone. We assessed ecohydrological changes in the lower Tarim River from 2000 to 2023 using annual water conveyance records, groundwater observations, growing season Carnegie–Ames–Stanford Approach (CASA) net primary productivity (NPP), Landsat-derived Normalized Difference Salinity Index (NDSI), climate data, and four operational NPP-NDSI states. Annual conveyance averaged 4.08 × 10⁸ m³ and peaked in 2017 at 12.09 × 10⁸ m³. Mean groundwater depth decreased from 7.49 m in 2010 to 4.25 m in 2023. Current-inclusive multi-year conveyance windows were more strongly negatively associated with groundwater depth than same-year conveyance (W3: rho = −0.743; W4: rho = −0.782); Newey–West tests remained significant at lags 1–3, and moving-block bootstrap intervals excluded zero for W3 and W4, whereas the strictly antecedent three-year metric was less robust. The high-NDSI area decreased from 1795.04 to 42.19 km², whereas the higher-productivity area increased from 91.81 to 1127.81 km². A paired Enhanced Thematic Mapper Plus/Operational Land Imager (ETM+/OLI) analysis showed high NDSI correspondence (r = 0.980) but a mean OLI-minus-ETM+ bias of −0.0122; the corresponding 2010 cross-sensor adjustment was 63.9% of the absolute raw 2010–2015 mean-NDSI shift. Cross-era NDSI class areas and sampled-state trajectories are therefore treated as sensor-sensitive descriptive quantities. Spatial support sensitivity was also important: the functionally limited low-salinity state (FLLS) was the largest 2023 state on the fine-alignment grid (53.93%), whereas the higher-productivity low/moderate-NDSI state (HPLN) became dominant after aggregation to 500 m (66.57%); so, no scale-invariant claim is made about the dominant 2023 state. A fixed-2000-land-cover CASA run produced no material change in NPP because the land-cover transitions represented in the common mask occurred among classes sharing the same NDVI endmembers and εmax. Overall, the results support asynchronous indicator change and a state-based monitoring framework, while temporal dependence, irregular groundwater sampling, residual spatial autocorrelation, cross-sensor discontinuity, and spatial support constrain causal and fine-scale inference.

Soil organic carbon (SOC) plays a crucial role in the terrestrial carbon cycle and climate regulation. Quantifying the sensitivity of SOC to climate change is essential for developing effective strategies to address climate change and optimizing agricultural production. This study compares the performance of four machine learning models in assessing SOC, ultimately selecting the optimal Extreme Gradient Boosting model for spatial predictions of surface SOC (0–30 cm) across the country. The results indicate that areas with higher organic carbon density are primarily concentrated in the Tibetan Plateau and northeastern regions. Notably, regions with high uncertainty in predictions correspond to areas of elevated organic carbon density. Average temperature, average precipitation, and the Normalized Difference Vegetation Index were identified as the most influential factors across all models. Based on the predictions from the optimal model and a bottom-up framework, various potential climate change scenarios were considered, allowing for the quantification of SOC sensitivity to climate change. Under scenarios of increased temperatures and decreased precipitation, SOC loss intensified, hindering SOC accumulation. When the average temperature rose by 1.45 °C and precipitation decreased by 14.67%, a loss of 10% in SOC was projected for most regions of China. These findings provide critical insights for the proactive formulation of climate adaptation strategies, soil health preservation, and the maintenance of ecosystem stability.

The overexploitation and misuse of natural resources in oaseshave put a significant strain on the ecosystem’s fragility. Therefore, a rigorous study of the ecological environment’s quality is required to assure the sustainability of oasis growth. The GEE platform has the features of timeliness and large data cloud processing, which accelerating the development of the remote sensing ecological index. The MODIS data of the research region from 2000 to 2020 were uploaded online to the GEE platform in order to calculate the humidity, greenness, dryness, and heat indices for each year. Principal component analysis was then used to develop the remote sensing ecological index after normalization. In addition, Pearson correlation coefficient, Moran’s I index, geo-detector, and the MK trend test were employed to determine the dependability of the RSEI comprehensive index, analyze the ecological environment status and its change trend in the Aksu River Basin from 2000 to 2020, and investigate the external driving factors of RSEI spatial heterogeneity. (1) The average correlation degree of RSEI is as high as 0.820, and the Moran’s I index is larger than 0.9118; thus, its practicability, dependability, and spatial rationality are enhanced. (2) The natural environment quality of Aksu basin is impacted in two ways by human influences. (1) The adoption of ecological protection measures to support the growth of groundwater reserves in the Aksu basin, increase plant covering, and so improve and enhance the ecological environment’s quality. Following the adoption of ecological protection measures, the average RSEI rose by 12.89%, the ecological quality of the farmland-based region improved considerably, and the quality of the ecological environment was enhanced. (2) Urban growth inhibits environmental progress. The acceleration of urbanization and the large rise in NDBSI have exerted pressure on the development of RSEI, while the growth of cities and towns has decreased the vegetation cover in urban areas and impeded the improvement of ecological environment quality. (3) Both human and environmental causes contribute to the regional variability of RSEI in Aksu Basin. The geographical heterogeneity is mostly caused by temperature and land use, with land use being the most important driver. Strengthening research on the connection between groundwater storage change, land use, vegetation cover, and NDBSI may facilitate the growth of regional green economies.