Zhenliang Yin

Accurate runoff simulation in glacier-fed alpine basins is essential for sustainable water resource management and water security in downstream arid regions, particularly under climate warming and accelerating glacier retreat. To address the limited representation of glacier processes in the Soil and Water Assessment Tool (SWAT) and the challenges of runoff modelling in data-scarce alpine basins, this study compared monthly runoff simulations from SWAT, SWAT_Glacier, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) in the Yurungkash and Karakash River Basins, two glacier-fed tributaries of the upper Hotan River Basin, Xinjiang, China. Pearson correlation analysis was used to screen runoff-related predictors, and SHapley Additive exPlanations (SHAP) was applied to quantify model-specific feature attribution in runoff simulations. SWAT_Glacier improved validation-period Nash–Sutcliffe efficiency (NSE) from 0.50–0.57 to 0.80–0.81 and modified Kling–Gupta efficiency (KGE) from 0.57–0.66 to 0.76–0.82 relative to SWAT, while reducing root mean square error (RMSE) by 32.4–37.0%. LightGBM and XGBoost achieved comparable testing performance, with NSE and R² of 0.81–0.82 and KGE of 0.80–0.86. Across the two basins, the relative performance of SWAT_Glacier and the machine-learning models varied by evaluation metric and basin characteristics, rather than showing a consistent model ranking. Temperature-related variables showed the strongest statistical associations and model attributions in both basins. These findings demonstrate that model selection should consider glacier cover, process representation, and data availability to improve runoff simulation reliability and support sustainable water resources management in glacier-fed basins.

Vegetation drought and the cascading effects of its drivers are critical to ecological security and water supply in river water conservation areas. Using the Temperature–Vegetation–Precipitation Drought Index (TVPDI), this study examined vegetation drought in the Yellow River Water Conservation Area (YRWC) during 2001–2022 and investigated its nonlinear causality, cascading pathways, and spatial heterogeneity. Vegetation drought showed a slight alleviating trend (0.003/10a), with clear monthly periodicity and a stepwise spatial pattern. Precipitation (PRE) and the Normalized Difference Vegetation Index (NDVI) increased significantly, whereas Land Surface Temperature (LST) decreased slightly. PRE was the dominant internal factor affecting TVPDI, with a total effect coefficient of 0.92. Among external drivers, Relative Humidity (RH), Temperature (TEM), Available Water Content (AWC), Actual Evapotranspiration (ET), and Elevation showed strong causal relationships with TVPDI. Structural equation modeling indicated positive total effects of RH and ET on TVPDI (0.20 and 0.14, respectively), but a negative effect of TEM (−0.11). Multiscale geographically weighted regression further revealed pronounced spatial heterogeneity in driver effects. These findings support vegetation drought monitoring, ecological restoration planning, and ecological water management in the YRWC.

Water resource carrying capacity (WRCC) assessment provides an important basis for regional socioeconomic development planning and water resource management. In this study, an 18-indicator WRCC evaluation system was constructed for the Eighth Division of the Xinjiang Production and Construction Corps based on the Supply–Water-use Efficiency–Demand Pressure–Economy and Society–Regulation (SWDER) framework. Indicator weights were determined using a combined method integrating the Criteria Importance Through Intercriteria Correlation method and the entropy weight method. The Technique for Order Preference by Similarity to an Ideal Solution was then applied to evaluate WRCC across six irrigation districts during 2015–2025 and under different scenarios in 2030. An obstacle factor diagnosis model was further used to identify the main limiting factors and support differentiated management recommendations. The results showed that: (1) the regulation and ecological constraints subsystem was the dominant criterion layer affecting WRCC in the Eighth Division, and industrial value added, ecological water use ratio, and supply–demand ratio were the key high-weight indicators; (2) WRCC in the Eighth Division showed a slow upward trend from 2015 to 2025, with the average evaluation value of the six irrigation districts increasing from 0.2325 to 0.2543, although phased declines occurred in 2020 and 2022. Marked differences were observed among irrigation districts, with the Ningjiahe and Shihezi irrigation districts generally showing higher carrying capacity levels than the other districts; and (3) under both the normal scenario and the rigid water resource constraint scenario in 2030, the average WRCC of the study area increased compared with 2025, but inter-district differentiation remained significant. Except for the Mosuowan Irrigation District, the WRCC evaluation values of the other irrigation districts were lower under the rigid constraint scenario than under the normal scenario. These results provide a scientific basis for rigid water resource constraint management, optimized water resource allocation, and differentiated regulation of irrigation districts in arid regions.

Studying the impact of extreme climate events on vegetation dynamics is crucial for maintaining ecosystem stability. Based on ERA5-Land temperature and precipitation data, as well as MOD13C2 data, this study employs Pearson correlation analysis, wavelet analysis, and the XGBoost-SHAP model to analyze the response of the Normalized Difference Vegetation Index (NDVI) in China to extreme climate variations. The findings reveal the following. (1) NDVI increased steadily at a rate of 0.021/10 yr from 2001 to 2024. Extreme temperature and precipitation indices show an increasing trend in most regions. (2) NDVI was positively correlated with most extreme temperature and precipitation indices, and showed a significant negative correlation exclusively with Frost Days FDO (Frost Days) and CDD (Consecutive Dry Days). (3) R25 (Number of Heavy Precipitation Days) and SDII (Simple daily intensity index) are the primary drivers of nationwide NDVI changes, with contribution rates of 31.6% and 12.5%. Extreme climate indices can significantly affect vegetation growth when surpassing certain thresholds. For instance, the thresholds for R25 and SDII are 1.85 days and 4.35 mm/day. This study provides a scientific basis for understanding and managing vegetation responses to increasing climate extremes.

Vegetation drought is a critical manifestation of ecosystem vulnerability in high-altitude, water-limited regions under climate change. The Yellow River Water Conservation Area (YRWC), as the core water source of the Yellow River Basin, is highly sensitive to variations in hydrothermal conditions. In this study, a Temperature–Vegetation–Precipitation Drought Index (TVPDI) was constructed to characterize the spatio-temporal dynamics of vegetation drought in the YRWC for 2003, 2012, and 2019. The XGBoost–SHAP framework was further employed to quantitatively analyze the nonlinear response characteristics and relative contributions of key factors within the TVPDI framework. Scenario-based spatial simulations of vegetation drought for 2035 are then conducted based on the GeoSOS-FLUS model. The results indicate that vegetation drought in the YRWC exhibits a relatively stable spatial pattern, with drought severity gradually intensifying from southeast to northwest and moderate drought as the dominant type. Precipitation is the key variable of TVPDI, followed by land surface temperature, while NDVI mainly plays a nonlinear regulatory role. Among external factors, atmospheric moisture conditions show relatively higher explanatory relevance, whereas topographic and human activity factors exert comparatively weaker influences. Scenario-based simulation results suggest that vegetation drought may be alleviated under low-emission pathways, whereas high-emission scenarios substantially exacerbate drought severity and associated risks. This study presents an interpretable, index-based analytical framework combined with scenario-based spatial simulation for characterizing vegetation drought in the YRWC, thereby providing scientific support for ecological management and climate adaptation strategies in the Yellow River Basin.

The coupling coordination relationship between ecosystem services supply–demand and human well-being in arid inland regions is increasingly vulnerable to imbalance risks under the combined pressures of climate change and intensified anthropogenic activities. Here, we assessed dynamic changes in ecosystem services supply–demand, human well-being, their coupling relationships and influencing factors in the Hexi Regions by integrating remote sensing data, ecological model, ecosystem services supply–demand ratio (ESDR), coupling coordination degree (CCD) model, and the partial least squares structural equation modeling (PLS-SEM). Our results showed that the six key ecosystem services supply, demand, and ESDR in the Hexi Regions from 1990 to 2020 exhibited greater ecosystem services surplus in the Qilian Mountains and stronger deficits in urban and surrounding areas of the Hexi Corridor. The deficit of water yield accounted for 32% in the Hexi Corridor with large cropland irrigated, four times that of the Qilian Mountains, indicating a serious supply–demand mismatch in space and time. Additionally, survival-oriented human well-being across regions is still dominant. Overall, the coupling relationship between ESDR and human well-being in the Hexi Regions progressed towards a high level of coordination, with higher values observed in the oases of the Hexi Corridor and the central and eastern Qilian Mountains. The ESDR of food production and water yield showed a higher coupling coordination level with human well-being in the Qilian Mountains, where the CCD was generally exceeded by 0.7. Climate, vegetation, and land use intensity were key drivers of spatial heterogeneity in CCD. Human well-being made a greater contribution to CCD than other elements in the influence paths. Our results can provide a reference for promoting coordinated development of the ecological environment and sustainable human well-being in arid inland regions.

The launch of Sentinel-1 satellite introduces a novel approach for synthetic aperture radar (SAR) interferometry (InSAR). However, its capabilities for topographic mapping are reportedly limited. It is very challenging to create high-quality digital elevation models (DEMs) from InSAR data in mountain area. The main goal of the study was to generate a new high-quality DEM by combining Sentinel-1A and Sentinel-1B data with a short temporal baseline and multiple perpendicular baselines from 43 to 143 m. Five DEMs were produced from five interferometric pairs. The five DEMs were fused to generate a more reliable fused DEM. The performance of the new DEMs was evaluated against ICESat-2/ATL06 product and global DEM products (NASADEM, advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), and advanced land observing satellite ALOS World 3D-30 m). The results showed that all Sentinel-1 DEMs performed better than ASTER GDEM, four of DEMs had higher accuracies than NASADEM. The fused DEM with the vertical error of 9.08 m for steep terrain (slope>20°) revealed higher accuracy than three global DEMs. The accuracy of DEM was related to terrain slope and land cover type. The accuracies of DEMs decreased as slope increased. The DEMs in glacier area revealed higher errors than those in bare rock. Besides, there was no clear relationship between the perpendicular baseline and vertical accuracy of DEM. The interferometric pair with the shortest baseline (43 m) produced the worst quality DEM, while the interferometric pair with slightly shorter baseline (69 m) produced the highest quality DEM. The study revealed the outstanding accuracy of the new DEM product, which is very valuable data for local glacier research.

Steady glacier runoff is related to the security and resilience of water resources in meltwater recharge basins, so the status and change of glaciers and their response to climate change in the upper reaches have received widespread concerns. Here, the spatiotemporal characteristics of glacier wastage in the Upper Reaches of Shule River Basin (URSRB) driven by climate change were analyzed based on multi-source and multi-temporal remotely sensed images. Firstly, we extracted multi-temporal glacier outlines from the Landsat time series data using Google Earth Engine (GEE) for seven different periods every approximately 5 years from 1990 to 2020. The spatiotemporal analysis of URSRB glaciers demonstrates a sustained reduction in glacier area from 481.07 ± 24.24 km² in 1990 to 384.05 ± 22.71 km² in 2020, corresponding to a glacier shrinkage rate of − 0.67 ± 0.23%/year, characterized by considerable temporal variability. Secondly, multi-temporal DEMs derived from ASTER stereo imagery spanning from 2000 to 2020 were used to compute the glacier surface elevation changes and determine the glacier mass loss. The overall glacier surface elevation change rate was − 0.32 ± 0.14 m/year, equivalent to a mass balance of − 0.28 ± 0.12 m w.e./year. Lastly, to better apprehend the long-term response of URSRB glaciers to climate change, studies on climate change were carried out based on the EAR5-Land reanalysis dataset. The long-term trend of glacier wastage is attributed to the increase in summer temperature, and the negative effects of increased summer temperature on glaciers exceeded the positive effects of increased annual precipitation. In summary, glaciers in the URSRB have experienced a significant area reduction and accelerated mass loss against the backdrop of climatic warming and humidification.