Search Results (96)

Under the background of global climate warming, glaciers in the Three-River Headwaters Region, as a crucial component of the “Asian Water Tower,” exert profound influences on regional water resource security and ecological stability through their mass balance variations. Due to the scarcity of in situ observations caused by the harsh high-altitude environment, long-term monitoring based on remote sensing techniques is urgently required. In this study, the geodetic method was employed, using the SRTM-C DEM acquired in 2000 as the reference, and recent glacier surface DEMs were generated from high-resolution ZiYuan-3 tri-stereo imagery obtained during 2024–2025. Through refined DEM co-registration, differencing, and systematic error corrections, the glacier mass balance in the Three-River Headwaters Region from 2000 to 2025 was systematically estimated. The results indicate that the glaciers in the study area exhibited an overall negative mass balance during the study period, with significant spatial heterogeneity. Among the sub-regions, the Lancang River source region experienced the most pronounced mass loss (−0.70 ± 0.07 m w.e. yr⁻¹), whereas the Yellow River source region showed the lowest mass loss (−0.37 ± 0.09 m w.e. yr⁻¹). Compared with earlier studies, glacier mass loss has accelerated in recent years and is closely associated with regional climatic characteristics. This study provides a scientific basis for understanding glacier changes and their hydrological and ecological impacts in the Three-River Headwaters Region. Full article

Plateau waters in Northern Sichuan, China, act as critical headwaters of the Yellow River. Microbial communities in water bodies and soil matrices within this region are increasingly pressured by intensive animal rearing; yet few studies have characterized microbial shifts across entire riverine niches. In this study, we employed next-generation sequencing based metagenomics to investigate microbial features, community structure and diversity, metabolic potentials, and antibiotic resistance genes (ARGs) in the Baihe River, a tributary in the source region of the Yellow River. Sampling locations covered the main stem and three tributaries of the Baihe River, spanning from its source, through upstream and downstream segments, to the convergence site with the main stem of the Yellow River. Results revealed that Pseudomonadota and Bacteroidota were the most abundant phyla. The relative abundance of most taxa at multiple taxonomic levels exhibited an increasing trend along the river continuum driven by rising total nitrogen (TN) and total phosphorus (TP) concentrations; however, a notable exception occurred at BH1 (the Baihe source), where the abundance of numerous taxa was markedly higher than in downstream samples. We detected abundant ARGs predominantly associated with antibiotic resistance. Furthermore, prevalent viruses affiliated with the phyla Uroviricota and Nucleocytoviricota, together with pathogenic bacteria, were identified as etiological agents of diverse infectious diseases. This study provides novel perspectives for managing aquatic contamination in plateau river ecosystems by linking environmental variables, microbial succession, and resistome distribution. Full article

Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the Tarim River headwater region and Awati County, Xinjiang, from July to October 2023. The aim was to estimate CHD using hyperspectral data combined with machine learning and to evaluate the effect of LWC on model accuracy. Raw spectra were preprocessed using Savitzky–Golay (SG) smoothing and continuous wavelet transform (CWT). A two-step feature selection strategy comprising Random Frog and iterative retaining informative variables (IRIV) was applied to extract characteristic bands. Three machine learning models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—were developed for CHD estimation with and without LWC as an additional input. Incorporating LWC consistently improved the predictive performance of all models. Without LWC, the RF model achieved the best accuracy (training R² = 0.842, test R² = 0.830), whereas after LWC integration, XGBoost reached the optimal performance (training R² = 0.871, test R² = 0.865). SHAP analysis identified the 687 nm wavelength and its interaction with LWC as the most important predictors. These results indicate that combining spectral information with LWC effectively improves the accuracy and stability of CHD estimation for Populus pruinosa, providing a reliable non-destructive approach for assessing forest ecosystem physiological status—a key contribution to the sustainable management of arid riparian forests. Full article

Quantifying glacier contributions to river discharge is challenging because many land surface models (LSMs) lack glacier processes, whereas standalone glacier models are often disconnected from catchment hydrology. Here we develop the Conjunctive Surface–Subsurface Process model version 2-glacier coupled model (CSSPv2-GLC), and evaluate it over the Three-River Headwaters Region (TRHR) at 3 km during 1979–2017. The glacier coupling raises Nash–Sutcliffe Efficiency for monthly streamflow simulation at Tuotuohe station from 0.63 to 0.79 during calibration and from 0.61 to 0.76 during validation. CSSPv2-GLC reduces glacier surface temperature error to 1.85 K, compared with 3.09 K for the CSSPv2. Glacier meltwater contributions to total discharge reached 11.5% in July and 10.8% in August in the Yangtze headwaters. In contrast, the Lancang and Yellow headwaters contributed up to 4.5% and 1.8% in August. Dry-year contributions are 2–3 times higher than wet-year values, indicating a transient drought-buffering effect. These results demonstrate the value of integrating physically explicit glacier processes into land surface modeling frameworks for water resource assessment in glacierized headwater regions, and highlight the necessity of accounting for non-stationary glacier contributions to streamflow. Full article

Evaluating whether zoning-based management is associated with measurable ecosystem function benefits is crucial for China’s national park system reform, yet most existing assessments emphasize greening or productivity alone. Here, we evaluate zoning-associated patterns in the Three-River Headwaters Region by combining MODIS-derived carbon use efficiency (CUE = NPP/GPP; 2001–2024), a boundary–buffer comparison with environmental matching, and an explainable machine learning attribution framework. NPP increased across all zones, whereas CUE remained stable to slightly declining, indicating a productivity–efficiency decoupling in the remote sensing record. Core and Buffer zones maintained higher long-term median CUE than the Outside zone, but matched boundary contrasts were heterogeneous, and the Experimental–Outside CUE contrast, although robust in sign, was small in magnitude. Zone–year attribution (2002–2020) suggests that interannual CUE variability is dominated by climate and land surface structure/change, while human pressure shows a smaller negative association; these grouped SHAP contributions should be interpreted as indicative rather than precise estimates. Post-2020 climate baseline residuals show persistent negative CUE anomalies in Buffer and Experimental zones, suggesting additional non-climatic influences but not demonstrating causality. Given the temperature-sensitive structure of MOD17 and the representativeness limits of QC-filtered 500 m observations, we interpret these results as management-consistent patterns rather than stand-alone causal proof. The findings support incorporating carbon use efficiency into zonal monitoring and may inform differentiated, efficiency-oriented management review. Full article

The Aksu River Basin, the main headwater of the Tarim River, contributes more than 70% of the main stream’s runoff and is therefore critical in maintaining hydrological stability in this arid river system. In recent decades, rapid oasis expansion and growing agricultural water withdrawals have intensified competition for surface and groundwater, posing increasing ecological risks to the downstream Tarim River Basin. To quantitatively characterize river–groundwater hydrological responses under intensive water use, we combined statistical analysis, field observations, and distributed hydrological modeling within a basin-scale conceptual framework. Multiple lines of evidence—water level monitoring, hydrochemical tracers, stable isotopes, and the integrated surface–groundwater model MIKE SHE—were used to identify river–groundwater interaction mechanisms in the Aksu alluvial plain. Results reveal a typical three-stage spatial exchange pattern: river recharge to groundwater in the upstream reach, groundwater discharge to the river in the midstream, and renewed river infiltration to groundwater downstream. The patterns inferred from water levels, hydrochemistry, and isotopes are broadly consistent, while water-level data better resolve left–right bank asymmetry. The MIKE SHE model supports the seasonal bidirectional exchange dynamics and reproduces runoff behavior with acceptable performance (RMSE and residual standard deviation within 20% of observed means and R² > 0.7 during both calibration (2010–2017) and validation (2018–2021)). The proposed multi-evidence framework captures the spatio-temporal variability of river–groundwater interactions in arid regions and provides spatially differentiated guidance for conjunctive surface–groundwater regulation and integrated water resources management in the Tarim River Basin. Full article

As a major water conservation region and ecological security barrier in China, the Three-River Source Region (TRSR) of the Qinghai–Tibet Plateau (QTP) is underlain by extensive permafrost. However, how permafrost degradation alters regional water conservation, particularly the existence of critical thresholds and time-lagged responses, remains insufficiently understood. To clarify these issues, spatiotemporal variations in water conservation (1990–2020) were quantified, and their nonlinear, lagged, and spatially heterogeneous responses to active layer thickness (ALT) were assessed. Using multi-source remote sensing and in situ observations from 1990 to 2020, spatiotemporal variations in water conservation were quantified with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and responses to permafrost degradation were examined by integrating Sen’s slope, GeoDetector, geographically weighted regression (GWR), and structural equation modeling (SEM) methods. The results showed that water conservation increased overall during 1990–2020 and exhibited a pronounced southeast–northwest gradient (higher in the southeast and lower in the northwest); the rates of change in the Lancang, Yellow, and Yangtze headwaters were 63.5, 56.5, and 31.0 mm a⁻¹, respectively. GeoDetector results indicate that precipitation was the dominant control on the spatial heterogeneity of water conservation (q = 0.704), and its interaction with active layer thickness (ALT) further increased explanatory power (q = 0.736). ALT also interacted with vegetation (q = 0.224) and topography (q = 0.157), suggesting that permafrost effects are modulated by vegetation condition and topographic setting in addition to water inputs. Piecewise regression identified a potential threshold at ALT = 1.77 m, indicating a shift in the ALT–water conservation relationship across this threshold. A 5–7-year lag in the response of water conservation to ALT was also detected, particularly apparent in continuous permafrost zones. Overall, water conservation exhibits a clear southeast–northwest gradient and a delayed response to ALT changes. In addition, the response exhibits clear spatial clustering, with the strongest sensitivity observed in areas with ice-rich permafrost overlain by alpine meadow, and a potential ALT breakpoint further suggests nonlinear permafrost–water conservation coupling. Full article

Changes in vegetation coverage reflect the status and dynamic processes of ecosystems and serve as a crucial foundation for regional ecological protection. Using Landsat-5 and Sentinel-2 data, this study calculated the vegetation coverage in the Three-River Headwaters (TRH) region from 1990 to 2022 with the pixel dichotomy model, identified land cover changes over the past three decades via a deep neural network, and analyzed the primary influencing factors behind vegetation coverage dynamics. The results indicate that vegetation coverage in TRH has generally increased, as very high vegetation coverage expanded by 10.3%, while very low and low vegetation coverage decreased by 4.2%. Extensive bare land in the western region decreased and transformed into grassland, while the areas of shrubland and forest in the central and eastern TRH areas increased. The areas of grassland, shrubland, and forest increased by 3.7 × 10⁴ km², 2.1 × 10⁴ km², and 4.7 × 10³ km², respectively. Precipitation, elevation, and temperature are the main factors influencing the spatial variation in vegetation coverage. We found that the contributions of the permafrost active layer thickness and precipitation to changes in vegetation coverage are high. Finally, we provide a detailed and timely analysis of recent vegetation distribution and type changes on the Tibetan Plateau, offering a strengthened scientific foundation for monitoring, assessment, and ecological conservation efforts aimed at supporting ecosystem restoration in the region. Full article

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions. Full article

Understanding the drivers of water yield (WY) changes in ecologically sensitive, data-scarce watersheds is crucial for sustainable management, particularly in the context of accelerating forest expansion and urbanization. This study focuses on the upper Yellow River Basin (UYRB), a critical headwater region that supplies 60% of the Yellow River’s flow and is undergoing rapid land use transitions from 1990 to 2100. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Future Land-Use Simulation (FLUS) model, we quantify historical (1990–2020) and projected (2025–2100) WY dynamics under three SSP scenarios (SSP126, SSP370, and SSP585). InVEST, a spatially explicit ecohydrological model based on the Budyko framework, estimates WY by balancing precipitation and evapotranspiration. The FLUS model combines cellular automata (CA) with an artificial neural network (ANN)-based suitability evaluation and Markov chain-derived transition probabilities to simulate land-use change under multiple scenarios. Results show that WY increased significantly during the historical period (1990–2020), primarily driven by increased precipitation, with climate change accounting for 94% and land-use change for 6% of the total variation in WY. Under future scenarios (SSP126, SSP370, and SSP585), WY is projected to increase to 217 mm, 206 mm, and 201 mm, respectively. Meanwhile, the influence of land-use change is expected to diminish, with its contribution decreasing to 9.1%, 5.7%, and 3.1% under SSP126, SSP370, and SSP585, respectively. This decrease reflects the increasing strength of climate signals (especially extreme precipitation and evaporative demand), which masks the hydrological impacts of land-use transitions. These findings highlight the dominant role of climate change, the scenario-dependent effects of land-use change, and the urgent need for integrated climate–land management strategies in forest-urbanizing watersheds. Full article

It is crucial to clarify the grassland carrying capacity (CC) and the balance between grass and livestock under different scenarios for ecological protection and sustainable development in the Three River Headwaters Region (TRHR). This study focused on the TRHR and used livestock data, MODIS Net Primary Productivity (NPP) data, and artificial supplementary feeding data to analyze grassland CC and explore changes in the grass–livestock balance across various scenarios. The results showed that the theoretical CC of edible forage under complete grazing conditions was much lower than that of crude protein under nutritional carrying conditions. Furthermore, without increasing the grazing intensity of natural grasslands, artificial supplementary feeding reduced overstocking areas by 21%. These results suggest that supplementary feeding effectively addresses the imbalance between forage supply and demand, serving as a key measure for achieving sustainable grassland livestock husbandry. Despite the effective mitigation of grassland degradation in the TRHR due to strict grass–livestock balance policies and ecological restoration projects, the actual livestock CC exceeded the theoretical capacity, leading to overgrazing in some areas. To achieve desired objectives, more effective grassland management strategies must be implemented in the future to minimize spatiotemporal conflicts between grasses and livestock and ensure the health and stability of grassland ecosystems. Full article

Sediment fingerprinting was utilized to identify potential hotspots of soil erosion and sediment transport pathways in the Nile Nyabarongo Upper Catchment (NNYU) in Rwanda, where rivers and reservoirs are suffering from alarmingly high levels of sedimentation. Sediment fingerprinting is a practical approach used to identify erosional hotspots and sediment transport processes in highly mountainous regions undergoing swift land use transformation. This technique involves a statistical comparison of the elemental composition of suspended sediments in river water with the elemental composition of soils belonging to different geological formations present in the catchment, thereby determining the sources of the suspended sediment. Suspended sediments were sampled five times over dry and wet seasons in all major headwater tributaries, as well as the main river channel, and compared with soils from respective delineated watersheds. Elemental composition was obtained using laser ablation inductively coupled plasma mass spectrometry, and elements were chosen that could reliably distinguish between the various geological types. The final results indicate different levels of sediment contribution from different geological types. A three-level intervention priority system was devised, with Level 1 indicating the areas with the most serious erosion. Potential sources were located on an administrative map, with the highest likely erosion over the study period (Level 1) occurring in Kabuga cell in the Mwogo sub-catchment, Nganzo and Nyamirama cells in the Nyagako sub-catchment and Kanyana cell in the NNYU downstream sub-catchment. This map enables the pinpointing of site visits in an extensive and rugged terrain to verify the areas and causes of erosion and the pathways of sediment transport. Sediment concentrations (mg L⁻¹) were the highest in the Secoko and Satinsyi tributaries. The composition of suspended sediment was seen to be temporally and spatially dynamic at each sampling point, suggesting the need for an adequate number of sampling locations to identify erosion hotspots in a large mountainous watershed. Apart from prioritizing rehabilitation locations, the detailed understanding of critical zone soil–land cover–climate processes is an important input for developing region-specific watershed management and policy guidelines. Full article

Grassland productivity is crucial for sustainable alpine livestock farming, yet the combined effects of climate change and topography remain unclear. Using long-term time series data of grassland NPP derived from Landsat imagery, along with meteorological and DEM data, this study employed correlation analysis and SEM to quantify climate-driven grassland NPP dynamics and topography-mediated regulatory effects in the Three-River Headwaters Region between 1990 and 2020. Significant spatiotemporal dynamics of grassland NPP were found in response to climate change over the past thirty years. Grassland NPP declined before 1994 and then grew significantly after 1995 at an average rate of 0.88 gC·m⁻²·a⁻¹ (p < 0.01). Spatially, NPP increased in 69% of the region, with significant and highly significant growth in 9.5% (p < 0.05) and 35.7% (p < 0.01), mainly in the southeast. Driven by general warming and wetting, topographic modulation of hydrothermal conditions had intensified a mismatch in both time and space between grassland NPP and climate change, particularly in temperature sensitivity. The positive effect of temperature on NPP shifted to higher elevations (4000–5000 m) and lower slopes (5–25°), with NPP at higher elevations exhibiting greater sensitivity to temperature changes. However, the most substantial contributions to the overall rise in NPP occurred at altitudes of 3000–4000 m and slopes of 0–25°. The key mechanism is that NPP growth above 4000 m was constrained by precipitation scarcity despite thermal limitation alleviation from warming. Overall, the direct effects of climate change outweighed those of various topographic factors, with both showing slight declines since 2010. These findings highlight the need for differentiated governance, restoration, and adaptive management of grasslands across diverse topographic gradients. Full article

Remote sensing plays an important role in understanding the degradation and restoration processes of alpine grasslands. However, the extreme climatic conditions of the region pose difficulties in collecting field spectral data on which remote sensing is based. Thus, in-depth knowledge of the spectral characteristics of alpine grasslands and an accurate assessment of their restoration status are still lacking. In this study, we collected the canopy hyperspectral data of plant communities in the growing season from severely degraded grasslands and actively restored grasslands of different ages in 13 counties of the “Three-River Headwaters Region” and determined the absorption characteristics in the red-light region as well as the trends of red-light parameters. We generated a model for estimating the crude protein content of plant communities in different grasslands based on the screened spectral characteristic covariates. Our results revealed that (1) the raw reflectance parameters of the near-infrared band spectra can distinguish alpine Kobresia meadow from extremely degraded and actively restored grasslands; (2) the wavelength value red-edge position (REP), corresponding to the highest point of the first derivative (FD) spectral reflectance (680–750 nm), can identify the extremely degraded grassland invaded by Artemisia frigida; and (3) the red valley reflectance (Rrw) parameter of the continuum removal (CR) spectral curve (550–750 nm) can discriminate among actively restored grasslands of different ages. In comparison with the Kobresia meadow, the predictive model for the actively restored grassland was more accurate, reaching an accuracy of over 60%. In conclusion, the predictive modeling of forage crude protein content for actively restored grasslands is beneficial for grassland management and sustainable development policies. Full article

Noxious weeds (NWs) are increasingly recognized as a significant threat to the native alpine grassland ecosystems of the Qinghai–Tibetan Plateau (QTP). However, large-scale quantification of their continuous fractional cover remains challenging. This study proposes a pixel-level estimation framework utilizing time-series Sentinel-2 imagery. A Dynamic Mask Non-Stationary Transformer (DMNST) model was developed and trained using multi-temporal multispectral data to map the spatial distribution of NWs in the Three-River Headwaters Region. The model was calibrated and validated using field data collected from 170 plots (1530 quadrats). The results demonstrated that both the dynamic masking module and the non-stationary normalization significantly enhanced the prediction accuracy and robustness, particularly when applied jointly. The model performance varied across different combinations of spectral bands and temporal inputs, with the optimal configurations achieving a test R² of 0.770, MSE of 0.009, and RMSE of 0.096. These findings underscore the critical role of the input configuration and architectural enhancements in accurately modeling the fractional cover of NWs. This study confirms the applicability of Sentinel-2 time-series imagery for modeling the continuous fractional cover of NWs and provides a scalable tool for invasive species monitoring and ecological risk assessment in alpine ecosystems. Full article