Search Results (225)

As a globally renowned alpine gorge region and seismically active zone, the eastern margin of the Qinghai–Tibet Plateau (QTP) is highly prone to landslide dam formation. Considering unstable landslide dams often pose catastrophic risks to downstream areas, current research on landslide dams along QTP primarily focuses on the breach mechanisms of unstable dams, while studies on the formation mechanisms of stable landslide dams—which can provide multiple benefits to downstream regions—remain limited. This paper selected the Conaxue Co landslide dam on the eastern margin of the QTP as one case example. Field investigation, sampling, numerical simulation, and comprehensive analysis were carried out to disclose its formation mechanisms. Field investigation shows that the Conaxue Co landslide dam was formed by a high-speed long-runout landslide blocking the river, with its structure exhibiting a typical inverse grading pattern characterized by coarse-grained rock overlying fine-grained layers. The inverse grading structure plays a critical role in the stability of the Conaxue Co landslide dam. On one hand, the coarse, hard rock boulders in the upper dam mitigate fluvial erosion of the lower fine-grained sediments. On the other hand, the fine-grained layer in the lower dam acts as a relatively impermeable aquitard, preventing seepage of dammed lake water. Additionally, the step-pool system formed in the spillway of the Conaxue Co landslide dam contributes to the protection of the dam structure by dissipating 68% of the river’s energy (energy dissipation rate η = 0.68). Understanding the formation mechanisms of the Conaxue Co landslide dam can provide critical insights into managing future landslide dams that may form in the QTP, both in emergency response and long-term strategies. Full article

The Qinghai–Tibet Plateau, a region susceptible to global change, stores substantial amounts of soil organic carbon (SOC) in its alpine grassland. However, little is known about how SOC is regulated by soil microbial communities, which vary with elevation, mean annual temperature (MAT), and mean annual precipitation (MAP). This study integrates phospholipid fatty acid (PLFA) analysis to simultaneously resolve microbial biomass, community composition, and membrane lipid adaptations along an elevational gradient (2861–5090 m) on the Qinghai–Tibet Plateau. This study found that microbial PLFAs increased significantly with rising MAP, while the relationship with MAT was nonlinear. PLFAs of different microbial groups all had a positive effect on SOC storage. At higher altitudes (characterized by lower MAP and lower MAT), Gram-positive bacteria dominated bacterial communities, and fungi dominated the overall microbial community, highlighting microbial structural adaptations as key regulators of carbon storage. Saturated fatty acids with branches of soil microbial membrane dominated across sites, but their prevalence over unsaturated fatty acids decreased at high elevations. These findings establish a mechanistic link between climate-driven microbial community restructuring and SOC vulnerability on the QTP, providing a predictive framework for carbon–climate feedbacks in alpine systems under global warming. Full article

Species characterized by undetermined clade affiliations, limited research coverage, and deficient systematic investigation serve as enigmatic entities in plant and animal taxonomy, yet hold critical significance for exploring phylogenetic relationships and evolutionary trajectories. Subgenus Cyathophora (Allium, Amayllidaceae), a small taxon comprising approximately five species distributed in the Qinghai–Tibet Plateau (QTP) and adjacent regions might contain an enigmatic species that has long remained unexplored. In this study, we collected data on species from subgenus Cyathophora and its close relatives in subgenus Rhizirideum, as well as the enigmatic species Allium siphonanthum. Combining phylogenomic datasets and morphological evidence, we investigated species relationships and the underlying mechanism of phylogenetic discordance. A total of 1662 single-copy genes (SCGs) and 150 plastid loci were filtered and used for phylogenetic analyses based on concatenated and coalescent-based methods. Furthermore, to systematically evaluate phylogenetic discordance and decipher its underlying drivers, we implemented integrative analyses using multiple approaches, such as coalescent simulation, Quartet Sampling (QS), and MSCquartets. Our phylogenetic analyses robustly resolve A. siphonanthum as a member of subg. Cyathophora, forming a sister clade with A. spicatum. This relationship was further corroborated by their shared morphological characteristics. Despite the robust phylogenies inferred, extensive phylogenetic conflicts were detected not only among gene trees but also between SCGs and plastid-derived species trees. These significant phylogenetic incongruences in subg. Cyathophora predominantly stem from incomplete lineage sorting (ILS) and reticulate evolutionary processes, with historical hybridization events likely correlated with the past orogenic dynamics and paleoclimatic oscillations in the QTP and adjacent regions. Our findings not only provide new insights into the phylogeny of subg. Cyathophora but also significantly enhance our understanding of the evolution of species in this subgenus. Full article

As a globally critical carbon reservoir, the response mechanism of wetland ecosystems to climate change on the Qinghai–Tibet Plateau (QTP) has attracted significant scientific scrutiny. This study investigated the temperature sensitivity of cbbL-harboring carbon-sequestering microbial communities and their coupling with carbon–nitrogen cycle dynamics through a simulated field warming experiment conducted in the Wayan Mountains’ river source wetland in the northeastern QTP. Key findings revealed that warming markedly elevated Alpha diversity (ACE and Chao1 indices), whereas Shannon and Simpson indices remained stable, indicating that temperature increases primarily altered community composition by enhancing species richness rather than evenness. Taxonomic analysis demonstrated significant increases in the relative abundances of Cyanobacteria and Actinobacteria, while Proteobacteria retained dominance but exhibited reduced relative abundance. At the genus level, Thioflexothrix, Ferrithrix, and Rhodospirillum dominated the community, with Thioflexothrix and Ferrithrix showing warming-induced abundance increments. Functional predictions indicated that warming preferentially stimulated heterotrophic and photoheterotrophic functional guilds. Soil physicochemical analyses further revealed warming-driven increases in nitrate nitrogen (NN), total carbon (TC), and total nitrogen (TN), concurrent with decreased soil moisture. Redundancy analysis identified TC as the predominant determinant of microbial community structure (followed by TN > NN), while pH and ammonium nitrogen (AN) exerted comparatively limited influence. Strong positive correlations between microbial communities and carbon/nitrogen indicators suggested that enhanced carbon–nitrogen resource availability served as the central driver of community succession. These findings elucidate the temperature-responsive mechanisms of cbbL-type carbon-sequestering microorganisms in alpine wetlands, offering critical insights for the adaptive management of carbon cycling in high-altitude ecosystems and advancing strategies toward achieving carbon neutrality goals. Full article

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. Full article

Limited surface energy and mass flux data hinder the understanding of glacier retreat mechanisms on the Qinghai–Tibet Plateau (QTP). Glaciers in the west Nyainqentanglha Range (WNR) supply meltwater to the densely populated Lhasa River basin (LRB) and Nam Co, the QTP’s second-largest endorheic lake. In this study, we used a glacier mass balance model based on the degree-day method (GMB-DDM) to understand the response of glacier changes to climate warming. The spatiotemporal variation in degree-day factors for ice (DDF_ice; plural form: DDFs_ice) was assessed to characterize the sensitivity of glacier melt to warming over 44 years in the WNR. Our results demonstrate that the GMB_DDM effectively captured the accelerated mass loss and regional heterogeneity of WNR glaciers from 2000 to 2020, particularly the intensified negative balance after 2014. Moreover, glacier ablation was more sensitive to warming in the WNR during 2000–2020 than 1976–2000, with DDF_ice increases of 21% ± 8% in the LRB and 31% ± 10% in the Nam Co basin (NCB). Increased precipitation during the ablation season and reduced glacier surface albedo can explain the increased sensitivity to warming during 2000–2020. These findings could support sustainable water resource management in the LRB, NCB, and the surrounding areas of the QTP. Full article

Background/Objectives: Saussurea obvallata (DC.) Edgew., Asteraceae, is a traditional medicinal herbnative to the Qinghai–Tibet Plateau (QTP). Pharmacological investigationshave validated its pharmacological effects in anti-tumor, anti-inflammatory, heat-clearing, detoxifying, and analgesia. S. obv is presently facing habitat fragmentation and population decline. Therefore, we analyzed its genetic and chemical diversity to provide a scientific basis for the conservation and sustainable use of S. obv. Methods: Seven populations of S. obv were sampled from Xizang, China. The genetic diversity was analyzed using inter-simple sequence repeat (ISSR) markers, and metabolites were identified by ultra-high-performance liquid chromatography-tandem-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS). Correlation analysis among genetic diversity, differential metabolites, and climatic factors were performed by R. Results: The genetic diversity among and within populations were both lowly and significantly correlated with geographical distance, showing a decreasing trend from east to west of the QTP. A total of 110 compounds were identified, including flavonoids, phenylpropanoids, lipids, fatty acids, terpenoids, alkaloids, etc. The metabolite contents among populations varied greatly and were related to environmental factors, mainly annual mean temperature and temperature fluctuation. The genetic diversity had little effect on the metabolic differences. Conclusions: These findings provided valuable baseline information for the conservation and pharmacological utilization of S. obv. Meanwhile, further research is necessary for the efficacy evaluation of anti-inflammatory, anti-tumor, radiation protection, and scar removal both in vitro and in vivo. Full article

Accurate runoff forecasting is crucial for disaster prevention and mitigation, as well as water resource allocation planning. However, the accuracy of runoff forecasting in high mountain watersheds is limited by the complexity of terrain and the scarcity of observation data. In recent years, machine learning models have been widely used for runoff prediction. In order to explore the application effect of the Long Short-Term Memory (LSTM) network in high mountain watersheds, this paper takes the Nyang River Basin (NRB) in a typical watershed on the Qinghai–Tibet Plateau (QTP) as the research object, and uses LSTM models to study the impact of different input variable combinations on runoff prediction under multiple prediction periods. The results indicate that with the extension of the forecast period, the impact of historical runoff on runoff prediction accuracy gradually decreases, while the impact of precipitation and temperature on runoff prediction accuracy gradually increases. When the forecast period exceeds 13 days, the contribution of precipitation increases more significantly. The use of historical runoff and forecasting that includes historical runoff and precipitation yields the most robust results, with good forecasting performance within 25 days of the forecast period. Moreover, the larger the watershed area, the better the runoff forecasting effect. Full article

Lake surface water area (LSWA) and lake surface water temperature (LSWT) are critical indicators of climate change, responding rapidly to global warming. However, studies on the synergistic variations of LSWA and LSWT are scarce, and the coupling relationships among lakes with different environmental characteristics remain unclear. In this study, the relative growth rate of LSWA (RK_LSWA); the absolute growth rates of annual maximum, mean, and minimum LSWTs (i.e., K_{LSWT_max}, K_{LSWT_mean}, K_{LSWT_min}); and the absolute growth rates of the difference between maximum and minimum LSWT (LSWT_mmd) (K_{LSWT_mmd}) were investigated across more than 4000 lakes in China using long-term Landsat data, and their coupling relationships among different lake types (i.e., permafrost and non-permafrost recharge, endorheic or exorheic lakes, and natural and artificial lakes) were comprehensively analyzed. Results indicate significant differences in the trends of LSWA and LSWT, as well as their interrelationships across various regions and lake types. In the Qinghai–Tibet Plateau (QTP), 57.8% of lakes showed an increasing trend in LSWA, with 2.4% of the lakes showing moderate expansion (RK_LSWA values of 0.1–0.2), while over 27.5% of lakes in the South China (SC) region displayed shrinkage in LSWA (RK_LSWA values were between −0.1~0%/year). Regarding LSWTs, 49.8% of lakes in the QTP exhibited a K_{LSWT_max} greater than 0, and 47.9% of lakes showed a K_{LSWT_mean} greater than 0. In contrast, 48.1% of lakes in the Middle and Lower Yangtze River Plain (MLYP) had a K_{LSWT_max} less than 0, and 48.5% of lakes had a K_{LSWT_mean} less than 0. Additionally, lakes supplied by permanent permafrost demonstrated more significant growth in both LSWA and LSWT than those supplied by non-permanent permafrost. Further analysis revealed that approximately 20.2% of the lakes experienced a concurrent increase in both mean LSWT and LSWA, whereas around 18.9% of the lakes exhibited a simultaneous rise in both LSWT_mmd and LSWA. This suggests that the expansion of lakes in China is correlated with both rising temperatures and greater temperature differences. This study provides deeper insights into the response of Chinese lakes to climate change and offers important references for lake resource management and ecological conservation. Full article

The Qinghai–Tibetan Plateau (QTP) is under serious desertification stress, which has been receiving increasing attention. Although the restoration of surface vegetation is crucial, the growth of plants is often hindered by unfavorable nutrient-deficient conditions. The plant-associated endophytic microbiome is considered the secondary genome of the host and plays a significant role in host survival under environmental stresses. However, the community compositions and functions of plant-endophytic microorganisms in the QTP desertification environments remain unclear. Therefore, this study investigated the endophytic microbiome of the pioneer plant Gueldenstaedtia verna on the QTP and its contribution to host growth under stressful conditions. The results showed that nutrient-deficient stresses strongly influenced the microbial community structures in the rhizosphere. The impacts of these stresses, however, decreased from the rhizosphere community to the plant endophytes, resulting in consistent plant endophytic microbial communities across different sites. Members of Halomonas were recognized as keystone taxa in the endophytic microbiome of G. verna. Correlation analysis, metagenome-assembled genomes (MAGs), and comparative genome analyses have shown that the keystone taxa of the plant endophytic microbiome may promote plant growth through pathways such as nitrogen fixation, IAA, and antioxidant production, which are important for improving plant nutrient acquisition and tolerance. This finding may provide a crucial theoretical foundation for future phytoremediation efforts in desertification environments on the Qinghai-Tibet Plateau. Full article

The Qinghai–Tibet Plateau (QTP), a critical ecological buffer for Asia, faces intensifying pressures from climate change and infrastructure expansion. The Qinghai–Tibet Railway (QTR), as the world’s highest-altitude railway, traverses this fragile yet economically vital region, where balancing ecosystem integrity and development remains a global sustainability challenge. While previous studies have documented localized environmental impacts of the QTR, systematic assessments of long-term ecological-economic interactions—particularly the synergies and trade-offs between ecosystem service value (ESV) and economic growth—are lacking. This gap hinders targeted policy design to reconcile conservation and development in extreme environments. The present research integrates an enhanced ecosystem service valuation framework with spatial econometric modeling to quantify environmental changes and ecological-economic coordination in the Qinghai–Tibet Railway Region (QTRR) during 1990–2020. The analysis reveals a cumulative ESV increase of USD 54.4 billion over the past 30 years, driven primarily by grassland restoration and regulated land use transitions. Notably, county-level ecological-economic coordination improved significantly, with harmonization indices rising by 32–68% across all jurisdictions. However, latent risks emerged: five counties exhibited severe ecosystem-health-to-economy mismatches by 2020. These findings demonstrate that infrastructure corridors in fragile ecosystems can achieve partial ecological-economic coordination through policy interventions, yet persistent local disparities demand spatially differentiated management. By linking ESV dynamics to governance pathways—including livestock–forage balance mechanisms and green urban zoning—the present study provides a transferable framework for assessing sustainability trade-offs in extreme environments. Broader implications highlight the necessity of embedding adaptive ecological thresholds into infrastructure planning, offering experiences for the Belt and Road Initiative and other high-altitude development frontiers. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Thermokarst lakes are widely distributed on the Qinghai-Tibet Plateau (QTP). However, owing to the lack of high-precision remote sensing imagery and the difficulty of in situ monitoring of permafrost regions, quantifying the changes in the distribution of thermokarst lakes is challenging. In this study, we used four machine learning methods—random forest (RF), gradient boosting decision tree (GBDT), classification and regression tree (CART), and support vector machine (SVM)—and combined various environmental factors to assess the distribution of thermokarst lakes from 2015 to 2020 via the Google Earth Engine (GEE). The results indicated that the RF model performed optimally in the extraction of thermokarst lakes, followed by GBDT, CART, and SVM. From 2015 to 2020, the number of thermokarst lakes increased by 52%, and the area expanded by 1.6 times. A large proportion of STK lakes (with areas less than or equal to 1000 m²) gradually developed into MTK lakes (with areas between 1000 and 10,000 m²) in the central part of the QTP. Additionally, thermokarst lakes are located primarily at elevations between 4000 and 5000 m, with slopes ranging from 0 to 5°, and the sand content is approximately 65%. The normalized difference water index (NDWI) and enhanced vegetation index (EVI) were the most favourable factors for thermokarst lake extraction. The results provide a scientific reference for the assessment and prediction of dynamic changes in thermokarst lakes on the QTP in the future, which will have important scientific significance for the studies of carbon and water processes in alpine ecosystems. Full article

The Qinghai–Tibet Plateau (QTP), one of China’s most snow-rich regions, has an extremely fragile ecosystem, with drought being the primary driver of ecological degradation. Given that the water resources in this region predominantly exist in the form of snow, high-spatiotemporal-resolution snow mapping is essential for understanding snow distribution and managing snow water resources effectively. However, although FY-4A/AGRI is capable of obtaining wide-area remote sensing data, only the first to third bands have a resolution of 1 km, which greatly limits its ability to produce high-resolution snow maps. This study proposes U-SwinFusionNet (USFNet), a deep learning-based snow cover retrieval algorithm that leverages the multi-scale advantages of FY-4A/AGRI remote sensing data in the shortwave infrared and visible bands. By integrating 1 km and 2 km resolution remote sensing imagery with auxiliary terrain information, USFNet effectively enhances snow cover mapping accuracy. The proposed model innovatively combines Swin Transformer and convolutional neural networks (CNNs) to capture both global contextual information and local spatial details. Additionally, an Attention Feature Fusion Module (AFFM) is introduced to align and integrate features from different modalities through an efficient attention mechanism, while the Feature Complementation Module (FCM) facilitates interactions between the encoded and decoded features. As a result, USFNet produces snow cover maps with a spatial resolution of 1 km. Experimental comparisons with Artificial Neural Networks (ANNs), Random Forest (RF), U-Net, and ResNet-FSC demonstrate that USFNet exhibits superior robustness, enhanced snow cover continuity, and lower error rates. The model achieves a correlation coefficient of 0.9126 and an R² of 0.7072. Compared to the MOD10A1 snow product, USFNet demonstrates an improved sensitivity to fragmented and low-snow-cover areas while ensuring more natural snow boundary transitions. Full article

River ecosystem health assessment (REHA) is crucial for sustainable river management and water security. However, existing REHA methodologies still fail to consider the multiple effects of input uncertainty, environmental stochasticity, and the decision-maker’s bounded rationality. Moreover, REHA studies primarily focused on plain areas, leaving the Qinghai–Tibet Plateau (QTP) understudied despite its ecosystems’ heightened fragility and complexity. To address these gaps, this study combined Pythagorean fuzzy sets with cloud modeling and proposed the Pythagorean fuzzy cloud (PFC) approach. Accordingly, a novel robust model (PFC-TODIM) was created by expanding the conventional TODIM method to the PFC algorithm. We provided an REHA indicator system tailored to the distinctive characteristics in the QTP, leveraging multisource data. River ecosystem health, driving mechanisms, and potential threats were investigated in the Lhasa River (LR) using the PFC-TODIM model. Results showed that the created model effectively took multiple uncertainties into consideration, thereby improving the REHA accuracy and robustness. In the LR, health conditions demonstrated substantial spatial disparities. Sampling sites of 28%, 48%, and 24% were subhealthy, healthy, and excellent, respectively. Findings showed that anthropogenic factors, such as dams, urban development, and fish release adversely affect river health and should be properly managed. Full article