Search Results (109)

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. Full article

The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km² in 1990 to 24,485 km² by 2020, with projections indicating a further decrease to only 2844 km² by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article

Complete chloroplast genome sequences are widely used in the analyses of phylogenetic relationships among angiosperms. As a species-rich genus, species diversity centers of Saxifraga L. include mountainous regions of Eurasia, such as the Alps and the Qinghai–Tibetan Plateau (QTP) sensu lato. However, to date, datasets of chloroplast genomes of Saxifraga have been concentrated on the QTP species; those from European Alps are largely unavailable, which hinders comprehensively comparative and evolutionary analyses of chloroplast genomes in this genus. Here, complete chloroplast genomes of 19 Saxifraga species were de novo sequenced, assembled and annotated, and of these 15 species from Alps were reported for the first time. Subsequent comparative analysis and phylogenetic reconstruction were also conducted. Chloroplast genome length of the 19 Saxifraga species range from 149,217 bp to 152,282 bp with a typical quadripartite structure. All individual chloroplast genome included in this study contains 113 unique genes, including 79 protein-coding genes, four rRNAs and 30 tRNAs. The IR boundaries keep relatively conserved with minor expansion in S. consanguinea. mVISTA analysis and identification of polymorphic loci for molecular markers shows that six intergenic regions (ndhC-trnV, psbE-petL, rpl32-trnL, rps16-trnQ, trnF-ndhJ, trnS-trnG) can be selected as the potential DNA barcodes. A total of 1204 SSRs, 433 tandem repeats and 534 Large sequence repeats were identified in the 19 Saxifraga chloroplast genomes. The codon usage analysis revealed that Saxifraga chloroplast genome codon prefers to end in A/T. Phylogenetic reconstruction of 33 species (31 Saxifraga species included) based on 75 common protein coding genes received high bootstrap support values for nearly all identified nodes, and revealed a tree topology similar to previous studies. 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

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

Plateau pika (Ochotona curzoniae, hereafter pika) is a key species in the alpine grasslands on the Qinghai-Tibetan Plateau (QTP). They are susceptible to the influence of external disturbance and may present great variation, which is important to evaluate their ecological role in alpine grasslands. However, our knowledge regarding their interannual variation and the influencing mechanism is still limited due to the lack of long-term observation of pika density. This study aimed to investigate the spatiotemporal variations in pika and the associated key influencing factors by aerial photographing at 181 sites in Gannan Tibetan Autonomous Prefecture in 2016, 2019, and 2022. Our findings showed that: (1) pika primarily distributed in the central and northeastern Maqu County and the southwestern part of Luqu County, and their average density was in a range of 9.87 ha⁻¹ to 14.43 ha⁻¹ from 2016 to 2022; (2) high pika density were found in 1.22 to 3.61 °C for annual mean temperature, 12.86 to 15.06 °C for diurnal temperature range, 3400 to 3800 m for DEM and less than 3° for slope; and (3) pika density showed varied response to interannual changes in mean diurnal range, annual precipitation and precipitation of the driest month in different years. Our results concluded that pika density showed significant spatiotemporal variations, and climate and terrain variables dominantly affected pika density. Given the great interannual fluctuation of climate variables and different responses of pika density to these variables, our results suggested that long-term monitoring of pika is crucial to reveal their real distribution, response mechanism to habitat environment, and role in alpine grasslands. Moreover, unmanned aerial vehicles are cost-effective tools for the long-term monitoring of pika. Full article

Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article

Alpine grassland degradation in permafrost regions seriously affects the provision of ecosystem services, posing a threat to ecological security. Revegetation is a key strategy for the restoration of alpine grassland ecosystems on the Qinghai–Tibetan Plateau (QTP). However, there is a lack of comprehensive research evaluating ecosystem services after revegetation, especially in permafrost regions. In this study, we assessed the changes in ecosystem services following revegetation in the alpine permafrost regions of the QTP through on-site monitoring and sampling, using extremely degraded alpine grassland as a control. In addition, we analyzed trade-offs among ecosystem services and identified key drivers. Our results indicate that (1) revegetation significantly increased forage supply, carbon storage, and soil retention values (p < 0.05), while water retention and permafrost stability showed no significant changes (p > 0.05); (2) vegetation restoration effectively reduced the trade-offs among ecosystem services; and (3) the main drivers were vegetation coverage, precipitation, belowground biomass, and restoration duration. Overall, this study demonstrates that revegetation improves ecosystem services. The enhancement in these services provides valuable data for future research on ecosystem services in alpine grassland. Full article

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R² = 0.611, RMSE = 69.162 gC·m⁻²·month⁻¹), is higher than that of the multiple linear regression model, regGPP (R² = 0.429, RMSE = 86.578 gC·m⁻²·month⁻¹), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R² = 0.360, RMSE = 91.764 gC·m⁻²·month⁻¹). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R² = 0.601, RMSE = 21.04 mm·month⁻¹), is higher than that of the product derived through mean processing, MEAN_ET (R² = 0.591, RMSE = 25.641 mm·month⁻¹). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future. Full article

Temperate glaciers in the Kangri Karpo region of the southeastern Qinghai–Tibet Plateau (QTP) have experienced significant ablation in recent decades, increasing the risk of glacier-related hazards and impacting regional water resources. However, the spatial and temporal pattern of mass loss in these glaciers remains inadequately quantified. In this study, we used ASTER L1A stereo images to construct a high-resolution elevation time series and provide a comprehensive spatial–temporal assessment of glacier elevation change from 2000 to 2024. The results indicate that almost all glaciers have experienced rapid ablation, with an average surface elevation decrease of −18.35 ± 5.13 m, corresponding to a rate of −0.76 ± 0.21 m yr⁻¹. Glaciers in the region were divided into the northern and southern basins, with average rates of −0.79 ± 0.17 m yr⁻¹ and −0.72 ± 0.13 m yr⁻¹, respectively. A notable difference in acceleration trends between the two basins was observed, with the elevation rate increasing from −0.78 ± 0.17m yr⁻¹ to −1.04 ± 0.17 m yr⁻¹ and from −0.52 ± 0.13 m yr⁻¹ to −0.92 ± 0.13 m yr⁻¹, respectively. The seasonal cycle was identified in glacier surface elevation change, with an accumulation period from November to March followed by a prolonged ablation period. The seasonal amplitude decreased with elevation, with higher elevations exhibiting longer accumulation periods and less ablation. Correlation analysis with meteorological data indicated that higher summer temperatures and increased summer rainfall intensify elevation loss, while increased spring snowfall may reduce ablation. Our analysis highlights distinct variations in glacier elevation changes across different locations, elevations, and climatic conditions in the Kangri Karpo region, providing valuable insights into glacier responses to environmental changes on the Tibetan Plateau. Full article

Understanding the change in the habitat distributions and abundance of wildlife in space and time is critical for the conservation of biodiversity and mitigate human–wildlife conflicts (HWCs). Tibetan antelope or chiru (Pantholops hodgsonii), Tibetan gazelle or goa (Procapra picticaudata), Tibetan wild ass or kiang (Equus kiang), and Wild yak (Bos mutus) have been sympatric on the Qinghai–Tibetan plateau (QTP) for numerous generations. However, reviews on the habitat distributions and abundance of these four wild herbivores (WHs), as well as the methods examining the changes in these aspects, are still lacking. Here, we firstly review the distributions and abundance of four major WHs on the QTP across different periods, examining the underlying causes of changes and HWCs. Furthermore, we critically compare three aspects of methods: transect surveys, machine learning (ML), and deep learning (DL) methods of studying WHs. The results show that since the 1990s, the distributions and abundance of WHs have exhibited a trend of initial decline followed by recovery, largely attributed to global climate warming and a decrease in illegal hunting. However, in recent years, the primary challenge has shifted from wildlife protection to balancing the human and wildlife interests within the constraints of limited resources. In the future, we should focus on enhancing the ecological functions of habitats to achieve harmonious coexistence between humans and nature, as well as establishing a scientific compensation mechanism to mitigate human–wildlife conflicts. In order to accurately calculate the changes, we should select appropriate models to analyze the habitats of wildlife based on their specific characteristics and the environmental conditions. Additionally, with the advancement of large models, AI (artificial intelligence) should be utilized for precise and rapid wildlife conservation. The findings of this study also provide guidance and reference for addressing the issues related to wildlife habitats and abundance in other regions globally. Full article

The Qinghai–Tibetan Plateau (QTP) has the largest area of natural grassland in China, and continuous grassland degradation poses a serious threat to regional ecological security and sustainable resource management. It is essential to comprehensively evaluate the cost–benefit differences and drivers of grassland degradation across various zones to enhance sustainable management practices. This study presents a zonal management framework for the ecological restoration of degraded grasslands based on cost–benefit analysis, specifically applied to Qinghai in the Northeastern QTP. The results indicate: (1) Although the overall NDVI of grasslands shows an upward trend, some areas still exhibit significant degradation. (2) Cost–benefit analysis can divide degraded grasslands into four types of Ecological Management Zones (EMZs): high-cost–high-benefit zone, high-cost–low-benefit zone, low-cost–low-benefit zone, and low-cost–high-benefit zone. (3) The driving factors of grassland degradation show significant differences in different EMZs. Based on these research findings, differentiated spatial planning and management strategies for grassland ecological restoration were developed for each EMZ. This study not only provides a scientific methodology for grassland ecological restoration but also offers important insights for the sustainable management of grassland resources in the QTP and other ecologically sensitive areas. Full article

Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics of the Tibetan Plateau ecosystem, variations in grassland coverage are crucial to its ecological stability. This study utilizes the Google Earth Engine (GEE) platform to retrieve long-term MODIS data and analyzes the spatiotemporal distribution of grassland FVC across the Qinghai–Tibet Plateau (QTP) over 24 years (2000–2023). The grassland growth index (GI) is used to evaluate the annual grassland growth at the pixel level. GI is an important indicator for measuring grassland growth status, which can effectively measure the changes in grassland growth in each year relative to the base year. FVC trends are monitored using Sen-Mann-Kendall slope estimation, the coefficient of variation, and the Hurst exponent. Geographic detectors and partial correlation analysis are then applied to explore the contribution rates of key driving factors to FVC. The results show: (1) From 2000 to 2023, FVC exhibited an overall upward trend, with an annual growth rate of 0.0881%. The distribution of FVC on the QTP follows a pattern of higher values in the east and lower values in the west; (2) Over the past 24 years, 54.05% of the total grassland area has shown a significant increase, 23.88% has remained stable, and only a small portion has shown a significant decrease. The overall trend is expected to continue with minimal variability, covering 82.36% of the total grassland area. The overall grassland GI suggests a balanced state of growth; (3) precipitation (Pre) and soil moisture (SM) are the main single factors affecting FVC changes in grasslands on the Tibetan Plateau (q = 0.59 and 0.46). In the interaction detection, in addition to the highest interaction between Pre and other factors, the interaction between SM and other factors also showed a significant impact on the changes in FVC of the QTP grassland; partial correlation analysis of hydrothermal factors and FVC of the QTP grassland. It shows that precipitation has a stronger correlation with QTP grassland FVC changes than temperature. This study has enhanced our understanding of grassland vegetation change and its driving factors on the QTP and quantitatively described the relationship between vegetation change and driving factors, which is of great significance for maintaining the sustainable development of grassland ecosystems. Full article

Plateau pika (Ochotona curzoniae) is crucial to soil organic carbon (SOC) storage in the Qinghai–Tibetan plateau (QTP), but its role in bacterial SOC metabolisms across different degraded alpine grasslands remains unclear. In this study, we investigated the soil physicochemical properties and the composition and function of the bacterial communities in control and pika-disturbed grasslands experiencing different degradation levels (undegraded, UDM; lightly, LDM; moderately, MDM and severely, SDM). The results demonstrate that (i) the primary bacterial phyla include Proteobacteria, Acidobacteriota, Actinobacteriota, Bacteroidota and Verrucomicrobiota. Soil physicochemical properties significantly impact the composition of the bacterial communities and determine the influence of pika disturbance. Pika disturbance increases bacterial OTUs by 7.5% in LDP (p > 0.05) and by 50.5% in MDP (p < 0.05), while decreases OTUs by 21.4% in SDP (p < 0.05). (ii) Pika disturbance downregulates the exoenzyme abundance associated with simple and complex organic matter decomposition by 9.5% and 13.9% in LDP, and 29.4% and 26.3% in MDP (p < 0.05), while upregulates these exoenzymes by 23.6% and 37.9% in SDP (p < 0.05). These changes correspond to the increase in TC and SOC in LDP and MDP but declines in SDP. (iii) Plateau pika disturbance can enhance SOC accumulation through upregulating the C cycle pathway of ethanol production in LDP and MDP. However, it upregulates the pathway of pyruvate to CO₂ conversion in SDP, leading to negative influence on SOC storage. Full article

For regional desertification control and sustainable development, it is critical to quickly and accurately understand the distribution pattern and spatial and temporal changes of deserts. In this work, five different machine learning algorithms are used to classify different desert types on the Qinghai–Tibetan Plateau (QTP), and their classification performance is evaluated on the basis of their classification results and classification accuracy. Then, on the basis of the best classification model, the Shapely Additive Explanations (SHAP) method is used to clarify the contribution of each classification feature to the identification of desert types during the machine learning classification process, both globally and locally. Finally, the independent and interactive effects of each factor on desert change on the Qinghai-Tibetan Plateau during the study period are quantitatively analyzed via geodetector. The main results are as follows: (1) Compared with other classification algorithms (GTB, CART, KNN, and SVM), the RF classifier achieves the best performance in classifying QTP desert types, with an overall accuracy (OA) of 87.11% and a kappa coefficient of 0.83. (2) From the perspective of the overall classification of deserts, the five features, namely, elevation, slope, VV, VH, and GLCM, contribute most significantly to the features. In terms of the influence of each classification feature on the extraction of different types of deserts, the radar backscattering coefficient VV serves the most important role in distinguishing sandy deserts; the VH is helpful in distinguishing the four types of deserts: rocky desert, alpine cold desert, sandy deserts, and loamy desert; slope is more effective in distinguishing between the two desert types (rocky desert and alpine cold desert) and other types of deserts; and elevation has a significant role in the identification of alpine cold deserts; and the short-wave infrared band SR_B7 has an important role in the identification of salt crusts and saline deserts. (3) During the study period, the QTP deserts exhibited a reversing trend, and the proportion of desert area decreased from 28.62% to 26.20%. (4) Compared with other factors, slope, precipitation, elevation, vegetation type, and the human footprint have greater effects on changes in the QTP desert area, and the interactions among the factors affecting changes in the desert area all show bidirectional enhancement or nonlinear enhancement effects. Full article