Three-River Headwaters Region Research Articles

Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R2 of 0.44 and an RMSE of 2.56 n/m2 on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management.

An Assessment of Vegetation Changes in the Three-River Headwaters Region, China: Integrating NDVI and Its Spatial Heterogeneity

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems.

Spatial Distribution Characteristics of Plant Community and the Identification of Driving Factors in Riparian Zone of the Three-River Headwaters Region, China

The unique geographical and climatic conditions in the Three-River Headwaters Region gave birth to distinctive plant species and vegetation types. To reveal the spatial distribution of plant communities and soil habitats along the riparian zone of the Sanjiangyuan Region and their influencing mechanisms, 14 survey plots were set up （ten from the Yangtze River source, two from the Lancang River source, and two from the Yellow River source）, and the effects of soil nutrient characteristics （especially soil phosphorus morphology）, climate factors, and river topography on plant community characteristics were quantitatively analyzed. The results showed that the plant community composition in the riparian zone of the source of the three rivers was dominated by perennial herbs （72.2%）, followed by annual herbs （20.4%） and shrubs （7.4%）. The dominant plants were Stipa purpurea, Polygonum orbiculatum, Carex parvula, Potentilla anserina, and Gentiana straminea. The average plant coverage, Shannon-Wiener index, and Pielou index were （64.4% ±23.6%）, （1.31 ±0.42）, and （0.84 ±0.08）, respectively. The plant community diversity index was the highest in the Yangtze River source, followed by that in the Lancang River source, and the lowest in the Yellow River source. The soil pH of the riparian zone of the Yangtze River source was significantly higher than that of the Lancang River source, whereas the mean contents of organic matter, total nitrogen, and Fe-Al combined phosphorus were significantly lower than those of the Lancang River source. The calcium and magnesium-combined phosphorus was the main form of phosphorus in riparian soil （63.89%）. Temperature, soil organic phosphorus content, and pH had significant effects on plant composition in the riparian zone of the Three-River Headwaters Region, whereas soil calcium and magnesium-combined phosphorus content had significant effects on plant community diversities. These results may deepen the scientific understanding of the evolution trend and genetic mechanism of plant communities in the riparian zone of the Three-River Headwaters Region.

Beyond grassland degradation: Pathways to resilience for pastoralist households in alpine grassland ecosystems

Sustaining the development of rural and pastoral communities' hinges on livelihood resilience. Pastoralist household resilience relies on resource availability and decision-making abilities. Despite extensive studies on pastoralist livelihoods, a significant knowledge gap remains in understanding the nuanced adaptive capacities of diverse households, particularly amid grassland degradation. Thus, this study investigates the household-based livelihood resilience of pastoralists in China's Three-River Headwater Region, offering policy recommendations for resilient livelihoods. Using stratified random sampling, 758 pastoralist household heads underwent semi-structured interviews to collect data. Five household characteristics, encompassing age, gender, energy use, well-being perception, and multi-household grazing participation, were evaluated. Looking ot the nature of data, descriptive statistics and non-parametric tests were performed in this study to draw the valid inferences. The results revealed a positive correlation (p < 0.05) between household head age and livelihood resilience, with divergent resilience across age groups. Varied energy usage yielded distinct impacts; households employing solar or mixed energy sources exhibited heightened resilience (p < 0.05). Household well-being emerged as an invariant variable concerning resilience. Furthermore, engagement in multi-household grazing (an informal institution) significantly (p < 0.05) influenced pastoralist livelihood resilience. These insights advocate targeted support for young household heads and the adoption of clean energy. Exploring the deeper strategies and mechanisms of multi-household grazing can enhance understanding and policy integration, guiding eco-friendly progress within rustic landscapes for pastoral communities.

Ecosystem Resilience Trends and Its Influencing Factors in China’s Three-River Headwater Region: A Comprehensive Analysis Using CSD Indicators (1982–2023)

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the “Water Tower of China”. We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world.

Understanding the Dry-to-Wet Transition of Summer Precipitation over the Three-Rivers Headwater Region: Atmospheric Circulation Mechanisms

Summer precipitation has changed over the Three-Rivers Headwater (TRH) region, which may have an impact on droughts and floods in Asia. This study examines the notable interdecadal variation from dry to wet conditions in summer (June to August) precipitation over the TRH region during the period of 1979–2020. The changes could have been influenced by atmospheric circulations. This study aims to improve our understanding of the interdecadal variation in summer precipitation over the TRH region. Our findings reveal that a zonally oriented teleconnection wave train is generated across the Eurasian mid-to-high latitudes, originating from the North Atlantic and propagating to northern East Asia along the westerly jet. This results in a weakened and northward-shifted westerly jet. Additionally, anticyclonic circulation anomalies over the northern Tibetan Plateau contribute to easterly water-vapor transport anomalies in the region, reducing water-vapor export at the eastern boundary. Concurrently, an anomalous cyclone over the Arabian Sea and an anomalous anticyclone over the Bay of Bengal enhance the influx of oceanic water vapor into the TRH region. The enhanced Walker circulation further augments the equatorial easterly, which in turn strengthens the anomalous anticyclone over the Bay of Bengal. Consequently, these atmospheric changes contribute to the increased summer precipitation over the TRH region, elucidating the mechanisms behind the observed dry-to-wet transition.

Temporal and spatial evolution of net primary productivity in the Three-River Headwaters Region under phenological changes and anthropogenic influence.

Understanding the spatiotemporal changes in net primary productivity (NPP) and the driving factors behind these changes in climate-vulnerable regions is crucial for ecological conservation. This study simulates the actual NPP (NPPA) and climate potential NPP (NPPC) in the Three-River Headwaters Region from 2000 to 2020. The Theil-Sen Median method and Mann-Kendall mutation analyses are employed to explore their spatiotemporal variation patterns, while geographic weighted regression and machine learning are used to investigate the influence of anthropogenic activities and climatic factors on NPPA, the results indicate that the average NPPA across the entire region over multiple years is 382.506 , which is 0.132 times the average annual NPPC over the past 21years, showing an overall distribution pattern of low in the northwest and high in the southeast. The annual increase in NPPA from 2000 to 2020 is approximately 1.034 . The source region of the Yangtze River shows the largest improvement in vegetation, with 74.1% of the area showing improvement. Between 2002 and 2003, the annual NPPA in the Three-River Headwaters Region experienced a sudden change, lagging behind the NPPC change by 1year, and after 2005, the upward trend in NPPA became more pronounced. The impact of anthropogenic activities on NPPA shifted from positive to negative to positive from 2000 to 2020, with significant impact areas mainly concentrated in the northeast and a few areas in the central and southern parts. The proportion of areas with extremely significant impact increased from 1.9% in 2000 to 3.7% in 2020. Over the past 21years, the main factors influencing NPPA changes in the Three-River Headwaters Region have been soil moisture and precipitation, with the influence of different climate factors on NPP changing over time. Additionally, NPP is more sensitive to changes in altitude in low-altitude areas. This study can provide more accurate theoretical support for ecological environment assessment and subsequent protection efforts in the Three-River Headwaters Region.

A dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015

Evapotranspiration (ET) plays an important in the exchange of water, carbon and energy on land. The ET estimates based on different models and remote sensing data have different degrees of uncertainty. Bayesian model averaging (BMA) provides a way to reduce the uncertainty. Based on water and heat flux observation data in the Three-River Headwaters Region of China, evapotranspiration simulated by ARTS and PT-JPL models, and internationally shared MOD16 and SSEBop remote sensing evapotranspiration products, we conducted a BMA integration study, resulting in a dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015. By verifying the results of each input model and BMA integrated model, it is found that the correlation between ET based on BMA and flux observation data is 0.94, which can explain 89% of the seasonal variation data, outperforming the performance of a single model. The results show that BMA model integration can integrate the inherent advantages of different models and reduce the uncertainty of result estimation for more reliable estimation results. This dataset can provide more accurate data for studying hydrothermal changes and evaluating ecosystem regulation functions in the Three-River Headwaters Region.

A dataset of vegetation net primary productivity at 250-meter spatial resolution in the Three-River Headwaters Region of Qinghai Province from 2000 to 2018

Net primary productivity (NPP) is a fundamental indicator quantifying photosynthetic production in ecology. With the development of ecosystem process simulation and satellite remote sensing technology, NPP is widely applied to exploring long-term spatiotemporal dynamics and underlying mechanism in terrestrial ecosystems with high spatial heterogeneity. Using the remote sensing ecological process coupling model GLOPEM-CEVSA and incorporating meteorological data interpolated from 2000 to 2018 the remote sensing-based vegetation photosynthetically active radiation absorption (FPAR) data, we obtained a dataset of vegetation net primary productivity at 250-meter spatial resolution for the Three-River Headwaters Region of Qinghai Province from 2000 to 2018. The dataset was made accessible to the public through the National Data Center for Ecological Sciences. The data sharing is expected to provide reliable scientific data to support for the study of spatial and temporal changes and the decision making related to sustainable development in the region.

Effects of hillslope aspect on erosion rate of alpine meadows in the Three-River Headwater Region, China

Soil erosion causes serious grassland degradation in the Three-River Headwater Region (TRHR) of the Qinghai-Tibet Plateau. Identifying source areas of erosion is important, including understanding the influences of hillslope aspect on soil erosion. However, the effect of hillslope aspect on soil erosion is a much-debated process, wherein contrasting results were previously reported. We hypothesized that aspect-induced differences in environmental variables will be related to differences in erosion magnitude and spatial pattern, and that these differences will impact soil physiochemical characteristics as well. To test this hypothesis, soil samples were collected at conjoined south- and north-facing hillslopes along elevational profiles in a small watershed in Yushu County, with measurements of soil water content (SWC), soil organic carbon (SOC), soil organic matter (SOM), soil particle size distribution, soil total nitrogen (STN) and phosphorus (STP). Soil erosion rate was estimated using 137Cs technology. The results showed that soil erosion rates of the two hillslopes ranged from 0.073 to 10.83 t ha-1 yr-1, and were 3 times greater on the south-facing hillslope. Soil water content ranged from 7.6% to 76.8% on the two hillslopes with lower values on the south-facing hillslope due to the greater solar radiation. By Spearman correlation analysis and path analysis, we concluded that lower soil water content on the south-facing hillslope restricts vegetation growth, and hence resulting in greater soil erosion under the case where vegetation growth is dominantly controlled by water availability. Greater soil erosion on the south-facing hillslope caused greater surficial accumulation of coarser materials and greater loss of soil nutrients, which are expected to complicate the hydrological erosion process. Moreover, the downslope increased soil erosion rate should be fully considered in conservation practices. These results will improve our understanding of the interaction and feedback mechanisms between soil characteristics, vegetation function, and soil erosion in the TRHR.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Soil water sources and their implications for vegetation restoration in the Three-Rivers Headwater Region during different ablation periods

Abstract. Amid global warming, the timely supplementation of soil water is crucial for the effective restoration and protection of the ecosystem. It is therefore of great importance to understand the temporal and spatial variations of soil water sources. The research collected 2451 samples of soil water, precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater in June, August, and September 2020. The goal was to quantify the contribution of various water sources to soil water in the Three-Rivers Headwater Region (China) during different ablation periods. The findings revealed that precipitation, ground ice, and snow meltwater constituted approximately 72 %, 20 %, and 8 % of soil water during the early ablation period. The snow is fully liquefied during the latter part of the ablation period, with precipitation contributing approximately 90 % and 94 % of soil water, respectively. These recharges also varied markedly with altitude and vegetation type. The study identified several influencing factors on soil water sources, including temperature, precipitation, vegetation, evapotranspiration, and the freeze–thaw cycle. However, soil water loss will further exacerbate vegetation degradation and pose a significant threat to the ecological security of the “Chinese Water Tower”. It emphasizes the importance of monitoring soil water, addressing vegetation degradation related to soil water loss, and determining reasonable soil and water conservation and vegetation restoration models.

Primary driving factors of ecological environment system change based on directed weighted network illustrating with the Three-River Headwaters Region

The primary driving factors of ecological environment change have received significant attention. However, previous research methods for identifying the main drivers of ecological environment change have primarily relied on correlation analysis and regression analysis. While these methods can reveal co-occurrences, associations, and correlations among elemental characteristics, they often struggle to uncover the deep-seated interactions among elements within complex, unstable, nonlinear, and high-dimensional systems. To address this, we used the Three-River Headwaters Region as a case study and introduced a complex network model from the perspective of the ecological environment system to investigate the main driving factors of ecological environment change. In our analysis, we considered 12 factors related to the atmosphere, hydrology, vegetation, and soil, including evaporation, long-wave radiation, short-wave radiation, specific humidity, soil temperature, precipitation rate, soil water content, air temperature, air pressure, vegetation normalization index, wind speed, and natural surface runoff. Watersheds were selected as the fundamental units for constructing ecological environment datasets. We applied the Ensemble Empirical Mode Decomposition (EEMD) method and Hilbert-Huang Transform (HHT) to analyze causal relationships between time series pairs and constructed two directed weighted network models based on sub-catchments. The results showed that both network models yielded consistent conclusions, with the sparse network exhibiting higher efficiency. Radiation and temperature were identified as the primary driving factors of ecosystem change, and the water cycle was determined to be the ultimate manifestation of ecological system change throughout the Three-River Headwaters Region. Furthermore, based on node out-strength, we generated a vegetation protection priority map.

Enhancing sustainable livelihoods in the Three Rivers Headwater Region: A geospatial and obstacles context

Applying novel approaches to assess the sustainable livelihood potentially contribute to learning and managing social-ecological systems. This research demonstrates the trajectory of sustainable livelihoods in the Three Rivers Headwater Region (TRHR) of Western China for a period of 2003 to 2017, utilizing a bespoke Sustainable Livelihood Index (SLI). Devised through a top-down data collation method, the SLI integrates comprehensive official statistics, encompassing socio-economic metrics alongside climatic and other environmental conditions. We employed spatial visualization and cold hotspot analysis to discern the spatio-temporal nuances of the SLI. Furthermore, the Obstacle Factor Analysis (OFA) was implemented to identify salient barriers impacting the SLI. Results indicated a steady rise in the SLI over the examined period, indicating progressive enhancements in the livelihood sustainability. However, marked disparities were observed across the TRHR counties, reflecting heterogeneity in socioeconomic progress and resource allocation. While Yushu county displayed commendable growth, Jiuzhi county stood out as a significant cold spot. Notably, natural and financial resources surfaced as paramount stresses to SLI progression, emphasizing the necessity for robust resource management and economic support. Key challenges, such as the grassland carrying pressure index, deteriorated grassland territories, and per capita disposable income were identified as critical obstacles to the sustainable livelihood of pastoral communities. These factors collectively influence economic viability, grassland resource stewardship, and human capital development, which are intertwined with local economic gains and future potentials. In nutshell, this study highlights the multifaceted challenges faced by the TRHR in charting sustainable livelihood pathways and champions recalibrated policies to protect natural assets, amplify economic support, reinforce human capital, and nurture regional synergies for sustainable evolution. The key insights suggest the pressing need for innovative, nature-based solutions that amplify regional-scale livelihood resilience, especially considering the challenges related to natural assets, financial support, and human capital development.

Trends and contribution of different grassland types in restoring the Three River Headwater Region, China, 1988–2012

Rapid greening of the Qinghai–Tibet Plateau had been confirmed, but the contributions to the overall change and its causes in various grassland types has been less studied. Previous research has focused on exogenous factors such as climate change and human activities, rather than on endogenous factors, such as grassland types. Using net primary productivity (NPP), precipitation and temperature data, we applied trend, contribution and pull contribution analysis to understand the spatiotemporal evolution and driving factors of six different grassland types at a pixel scale in the Three River Headwater Region (TRHR) of China from 1988 to 2012. The results showed that grassland NPP in the TRHR increased at an average growth amount of 3.46 gC m−2 yr−1 and an average growth rate of 2.26 %. The average growth amount of alpine desert and alpine steppe (0.42 gC m−2 yr−1, 1.74 gC m−2 yr−1, respectively) showed great potential improvement. The average growth rate (1.27 %, 1.87 %) of montane meadow and alpine meadow, respectively, presented a high potential to increase (P < 0.05). Alpine meadow, montane meadow and temperate steppe were positive pullers to the average growth amount. Alpine steppe and alpine desert were positive pullers to the average growth rate. In general, alpine meadow had the highest growth amount contribution (84.86 %), while alpine meadow and alpine steppe had the highest contribution to the growth rate (62.16 %, 34.24 %, respectively). The study implied that, in addition to external factors, differences in internal factors such as the community composition and structure of different grassland types could also affected the grassland recovery process. These results contribute to understanding the specific differences in the contribution of regional grassland restoration processes from vegetation composition. Assessing grasslands with the potential to increase productivity, we can provide scientific reference for implementing more precise and efficient measures in future grassland management restoration.

Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.

Short-term restoration effects of ecological projects detected using the turning point method in the Three River Headwater Region, China.

The Three River Headwater Region (TRHR) is an important river source area providing important ecological functions. Decades ago, climate change and human activities severely degraded the ecosystem in the TRHR. To restore vegetation, a series of ecological projects have been implemented since 1989. Using net primary productivity (NPP) data from 1988 to 2012, a sequential Mann-Kendall trend test (SQ-MK) method was applied to identify the turning point of vegetation NPP. This approach was able to represent the critical response time of the vegetation to important disturbances. A 3-year time window was set after the implementation of one ecological project to detect and analyze its short-term effects. The ecological projects included the Yangtze River Basin Shelterbelt System Construction Project (YRCP), the TRHR Nature Reserve Construction Project (TNR), the Returning Grazing Land to Grassland Project (RGLGP), and the first phase of the Ecological Conservation and Restoration Project of the TRHR (ECRP). Our results showed that the vegetation in the TRHR responded positively to restoration: 89% of pixels showed an increasing trend and 54% of pixels underwent an abrupt change. The accelerated growth type accounted for the highest proportion among all types of detected turning points. In the ECRP's window, the positive turns rose rapidly, from 41% in 2005 to 86% in 2008, and it showed the most balanced restoration effects across grasslands. The alpine meadow and montane meadow restoration was largely influenced by the ECRP and the RGLGP (both >40%). The alpine steppe restoration was mainly attributed to the ECRP (68%). On the county scale, the positive turns in Yushu at the source of the Yangtze River mainly benefited from the RGLGP (56%), while the positive turns in Maduo at the source of the Yellow River benefited from the ECRP (77%). Nangqian, Tanggula and Zaduo County were still in need of intervention for restoration (< 3%). The results of the study can enhance our understanding of the spatio-temporal distribution of the short-term ecological benefits of different ecological projects, thus provide a scientific and timely reference for future planning and adjustment of the conservation and restoration projects.

Spatiotemporal Changes in Water Storage and Its Driving Factors in the Three-River Headwaters Region, Qinghai–Tibet Plateau

Water storage (WS) is a crucial terrestrial ecosystems service function. In cold alpine regions (CAR), the cryosphere elements are important solid water resources, but the existing methods for quantitatively assessing WS usually ignore cryosphere elements. In this study, a revised Seasonal Water Yield model (SWY) in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), which considers the effects of frozen ground (FG) and snow cover (SC) on WS, was employed to estimate the spatiotemporal distribution and changes in WS in the Three-Rivers Headwaters region (TRHR) from 1981 to 2020. Sensitivity analyses were conducted to understand the overall effects of multiple factors on WS, as well as the dominant driving factors of WS change at the grid scale in the TRHR. The results show that (1) the WS in the TRHR generally increased from 1981 to 2020 (0.56 mm/year), but the spatial distribution of WS change varied greatly, with a significant increasing trend in the northwest part and a significant decreasing trend in the southeast part. (2) In the last 40 years, increased precipitation (Pre) positively affected WS, while increased potential evapotranspiration (ET0) reduced it. Increased permeability caused by degradation of frozen ground increased WS, while snow cover and LULC changes reduced it. (3) In the TRHR, Pre primarily affected the WS with the largest area ratio (32.62%), followed by land use/land cover (LULC) (19.69%) and ET0 (18.49%), with FG being fourth (17.05%) and SC being the least (6.64%). (4) The highly important and extremely important zones generally showed a decreasing trend in WS and should be treated as key and priority conservation regions. It is expected that this research could provide a scientific reference for water management in the TRHR.

Characteristics of Solar-Induced Chlorophyll Fluorescence in the Three River Headwaters Region, Qinghai-Tibetan Plateau during 2001 to 2020

The ecology of the Three River Headwaters Region (TRHR) is related to the long-term sustainable development of Qinghai Province and the whole of China. The change in chlorophyll fluorescence is an important index to measure the ecological environment. Therefore, it is of great significance to study the spatial and temporal distributions of Solar-induced chlorophyll fluorescence (SIF)and the related influencing factors in the TRHR. In this study, a high-resolution SIF dataset (2001–2020) was selected to be averaged on a time scale of years and months to investigate the annual and seasonal SIF characteristics, and the influencing climate factors were analyzed in combination with meteorological data by statistical method. The results showed that the SIF values ranged from 0.05 to 0.073 during 2001–2020, with a peak value of 0.073 in 2005 and 2009 and a minimum value of 0.05 in 2002. The averages of SIF values were higher in the source regions of the Yellow River source region (YR) and Langcang (Mekong) River source region (LCR) than in the source region of the Yangtze River source region (YZR). The SIF values of the TRHR in July, August and September were significantly higher than those in other months. The maximum value occurred in August at 0.11, and the minimum value was 0.008 in December. The precipitation had greater effect on the inter-annual variations in SIF. The monthly variation of SIF is influenced by precipitation, temperature and relative humidity. In addition, the influence of human activities and altitude on SIF should not be ignored. The results have certain reference value for protecting vegetation in the TRHR, and provide a reference for other regions to analyze the spatiotemporal changes and influencing factors by using SIF data.

Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD &gt; 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p &lt; 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.