Three-River Headwaters Region Research Articles

Spatial and Temporal Evolution Characteristics of Water Conservation in the Three-Rivers Headwater Region and the Driving Factors over the Past 30 Years

The Three-Rivers Headwater Region (TRHR), located in the hinterland of the Tibetan Plateau, serves as the “Water Tower of China”, providing vital water conservation (WC) services. Understanding the variations in WC is crucial for locally tailored efforts to adapt to climate change. This study improves the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) water yield model by integrating long-term time series of vegetation data, emphasizing the role of interannual vegetation variation. This study also analyzes the influences of various factors on WC variations. The results show a significant increase in WC from 1991 to 2020 (1.4 mm/yr, p < 0.05), with 78.17% of the TRHR showing improvement. Precipitation is the primary factor driving the interannual variations in WC. Moreover, distinct interactions play dominant roles in WC across different eco-geographical regions. In the north-central and western areas, the interaction between annual precipitation and potential evapotranspiration has the highest influence. Conversely, the interaction between annual precipitation and vegetation has the greatest impact in the eastern and central-southern areas. This study provides valuable insights into the complex interactions between the land and atmosphere of the TRHR, which are crucial for enhancing the stability of the ecosystem.

Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020

Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020

Artificial grassland mapping using artificial grassland detection index of vegetation growth in the Three-River Headwaters region

Mapping the spatial distribution of artificial grassland for ecological restoration is of great significance for evaluating its secondary degradation and negative consequences, such as nonpoint source pollution of water bodies in the Three-River Headwaters (TRH) region. Because of the numerous challenges faced in obtaining ground training samples caused by adverse natural conditions, inclement cloudy weather and spectral similarity between natural and artificial grassland, commonly used classification or temporal-profile extraction methods have proven ineffective in identifying artificial grasslands. To overcome these challenges, we present a novel artificial grassland detection index for mapping their distribution using optical images with a resolution of 10 ∼ 30 m, along with their corresponding quality control data based on the Google Earth Engine cloud computing platform. The index is calculated using the ratio of the normalized difference vegetation index during the sowing and emergence period and the growth peak period of artificial grassland. A case study was conducted in Maqin County in the TRH region covering an area of 1.35 × 104 km2 from 2017 to 2021. Our proposed method demonstrated high accuracy and achieved a favorable balance between commission errors and omission errors. Over the study period, the average overall accuracy and Cohen's kappa were 96.2% and 0.91, respectively; with average precision, recall, and F1-score of artificial grassland being 89.6%, 99.2%, and 94.0%, respectively. The proposed method exhibited excellent robustness for the critical threshold used, with the average overall accuracy, F1-score, precision, and recall of artificial grassland between 2017 and 2021 consistently exceeding 90% for threshold values ranging from 1.5 to 2.0 throughout the study period. These findings suggest that our proposed method is capable of efficiently and accurately obtaining the detailed spatiotemporal distribution of artificial grassland in the TRH region. Moreover, the method also meets the pressing requirement for the rapid acquisition of detailed spatiotemporal distribution of artificial grassland across the Qinghai-Tibet Plateau.

A New Tool for Mapping Water Yield in Cold Alpine Regions

Watershed management requires reliable information about hydrologic ecosystem services (HESs) to support decision-making. In cold alpine regions, the hydrology regime is largely affected by frozen ground and snow cover. However, existing special models of ecosystem services usually ignore cryosphere elements (such as frozen ground and snow cover) when mapping water yield, which limits their application and promotion in cold alpine regions. By considering the effects of frozen ground and snow cover on water yield, a new version of the Seasonal Water Yield model (SWY) in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) was presented and applied in the Three-River Headwaters Region (TRHR) in southeastern Qinghai-Tibetan Plateau (QTP). Our study found that incorporating the effects of frozen ground and snow cover improved model performance. Frozen ground acts as a low permeable layer, reducing water infiltration, while snow cover affects water yield through processes of melting and sublimation. Both of these factors can significantly impact the distribution of spatial and temporal quickflow and baseflow. The annual average baseflow and water yield of the TRHR would be overestimated by 13 mm (47.58 × 108 m3/yr) and 14 mm (51.24 × 108 m3/yr), respectively, if the effect of snow cover on them is not considered. Furthermore, if the effect of frozen ground on water yield were not accounted for, there would be an average of 6 mm of quickflow misestimated as baseflow each year. Our study emphasizes that the effects of frozen ground and snow cover on water yield cannot be ignored, particularly over extended temporal horizons and in the context of climate change. It is crucial to consider their impacts on water resources in cold alpine regions when making water-related decisions. Our study widens the application of the SWY and contributes to water-related decision-making in cold alpine regions.

Increased southerly and easterly water vapor transport contributed to the dry-to-wet transition of summer precipitation over the Three-River Headwaters in the Tibetan Plateau

The Three-River Headwaters (TRH) region in the Tibetan Plateau is vulnerable to climate change; changes in summer (June–August) precipitation have a significant impact on water security and sustainability in both local and downstream areas. However, the changes in summer precipitation of different intensities over the TRH region, along with their influencing factors, remain unclear. In this study, we used observational and ERA5 reanalysis data and employed a precipitation categorization and water vapor budget analysis to quantify the categorized precipitation variations and investigate their possible linkages with the water vapor budget. Our results showed an increasing trend in summer precipitation at a rate of 0.9 mm per year (p < 0.1) during 1979–2020, with a significant dry-to-wet transition in 2002. The category ‘very heavy precipitation’ (≥10 mm d−1) contributed 65.1% of the increased summer precipitation, which occurred frequently in the northern TRH region. The dry-to-wet transition was caused by the effects of varied atmospheric circulations in each subregion. Southwesterly water vapor transport through the southern boundary was responsible for the increased net water vapor flux in the western TRH region (158.2%), while southeasterly water vapor transport through the eastern boundary was responsible for the increased net water vapor flux in the central TRH (155.2%) and eastern TRH (229.2%) regions. Therefore, we inferred that the dry-to-wet transition of summer precipitation and the increased ‘very heavy precipitation’ over the TRH was caused by increased easterly and southerly water vapor transport.

Moisture source anomalies connected to flood‐drought changes over the three‐rivers headwater region of Tibetan Plateau

AbstractThe Flood‐Drought condition over the Three‐River Headwater region (TRHR) of the Tibetan Plateau (TP) exerts vital impacts on regional climate and downstream livelihoods. However, the reasons behind precipitation variability over this region remain elusive. In this study, the multi‐year moisture sources of water vapour reaching the TRHR were identified to reveal their connection to the interannual variability of summer precipitation by utilizing a Lagrangian diagnosis analysis. The results show that the moisture sources could be tracked backward covering vast areas, with the peak values located mostly at the northern Indian continent and adjacent regions of TRH itself. The variability of integrated moisture sources' contribution agrees well with the summer precipitation on the interannual scale; However, the role of different regions varies in line with their geographical location. More specifically, summer precipitation is positively correlated with the moisture source over the adjacent areas of TRH and the remote terrestrial‐oceanic regions, in contrast to a negative correlation to the water vapour departing from the Northeastern Asian and western regions to the TP. The Indian summer monsoon circulation and local recycling process play a dominant role in modulating the summer precipitation on the interannual scale, whereas the role of the westerlies is almost negligible. The abnormal atmosphere circulations triggered by mid‐high latitude wave trains and anomalies in SST in the central and eastern Pacific Ocean and Indian Ocean could serve as possible drivers for the precipitation variability over TRHR.

Spatiotemporal variations of precipitation during the rainy season over the three-rivers headwater region of tibetan plateau from 1990 to 2020

Spatiotemporal variations of precipitation during the rainy season over the three-rivers headwater region of tibetan plateau from 1990 to 2020

The Radial Growth of Juniperus squamata Showed Sharp Increase in Response to Climate Warming on the Three-River Headwaters Region of Tibetan Plateau since the Early 21st Century

In order to explore the impact of climate change on the ecosystem at high altitudes, dendroclimatology was used to study the response of radial growth of Juniperus squamata Buch.-Ham. ex D.Don to the rapid warming in Nangqian County over the past 60 years, and a tree-ring width chronology for 115 years was established. (1) Meteorological data showed that the temperature in Nangqian County of the Tibetan Plateau has increased continuously during the past 60 years, and the minimum temperature has had the most significant change (0.63 °C/10a), especially between 2000–2019 (0.12 °C/a). Over the same time period precipitation has not changed significantly (0.94 mm/a, p &gt; 0.10). The standard chronology was used to reconstruct the mean temperature series from July to September in Nangqian meteorological station during the past 115 years (1905–2019). The explained variance of the reconstructed equation was 42.8% (40.8%, after adjusting for degrees of freedom). The reconstructed temperature series can be roughly divided into two stages: from 1905 to 1999, the temperature fluctuated around the average value, 12.10 °C, and from 2000 to 2019, the temperature showed a significant upward trend. (2) The analysis of the climate-tree growth relationship indicated that the response of radial growth of Juniperus squamata to temperature was significantly stronger than the response to precipitation; especially in the last 20 years, when the radial growth of Juniperus squamata was positively correlated with temperature (p &lt; 0.01). Compared to the maximum temperature and mean temperature, the correlation between radial growth of Juniperus squamata and minimum temperature was more significant. (3) Under the background of climate warming, the radial growth trend of Juniperus squamata in Nangqian county was consistent with temperature changes. Particularly in the past 20 years, the radial growth of Juniperus squamata showed a significantly increased trend and entered a rapid growth period.

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

The Responses of Vegetation NPP Dynamics to the Influences of Climate–Human Factors on Qinghai–Tibet Plateau from 2000 to 2020

The dominant influencing factors of changes in vegetation NPP and the relative roles of climate–human factors on the Qinghai–Tibet Plateau (QTP) differ between historical periods and are unclear. Therefore, there is an urgent need to systematically and quantitatively analyze the evolution process of the QTP’s ecosystem pattern and the driving factors of this process. Based on MOD17A3H and meteorological data, the Miami model, correlation analysis, and the residual coefficient method were used to investigate the spatiotemporal patterns of changes in vegetation NPP on the QTP from 2000 to 2020. We then quantitatively distinguished the relative roles of climate change and human activity in the process of vegetation NPP change during different historical periods. The results show the following: (1) From 2000 to 2020, zones with increasing vegetation NPP (10–30%) were the most widely distributed, and were mainly located in the Three-Rivers Headwater Region and the northern part of the Hengduan Mountains. (2) From 2000 to 2020, zones with a significant positive correlation between vegetation NPP and annual precipitation were mostly distributed in the northeastern QTP and the Three-Rivers Headwater Region, while zones with a positive correlation between vegetation NPP and annual average temperature were mostly located in southern Tibet. Zones with a significant positive correlation between NPP and annual sunshine hours were mainly distributed in the southeastern part of the QTP and the southern part of the Tanggula Mountains. In contrast, zones with a significant positive correlation between NPP and accumulated temperature (&gt;10 °C) were mainly concentrated in the northern and eastern parts of the QTP. (3) During different historical periods, the relative roles of climate–human factors in the process of vegetation NPP change on the QTP had obvious spatiotemporal differences. These results could provide scientific support for the protection and restoration of regional ecosystems on the QTP.

Precipitation Characteristics across the Three River Headwaters Region of the Tibetan Plateau: A Comparison between Multiple Datasets

Precipitation is crucial for managing water resources in the Three River Headwaters (TRH) region of the Tibetan Plateau (TP). Gridded precipitation datasets across the TRH region exhibit significant discrepancies in their results. Previous studies have primarily focused on assessing average or extreme precipitation for a single dataset or several datasets. In this study, based on the observed gridded precipitation dataset (CN05.1), a comprehensive evaluation of the climatic features and extreme precipitation across the TRH region from 1983 to 2014 is performed by employing two gauge-based gridded datasets (GPCC and CRU), two satellite-derived precipitation datasets (P-CDR and IMERG), and two reanalysis precipitation datasets (ERA5 and CRA40). The results show that all datasets are consistent in reproducing the climatology, interannual variability, and annual cycle of precipitation in the TRH region. However, the different datasets exhibit significant discrepancies in characterizing the long-term trends and extreme precipitation events. P-CDR and GPCC provide a good representation of the spatial variability of the annual mean climatology. ERA5 and CRU are more reliable in capturing interannual variabilities. The long-term trends can be closely described by employing CRU. P-CDR and GPCC exhibit higher skills in terms of the annual cycle. P-CDR performs better than IMERG for daily precipitation in terms of probability distributions and other assessment metrics. P-CDR and IMERG have advantages and disadvantages in characterizing the nine extreme precipitation indices. This study demonstrates a comprehensive comparison method using multiple precipitation datasets to gain essential insight into the strengths and weaknesses of various datasets across the TRH region.

Mapping Alpine Grassland Fraction Coverage Using Zhuhai-1 OHS Imagery in the Three River Headwaters Region, China

The widely spread alpine grassland ecosystem in the Three River Headwaters Region (TRHR) plays an essential ecological role in carbon sequestration and soil and water conservation. In this study, we test the latest high spatial resolution hyperspectral (Zhuhai-1 OHS) remote sensing imagery to examine different alpine grassland coverage levels using Multiple Endmember Spectral Mixture Analysis (MESMA). Our results suggest that the 3-endmember (3-EM) MESMA model can provide the highest image pixel unmixing percentage, with a percentage exceeding 97% and 96% for pixel scale and landscape scale, respectively. The overall accuracy shows that Zhuhai-1 OHS imagery obtained the highest overall accuracy (83.7%, k = 0.77) in the landscape scale, but in the pixel scale, it is not as good as Landsat 8 OLI imagery. Overall, we can conclude that the hyperspectral imagery combined 3-EM MESMA model performs better in both pixel scale and landscape scale alpine grassland coverage mapping, while the multispectral imagery with the 3-EM MESMA model can satisfy requirements of alpine grassland coverage mapping at the pixel scale. The approaches and workflow to mapping alpine grassland in this study can help monitor alpine grassland degradation; not only in the Qinghai–Tibetan Plateau (QTP), but also in other grassland ecosystems.

More management is needed to improve the effectiveness of artificial grassland in vegetation and soil restoration on the three-river headwaters region of China.

Establishing an artificial grassland is a common measure employed to restore heavily degraded alpine grasslands for regional sustainability. The Three-River Headwaters Region in China has significant areas of black-soil-type grassland which is typified by heavy degradation; nearly 35% of the grassland regions in the Three-River Headwaters Region has degraded into this type. There are different plant community types of black-soil-type grasslands, however, it is not clear which restoration measures should be adopted for different kinds of black-soil-type grasslands. Here, we investigate the plant community characteristics and soil physicochemical properties of artificial grasslands, two types of black-soil-type grasslands, and native undegraded grassland in the Three-River Headwaters Region, then analyzed the direct and indirect interactions between the plant and soil properties by partial least squares path models (PLS-PM). Our results revealed that establishing artificial grassland significantly increased aboveground biomass and plant community coverage, and also decreased plant species richness and diversity and soil water content, soil organic carbon and total nitrogen in the 0-10 cm soil layer as compared with black-soil-type grasslands. Plant community diversity had a positive effect on plant community productivity, soil nutrient, and soil water content in native undegraded grassland. These results suggest that more management interventions are needed after establishing an artificial grassland, such as reducing dominant species in two types of black-soil-type grasslands, water regulation in the A. frigida-dominated meadow, diversifying plant species (i.e., Gramineae and sedges), and fertilizer addition.

Relationship between plant species diversity and aboveground biomass in alpine grasslands on the Qinghai–Tibet Plateau: Spatial patterns and the factors driving them

Alpine grasslands are important ecosystems on the Qinghai–Tibet Plateau and are extremely sensitive to climate change. However, the spatial responses of plant species diversity and biomass in alpine grasslands to environmental factors under the background of global climate change have not been thoroughly characterized. In this study, a random forest model was constructed using grassland ground monitoring data with satellite remote sensing data and environmental variables to characterize the plant species diversity and aboveground biomass of grasslands in the Three-River Headwaters Region within the Qinghai–Tibet Plateau and analyze spatial variation in the relationship between the plant species diversity and aboveground biomass and their driving factors. The results show that (1) the selection of characteristic variables can effectively improve the accuracy of random forest models. The stepwise regression variable selection method was the most effective approach, with an R2 of 0.60 for the plant species diversity prediction model and 0.55 for the aboveground biomass prediction model, (2) The spatial distribution patterns of the plant species diversity and aboveground biomass in the study area were similar, they were both high in the southeast and low in the northwest and gradually decreased from east to west. The relationship between the plant species diversity and aboveground biomass varied spatially, they were mostly positively correlated (67.63%), but they were negatively correlated in areas with low and high values of plant species diversity and aboveground biomass, and (3) Analysis with geodetector revealed that longitude, average annual precipitation, and elevation were the main factors driving variation in the plant species diversity and aboveground biomass relationship. We characterized plant species diversity and aboveground biomass, as well as their spatial relationships, over a large spatial scale. Our data will aid biodiversity monitoring and grassland conservation management, as well as future studies aimed at clarifying the relationship between biodiversity and ecosystem functions.

Contribution of the cryosphere to runoff in “Chinese water tower” based on environmental isotopes

Cryospheric meltwater is an important runoff component and it profoundly influences changes in water resources in the Tibetan Plateau. Significant changes in runoff components occur in the three-river headwater region (TRHR), which is an important part of “Chinese Water Tower” due to climate warming. However, these effects remain unclear owing to the sparse and uneven distribution of monitoring sites and limited field investigations. Quantifying the contribution of cryospheric meltwater to outlet runoff is a key scientific question that needs to be addressed. In this study, we analyzed 907 precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater samples collected from October 2019 to September 2020 in the TRHR. The following results were obtained: (1) There was significant spatio-temporal variation in stable isotopes in different waters; (2) The seasonal trends of stable isotopes for different waters, the relationship between each water body and the local meteoric water line (LWML) confirmed that river water was mainly recharged by precipitation, supra-permafrost water, and glacier snow meltwater; (3) Precipitation, supra-permafrost water, and glacier snow meltwater accounted for 52%, 39%, and 9% of river water, respectively, during the ablation period according to the end-member mixing analysis (EMMA); (4) In terms of future runoff components, there will be many challenges due to increasing precipitation and evaporation, decreasing snow cover, glacier retreat, and permafrost degradation. Therefore, it is crucial to establish the “star-machine-ground” observation networks, forecast extreme precipitation and hydrological events, build the “TRHE on the Cloud” platform, and implement systematic hydraulic engineering projects to support the management and utilization of water resources in the TRHR. The findings of environmental isotope analysis provide insights into water resources as well as scientific basis for rational use of water resources in the TRHR.

Ecosystem changes revealed by land cover in the three-river headwaters region of Qinghai, China (1990–2015)

The Three-River Headwaters Region (TRHR) of Qinghai Province, in the Tibetan Plateau of China, is the main source of the Yangtze, Yellow, and Lancang rivers, and is very significant to the security of freshwater resources for China and southeastern Asia. It is a critical ecological region of China for its ecological functions, and has been changed or even degraded in recent decades owing to climate change and human pressure. To effectively protect and restore the degraded ecosystems, the Chinese government initiated a series of ecological conservation projects in TRHR. It is essential to quantitatively assess ecosystem changes and their relationship to driving factors for in-depth understanding of long-term changes of ecosystems and effects of ecological restoration policies and offer practical insights for ecological restoration. Here, land cover data has been interpreted with the series data of Landsat during 1990–2015. The patterns of different ecosystems and their developing process have been derived from land cover change. The results show that ecosystem types in TRHR include forest, grassland, cropland, wetland, artificial surface and barren land, accounting for 4.51%, 70.80%, 0.15%, 9.47%, 0.16% and 14.90%, respectively. Barren land converted to wetland was the significant ecosystem change from 1990 to 2015. Increases in temperature and precipitation and implementation of ecological rehabilitation helped maintain relatively stable ecosystem patterns. It is necessary to continue ecological projects to improve and/or maintain the ecosystems in TRHR because there is still a risk of land degradation under increasing climate change and human activity.

Improved soil surface nitrogen balance method for assessing nutrient use efficiency and potential environmental impacts within an alpine meadow dominated region

The soil surface nitrogen balance (SSNB) method is commonly used to assess the nutrient use efficiency (NUE) of agricultural systems and any associated potential environmental impacts. However, the nitrogen flow of wide natural grasslands and other natural areas differ from that of artificial croplands and mown grasslands. In this study, we integrated root growth and the important nutrient resorption process into the SSNB model and used the improved model to clarify the nitrogen (N) flow and balance in the Three Rivers Headwater Region (TRHR)—an area dominated by alpine meadows—from 2012–2019. In the grassland system, the N surplus (ΔN) was 0.274 g m−2 year−1, and root return (BLD) dominated the N input, accounting for 67% of the total input (3.924 g m−2 year−1). N resorption was the main internal N flow in the grassland system (1.079 g m−2 year−1), and 30% of grassland uptake (NUP−grass). The ΔN of the agricultural system was 1.097 g m−2 year−1, which was four times that of the grassland, and chemical fertilizer was the largest input, accounting for 84% of the total input. The NUE in grassland was 93%, which suggests a risk of soil mining and degradation, while that of cropland was 76% and within an ideal range. The ΔN provides a robust measure of river N export, the TRHR was divided into three catchments, and the export coefficient was 16.14%–55.68%. The results of this study show that the improved SSNB model can be applied to a wide range of natural grasslands that have high root biomass and resorption characteristics.

Horizontal Variation Characteristics of Alpine Grassland Soil Function and Vertical Changes Along Soil Genetic Horizons in the Three-River Headwaters Region

The Three-River Headwaters region is a hotspot for studying the response of soil function to climate change. To study the horizontal variation characteristics of alpine grassland soil function and vertical changes along soil genetic horizons, soil functional indicators (including respiration, nitrogen conversion rate, and enzymatic activity) of different genetic horizons in alpine grassland soil profiles and their correlations with environmental factors were analyzed. The results showed that there were no significant differences in soil functional characteristics between alpine meadows and steppes, and topsoil had higher respiration rates, nitrogen conversion rates, and enzymatic activities than those of subsoil. Total nitrogen was a key driver of soil functional characteristics in different genetic horizons, explaining 18.3%, 21.4%, and 27.5% of the horizontal variation in functional characteristics, respectively. Climate and vegetation factors mainly affected soil function indirectly by changing soil physicochemical properties in topsoil, but atmospheric nitrogen deposition still affected soil function in subsoil. These results indicate the significant nitrogen limitation of alpine grassland soil in the Three-River Headwaters region, and the findings provide a new insight into the maintenance of soil functional diversity and the response to climate change in the context of global climate change.

Dynamic Response and Adaptation of Grassland Ecosystems in the Three-River Headwaters Region under Changing Environment: A Review.

The Three-River Headwaters Region (TRHR) is crucial to the sustainable development of China and Southeast Asia. The sustainability of grassland ecosystems in the region has been seriously challenged in recent years. This paper reviewed the changes in the grasslands of the TRHR and their responses to climate change and human activities. The review showed that accurate monitoring of grassland ecological information is the basis for effective management. Although alpine grassland coverage and the above-ground biomass of the alpine grassland have generally increased in the region over the past 30 years, the degradation has not been fundamentally curbed. Grassland degradation substantially reduced topsoil nutrients and affected their distribution, deteriorated soil moisture conditions, and aggravated soil erosion. Grassland degradation led to loss of productivity and species diversity, and this is already harming the well-being of pastoralists. The "warm and wet" trend of the climate promoted the restoration of alpine grasslands, but widespread overgrazing is considered as one of the main reasons for grassland degradation, and related differences still exist. Since 2000, the grassland restoration policy has achieved fruitful results, but the formulation of the policy still needs to integrate market logic effectively and strengthen the understanding of the relationship between ecological protection and cultural protection. In addition, appropriate human intervention mechanisms are urgently needed due to the uncertainty of future climate change. For mildly and moderately degraded grassland, traditional methods are applicable. However, the severely degraded "black soil beach" needs to be restored by artificial seeding, and the stability of the plant-soil system needs to be emphasized to establish a relatively stable community to prevent secondary degradation.

Net Primary Productivity Estimation Using a Modified MOD17A3 Model in the Three-River Headwaters Region

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO2 concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 104 km2. The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm−2yr−1, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.