Three-River Headwaters Region Research Articles

Reconstructing the distribution of the paleobiome types in the Three-River Headwaters Region in China from the middle Holocene:

This study examined the spatial and temporal patterns of the paleobiome types in the Three-River Headwaters Region (TRHR, Sanjiangyuan) in China from the middle Holocene (i.e. 6000 BP). Existing st...

Reconstructing the distribution of the paleobiome types in the Three-River Headwaters Region in China from the middle Holocene

This study examined the spatial and temporal patterns of the paleobiome types in the Three-River Headwaters Region (TRHR, Sanjiangyuan) in China from the middle Holocene (i.e. 6000 BP). Existing studies explored the patterns of paleobiome types by identifying the type of biomes in pollen sites, based on the taxonomical assemblage of pollen samples. This site-wise identification, however, is not suited for the analysis of the TRHR because pollen sites in the region-level are discrete and low-resolution. In this study – to solve the lack of the pollen sites – climate data, which are easy to interpolate, were extracted from the pollen data by pollen transfer functions. Next, the extracted climate data were calibrated and interpolated over the TRHR and study period. Then, a physiological biome model based on climate was used to produce the chronology of the distribution of the biome types. Consequently, the chronology was reconstructed with a time interval of 50 years and a spatial cell size of 0.5 ° × 0.5 °. From the results, the variations in the distribution of the paleobiome types were mainly dominated by 36 patterns over 10 biome types. In detail, tundra and semidesert were the main types, and the alternation of these two types was the main pattern. Further, the long-term evolution was from tundra to semi-desert, and the precipitation was the main driving force for changes in biomes, while temperature also had some influence.

Internal and external coupling of Gaussian mixture model and deep recurrent network for probabilistic drought forecasting

Developing an accurate drought forecasting model is significantly important for minimizing the economic and ecological damage due to drought hazards. The competitive performances of deep neuron networks in drought forecasting have been proved in the last few years. However, the uncertainty of drought events has not been effectively addressed owing to massive computational requirements involved in deep learning framework. Instead of developing a heavy ensemble probabilistic model, this study introduced an improved long short-term memory network that is aggregated with a conditional probability model for probabilistic drought forecasting. Two different aggregation strategies are carefully discussed, implemented, and evaluated. For external coupling, the conditional probability model is used as a postprocessing technique that applied on deterministic forecasting. For internal coupling, the conditional probability model is embedded into the internal structure of a long short-term memory network, making the improved long short-term memory network model probabilistic in nature. The proposed methods were demonstrated though actual drought forecasts in the three-river headwater region of China. In addition, three state-of-the-art models: autoregressive moving average, generalized linear regression, and artificial neural networks, were developed for drought forecasting served as a comparison. The forecasting abilities were evaluated using four types of verification methods with respect to the predictive mean and interval. The obtained results showed that the improved model could successfully forecast drought index as well as uncertainty information. Moreover, the internal aggregation strategy was validated as superior in the probabilistic drought forecasting in the three-river headwater region.

Climate warming benefits alpine vegetation growth in Three-River Headwater Region, China

Environmental factors that drive vegetation change in the Three River Headwater Region (TRHR) on Qinghai-Tibetan Plateau are largely unknown. In particular, the response of alpine grasslands in the TRHR to changing climate and ecological compensations is still poorly understood. Here, we present data on vegetation trends of the TRHR from 1982 to 2015 by employing multiple high-resolution satellite data to determine the mean annual normalized difference vegetation index (NDVI). In addition, spatio-temporal changes in climate were monitored by long-term climate data collection and by using the distributed modeling system. It emerged that: 1) there was a weak increasing trend, albeit not significant, in overall TRHR NDVI, ranging between 0.23 and 0.27; whereas, grassland NDVI ranged between 0.43 and 0.50, and displayed a significant (r2adj = 0.46; P = 0.004) linear increase with year; 2) annual average temperature was below 0 °C and increased linearly (r2adj = 0.60; P = 0.01) at a rate of 0.06 °C/yr from 2000 to 2015, which was almost four times faster than the rate of global warming; and 3) average rainfall was 493 mm/yr, with no significant yearly trend. In conclusion, climate warming enhanced vegetation growth and recovery in the TRHR since 2000; whereas, rainfall did not show a trend. However, vegetation changes on the spatial scale demonstrated zoning and segmentation effects. Consequently, for restoration of degraded lands in the TRHR, effective one-to-one ecological conservation projects, which are particular to an eco-fragile area, should be implemented. In addition, these results are important for regional planning of livestock stocking rates and animal husbandry systems, which can have great impact on the livelihood of the people in the area.

The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China

ABSTRACT Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R 2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions.

Spatial-temporal variation and tradeoffs/synergies analysis on multiple ecosystem services: A case study in the Three-River Headwaters region of China

• Distribution of ecosystem services in the Three-River Headwaters region is obviously different. • Regulating services are the largest in all ecosystem services. • Synergies are the dominant correlation of ecosystem services in the Three-River Headwaters region. • The strength of a same pair-wise ecosystem services varies at different scales. We analyzed the spatial-temporal variation of ecosystem services in the Three-River Headwaters (TRH) region from 1990 to 2015 and their tradeoffs/synergies at the county-scale and grid-scale by using ecosystem service value model, correlation coefficient and bivariate spatial autocorrelation analysis. The results showed that: (i) From 1990 to 2015, the ecosystem services value (ESV) of the TRH region showed an upward trend, an increase of 60.681 billion yuan from 126.967 billion yuan in 1990 to 187.648 billion yuan in 2015. Moreover, the dynamic degree of ecosystem services was 6.59%. Regulating services accounting for 69.51% were the largest in all ecosystem services, followed by supporting, provisioning, and cultural services. Among the individual ecological services, only the values of gas regulation and environment depuration decreased, other services increased. Among various land use, the ecosystem services of grassland accounting for 47% were the largest, followed by watershed, wetland, forestland, desert, and farmlands. (ii) The spatial distribution of ecosystem services showed an increasing pattern from northwest to southeast. High-value areas of ecosystem services per unit area distributed in the lakes and basins of Zhidoi county, Madoi county, and Tanggulashan town. Low-value areas mainly distributed in the northwest of Zhidoi county. (iii) There was a significant positive correlation between climate regulation and soil conservation. Aesthetic landscape and all provisioning services were weak synergies, gas regulation and other services were weak tradeoffs. (iv) Analysis of multi-scale tradeoffs/synergies showed that synergies dominated ecosystem services, and tradeoffs transformed into synergies with the increase of scale. Quantifying the ESVs and clarifying the correlations among multiple ecosystem services at multi-scale can provide a basis for regional planning, and promote the sharing of ecological well-being among the people in the TRH region.

Assessment of Ecological Carrying Capacity and Ecological Security in China’s Typical Eco-Engineering Areas

The implementation of ecological restoration programs is intensively changing the original ecological carrying capacity and the status of ecological security. To evaluate the spatiotemporal variation of ecological carrying capacity and ecological security in China’s eco-engineering areas, an indicator system of ecological carrying capacity and ecological security should be developed. This study developed an evaluation indicator system that contains 29 indicators. The indicators were generated by long-time series and multi-source data. The indicator system presents the relationship between ecological carrying capacity and ecological security and reflects the dynamic change of them in eco-engineering areas. We selected the Three-River Headwaters Region (TRHR) and implemented the Ecological Conservation and Construction Program (ECCP) as a case study. The results showed the variation of ecological carrying capacity (ECC) and ecological security (ES) in the TRHR before (2000–2004), during early term (2005–2009), and during medium term (2010–2015) implementation of ECCP, and limiting factors of ecological carrying capacity and ecological security in TRHR was analyzed. The results showed that the ECC index and the ES index were significantly increase, indicating that the ECC improved and that the ES state got better in the TRHR after implementing ECCP. The water conservation was the major factors limiting the increase of the ECC. The leading factors limiting the improvement of the ES were educational expenditure before 2010 and turned into proportion of tertiary industry and investments for ecological restoration after 2010. The implementation of the ECCP has improved the ES state but has also resulted in new problems. It provides a scientific reference for future research on the indicator system of ecological carrying capacity and ecological security in eco-engineering areas and also has vital practical significance to guide the sustainable development of ecological restoration programs.

Multiscale variability of streamflow in the Three Rivers Headwater Region, China, and links to large-scale atmospheric circulation indices

Abstract Exploring the relations between streamflow and large-scale atmospheric circulation systems can assist in identifying potentially useful indicators for the modeling of hydrological processes. With the help of ensemble empirical mode decomposition and the wavelet analysis method, this research explored streamflow variations and its links to large-scale atmospheric circulation indices during 1960–2012 in the Three Rivers Headwater Region (TRHR). A steady increasing trend was detected in the streamflow of the source region of Yangtze River (SYR), and a steady decreasing trend was detected in the streamflow of the source region of Lancang River (SLR). The streamflow of the source region of Yellow River (SYeR) had an increasing trend in the early years of the study period and subsequently exhibited a decreasing trend. The Tibetan Plateau monsoon (TPM), Arctic Oscillation (AO), and South Asia monsoon (SAM) are the key factors influencing streamflow changes in the SYR, SYeR, and SLR, respectively. At interannual time-scale variation with the period of about 3–9 years, an antiphase relationship exists between SYR streamflow and TPM indices, while in-phase relationships are detected between SYeR (SLR) streamflow and AO (SAM) indices.

Does recent climate warming drive spatiotemporal shifts in functioning of high-elevation hydrological systems?

Reference evapotranspiration (ETo) is the basic component of the hydrological cycle. This study systematically analyzed the spatiotemporal variations of ETo in the Three-River Headwaters Region (TRHR) by utilizing the FAO Penman–Monteith equation during the period of 1961–2016, and studied the impact of climatic variables on ETo fluctuation based on sensitivity analysis and contribution analysis. Over the past 56 years, ETo increased in most months and annually in most regions of the TRHR, and the spatial distribution for annual ETo varied apparently. Sensitivity analysis revealed that ETo responded more to changes in maximum temperature and sunshine duration than relative humidity, wind speed, and minimum temperature. The relative importance of climatic variables to ETo was influenced by the ETo sensitivity and the fluctuation of climatic variables. Contribution analysis indicated that the significant temperature increase was mainly responsible for the observed increase in ETo. Besides, spatially distributed analysis showed that the annual TRHR ETo tended to increase from east to west, north to south, and high to low elevation. The analysis also indicated a dominant altitudinal spatial gradient in the sensitivity of ETo to changes in different climatic variables. The physical mechanisms for ETo variations discussed in this study might provide a reference for water resources management and conservation of high-elevation areas.

Multidimensional Response Evaluation of Remote-Sensing Vegetation Change to Drought Stress in the Three-River Headwaters, China

The impact of drought on ecosystems has become increasingly prominent. This study used the MODIS remote-sensing vegetation index and the standardized precipitation index to quantify vegetation coverage status and meteorological drought level. Using the copula method, the joint distribution of the drought index and related vegetation cover variables were simulated for the first time. The conditional distribution of vegetation biomass was deduced, and the multidimensional response between the vegetation biomass and drought was explored to understand the possible vegetation loss under different drought severity conditions. The three-river headwaters (TRH) region is the source place of the Yangtze River, Yellow River, and Lancang River, where the ecosystem is characterized by the innate vulnerability. Using this region as a case study, the results show that the spatiotemporal evolution of drought and vegetation in the TRH region has apparent regional heterogeneity. The vegetation cover in the eastern region is significantly better than that in the northwestern region, while the vegetation growth trend in the northwestern region is stronger than that in the southeastern region. It is feasible to build a vegetation-drought multidimensional response model based on the copula method. In 92.5% of the TRH region, vegetation cover was significantly affected by the severity of the drought. The impact on the growth of vegetation caused by persistent drought events was higher than that of short-term but high-intensity drought. The vegetation in the TRH area has a certain degree of drought resistance. This study provides outstanding theoretical support and reference for the protection of the TRH basin ecosystem.

Fragmentation and percolation thresholds in the degradation process of alpine meadow in the Three-River Headwaters region of Qinghai-Tibetan Plateau, China

Understanding the process and mechanisms of alpine meadow degradation is crucial for restoration and management in the Three-River Headwaters region, Qinghai-Tibetan Plateau, China. However, little is known about this complex and controversial problem because identification and quantification of the underlying causes is difficult. This research aimed to build a spatiotemporal dynamical model for alpine meadow degradation, capturing the natural process of erosion at the interface of barren patches and undamaged meadow. The model clarified the role of barren patches and meadow connectivity in degradation, and identified the ecological mechanisms and processes accounting for the spatial and temporal pattern of degradation. A fragmentation and percolation threshold exists in the process of meadow degradation, independent of spatial scale. An impulsive differential equation was used to investigate the consequence of periodic restoration of degraded meadow. Both the level of meadow degradation and the restoration period play crucial roles in determining whether the meadow can be successfully restored. This research has demonstrated theoretically that the effectiveness of meadow restoration by periodic effort depends on the degree of degradation.

基于IPA分析的三江源区游客满意度研究

基于IPA分析的三江源区游客满意度研究

Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.

Changes in the depths of seasonal freezing and thawing and their effects on vegetation in the Three-River Headwater Region of the Tibetan Plateau

Frozen ground degradation plays an important role in vegetation growth and activity in high-altitude cold regions. This study estimated the spatiotemporal variations in the active layer thickness (ALT) of the permafrost region and the soil freeze depth (SFD) in the seasonally frozen ground region across the Three Rivers Source Region (TRSR) from 1980 to 2014 using the Stefan equation, and differentiated the effects of these variations on alpine vegetation in these two regions. The results showed that the average ALT from 1980 to 2014 increased by 23.01 cm/10a, while the average SFD decreased by 3.41 cm/10a, and both changed intensively in the transitional zone between the seasonally frozen ground and permafrost. From 1982–2014, the increase in the normalized difference vegetation index (NDVI) and the advancement of the start of the vegetation growing season (SOS) in the seasonally frozen ground region (0.0078/10a, 1.83d/10a) were greater than those in the permafrost region (0.0057/10a, 0.39d/10a). The results of the correlation analysis indicated that increases in the ALT and decreases in the SFD in the TRSR could lead to increases in the NDVI and advancement of the SOS. Surface soil moisture played a critical role in vegetation growth in association with the increasing ALT and decreasing SFD. The NDVI for all vegetation types in the TRSR except for alpine vegetation showed an increasing trend that was significantly related to the SFD and ALT. During the study period, the general frozen ground conditions were favorable to vegetation growth, while the average contributions of ALT and SFD to the interannual variation in the NDVI were greater than that of precipitation but less than that of temperature.

Assessing the Snow Disaster and Disaster Resistance Capability for Spring 2019 in China’s Three-River Headwaters Region

Frequent snowfall and low temperatures led to a considerable snow disaster in some areas of China’s Three-River Headwaters Region (TRHR) in Qinghai province in the spring of 2019, exerting a considerably negative influence on animal husbandry production in local grasslands. Based on a model of snow disaster classification and quantitative estimations of disaster-stricken animal husbandry, we propose a comprehensive disaster resistance capability index (CDRCI) using remote sensing, ground monitoring, and statistical investigations. With a comprehensive assessment of the space distribution and the magnitude of snow disasters, combined with a quantitative determination of disaster-stricken animal husbandry, the proposed CDRCI calculates how grassland animal husbandry is affected by snow disasters in different counties of the TRHR. The results indicate that approximately 2.31 million sheep and yaks were affected by moderate to severe snow disasters in the TRHR, accounting for 78.3% of the total livestock in the affected region. Of these affected livestock, approximately 1.54 million sheep and yaks were specifically affected by severe snow disasters, accounting for 52.1% of the total number of livestock. The CDRCIs for grassland animal husbandry in both Yushu and were moderate, being higher for the former than for the latter. We confirmed that the proposed CDRCI can accurately evaluate the magnitude of snow disasters in terms of how they affect grassland animal husbandry. The CDRCI is a way of relating the number of animal deaths to spatial disaster prevention and resistance. We expect that this research will provide important theoretical support for formulating snow disaster resistance policy, for example for increasing the construction of grassland animal husbandry infrastructure as well as providing greater stored forage material.

Mapping of native plant species and noxious weeds to investigate grassland degradation in the Three-River Headwaters region using HJ-1A/HSI imagery

ABSTRACT Characterized by an alpine meadow, the ecological environment system in the ‘Three-River Headwaters’ region (TRHR) is considered to be a typical fragile ecological system. Numerous observations and research results have indicated that grassland degradation has occurred in the TRHR in recent years. However, research related to utilize the species information of grass communities to monitor grassland degradation remains rare. Therefore, the aim of this study is to produce the distribution maps of native plant species and noxious weeds to investigate grassland degradation for livestock farming perspective. In this study, the fused HJ-1A/HSI data was combined with field investigation samples to define the coverage of native plant species and noxious weeds at different coverage levels. Then, coverage distribution maps of native plant species and noxious weeds were produced by using support vector machine (SVM) classification and random forests (RF) regression methods. Meanwhile, the overall accuracy (OA) and root-mean-square error (RMSE) of each coverage map were assessed. Finally, a grassland degradation map was derived according to the native plant species and noxious weeds cover information. The experimental results show that (1) the spectral feature of native plant species and noxious weeds can be distinguished based on field measurement spectra in the TRHR; (2) the fractional coverage of native plant species and noxious weeds can be relatively accurately estimated when coverage is divided into nine levels; (3) the grass coverage estimation accuracies of SVM classification are similar with these of RF regression method. The OAs of SVM classification are 69.7% at nine grassland coverage levels for native plant species and noxious weeds, and corresponding RMSEs are 8.2% and 8.0%, respectively; and (4) the coverage of native plant species is generally higher than that of the noxious weeds in the study area.

Changes of Soil Erosion and Possible Impacts from Ecosystem Recovery in the Three-River Headwaters Region, Qinghai, China from 2000 to 2015

Soil erosion poses a great threat to the sustainability of the ecological environment and the harmonious development of human well-being. The revised universal soil loss equation (RUSLE) was used to quantify soil erosion in the Three-River Headwaters region (TRH), Qinghai, China from 2000 to 2015. The possible effects of an ecosystem restoration project on soil erosion were explored against the background of climatic changes in the study area. The model was validated with on-ground observations and showed a satisfactory performance, with a multiple correlation coefficient of 0.62 from the linear regression between the estimations and observations. The soil erosion modulus in 2010–2015 increased 6.2%, but decreased 1.2% compared with those in the periods of 2000–2005 and 2005–2010, respectively. Based on the method of overlay analysis, the interannual change of the estimated soil erosion was dominated by climate (about 64%), specifically by precipitation, rather than by vegetation coverage (about 34%). Despite some uncertainties in the model and data, this study quantified the relative contribution of ecological restoration under global climatic change; meanwhile the complexity, labor-intensiveness and long-range character of ecological restoration projects have to be recognized. On-ground observations over the long-term, further parameterization, and data inputs with higher quality are necessary and essential for decreasing the uncertainties in the estimations.

Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China.

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

Dynamic response of water retention to grazing activity on grassland over the Three River Headwaters region

Known as the “Chinese water tower,” the Three River Headwaters (TRH) region serves an important water retention function. In addition, grassland in this region is mainly used for grazing. However, grazing pressure potentially threatens the balance of this alpine grassland ecosystem. Thus, the influence of grazing activity on water retention needs to be explored. Based on remote sensing, meteorological, government statistics, hydrological records, and field observation data, we used the Integrated Valuation of Environmental Services and Tradeoffs model, grazing pressure index (GPI), and regression analysis to assess water yield (WY) variations and investigate the influence of grazing activity on water retention. The results showed that the grasslands were overloaded in the southern and eastern areas, whereas they had the capacity to carry more livestock in the western and central areas. Grazing pressure negatively impacts on WY in most of the TRH region. Source regions of the Yangtze, Yellow, and Lantsang Rivers also exhibited significant negative correlations between GPI and WY. Grazing decreased the soil water content in the alpine steppe, alpine meadow, and temperate steppe sites at soil depths of 0–30 cm, and a significant decrease was observed in the alpine meadow site at a depth of 10–20 cm. In all the areas, WY tended to decrease with GPI. Regression analyses indicate that GPI could better explain the total WY variation in the alpine meadow than in temperate steppe and alpine steppe. This implies that grazing activity has a negative effect on water retention, particularly in alpine meadows.

Research on Sustainable Development in an Alpine Pastoral Area Based on Equilibrium Analysis Between the Grassland Yield, Livestock Carrying Capacity, and Animal Husbandry Population

Equilibrium of the grassland yield, livestock carrying capacity, and animal husbandry population is the key factor that influences the ecological environment and sustainable development in pastoral areas. It is of great importance to define the animal husbandry pressure and carrying capacity of an animal husbandry population for grassland management policy-making and the sustainable development of animal husbandry. As one of the areas with the most sensitive and vulnerable ecosystem in China, the Three-River Headwaters Region is an ecological barrier for the environmental security and regional sustainable development of Southeast Asian nations. It is of great significance to define the livestock carrying capacity and population pressure in the area. This research estimates the net primary productivity (NPP) of vegetation in the Three-River Headwaters Region using Carnegie-Ames-Stanford approach (CASA) model, estimates the grass yield on the basis of NPP data, and then estimates the reasonable livestock carrying capacity according to the grass yield. Meanwhile, combined with herdsmen investigation data, it establishes a quantitative relationship between the proper livestock carrying capacity and reasonable animal husbandry population. In addition, the spatial distribution of an overloading animal husbandry population is analyzed through spatial interpolation, which can provide theoretical support for the establishment of scientific ecological immigration policy and the sustainable development of local animal husbandry. The results show that (1) the total grass yield of the grassland in the Three-River Headwaters Region is 13.96 million tons, and the average grass yield is 529.87 kg/hm2, whilst the spatial distribution presents a decreasing trend from the east and southeast to the west and northwest; (2) the reasonable livestock carrying capacity is 14.03 million sheep units (hereinafter referred as “SU”), and the average livestock carrying capacity is 55.14 SU/km2; and (3) the reasonable animal husbandry population carrying capacity is 512,500 people, while the actual amount is 645,300, with 132,800 people beyond the carrying capacity, especially in Xinghai, Tongde, Zekog, Yushu, Nangqen, and Chindu County.