Abstract
The
Three-Rivers Headwaters Region (TRHR) is crucial to the sustainable
development of China and Southeast Asian countries. For various reasons,
the sustainability of grassland ecosystems in the region has been
seriously challenged. This paper reviews remote sensing-based monitoring
and simulation of TRHR grassland ecosystems; quantitative assessment of
grassland degradation and its ecological effects; driving factors and
mechanisms of grassland degradation; grassland conservation policies and
restoration for degraded grassland. The review shows that although TRHR
alpine grassland coverage and above-ground biomass of alpine grassland
(AG-AGB) have generally increased over the past 30 years, the
degradation has not been fundamentally curbed. Grassland degradation
significantly reduced the surface soil nutrients and affected their
distribution, and also aggravated soil erosion and deteriorated soil
moisture conditions. Grassland degradation leads to loss of productivity
and species diversity. Its adverse impact on production will reduce the
well-being of pastoralists. The ”warm and wet” trend of the TRHR climate
promotes the restoration of alpine grasslands, but the widespread
overgrazing is considered to be the main reason for grassland
degradation. However, the two have very complex impacts on grassland,
and further research is needed. Since 2000, the TRHR grassland
restoration policy has achieved great results, but the formulation of
the policy still needs to effectively integrate the market logic and
strengthen the understanding of the relationship between ecological
protection and cultural protection. In addition, appropriate human
intervention mechanisms are urgently needed for the uncertainty of
future climate change. It is recommended to implement technologies such
as rodent control, light grazing, enclosure, weeding, and fertilization
to restore slightly and moderately degraded grasslands. However, for the
severely degraded “black soil beach”, it 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.
Keywords: Three-River Headwaters Region; Climate Change;
Grassland degradation and restoration; Sustainable grazing
1 Introduction
The three-river headwaters region
(TRHR) is the source of the Yangtze River, Yellow River and Lancang
River. Located in the southern part of Qinghai Province, it is a
veritable ”Chinese Water Tower” (Figure
1). It is one of the most
sensitive and fragile ecosystems, and is crucial to the sustainable
development of Southeast Asian countries (Zhang et al.,
2019a). It also occupies a
special position in Chinese animal husbandry (Zhang et al., 2014).
Grassland is the most dominant
type of cover in TRHR and provides the most ecosystem services (Zheng et
al., 2020).
However, single and continuous
alpine meadows combined with harsh natural environment are the reasons
for the fragile nature of TRHR alpine grasslands (Jiang and Zhang,
2016). Under climate change conditions, TRHR agricultural economic
development, overgrazing, grassland abandonment and construction etc.
lead to grassland degradation (Han et al., 2018). Important service
functions of grassland ecosystems are degraded, especially ecological
service functions, production services and herders’ livelihoods are also
affected. Sustainability of TRHR grassland ecosystems challenged (Dong
and Sherman, 2015). The importance of addressing the challenges should
be fully recognized, which is crucial for the sustainable development of
TRHR as well as the middle and lower reaches.
Given the unique geography and strategic location of TRHR, there is an
urgent need to review the scientific understanding of grassland
ecosystems in the region, which is critical for innovative approaches to
maintaining ecosystem services and improving the resilience of grassland
ecosystems to global change. For its critical and unique ecosystem,
innovative theories are desperately needed. Therefore, this paper
reviews the dynamic monitoring of grassland in the TRHR under changing
environment; TRHR grassland degradation and its quantitative assessment;
ecological effects of grassland degradation; driving factors and
mechanisms of grassland degradation; grassland ecosystem protection
policies and restoration measures for degraded grasslands.
2 Dynamic monitoring of the
Three-River Headwaters Region grassland under changing environment
2.1 Grassland monitoring and simulation based on NDVI
The normalized difference vegetation index (NDVI) in TRHR has been
extensively studied by researchers. Most studies show an increasing
trend in vegetation cover. E.g, Bai et al. (2020) synthesized multiple
satellite data to calculate the annual average NDVI, and the results
showed that the NDVI of TRHR showed a weak overall trend of growth from
2000 to 2015, and the grassland NDVI changed between 0.43 and 0.50, and
the change range was between 0.23 and 0.27. The results of Ge et al.
(2018) showed that in the first 15 years of the 21st century, 59.9% of
the grassland vegetation coverage in the headwater of the Yellow River
showed an upward trend. A study by Qian et al. (2010) based on data from
the US Earth Resources Observation System showed that in TRHR, grassland
NDVI increased after 1994, and since 2004, the increase has been larger.
However, the study by Gillespie et al. (2019) showed (based on 8km × 8km
pixel resolution images) during 1982-2015, no changes in NDVI were
detected in TRHR, but the regional differences were significant, with an
increase in the western region and a decrease in the eastern region.
Zheng et al. (2018) showed that NDVI showed a weak downward trend in the
first 12 years of the 21st century. One of the reasons for the
discrepancies between the findings of these studies may be due to
different data sources and different temporal resolutions.
The NDVI of TRHR has obvious regional differences (Liang et al., 2016)
and has a high degree of spatiotemporal
variability. Human-induced
fragmentation of the landscape has contributed to this regional
disparity (Bai et al., 2020).
Further projections suggest that
TRHR vegetation may show an increasing trend until the end of the 21st
century (Zheng et al., 2018). There is still the possibility of mutation
in the future, and grassland degradation may still occur (Shen et al.,
2018), and continuous dynamic monitoring is still required to prevent
the occurrence of degradation risks.
2.2 Grassland cover monitoring and simulation
Grassland cover and alpine grassland aboveground biomass (AG-AGB) are
important for the restoration and management of TRHR grassland
ecosystems. Vegetation coverage is an important index to describe
vegetation changes (Jiapaer et al., 2011). Remote sensing is the only
effective means to estimate grassland coverage and monitor its long-term
dynamic changes in a wide range of harsh environments. Relatively few
spectral mixture analysis studies have been used in the TRHR region.
High-resolution satellite imagery over large areas is limited by high
cost and weather conditions, while medium-resolution satellite images
are more suitable for large-area scales due to their wide coverage and
higher temporal resolution.
Studies found that when using
MODIS data to extract its endmember vegetation index (VI), the pixel
dichotomy model was not suitable for simulating the grassland coverage
of TRHR (Ge et al., 2018).
Empirical models are also one of the common methods for determining
vegetation coverage. The empirical formula is used to estimate the
vegetation cover and has been well verified in practice (Kergoat et al.,
2015). VI is very well applied in this regard, but due to environmental
differences in different regions, the performance of various vegetation
indices is not the same. In simulating TRHR grassland coverage, NDVI
performed better, while Enhanced Vegetation Index (EVI) was second (Ge
et al., 2018). Also, using a single VI-based model often yields good
results for only the specified area (Yang et al., 2018b). Multiple
regression models further constrain the model by adding more correlated
other variables and are therefore better than univariate VI models (Ge
et al., 2018). Machine learning models are a more advanced approach.
Many scholars have compared the prediction effects of different models
in TRHR. For example, Ge et al. (2018) found that the support vector
machine (SVM) model was the optimal model after comparing multiple
models (pixel binary model, univariate VI model, multivariate model and
SVM model). Similar to this result, Ai et al. (2019) compared four
commonly used alpine grassland coverage estimation methods, including
random forest classification (RFC), regression analysis (RA),
multi-endmember spectral mixture analysis (MESMA), and SVMR, and showed
that the SVMR-based grassland coverage estimation accuracy was similar
to the RFC method, and the stability was also better.
Most of these studies show that the overall vegetation cover of TRHR is
getting better (Bai et al., 2020). However, the changes of grassland
cover in the TRHR area are complex and unstable. Therefore, the impact
of subsequent warm-wet processes on vegetation will be difficult to
predict (Liu et al., 2018), and should be monitored continuously. It
should be noted that most of these monitoring methods only consider
changes in overall grassland coverage, while ignoring species-related
factors. However, the upward trend in grassland cover can often be the
result of severe invasion by weeds. In order to reflect the real
situation of grassland, Ai et al. (2019) classified grassland plant
types into native plants and noxious weeds, and generated a distribution
map based on their spectral difference bands. The results indicated that
the distribution of native plant species was generally dominant.
2.3 Monitoring and simulation of aboveground biomass in alpine grassland
Grassland aboveground biomass (AGB) can characterize grassland
attributes and grassland quality. It is one of the important indicators
to study grassland ecosystem health, ecological service value and
grassland degradation (Kong et al., 2019, Zeng et al., 2019, Zhao et
al., 2021). There are two main methods for monitoring AG-AGB, namely
traditional field measurement and remote sensing image-driven
estimation. Traditional
terrestrial methods estimate biomass by sampling in the field (Yang et
al., 2018b). Field sampling can obtain accurate AGB, but its cost is
relatively high, and the spatial difference is insufficiently considered
(Li et al., 2016). Remote
sensing-based methods use the relationship between spectrum,
environment, and AGB to build a model to evaluate AGB (Liang et al.,
2016). The selection of indicators and models is very important for
estimation. The accuracy of model estimation depends on the selection of
indicators. The increase in the number of indicators will inevitably
improve the simulation quality of the model, but it will reduce the work
efficiency, and the improvement of the model accuracy is also limited.
However, the screening of key indicators in specific regions is still
challenging. In the actual
research of TRHR, the selection of indicators in various studies varies
greatly (Zhao et al., 2021, Tang et al., 2021, Liang et al., 2016, Zeng
et al., 2021). Zhao et al. (2021) focused on the selection of
indicators, and identified 6 items (EVI, radiation, altitude, B5/B7,
latitude, and precipitation) out of a total of 33 items that are of
great significance for estimating AG-AGB. In terms of model selection,
the prediction accuracy of the AG-AGB model constructed by the machine
learning algorithm is higher than that of the traditional multiple
regression model (Yang et al., 2018b, Tang et al., 2021). In the
model-based research of TRHR, many scholars compared the models
constructed by multiple algorithms. The results show that RF model
predicts AG-AGB better than models constructed by Multiple Linear
Regression (MLR), Backpropagation Artificial Neural Network (BP-ANN),
SVM, Cubist, Classification and regression tree (CART), etc. (Liu et
al., 2018, Zeng et al., 2021, Tang et al., 2021, Zhao et al., 2021). RF
models also exhibit higher stability and accuracy (Liu et al., 2018).
Most models show a gradual increase in TRHR AG-AGB from northwest to
southeast, and an overall increase from 2000 onwards (Zhao et al., 2021,
Tang et al., 2021, Liang et al., 2016, Yang et al., 2018b).
However, there are also studies
showing that the interannual changes of grassland AGB in most areas of
TRHR from 2000 to 2018 were not obvious (Zeng et al., 2021). Some models
also showed that the trend of change was different in different regions,
with TRHR far east (Zeku, Henan) and far southwest (parts of Golmud and
Yushu) AG-AGB increased significantly (16.5%), while Zhiduo northwest
AG-AGB decreased significantly (3.8%) (Yang et al., 2018b).
The monitoring results of grassland in the TRHR area mostly showed an
increase in grassland coverage and AG-AGB. Many researchers attribute
the increasing trend to a combination of climate change and human
factors. However, it should be noted that most of these monitoring
methods only considered changes in overall grassland coverage or AG-AGB,
but did not consider changes in grassland structural composition. But
severe invasion by noxious weeds may lead to an increase in grassland
biomass rather than a decrease. Therefore, in order to have a clearer
understanding of the changes in grassland ecosystems, future research
needs to pay attention to the related changes in the composition of
grass species. (Table 1)
3 Degradation of the Three-River
Headwaters Region Grassland
Multiple studies have shown that TRHR grassland ecosystem was degraded
to varying degrees, which is proved by field surveys and satellite
images. In the middle and late 1970s, the grassland degradation of TRHR
basically began to form. From the 1970s to the 1990s, the grassland
degradation process continued to occur, and different regions showed
distinct patterns (Liu et al., 2008, Wang et al., 2006). However, some
studies have also shown that after 1990, the increase in grassland
coverage and AG-AGB of TRHR was greater than the decrease, showing an
overall increasing trend, and the regional macro-ecological environment
tended to improve (Liu et al., 2016a, Chen et al., 2020). At the
watershed scale, the grassland in the headwater of the Yellow River has
recovered relatively well, followed by the grassland in the headwater of
the Yangtze River, and the grassland in the Lancang River has a poor
status (Zhang et al., 2019b). From the perspective of the spatial
distribution of grassland changes, there is a general recovery trend in
the southeastern and central regions of TRHR, while the grassland
quality tends to deteriorate in the northwest of TRHR (An et al., 2021,
Yang et al., 2018b).
However, the restoration of TRHR grasslands is partial and temporary,
and does not reflect overall or fundamental improvement, and grassland
degradation has not been fundamentally suppressed (Shao et al., 2013,
Cao et al., 2020). Most of the areas with larger increases in grassland
coverage originally had lower grassland coverage, and most areas with
decreased grassland coverage originally had higher grassland coverage
(Ge et al., 2018). And it’s slow when grasslands recover and fast when
grasslands degrade (Bai et al., 2020). Some areas of TRHR (especially
the high-altitude areas in the northwest) still have obvious degradation
(Han et al., 2017, Yu et al., 2019, An et al., 2017, Xiong et al.,
2019), and the degree of desertification and salinization is still
expanding (Li et al., 2013a). Liu et al. (2014) used linear regression
analysis and Hurst index analysis to reveal that the vegetation coverage
increased in the northern part of TRHR and decreased in the southern
part during 2000-2011. The uncertainty of grassland changes reflects the
nature of the grassland in this region, which is prone to mutation and
fragility. Studies have shown that the area of extremely degraded
grassland in this region accounts for 5.68% of the TRHR area (Ai et
al., 2020), which further illustrates the severe unhealth status of the
TRHR grassland.
3.1 Quantitative assessment of grassland degradation
The diagnosis of grassland ecosystem degradation degree is the premise
of grassland restoration (Wen et al., 2010, Wang et al., 2014). The
earliest assessment of grassland degradation in TRHR was that Ma et al.
(2002) integrated some visibility indicators such as grassland coverage,
plant height, AG-AGB and proportion of palatable plants to classify
grassland degradation into five grades: no degradation, mild, moderate,
severe and extreme degradation. Mildly and moderately degraded
grasslands are generally distributed in summer pastures and transitional
pastures. Severely and extremely degraded grasslands are mostly
distributed near residential sites or in the center of drinking water
points. The terrain is generally gentle, and most of them are winter
pastures. To further quantify the degree of alpine meadow degradation,
Wen et al. (2010) constructed a comprehensive evaluation system on the
basis of Ma et al. (2002) to define the Grassland Degradation Index
(GDI). Using GDI to evaluate the TRHR Maqin alpine grassland, it is
found that the dominant and sub-dominant grassland species have changed
greatly at different degradation levels.
However, grassland degradation is a complex ecological process, which
includes not only vegetation degradation, but also soil changes. In
addition to visible indicators, invisible indicators (such as
underground biomass, nutrients in soil, etc.) are also important
parameters reflecting the degradation of grassland ecosystems (Lin et
al., 2015). Plant species
diversity and soil nutrients are important predictors of different
degradation stages of alpine meadows, and severe degradation will lead
to the migration of alpine meadow plant communities (Wang et al., 2014).
Therefore, soil-plant systems must be analyzed from the perspective of a
multidisciplinary strategy (Brevik et al., 2015). Urease, ratio of
microbial biomass nitrogen (MBN) to total nitrogen (TN), hydrolase and
soil organic carbon (SOC) were the most important indicators for
evaluating soil quality. The ratio of microbial biomass carbon (MBC)/MBN
is a key factor affecting grasslands above moderately degraded levels.
In extremely degraded grasslands, almost all parameters are key factors,
which means that human disturbance has a significant impact on soil
quality (Li et al., 2013c). Li et
al. (2013c) comprehensively considered the physical and chemical
properties of soil and soil organisms, and constructed a systematic
index to apply it to soil quality evaluation of TRHR plateau alpine
grassland under different disturbance intensities, and divided grassland
into three categories: non-degraded grassland with high soil quality
index (SQI) , moderately degraded grassland with medium SQI, and
severely degraded grassland with low SQI. Lin et al. (2015) divided the
entire degradation succession of Tibetan alpine Kobresia grasslands into
six stages. Their study found that easily observable features such as
plant functional group (PFG) type and mattic epipedon state were
associated with less observable features such as root state. Therefore,
PFG type, root system, and soil status can measure the degradation level
of grassland ecosystems. This will help the grassland to determine the
degradation level more easily, so the grassland can be protected more
reasonably.
In order to assess the degree of
grassland degradation in TRHR, An et al. (2017) comprehensively
considered topography, hydrothermal factors, and soil factors, and
divided grassland into 20 grassland productivity units. The grassland
degradation level was measured by the change in net primary productivity
(NPP) of grassland using the grassland productivity unit technique. The
results showed that the grassland degradation degree in this area was
32.86% in 1990, 36.7% in 2004, and increased by 3.84% in 15 years.
Banma, Gande, Henan, Jiuzhi,
Tongde and Zeku are the least degraded in the eastern region of TRHR.
The Qumalai degradation level was relatively highest. In 1990, the
proportion of degraded grassland in Qumalai reached 63.33%, and the
proportion increased to 77.47% in the following 14 years. Maduo and
Chengduo were relegated by more than 40%. However, it should be noted
that since the interannual variation of AG-AGB may be the result of
climatic factors or grassland degradation, the influence of climatic
factors needs to be corrected in the relevant assessment of grassland
status based on temporal and spatial changes of AG-AGB. Moreover, the
Climate Use Efficiency Index (CUE) index combined with local climatic
conditions can show the degradation of soil to a certain extent. In
addition to this, extreme weather events are also an important factor,
as they can lead to degradation of grassland ecosystems in a short
period of time. Therefore, some
scholars use CUE to judge the dynamics of TRHR grassland ecosystems. An
et al. (2021) proposed a new climate use efficiency index (NCUE) to
monitor grassland changes by comprehensively considering a series of
climatic factors closely related to vegetation growth and their
coordinated climatic factors. The results showed that during the 31
years from 1982 to 2012, grassland degradation and restoration
coexisted, accounting for 20.49% and 23.89%, respectively. Zhang et
al. (2019b) further combined CUE with vegetation net primary
productivity (NPP), grassland coverage and surface bare rate to
construct a more complete evaluation index to evaluate regional
grassland dynamics. The results showed that from 2001 to 2016, the
headwater of the Yellow River had high NPP, grassland vegetation
coverage and CUE, and a low degree of desertification; the headwater of
the Yangtze River had low NPP, grassland vegetation coverage and CUE,
and a high degree of desertification. During this period, the vegetation
coverage and VI of TRHR grassland showed an upward trend, and the bald
spot rate and NPP showed a decreasing trend.
3.2 Ecological effects of grassland degradation
The degradation of the TRHR alpine grassland ecosystem significantly
affects the service functions of the grassland ecosystem, and the impact
of grassland degradation can be reflected in the aspects of ecology,
production and livelihoods (Long, 2007, Dong et al., 2020).
Ecological functions are inherent
in the system and are the basis for the maintenance and development of
the system. From an ecological point of view, grassland degradation in
TRHR has greatly weakened ecological functions such as carbon sinks,
climate regulation, soil conservation, water conservation, biodiversity
conservation, and nutrient cycling (Dong et al., 2020, Wang et al.,
2014). Grassland degradation can lead to poor soil quality or even to
desert grasslands in the region, resulting in changes in seed banks, and
changes in soil properties such as soil moisture, SOC, TN and soil bulk
density, soil microorganisms, and soil enzymes (An et al., 2021). With
soil degradation in this area, the soil fertility of the uppermost soil
layer within 30 cm decreased significantly (Wang et al., 2014). In
different degradation succession stages, the correlation between the
biomass of alpine meadow community and soil nutrients (TN, available
nitrogen, total phosphorus, available phosphorus, SOC and soil MBC,
etc.) in the previous succession stage was positive. With grassland
degradation, SOC and TN showed a downward trend, and the distribution of
SOC was greatly affected. The proportion of light fraction carbon in
total organic carbon (TOC) gradually decreased, while the proportion of
heavy fraction carbon in TOC gradually increased (Wang et al., 2009). A
study conducted at TRHR showed that non-degraded grassland had the
highest SOC content. Compared with the non-degraded grassland, the SOC
content decreased by 21.89%, 38.30% and 43.15% with the development
of degradation, respectively. The TN content in the non-degraded
grassland was also higher than that in any of the degraded grasslands
(0.908 kg·m-2, 0.786 kg·m-2 and
0.769 kg·m-2 for moderate, severe and extreme
degradation, respectively) (Li et al., 2014). The loss of SOC caused by
grassland degradation will have a positive feedback on climate warming,
which will intensify the warming.
The high soil erosion intensity in the source area increased with the
deterioration of meadow vegetation (Li et al., 2009). The average soil
erosion modulus decreased linearly with the increase of vegetation
coverage, and the correlation coefficient R2≥0.997.
The average erosion modulus of severely degraded meadow is 2.23 times
that of mildly degraded meadow, and the maximum erosion modulus is
2.96×106kg·km-2·a-1. About
121.28×107 t of soil and water conservation capacity
is lost due to degradation every year in the TRHR “wasteland”
grassland (XU et al., 2013). Grassland degradation not only worsens soil
holding capacity, but also worsens soil moisture conditions. Land
degradation causes severe water scarcity near the soil surface, and the
detrimental effect of land degradation on moisture conditions may be
greater than expected since the effect will be doubled by a larger
active layer thickness due to degradation compared to the experimental
warming effect alone (Xue et al., 2017). For example, when alpine meadow
grassland is degraded, its coverage is reduced, and when weeds replace
the original dense-rooted pine grass and grasses, the soil water-holding
capacity will be significantly reduced, and the soil will tend to be
dry. Changes in soil and root systems provide higher thermal
conductivity, which in turn accelerates soil degradation processes,
leading to water infiltration and ultimately a significant decrease in
water conservation (WANG et al., 2003). Declining soil water-holding
capacity also makes summer flooding worse, and future climate change
could lead to more frequent extreme flooding in the Yangtze River Basin.
So putting the two together will make things worse (Li et al., 2017).
Grassland degradation is a crucial factor affecting vegetation
composition and plant diversity (Xing et al., 2021). The species
diversity and productivity of TRHR alpine meadows decreased
significantly with different degradation stages, and the severe
degradation degree led to the migration of alpine meadow plant
communities (Wang et al., 2014, Wang et al., 2009). Degradation of
grasslands will undoubtedly lead to the fragmentation of these specific
habitats for TRHR. Endangered
plant species will be more likely to be compromised by fragmentation of
alpine grasslands, as this will include reducing fragment size and
increasing distance to sites with similar habitats (Li et al., 2013a).
Those species with shorter life cycles may therefore experience greater
variation in individual numbers, a combination that could be dangerous
for small populations with shrinking habitats, which will undoubtedly
lead to a loss of species diversity in grassland ecosystems. And
predictably the harsh environment of TRHR makes this sort of thing even
more devastating.
Grassland degradation reduces
vegetation cover, height and productivity, changes in grassland
community composition, and allows the invasion of noxious weeds, all of
which undermine grassland ecological sustainability (Xing et al., 2021).
From a production point of view, grassland degradation will undoubtedly
impair the yields of primary products (grass, medicinal materials,
fungi, fuel) and secondary products (milk, meat, hair, hides). From the
perspective of herdsmen’s livelihood, grassland degradation has reduced
herdsmen’s well-being, resulting in the emergence of a large number of
ecological refugees (Dong et al., 2020). (Figure 2)
4 Driving factors and mechanisms
of grassland degradation
The combination of the fragility of the ecosystem formed by its natural
environment and the excessive human disturbance has led to the
degradation of grassland on the Qinghai-Tibet Plateau (Dong et al.,
2020). The bald spots of alpine meadows occurs and develops due to the
unreasonable use of vegetation, burrowing by rodents, soil loosening,
and wind and water erosion (YANG et al., 2005). Habitat aridification
caused by the bald spots of the grass layer in the alpine meadow
prompted the reverse succession of the top plant community dominated by
stable Artemisia species. As the area of these bald spots increased, the
native vegetation with Artemisia as the dominant species was gradually
replaced by poisonous weeds. The alpine meadow is gradually replaced by
the bald spot landscape - ”black soil beach” (YANG et al., 2005).
Studies have shown that at the beginning of degeneration, PFG types
gradually from rhizome bunchgrasses to rhizome plexus and denseplexus
grasses, then root overgrowth leads to mattic epipedon thickening, the
balance between nutrients and water in the soil is broken, the
degradation gradually accelerates followed by fissures and collapse of
the mattic epipedon (Lin et al., 2015). The ”hydrothermal hole effect”
also appears (Shang et al., 2018). Moisture and heat will be reduced
through these hollows, destabilizing the “root-soil-permafrost” system
in and around the hollows, creating island-like meadows, and this will
accelerate the loss of native forages (Shang et al., 2016). In addition,
grassland degradation affects the composition and quantity of seeds in
seed rain, and its involvement with vegetation is also affected, and the
ability of many weed species to produce large amounts of seed rain leads
to further grassland degradation (Shang et al., 2013). With the further
aggravation of grassland degradation, harmful weed species flourished
and allelopathically inhibited other plants (Shang et al., 2017),
gradually establishing sustainable populations.
After establishing sustainable
communities, noxious weed species begin to spread nearby or beyond with
the help of factors such as climate warming and overgrazing (Xing et
al., 2021). After severe grassland degradation, the alpine meadow plant
community has migrated, and the response of plant diversity, plant
productivity, and soil nutrients has mutated (Wang et al., 2014), and
the grassland will also lose its ability to restore itself.
It should be noted that the degradation characteristics of different
grassland types are different. Alpine meadow and alpine steppe
ecosystems are more stable than alpine desert ecosystems and show
stronger resistance to disturbance (Tang et al., 2015). The underlying
mechanism is that the more complex structure and composition of alpine
meadow and steppe ecosystems may make them more elastic and more stable
(Joner et al., 2011).
4.1 Impact of climate change on grassland ecosystem
Over the past 60 years, the TRHR has experienced significant warming,
and the rate is higher than that of the global and overall Qinghai-Tibet
Plateau (Bai et al., 2020, Chen et al., 2020, Jin et al., 2022, Hu et
al., 2022, Li et al., 2013a, Bardgett et al., 2013, Jiang et al., 2017).
Warming varies by season and region
(Liang et al., 2013). The warming
of TRHR since the 21st century is mainly the result of the increase in
temperature in the cold season (Liang et al., 2013).
From 1982 to 2014, the growing
season temperature in the TRHR region showed the most significant upward
trend in the northwest and southeast (Chen et al., 2020).
Precipitation has increased
slightly in the TRHR region in recent decades (Jin et al., 2022, Li et
al., 2013a, Hu et al., 2022, Bai et al., 2020). The regional differences
in precipitation increase are obvious. Chen et al. (2020) showed that
the areas with significant increases in growing season precipitation in
the TRHR region from 1982 to 2014 were located in the northeastern and
western parts of the TRHR, with a maximum rate of up to 1.45 mm
yr-1.
Therefore, in general the climate
in the region has been in line with the ”warm and wet” trend for at
least the past few decades (Bardgett et al., 2013, Jiang et al., 2017,
Qian et al., 2010). But there are also studies showing that the TRHR
climate has shown a ”warm and dry” trend in the past 40 years (Li et
al., 2013a). The trend of
”warm-dry” or ”warm-wet” in the TRHR region varies significantly in
different regions (Liang et al., 2013). In addition, the increasing
trend of reference evapotranspiration (ETO) in the TRHR
suggests a possible future climate transition to warmer and drier
climates (Wang et al., 2020, Li et al., 2012). Climate warming has also
caused serious environmental problems. For example, the TRHR region has
reported a significant reduction in the area of permafrost in recent
decades, and those areas where the permafrost disappears consistently
show the highest temperature increases, resulting in a thinning of
permafrost thickness and eventual disappearance (Hu et al., 2022).
4.1.1 Effects of climate change on plant growth and composition
Most remote sensing-based model studies show that climate change has
promoted the growth of vegetation in the TRHR over the past 20 years
(Zhang et al., 2016a), mainly due to climate
warming (Bai et al., 2020, Zeng
et al., 2021, Cao et al., 2020, Fan et al., 2010). TRHR has a
low-temperature and rainy climate, which suggests that temperature
constrains alpine grassland vegetation more strongly than rainfall (Xu
et al., 2011). The growth of grassland vegetation benefits from a better
thermal environment created by rising surface temperature, and
permafrost is no longer a limiting factor for root growth. The
degradation rate of organic matter is also accelerated. Elevated
temperature may increase the photosynthetic rate of grassland, thereby
increasing its NPP (Yang et al., 2009). Warming temperatures also lead
to an earlier start of the growing season of alpine grasslands (Zhang et
al., 2013a), which may prolong the vegetation growth period (Han et al.,
2018) and increase its carbon sink capacity (Piao et al., 2007). In
addition, the regulating effect of temperature on precipitation and
cloud cover will also promote vegetation growth (Chen et al., 2020).
Rising
temperatures also have many negative impacts on TRHR grasslands (Xiong
et al., 2019). In the western TRHR, due to poorer moisture conditions,
warmer temperatures may increase vegetation evapotranspiration, leading
to increased moisture constraints on grassland vegetation growth.
Studies have shown that climate warming is beneficial to healthy alpine
ecosystems, but in degraded meadows at TRHR, rising temperatures
exacerbate degradation by exacerbating degradation-induced droughts (Xue
et al., 2017), thereby inhibiting meadow plant growth. In addition, the
degradation of permafrost, linked to rising temperatures, leads to a
drop in the groundwater table, which in turn inhibits the growth of
shallow-rooted plants, leading to the degradation of alpine meadows
(Song et al., 2018, Hu et al., 2022). The long-term effect of the
deepening of the ecological groundwater level will promote the change of
vegetation species and the regional evolution of plants. This in turn
leads to the degradation and even desertification of alpine grasslands
(Jin et al., 2022).
The effect of temperature on grassland productivity is nonlinear. And
there are obvious differences in different regions of TRHR. The annual
average temperature in most grassland areas of TRHR has a positive
effect on AGB and NPP (Xiong et al., 2019, Zeng et al., 2021). Studies
have shown that the effect of temperature on AG-AGB in the eastern
region of TRHR is significantly stronger than that in the western and
southwestern regions. The rise in temperature in the western region of
TRHR may lead to the worsening of the originally severe water conditions
in the region due to vegetation evapotranspiration, resulting in
increased water restrictions on vegetation growth (Zeng et al., 2021).
However, this nonlinear increase and different responses to temperature
increase in different climates suggest the existence of interactions
between other non-temperature factors and temperature on plant growth
(Piao et al., 2014).
Most areas of TRHR belong to a semi-arid and semi-humid climate, and the
lack of water severely limits the growth of plants. Precipitation is an
important water source for grassland growth and affects the
spatiotemporal pattern of AG-AGB. Studies have shown that in the TRHR
region, taking 2001 as a node, the temperature and radiation before 2001
were the factors restricting the increase of NPP and then changed to
precipitation (Zhang et al., 2016a), which highlights the importance of
the impact of precipitation on grassland vegetation. Zheng et al. (2018)
showed that precipitation, rather than air temperature, was considered
to be the key factor responsible for changes in TRHR NDVI values,
especially the average precipitation for 2 consecutive months. And, in
terms of overall vegetation, rainfall is a decisive factor in improving
vegetation productivity, because increased rainfall can lengthen the
growth period of vegetation, which is beneficial to the accumulation of
NPP (Bai et al., 2020). Furthermore, TRHR precipitation affects the
sensitivity of AG-AGB to mean annual precipitation (MAP). If the region
has a high drought risk (MAP<400mm), the sensitivity of AG-AGB
to annual mean precipitation is low, and its change is small. Probably
because of the high resistance of vegetation in these arid and semi-arid
environments (Gao et al., 2019a); AG-AGB was susceptible to
precipitation when MAP varied between 400-700 mm, the increase in
precipitation is favorable for AG-AGB; In humid areas
(MAP>700mm), AG-AGB was not significantly affected by MAP,
or even negatively affected. This reflects that sufficient precipitation
can promote the growth of grassland leaves and leaf photosynthetic
capacity, thereby improving grassland productivity. However, excessive
precipitation, whether in saturated or excess conditions, may unbalance
soil moisture and gases, affecting plant uptake of nutrients and light,
such that increased precipitation does not contribute significantly to
productivity, or even leads to reduced productivity (Zeng et al., 2021).
Zhang et al. (2016a) showed that since 2000, more precipitation in the
central region of TRHR is not conducive to plant growth, and high
precipitation leads to lower air temperature and radiation (Han et al.,
2018), which inhibits plant synthesis of organic carbon. In addition,
high rainfall also reduces vegetation productivity by increasing topsoil
loss, thereby reducing soil organic matter (Gao et al., 2013).
Although in the past 20 years, climate change in the TRHR has generally
promoted the growth of grassland vegetation, the dominant factors are
different in different regions and different periods. For example: The
dominant factors in TRHR vegetation growth during 1995-2014 were
precipitation in the west, temperature in the southeast and south, and
solar radiation in the northeast (Chen et al., 2020). Vegetation changes
have obvious zonal characteristics, which may be due to the
inconsistency in the rate of temperature increase in each region of the
TRHR (Bai et al., 2020). Although projections suggest that the
vegetation of the TRHR will increase until the end of the 21st century
under the RCP4.5 climate change
scenario (Zheng et al., 2018),
the risk of future regional degradation remains due to the potentially
abrupt and fragile nature of the TRHR.
Futhermore, The increasing trend
of ETO in the TRHR suggests that a future climate
transition to a warmer and drier climate will accelerate the transition
from grassland to heathland, especially in the high-altitude regions of
the midwest of TRHR (Wang et al., 2020). And with the improvement of
grassland degradation level in this area, the proportion of weeds
gradually increased, and with the trend of warm and humid climate, the
invasion rate of weeds was further accelerated (Xing et al., 2021).
4.1.2 The impact of climate change on grassland plant phenology
The main drivers of plant
phenological changes are air temperature and precipitation. In addition,
grazing and nitrogen deposition may also affect phenological changes
(Shen et al., 2022), and snowfall may also play a key role in the actual
growing season (Liu et al., 2016b). Studies have reported that since the
1980s, many alpine grasslands have generally experienced early start and
delayed end of the growth period, which has prolonged the growth period
(Shen et al., 2022, Piao et al., 2007). Piao et al. (2007) suggested
that the increase in the length of the growing season in alpine meadows
from 1982 to 1999 may be due to the increase in temperature. In order to
further reveal the related mechanism of phenological change, some
scholars also studied the thermal growth season change of TRHR, and then
compared it with the actual growth season. The thermal growing season is
closely related to temperature changes. Liu et al. (2016b) used TRHR
1960-2013 daily temperature data and field observation phenology data to
show that the temperature increase in winter and spring is a key factor
affecting the early start of the actual growing season. For the actual
growing season, the early end of the season is affected by the increase
in summer temperature, while the delay of its end is affected by the
increase in summer precipitation. Although the duration of the thermal
growing season has become longer due to rising temperatures, the
duration of the actual growing season may not have increased, but rather
an overall advance. The earlier start of both was associated with the
response to temperature increase, while the later end of the thermal
growing season was associated with the earlier end of the actual growing
season. One of the possible explanations is that, due to the short
growth period of grassland in this area, the increase of thermal energy
can accelerate the completion of the growth cycle. Another possible
explanation is that warm summers lead to water scarcity and inhibit
grassland growth (Richardson et al., 2013, Angert et al., 2005). In
addition, the effects of temperature and water on phenology have obvious
interactions, and the sensitivity of grassland plant phenology to
temperature increase is also regulated by water (Shen et al., 2022). Due
to the regional differences in TRHR climate and its changes, there may
be regional differences in its phenological responses. Changes in the
phenology of TRHR grassland ecosystems may, in turn, affect the
interactions between grassland plant species, resulting in changes in
ecosystem structure and impacts on ecosystem functions such as carbon
and water cycles.
4.1.3 Impact of climate change on soil
Climate warming increases
evapotranspiration and aggravates surface water infiltration, which
gradually induces land degradation in TRHR alpine meadows, eventually
leading to topsoil drought (Xue et al., 2017). The increase in air
temperature makes the soil temperature rise and the permafrost thaw.
Soil moisture in the root zone infiltrates due to the thawing of the
permafrost, which eventually leads to poor soil moisture status (Hu et
al., 2022).
Climate change negatively affects soil carbon and nitrogen pools (Su et
al., 2015). Changes in soil organic carbon density (SOCD) of TRHR are
sensitive to temperature increase. SOCD in TRHR showed significant
interannual variation from 1981-2010, but the trends over 30 years were
not consistent. The downward trend in SOCD after 2003 was associated
with climate warming and increased spatial heterogeneity of
precipitation. After 2003, the decrease in precipitation in the east
often leads to the decomposition of SOC, while the increase in
precipitation in the west leads to a relatively stable SOC. Model
projections suggest that due to the warm-wet trend of the future climate
in the TRHR, heterotrophic respiration will increase and may overtake
plant production during 2011-2070, leading to a decrease in SOC. After
2071, the increment of NPP is higher than that of heterotrophic
respiration, and SOCD starts to rise, especially in the west. And, the
simulated SOC responses to climate change also have significant regional
differences. The decrease in SOCD in the east and the increase in the
west may be caused by the regional differences in carbon input produced
by plants. The carbon input in the western region may increase, but the
carbon input in the eastern region remains relatively unchanged or even
decreases (Zhao et al., 2013).
4.2 Effects of grazing on grassland ecosystems
With the rapid development of the TRHR agricultural economy since the
1980s, overgrazing, grassland abandonment, and construction have all had
a significant impact on ecological processes, resulting in grassland
degradation, habitat loss, and landscape fragmentation (Wang et al.,
2016b, Zhang et al., 2016a). These effects significantly affect the
ecological welfare of downstream residents (Fang, 2013). And, grazing
partially offsets the positive contribution of climate change to
grasslands (Chen et al., 2020). Many researchers agree that overgrazing
is the main cause of grassland desertification (Gao et al., 2020).
4.2.1 Status of overgrazing
Most studies show that although the grazing pressure in TRHR has
decreased in the past 20 years, it is still in a state of overgrazing
(Zhang et al., 2014, Dong et al., 2015, Fan et al., 2010), and it is
necessary to further reduce the number of livestock in TRHR to alleviate
the grazing pressure(Zhang et al., 2017, Zhang et al., 2019a, Gao et
al., 2020, Yu et al., 2021, Yang et al., 2018a). All winter pastures in
the TRHR region are overgrazed.
However, only 37.5% of the
summer pasture area is overgrazed (Dong et al., 2015). Grazing pressure
on winter pastures is much higher than summer pastures because these
pastures are closer to settlements and watering facilities. Longer
grazing time and greater grazing intensity usually result in more
degraded pastures in winter than in summer (ZHANG et al., 2006, Zhang et
al., 2017). Furthermore, The
degree of overgrazing varies in different counties. Zhang et al. (2014)
showed that in 2010, the total number of overgrazing sheep in TRHR was
652×104 sheep units (SU), the average overgrazing rate
was 67.88%, and the average overgrazing number was 27.43 SU
km-2. Tongde,
Xinghai, Yushu, Henan and Zeku had higher overgrazing rates, Zhiduo,
Golmud and Dari, Qumalai and Maduo had no overgrazing, and the rest of
the counties also had overgrazing. Zhang et al. (2019a) showed that the
grazing population in the TRHR area exceeded the carrying capacity by
132,800 people from 2000 to 2015, especially in counties such as
Xinghai, Tongde, Zeku, Yushu, Nangqian, and Chengduo. In addition, the
balance between grassland carrying capacity and livestock and wildlife
is critical to maintaining the stability of grassland ecosystems (Gao et
al., 2020). The ratio of domestic
animals to wild ungulates in the TRHR area is estimated to be
approximately 4.5:1 (Yu et al., 2021, Yang et al., 2018a). A study in
Maduo County showed that grasslands are mildly overloaded when only
livestock are considered, but moderately overloaded when livestock and
large wild herbivores are considered. Therefore, if large wild
herbivores are not considered when calculating the forage balance,
grazing pressure will be underestimated by about 22% (Yang et al.,
2018a). In addition, the division of national park functional areas has
a significant impact on the balance of grass and livestock in the Yellow
River Source National Park. After the implementation of the zoning plan,
the grassland in the Yellow River Source National Park is still
overloaded. Under the condition that the number of grazing livestock
remains unchanged, the grazing pressure has doubled, and the conflict
between pasture and livestock has become more obvious (Yang et al.,
2019).
4.2.2 Effects of grazing on plants
Studies have shown that moderately grazing yaks significantly reduced
the maximum height of adult grassland vegetation (Niu et al., 2010).
Fencing significantly increases above-ground vegetation productivity (Wu
et al., 2009). Under long-term
enclosure conditions, the total aboveground biomass of fenced and
non-grazing grasslands was higher than that of free-grazing grasslands
(Fan et al., 2013). This suggests that fencing management can promote
plant biomass, especially herb biomass and belowground biomass in the
uppermost 0-10 cm. Vegetation shift to low and sparse type due to
excessive consumption and trampling of grassland by livestock (Wang et
al., 2009).
Grazing also has effects on plant composition and diversity. Vertebrate
herbivores control diversity by altering species composition, including
the invasion of non-native species and the extinction of native species.
Herbivores can maintain plant diversity as grazing benefits native
flowering plants and increases ground light (Borer et al., 2014). The
disturbance intensity determines the composition and diversity of
grassland species in different pasture types to a certain extent.
Fencing reduces plant species diversity (Fan et al., 2013), but
long-term fencing is beneficial to the improvement of forage functional
groups and inhibits the development of harmful weed functional groups
(Wu et al., 2009). Mild and moderate grazing intensities grazing
intensity can promote plant diversity (Tang et al., 2015) and nectar
production in alpine grasslands due to increased numbers of florets and
flower heads, reduced competition for light, and increased numbers of
flowering individuals per plot (Mu et al., 2016), which supports
moderate perturbation hypothesis. The plant diversity-biomass-cover
relationship on alpine plateaus may be decoupled by overgrazing
livestock (Fayiah et al., 2019), and overgrazing creates bare patches
that provide suitable habitats for receiving weed seed rain and
cultivating weed seedlings. On
the impact of grazing practices on grass species diversity. Studies have
found that multifamily grazing pastures (pastures are not fenced and are
grazing by multiple households) have higher species richness than
single-family grazing pastures (pastures are separated by fences and are
grazing by single households), because single-family operations may
concentrate sustained, higher grazing pressure on small areas of
grassland, leading to a reduction in plant diversity (Cao et al., 2011).
Grazing not only affected species richness, but also adversely affected
soil moisture and TN. Not only that, but indirectly increase the density
of spring seed bank and decrease the density of summer seed bank through
these effects. Grazing regimes changed the species composition of the
vegetation, but the seed bank composition did not change much. Although
seed bank size did not change much with grazing intensity, it reduced
the number of persistent seeds (Ma et al., 2018), and persistent seed
banks are important for grassland restoration.
4.2.3 Effects of grazing on soil
Grazing alters soil properties by altering plant functional group
composition, biomass loss, and nutrient cycling in alpine pasture
ecosystems, with significant negative effects on soil physical and
nutrient properties. Grazing intensity is one of the key factors
affecting soil properties in grassland ecosystems (Dong et al., 2012).
In recent decades, TRHR soil erosion acceleration was significantly
associated with livestock numbers and intensive grazing, but not with
precipitation. Grazing is more important than climate change for soil
erosion. Overgrazing reduces vegetation cover and fine roots and thus
accelerates soil erosion (Li et al., 2019). Grazing activities have a
negative impact on soil moisture retention. Grazing reduces the moisture
content in the upper 30 cm soil layer of alpine steppe, alpine meadows
and temperate steppe, especially in the 10-20 cm soil layer of alpine
meadows (Wang et al., 2019). The upper soil hardness and pH of different
grazing treatments in the alpine meadow ecosystem showed an increasing
trend with increasing grazing activity, but a significant difference in
hardness was observed between the summer pasture and winter pasture
grazing treatments. Grazing had a significant effect on soil total
phosphorus and available phosphorus content. With the increase of
grazing intensity and the increase of total potassium and available
potassium, SOM, SOC and TN decreased significantly, and the C/N ratio
also showed a similar law. Soil
properties such as soil carbon and nitrogen generally decreased with
increasing grazing intensity, possibly due to the increased turnover of
plant matter and litter, as well as physical damage to soil, accelerated
soil C and N loss due to high grazing intensity (Dong et al., 2012).
Similar to Fan et al. (2013), the total carbon and C/N ratios in the
aboveground tissues of fenced and ungrazed grasslands were significantly
higher than those of free-grazing grasslands.
4.3 The influence of plateau pika on grassland ecosystem
Pika activity is also one of the factors contributing to TRHR grassland
degradation. Studies have shown that pika activity reduced soil
moisture, hardness, SOC and TN (Chen et al., 2017). With the increase of
pika density, the aboveground biomass, species number, cover and leaf
area index (LAI) of grassland decreased. Higher cave densities decreased
net ecosystem CO2 exchange (NEE), gross ecosystem productivity (GEP),
and ecosystem respiration (ER) (Liu et al., 2013). However, Yi et al.
(2016) have shown that pikas deplete 8 to 21 percent of the average
annual NPP of alpine grasslands. The effect of pika mound on the
reduction of vegetation cover, biomass, soil carbon and nitrogen was
much smaller than that of bald spot, all less than 10%. Large voids in
pristine grasslands are the result of strong root systems, while those
in new and old pika mounds and bald patch soils are smaller and
discontinuous. Water generally preferentially passes through soil
macropores, while in new and old pika mounds and bald patches more
readily occurs at the surface, resulting in topsoil loss (Hu et al.,
2020). In addition, fine-grained soils loosened by pika activity can be
blown away by frequent strong winds, thereby increasing the proportion
of gravel in the soil (Liu et al., 2013), thereby increasing soil
erosion and hindering vegetation restoration (Chen et al., 2017).
Coupling of overgrazing and rodent infestation may lead to the formation
of bare patches, ultimately leading to severe degradation of TRHR
grasslands (Wen et al., 2013) (Figure 3).
5 Restoration of degraded
grasslands
5.1 Protection policy of grassland ecosystem
Since 2000, due to the obvious adverse effects caused by grassland
degradation, the government and researchers have gradually realized the
importance of TRHR grassland protection, and formulated a series of
environmental protection policies, which have achieved remarkable
results (Wang et al., 2017). In order to achieve effective management,
TRHR environmental protection projects pay more attention to the
integration of market-based logic in the formulation of conservation
policies. However, the market mechanism emphasized in the actual policy
design is not reflected in practice, because there is no well-integrated
scientific measurement standard, lack of accountability mechanism, and
weak supervision and implementation.. Furthermore, this negatively
affects pastoralists due to the coercive nature of registration. The
effectiveness of market logic-based environmental policies can thus be
harnessed by using more scientific metrics, more liberal registration,
and subsidizing money based on actual restoration results of grasslands
(Wang et al., 2016a). Policy formulation should pay more attention to
the adjustment of market prices, the implementation of incentive
policies and the promotion of emerging technologies, to guide the
rational use of land, to promote rational use of land, and to ensure the
safe and sustainable development of ecosystems (Zhang et al., 2013c). In
addition, it is important to help residents of TRHR to deeply identify
with grassland restoration plans and use conservation techniques (Sheng
et al., 2019). This is because residents’ participation is largely
dependent on the benefits they receive and their perception of the
benefits of the program. Therefore, we can ensure the successful
implementation of the policy through education and the use of incentive
policies (Sheng et al., 2019). Furthermore, the trigger and feedback of
family decision-making are very important and need enough attention,
which has important practical significance for the protection of alpine
grassland. (Su et al., 2022). Managers should also enhance their
understanding of the relationship between ecological and cultural
conservation in the TRHR region, which will improve future ecosystem
management and cultural conservation. This is because successful
management cannot happen without acknowledging the local Tibetan people
and their traditional customs and culture as part of the conservation
process (Shen and Tan, 2012). Resource management of any kind is
essentially about how to manage sustainably while preserving, and even
improving, the lifestyles and cultures of those who harvest and use
resources.
5.2 Restoration measures for degraded grassland
5.2.1 Adaptation to climate change
Due to regional differences in geographical background, climate change,
hydrothermal conditions, and conflicts between pastures and livestock,
there are great differences in the types, degrees, scales and time
courses of regional grassland degradation.
Therefore, it is very important
to plan ecosystem restoration projects in a targeted manner according to
the regional differences in each region (Liu et al., 2008). The trend of
increased ETO observed in summer months poses a threat
to the growth of natural vegetation, suggesting that TRHR requires more
irrigation, which has been determined to have a greater impact on
vegetation than the observed decrease in ETO in winter.
Therefore, more measures will be needed in the future, such as
artificial rainfall, to counteract the negative effects that occur
during the summer months (Wang et al., 2020). In addition, research
shows that recovering new species assemblages that have emerged due to
climate change is very difficult, if not impossible (Li et al., 2013b).
According to the trend analysis of grazing ability, climatic factors
have a significant impact on the grazing ability of TRHR, and the
grazing potential has changed drastically. Therefore, it is necessary to
select and adjust strategies in a timely manner, optimize livestock
breeds, and select grass species that are more adaptable to climate
change. In addition, it is necessary to make various preparations for
climate change as early as possible, and develop artificial intervention
mechanisms to enable the healthy development of animal husbandry (Zhang
et al., 2013b).
Ecological restoration measures should be prioritized in areas with
relatively warm and humid climates, where grassland productivity will be
greater (Xiong et al., 2014). However, from the perspective of the
fragility of the ecosystem, arid regions also need to prioritize the
implementation of ecological restoration measures (Di et al., 2017,
Zhang et al., 2017). Promoting the use of solar energy in households is
also important for improving ecosystem functioning and adapting to
climate change. The use of solar energy equipment will provide more
manure to the grassland, thereby greatly improving the ecosystem service
value of the grassland, especially the alpine grassland in this area
(Zhang et al., 2016b). In general, further comparative studies and
mechanism analysis are essential to formulate better ecological
restoration strategies in TRHR to cope with the uncertainties of future
environmental changes (Zhang et al., 2017).
5.2.2 Sustainable grazing management
Moderate grazing is a good way to maintain grassland biodiversity,
maintain the function of grazing ecosystem and develop the productivity
of grassland ecosystem. Rational
use is the best protection strategy (Wang et al., 2009). Based on the
carrying capacity of the grassland, the yield of grass, the number of
livestock and the number of herdsmen should be kept in balance. It is
necessary to continue to reduce livestock, increase the slaughter rate,
control the number of livestock, ease the pressure on grazing, and
promote scientific breeding methods. In addition, it is necessary to
regulate the number of herdsmen population, carry out population
transfer, or directly realize the transformation from traditional animal
husbandry to other industries (Zhang et al., 2019a). In addition,
improved management efforts should go directly to cool-season pastures,
and adjusting the proportion of seasonal grazing area is also an
effective alternative strategy, which will achieve a ”win-win” for both
grasslands and households (Dong et al., 2015). Due to the deterioration
of the balance between supply and demand in animal husbandry, planting
forage crops or using highland barley varieties for crop rotation is an
effective way to alleviate the imbalance between supply and demand of
forage grass from the perspective of supply (Yang et al., 2021). Studies
based on grassland sensitivity and impacts suggest that efforts to
improve grassland adaptive capacity should be based on increasing the
area of fenced pastures, warm sheds, sown grasslands, and reducing
livestock densities, as well as strengthening TRHR ecological
engineering protection. (Fang et al., 2021).
5.2.3 Restoration of ”Black Soil Beach”
Diagnosis of grassland conditions is an important first step in
grassland ecosystem management. The degree of degradation and recovery
period of alpine meadows determine the probability of successful meadow
recovery. The effectiveness of meadow restoration through long-term
efforts is strongly related to the level of degradatio (Lin and Zhang,
2020). Depending on the degree of
degradation, different measures should be taken (Wen et al., 2013). The
study found that biotic drivers are more important than abiotic drivers
in the vegetation heterogeneity of small-scale degraded alpine
grasslands on sunny slopes. Therefore, to prevent grassland degradation,
the primary task is to carry out reasonable grazing management on
non-degraded grassland. For mildly and moderately degraded grasslands,
it is recommended to implement rodent control, limit grazing to low
stocking levels (Wen et al., 2013), and use fencing, weeding, and
fertilization techniques for grassland
restoration (Ma et al., 2002).
However, for the severely degraded ”black soil beach”, grazing
prohibition and rodent control alone cannot restore severely degraded
grasslands, and even if the stressors are removed, it is impossible to
restore the grassland ecosystem to its original state. Studies have
shown that the restoration of ”black soil shoals” by fencing and
abandoning tillage increases the stability of secondary plants in
degraded grasslands of ”black soil shoals”, which is not conducive to
the restoration of ”black soil shoals” (Shang et al., 2008).
Therefore, feasible protection
and restoration measures need to be taken to prevent the alpine
grassland from further degrading into a collapsed ”black soil flat”
state, that is, an irreversible state of grassland degradation (Wang et
al., 2014). Targeted human
interventions, including selective planting of pasture and artificial
grass seeding, as well as ecological and biological control of
high-altitude rodent populations, are recommended to restore
’irreversibly’ degraded pastures (Li et al., 2013b).
Studies have shown that weed
species richness decreased as native forage grasses were artificially
seeded, altering the plant composition of ”black soil flats” in the
short term (Shang et al., 2008). ”Black soil beach” showed visible
changes after 3 years of restoration, and the quality of forages
increased with the increase of restoration time (Wu et al., 2022). Xu et
al. (2022) showed that restoration behaviors (restoration of planting
and enclosure) improved AG-AGB weakly, but significantly promoted
species richness, target species richness, and target species AGB. This
means that grassland cultivation can accelerate the restoration of
degraded grasslands, allowing target species and communities to be
established and developed.
But generally speaking, ”black soil beach” is dominated by perennial
weeds, which are more difficult to remove than annual weeds. Therefore,
abandoning tillage and not replanting is not conducive to the
restoration of ”black soil beach” (Shang et al., 2008).
It takes more than 9 years to
restore the soil carbon and nitrogen storage in the alpine grassland
through vegetation reconstruction on the ”black soil beach”, and the C
and N storage changes in a ”V” shape with the vegetation restoration
time (Su et al., 2015). Scientific management of soil nitrogen
availability during restoration and succession can delay the occurrence
of secondary degradation of vegetation and grassland (Shi et al., 2022).
During grassland reconstruction, proper planning is needed to enhance
soil carbon and nitrogen storage potential, which is essential to
maintain the healthy development of the ecosystem. Studies have shown
that plants, soils, and plant-soil systems exhibit nonlinear resilience
of artificially reconstructed grasslands along a temporal gradient.
Plant resilience was highest in the 12th year. After 13 years of
revegetation, revegetated grassland soils outperformed severely degraded
grasslands. The elasticity of soil and the elasticity of plants are not
synchronized in time gradient. Plant-soil system resilience was highest
in the 16th recovery year. Therefore, from a system-wide perspective,
the rebuilding time for severely degraded level grasslands will take at
least 16 to 18 years to stabilize. It should be noted that although
restored grasslands can be relatively stable after 16 to 18 years of
reconstruction, the restored ecosystems are still much lower than
healthy alpine meadows in terms of both plant and soil quality (Gao et
al., 2019b). After 4 years of artificial planting and restoration of
degraded grassland, the number of unrelated species in the community
decreased, and the community’s sensitivity to external disturbances
increased, and there was a trend of reverse succession. The development
of arable grassland leads to an increase in neutral interactions among
plant species with prolonged recovery time.
The proportion of positively and
negatively correlated species decreased. In the later stage of recovery,
the niche occupied by a single species is narrow, and species coexist
harmoniously, reaching a relatively stable state (Wu et al., 2022).
Therefore, in the long run,
continuous monitoring should be carried out, and the monitoring accuracy
and accuracy should be further improved, and appropriate manual
intervention should be taken according to the situation to prevent the
secondary degradation of artificial grassland (Li et al., 2014).
6 Conclusions
In terms of dynamic monitoring of
the TRHR grassland under changing environments, many model comparison
studies have determined that the SVMA method is a more suitable method
(Ge et al., 2018, Ai et al., 2019). For the simulation of AG-AGB, the
satellite-driven model emphasizes the selection of indicators and models
for the accuracy of prediction, and the RF model usually shows higher
stability and accuracy (Liu et al., 2018, Zeng et al., 2021, Tang et
al., 2021, Zhao et al., 2021). Most studies show that TRHR overall
grassland cover and AGB show a trend of improvement. In addition, the
monitoring of TRHR grassland needs to pay more attention to considering
changes in the composition of grassland structures, so as to have a
clearer understanding of changes in grassland ecosystems.
TRHR grasslands have been degraded to varying degrees in the past 50
years. Although grassland coverage and AG-AGB showed an overall
increasing trend after 1990, this recovery did not reflect overall or
fundamental improvement, grassland degradation has not been
fundamentally curbed (Shao et
al., 2013, Cao et al., 2020), and the risk of future regional
degradation still exists. Grassland degradation is a very complex
ecological process, including changes in both vegetation and soil. The
assessment of grassland emphasizes the importance of analyzing the
soil-plant system as a whole from the perspective of a multidisciplinary
strategy (Brevik et al., 2015), and constructing a reasonable monitoring
system for grassland degradation simulation. Once grasslands are graded
for degradation, adjusting their use according to our degradation system
will help prevent irreversible degradation of important grasslands. The
ecological effects of TRHR alpine grassland degradation can be reflected
in ecology, production and livelihoods (Long, 2007, Dong et al., 2020).
Grassland degradation greatly weakens ecosystem functions such as carbon
sequestration, climate regulation, soil conservation, water
conservation, biodiversity protection, and nutrient cycling (Dong et
al., 2020, Wang et al., 2014), and affects grassland production
functions and herders’ livelihoods. Grassland degradation significantly
reduced surface soil nutrients and greatly affected the distribution of
SOC; the resulting loss of SOC and a positive feedback on climate
warming. The degree of grassland degradation is proportional to the
intensity of soil erosion. And grassland degradation worsens soil
moisture conditions, and the effects of this deterioration may be
greater than previously thought. Grassland degradation not only leads to
the decline of grassland species diversity and productivity, but also
leads to fragmentation of specific habitats, which will undoubtedly
accelerate the loss of species diversity. The adverse effects of
degradation on the yield of primary and secondary products will also
reduce the well-being of pastoralists.
The TRHR climate has generally shown a ”warm and wet” trend in recent
decades, and climate change has generally promoted the recovery and
growth of alpine grasslands. Overgrazing is common in all counties and
townships of TRHR, and is considered by many researchers to be one of
the main reasons for grassland degradation. However, climate change and
livestock grazing activities have a very complex impact on the grassland
ecosystem of TRHR, and related research is still controversial, and
further research is needed to unify these differences. The grassland
changes and the dominant factors of the changes in different regions of
the TRHR in different periods are different, and the uncertainty of
changes will further increase under the background of climate warming.
It is necessary to pay attention to the research on the driving
mechanism of grassland change, and there are still relatively few
quantitative studies on the impact of climate change and human
activities on the grassland ecology of TRHR, which are crucial for the
protection of grassland ecosystems, and a clear understanding will
increase the likelihood of successful restoration of degraded
grasslands. Therefore, the research on grassland change and its
influencing factors is undoubtedly a process of continuous efforts. In
addition, the impact of pika on the TRHR grassland needs to be further
evaluated to determine the degree of impact on the grassland ecosystem
as a whole to determine the status of the pika.
The TRHR ecological protection policy needs to emphasize the effective
integration of market logic, consider the feedback and triggers of
family decision-making, and help herdsmen understand the restoration
plan and master restoration techniques through education and publicity
(Sheng et al., 2019), so as to achieve all-round effective management.
Conservation policies also need to enhance awareness of the relationship
between ecological and cultural conservation in TRHR, and acknowledge
that local Tibetans and their traditional customs and cultures are part
of the conservation process so that successful management can be
achieved.
The grazing capacity of TRHR has changed drastically due to climatic
factors. In response to this, grazing capacity should be effectively
regulated, better livestock breeds should be selected, and grass species
more adaptable to climate change should be cultivated. In addition, it
is necessary to make various preparations for climate change as early as
possible, and to formulate artificial intervention mechanisms to enable
the healthy development of local animal
husbandry (Zhang et al., 2013b).
Rational utilization is the best strategy for grassland protection. The
likelihood of success of long-term grassland restoration strategies
depends on the level of grassland degradation (Lin and Zhang, 2020), and
different measures should be taken according to the degree of
degradation (Wen et al., 2013). The effectiveness of rehabilitating
meadows through cyclical efforts depends on the degree of degradation,
and depending on the degree of degradation, different measures should be
taken. It is recommended to implement rodent control, light grazing,
enclosure, weeding, fertilization and other techniques for restoration
of grasslands with mild and moderate degradation levels. But for the
severely degraded ”black soil beach”, it needs to be restored by
artificial seeding. Appropriate manual intervention is required to
prevent secondary degradation during the restoration succession.
Research in grassland restoration needs to emphasize the stability of
plant-soil systems (Gao et al., 2019b) to establish relatively stable
communities.
Conflict of Interest
The authors declare that there is no conflict of interests regarding the
publication of
this paper.
Author contribution
Yao-wen kou : data curation, formal analysis, software,
validation, visualization and writing-original draft
Quan-Zhi Yuan: Funding acquisition, project administration and
writng-review & editing
Xiang-shou Dong: investigation
Shu-jun Li : methodology
Wei Deng: conceptualization
Ping Ren: resources, supervision
Acknowledgments
This research was funded by the
Projects of National Natural Science Foundation of China (grant No.
41930651)
References
AI, Z. T., AN, R., CHEN, Y. H. & HUANG, L. J. 2019. Comparison of
hyperspectral HJ-1A/HSI and multispectral Landsat 8 and Sentinel-2A
imagery for estimating alpine grassland coverage in the Three-River
Headwaters region. Journal of Applied Remote Sensing, 13(1), 19.https://doi.org/10.1117/1.Jrs.13.014504
AI, Z. T., AN, R., LU, C. H. & CHEN, Y. H. 2020. Mapping of native
plant species and noxious weeds to investigate grassland degradation in
the Three-River Headwaters region using HJ-1A/HSI imagery.International Journal of Remote Sensing, 41(5), 1813-1838.https://doi.org/10.1080/01431161.2019.1675324
AN, R., WANG, H. L., FENG, X. Z., WU, H., WANG, Z., WANG, Y., SHEN, X.
J., LU, C. H., QUAYE-BALLARD, J. A., CHEN, Y. H. & ZHAO, Y. H. 2017.
Monitoring rangeland degradation using a novel local NPP scaling based
scheme over the ”Three-River Headwaters” region, hinterland of the
Qinghai-Tibetan Plateau. Quaternary International, 444, 97-114.https://doi.org/10.1016/j.quaint.2016.07.050
AN, R., ZHANG, C., SUN, M. Q., WANG, H. L., SHEN, X. J., WANG, B. L.,
XING, F., HUANG, X. L. & FAN, M. Y. 2021. Monitoring grassland
degradation and restoration using a novel climate use efficiency (NCUE)
index in the Tibetan Plateau, China. Ecological Indicators, 131,
14.https://doi.org/10.1016/j.ecolind.2021.108208
ANGERT, A., BIRAUD, S., BONFILS, C., HENNING, C. C., BUERMANN, W.,
PINZON, J., TUCKER, C. J. & FUNG, I. 2005. Drier summers cancel out the
CO2 uptake enhancement induced by warmer springs. Proceedings of
the National Academy of Sciences of the United States of America,102(31), 10823-10827.https://doi.org/10.1073/pnas.0501647102
BAI, Y. F., GUO, C. C., DEGEN, A. A., AHMAD, A. A., WANG, W. Y., ZHANG,
T., LI, W. Y., MA, L., HUANG, M., ZENG, H. J., QI, L. Y., LONG, R. J. &
SHANG, Z. H. 2020. Climate warming benefits alpine vegetation growth in
Three-River Headwater Region, China. Science of the Total
Environment, 742, 10.https://doi.org/10.1016/j.scitotenv.2020.140574
BARDGETT, R. D., MANNING, P., MORRIEN, E. & DE VRIES, F. T. 2013.
Hierarchical responses of plant-soil interactions to climate change:
consequences for the global carbon cycle. Journal of Ecology,101(2), 334-343.https://doi.org/10.1111/1365-2745.12043
BORER, E. T., SEABLOOM, E. W., GRUNER, D. S., HARPOLE, W. S.,
HILLEBRAND, H., LIND, E. M., ADLER, P. B., ALBERTI, J., ANDERSON, T. M.,
BAKKER, J. D., BIEDERMAN, L., BLUMENTHAL, D., BROWN, C. S., BRUDVIG, L.
A., BUCKLEY, Y. M., CADOTTE, M., CHU, C. J., CLELAND, E. E., CRAWLEY, M.
J., DALEO, P., DAMSCHEN, E. I., DAVIES, K. F., DECRAPPEO, N. M., DU, G.
Z., FIRN, J., HAUTIER, Y., HECKMAN, R. W., HECTOR, A., HILLERISLAMBERS,
J., IRIBARNE, O., KLEIN, J. A., KNOPS, J. M. H., LA PIERRE, K. J.,
LEAKEY, A. D. B., LI, W., MACDOUGALL, A. S., MCCULLEY, R. L., MELBOURNE,
B. A., MITCHELL, C. E., MOORE, J. L., MORTENSEN, B., O’HALLORAN, L. R.,
ORROCK, J. L., PASCUAL, J., PROBER, S. M., PYKE, D. A., RISCH, A. C.,
SCHUETZ, M., SMITH, M. D., STEVENS, C. J., SULLIVAN, L. L., WILLIAMS, R.
J., WRAGG, P. D., WRIGHT, J. P. & YANG, L. H. 2014. Herbivores and
nutrients control grassland plant diversity via light limitation.Nature, 508(7497), 517-+.https://doi.org/10.1038/nature13144
BREVIK, E. C., CERDA, A., MATAIX-SOLERA, J., PEREG, L., QUINTON, J. N.,
SIX, J. & VAN OOST, K. 2015. The interdisciplinary nature of SOIL.Soil, 1(1), 117-129.https://doi.org/10.5194/soil-1-117-2015
CAO, J., HOLDEN, N. M., LU, X. T. & DU, G. 2011. The effect of grazing
management on plant species richness on the Qinghai-Tibetan Plateau.Grass and Forage Science, 66(3), 333-336.https://doi.org/10.1111/j.1365-2494.2011.00793.x
CAO, W., WU, D., HUANG, L. & LIU, L. L. 2020. Spatial and temporal
variations and significance identification of ecosystem services in the
Sanjiangyuan National Park, China. Scientific Reports, 10(1), 13.https://doi.org/10.1038/s41598-020-63137-x
CARTER, T. R. 1998. Changes in the thermal growing season in Nordic
countries during the past century and prospects for the future.Agricultural and Food Science, 7(2), 161-179.https://doi.org/10.23986/afsci.72857
CHEN, C., LI, T. J., SIVAKUMAR, B., LI, J. Y. & WANG, G. Q. 2020.
Attribution of growing season vegetation activity to climate change and
human activities in the Three-River Headwaters Region, China.Journal of Hydroinformatics, 22(1), 186-204.https://doi.org/10.2166/hydro.2019.003
CHEN, J. J., YI, S. H. & QIN, Y. 2017. The contribution of plateau pika
disturbance and erosion on patchy alpine grassland soil on the
Qinghai-Tibetan Plateau: Implications for grassland restoration.Geoderma, 297, 1-9.https://doi.org/10.1016/j.geoderma.2017.03.001
DI, L., CAO, C. X., DUBOVYK, O., RONG, T., WEI, C., ZHUANG, Q. F., ZHAO,
Y. J. & MENZ, G. 2017. Using fuzzy analytic hierarchy process for
spatio-temporal analysis of eco-environmental vulnerability change
during 1990-2010 in Sanjiangyuan region, China. Ecological
Indicators, 73, 612-625.https://doi.org/10.1016/j.ecolind.2016.08.031
DONG, Q. M., ZHAO, X. Q., WU, G. L. & CHANG, X. F. 2015. Optimization
yak grazing stocking rate in an alpine grassland of Qinghai-Tibetan
Plateau, China. Environmental Earth Sciences, 73(5), 2497-2503.https://doi.org/10.1007/s12665-014-3597-7
DONG, Q. M., ZHAO, X. Q., WU, G. L., SHI, J. J., WANG, Y. L. & SHENG,
L. 2012. Response of soil properties to yak grazing intensity in a
Kobresia parva-meadow on the Qinghai-Tibetan Plateau, China.Journal of Soil Science and Plant Nutrition, 12(3), 535-546.https://doi.org/10.4067/s0718-95162012005000024
DONG, S. K., SHANG, Z. H., GAO, J. X. & BOONE, R. B. 2020. Enhancing
sustainability of grassland ecosystems through ecological restoration
and grazing management in an era of climate change on Qinghai-Tibetan
Plateau. Agriculture Ecosystems & Environment, 287, 16.https://doi.org/10.1016/j.agee.2019.106684
DONG, S. K. & SHERMAN, R. 2015. Enhancing the resilience of coupled
human and natural systems of alpine rangelands on the Qinghai-Tibetan
Plateau. Rangeland Journal, 37(1), I-III.https://doi.org/10.1071/rj14117
FAN, J. W., SHAO, Q. Q., LIU, J. Y., WANG, J. B., HARRIS, W., CHEN, Z.
Q., ZHONG, H. P., XU, X. L. & LIU, R. G. 2010. Assessment of effects of
climate change and grazing activity on grassland yield in the Three
Rivers Headwaters Region of Qinghai-Tibet Plateau, China.Environmental Monitoring and Assessment, 170(1-4), 571-584.https://doi.org/10.1007/s10661-009-1258-1
FAN, Y. J., HOU, X. Y., SHI, H. X. & SHI, S. L. 2013. Effects of
grazing and fencing on carbon and nitrogen reserves in plants and soils
of alpine meadow in the three headwater resource regions. Russian
Journal of Ecology, 44(1), 80-88.https://doi.org/10.1134/s1067413612050165
FANG, Y. P. 2013. Managing the Three-Rivers Headwater Region, China:
From Ecological Engineering to Social Engineering. Ambio, 42(5),
566-576.https://doi.org/10.1007/s13280-012-0366-2
FANG, Y. P., ZHU, F. B., YI, S. H., QIU, X. P. & DING, Y. J. 2021.
Ecological carrying capacity of alpine grassland in the Qinghai-Tibet
Plateau based on the structural dynamics method. Environment
Development and Sustainability, 23(8), 12550-12578.https://doi.org/10.1007/s10668-020-01182-2
FAYIAH, M., DONG, S. K., LI, Y., XU, Y. D., GAO, X. X., LI, S., SHEN,
H., XIAO, J. N., YANG, Y. F. & WESSELL, K. 2019. The relationships
between plant diversity, plant cover, plant biomass and soil fertility
vary with grassland type on Qinghai-Tibetan Plateau. Agriculture
Ecosystems & Environment, 286, 11.https://doi.org/10.1016/j.agee.2019.106659
GAO, H. M., JIANG, F., CHI, X. W., LI, G. Y., CAI, Z. Y., QIN, W.,
ZHANG, J. J., WU, T. & ZHANG, T. Z. 2020. The carrying pressure of
livestock is higher than that of large wild herbivores in Yellow River
source area, China. Ecological Modelling, 431, 7.https://doi.org/10.1016/j.ecolmodel.2020.109163
GAO, J. B., ZHANG, L. L., TANG, Z. & WU, S. H. 2019a. A synthesis of
ecosystem aboveground productivity and its process variables under
simulated drought stress. Journal of Ecology, 107(6), 2519-2531.https://doi.org/10.1111/1365-2745.13218
GAO, X. X., DONG, S. K., XU, Y. D., WU, S. N., WU, X. H., ZHANG, X.,
ZHI, Y. L., LI, S., LIU, S. L., LI, Y., SHANG, Z. H., DONG, Q. M., ZHOU,
H. K. & STUFKENS, P. 2019b. Resilience of revegetated grassland for
restoring severely degraded alpine meadows is driven by plant and soil
quality along recovery time: A case study from the Three-river Headwater
Area of Qinghai-Tibetan Plateau. Agriculture Ecosystems &
Environment, 279, 169-177.https://doi.org/10.1016/j.agee.2019.01.010
GAO, Y. H., ZHOU, X., WANG, Q., WANG, C. Z., ZHAN, Z. M., CHEN, L. F.,
YAN, J. X. & QU, R. 2013. Vegetation net primary productivity and its
response to climate change during 2001-2008 in the Tibetan Plateau.Science of the Total Environment, 444, 356-362.https://doi.org/10.1016/j.scitotenv.2012.12.014
GE, J., MENG, B. P., LIANG, T. G., FENG, Q. S., GAO, J. L., YANG, S. X.,
HUANG, X. D. & XIE, H. J. 2018. Modeling alpine grassland cover based
on MODIS data and support vector machine regression in the headwater
region of the Huanghe River, China. Remote Sensing of
Environment, 218, 162-173.https://doi.org/10.1016/j.rse.2018.09.019
GILLESPIE, T. W., MADSON, A., CUSACK, C. F. & XUE, Y. K. 2019. Changes
in NDVI and human population in protected areas on the Tibetan Plateau.Arctic Antarctic and Alpine Research, 51(1), 428-439.https://doi.org/10.1080/15230430.2019.1650541
HAN, Z., SONG, W., DENG, X. Z. & XU, X. L. 2017. Trade-Offs and
Synergies in Ecosystem Service within the Three-Rivers Headwater Region,
China. Water, 9(8), 24.https://doi.org/10.3390/w9080588
HAN, Z., SONG, W., DENG, X. Z. & XU, X. L. 2018. Grassland ecosystem
responses to climate change and human activities within the Three-River
Headwaters region of China. Scientific Reports, 8, 13.https://doi.org/10.1038/s41598-018-27150-5
HU, J. A., NAN, Z. T. & JI, H. L. 2022. Spatiotemporal Characteristics
of NPP Changes in Frozen Ground Areas of the Three-River Headwaters
Region, China: A Regional Modeling Perspective. Frontiers in Earth
Science, 10, 12.https://doi.org/10.3389/feart.2022.838558
HU, X., LI, X. Y., LI, Z. C. & LIU, L. Y. 2020. 3-D soil macropore
networks derived from X-ray tomography in an alpine meadow disturbed by
plateau pikas in the Qinghai Lake watershed, north-eastern
Qinghai-Tibetan Plateau. Journal of Soils and Sediments, 20(4),
2181-2191.https://doi.org/10.1007/s11368-019-02560-8
JIANG, C., LI, D. Q., GAO, Y. N., LIU, W. F. & ZHANG, L. B. 2017.
Impact of climate variability and anthropogenic activity on streamflow
in the Three Rivers Headwater Region, Tibetan Plateau, China.Theoretical and Applied Climatology, 129(1-2), 667-681.https://doi.org/10.1007/s00704-016-1833-7
JIANG, C. & ZHANG, L. B. 2016. Effect of ecological restoration and
climate change on ecosystems: a case study in the Three-Rivers Headwater
Region, China. Environmental Monitoring and Assessment, 188(6),
20.https://doi.org/10.1007/s10661-016-5368-2
JIAPAER, G., CHEN, X. & BAO, A. M. 2011. A comparison of methods for
estimating fractional vegetation cover in arid regions.Agricultural and Forest Meteorology, 151(12), 1698-1710.https://doi.org/10.1016/j.agrformet.2011.07.004
JIN, X. Y., JIN, H. J., LUO, D. L., SHENG, Y., WU, Q. B., WU, J. C.,
WANG, W. H., HUANG, S., LI, X. Y., LIANG, S. H., WANG, Q. F., HE, R. X.,
SERBAN, R. D., MA, Q., GAO, S. H. & LI, Y. 2022. Impacts of Permafrost
Degradation on Hydrology and Vegetation in the Source Area of the Yellow
River on Northeastern Qinghai-Tibet Plateau, Southwest China.Frontiers in Earth Science, 10, 12.https://doi.org/10.3389/feart.2022.845824
JONER, F., SPECHT, G., MULLER, S. C. & PILLAR, V. D. 2011. Functional
redundancy in a clipping experiment on grassland plant communities.Oikos, 120(9), 1420-1426.https://doi.org/10.1111/j.1600-0706.2011.19375.x
KERGOAT, L., HIERNAUX, P., DARDEL, C., PIERRE, C., GUICHARD, F. &
KALILOU, A. 2015. Dry-season vegetation mass and cover fraction from
SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the
Sahel. International Journal of Applied Earth Observation and
Geoinformation, 39, 56-64.https://doi.org/10.1016/j.jag.2015.02.011
KONG, B., YU, H., DU, R. X. & WANG, Q. 2019. Quantitative Estimation of
Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing.Rangeland Ecology & Management, 72(2), 336-346.https://doi.org/10.1016/j.rama.2018.10.005
LI, F., ZENG, Y., LUO, J. H., MA, R. H. & WU, B. F. 2016. Modeling
grassland aboveground biomass using a pure vegetation index.Ecological Indicators, 62, 279-288.https://doi.org/10.1016/j.ecolind.2015.11.005
LI, H. X., LIU, G. H. & FU, B. J. 2012. Estimation of Regional
Evapotranspiration in Alpine Area and Its Response to Land Use Change: A
Case Study in Three-River Headwaters Region of Qinghai-Tibet Plateau,
China. Chinese Geographical Science, 22(4), 437-449.https://doi.org/10.1007/s11769-012-0550-0
LI, J., LIU, D., WANG, T., LI, Y. N., WANG, S. P., YANG, Y. T., WANG, X.
Y., GUO, H., PENG, S. S., DING, J. Z., SHEN, M. G. & WANG, L. 2017.
Grassland restoration reduces water yield in the headstream region of
Yangtze River. Scientific Reports, 7, 9.https://doi.org/10.1038/s41598-017-02413-9
LI, N., WANG, G. X., LIU, G. S., LIN, Y. & SUN, X. Y. 2013a. The
ecological implications of land use change in the Source Regions of the
Yangtze and Yellow Rivers, China. Regional Environmental Change,13(5), 1099-1108.https://doi.org/10.1007/s10113-013-0419-5
LI, X. L., GAO, J., BRIERLEY, G., QIAO, Y. M., ZHANG, J. & YANG, Y. W.
2013b. RANGELAND DEGRADATION ON THE QINGHAI-TIBET PLATEAU: IMPLICATIONS
FOR REHABILITATION. Land Degradation & Development, 24(1),
72-80.https://doi.org/10.1002/ldr.1108
LI, Y., LI, J. J., ARE, K. S., HUANG, Z. G., YU, H. Q. & ZHANG, Q. W.
2019. Livestock grazing significantly accelerates soil erosion more than
climate change in Qinghai-Tibet Plateau: Evidenced from Cs-137 and
(210)Pbex measurements. Agriculture Ecosystems & Environment,285, 8.https://doi.org/10.1016/j.agee.2019.106643
LI, Y. S., WANG, G. X., DING, Y. J., ZHAO, L. & WANG, Y. B. 2009.
Application of the Cs-137 tracer technique to study soil erosion of
alpine meadows in the headwater region of the Yellow River.Environmental Geology, 58(5), 1021-1028.https://doi.org/10.1007/s00254-008-1581-9
LI, Y. Y., DONG, S. K., WEN, L., WANG, X. X. & WU, Y. 2013c. Assessing
the soil quality of alpine grasslands in the Qinghai-Tibetan Plateau
using a modified soil quality index. Environmental Monitoring and
Assessment, 185(10), 8011-8022.https://doi.org/10.1007/s10661-013-3151-1
LI, Y. Y., DONG, S. K., WEN, L., WANG, X. X. & WU, Y. 2014. Soil carbon
and nitrogen pools and their relationship to plant and soil dynamics of
degraded and artificially restored grasslands of the Qinghai-Tibetan
Plateau. Geoderma, 213, 178-184.https://doi.org/10.1016/j.geoderma.2013.08.022
LIANG, L. Q., LI, L. J., LIU, C. M. & CUO, L. 2013. Climate change in
the Tibetan Plateau Three Rivers Source Region: 1960-2009.International Journal of Climatology, 33(13), 2900-2916.https://doi.org/10.1002/joc.3642
LIANG, T. G., YANG, S. X., FENG, Q. S., LIU, B. K., ZHANG, R. P., HUANG,
X. D. & XIE, H. J. 2016. Multi-factor modeling of above-ground biomass
in alpine grassland: A case study in the Three-River Headwaters Region,
China. Remote Sensing of Environment, 186, 164-172.https://doi.org/10.1016/j.rse.2016.08.014
LIN, H. L. & ZHANG, F. 2020. Fragmentation and percolation thresholds
in the degradation process of alpine meadow in the Three-River
Headwaters region of Qinghai-Tibetan Plateau, China. Rangeland
Journal, 42(3), 171-177.https://doi.org/10.1071/rj20005
LIN, L., LI, Y. K., XU, X. L., ZHANG, F. W., DU, Y. G., LIU, S. L., GUO,
X. W. & CAO, G. M. 2015. Predicting parameters of degradation
succession processes of Tibetan Kobresia grasslands. Solid Earth,6(4), 1237-1246.https://doi.org/10.5194/se-6-1237-2015
LINDERHOLM, H. W., WALTHER, A. & CHEN, D. L. 2008. Twentieth-century
trends in the thermal growing season in the Greater Baltic Area.Climatic Change, 87(3-4), 405-419.https://doi.org/10.1007/s10584-007-9327-3
LIU, J. Y., XU, X. L. & SHAO, Q. Q. 2008. Grassland degradation in the
”Three-River Headwaters” region, Qinghai Province. Journal of
Geographical Sciences, 18(3), 259-273.https://doi.org/10.1007/s11442-008-0259-2
LIU, L. L., CAO, W., SHAO, Q. Q., HUANG, L. & HE, T. 2016a.
Characteristics of Land Use/Cover and Macroscopic Ecological Changes in
the Headwaters of the Yangtze River and of the Yellow River over the
Past 30 Years. Sustainability, 8(3), 20.https://doi.org/10.3390/su8030237
LIU, N. J., YANG, Y. P., YAO, L. & YUE, X. F. 2018. A Regionalized
Study on the Spatial-Temporal Changes of Grassland Cover in the
Three-River Headwaters Region from 2000 to 2016. Sustainability,10(10), 24.https://doi.org/10.3390/su10103539
LIU, X. F., ZHANG, J. S., ZHU, X. F., PAN, Y. Z., LIU, Y. X., ZHANG, D.
H. & LIN, Z. H. 2014. Spatiotemporal changes in vegetation coverage and
its driving factors in the Three-River Headwaters Region during
2000-2011. Journal of Geographical Sciences, 24(2), 288-302.https://doi.org/10.1007/s11442-014-1088-0
LIU, X. F., ZHU, X. F., PAN, Y. Z., ZHU, W. Q., ZHANG, J. S. & ZHANG,
D. H. 2016b. Thermal growing season and response of alpine grassland to
climate variability across the Three-Rivers Headwater Region, China.Agricultural and Forest Meteorology, 220, 30-37.https://doi.org/10.1016/j.agrformet.2016.01.015
LIU, Y. S., FAN, J. W., HARRIS, W., SHAO, Q. Q., ZHOU, Y. C., WANG, N.
& LI, Y. Z. 2013. Effects of plateau pika (Ochotona curzoniae) on net
ecosystem carbon exchange of grassland in the Three Rivers Headwaters
region, Qinghai-Tibet, China. Plant and Soil, 366(1-2), 491-504.https://doi.org/10.1007/s11104-012-1442-x
LONG, R. J. 2007. Functions of Ecosystem in the Tibetan Grassland.Science & Technology Review, 25(09), 26-28.
MA, M. J., WALCK, J. L., MA, Z., WANG, L. P. & DU, G. Z. 2018. Grazing
disturbance increases transient but decreases persistent soil seed bank.Ecological Applications, 28(4), 1020-1031.https://doi.org/10.1002/eap.1706
MA, Y., LANG, B., LI, Q., SHI, J. & DONG, Q. 2002. Study on
rehabilitating and rebuilding technologies for degenerated alpine meadow
in the Changjiang and Yellow river source region. Pratacultural
science, 19(9), 1-5.
MU, J. P., ZENG, Y. L., WU, Q. G., NIKLAS, K. J. & NIU, K. C. 2016.
Traditional grazing regimes promote biodiversity and increase nectar
production in Tibetan alpine meadows. Agriculture Ecosystems &
Environment, 233, 336-342.https://doi.org/10.1016/j.agee.2016.09.030
NIU, K. C., ZHANG, S. T., ZHAO, B. B. & DU, G. Z. 2010. Linking grazing
response of species abundance to functional traits in the Tibetan alpine
meadow. Plant and Soil, 330(1-2), 215-223.https://doi.org/10.1007/s11104-009-0194-8
PIAO, S. L., FANG, J. Y., ZHOU, L. M., TAN, K. & TAO, S. 2007. Changes
in biomass carbon stocks in China’s grasslands between 1982 and 1999.Global Biogeochemical Cycles, 21(2), 10.https://doi.org/10.1029/2005gb002634
PIAO, S. L., NAN, H. J., HUNTINGFORD, C., CIAIS, P., FRIEDLINGSTEIN, P.,
SITCH, S., PENG, S. S., AHLSTROM, A., CANADELL, J. G., CONG, N., LEVIS,
S., LEVY, P. E., LIU, L. L., LOMAS, M. R., MAO, J. F., MYNENI, R. B.,
PEYLIN, P., POULTER, B., SHI, X. Y., YIN, G. D., VIOVY, N., WANG, T.,
WANG, X. H., ZAEHLE, S., ZENG, N., ZENG, Z. Z. & CHEN, A. P. 2014.
Evidence for a weakening relationship between interannual temperature
variability and northern vegetation activity. Nature
Communications, 5, 7.https://doi.org/10.1038/ncomms6018
QIAN, S. A., FU, Y. & PAN, F. F. 2010. Climate change tendency and
grassland vegetation response during the growth season in Three-River
Source Region. Science China-Earth Sciences, 53(10), 1506-1512.https://doi.org/10.1007/s11430-010-4064-2
RICHARDSON, A. D., KEENAN, T. F., MIGLIAVACCA, M., RYU, Y., SONNENTAG,
O. & TOOMEY, M. 2013. Climate change, phenology, and phenological
control of vegetation feedbacks to the climate system.Agricultural and Forest Meteorology, 169, 156-173.https://doi.org/10.1016/j.agrformet.2012.09.012
SHANG, Z., DONG, Q., DEGEN, A. & LONG, R. 2016. Ecological restoration
on Qinghai-Tibetan plateau: Problems, strategies and prospects.Ecological restoration: Global challenges, social aspects and
environmental benefits. Nova Science Publishers, Inc., pp. 151-176.
SHANG, Z. H., DONG, Q. M., SHI, J. J., ZHOU, H. K., DONG, S. K., SHAO,
X. Q., LI, S. X., WANG, Y. L., MA, Y. S., DING, L. M., CAO, G. M. &
LONG, R. J. 2018. Research Progress in Recent Ten Years of Ecological
Restoration for ‘Black Soil Land’ Degraded Grassland on Tibetan
Plateau——Concurrently Discuss of Ecological Restoration in
Sangjiangyuan Region. Acta Agrestia Sinica, 26(01), 1-21.https://doi.org/10.11733/j.issn.1007-0435.2018.01.001
SHANG, Z. H., HOU, Y., LI, F., GUO, C. C., JIA, T. H., DEGEN, A. A.,
WHITE, A., DING, L. M. & LONG, R. J. 2017. Inhibitory action of
allelochemicals from Artemisia nanschanica to control Pedicularis
kansuensis, an annual weed of alpine grasslands. Australian
Journal of Botany, 65(4), 305-314.https://doi.org/10.1071/bt17014
SHANG, Z. H., MA, Y. S., LONG, R. J. & DING, L. M. 2008. EFFECT OF
FENCING, ARTIFICIAL SEEDING AND ABANDONMENT ON VEGETATION COMPOSITION
AND DYNAMICS OF ’BLACK SOIL LAND’ IN THE HEADWATERS OF THE YANGTZE AND
THE YELLOW RIVERS OF THE QINGHAI-TIBETAN PLATEAU. Land Degradation
& Development, 19(5), 554-563.https://doi.org/10.1002/ldr.861
SHANG, Z. H., YANG, S. H., SHI, J. J., WANG, Y. L. & LONG, R. J. 2013.
Seed rain and its relationship with above-ground vegetation of degraded
Kobresia meadows. Journal of Plant Research, 126(1), 63-72.https://doi.org/10.1007/s10265-012-0498-2
SHAO, Q. Q., LIU, J. Y., HUANG, L., FAN, J. W., XU, X. L. & WANG, J. B.
2013. Integrated assessment on the effectiveness of ecological
conservation in Sanjiangyuan National Nature Reserve. Geographical
Research, 32(09), 1645-1656.https://doi.org/10.11821/dlyj201309007
SHEN, M., WANG, S., JIANG, N., SUN, J., CAO, R., LING, X., FANG, B.,
ZHANG, L., ZHANG, L., XU, X., LV, W., LI, B., SUN, Q., MENG, F., JIANG,
Y., DORJI, T., FU, Y., ILER, A., VITASSE, Y., STELTZER, H., JI, Z.,
ZHAO, W., PIAO, S. & FU, B. 2022. Plant phenology changes and drivers
on the Qinghai–Tibetan Plateau. Nature Reviews Earth &
Environment .https://doi.org/10.1038/s43017-022-00317-5
SHEN, X. J., AN, R., FENG, L., YE, N., ZHU, L. J. & LI, M. H. 2018.
Vegetation changes in the Three-River Headwaters Region of the Tibetan
Plateau of China. Ecological Indicators, 93, 804-812.https://doi.org/10.1016/j.ecolind.2018.05.065
SHEN, X. L. & TAN, J. X. 2012. Ecological Conservation, Cultural
Preservation, and a Bridge between: the Journey of Shanshui Conservation
Center in the Sanjiangyuan Region, Qinghai-Tibetan Plateau, China.Ecology and Society, 17(4), 9.https://doi.org/10.5751/es-05345-170438
SHENG, W. P., ZHEN, L., XIAO, Y. & HU, Y. F. 2019. Ecological and
socioeconomic effects of ecological restoration in Chins’s Three Rivers
Source Region. Science of the Total Environment, 650, 2307-2313.https://doi.org/10.1016/j.scitotenv.2018.09.265
SHI, G. X., YANG, Y., LIU, Y. J., UWAMUNGU, J. Y., LIU, Y. M., WANG, Y.
B., FENG, H. Y., YAO, B. Q. & ZHOU, H. K. 2022. Effect of Elymus nutans
on the assemblage of arbuscular mycorrhizal fungal communities enhanced
by soil available nitrogen in the restoration succession of revegetated
grassland on the Qinghai-Tibetan Plateau. Land Degradation &
Development, 33(6), 931-944.https://doi.org/10.1002/ldr.4201
SONG, Y., JIN, L. & WANG, H. B. 2018. Vegetation Changes along the
Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate
Change and Human Activities. Remote Sensing, 10(1), 21.https://doi.org/10.3390/rs10010095
SU, X. K., SHEN, Y., DONG, S. K., LIU, Y. Q., CHENG, H., WAN, L. F. &
LIU, G. H. 2022. Feedback and Trigger of Household Decision-Making to
Ecological Protection Policies in Sanjiangyuan National Park.Frontiers in Plant Science, 12, 12.https://doi.org/10.3389/fpls.2021.827618
SU, X. K., WU, Y., DONG, S. K., WEN, L., LI, Y. Y. & WANG, X. X. 2015.
Effects of grassland degradation and re-vegetation on carbon and
nitrogen storage in the soils of the Headwater Area Nature Reserve on
the Qinghai-Tibetan Plateau, China. Journal of Mountain Science,12(3), 582-591.https://doi.org/10.1007/s11629-014-3043-z
TANG, L., DONG, S. K., SHERMAN, R., LIU, S. L., LIU, Q. R., WANG, X. X.,
SU, X. K., ZHANG, Y., LI, Y. Y., WU, Y., ZHAO, H. D., ZHAO, C. & WU, X.
Y. 2015. Changes in vegetation composition and plant diversity with
rangeland degradation in the alpine region of Qinghai-Tibet Plateau.Rangeland Journal, 37(1), 107-115.https://doi.org/10.1071/rj14077
TANG, R., ZHAO, Y. T. & LIN, H. L. 2021. Spatio-Temporal Variation
Characteristics of Aboveground Biomass in the Headwater of the Yellow
River Based on Machine Learning. Remote Sensing, 13(17), 16.https://doi.org/10.3390/rs13173404
WANG, C. T., LONG, R. J., WANG, Q. L., JING, Z. C. & SHI, J. J. 2009.
CHANGES IN PLANT DIVERSITY, BIOMASS AND SOIL C, IN ALPINE MEADOWS AT
DIFFERENT DEGRADATION STAGES IN THE HEADWATER REGION OF THREE RIVERS,
CHINA. Land Degradation & Development, 20(2), 187-198.https://doi.org/10.1002/ldr.879
WANG, G. X., SHEN, Y. P., QIAN, J. & WANG, J. D. 2003. Study on the
Influence of Vegetation Change on Soil Moisture Cycle in Alpine Meadow.Journal of Glaciology and Geocryology, 25(06), 653-659.
WANG, G. X., WANG, Y. B., QIAN, J. & WU, Q. B. 2006. Land cover change
and its impacts on soil C and N in two watersheds in the center of the
Qinghai-Tibetan Plateau. Mountain Research and Development,26(2), 153-162.https://doi.org/10.1659/0276-4741(2006)26[153:LCCAII]2.0.CO;2
WANG, K. Y., XU, Q. & LI, T. J. 2020. Does recent climate warming drive
spatiotemporal shifts in functioning of high-elevation hydrological
systems? Science of the Total Environment, 719, 14.https://doi.org/10.1016/j.scitotenv.2020.137507
WANG, P., DENG, X. Z. & JIANG, S. J. 2017. Diffused impact of grassland
degradation over space: A case study in Qinghai province. Physics
and Chemistry of the Earth, 101, 166-171.https://doi.org/10.1016/j.pce.2017.06.006
WANG, P., WOLF, S. A., LASSOIE, J. P., POE, G. L., MORREALE, S. J., SU,
X. K. & DONG, S. K. 2016a. Promise and reality of market-based
environmental policy in China: Empirical analyses of the ecological
restoration program on the Qinghai-Tibetan Plateau. Global
Environmental Change-Human and Policy Dimensions, 39, 35-44.https://doi.org/10.1016/j.gloenvcha.2016.04.004
WANG, S. Z., FAN, J. W., LI, Y. Z., WU, D., ZHANG, Y. X. & HUANG, L.
2019. Dynamic response of water retention to grazing activity on
grassland over the Three River Headwaters region. Agriculture
Ecosystems & Environment, 286, 12.https://doi.org/10.1016/j.agee.2019.106662
WANG, X. X., DONG, S. K., YANG, B., LI, Y. Y. & SU, X. K. 2014. The
effects of grassland degradation on plant diversity, primary
productivity, and soil fertility in the alpine region of Asia’s
headwaters. Environmental Monitoring and Assessment, 186(10),
6903-6917.https://doi.org/10.1007/s10661-014-3898-z
WANG, Z. Q., ZHANG, Y. Z., YANG, Y., ZHOU, W., GANG, C. C., ZHANG, Y.,
LI, J. L., AN, R., WANG, K., ODEH, I. & QI, J. G. 2016b. Quantitative
assess the driving forces on the grassland degradation in the
Qinghai-Tibet Plateau, in China. Ecological Informatics, 33,
32-44.https://doi.org/10.1016/j.ecoinf.2016.03.006
WEN, L., DONG, S. K., LI, Y. Y., SHERMAN, R., SHI, J. J., LIU, D. M.,
WANG, Y. L., MA, Y. S. & ZHU, L. 2013. The effects of biotic and
abiotic factors on the spatial heterogeneity of alpine grassland
vegetation at a small scale on the Qinghai-Tibet Plateau (QTP), China.Environmental Monitoring and Assessment, 185(10), 8051-8064.https://doi.org/10.1007/s10661-013-3154-y
WEN, L., DONG, S. K., ZHU, L., LI, X. Y., SHI, J. J., Y.L.WANG & MA, Y.
S. 2010. The construction of Grassland Degradation Index for Alpine
Meadow in Qinghai-Tibetan Plateau. Procedia Environmental
Sciences, 2, 1966-1969.https://doi.org/10.1016/j.proenv.2010.10.210
WU, G. L., DU, G. Z., LIU, Z. H. & THIRGOOD, S. 2009. Effect of fencing
and grazing on a Kobresia-dominated meadow in the Qinghai-Tibetan
Plateau. Plant and Soil, 319(1-2), 115-126.https://doi.org/10.1007/s11104-008-9854-3
WU, S. N., WEN, L., DONG, S. K., GAO, X. X., XU, Y. D., LI, S., DONG, Q.
M. & WESSELL, K. 2022. The Plant Interspecific Association in the
Revegetated Alpine Grasslands Determines the Productivity Stability of
Plant Community Across Restoration Time on Qinghai-Tibetan Plateau.Frontiers in Plant Science, 13, 9.https://doi.org/10.3389/fpls.2022.850854
XING, F., AN, R., WANG, B. L., MIAO, J., JIANG, T., HUANG, X. L. & HU,
Y. N. 2021. Mapping the occurrence and spatial distribution of noxious
weed species with multisource data in degraded grasslands in the
Three-River Headwaters Region, China. Science of the Total
Environment, 801, 12.https://doi.org/10.1016/j.scitotenv.2021.149714
XIONG, D. P., SHI, P. L., SUN, Y. L., WU, J. S. & ZHANG, X. Z. 2014.
Effects of grazing exclusion on plant productivity and soil carbon,
nitrogen storage in alpine meadows in northern Tibet, China.Chinese Geographical Science, 24(4), 488-498.https://doi.org/10.1007/s11769-014-0697-y
XIONG, Q. L., XIAO, Y., HALMY, M. W. A., DAKHIL, M. A., LIANG, P. H.,
LIU, C. G., ZHANG, L., PANDEY, B., PAN, K. W., EL KAFRAWAY, S. B. &
CHEN, J. 2019. Monitoring the impact of climate change and human
activities on grassland vegetation dynamics in the northeastern
Qinghai-Tibet Plateau of China during 2000-2015. Journal of Arid
Land, 11(5), 637-651.https://doi.org/10.1007/s40333-019-0061-2
XU, C., ZHANG, L. B., DU, J. Q., GUO, Y., WU, Z. F., XU, Y. D., LI, F.
& WANG, F. Y. 2013. Impact of alpine meadow degradation on soil water
conservation in the source region of three rivers. Acta Ecologica
Sinica, 33(08), 2388-2399.https://doi.org/10.5846/stxb201210181449
XU, K. X., SU, Y. J., LIU, J., HU, T. Y., JIN, S. C., MA, Q., ZHAI, Q.
P., WANG, R., ZHANG, J., LI, Y. M., LIU, O. A. & GUO, Q. H. 2020.
Estimation of degraded grassland aboveground biomass using machine
learning methods from terrestrial laser scanning data. Ecological
Indicators, 108, 9.https://doi.org/10.1016/j.ecolind.2019.105747
XU, W. X., GU, S., ZHAO, X. Q., XIAO, J. S., TANG, Y. H., FANG, J. Y.,
ZHANG, J. & JIANG, S. 2011. High positive correlation between soil
temperature and NDVI from 1982 to 2006 in alpine meadow of the
Three-River Source Region on the Qinghai-Tibetan Plateau.International Journal of Applied Earth Observation and
Geoinformation, 13(4), 528-535.https://doi.org/10.1016/j.jag.2011.02.001
XU, Y. D., DONG, S. K., GAO, X. X., WU, S. N., YANG, M. Y., LI, S.,
SHEN, H., XIAO, J. N., ZHI, Y. L., ZHAO, X. Y., MU, Z. Y. & LIU, S. L.
2022. Target species rather than plant community tell the success of
ecological restoration for degraded alpine meadows. Ecological
Indicators, 135, 9.https://doi.org/10.1016/j.ecolind.2021.108487
XUE, X., YOU, Q. G., PENG, F., DONG, S. Y. & DUAN, H. C. 2017.
Experimental Warming Aggravates Degradation-Induced Topsoil Drought in
Alpine Meadows of the Qinghai-Tibetan Plateau. Land Degradation &
Development, 28(8), 2343-2353.https://doi.org/10.1002/ldr.2763
YANG, F., SHAO, Q. Q., GUO, X. J., TANG, Y. Z., LI, Y. Z., WANG, D. L.,
WANG, Y. C. & FAN, J. W. 2018a. Effect of Large Wild Herbivore
Populations on the Forage-Livestock Balance in the Source Region of the
Yellow River. Sustainability, 10(2), 18.https://doi.org/10.3390/su10020340
YANG, F., SHAO, Q. Q. & JIANG, Z. G. 2019. A Population Census of Large
Herbivores Based on UAV and Its Effects on Grazing Pressure in the
Yellow-River-Source National Park, China. International Journal of
Environmental Research and Public Health, 16(22), 20.https://doi.org/10.3390/ijerph16224402
YANG, L. J., LI, X. L., SHI, D. J., SUN, H. Q. & YANG, Y. W. 2005.
Research or Regulation of Vegetation Succession in Degraded Grassland in
Qinghai and Tibetan plateau. Qinghai Prataculture, 14(01),
2-5+15.
YANG, S. X., FENG, Q. S., LIANG, T. G., LIU, B. K., ZHANG, W. J. & XIE,
H. J. 2018b. Modeling grassland above-ground biomass based on artificial
neural network and remote sensing in the Three-River Headwaters Region.Remote Sensing of Environment, 204, 448-455.https://doi.org/10.1016/j.rse.2017.10.011
YANG, T., ZHANG, G. L., LI, Y. Z., FAN, J. W., SUN, D. F., WANG, J., DI,
Y. Y., YOU, N. S., LIU, R. Q., ZHANG, Q. & DOUGHTY, R. B. 2021.
Satellite observed rapid green fodder expansion in northeastern Tibetan
Plateau from 2010 to 2019. International Journal of Applied Earth
Observation and Geoinformation, 102, 15.https://doi.org/10.1016/j.jag.2021.102394
YANG, Y. H., FANG, J. Y., PAN, Y. D. & JI, C. J. 2009. Aboveground
biomass in Tibetan grasslands. Journal of Arid Environments,73(1), 91-95.https://doi.org/10.1016/j.jaridenv.2008.09.027
YI, S. H., CHEN, J. J., QIN, Y. & XU, G. W. 2016. The burying and
grazing effects of plateau pika on alpine grassland are small: a pilot
study in a semiarid basin on the Qinghai-Tibet Plateau.Biogeosciences, 13(22), 6273-6284.https://doi.org/10.5194/bg-13-6273-2016
YU, H., LIU, B. T., WANG, G. X., ZHANG, T. Z., YANG, Y., LU, Y. Q., XU,
Y. X., HUANG, M., YANG, Y. & ZHANG, L. 2021. Grass-livestock balance
based grassland ecological carrying capability and sustainable strategy
in the Yellow River Source National Park, Tibet Plateau, China.Journal of Mountain Science, 18(8), 2201-2211.https://doi.org/10.1007/s11629-020-6087-2
YU, Y., GUO, Z. & WANG, Y. C. 2019. Spatial patterns and driving forces
of land change in Tibetan-inhabited Three Rivers Headwaters region,
China. Journal of Mountain Science, 16(1), 207-225.https://doi.org/10.1007/s11629-018-5217-6
ZENG, N., REN, X. L., HE, H. L., ZHANG, L., LI, P. & NIU, O. G. 2021.
Estimating the grassland aboveground biomass in the Three-River
Headwater Region of China using machine learning and Bayesian model
averaging. Environmental Research Letters, 16(11), 14.https://doi.org/10.1088/1748-9326/ac2e85
ZENG, N., REN, X. L., HE, H. L., ZHANG, L., ZHAO, D., GE, R., LI, P. &
NIU, Z. E. 2019. Estimating grassland aboveground biomass on the Tibetan
Plateau using a random forest algorithm. Ecological Indicators,102, 479-487.https://doi.org/10.1016/j.ecolind.2019.02.023
ZHANG, G. L., ZHANG, Y. J., DONG, J. W. & XIAO, X. M. 2013a. Green-up
dates in the Tibetan Plateau have continuously advanced from 1982 to
2011. Proceedings of the National Academy of Sciences of the
United States of America, 110(11), 4309-4314.https://doi.org/10.1073/pnas.1210423110
ZHANG, J. P., ZHANG, L. B., LIU, W. L., QI, Y. & WO, X. 2014.
Livestock-carrying capacity and overgrazing status of alpine grassland
in the Three-River Headwaters region, China. Journal of
Geographical Sciences, 24(2), 303-312.https://doi.org/10.1007/s11442-014-1089-z
ZHANG, J. P., ZHANG, L. B., LIU, X. N. & QIAO, Q. 2019a. Research on
Sustainable Development in an Alpine Pastoral Area Based on Equilibrium
Analysis Between the Grassland Yield, Livestock Carrying Capacity, and
Animal Husbandry Population. Sustainability, 11(17), 11.https://doi.org/10.3390/su11174659
ZHANG, L. X., FAN, J. W., ZHOU, D. C. & ZHANG, H. Y. 2017. Ecological
Protection and Restoration ProgramReduced Grazing Pressure in the
Three-River Headwaters Region, China. Rangeland Ecology &
Management, 70(5), 540-548.https://doi.org/10.1016/j.rama.2017.05.001
ZHANG, R. R., LI, Z. H., YUAN, Y. W., LI, Z. H. & YIN, F. 2013b.
Analyses on the Changes of Grazing Capacity in the Three-River
Headwaters Region of China under Various Climate Change Scenarios.Advances in Meteorology, 2013, 9.https://doi.org/10.1155/2013/951261
ZHANG, T., ZHAN, J. Y., HUANG, J., YU, R. & SHI, C. C. 2013c. An
Agent-Based Reasoning of Impacts of Regional Climate Changes on Land Use
Changes in the Three-River Headwaters Region of China. Advances in
Meteorology, 2013, 9.https://doi.org/10.1155/2013/248194
ZHANG, Y., ZHANG, C. B., WANG, Z. Q., AN, R. & LI, J. L. 2019b.
Comprehensive Research on Remote Sensing Monitoring of Grassland
Degradation: A Case Study in the Three-River Source Region, China.Sustainability, 11(7), 15.https://doi.org/10.3390/su11071845
ZHANG, Y., ZHANG, C. B., WANG, Z. Q., CHEN, Y. Z., GANG, C. C., AN, R.
& LI, J. L. 2016a. Vegetation dynamics and its driving forces from
climate change and human activities in the Three-River Source Region,
China from 1982 to 2012. Science of the Total Environment, 563,
210-220.https://doi.org/10.1016/j.scitotenv.2016.03.223
ZHANG, Y. L., LIU, L. S., BAI, W. Q., SHEN, Z. X., YAN, J. Z., DING, M.
J., LI, S. C. & ZHENG, D. 2006. Grassland Degradation in the Source
Region of the Yellow River. Acta Geographica Sinica, 61(01),
3-14.https://doi.org/10.11821/xb200601001
ZHANG, Y. X., MIN, Q. W., ZHAO, G. G., JIAO, W. J., LIU, W. W. &
BIJAYA, G. C. D. 2016b. Can Clean Energy Policy Improve the Quality of
Alpine Grassland Ecosystem? A Scenario Analysis to Influence the Energy
Changes in the Three-River Headwater Region, China.Sustainability, 8(3), 14.https://doi.org/10.3390/su8030231
ZHAO, D. S., WU, S. H. & YIN, Y. H. 2013. Dynamic responses of soil
organic carbon to climate change in the Three-River Headwater region of
the Tibetan Plateau. Climate Research, 56(1), 21-32.https://doi.org/10.3354/cr01141
ZHAO, L., ZHOU, W., PENG, Y. P., HU, Y. M., MA, T., XIE, Y. K., WANG, L.
Y., LIU, J. C. & LIU, Z. H. 2021. A new AG-AGB estimation model based
on MODIS and SRTM data in Qinghai Province, China. Ecological
Indicators, 133, 15.https://doi.org/10.1016/j.ecolind.2021.108378
ZHENG, D. F., WANG, Y. H., HAO, S., XU, W. J., LV, L. T. & YU, S. 2020.
Spatial -temporal variation and tradeoffs/synergies analysis on multiple
ecosystem services: A case study in the Three -River Headwaters region
of China. Ecological Indicators, 116, 11.https://doi.org/10.1016/j.ecolind.2020.106494
ZHENG, Y. T., HAN, J. C., HUANG, Y. F., FASSNACHT, S. R., XIE, S., LV,
E. Z. & CHEN, M. 2018. Vegetation response to climate conditions based
on NDVI simulations using stepwise cluster analysis for the Three-River
Headwaters region of China. Ecological Indicators, 92, 18-29.https://doi.org/10.1016/j.ecolind.2017.06.040