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\begin{document}

\title{}



\author[1]{Yanmin Teng}%
\author[1]{Jinyan Zhan}%
\author[1]{Wei Liu}%
\author[1]{Yongxiu Sun}%
\author[1]{Boappeah Frank }%
\author[2]{Li Liang}%
\author[3]{Zhihui Li}%
\affil[1]{Beijing Normal University}%
\affil[2]{Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences}%
\affil[3]{Institute of Geographic Sciences and Natural Resources Research CAS}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Wind erosion has become one of the major causes of land degradation on
the Qinghai-Tibet Plateau (QTP). Assessing wind erosion dynamics is
critical to forecast wind erosion and formulate desertification control
strategies. In this study, the spatial and temporal variations in wind
erosion on the QTP from 1980 to 2015 were evaluated by the revised wind
erosion equation (RWEQ). Furthermore, the relative contributions of
climate change and human activities to wind erosion were analysed to
identify the drivers. The results showed that wind erosion intensity on
the QTP increased continuously from southeast to northwest. Areas with
moderate or high soil erosion intensity (\textgreater{}25 t[?]hm2[?]a-1),
accounting for 53.48\% of the region, were mainly distributed on the
western and northern parts of the plateau. The mean annual soil wind
erosion modulus (SWEM) of the total region during 1980-2015 was 32.08
t[?]hm2[?]a-1, with an obvious decline at a rate of approximately 0.14
t[?]hm2[?]a-1 in this period. However, 9.99\% of the study area showed a
relatively significant increase, and these areas were mostly located in
the northwestern part of the region. Climate change, especially
variation in wind speed, was the dominant factor that affected wind
erosion over the whole plateau, whereas the impacts of human activities
such as grazing and ecological restoration policies and programmes were
comparatively small and usually more localized.%
\end{abstract}%



\sloppy


\textbf{Impact assessment of climate change and human activities on wind
erosion on the Qinghai-Tibet Plateau, China}

Yanmin Teng\textsuperscript{a}, Jinyan Zhan\textsuperscript{a*}, Wei
Liu\textsuperscript{a}, Zhihui Li\textsuperscript{b}, Yongxiu
Sun\textsuperscript{a}, Frank Boappeah Agyemang\textsuperscript{a}, Li
Liang\textsuperscript{b}

\textsuperscript{a} State Key Laboratory of Water Environment
Simulation, School of Environment, Beijing Normal University, Beijing
100875, China

\textsuperscript{b} Key Laboratory of Land Surface Pattern and
Simulation, Institute of Geographic Sciences and Natural Resources
Research, Chinese Academy of Sciences, Beijing 100101, China

\textbf{Abstract:} Wind erosion has become one of the major causes of
land degradation on the Qinghai-Tibet Plateau (QTP). Assessing wind
erosion dynamics is critical to forecast wind erosion and formulate
desertification control strategies. In this study, the spatial and
temporal variations in wind erosion on the QTP from 1980 to 2015 were
evaluated by the revised wind erosion equation (RWEQ). Furthermore, the
relative contributions of climate change and human activities to wind
erosion were analysed to identify the drivers. The results showed that
wind erosion intensity on the QTP increased continuously from southeast
to northwest. Areas with moderate or high soil erosion intensity
(\textgreater{}25 t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}),
accounting for 53.48\% of the region, were mainly distributed on the
western and northern parts of the plateau. The mean annual soil wind
erosion modulus (SWEM) of the total region during 1980-2015 was 32.08
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}, with an obvious decline
at a rate of approximately 0.14
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1} in this period. However,
9.99\% of the study area showed a relatively significant increase, and
these areas were mostly located in the northwestern part of the region.
Climate change, especially variation in wind speed, was the dominant
factor that affected wind erosion over the whole plateau, whereas the
impacts of human activities such as grazing and ecological restoration
policies and programmes were comparatively small and usually more
localized.

\textbf{Keywords:} Wind erosion, Revised wind erosion equation (RWEQ),
Climate change, Human activities, Land degradation, Qinghai-Tibet
Plateau

\textbf{Introduction}

Soil erosion by wind has become an important cause of land degradation
in arid and semi-arid regions of China (Chi et al., 2019). Wind erosion
causes a large loss of clay, silt and organic matter, which leads to a
decrease in soil fertility and deterioration of soil hydrothermal
conditions, which are not conducive to plant growth (Li et al., 2006; He
\& Richards, 2015, 2017; Peng et al., 2018). As a result, a range of
ecosystem services such as carbon sequestration and water conservation
will weaken or disappear, and it becomes difficult for the ecosystem to
recover naturally (Hopping et al., 2018). Therefore, assessing regional
wind erosion intensity and analysing its influencing factors are
critical to forecast wind erosion and provide a basis for
desertification control.

The QTP, also known as the Third Pole, has an urgent need for ecological
protection and desertification control to protect the area from wind
erosion. Lack of rainfall and frequent strong wind makes the land on the
plateau, which is mostly arid and semi-arid, prone to desertification
(Wang et al., 2015; Huang et al., 2016; Yan et al., 2018). In 2015, the
area of desertification in this region was 392913 km\textsuperscript{2},
accounting for 15.1\% of the total QTP (Li et al., 2018). Additionally,
the QTP is the source of the nine largest rivers in Asia and an
important habitat for endemic organisms, providing fresh water, food,
and other irreplaceable ecosystem services (Zhu et al., 2017; Liu et
al., 2018), while desertification in this region poses a major threat to
these ecosystem services. However, wind erosion studies in this area
have been limited and have mainly focused on soil erosion processes
(Zhang et al., 2016; Xie et al., 2018): the primary methods used were
sample-based wind tunnel simulation (Lu et al., 2013)
and\textsuperscript{137}Cs techniques (Ping et al., 2001; Li et al.,
2019). In addition, most studies have involved only local areas, such as
the Three-River Headwaters Region in Qinghai Province (Jiang et al.,
2016; Li et al., 2019). Accordingly, few studies have comprehensively
described the wind erosion and its dynamic characteristics over the
whole QTP.

Soil wind erosion is mainly affected by local vegetation and climatic
conditions. The comprehensive effects of climate change and
high-intensity human activities have led to a wide range of grassland
degradation on the QTP (Sun et al., 2012; Li et al., 2013; Liu et al.,
2018). In terms of human activities, overgrazing is considered the
leading factor responsible for the vegetation degradation and desert
expansion (Feng et al., 2017; Zhang et al., 2018; Li et al., 2019). For
example, Deng et al. (2017) found that higher grazing density severely
reduced grassland productivity in Qinghai Province by using simultaneous
equations. Furthermore, climate, especially near-surface wind speed, can
strongly influence the process of wind erosion by removing the topsoil
or carrying sand from riverbeds or lakeshores (Xu et al., 2019). Studies
have shown that the maximum wind speed is located in the central and
southwest parts of the QTP, with the maximum exceeding 4.5 m/s, which
creates favourable conditions for local soil erosion (Yao \& Li, 2016).
Separating the wind erosion variations induced by climate change and
human activities and identifying their main drivers can provide
essential information for developing proposals to reduce wind erosion
(Zhang et al., 2018). Previous studies have analysed the effects of land
use/cover changes (Chi et al., 2019) and overgrazing (Li et al., 2019)
on wind erosion statistically. However, there are fewer studies on the
spatial identification of the relative contributions of climatic and
anthropogenic factors to wind erosion dynamics on the QTP.

The revised wind erosion equation (RWEQ) model (Fryrear et al., 1998)
has been relatively widely used in wind erosion assessment, which can
overcome the shortcomings of field measurements (e.g., isotopic tracing,
field sand collection and wind tunnel simulation) that are difficult to
generalize to regional scales and require a large amount of person power
and financial resources (Gong et al., 2014). The RWEQ fully considers
climatic conditions, vegetation coverage, soil erodibility, the soil
crust, surface roughness and other factors. Studies have shown that the
model can predict wind erosion well and estimate long time series of
wind erosion at the regional scale (Zobeck et al., 2000; Guo et al.,
2013; Jiang et al., 2015). Therefore, we used the RWEQ model to evaluate
actual wind erosion and its temporal and spatial variation. By comparing
the trends and correlation coefficients of climate-induced and
human-induced wind erosion over time, we analysed whether climatic
factors or human activities were determining the changes in wind
erosion. The objectives of this study were (1) to analyse the spatial
and temporal changes in wind erosion on the QTP from 1980 to 2015; (2)
to explore the relative roles of climate change and human activities in
wind erosion dynamics; and (3) to provide a scientific reference and
guidelines for management and planning related to the prevention and
control of desertification on the QTP.

\textbf{Materials and methods}

2.1. Study area

The QTP is located in southwestern China, between 73\selectlanguage{ngerman}°18\selectlanguage{english}' and 104deg46\selectlanguage{english}'E
and between 26deg00\selectlanguage{english}' and 39deg46\selectlanguage{english}'N (Fig. 1(a)). Also known as the Roof of
the World, the average elevation in this region is greater than 4000 m,
and the annual average temperature ranges from \selectlanguage{english}-5.6 \textsuperscript{deg}C
to 17.6 \textsuperscript{deg}C across the region. The hinterland of the
plateau has the highest altitude (Fig. 1(b)), with an average annual
temperature below 0 \textsuperscript{deg}C and less than
10\textsuperscript{deg}C in the warmest month. Precipitation is unevenly
distributed on this plateau. Annual average precipitation in the
southeastern, northeastern and northwestern regions of the plateau are
above 2000 mm, 300-500 mm and 20-75 mm, respectively (Huang et al.,
2016). Under the unique alpine and arid climate of this region,
grassland is the most important land cover type on the QTP, accounting
for one third of the total grassland area in China (Fig. 1(a)). From
southeast to northwest, alpine meadow, alpine steppe and alpine desert
are successively distributed, and desertified land is widely spread over
the western and northern parts of the plateau (Fig. 1(c)).

2.2. Data acquisition and processing

Meteorological datasets were obtained from 131 meteorological stations
within and close to the QTP and were available from the China
Meteorological Data Service Center (CMDC) (\emph{http://data.cma.cn/}).
The datasets mainly included daily, monthly and annual wind speed, wind
direction, precipitation, temperature, relative humidity, solar
radiation and sunlight hour data from 1980 to 2015. Snow cover data with
a 25-km resolution were obtained from a long-term snow depth dataset
(1979-2016) (Che et al., 2008; Dai et al., 2012, 2015), which was
accessed from the Cold and Arid Region Science Data Center (CARSDC)
(\emph{http://westdc.westgis.ac.cn}).

The normalized difference vegetation index (NDVI) products with an 8-km
resolution and 15-day interval were obtained from GIMMS3g data for the
period 1982--2013 (\emph{https://ecocast.arc.nasa.gov/data/pub/gimms/}),
and the 1-km resolution monthly composite NDVI data during 1998--2015
using maximum synthesis were acquired from the RESDC. Details on data
preprocessing were provided in a previous study (Zhang et al., 2018).
The NDVI data were not available for the years of 1980 and 1981, so the
value of NDVI in 1982 was used as an alternative.

Digital elevation model (DEM) data with a 30-m spatial resolution and
land use/cover data with 1-km grids and a 5- or 10-year interval from
1980 to 2015 were acquired from the Data Center for Resources and
Environmental Sciences, Chinese Academy of Sciences (RESDC)
(\emph{http://www.resdc.cn}). The land use/cover data were divided into
six classes (cropland, forest, grassland, water body, built-up area, and
unused land) and 25 sub-classes (Liu et al., 2018; Ning et al., 2018).
To analyse soil wind erosion under different land use types more
accurately, we re-categorized bare soil and bare rock from unused land
as bare land, merged swampland from unused land with water body, and
categorized other types of unused land as desert according to the
average SWEM of different land use/cover sub-classes.

The 1:1 million soil data including soil texture and organic content
were derived from the Harmonized World Soil Database (HWSD 1.2 version),
which was accessed from the CARSDC. The soil texture was reclassified
from the Chinese categories to the American categories using a logistic
growth model (Skaggs et al., 2001). The 1:4 million soil calcium
carbonate content data were accessed from the Data Sharing
Infrastructure of Earth System Science (\emph{http://www.geodata.cn}).
All the data above were interpolated or resampled to a 1-km spatial
resolution to avoid scale mismatch problems.

Livestock number data were accessed from the China Economic and Social
Development Statistical Database (\emph{http://tongji.cnki.net}). Small
livestock including sheep and goats were treated as equivalent to
standardized sheep units (SUs), and large domestic animals including
horses and cattle were regarded as 5 SUs (Su et al., 2003).

2.3. Wind erosion evaluation

The annual SWEM from 1980 to 2015 was calculated using the RWEQ model.
The SWEM was first computed at a time scale of one month and then was
summed to acquire the annual SWEM. Wind erosion is a continuous dynamic
process involving interactions between the atmosphere and soil interface
and is the result of the combined action of multiple factors, including
climate factors (e.g., wind velocity, solar radiation, temperature,
precipitation, snow cover), vegetation coverage, soil physicochemical
properties (e.g., texture, organic matter, moisture), land use/cover,
topography and geomorphology. The RWEQ model is generally expressed as
follows (Fryrear et al., 2000):

\(\text{WE}_{A}=\frac{2z}{S^{2}}Q_{\max}e^{{\frac{-(z}{S})}^{2}}\)(1)

\(Q_{\max}=109.8(WF\times EF\times SCF\times K^{\prime}\times C)\) (2)

\(S={150.71(WF\times EF\times SCF\times K^{\prime}\times C)}^{-0.3711}\)(3)

where \emph{WE\textsubscript{A}} is the actual wind erosion amount
(t\selectlanguage{english}[?]hm\textsuperscript{-2}\selectlanguage{english}[?]a\textsuperscript{-1});\emph{Q\textsubscript{max}}
is the maximum sand transport capacity (kg\selectlanguage{english}[?]m\textsuperscript{-1});
\emph{S}is the critical plot length (m); \emph{z} is the maximum
downwind erosion distance (m); \emph{WF} is the weather factor
(kg\selectlanguage{english}[?]m\textsuperscript{-1}); \emph{EF} is the soil erodibility
factor;\emph{SCF} is the soil crust factor (dimensionless); \emph{K'} is
the surface roughness factor (dimensionless); and \emph{C} is the
combined vegetation factor including growing vegetation, withered
vegetation, crops and other vegetation residues.

The \emph{WF} can be calculated as follows (Zhang et al., 2018):

\(WF=W_{f}\times\frac{\rho}{g}\times SW\times SD\) (4)

\(W_{f}=u_{2}{(u_{2}-u_{1})}^{2}\times N_{d}\) (5)

\(\rho=348.0\left(\frac{1.013-0.1183EL+0.0048\text{EL}^{2}}{T}\right)\)(6)

\(\text{SW}=\frac{\text{ET}_{p}-(R+I)\frac{R_{d}}{N_{d}}}{\text{ET}_{p}}\)(7)

\(\text{ET}_{p}=0.0162\left(\frac{\text{SR}}{58.5}\right)\left(DT+17.8\right)\)(8)

\(\text{SD}=1-P\left(snow\ depth>25.4\ mm\right)\) (9)

where \emph{W\textsubscript{f}} is the wind factor
(m\textsuperscript{3}\selectlanguage{english}[?]s\textsuperscript{-3}); \selectlanguage{greek}\emph{ρ} \selectlanguage{english}is the air
density (kg[?]m\textsuperscript{-3}); \emph{g} is the gravitational
acceleration (m[?]s\textsuperscript{-2}); \emph{SW} is the soil wetness
factor (dimensionless); \emph{SD} is the snow cover factor
(dimensionless); \emph{u\textsubscript{1}} is the threshold wind speed
at a height of 2 m (m[?]s\textsuperscript{-1}), which was suggested to be
6 m[?]s\textsuperscript{-1} (Li et al., 2014); \emph{u\textsubscript{2}}is
the wind speed at a height of 2 m
(m[?]s\textsuperscript{-1});\emph{N\textsubscript{d}} is the number of
days with a wind speed exceeding the threshold; \emph{EL} is altitude
(km); \emph{T} is the absolute temperature (K);
\emph{ET\textsubscript{p}} is the potential relative evaporation (mm);
\emph{R} is rainfall (mm); \emph{I} is irrigation quantity (mm);
\emph{R\textsubscript{d}} is the number of days with rainfall or
irrigation; \emph{SR} is solar radiation (cal[?]cm\textsuperscript{-2});
\emph{DT} is average temperature (\selectlanguage{ngerman}\textsuperscript{°}C); and \emph{P} is
the probability of a snow depth greater than 25.4 mm.

The \emph{EF} and \emph{SCF} can be calculated as:

\(EF=\frac{29.09+0.31SA+0.17SI+0.33\left(\frac{\text{SA}}{\text{CL}}\right)-2.59SOM-0.95CC}{100}\)(10)

\(SCF=\frac{1}{1+0.0066\left(\text{CL}\right)^{2}+0.021\left(\text{SOM}\right)^{2}}\)(11)

where \emph{SA} , \emph{SI} , \emph{CL} , \emph{SOM} and
\emph{CC}represent the sand, silt, clay, soil organic matter and calcium
carbonate content (\%), respectively. In this study, we assumed that
the\emph{EF} and \emph{SCF} did not change over time.

The vegetation factor (\emph{C} ) can be calculated as:

\(C=e^{-0.0483\left(\text{VC}\right)}\) (12)

\(\text{FVC}=\frac{\left(NDVI-\text{NDVI}_{\text{soil}}\right)}{\left(\text{NDVI}_{\text{veg}}-\text{NDVI}_{\text{soil}}\right)}\)(13)

where \emph{FVC} is the fractional vegetation cover (\%), \emph{NDVI} is
the normalized difference vegetation
index;\emph{NDVI\textsubscript{soil}} is the NDVI value of a pure soil
pixel; and \emph{NDVI\textsubscript{veg}} is the NDVI value of a pure
vegetation pixel. In this study, \emph{NDVI\textsubscript{veg}}
and\emph{NDVI\textsubscript{soil}} represented the NDVI value at a 95\%
and 5\% cumulative frequency, respectively.

\selectlanguage{english}\emph{K'} can be calculated as (Xu et al., 2019):

\(K^{{}^{\prime}}=\cos\alpha\) (14)

where \selectlanguage{greek}\emph{α} \selectlanguage{english}represents the slope gradient and is calculated by DEM
data.

The relative contributions of climate change and human activities to
wind erosion

The variation in actual soil wind erosion (\emph{WE\textsubscript{A}} )
is the result of climate change and human activity. Human-induced wind
erosion (\emph{WE\textsubscript{H}} ) was calculated by a variable
control method that uses the 36-year average of the weather factor
(\emph{WF} ) instead of actual annual values. Thus, the climate-driven
wind erosion (\emph{WE\textsubscript{C}} ) can be calculated as follows:

\(\text{WE}_{C}\)=\(\text{WE}_{A}-\text{WE}_{H}\) (15)

To identify the relative effects of climate change and human activities
on \emph{WE\textsubscript{A}} dynamics spatially, the absolute slopes
of\emph{WE\textsubscript{C}} and \emph{WE\textsubscript{A}} in the same
period were compared for each pixel. Furthermore, the threshold
segmentation method was adopted to separate areas where annual SWEM
change was mainly affected by either climate change or human activities.
Detailed information can be found in a previous study (Zhang et al.,
2018).

2.5. Change trend and correlation analysis

The linear slopes of analytical factors (wind erosion, FVC, climatic
factors and livestock numbers) over time can reflect changing trends and
spatial differences more precisely (Zhang et al., 2018). The slope of
the temporal variation trend for each factor and for a certain period is
calculated as follows (Pan et al., 2017):

\(slope=\frac{n\times\sum_{i=1}^{n}{i\times X_{i}-(\sum_{i=1}^{n}{i)\times(\sum_{i=1}^{n}{X_{i})}}}}{n\times\sum_{i=1}^{n}i^{2}-{(\sum_{i=1}^{n}i)}^{2}}\)(16)

where \emph{i} is 1 for the first year; n is the total number of years
in the study period; and \emph{X\textsubscript{i}} is the value of
factors for the year \emph{i} . Positive slopes represent increasing
values, and negative slopes indicate an overall decreasing trend.

Pearson correlation analysis with a linear regression model was used to
identify correlations between the SWEM and vegetation coverage, climatic
conditions (wind speed, temperature and precipitation) and human
activities (livestock numbers). The calculation formula is as follows:

\(R=\frac{\sum_{i=1}^{n}{{(X}_{i}-\overset{\overline{}}{X}})(Y_{i}-\overset{\overline{}}{Y})}{\sqrt{\sum_{i=1}^{n}{(X_{i}-\overset{\overline{}}{X})}^{2}}\sum_{i=1}^{n}{(Y_{i}-\overset{\overline{}}{Y})}^{2}}\)(17)

where \emph{R} is the correlation coefficient; n is the time series from
1 to 36; \emph{X\textsubscript{i}} is the SWEM of the\emph{i}
\textsuperscript{th} year; \emph{Y\textsubscript{i}} is the vegetation
coverage, climatic conditions or human activities of the\emph{i}
\textsuperscript{th} year; \(\overset{\overline{}}{X}\) is the mean value of the
SWEM during 1980-2015; and\(\overset{\overline{}}{Y}\) is the mean value of
vegetation coverage, climatic conditions or human activities during
1980-2015. When\emph{R} \textgreater{}0, the variables are positively
correlated, and when \emph{R} \textless{}0, they are negatively
correlated. As the value approaches 1 or -1, the correlation becomes
stronger.

\textbf{Results}

3.1. Model accuracy assessment and spatial changes in soil wind erosion

\textsuperscript{137}Cs tracing is a common method used to estimate real
values of the soil wind erosion rates of different land use/covers. To
verify the accuracy of the model results, a comparison was made between
the evaluated values and values from previous studies using
the\textsuperscript{137}Cs method in this area (Ping et al., 2001; Yan
\& Dong, 2003). The result showed that the SWEM values acquired from the
RWEQ were significantly correlated with the plot measurements
(R\textsuperscript{2} = 0.93, p \textless{} 0.01) (Fig. 2(b)).

Soil wind erosion intensity on the QTP gradually increased from
southeast to the northwest (Fig. 2 (a)). This trend has a strong
correlation with rainfall and vegetation coverage. According to the
``Standard for classification and gradation of soil erosion''
(SL190--2007) published by the Ministry of Water Resources of the
People's Republic of China, the wind erosion intensity of the study area
was classified into six grades. Areas with tolerable (\textless{} 2
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}) and slight erosion (2--25
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), which were located in
the southeastern part of the QTP, accounted for 13.66\% and 32.85\% of
the total area, respectively. These places had considerable
precipitation and were mainly covered by forest and dense grass. The
percentages of regions with moderate (25--50
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), severe (50--80
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), very severe (80--150
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), and destructive
(\textgreater{}150 t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1})
erosion, mainly located in the western and northern parts of the QTP,
accounted for 18.43\%, 21.29\%, 10.31\%, and 3.45\% of the total area,
respectively. The climate in these areas is cold and dry, with large
areas of desert (Qaidam basin desert), sandy lands and sparse grassland.

3.2. Temporal variations in soil wind erosion

The overall SWEM on the QTP showed a declining trend from 1980 to 2015
at a rate of approximately 0.14
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1} (Fig. 3(b)). The average
annual SWEM during 1980-2015 was 32.08
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}, with the lowest value of
27.05 t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1} occurring in 1997
and the highest value of 37.32
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1} in 1984. Spatially,
34.00\% of the study area showed a trend of strengthening, and 66.00\%
of the area showed a trend of weakening (Fig. 3(a)). The annual
variation in SWEM in approximately 71.77\% of the total region was less
than 0.5 t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}. Areas with an
annual SWEM that increased by more than 0.5
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1} accounted for 9.99\% of
the QTP and were mostly distributed in the northwestern corner of the
QTP, on the Chang Tang Plateau and in the Gangdise Mountains. Areas with
an annual SWEM that decreased by more than 0.5
t[?]km\textsuperscript{2}[?]a\textsuperscript{-1} were mainly located in the
Qaidam basin, accounting for 18.23\% of the study area.

3.3. Changes in wind erosion of different land use/cover types

The land use/cover changes, mean SWEM and total amount of wind erosion
of different land use/cover types are shown in Table 1. Between 1980 and
2015, land use/cover changes were inconspicuous. Grassland and
cultivated land increased, while woodland and unused land decreased.
During 1980-2015, the average SWEM of desert was 96.14
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}, which was approximately
2.48-, 2.65-, 3.87-, 4.38-, 8.31-, and 36.09-fold that of bare land
(38.73 ), sparse grass (36.26
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), moderate grass (24.84
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), dense grass (19.73
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), cultivated land (11.57
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), and woodland (2.66
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}), respectively. The
average SWEM of desert areas showed a significant decline during
1980-2015 at a rate of 0.60
t[?]hm\textsuperscript{2}[?]a\textsuperscript{-1}, while the values of
woodland, cultivated land and dense grass had a small reduction, and the
values of moderate grass and sparse grass rose slightly. Grassland,
desert and bare land were the main sources of soil wind erosion on the
QTP. During 1980-2015, the average amount of total soil wind erosion
from desert areas was 19.72\selectlanguage{ngerman}×10\textsuperscript{8} t, which accounted for
24.72\% of the total amount on the QTP. This was followed by sparse
grass (19.14, 24.00\%), bare land (17.60×10\textsuperscript{8} t,
22.06\%), moderate grass (13.99×10\textsuperscript{8} t, 17.54\%), dense
grass (8.34×10\textsuperscript{8} t, 10.46\%), woodland
(0.72×10\textsuperscript{8} t, 0.90\%), and cultivated land
(0.24×10\textsuperscript{8} t, 0.30\%).

\textbf{Table 1} Changes in land use/cover and wind erosion on the QTP
from 1980 to 2015 (area: 10\textsuperscript{3} km\textsuperscript{2},
percentage/rate: \%, amount:
10\textsuperscript{8}\selectlanguage{english}[?]t\selectlanguage{english}[?]a\textsuperscript{-1}, SWEM/slope:
t\selectlanguage{english}[?]hm\textsuperscript{2}\selectlanguage{english}[?]a\textsuperscript{-1}).\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllll@{}}
\toprule
\begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Land use/cover types\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
1980\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
1980\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
1980\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
2015\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
2015\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
2015\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
2015\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Land use/cover changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Land use/cover changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Land use/cover changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Land use/cover changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Wind erosion changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Wind erosion changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Wind erosion changes\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Wind erosion changes\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Area\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Percentage\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Area\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Percentage\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Area\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Rate\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
SWEM\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Amount\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Slope\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Cultivated land\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
22.65\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
22.85\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
22.85\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.88\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.88\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.20\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.20\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
11.57\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
11.57\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.24\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.05\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Woodland\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
271.90\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
10.49\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
10.49\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
270.19\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
270.19\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
10.42\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
10.42\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.71\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.71\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.63\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.63\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
2.66\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
2.66\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.72\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.04\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Desert\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
204.81\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
7.90\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
7.90\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
205.76\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
205.76\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
7.94\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
7.94\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.94\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.94\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
96.14\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
96.14\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
19.72\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.60\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Bare land\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
460.38\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17.75\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17.75\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
454.20\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
454.20\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17.52\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17.52\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-6.18\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-6.18\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.34\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.34\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
38.73\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
38.73\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17.60\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.00\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Dense grass\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
412.26\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
15.90\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
15.90\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
423.55\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
423.55\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
16.33\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
16.33\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
11.28\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
11.28\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
2.74\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
2.74\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
19.73\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
19.73\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
8.34\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.01\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Moderate grass\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
568.29\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
21.92\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
21.92\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
558.85\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
558.85\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
21.55\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
21.55\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-9.44\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-9.44\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.66\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-1.66\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
24.84\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
24.84\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
13.99\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.04\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
Sparse grass\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
524.94\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
20.24\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
20.24\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
525.81\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
525.81\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
20.28\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
20.28\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.87\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.17\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.17\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
36.26\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
36.26\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
19.14\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

3.4. Relative roles of climate change and human activity in wind erosion

To clearly identify the drivers of wind erosion dynamics over the past
36 years, the driving forces for the variations on the QTP were divided
into two categories, namely, climate change and human activities. As
shown in Fig. 4, significant changes in wind erosion caused by climate
change accounted for 54.74\% of the study area, of which the increase
was 29.44\% and the decrease was 25.30\%. Significant changes in wind
erosion attributed to human activity accounted for 18.89\% of the
region, of which the increase was 6.05\% and the decrease was 12.84\%.

\textbf{Discussion}

4.1. Vegetation coverage change and its effect on wind erosion

Vegetation covering the soil surface acts as an insulator to protect
surface soil, regulate wind speed, and consolidate sand to reduce the
potential for wind erosion. As a common quantitative indicator of
vegetation status, fractional vegetation cover (FVC) is able to reflect
vegetation dynamics and the soil conservation function comprehensively.
Increasing FVC can effectively reduce the amount of soil erosion: areas
with severe wind erosion mainly occurred in areas with low FVC. As shown
in Fig. 5(a), FVC had an obvious decreasing trend from southeast to
northwest on the QTP, while wind erosion intensity had an opposite
ascending trend (Fig. 2(a)). The average FVC of the QTP showed an
obvious increasing trend during 1980-2015, and the annual growth rate
was approximately 0.10\% (Fig. 5(b)). Studies have found that the
warming and humidification of climate and ecological construction are
the main reasons for the recent greening of the plateau (Chen et al.,
2014; Li et al., 2016). With the increase in FVC, soil wind erosion
showed an opposite decline (Fig. 3(b)), which indicated that the
improvement of FVC on the QTP was helpful in slowing down wind erosion.

According to the correlation coefficients, 61.96\% of the total area
showed changes in FVC that were negatively correlated with the changes
in soil wind erosion (Fig. 5(d)), which was scattered across the QTP.
Nevertheless, for some areas with relatively serious wind erosion (e.g.,
the Chang Tang Plateau), the enhancement of FVC had no clear impact on
reducing wind erosion. This may be because the increase in FVC in the
area with high wind erosion was not significant (Fig. 5(c)). Even though
the vegetation coverage increased, it was still low (less than 10\%) and
therefore did not play a significant role in reducing wind erosion (Zhao
et al., 2017). In addition, changes in other factors such as wind
velocity may have a greater effect on soil wind erosion (Zhang et al.,
2019).

4.2. Climate change and its effect on soil wind erosion

Climatic changes, including variations in wind speed, precipitation and
temperature, are the leading factors that affect the soil wind erosion
in a region overall (Li et al., 2020). Wind carries away surface soil
and transports sand, which is the most immediate factor affecting soil
erosion, and wind speed is the decisive factor influencing wind erosion
intensity (Zhang et al., 2019). Rainfall and air temperature affect both
soil moisture and vegetation growth (Sharratt et al., 2015; Yan et al.,
2015). These factors will further influence soil wind erosion in a
comprehensive way.

Mean annual wind speed was selected as an indicator of wind changes in
this study. The area with high wind speed was mainly distributed in the
central and western parts of the QTP (Fig. 6(a)). During 1980-2015, the
mean annual wind speed of the QTP first dropped sharply, reaching its
lowest level in 1997, and then rose in volatility (Fig. 6(d)). The
variation in SWEM and wind speed showed a relatively consistent trend
(Fig. 3(b)). Spatially, changes in mean annual wind speed had a
significant positive correlation with the SWEM over the vast majority of
the QTP (Fig. 7(b)). This result indicates that wind speed was one of
the most important factors affecting the wind erosion in the study area.

A negative correlation between wind erosion and precipitation occurred
in most areas of the QTP (Fig. 7(d)). From 1980 to 2015, the
precipitation on the QTP tended to increase with a slope of 1.50
mm[?]a\textsuperscript{-1} (Fig. 6(e)). For the spatial distribution, the
precipitation increased obviously as a whole, except in some marginal
areas (e.g., the southeastern and western edges) (Fig. 7(c)). The
increase in rainfall reduced wind erosion to a certain extent by
promoting vegetation growth and increasing FVC (Fig. 3(b)). In contrast,
the wind erosion in some regions (e.g., most parts of the Chang Tang
Plateau and the northwestern part of the Qaidam Basin Desert) increased
further as precipitation increased (Fig. 7(d)). This may be because the
climate in these areas was relatively dry (Fig. 6(b)) and the increase
in temperature was more pronounced (Fig. 7(e)), which may have led to
warmer and drier meteorological conditions. In general, the effect of
precipitation on reducing wind erosion was not obvious, and rainfall was
more likely to affect wind erosion indirectly by influencing vegetation
growth.

As opposed to precipitation, the intensity of wind erosion increased
with the rise in temperature in most parts of the QTP (Fig. 7(f)).
During the period of 1980-2015, the temperature of the region clearly
increased at a rate of approximately
0.05\selectlanguage{ngerman}\textsuperscript{°}C\selectlanguage{english}[?]a\textsuperscript{-1} (Fig. 6(f)). Spatially,
the areas with the most apparent warming were mainly distributed in the
central and western parts of the QTP (Fig. 7(e)), which were covered
with desert and sparse grass of low coverage. Climate warming promoted
the evaporation of soil surface water, leading to the soil becoming
drier and more vulnerable to wind erosion, especially in deserts and
areas lacking plants (D'Odorico et al., 2013). In these areas, the
surface temperature rose faster, and the rate of evaporation increased
compared with those in surrounding regions, which brought about
increased soil erodibility, stronger convection due to cold air invasion
and more readily occurring wind erosion (Zhang et al., 2018). Therefore,
this result could indicate that temperature was the principal element
determining wind erosion on the QTP by altering hydrothermal and air
flow conditions.

4.3. Effect of grazing on soil wind erosion

With approximately 130 million hectares of pasture and 70 million
livestock, the grassland on the QTP has become one of the largest
grazing ecosystems in the world (He et al., 2008). Animal husbandry is
the only agricultural activity in 57 of the plateau's 188 counties
(Zhang et al., 2012). Excessive animal grazing has exacerbated grassland
degradation and desertification in this region (Li et al., 2013; Miehe
et al., 2019). Long-term overgrazing, especially in arid areas with low
FVC, could reduce the height and coverage of vegetation, destroy soil
surfaces and damage soil crusts through livestock foraging and trampling
(Du et al., 2019). After grassland degradation, the soil surface was
exposed, making it prone to desertification.

In the early 1960s, the number of livestock on the QTP remained at a low
level, rising sharply over the next decade (Fig. 8(b)). Since the 1970s,
livestock numbers were maintained at a very high level
(41.66x10\textsuperscript{6} SUs and 45.45x10\textsuperscript{6} SUs in
Qinghai Province and Tibet on average, respectively), which exceeded the
local theoretical carrying capacity (16.08x10\textsuperscript{6} SUs and
43.92 x10\textsuperscript{6} SUs in Qinghai Province and Tibet,
respectively) (Li et al., 2012; Zhao et al., 2015). (Liu et al., 2008)
discovered that the grassland degradation pattern in the Three-River
Headwaters region of the QTP was initially formed in the middle and late
1970s. It is possible that the dramatic increase in livestock numbers
during this period led to the degradation of grasslands. Therefore, the
effect of grazing on soil wind erosion can be very strong over a longer
time scale.

In this study, the counties with an average SWEM greater than 25
t\selectlanguage{english}[?]hm\textsuperscript{2}\selectlanguage{english}[?]a\textsuperscript{-1} were chosen to analyse the
correlation between livestock carrying capacity and soil wind erosion.
Counties with significant increases in livestock numbers appeared in
Bange, Nima, Hetian, Gaize, Zhongba, Angren, and other areas (Fig.
8(c)), and obvious intensification of the SWEM also occurred in these
areas (Fig. 3(a)). The Pearson correlation analysis results showed that
the change in livestock numbers was significantly correlated with the
change in the mean SWEM. Hence, overgrazing is an important factor
affecting soil wind erosion in the QTP, primarily by damaging natural
vegetation and destroying soil structure. However, we also found that
the livestock density was relatively low in areas where soil wind
erosion changed significantly (Fig. 8(a), 3(a)). Although the density of
livestock in these areas was low, the natural conditions such as climate
and natural vegetation cover were also poor (Fig. 5(a), 6(a), 6(b),
6(c)). These harsh natural conditions are conducive to soil wind
erosion.

4.4. Effect of implementation of ecological conservation programmes and
policies on wind erosion

To control grassland degradation and desertification, a series of
ecological conservation and restoration programmes and policies (e.g.,
Grazing Forbidden Policy, Grassland Ecological Protection Subsidies and
Rewards Program, Protection and Construction Planning of Ecological
Safety Shelter for Tibet (2008-2030)) have been implemented throughout
the QTP (Li et al., 2016; Zhang et al., 2018). For example,
approximately 0.11 million hectares of land has been devoted to
combating desertification in Tibet from 2009 to 2015 for the
construction of ecological safety shelters (Wang et al., 2017). The
implementation of these policies and plans on the one hand reduced the
number of livestock on the plateau to a certain extent (Fig. 8(b)) and
on the other hand promoted the implementation of a great deal of
ecological restoration projects, which mainly included pasture
enclosure, sand barrier setting, shelterbelt construction and artificial
grassland establishment (Li et al., 2016; Wen et al., 2018).

In this study, these measures proved to be effective. The decrease in
wind erosion area due to human interventions was much greater than the
increase (Fig. 4). With these human interventions in the degraded
grassland, the vegetation on the plateau has been effectively restored
(Chen et al., 2014), and aeolian desertification began to decrease after
2000 (Zhang et al., 2018). The SWEM of the QTP has shown a clear
downward trend since 2000 (Fig. 3(b)), probably because of the
restoration of grasslands and the reduction in deserts. However,
compared with the influence of climate on wind erosion, current
protection and recovery measures can play only a limited role in local
areas. The recovery of the desertified land and the decrease in SWEM on
the QTP were due to a combination of favourable climatic changes and
ecological conservation policies and projects during this period.

\textbf{Conclusion}

In this study, we assessed spatial and temporal changes in wind erosion
on the QTP from 1980 to 2015. We also explored the relative impacts of
climate change and human activities on wind erosion dynamics. The
results indicate that areas with moderate or high soil erosion
intensity, accounting for 53.48\% of the QTP, are mainly located in the
western and northern parts of the region, where vegetation coverage was
very low and large areas of desert and sparse grassland were
distributed. The annual mean SWEM on the plateau showed a declining
trend from 1980 to 2015 at a rate of approximately 0.14
t\selectlanguage{english}[?]hm\textsuperscript{2}\selectlanguage{english}[?]a\textsuperscript{-1}. Climate change was the
dominant factor affecting wind erosion dynamics over the whole plateau.
Among the climatic factors, the change in wind speed was the most direct
and obvious factor controlling soil wind erosion, and the increases in
temperature and rainfall somewhat promoted and inhibited soil wind
erosion, respectively. Compared to climate change, the effects of human
activities on wind erosion were comparatively small and usually more
localized. Over the course of 36 years, the climate became warmer,
wetter and less windy; additionally, with the implementation of
ecological conservation programmes and policies, vegetation coverage has
continued to improve while soil wind erosion has declined continuously.
However, when affected by climate and overgrazing, wind erosion
continued to increase in some regional areas.

\textbf{Acknowledgments}

The work was supported by the Second Scientific Expedition to the
Qinghai-Tibet Plateau (No. 2019QZKK0405-05), the Strategic Priority
Research Program of Chinese Academy of Sciences (Grant No. XDA20010302)
and the major research plan of the National Natural Science Foundation
of China (Grant No. 91325302).

\textbf{Conflict of Interest Statement}

The authors declare no conflicts of interest.

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