[go: up one dir, main page]
Next Article in Journal
Stoichiometric Coupling of C, N, P, and K in the Litter and Soil of Rosa roxburghii Tratt Woodlands across Rocky Desertification Grades and Seasons
Previous Article in Journal
Simulation of LUCC Scenarios and Analysis of the Driving Force of Carbon Stock Supply Changes in the North China Plain in the Context of Urbanization
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 8
10.3390/f15081416
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatio-Temporal Change and Drivers of the Vegetation Trends in Central Asia

by
Moyan Li
1,
Junqiang Yao
2,3,* and
Jianghua Zheng
4,*
1
College of Geography and Tourism, Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Xinjiang Normal University, Urumqi 830054, China
2
Key Laboratory of Tree-Ring Physical and Chemical Research of CMA, Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
3
Field Scientific Experiment Base of Akdala Atmospheric Background of CMA, Akdala 836499, China
4
Key Laboratory of Oasis Ecology of Ministry of Education, Institute of Arid Ecology and Environment, College of Geographical Sciences, Xinjiang University, Urumqi 830046, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(8), 1416; https://doi.org/10.3390/f15081416
Submission received: 25 May 2024 / Revised: 18 July 2024 / Accepted: 9 August 2024 / Published: 13 August 2024
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
Browse Figures
Figure 1
<p>Spatial distribution of vegetation NDVI in Central Asia during 1982–2015 (<b>a</b>) growing season, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn.</p> "> Figure 2
<p>The cumulative anomaly curve of vegetation NDVI in Central Asia during the growing seasons of 1982–2015.</p> "> Figure 3
<p>Change trends of vegetation NDVI in Central Asia during the growing seasons of 1982–2015 (gray, blue, and red dashed lines represent the variation trends in 1982–2015, 1982–1998, and 1998–2015, respectively).</p> "> Figure 4
<p>Variation trend of monthly NDVI in Central Asia from 1982 to 2015 (shaded area is the growing season).</p> "> Figure 5
<p>Seasonal variation of NDVI in Central Asia from 1982 to 2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).</p> "> Figure 6
<p>Variation trends of NDVI in Central Asia during 1982–2015 (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and the significance test (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). (<b>a</b>,<b>b</b>) growing season, (<b>c</b>,<b>d</b>) spring, (<b>e</b>,<b>f</b>) summer, and (<b>g</b>,<b>h</b>) autumn.</p> "> Figure 7
<p>Variation trends of vegetation NDVI in Central Asia during 1982–1998 (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and the significance test (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). (<b>a</b>,<b>b</b>) Growing season, (<b>c</b>,<b>d</b>) spring, (<b>e</b>,<b>f</b>) summer, and (<b>g</b>,<b>h</b>) autumn.</p> "> Figure 8
<p>Variation trends of vegetation NDVI in Central Asia during 1998–2015 (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and the significance test (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). (<b>a</b>,<b>b</b>) Growing season, (<b>c</b>,<b>d</b>) spring, (<b>e</b>,<b>f</b>) summer, and (<b>g</b>,<b>h</b>) autumn.</p> "> Figure 9
<p>Variation trend of the growing seasons’ vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).</p> "> Figure 10
<p>Seasonal variation trends of vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).</p> "> Figure 11
<p>Correlation changes of the vegetation NDVI with (<b>a</b>) VPD and (<b>b</b>) precipitation in western Central Asia during the growing seasons between 1982–2015 (solid line represents a low-pass filtering curve, and red dashed line represents the year 1998).</p> "> Figure 12
<p>Partial correlation coefficients between NDVI and climate factors during the (<b>a</b>) growing seasons, (<b>b</b>) springs, (<b>c</b>) summers, and (<b>d</b>) autumns of 1982–1998 and 1998–2015 in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; the triangle represents <span class="html-italic">p</span> &lt; 0.05).</p> "> Figure 13
<p>(<b>a</b>) Contribution and (<b>b</b>) relative contribution of climate factors to vegetation NDVI during the growing seasons in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).</p> "> Figure 14
<p>(<b>a</b>) Contribution and (<b>b</b>) relative contribution of climate factors to vegetation NDVI changes in summertime in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).</p> "> Figure 15
<p>The vegetation NDVI structural equation model during the growing seasons in western Central Asia.</p> "> Figure 16
<p>The vegetation NDVI structural equation model during the summertime in western Central Asia.</p> ">
Versions Notes

Abstract

:
The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons’ NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced “greening” process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant “browning” trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of “greening in the east and browning in the west” of Central Asia. During spring, the dominant theme was the “greening” of vegetation NDVI, although there was noticeable “browning” observed in southwest region of Central Asia. During summer, the “browning” of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the “browning” trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the “greening” trend of vegetation NDVI in arid regions, which further aggravates the “browning” trend of vegetation NDVI.

1. Introduction

Central Asia is the largest non-zonal arid zone on earth. It has a unique mountain-basin topography comprising desert, oasis, snow, and ice, with over 80% of temperate deserts distributed in Central Asia. The ecosystem, primarily dominated by desert vegetation is fragile and sensitive to climate variations. Hence, this is a key and vulnerable global climate change region and is representative of the arid regions in the world [1,2,3,4,5,6,7]. The temperature in Central Asia demonstrated a substantial rise in 1998 and has maintained a fluctuating high-temperature trend ever since [6,8,9]. Because of the high-temperature fluctuations, the Palmer Drought Severity Index (PDSI) in Central Asia has demonstrated a significant decreasing trend since 2000, and approximately 65% of the Central Asian region has experienced increased aridity [8]. Since the onset of the 21st century, ecological and environmental issues have been extremely prominent, including those in the Aral Sea and Lake Balkhash [10].
Consequently, the risks of water resources and crisis have been continuously aggravated, which has severely restricted economic and social development in Central Asia [11]. Additionally, Central Asia is also the key upstream area affecting the weather and climate of China; hence, the ecological and environmental upheaval in this region can severely impact the weather, climate, and environmental health of China [4].
Vegetation is a key link connecting the atmosphere, soil, hydrosphere, and biosphere, with the spatio-temporal change in growing vegetation playing an important role in the regional ecological patterns [12,13,14,15,16,17,18]. The response of vegetation in arid areas to the changing climate has garnered substantial attention in the global research arena, but the changes in vegetation and its influence mechanisms in arid areas are still controversial [16,19,20,21,22]. Central Asia has fragile ecosystems, with the desert vegetation ecosystem being specifically vulnerable to the impact of climate change. Scientists have found substantial spatial differences in the vegetation changes in Central Asia since the 1980s, among which the sparse vegetation in the western desert and around the Aral Sea has deteriorated severely [8]. In the context of the driving factors that lead to vegetation change, certain studies speculated that hydrological factors potentially dominate the vegetation growth patterns in Central Asia, with air temperature negatively correlated to vegetation change [23]. Hydrothermal change has affected vegetation growth in Central Asia, and climate warming has caused desert vegetation decline and ecosystem degradation [8]. Precipitation is the major climate factor affecting vegetation change in northern Central Asia, while temperature is the primary factor controlling seasonal vegetation changes in mountainous regions and the Aral Sea Basin. Additionally, drought has led to sparse vegetation degradation in deserts [8].
The potential impact of climatic variability on vegetation changes has garnered substantial attention from researchers. Previous studies concentrated more on the effect of high temperatures and drought on vegetation change [24,25], as well as on the impact of decreased precipitation [26,27] and increased CO2 concentration [28,29]. Previous studies have indicated that the rise of CO2 concentration in the atmosphere is a major driving force behind global terrestrial vegetation change and is also a crucial element influencing vegetation growth in arid regions [20]. On the other hand, climate warming is the primary driver of vegetation growth over the Tibetan Plateau and the high latitudes of northern China [29]. Previous studies have established that an increase in CO2 concentration and climate warming are the primary factors regulating vegetation change [29]. However, the vital function of atmospheric water stress caused by global warming has been overlooked. In recent years, scientists have begun to pay attention to the influence of vapor pressure deficit (VPD) and have found that a long-term high value of VPD can result in the large-scale death of trees in forest ecosystems, with the death rate of tropical trees increasing substantially with the aggravation of atmospheric water stress [30,31,32,33]. Additionally, VPD also exhibits a positive correlation with wildfire [34,35] and contributes to reduced crop yield [36,37]. Some studies have demonstrated that the reduction in vegetation productivity caused by an increase in VPD since the end of the 1990s has counteracted the “fertilization effect” attributed to the increased CO2 concentration on vegetation growth [38]. In contrast, other studies have concluded that, in comparison to the stress effect induced by a heightened atmospheric moisture deficit on vegetation growth, the physiological impact of CO2 enrichment in the atmosphere exerts a more pronounced effect on vegetation water stress [20].
All of the studies mentioned earlier have focused on climate, ecology, and vegetation changes in Central Asia [1,3,6,39,40,41,42]. However, regarding the spatio-temporal distribution of vegetation in Central Asia, there exists a lack of comprehensive understanding, and the study domains are fragmented. For instance, the impact of climate factors, including temperature, rainfall, and VPD, on vegetation growth in Central Asia has been studied [15,17,21], but a limited number of studies have quantified these elements, and the contribution of VPD change in the atmosphere to vegetation dynamic in Central Asia is still uncertain. To address this gap, our study focuses on the spatio-temporal variation trend and the characteristics of the NDVI in Central Asia. Additionally, we quantitatively estimate the contribution rates of climatic elements to vegetation dynamic in Central Asia using the partial derivative equation method and structural equation model (SEM). The research findings will hold scientific significance and practical value, contributing to a deeper understanding of the connection with climate and vegetation in arid regions.

2. Materials and Methods

2.1. Study Area

Central Asia (CA) is located in the range of 45°–95° E and 34°–54° N in the hinterland of Eurasia, with a total area of approximately 5.66 million km2, comprising five Central Asian countries and the Xinjiang region of China [7]. Central Asia has a mountain-basin landform with a significant elevation difference. The major mountain regions include the Tianshan Mountains, Pamir Plateau, and Altai and Kunlun Mountains, which collectively constitute the “water tower” in Central Asia [3]. Additionally, deserts are extensively distributed in Central Asia, serving as an important source of global sand-dust weather.

2.2. Data

2.2.1. The GIMMS-NDVI Data

The NDVI can reveal the surface vegetation growth and is the best index to monitor vegetation change. We employed NASA’s Global Inventory Modeling and Mapping Studies NDVI third-generation dataset NDVI3g (GIMMS NDVI3g) from NASA’s Goddard Space Flight Center. The GIMMS NDVI3g data are the longest time series of NDVI data, spanning from 1981 to 2015, with a spatial resolution of 1/12° (about 8 km) and a temporal resolution of 15 days [12,15]. GIMMS NDVI 3g data possess several advantages, including a long time series, extensive coverage, and robust characterization capabilities. Generally, an NDVI value below 0.1 is considered the threshold to distinguish whether the NDVI exists or not. Based on this criterion, April to October of each year is regarded as the growing season for Central Asian vegetation.

2.2.2. The ERA5 Dataset

The ERA5 dataset consists of the latest reanalysis data from the European Centre for Medium-Range Weather Forecasting (ECMWF). It combines the largest collection of historical global observations with advanced satellite remote sensing data and uses the latest data assimilation systems and numerical model simulations to obtain a precise atmospheric status [43]. In this study, we choose the ECMWF ERA-5 monthly temperature, relative humidity, precipitation, wind speed, and sunshine hours data, with the temporal coverage spanning from 1982 to 2015.
VPD is an important indicator of the increased atmospheric moisture demand and intensified drought due to rising temperatures [44,45]. The monthly VPD data were calculated based on the monthly temperature and relative humidity of the ERA5 datasets during 1982–2015. The calculation formula for VPD is as follows [33]:
VPD = es − ea
where ea is the actual water vapor pressure (kPa) and es is the saturation water vapor pressure (kPa).
The calculation formula for es is
es = 0.611exp[(17.27 × Ta)/(Ta + 237.3)]
where Ta is the mean temperature (°C).
The calculation formula for ea is
ea = RH × es/100
where RH is relative humidity (%).

2.3. Methods

In this study, we utilized the nonparametric Sen method to evaluate the changing climate and vegetation trends and used the Mann–Kendall test (MK) to examine the significance of the changing trends. Additionally, the Pearson correlation analysis was used to reveal the correlation between the NDVI and meteorological elements, expressed by correlation coefficient (CC). Partial correlation analysis (PCA) was employed to study the impact of diverse meteorological elements on NDVI changes, which was expressed as a partial correlation coefficient (PCC) [46].
Since there are difference between the NDVI and climatic elements, it is essential to normalize all variables when revealing the effects of climatic elements on vegetation changes. We use the data normalization method proposed by Li et al. [18]. Based on the partial derivative equation combined with the partial correlation analysis method proposed by Roderick et al. [47] and Naeem et al. [48], Li et al. [18] further implemented the elastic coefficient method and constructed a partial derivative equation method, which can quantitatively estimate the contribution rates of diverse meteorological elements to vegetation changes. In addition, the SEM is a statistical method based on the covariance matrix of variables used to reveal the relationship between variables. In this study, we also employ the SEM method to investigate the effect of climate factors on vegetation changes in Central Asia [49,50].

3. Results

3.1. NDVI Distribution in Central Asia

Figure 1 depicts the vegetation growth conditions during the growing season, spring, summer, and autumn in Central Asia. The vegetation NDVI in Central Asia during the growing season exhibited a spatial pattern of “high in the north and low in the south, high in the west and low in the east, and high in the mountains and low in the basin”. The vegetation coverage area (NDVI ≥ 0.1) accounted for 85% of the total acreage in Central Asia, of which the high vegetation coverage area (NDVI ≥ 0.5) that accounted for 32% was predominantly distributed in the Tianshan Mountains and the surrounding oasis areas, as well as in the area north of 50° N. The vegetation types were predominantly coniferous forest, scrub, and grassland. The area located south of 50° N was predominantly characterized by grassland and desert steppe, with 16% of the region being occupied by desert and wilderness.
In terms of seasonal distribution, the vegetation NDVI exhibited the highest value during summer and was slightly higher during spring in comparison to autumn. During spring and autumn, most regions had NDVI values ranging between 0.1 and 0.3, while the mountainous areas and oasis had NDVI values above 0.3. However, during summer, the vegetation NDVI differed significantly in terms of spatial distribution. Specifically, in the Tianshan Mountains, Ili Valley, Irtysh River Basin, and in areas north of 50° N, the NDVI value exceeded 0.5.

3.2. Temporal Variation of NDVI in Central Asia

3.2.1. NDVI Variations in Vegetation Growing Seasons

The growing season vegetation NDVI showed a slight increasing trend (p > 0.05) in Central Asia during 1982–2015, characterized by substantial stage changes. According to the evaluation of the cumulative anomaly curve, the year 1998 marked the turning point of the growing season NDVI (Figure 2). Therefore, this study focused on NDVI changes in Central Asia before and after 1998.
During 1982–1998, the vegetation NDVI presented an increasing trend at a rate of 0.012/10 yr (p < 0.05), while it exhibited a decreasing trend at a rate of −0.006/10 yr (p > 0.05) during 1998–2015. This indicated that the vegetation exhibited a significant “greening” trend during the 1982–1998 growing seasons and an insignificant “browning” after 1998 (Figure 3). Notably, the NDVI declined considerably from 1998 to 2010, and the changing trend was −0.01/10 yr (p < 0.05), demonstrating a significant “browning” trend, which gradually increased after 2010.
The NDVI demonstrated an increasing trend from March to June in 1982–2015 but a decreasing or insignificant trend in the remaining months (Figure 4). During 1982–1998, the monthly vegetation NDVI variations exhibited an upward trend in all months except December, indicating the “greening” trend in Central Asia in these years. The greening trend was the highest in May during 1982–1998 (0.021/10 yr, p < 0.05). The vegetation “greening” from June to October was approximately 0.01/10 yr (p < 0.05), but the variation trend of vegetation from January to March was not significant, which was predominantly associated with the snow cover in the high latitudes and mountainous areas of Central Asia. However, from 1998 to 2015, except for a slight increase in the NDVI in May, the NDVI in other months was mostly decreasing, demonstrating a consistent trend of the “browning” of vegetation. In the growing seasons, the vegetation “browning” trend was the most prominent in July, which was −0.012/10 yr (p < 0.05). This was predominantly influenced by a significant increase in temperature, with no significant increase in rainfall in July. Consequently, the effects of drought and water stress on vegetation growth was increased, which led to a substantial stagnation of vegetation growth.

3.2.2. Seasonal Variation of NDVI

During the spring season of 1982–2015, the vegetation NDVI demonstrated a slight “greening” trend at a rate of 0.005/10 yr (p > 0.05). From the point of stages, the NDVI exhibited an increasing trend during 1982–1998, with a rate of 0.009/10 yr (p > 0.05), but a decreasing trend from 1998 to 2015 (−0.007/10 yr, p > 0.05) (Figure 5a).
During the summer season of 1982–2015, NDVI variation demonstrated an insignificant declining trend (Figure 5b). Between 1982 and 1998, the NDVI showed a consistent increasing trend with a rate of 0.01/10 yr (p < 0.05), while the 1998–2015 NDVI exhibited a decrease at the rate of −0.008/10 yr, revealing a significant “browning” trend of vegetation in comparison to the spring season. Particularly, during 1998–2012, the NDVI reduced significantly, exhibiting a changing trend of −0.016/10 yr (p < 0.05), representing a significant “browning” trend. Post 2012, the NDVI gradually increased, from 0.24 in 2012 to 0.27 in 2015, indicating a new trend of vegetation change in recent years.
Between 1982 and 2015, the vegetation NDVI during the autumn season typically exhibited a downward trend at the rate of −0.003/10 yr (p > 0.05). Between 1982 and 1998, the autumn NDVI showed a significant increasing trend with a rate of 0.01/10 yr (p < 0.05), which was similar to that in summer and slightly higher than that in spring. However, during 1998–2015, the NDVI showed a significant downward trend during autumn with a rate of −0.012/10 yr (p < 0.05), indicating the evident “browning” trend. Hence, the “browning” trend of vegetation was substantially more significant during autumn than during spring and summer (Figure 5c).

3.3. Spatial Distribution of Vegetation NDVI Changes

3.3.1. During 1982–2015

During 1982–2015, the growing seasons NDVI in Central Asia exhibited a “greening” pattern in the east and a “browning” pattern in the west, with 70° E as the boundary. In most parts to the west located at 70° E, the vegetation demonstrates a dominant “browning” trend, especially in the region located south of 45° N (Figure 6a,b). The predominant regions where the “browning” trend in vegetation was observed are located in northwest Central Asia, including the Turgay Plateau, the Caspian Sea, and the Aral Sea.
The spring NDVI predominantly exhibited a “greening” trend during 1982–2015 (Figure 6c,d). The acreage of vegetation browning was relatively small, predominantly distributed in the surroundings of the Caspian Sea and Aral Sea, the east of Xinjiang, and the lower reaches of the Tarim River. During summer, the regions exhibiting a “browning” trend in the NDVI expanded eastward and westward in Central Asia in comparison to the “browning” trend observed during spring (Figure 6e,f). The significant “browning” areas were predominantly distributed in the lower reaches of the Caspian and Aral Sea, the areas located northwest of Kazakhstan, and the Turgay Plateau. On the other hand, the “greening” areas of vegetation in Central Asia were predominantly concentrated in the hilly areas of Kazakhstan and the Xinjiang, China.
In comparison to the spring and summer vegetation NDVI trend, the autumn “browning” area in Central Asia further expanded, and vegetation “browning” predominated throughout western Central Asia, with patches of “greening” found only in the northern Turgai low-lying land and the western Tianshan Mountains. Conversely, in Xinjiang, located in the east of Central Asia, “greening” was the predominant trend, except for vegetation “browning” observed in the eastern region of Junggar Basin (Figure 6g,h).
In conclusion, the spatial distribution pattern of the growing seasons NDVI was “greening in the east and browning in the west” in Central Asia during 1982–2015. Seasonally, “greening” was the predominant variation trend in Central Asia during spring, excluding the “browning” trend observed in the southwestern region. However, during summer, the vegetation “browning” trend developed eastward and covered the entirety of western Central Asia during autumn. The vegetation in Xinjiang, located in eastern Central Asia, predominantly exhibited the “greening” trend during summer, but during spring and autumn, the vegetation “browning” trend gradually expanded.

3.3.2. During 1982–1998

The growing seasons NDVI changing trend was predominantly manifested by vegetation “greening” in Central Asia during 1982–1998, and the significant “greening” regions included the Tianshan Mountains, Altai Mountains, and Ili Valley of Central Asia, as well as parts of northern Central Asia. Simultaneously, patches of “browning” areas were observed exclusively in Turgay Plateau and the lower reaches of the Tarim River (Figure 7a,b).
From a seasonal perspective, the variation of the NDVI during the spring season between 1982 and 1998 was dominated by vegetation “greening”. The significant “greening” regions were present in patches, predominantly found in mountain valleys and plain oases; however, the Turgay Plateau exclusively demonstrated an insignificant “browning” trend (Figure 7c,d). In comparison to the spring season, the acreage of vegetation “greening” during summer was more enlarged and the “greening” trend was enhanced. The areas of significant vegetation “greening” were mainly located on the north and south slopes of the Tianshan Mountains, Ili River Valley, and Syr Darya River Valley, as well as Amu Darya River Valley and oases located on both sides, with the variation rate of the NDVI reaching 0.033/10 yr (p < 0.05) (Figure 7e,f). The acreage and “browning” trend further intensified in the Tarim River Basin, Xinjiang. The vegetation “greening” areas during autumn were significantly consistent with those in summer, but the vegetation “browning” trend was enhanced in the surrounding area of the Turgay Plateau, located in northwest Central Asia. This trend weakened in southern Xinjiang, where the vegetation “browning” state in the lower reaches of Tarim River still existed (Figure 7g,h).
In summary, the growing season NDVI in Central Asia between 1982 and 1998 presented a general “greening” trend. Seasonally, spring was dominated by the “greening” trend of the NDVI, while the “browning” trend was predominantly observed in the Turgay low-lying area. During summer and autumn, the vegetation “greening” trend expanded further, but the vegetation “browning” trend persisted in the lower reaches of the Tarim River.

3.3.3. During 1998–2015

During 1998–2015, NDVI variation in growing seasons in Central Asia was dominated by vegetation “browning”, especially in the area west of 70° E. The significant “browning” trend predominantly occurred in the area surrounding the Caspian Sea, Aral Sea Basin, and Ili River Basin, whereas the “greening” trend was observed predominantly in the hilly area of eastern Kazakhstan, the northern slope of Tianshan Mountains, and southern Xinjiang (Figure 8a,b).
In the context of seasons, the spring NDVI in Central Asia from 1998 to 2015 was dominated by vegetation “browning”, with significant changes in southern Central Asia and northern Xinjiang. Simultaneously, patchy “browning” areas were observed in the Turgay Plateau in northern Central Asia, the Tianshan Mountains, and the mountain areas of southern Xinjiang. However, the vegetation “greening” areas were predominant in central and eastern Kazakhstan, the north slope of the Tianshan Mountains, and southern Xinjiang, with a variation trend of approximately 0.017/10 yr (p < 0.05) (Figure 8c,d). In comparison to the spring season, the area where the vegetation NDVI tended to be significantly “browning” during summer in Central Asia and extended further to the north during spring. The vegetation in the Turgay Plateau showed a significant “browning” trend at a rate exceeding −0.035/10 yr (p < 0.05). This was followed by the significant “browning” trend observed in the Ili River Valley to Balkhash Lake Basin. Contrastingly, the summer NDVI exhibited a substantial “greening” trend in comparison to spring in Xinjiang (Figure 8e,f). During autumn, the vegetation “browning” trend significantly expanded in western Central Asia and northern Xinjiang, while the vegetation “greening” trend was predominant in the areas surrounding the Tarim Basin (Figure 8g,h).
As seen from the earlier analysis, the “browning” of the vegetation NDVI in Central Asia during 1998–2015 was the predominant trend, exhibiting significant variations in western Central Asia and northern Xinjiang. During summer and autumn, the “browning” trend enlarged further, with the summer “browning” trend being the most prominent. The sustained vegetation “greening” was observed primarily in southern Xinjiang.

3.3.4. Variation Trend of Vegetation NDVI in Western Central Asia

In this study, our analysis demonstrated that there were significant regional differences in vegetation changes in Central Asia, with relatively consistent trends in the west (mainly in the five Central Asian countries) and large differences in trends in eastern Central Asia (Xinjiang). The following section focuses on the variation trend and stage characteristics of the NDVI in western Central Asia.
There was no significant change trend in the growing seasons’ vegetation NDVI during 1982–2015 in western Central Asia, but the NDVI showed stage characteristics. When the year 1998 was visualized as the boundary year, the vegetation NDVI during 1982–1998 exhibited a positive “greening” trend at the rate of 0.013/10 yr (p < 0.05). Contrastingly, the NDVI during 1998–2015 demonstrated a negative “browning” trend at the rate of −0.009/10 yr (p > 0.05) (Figure 9).
When the analysis was conducted from the perspective of seasons, the vegetation NDVI exhibited an overall slight increasing trend (0.006/10 yr, p > 0.05) during the spring season between 1982 and 2015, and the vegetation change was consistent. However, a slight turning of the NDVI occurred in the late 1990s, changing from an insignificant “greening” trend to an insignificant “browning” trend (Figure 10a). During summer, the vegetation NDVI typically exhibited a slight decreasing trend, with a rate of −0.003/10 yr (p > 0.05) from 1982 to 2015. During this period, the NDVI shifted from the upward trend observed during 1982–1998 (0.011/10 yr, p > 0.05) to a significant downward trend observed between 1998 and 2015 (−0.015/10 yr, p < 0.05) (Figure 10b). Notably, vegetation has been exhibiting an increasing “greening” trend since 2012. During the autumn seasons between 1982 and 2015, an indistinctive decline in the NDVI was typically observed with a trend rate of −0.005/10 yr (p > 0.05). This comprised a significant increasing trend of the NDVI (0.011/10 yr, p < 0.05), observed during 1982–1998, and a significant decreasing trend (−0.016/10 yr, p < 0.05) observed during 1998–2015 (Figure 10c). In contrast to the NDVI variations observed during spring and summer, all the changes in the trend of the NDVI before and after autumn were significant.

3.4. Impact Factors of Vegetation Changes in Western Central Asia

3.4.1. Correlations between Climate Factors and NDVI

To assess the effect of climate factors on the NDVI changes in western Central Asia, the correlations between climate factors and the NDVI in the growing seasons and the spring, summer, and autumn seasons of 1982–2015, 1982–1998, and 1998–2015 were computed separately. The results illustrated that, from 1982 to 2015, the vegetation NDVI was significantly correlated to VPD (vapor pressure deficit) and precipitation in the growing seasons. Specifically, the growing seasons NDVI showed a significant negative correlation with VPD (CC = −0.54, p < 0.05) and a significant positive correlation with precipitation (CC = 0.55, p < 0.05). In the context of wind speed and radiation (sunshine hours), the correlations were insignificantly negative. In temporal stages, the vegetation NDVI in the growing seasons exhibited stronger correlations with VPD and precipitation in 1998–2015 than in 1982–1998 (Figure 11), which indicated that the vegetation NDVI was affected significantly by VPD and precipitation in the growing seasons during 1998–2015.
During spring and autumn, the correlations between the vegetation NDVI and climate factors were not significant, but the variation of the NDVI during summer was significantly correlated to climatic factors. Specifically, the summer NDVI was positively correlated to precipitation during 1982–2015 (CC = 0.73, p < 0.01) but negatively correlated to both VPD and radiation (CC = −0.64, p < 0.05). The NDVI also exhibited a negative correlation with wind speed, but the correlation was relatively low (CC = =0.51, p < 0.05).
To reduce the influence of the mutual relations among climate factors on NDVI variation, the partial correlations between climate factors and the vegetation NDVI during the growing seasons and summers in different periods were computed (Figure 12). The partial correlation analysis suggested that the vegetation NDVI during the growing seasons from 1982 to 1998 was significantly positively correlated to precipitation and radiation and significantly negatively correlated to VPD and wind speed. During the growing seasons from 1998 to 2015, the vegetation NDVI exhibited a significant negative correlation with VPD and a significant positive correlation with precipitation. Moreover, the partial correlation coefficient (PCC) increased in comparison to the PCC in 1982–1998. During the spring seasons of 1998–2015, the vegetation NDVI demonstrated significant positive correlations with precipitation and radiation, while the partial correlations between the NDVI and climate factors during summer were not significantly related. The autumn NDVI during 1982–1998 exhibited a significant negative correlation exclusively with VPD.

3.4.2. Quantitatively Evaluating NDVI Changes in Western Central Asia

Based on the constructed partial differential attribution analysis equation, the contributions of major climate factors (VPD, precipitation, wind speed, and radiation) to the NDVI changes during the growing seasons and summers of 1982–1998 and 1998–2015 in western Central Asia were estimated.

During the Growing Seasons

From 1982 to 1998, the combination of the different factors, including a decrease in VPD, an increase in precipitation, and a decrease in wind speed and radiation caused vegetation “greening” during the growing seasons in western Central Asia, with the residual term contributing the most to the “greening” of the vegetation NDVI. However, during 1998–2015, an increase in VPD played a prominent role in the growing seasons’ “browning” in western Central Asia, which was followed by a decrease in precipitation and an increase in wind speed and radiation. Relative to the period of 1982–1998, the variation trend of climate elements during 1998–2015 exhibited a turning point, and the NDVI also experienced a turning point from vegetation “greening” to “browning”. Specifically, VPD decreased by 0.04 kPa/yr during the 1982–1998 growing seasons, resulting in an increase of 0.02/yr in the NDVI, while VPD increased by 0.06 kPa/yr during the 1998–2015 growing seasons, causing a decrease of −0.03/yr in the NDVI. In the context of precipitation, the rainfall amount in the 1982–1998 growing seasons increased by 0.05 (mm/d)/yr, leading to an elevation of 0.03/yr in the NDVI, whereas the rainfall in the 1998–2015 growing seasons decreased by 0.08 (mm/d)/yr, causing a decline of 0.01/yr in the NDVI (Figure 13a).
Figure 13b depicts the relative contribution of climate factors to the change of the vegetation NDVI during the growing seasons of 1982–1998 and 1998–2015 in western Central Asia. The figure depicts that from 1982 to 1998, the contribution rates of the increased precipitation and decreased VPD to vegetation “greening” were 31.3 and 20%, respectively. However, the highest contributing factor was the residual term, which made more than 40% of the contribution, depicting the significant contribution of non-climatic factors to vegetation “greening”. During 1998–2015, the increased VPD contributed 52.3% to vegetation “browning” during the growing seasons, which was followed by decreased precipitation with a contribution rate of 19.3%. Additionally, the contribution rate of residual term reached 33%.

During Summer

Both decreased VPD and increased precipitation were equal primary contributors to the “greening” trend observed during summer in the vegetation NDVI during 1982–1998. Similarly, increased VPD and decreased precipitation played a leading role in the “browning” during 1998–2015. In summary, during 1982–1998, summer VPD reduced by 0.07 kPa/yr, leading to a 0.03 /yr growth in the NDVI, and the precipitation increased by 0.11 (mm/d)/yr, which caused a rise of 0.03/yr in the corresponding NDVI. Comparably, during 1998–2015, an increase of 0.08 kPa/yr in VPD corresponded to a decline of 0.02/yr in the NDVI, while a decrease of 0.10 (mm/d)/yr in precipitation corresponded to a reduction of 0.01/yr in the NDVI (Figure 14a).
During 1982–1998, the “greening” of western Central Asia in summer was predominantly controlled by the precipitation and VPD changes, and the contribution rates of the increased precipitation and reduced VPD were 41.9 and 40.3%, respectively. Additionally, wind speed and radiation contributed 10.5 and 9.9%, respectively, towards the “greening” of the vegetation NDVI. Between 1998 and 2015, the vegetation NDVI in western Central Asia changed from “greening” to “browning” during the summer seasons. Attribution analysis indicated that the “browning” was dominated by the increase in VPD and the decrease in precipitation, of which the contribution rate of the increased VPD to the vegetation “browning” in western Central Asia was 37.3%, and that of the reduced precipitation was 19.2%. Furthermore, the contribution rate of the residual term also reached 31.2%, which indicates the influence of human activities and other factors on the vegetation “browning” in western Central Asia (Figure 14b).

3.4.3. Drivers of Vegetation Changes Based on SEM

The SEM is a model for evaluating the causal relationships between variables. We constructed an SEM to evaluate and quantify the climate factors on the vegetation NDVI changes during the growing seasons and summer of 1982–1998 and 1998–2015 in western Central Asia. We assessed the model fit and determined that the established SEM model was capable of reflecting the complex relationship between climate variables and vegetation change. During the growing seasons, the effects of VPD and RAD on vegetation NDVI change are negatively correlated, while those of PRE are positively correlated. PRE is the most influential latent factor during 1982–1998, while the most influential latent factor in 1998–2015 is VPD (Figure 15). Additionally, the WS can indirectly affect vegetation NDVI change through the direct variables. Additionally, the vegetation NDVI structural equation model during the summertime is similar to those in the model for the growing seasons (Figure 16).

4. Discussion and Conclusions

Based on the ECMWF ERA-5 and GIMMS NDVI3g datasets, we analyzed the spatial distribution of the NDVI in Central Asia during 1982–2015 and elucidated the spatial and temporal patterns of vegetation change trends during the growing seasons, springs, summers, and autumns in that period. Additionally, the spatial distribution and difference of the change trends in the NDVI in Central Asia before and after 1998 have been revealed, and the contribution rates of climate factors to the changes in the vegetation NDVI in Central Asia have been quantitatively estimated. The major conclusions are as follows:
(1)
The vegetation NDVI during the growing seasons of 1982–2015 in Central Asia demonstrated an insignificant upward trend with apparent stage features, with the vegetation NDVI exhibiting a significant “greening” trend (0.012/10 yr, p < 0.05) during the growing seasons from 1982 to 1998. However, this “greening” trend was replaced by an insignificant “browning” trend after 1998. Spatially, the variation trends of the NDVI in Central Asia presented a pattern of “greening in the east and browning in the west”. Seasonally, the NDVI exhibited a “greening” trend predominantly during spring, except for the “browning” trend observed in the southwest region of Central Asia. During summer, the “browning” of the vegetation NDVI further increased eastward and expanded to the western Central Asia during autumn. Conversely, the vegetation NDVI in Xinjiang, China, primarily exhibited a “greening” trend during summer, but a gradually expanding “browning” trend was observed during spring and autumn.
(2)
The growing seasons’ vegetation NDVI exhibited a general “greening” trend in Central Asia during 1982–1998. Seasonally, spring was dominated by the “greening” of vegetation NDVI, and the “greening” range expanded further during summer and autumn. However, during 1998–2015, “browning” turned out to be the primary variation trend of the NDVI, especially in western Central Asia and northern Xinjiang. The “browning” scopes of the vegetation NDVI were substantially higher during both summer and autumn, and the “browning” trend was the highest during summer. The persistent vegetation “greening” predominantly occurred in the Tarim River Basin of Xinjiang.
(3)
Between 1998 and 2015, the increase in VPD and the reduction in precipitation dominated the “browning” trend of the vegetation NDVI during the growing seasons in western Central Asia. The increased VPD contributed 52.3% to the NDVI changes. The increase in VPD reflected the drying condition of the atmosphere, which effectively reduced the “greening” trend in Central Asia, thus leading to the “browning” trend of the vegetation NDVI.
In this study, we quantified the contribution of the increased VPD to the “browning” of vegetation NDVI in Central Asia. Previous studies have demonstrated that increased temperature and decreased relative humidity together lead to an increase in VPD, aggravating atmospheric drought [33]. Water stress caused by atmospheric drought can effectively reduce the “greening” trend of vegetation, decelerating the “greening” trend or resulting in the “browning” trend of vegetation [38]. In this study, we have highlighted the significance of VPD as a limiting factor of the vegetation “greening” trend in arid areas. Therefore, we suggest that the effects of VPD on vegetation growth should be considered when evaluating arid ecosystem function under the global warming background.
Additionally, we focused on evaluating the influence of climate factors, especially the changes in VPD, on the vegetation NDVI in arid areas, without considering the effect of soil moisture on the changes in vegetation. Studies have demonstrated that both the atmospheric dryness caused by increased VPD and soil drought caused by soil moisture deficiency exert negative effects on vegetation growth [32,51].
However, due to the covariation of atmospheric water vapor and soil moisture deficit, it is difficult to distinguish the influence of atmospheric VPD and soil moisture change on vegetation growth [32,51]. This is an important factor that needs to be explored further in future studies. Furthermore, in current Earth system models, negative ecological effects caused by an increase in VPD are underestimated, and this is a common problem [38]. Therefore, it is essential to systematically evaluate whether Earth system models can accurately reflect the impact of the increased VPD on global ecosystems in the future.

Author Contributions

Conceptualization, M.L. and J.Y.; methodology, M.L.; software, M.L.; data curation, M.L. and J.Z.; writing—original draft preparation, M.L.; writing—review and editing, J.Y. and J.Z.; visualization, M.L. and J.Y.; project administration, J.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the “Tianchi Talents” (Xinjiang) Plan Project for their support to Moyan Li. This research was supported by the Shanghai Cooperation Organization (SCO) Science and Technology Partnership and International S&T Cooperation Program (2023E01022), and the Science and Technology Youth Top-notch Talent Support Program (Tianshan Talents) of Xinjiang (2022TSYCCX0005).

Data Availability Statement

The data that support the findings of this study are available by request to the corresponding author.

Acknowledgments

The authors would like to thank the data support provided by various data source websites in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, F.H.; Huang, W.; Jin, L.Y.; Chen, J.H.; Wang, J.S. Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming. Sci. China Earth Sci. Chin. J. 2011, 41, 1647–1657. [Google Scholar] [CrossRef]
  2. Chen, Y.; Li, Z.; Fan, Y.; Wang, H.; Deng, H. Progress and prospects of climate change impacts on hydrology in the arid region of northwest China. Environ. Res. 2015, 139, 11–19. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, Y.; Li, W.; Deng, H.; Fang, G.; Li, Z. Changes in Central Asia’s Water Tower: Past, Present and Future. Sci. Rep. 2016, 6, 35458. [Google Scholar] [CrossRef] [PubMed]
  4. Chen, F.; Chen, J.; Huang, W.; Chen, S.; Huang, X.; Jin, L.; Feng, S. Westerlies Asia and monsoonal Asia: Spatiotemporal differences in climate change and possible mechanisms on decadal to sub-orbital timescales. Earth Sci. Rev. 2019, 192, 337–354. [Google Scholar] [CrossRef]
  5. IPCC. Climate change 2013: The physical science basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
  6. Hu, Z.; Zhang, C.; Hu, Q.S.; Tian, H. Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets. J. Clim. 2014, 27, 1143–1167. [Google Scholar] [CrossRef]
  7. Yao, J.; Chen, Y.; Chen, J.; Zhao, Y.; Tuoliewubieke, D.; Li, J.; Yang, L.M.; Mao, W. Intensification of extreme precipitation in arid Central Asia. J. Hydrol. 2021, 598, 125760. [Google Scholar] [CrossRef]
  8. Li, Z.; Chen, Y.N.; Fang, G.H.; Li, Y.P. Multivariate assessment and attribution of droughts in Central Asia. Sci. Rep. 2017, 7, 1316. [Google Scholar] [CrossRef]
  9. Peng, D.; Zhou, T.; Zhang, L.; Zhang, W.; Chen, X. Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 °C less global warming. Clim. Dyn. 2019, 54, 543–560. [Google Scholar] [CrossRef]
  10. Yang, S.; Sun, L.; He, J.; Li, C.; Yu, Y. Evolution of the Aral Sea: Crisis and present situation. Arid. Land Geogr. 2024, 47, 181–191. [Google Scholar]
  11. Wang, X.; Chen, Y.; Li, Z.; Fang, G.; Wang, Y. Development and utilization of water resources and assessment of water security in Central Asia. Agric. Water Manag. 2020, 240, 106297. [Google Scholar] [CrossRef]
  12. Piao, S.; Fang, J.; Zhou, L.; Guo, Q.; Henderson, M.; Ji, W.; Li, Y.; Tao, S. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J. Geophys. Res. Atmos. 2003, 108, 4401. [Google Scholar] [CrossRef]
  13. Zhao, W.; Hu, Z.; Guo, Q.; Wu, G.; Chen, R.; Li, S. Contributions of climatic factors to inter-annual variability of vegetation index in northern China grasslands. J. Clim. 2020, 33, 175–183. [Google Scholar] [CrossRef]
  14. Liu, D.; Wang, T.; Yang, T.; Yan, Z.; Liu, Y.; Zhao, Y.; Piao, S. Deciphering impacts of climate extremes on Tibetan grasslands in the last fifteen years. Sci. Bull. 2019, 64, 446. [Google Scholar] [CrossRef] [PubMed]
  15. Hao, H.; Chen, Y.; Xu, J.; Li, Z.; Li, Y.; Kayumba, P.M. Water Deficit May Cause Vegetation Browning in Central Asia. Remote Sens. 2022, 14, 2574. [Google Scholar] [CrossRef]
  16. Yao, J.; Chen, Y.; Guan, X.; Zhao, Y.; Chen, J.; Mao, W. Recent climate and hydrological changes in a mountain-basin system in Xinjiang, China. Earth-Sci. Rev. 2022, 226, 103957. [Google Scholar] [CrossRef]
  17. Yu, T.; Jiapaer, G.; Bao, A.; Zheng, G.; Zhang, J.; Li, X.; Yuan, Y.; Huang, X.; Umuhoza, J. Disentangling the relative effects of soil moisture and vapor pressure deficit on photosynthesis in dryland Central Asia. Ecol. Indic. 2022, 137, 108698. [Google Scholar] [CrossRef]
  18. Li, M.; Guan, J.; Zheng, J. Climate Drivers Contribute in Vegetation Greening Stalls of Arid Xinjiang, China: An Atmospheric Water Drying Effect. Water 2022, 14, 2019. [Google Scholar] [CrossRef]
  19. Fu, B.; Stafford-Smith, M.; Wang, Y.; Wu, B.; Yu, X.; Lv, N.; Ojima, D.S.; Lv, Y.; Fu, C.; Liu, Y.; et al. The Global-DEP conceptual framework—Research on dryland ecosystems to promote sustainability. Curr. Opin. Environ. Sustain. 2021, 48, 17–28. [Google Scholar] [CrossRef]
  20. Lian, X.; Piao, S.; Chen, A.; Huntingford, C.; Fu, B.; Li, L.Z.; Roderick, M.L. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2021, 2, 232–250. [Google Scholar] [CrossRef]
  21. Zhang, W.; Li, Y.; Wu, X.; Chen, Y.; Chen, A.; Schwalm, C.R.; Kimball, J.S. Divergent response of vegetation growth to soil water availability in dry and wet periods over Central Asia. J. Geophys. Res. Biogeosci. 2021, 126, e2020JG005912. [Google Scholar] [CrossRef]
  22. Zhang, H.; Hu, Z.; Zhang, Z.; Li, Y.; Song, S.; Chen, X. How does vegetation change under the warm–wet tendency across Xinjiang, China? Int. J. Appl. Earth Obs. Geoinf. 2024, 127, 103664. [Google Scholar] [CrossRef]
  23. Luo, M.; Sa, C.; Meng, F.; Duan, Y.; Liu, T.; Bao, Y. Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia. J. Clean. Prod. 2020, 271, 122396. [Google Scholar] [CrossRef]
  24. Lindner, M.; Maroschek, M.; Netherer, S.; Kremer, A.; Barbati, A.; Garcia-Gonzalo, J.; Seidl, R.; Delzon, S.; Corona, P.; Kolström, M.; et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For. Ecol. Manag. 2010, 259, 698–709. [Google Scholar] [CrossRef]
  25. Way, D.A.; Oren, R. Differential responses to changes in growth temperature between trees from different functional groups and biomes: A review and synthesis of data. Tree Physiol. 2010, 30, 669–688. [Google Scholar] [CrossRef] [PubMed]
  26. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  27. Knapp, A.K.; Avolio, M.L.; Beier, C.; Carroll, C.J.W.; Collins, S.L.; Dukes, J.S.; Fraser, L.H.; Griffin-Nolan, R.J.; Hoover, D.L.; Jentsch, A.; et al. Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years. Glob. Chang. Biol. 2017, 23, 1774–1782. [Google Scholar] [CrossRef]
  28. Ainsworth, E.A.; Long, S.P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 2005, 165, 351–372. [Google Scholar] [CrossRef]
  29. Zhu, Z.; Piao, S.; Myneni, R.B.; Huang, M.; Zeng, Z.; Canadell, J.G.; Ciais, P.; Sitch, S.; Friedlingstein, P.; Arneth, A.; et al. Greening of the Earth and its drivers. Nat. Clim. Chang. 2016, 6, 791–795. [Google Scholar] [CrossRef]
  30. Breshears, D.D.; Adams, H.D.; Eamus, D.; McDowell, N.G.; Law, D.J.; Will, R.E.; Zou, C.B. The critical amplifying role of increasing atmospheric moisture demand on tree mortality and associated regional die-off. Front. Plant Sci. 2013, 4, 266. [Google Scholar] [CrossRef]
  31. Williams, A.P.; Allen, C.D.; Macalady, A.K.; Griffin, D.; Woodhouse, C.A.; Meko, D.M.; McDowell, N.G. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 2013, 3, 292–297. [Google Scholar] [CrossRef]
  32. Zhou, S.; Williams, A.P.; Berg, A.M.; Cook, B.I.; Zhang, Y.; Hagemann, S.; Gentine, P. Land–Atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl. Acad. Sci. USA 2019, 116, 18848–18853. [Google Scholar] [CrossRef] [PubMed]
  33. Li, M.; Yao, J.Q.; Guan, J.Y.; Zheng, J.H. Observed changes in vapor pressure deficit suggest a systematic drying of the atmosphere in Xinjiang of China. Atmos Res. 2020, 248, 105199. [Google Scholar] [CrossRef]
  34. Williams, A.P.; Seager, R.; Berkelhammer, M.; Macalady, A.K.; Crimmins, M.A.; Swetnam, T.W.; Rahn, T. Causes and implications of extreme atmospheric moisture demand during the record-breaking 2011 wildfire season in the southwestern United States. J. Appl. Meteorol. Climatol. 2014, 53, 2671–2684. [Google Scholar] [CrossRef]
  35. Seager, R.; Hooks, A.; Williams, A.P.; Cook, B.; Nakamura, J.; Henderson, N. Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 2015, 54, 1121–1141. [Google Scholar] [CrossRef]
  36. Asseng, S.; Ewert, F.; Martre, P.; Rotter, R.P.; Lobell, D.B.; Cammarano, D.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; et al. Rising temperatures reduce global wheat production. Nat. Clim. Chang. 2015, 5, 143. [Google Scholar] [CrossRef]
  37. Zhao, C.; Liu, B.; Piao, S.; Wang, X.; Lobell, D.B.; Huang, Y.; Huang, M.T.; Yao, Y.T.; Bassu, S.; Ciais, P.; et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. USA. 2017, 114, 9326–9331. [Google Scholar] [CrossRef] [PubMed]
  38. Yuan, W.; Zheng, Y.; Piao, S.; Ciais, P.; Lombardozzi, D.; Wang, Y.; Ryu, Y.; Chen, G.; Dong, W.; Hu, Z.; et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 2019, 5, eaax1396. [Google Scholar] [CrossRef]
  39. Yao, J.; Chen, Y.; Zhao, Y.; Guan, X.; Mao, W.; Yang, L. Climatic and associated atmospheric water cycle changes over the Xinjiang, China. J. Hydrol. 2020, 585, 124823. [Google Scholar] [CrossRef]
  40. Li, Z.; Chen, Y.; Li, W.; Deng, H.; Fang, G. Potential impacts of climate change on vegetation dynamics in Central Asia. J. Geophys. Res. Atmos. 2015, 120, 12345–112356. [Google Scholar] [CrossRef]
  41. Jiang, C.; Ryu, Y.; Fang, H.; Myneni, R.; Claverie, M.; Zhu, Z. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Glob. Chang. Biol. 2017, 23, 4133–4146. [Google Scholar] [CrossRef]
  42. Chen, P.; Yao, J.; Mao, W.; Ma, L.; Chen, J.; Dilinuer, T.; Li, S. Increased Extreme Precipitation in May over Southwestern Xinjiang in Relation to Eurasian Snow Cover in Recent Years. J. Clim. 2024, 37, 2695–2711. [Google Scholar] [CrossRef]
  43. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  44. Sherwood, S.; Fu, Q. A drier future? Science 2014, 343, 737–739. [Google Scholar] [CrossRef] [PubMed]
  45. Ficklin, D.L.; Novick, K.A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. Atmos. 2017, 122, 2061–2079. [Google Scholar] [CrossRef]
  46. Yadolah, D. The Concise Encyclopedia of Statistics; Springer: New York, NY, USA, 2008. [Google Scholar]
  47. Roderick, M.L.; Rotstayn, L.D.; Farquhar, G.D.; Hobbins, M.T. On the attribution of changing pan evaporation. Geophys. Res. Lett. 2007, 34, L17403. [Google Scholar] [CrossRef]
  48. Naeem, S.; Zhang, Y.; Zhang, X.; Tian, J.; Abbas, S.; Luo, L.; Meresa, H.K. Both climate and socioeconomic drivers contribute in vegetation greening of the loess plateau. Sci. Bull. 2021, 66, 1160–1163. [Google Scholar] [CrossRef]
  49. Hao, J.; Xu, G.; Luo, L.; Zhang, Z.; Yang, H.; Li, H. Quantifying the relative contribution of natural and human factors to vegetation coverage variation in coastal wetlands in China. Catena 2020, 188, 104429. [Google Scholar] [CrossRef]
  50. Shao, W.; Wang, Q.; Guan, Q.; Luo, H.; Ma, Y.; Zhang, J. Distribution of soil available nutrients and their response to environmental factors based on path analysis model in arid and semi-arid area of northwest China. Sci. Total Environ. 2022, 827, 154254. [Google Scholar] [CrossRef]
  51. Zhou, S.; Williams, A.P.; Lintner, B.R.; Berg, A.M.; Zhang, Y.; Keenan, T.F.; Cook, B.I.; Hagemann, S.; Seneviratne, S.I.; Gentine, P. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Chang. 2021, 11, 38–44. [Google Scholar] [CrossRef]
Forests 15 01416 g001
Figure 1. Spatial distribution of vegetation NDVI in Central Asia during 1982–2015 (a) growing season, (b) spring, (c) summer, and (d) autumn.
Figure 1. Spatial distribution of vegetation NDVI in Central Asia during 1982–2015 (a) growing season, (b) spring, (c) summer, and (d) autumn.
Forests 15 01416 g001
Forests 15 01416 g002
Figure 2. The cumulative anomaly curve of vegetation NDVI in Central Asia during the growing seasons of 1982–2015.
Figure 2. The cumulative anomaly curve of vegetation NDVI in Central Asia during the growing seasons of 1982–2015.
Forests 15 01416 g002
Forests 15 01416 g003
Figure 3. Change trends of vegetation NDVI in Central Asia during the growing seasons of 1982–2015 (gray, blue, and red dashed lines represent the variation trends in 1982–2015, 1982–1998, and 1998–2015, respectively).
Figure 3. Change trends of vegetation NDVI in Central Asia during the growing seasons of 1982–2015 (gray, blue, and red dashed lines represent the variation trends in 1982–2015, 1982–1998, and 1998–2015, respectively).
Forests 15 01416 g003
Forests 15 01416 g004
Figure 4. Variation trend of monthly NDVI in Central Asia from 1982 to 2015 (shaded area is the growing season).
Figure 4. Variation trend of monthly NDVI in Central Asia from 1982 to 2015 (shaded area is the growing season).
Forests 15 01416 g004
Forests 15 01416 g005
Figure 5. Seasonal variation of NDVI in Central Asia from 1982 to 2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Figure 5. Seasonal variation of NDVI in Central Asia from 1982 to 2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Forests 15 01416 g005
Forests 15 01416 g006
Figure 6. Variation trends of NDVI in Central Asia during 1982–2015 (a,c,e,g) and the significance test (b,d,f,h). (a,b) growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Figure 6. Variation trends of NDVI in Central Asia during 1982–2015 (a,c,e,g) and the significance test (b,d,f,h). (a,b) growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Forests 15 01416 g006
Forests 15 01416 g007
Figure 7. Variation trends of vegetation NDVI in Central Asia during 1982–1998 (a,c,e,g) and the significance test (b,d,f,h). (a,b) Growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Figure 7. Variation trends of vegetation NDVI in Central Asia during 1982–1998 (a,c,e,g) and the significance test (b,d,f,h). (a,b) Growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Forests 15 01416 g007
Forests 15 01416 g008
Figure 8. Variation trends of vegetation NDVI in Central Asia during 1998–2015 (a,c,e,g) and the significance test (b,d,f,h). (a,b) Growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Figure 8. Variation trends of vegetation NDVI in Central Asia during 1998–2015 (a,c,e,g) and the significance test (b,d,f,h). (a,b) Growing season, (c,d) spring, (e,f) summer, and (g,h) autumn.
Forests 15 01416 g008
Forests 15 01416 g009
Figure 9. Variation trend of the growing seasons’ vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Figure 9. Variation trend of the growing seasons’ vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Forests 15 01416 g009
Forests 15 01416 g010
Figure 10. Seasonal variation trends of vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Figure 10. Seasonal variation trends of vegetation NDVI in western Central Asia during 1982–2015 (gray, blue, and red dashed lines represent the variation trend lines during 1982–2015, 1982–1998, and 1998–2015, respectively).
Forests 15 01416 g010
Forests 15 01416 g011
Figure 11. Correlation changes of the vegetation NDVI with (a) VPD and (b) precipitation in western Central Asia during the growing seasons between 1982–2015 (solid line represents a low-pass filtering curve, and red dashed line represents the year 1998).
Figure 11. Correlation changes of the vegetation NDVI with (a) VPD and (b) precipitation in western Central Asia during the growing seasons between 1982–2015 (solid line represents a low-pass filtering curve, and red dashed line represents the year 1998).
Forests 15 01416 g011
Forests 15 01416 g012
Figure 12. Partial correlation coefficients between NDVI and climate factors during the (a) growing seasons, (b) springs, (c) summers, and (d) autumns of 1982–1998 and 1998–2015 in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; the triangle represents p < 0.05).
Figure 12. Partial correlation coefficients between NDVI and climate factors during the (a) growing seasons, (b) springs, (c) summers, and (d) autumns of 1982–1998 and 1998–2015 in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; the triangle represents p < 0.05).
Forests 15 01416 g012
Forests 15 01416 g013
Figure 13. (a) Contribution and (b) relative contribution of climate factors to vegetation NDVI during the growing seasons in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).
Figure 13. (a) Contribution and (b) relative contribution of climate factors to vegetation NDVI during the growing seasons in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).
Forests 15 01416 g013
Forests 15 01416 g014
Figure 14. (a) Contribution and (b) relative contribution of climate factors to vegetation NDVI changes in summertime in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).
Figure 14. (a) Contribution and (b) relative contribution of climate factors to vegetation NDVI changes in summertime in western Central Asia (PRE: precipitation; WS: wind speed; RAD: sunshine hours; R*: residual).
Forests 15 01416 g014
Forests 15 01416 g015
Figure 15. The vegetation NDVI structural equation model during the growing seasons in western Central Asia.
Figure 15. The vegetation NDVI structural equation model during the growing seasons in western Central Asia.
Forests 15 01416 g015
Forests 15 01416 g016
Figure 16. The vegetation NDVI structural equation model during the summertime in western Central Asia.
Figure 16. The vegetation NDVI structural equation model during the summertime in western Central Asia.
Forests 15 01416 g016
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, M.; Yao, J.; Zheng, J. Spatio-Temporal Change and Drivers of the Vegetation Trends in Central Asia. Forests 2024, 15, 1416. https://doi.org/10.3390/f15081416

AMA Style

Li M, Yao J, Zheng J. Spatio-Temporal Change and Drivers of the Vegetation Trends in Central Asia. Forests. 2024; 15(8):1416. https://doi.org/10.3390/f15081416

Chicago/Turabian Style

Li, Moyan, Junqiang Yao, and Jianghua Zheng. 2024. "Spatio-Temporal Change and Drivers of the Vegetation Trends in Central Asia" Forests 15, no. 8: 1416. https://doi.org/10.3390/f15081416

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop