Satellite-observed vegetation stability in response to changes in climate and total water storage in Central Asia

Integrating landsat NDVI data with climate and anthropogenic factors reveals drivers of vegetation dynamics in the semi-arid Basin of Western China

In remote sensing research, vegetation dynamics are often used as indicators of ecosystem conditions, especially in semi-arid areas. The Wei River Basin (WRB) is a semi-arid region in western China prone to climate change and sensitive to the environment. Driven by climate change and human activities, particularly the recent reforestation projects, the environment and landscape of this region have undergone significant changes. However, the quantitative contributions of the driving factors to vegetation dynamics have not yet been well established. Here, we use a first-difference multiple regression model to separate and quantify the impacts of climate change and human activities on normalized difference vegetation index (NDVI) from 1998 to 2023. The results indicate that: (1) the growing season NDVI has significantly increased (slope = 0.006, R2 = 0.85) during the previous 26 years. (2) The main factor limiting the improvement of NDVI is precipitation, accounting for 67.6% of the area (p < 0.05). (3) During 1998-2023, climate factors accounted for 27.5% of NDVI changes in the Wei River Basin (WRB), with precipitation contributing 63.2% of the climatic influence, making it the primary positive driver of vegetation growth. Meanwhile, anthropogenic factors contributed 72.5%, with ecological restoration projects promoting greening and urban expansion causing degradation. These findings provide a basis for future assessments of vegetation management strategies and ecological restoration policies under climate and anthropogenic pressures in semi-arid basins.

Water Use Efficiency Spatiotemporal Change and Its Driving Analysis on the Mongolian Plateau

Water use efficiency (WUE) connects two key processes in terrestrial ecosystems: the carbon and water cycles. Thus, it is important to evaluate temporal and spatial changes in WUE over a prolonged period. The spatiotemporal variation characteristics of the WUE in the Mongolian Plateau from 1982 to 2018 were analyzed based on the net primary productivity (NPP), evapotranspiration (ET), temperature, precipitation, and soil moisture. In this study, we used remote sensing data and various statistical methods to evaluate the spatiotemporal patterns of water use efficiency and their potential influencing factors on the Mongolian Plateau from 1982 to 2018. In total, 27.02% of the region witnessed a significant decline in the annual WUE over the 37 years. Two abnormal surges in the WUESeason (April-October) were detected, from 1997 to 1998 and from 2007 to 2009. The trend in the annual WUE in some broadleaf forest areas in the middle and northeast of the Mongolian Plateau reversed from the original decreasing trend to an increasing trend. WUE has shown strong resilience in previous analytical studies, whereas the WUE in the artificial vegetation area in the middle of the Mongolian Plateau showed weak resilience. WUE had a significant positive correlation with precipitation, soil moisture, and the drought severity index (DSI) but a weak correlation with temperature. WUE had strong resistance to abnormal water disturbances; however, its resistance to the effects of temperature and DSI anomalies was weak. The degree of interpretation of vegetation changes for WUE was higher than that for meteorological factors, and WUE showed weak resistance to normalized difference vegetation index (NDVI) disturbances. Delaying the start of the vegetation growing season had an increasing effect on WUE, and the interaction between phenological and meteorological vegetation factors had a non-linear enhancing effect on WUE. Human activities have contributed significantly to the increase in WUE in the eastern, central, and southern regions of the Mongolian Plateau. These results provide a reference for the study of the carbon-water cycle in the Mongolian Plateau.

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001-2020)

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001-2020). Meanwhile, the mean local latent heat increased by 4.65Wm-2, following the mild reduction of emitted longwave radiation (-0.8Wm-2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001-2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Exploring future ecosystem service changes and key contributing factors from a "past-future-action" perspective: A case study of the Yellow River Basin

Understanding the impacts of climate change and human activities on ecosystem services (ESs) and taking actions to adapt to and mitigate their negative impacts are of great benefit to sustainable regional development. In this paper, we integrate the System Dynamics Model (SD), the Future Land Use Simulation (FLUS) model, the Integrated Valuation and Trade-offs of ESs (InVEST) model, and the Structural Equation Model (SEM). We select three scenarios, SSP1-1.9, SSP2-4.5, and SSP5-8.5, from the Coupled Model Intercomparison Project 6 (CMIP6) to forecast future changes under these scenarios in the Yellow River Basin (YRB) by 2030. We predict future changes in water yield (WY), carbon storage (CS), soil retention (SR), and habitat quality (HQ) in the YRB. The results show that: (1) Under the SSP1-1.9 scenario, ecological land types such as forests, grasslands, and water bodies are protected and restored to a certain extent; under the SSP2-4.5 scenario, the degree of land spatial development occupies an intermediate state among the three scenarios; and under the SSP5-8.5 scenario, there is an obvious increase in the artificialization of the watershed's land use. (2) Under scenario SSP1-1.9, there is a comprehensive approach to sustainable development that significantly improves all ESs in the watershed, while the SSP5-8.5 and SSP2-4.5 scenarios demonstrate an increase in trade-offs between WY, HQ, and CS, especially in the downstream area. (3) Anthropogenic factors having more significant impacts in the SSP5-8.5 scenario. In this paper, we not only summarize the differences in trade-offs among various ESs but also provide an in-depth analysis of the key factors affecting future ESs, providing new ideas and insights for the sustainable development of ES in the future. In summary, we propose a prioritized development pathway for the future, a reduction of trade-offs between ESs, and an improved capacity to respond to challenges.

Ecological security warning in Central Asia: Integrating ecosystem services protection under SSPs-RCPs scenarios

Ecological security patterns (ESPs) are designed to enhance ecosystem structure and functionality while preserving vital ecosystem services (ESs). This study not only integrated the ES trade-offs related to ecological security warning, but also considered the effects of future climate changes and human activities on ESPs. By combining the revised universal soil loss equation (RUSLE), the revised wind erosion equation (RWEQ), the dry sedimentation (DS) model, the recreation opportunity map (ROM) and the integrated valuation of ESs and trade-offs (InVEST) model, this study projected provisioning services, regulation services and cultural services in Central Asia (CA) for historical periods (1995-2014) and future scenarios (2021-2099). An ecological security early-warning (source - corridor - barriers) framework was constructed based on the protection of ESs under the SSP126, SSP245 and SSP585 scenarios. The ordered weighted averaging method (OWA) was applied to this framework to identify ecological sources. The Minimum cumulative resistance model (MCR) and circuit theory were used to construct ecological corridors and barriers. Our results revealed that ES hotspot areas will decrease by 11.75 % to 16.42 % in CA under the SSP126, SSP245, and SSP585 scenarios. Under the ecological warning framework, the ecological source warning area will reach 792 km2-1942 km2 and 6591 km2-17,465 km2 under the SSP126 and SSP585 scenarios, respectively. In particular, in the 2050s under the SSP245 scenario, the number of key ecological corridor warnings will exceed 50 % of the total number of corridors. We found that ecological barrier warnings will mainly be distributed in desert areas with low vegetation coverage in southwestern CA. Building upon the reorganization of ESs within the ESP framework, we propose an ecological early warning strategy referred to as "one axis, two belts, two cores, and three zones". This novel approach aims to enhance our ability to predict and respond to ecological threats and challenges.

Land use significantly improved grassland degradation and desertification states in China over the last two decades

China possesses extensive grasslands primarily located in dryland regions, which are highly susceptible and fragile to climate change and human intervention, making them prone to degradation and desertification. In recent decades, China has implemented numerous ecological projects to improve the states of ecosystems. Additionally, recent studies have revealed the critical roles of rising CO2 on dryland greening. However, it is still limited to understand the contributions of anthropogenic recovery and CO2 fertilization, as well as other climate factors, to the dynamics of grassland degradation and desertification in China. To address these gaps, we employed a two-step approach to differentiate between grassland degradation and desertification as distinct processes across the grasslands and sparsely vegetated lands in China. This involved assessing degradation within existing grassland areas and identifying the conversion of grasslands into desert regions. The study period of 2000-2020 was examined to determine the occurrence of grassland desertification, which suggested a significant decrease in the desertification area in China. Subsequently, the time series of the Enhanced Vegetation Index (EVI) during the growing season was analyzed to track vegetation dynamics. Since the beginning of the 21st century, a significant greening trend has been observed in approximately 97% of the study area. Furthermore, we quantified the effects of anthropogenic climate change (ACC), climate change, and land use change on grassland degradation and desertification in China. The analyses indicated that 50.7% of the observed vegetation changes in the study area from 2000 to 2020 were primarily driven by land use, followed by the effects of rising CO2, accounting for 42.1% of the changes. These findings provided some insights on developing regionally-targeted strategies for grassland conservation in China.

Shift in the migration trajectory of the green biomass loss barycenter in Central Asia

Revealing the vegetation response law under drought stress has become a hot issue in global climate change research. Against the background of human beings actively responding to climate change, quantitatively revealing the change and migration laws of green biomass loss (GBL) caused by drought in historical and future periods is insufficient. In this regard, we innovatively constructed a joint kNDVI-SPEI (kernel normalized difference vegetation index and standardized precipitation evapotranspiration index) distribution based on copula theory to accurately capture GBL dynamic under various drought scenarios unlike previous studies conducted in a deterministic way. Taking the drought-sensitive and ecologically vulnerable Central Asia (CA) as a typical region, we verified that an average 94.4 % of region showed greater vegetation vulnerability in times of water shortage from May to October, which exhibited the greatest probability of GBL under different drought scenarios, mainly in Kazakhstan and Uzbekistan. Significantly intensified drought due to high emissions will cause an 18.16 percentage-point increase in GBL probability in the far future (FFP, 2061-2100) compared to the near future (NFP, 2019-2060), which is much higher than in the lower-emission (0.38 %) and moderate-emission scenarios (9.82 %). In the NFP, the GBL barycenter will shift from Kazakhstan to Xinjiang, China; in the FFP, it will shift back to Kazakhstan due to the measures taken by the Chinese government to conserve energy and reduce emissions. Results illustrate that against the background of worsening drought, active climate change coping strategies can reverse the migration trajectory of the GBL barycenter caused by drought, which provides a new idea for vegetation protection research in response to global climate change.

Impacts of climate change and evapotranspiration on shrinkage of Aral Sea

The massive desiccation of the Aral Sea, the fourth largest lake in the world, has led to severe ecological problems, expansion of cropland was thought to be the main factor driving that shrinkage. But this study performed a long-term land cover and use change assessment for Aral Sea Basin (ASB) to show that the cropland has stopped expanding in 2000, of which the cropland in the ASB plain area has decreased significantly (-140 km²/year) from 2001 to 2019. By contrast, this study finds the hydrological cycle in the ASB has intensified through a spatial and temporal scale approach based on Earth observation. Specifically, there is a 7.21 % (+304.56 × 10⁸ m³) increase in annual total precipitation and a 10.13 % (+376.21 × 10⁸ m³) increase in annual total actual evapotranspiration (AET) for the whole ASB during 1980-2019. In particular, the total annual AET in the ASB plain area has increased by 37.81 % (+718.92 × 10⁸ m³), which almost depletes the water that should have flowed into the Aral Sea. Therefore, the Aral Sea shrank by 5625 × 10⁸ m³ (or 42,944.32km²) from 1980 to 2019. Changing climate and increasing AET have accelerated the desiccation of the Aral Sea, and the expansion of cropland is no longer the main factor of that shrinkage. After more water was conserved in the ASB plain area, evapotranspiration plays a more vital role in the Aral Sea shrinkage. Reducing AET and unproductive water losses are key initiatives in future projects to save the Aral Sea. This study explores the causes of Aral Sea shrinkage from an integrated perspective of climate-land-water-ecological change across the ASB, bridging the limitations of previous studies that have focused on Aral Sea waters and subbasins.

Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.

The Spatiotemporal Response of Vegetation Changes to Precipitation and Soil Moisture in Drylands in the North Temperate Mid-Latitudes

Vegetation growth in drylands is highly constrained by water availability. How dryland vegetation responds to changes in precipitation and soil moisture in the context of a warming climate is not well understood. In this study, warm drylands in the temperate zone between 30 and 50° N, including North America (NA), the Mediterranean region (MD), Central Asia (CA), and East Asia (EA), were selected as the study area. After verifying the trends and anomalies of three kinds of leaf area index (LAI) datasets (GLASS LAI, GLEAM LAI, and GLOBAMAP LAI) in the study area, we mainly used the climate (GPCC precipitation and ERA5 temperature), GLEAM soil moisture, and GLASS LAI datasets from 1981 to 2018 to analyze the response of vegetation growth to changes in precipitation and soil moisture. The results of the three mutually validated LAI datasets show an overall greening of dryland vegetation with the same increasing trend of 0.002 per year in LAI over the past 38 years. LAI and precipitation exhibited a strong correlation in the eastern part of the NA drylands and the northeastern part of the EA drylands. LAI and soil moisture exhibited a strong correlation in the eastern part of the NA drylands, the eastern part of the MD drylands, the southern part of the CA drylands, and the northeastern part of the EA drylands. The results of this study will contribute to the understanding of vegetation dynamics and their response to changing water conditions in the Northern Hemisphere midlatitude drylands.

Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia

Maintaining the ecological security of arid Central Asia (CA) is essential for the sustainable development of arid CA. Based on the moderate-resolution imaging spectroradiometer (MODIS) data stored on the Google Earth Engine (GEE), this paper investigated the spatiotemporal changes and factors related to ecological environment quality (EEQ) in CA from 2000 to 2020 using the remote sensing ecological index (RSEI). The RSEI values in CA during 2000, 2005, 2010, 2015, and 2020 were 0.379, 0.376, 0.349, 0.360, and 0.327, respectively; the unchanged/improved/deteriorated areas during 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were about 83.21/7.66%/9.13%, 77.28/6.68%/16.04%, 79.03/11.99%/8.98%, and 81.29/2.16%/16.55%, respectively, which indicated that the EEQ of CA was poor and presented a trend of gradual deterioration. Consistent with the RSEI trend, Moran’s I index values in 2000, 2005, 2010, 2015, and 2020 were 0.905, 0.893, 0.901, 0.898, and 0.884, respectively, revealing that the spatial distribution of the EEQ was clustered rather than random. The high–high (H-H) areas were mainly located in mountainous areas, and the low–low (L-L) areas were mainly distributed in deserts. Significant regions were mainly located in H-H and L-L, and most reached the significance level of 0.01, indicating that EEQ exhibited strong correlation. The EEQ in CA is affected by both natural and human factors. Among the natural factors, greenness and wetness promoted the EEQ, while heat and dryness reduced the EEQ, and heat had greater effects than the other three indexes. Human factors such as population growth, overgrazing, and hydropower development are important factors affecting the EEQ. This study provides important data for environmental protection and regional planning in arid and semi-arid regions.

Quantifying Vegetation Vulnerability to Climate Variability in China

Climate variability has profound effects on vegetation. Spatial distributions of vegetation vulnerability that comprehensively consider vegetation sensitivity and resilience are not well understood in China. Furthermore, the combination of cumulative climate effects and a one-month-lagged autoregressive model represents an advance in the technical approach for calculating vegetation sensitivity. In this study, the spatiotemporal characteristics of vegetation sensitivity to climate variability and vegetation resilience were investigated at seasonal scales. Further analysis explored the spatial distributions of vegetation vulnerability for different regions. The results showed that the spatial distribution pattern of vegetation vulnerability exhibited spatial heterogeneity in China. In spring, vegetation vulnerability values of approximately 0.9 were mainly distributed in northern Xinjiang and northern Inner Mongolia, while low values were scattered in Yunnan Province and the central region of East China. The highest proportion of severe vegetation vulnerability to climate variability was observed in the subhumid zone (28.94%), followed by the arid zone (26.27%). In summer and autumn, the proportions of severe vegetation vulnerability in the arid and humid zones were higher than those in the other climate zones. Regarding different vegetation types, the highest proportions of severe vegetation vulnerability were found in sparse vegetation in different seasons, while the highest proportions of slight vegetation vulnerability were found in croplands in different seasons. In addition, vegetation with high vulnerability is prone to change in Northeast and Southwest China. Although ecological restoration projects have been implemented to increase vegetation cover in northern China, low vegetation resilience and high vulnerability were observed in this region. Most grasslands, which were mainly concentrated on the Qinghai–Tibet Plateau, had high vulnerability. Vegetation areas with low resilience were likely to be degraded in this region. The areas with highly vulnerable vegetation on the Qinghai–Tibet Plateau could function as warning signals of vegetation degradation. Knowledge of spatial patterns of vegetation resilience and vegetation vulnerability will help provide scientific guidance for regional environmental protection.

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia

The increasing frequency and intensity of droughts in a warming climate are likely to exacerbate adverse impacts on ecosystems, especially for water-limited regions such as Central Asia. A quantitative understanding of the impacts of drought on vegetation is required for drought preparedness and mitigation. Using the Global Inventory Modeling and Mapping Studies NDVI3g data and Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2015, we evaluate the vegetation vulnerability to drought in Central Asia based on a copula-based probabilistic framework and identify the critical regions and periods. Furthermore, a boosted regression trees (BRT) model was also used to explore the relative importance of environmental factors and plant traits on vegetation response to drought. Additionally, we also investigated to what extent irrigation could alleviate the impacts of drought. Results revealed that months from June to September was the critical period when vegetated areas were most vulnerable to drought stress. The probabilities of vegetation loss below 20th quantile under extremely dry in these months were 68.7%, 69.4%, 71.0%, and 67.0%, respectively. Regarding vegetation-vulnerable regions, they shifted with different growth stages. During the middle of the growing season, semi-arid areas were the most vulnerable regions, whereas the highest drought-vulnerable regions were observed in arid areas during other periods. The BRT results showed that plant traits accounted for a large fraction (58.9%) of vegetation response to drought, which was more important than ambient soil environment (20.8%). The analysis also showed that mitigations from irrigation during July to September were smaller than in other months. The results of this paper provide insight into the influences of drought on vegetation and may contribute to drought mitigation and land degradation measures in Central Asia under accelerating global warming.

Analysis of Effects of Recent Changes in Hydrothermal Conditions on Vegetation in Central Asia

Understanding the relationship of hydrothermal conditions to vegetation changes is conducive to revealing the feedback mechanism connecting climate variations and vegetation. Based on the methods of Theil–Sen median analysis, and the Mann–Kendall trend test, this research investigated the spatiotemporal vegetation dynamics in Central Asia using the Normalized Difference Vegetation Index (NDVI) and grid climate data from 1982 to 2015. Further, the contributions of hydrothermal conditions to vegetation changes were quantified using a boosted regression tree model (BRT). The results demonstrated that the spatiotemporal characteristics of vegetation dynamics exhibited significant differences in different seasons, and most pixels showed increasing trends in the growing season and spring. Boosted regression tree analysis indicated that the contributions of hydrothermal conditions to vegetation dynamics exhibited temporal and spatial heterogeneity. During the annual, growing season, and summer examination periods, the contribution value of the increase in warming conditions (temperature or potential evapotranspiration) to vegetation degradation in the region due to the hydrothermal tradeoff effect (water) was 49.92%, 44.10%, and 44.95%, respectively. Moreover, the increase in warming conditions promoted vegetation growth, with a contribution value of 59.73% in spring. The contribution value of the increase in wetting conditions (precipitation or soil moisture) to vegetation growth was 48.46% in northern Central Asia, but the contribution value of the increase in warming conditions to vegetation degradation was 59.49% in Ustyurt Upland and the Aral Sea basin in autumn. However, the increase in warming conditions facilitated irrigation vegetation growth, with a contribution value of 59.86% in winter. The increasing potential evapotranspiration was the main factor affecting vegetation degradation in the Kyzylkum Desert and Karakum Desert during the annual, growing season, and autumn examination periods. Precipitation and soil moisture played decisive roles in vegetation dynamics in northern Central Asia during the growing season, summer, and autumn. This research provides reference information for ecological restoration in Central Asia.

Assessing vegetation stability to climate variability in Central Asia

The dramatic climate change has far-reaching impacts on vegetation in drylands such as Central Asia. Recent attempts to assess vegetation stability to short-term climate variability often account solely for vegetation sensitivity or resilience but ignore the composite effects of these two indicators. Meanwhile, our understanding of the vegetation stability at the seasonal scale remains insufficient. In this study, considering the cumulative effects of vegetation response to three key climate factors, we assessed the stability of vegetation in Central Asia using normalized difference vegetation index (NDVI) and the meteorological data from 1982 to 2014 by integrating vegetation sensitivity and resilience, and further identified the critical regions and seasons of vegetation that experience high risks of pending change. The results show that the sensitivity of vegetation has a strong correlation (R² = 0.83, p < 0.001) with the aridity index (AI), with the vegetation of drier areas having lower sensitivities to climate variability. At the temporal scale, the sensitivity of vegetation to climate variability varied among different seasons. The average vegetation sensitivity index (VSI) is 41.17, 33.32 and 28.63 in spring, summer and autumn, respectively. Spatially, a trade-off between vegetation sensitivity and resilience is found both for the growing season (R² = 0.67) and seasonal scale (R² = 0.71, 0.32 and 0.43 for spring, summer and autumn, respectively), regions with high vegetation sensitivity were always accompanied by strong resilience. Based on the relationship between vegetation sensitivity and resilience, we further identify the critical regions and periods of vegetation with high change risk in Central Asia. Results suggest that herbaceous plants in semi-arid areas present high instability, especially in summer. This study offers a comprehensive perspective to assess vegetation stability to climate variability and the results will facilitate the protection of ecosystems and the implementation of sustainable development goals in Central Asia.

Identification of conservation priorities in the major basins of Central Asia: Using an integrated GIS-based ordered weighted averaging approach

Ecosystem services (ESs) provided by the major basins of Central Asia are critical to human well-being and have attracted the attention of the international community. The identification of conservation priorities is of great significance for the maintenance and protection of key ESs. In this study, we quantified the spatiotemporal changes of net primary productivity (NPP), soil conservation (SC), water yield (WY) and habitat quality (HQ) in the major basins of Central Asia from 1995 to 2015. In addition, a GIS-based ordered weighted averaging (OWA) multi-criterion valuation method was adopted to identify potential conservation areas under 11 scenarios. Conservation priorities were determined by comparing the conservation efficiency under each scenario. Then, a broad range of indicators were considered to distinguish the driving factors affecting ESs in conservation priorities. The results show that the average conservation efficiency in the Issyk-Kul Basin was the highest, followed by the Am Darya Basin, Ili-Balkhash Basin and Syr Darya Basin. We observed that the conservation efficiency of the four ESs declined continuously in the Ili-Balkhash Basin from 1995 to 2015, while it changed steadily in the other three basins. Correlation analysis indicated that natural factors (e.g., precipitation and topography) were the main driving factors of WY, SR and NPP in conservation priorities, while HQ was more affected by socio-economic factors (e.g., population density and both cropland and urban percentages). The identification of conservation priorities and their driving factors plays an important role in ensuring the ecological security of the lower reaches, regulating the regional water balance and stabilizing the climate pattern.

Impact of Winter Snowfall on Vegetation Greenness in Central Asia

Snowfall is an important climatic variable with remarkable impacts on vegetation greenness in the alpine and extra-tropical regions. Central Asia (CA) is located in the middle latitude with high mountains, and the ecosystem is vulnerable to climate change in CA. In this region, the vegetation response to winter snowfall and its interactions with growing season climatic factors on vegetation greenness is still unclear. Thus, this study attempted to examine the impact of winter snowfall on vegetation greenness with remote-sensing vegetation index and reanalysis climatic data, and to investigate the interactions between winter snowfall and growing season climatic factors and their influence on vegetation growth via path analysis. The results showed that there is a generally positive correlation between winter snowfall and vegetation greenness from May to September and during the whole growing season (April to September). This positive correlation was significant in 8% of the study area for the whole growing season. However, the increase in winter snowfall was not beneficial to the regional vegetation growth in April. The vegetation response to winter snowfall also relates to land-cover type and elevation. The vegetation greenness in grassland was depicted to be more sensitive to winter snowfall than that in forestland. The response turned from positive to negative when the elevation increased from below 3000 m to above 3000 m. Moreover, the impact of winter snowfall on vegetation greenness was not regulated by temperature and precipitation in the growing season in more than 70% of CA. The impact relates to the interaction with April temperature in Central Kazakhstan, and is regulated by growing season precipitation in North Kazakhstan where annual precipitation mainly occurs in the growing season. The impact of winter snowfall on vegetation greenness is more important than growing season precipitation and temperature in some areas, since annual precipitation does not concentrate in the growing season or the relative increase of winter snowfall is great in these places. The results of the present study improve the understanding of vegetation response to climate change, and provide a scientific reference for environmental protection in CA and similar regions.

Terrestrial biodiversity threatened by increasing global aridity velocity under high-level warming

Global aridification is projected to intensify. Yet, our knowledge of its potential impacts on species ranges remains limited. Here, we investigate global aridity velocity and its overlap with three sectors (natural protected areas, agricultural areas, and urban areas) and terrestrial biodiversity in historical (1979 through 2016) and future periods (2050 through 2099), with and without considering vegetation physiological response to rising CO₂ Both agricultural and urban areas showed a mean drying velocity in history, although the concurrent global aridity velocity was on average +0.05/+0.20 km/yr^-1 (no CO₂ effects/with CO₂ effects; "+" denoting wetting). Moreover, in drylands, the shifts of vegetation greenness isolines were found to be significantly coupled with the tracks of aridity velocity. In the future, the aridity velocity in natural protected areas is projected to change from wetting to drying across RCP (representative concentration pathway) 2.6, RCP6.0, and RCP8.5 scenarios. When accounting for spatial distribution of terrestrial taxa (including plants, mammals, birds, and amphibians), the global aridity velocity would be -0.15/-0.02 km/yr^-1 ("-" denoting drying; historical), -0.12/-0.15 km/yr^-1 (RCP2.6), -0.36/-0.10 km/yr^-1 (RCP6.0), and -0.75/-0.29 km/yr^-1 (RCP8.5), with amphibians particularly negatively impacted. Under all scenarios, aridity velocity shows much higher multidirectionality than temperature velocity, which is mainly poleward. These results suggest that aridification risks may significantly influence the distribution of terrestrial species besides warming impacts and further impact the effectiveness of current protected areas in future, especially under RCP8.5, which best matches historical CO₂ emissions [C. R. Schwalm et al., Proc. Natl. Acad. Sci. U.S.A. 117, 19656-19657 (2020)].

Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes

A systematic understanding of the dynamics of surface water resources and terrestrial water storage (TWS) is extremely important for human survival in Central Asia (CA) and maintaining the balance of regional ecosystems. Although several remote sensing products have been used to map surface water, the spatial resolution of some of them (hundreds of meters) is not sufficient to identify small surface water bodies, with monitoring data only being available for a few years or less. Thus, long-term continuous monitoring of surface water dynamics has not yet been achieved. To address this, we used all available Landsat images and the adjacent-years interpolation method to describe the dynamics of surface water in CA with a 30-m spatial resolution during 1990-2019. Subsequently, based on the multiple stepwise regression model, the climatic changes and human activity drivers affecting the surface water were systematically assessed. The permanent surface water areas (PSWA) of downstream countries with water scarcity decreased over time. The PSWA of Kazakhstan continues to decline at a maximum rate of 1189 km²/a. Additionally, human activities represented by population and reservoir areas are the dominant drivers affecting surface water resources in CA. The relationship between TWS and PSWA in CA and the constraints on social and economic development provided by the available water resources are discussed. The findings demonstrate that more than one-third of the croplands in CA are suffering from declining SWAs and TWS. The water crisis in CA has intensified, and the spatial mismatch between water and land resources is expected to remain one of the biggest challenges for future social and economic development in CA. Our dataset and findings provide high-precision surface water dynamics data that could be valuable for mitigating the water crisis in CA and provide a current scientific reference for achieving the United Nations' Sustainable Development Goals.

Divergent Trends of Water Storage Observed via Gravity Satellite across Distinct Areas in China

Knowledge of the spatiotemporal variations of terrestrial water storage (TWS) is critical for the sustainable management of water resources in China. However, this knowledge has not been quantified and compared for the different climate types and underlying surface characteristics. Here, we present observational evidence for the spatiotemporal dynamics of water storage based on the products from the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS) in China over 2003–2016. Our results were the following: (1) gravity satellite dataset showed divergent trends of TWS across distinct areas due to human factors and climate factors. The overall changing trend of water storage is that the north experiences a loss of water and the south gains in water, which aggravates the uneven spatial distribution of water resources in China. (2) In the eastern monsoon area, the depletion of water storage in North China (NC) was found to be mostly due to anthropogenic disturbance through groundwater pumping in plain areas. However, precipitation was shown to be a key driver for the increase of water storage in South China (SC). Increasing precipitation in SC was linked to atmospheric circulation enhancement and Pacific Ocean warming, meaning an unrecognized teleconnection between circulation anomalies and water storage. (3) At high altitudes in the west, the change of water storage was affected by the melting of ice and snow due to the rising temperatures, yet the topography determines the trend of water storage. We found that the mountainous terrain led to the loss of water storage in Tianshan Mountain (TSM), while the closed basin topography gathered the melted water in the interior of the Tibetan Plateau (ITP). This study highlights the impacts of the local climate and topography on terrestrial water storage, and has reference value for the government and the public to address the crisis of water resources in China.

Analysis on impacts of hydro-climatic changes and human activities on available water changes in Central Asia

Water resources in Central Asia are very scarce due to natural and anthropogenic impacts. Water shortages have been a major factor hampering the socio-economic development of Central Asia. Exploring internal interactions among climate change, human activities and terrestrial hydrological cycles will help to improve the management of water resources in Central Asia. In this paper, hydro-climatic and anthropogenic data for the period 2003-2016 from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), the Climatic Research Unit (CRU) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to analyze the influence of natural factors and human activities on changes of available water (AWC). The terrestrial water storage derived from GRACE and GLDAS remarkably declined in 2008, due to a serious drought, but increased thereafter. The AWC positively responded to the vegetation index, evapotranspiration, potential evapotranspiration and air temperature at a lag of 0-1 month, but to precipitation at a lag of 2-3 months. Results of correlation analysis with a spatial square moving window indicated that forests, grasses, croplands and water areas presented significantly positive correlations with AWC, while barren areas and urban areas were negatively correlated with AWC. According to the Boruta algorithm and the Random Forest model, natural factors, namely precipitation, evapotranspiration and potential evapotranspiration, were major factors for AWC in the whole Central Asia. Human activities had direct and indirect impacts on AWC. With the development of society and economy, croplands and urban areas gradually increased, resulting in a rising demand for water withdrawals for agriculture irrigation and industry. The unreasonable utilization and exploitation of water resources led to vegetation degradation and ecosystem deterioration, which would worsen the shortage of water resources in arid regions of Central Asia.

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.