Assessing vegetation stability to climate variability in Central Asia

A greater negative impact of future climate change on vegetation in Central Asia: Evidence from trajectory/pattern analysis

In the context of global warming, vegetation changes exhibit various patterns, yet previous studies have focused primarily on monotonic changes, often overlooking the complexity and diversity of multiple change processes. Therefore, it is crucial to further explore vegetation dynamics and diverse change trajectories in this region under future climate scenarios to obtain a more comprehensive understanding of local ecosystem evolution. In this study, we established an integrated machine learning prediction framework and a vegetation change trajectory recognition framework to predict the dynamics of vegetation in Central Asia under future climate change scenarios and identify its change trajectories, thus revealing the potential impacts of future climate change on vegetation in the region. The findings suggest that various future climate scenarios will negatively affect most vegetation in Central Asia, with vegetation change intensity increasing with increasing emission trajectories. Analyses of different time scales and trend variations consistently revealed more pronounced downward trends. Vegetation change trajectory analysis revealed that most vegetation has undergone nonlinear and dramatic changes, with negative changes outnumbering positive changes and curve changes outnumbering abrupt changes. Under the highest emission scenario (SSP5-8.5), the abrupt vegetation changes and curve changes are 1.7 times and 1.3 times greater, respectively, than those under the SSP1-2.6 scenario. When transitioning from lower emission pathways (SSP1-2.6, SSP2-4.5) to higher emission pathways (SSP3-7.0, SSP5-8.5), the vegetation change trajectories shift from neutral and negative curve changes to abrupt negative changes. Across climate scenarios, the key climate factors influencing vegetation changes are mostly evapotranspiration and soil moisture, with temperature and relative humidity exerting relatively minor effects. Our study reveals the negative response of vegetation in Central Asia to climate change from the perspective of vegetation dynamics and change trajectories, providing a scientific basis for the development of effective ecological protection and climate adaptation strategies.

Grassland productivity in arid Central Asia depends on the greening rate rather than the growing season length

Climate change has induced substantial impact on the gross primary productivity (GPP) of terrestrial ecosystems by affecting vegetation phenology. Nevertheless, it remains unclear which among the mean rates of grass greening (RG), yellowing (RY), and the length of growing season (LOS) exhibit stronger explanatory power for GPP variations, and how RG and RY affect GPP variations under warming scenarios. Here, we explored the relationship between RG, RY, LOS, and GPP in arid Central Asia (ACA) from 1982 to 2019, elucidating the response mechanisms of RG, RY, and GPP to the mean temperature (TMP), vapor pressure deficit (VPD), precipitation (PRE), and soil moisture (SM). The results showed that the multi-year average length of greening (LG) in ACA was 22.7 days shorter than that of yellowing (LY) and the multi-year average GPP during LG (GPPlg) was 38.28 g C m-2 d -1 more than that of during LY (GPPly). RG and RY were positively correlated with GPPlg and GPPly, although the degree of correlation between RG and GPPlg was higher than that between RY and GPPly. Increases in RG and RY contributed to an increase in GPPlg (55.44 % of annual GPP) and GPPly (35.44 % of annual GPP). The correlation between RG and GPPlg was the strongest (0.49), followed by RY and GPPly (0.33), and LOS and GPP was the weakest (0.21). TMP, VPD, PRE, and SM primarily affected GPP by influencing RG and RY, rather than direct effects. The positive effects of TMP during LG (TMPlg), PRE during LG (PRElg), and SM during LG (SMlg) facilitated increases in RG and GPPlg, and higher VPD during LY (VPDly) and lower PRE during LY (PREly) accelerated increases in RY. Our study elucidated the impact of vegetation growth rate on GPP, thus providing an alternate method of quantifying the relationship between vegetation phenology and GPP.

Effects of climate change and ozone on vegetation phenology on the Tibetan Plateau

The vegetation phenology, encompassing the start (SOS) and end (EOS) of the growing season on the Tibetan Plateau, has been significantly impacted by global climate change. Furthermore, ozone (O3) has gradually become the main pollutant in this region, substantially influencing carbon cycle and ecosystems on Earth. While ongoing studies have focused mainly on the implications of climate parameters, including temperature, precipitation, and radiation, the effects of O3 on the SOS and EOS remain unclear. Here, we compared the responses and sensitivities of the SOS and EOS to both climatic factors and O3 in this region. With the use of partial correlation analysis, we found that increased precipitation was the most important factor influencing the SOS and caused earlier occurrence (4.8 % vs. 21.9 %) for most plant functional types. In comparison, temperature only dominated in shrublands. In particular, we found that the EOS responded comparably to climatic factors with similar proportions between advancing and delaying patterns. However, higher O3 levels consistently advanced the EOS for almost all plant functional types and was the main factor controlling EOS variations based on the sensitivity analysis. Our results emphasized that O3 pollution should be considered for obtaining better phenological forecasts and determining the impacts of the environment and atmospheric composition on carbon sequestration in terrestrial ecosystems.

Satellite observed dryland greening in Asian endorheic basins: Drivers and implications to sustainable development

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEBs). As these retention basins are only being replenished by the intermittent and scarce rainfall, global warming coupled with ever-rising human demand for water is exerting unprecedented pressures on local water and ecological security. Recent studies revealed a persistent and widespread water storage decline across the AEBs, yet the response of dryland vegetation to this recent hydroclimatic trend and a spatially explicit partitioning of the impact into the hydroclimatic factors and human activities remain largely unknown. To fill in this knowledge gap, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53 %), encompassing Mongolia Plateau, Northwest China, Qinghai Tibet Plateau, and Western Asia (except the Arabian Peninsula), exhibited a significant greening trend over the past two decades. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3 %) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the AEB, our results therefore raised a significant concern on the ecological and societal sustainability in this region, where a mild increase in precipitation cannot catch up the rising evaporative demand and water consumption resulted from global warming and agriculture intensification.

Impacts of climate change on fractional vegetation coverage of temperate grasslands in China from 1982 to 2015

The vegetation coverage of temperate grasslands in China has changed substantially due to climate change during the past decades, which significantly affects the function of grassland ecosystems. To appropriately carry out adaptive management and protect temperate grassland vegetation, it is important to understand the variations in fractional vegetation coverage (FVC) of China's temperate grasslands and how they are responding to climate change. Using the GIMMS NDVI and climatic datasets, this study explored the dynamics of FVC and their climatic drivers across the temperate grassland region of China during 1982∼2015. The results showed that the growing season mean FVC increased by 0.12% per year during 1982∼2015. The increases in precipitation and minimum temperature in the growing-season (especially in spring) could enhance the FVC of various vegetation types. In summer, the FVC of temperate steppe and desert steppe could drastically increase with increasing precipitation. In addition, this study found that the impacts of daytime and night-time warming on the FVC of temperate grasslands were asymmetric. Daytime warming can moderately increase FVC of temperate grasslands, while night-time warming could significantly increase it. Furthermore, the increase in summer daytime and night-time temperatures leads to a weak decrease and a moderate increase in FVC, respectively. This asymmetric effect was more evident for the temperate steppe and desert steppe in the central area. In autumn, the temperatures increase had significant positive impacts on the FVC of temperate meadows and steppes. This study highlights the differences in the impacts of climate change at different time scales on the FVC of grasslands with various vegetation types, and indicates that the asymmetric influences of daytime and night-time temperatures in different seasons on FVC must be included in calculating the vegetation coverage of China's temperate grasslands. The results could provide information for maintaining grassland ecosystem functions and managing environmental systems.

Combining Multiple Plant Attributes to Reveal Differences in Community Structure in Two Distant Deserts in Central Asia

International interest is growing in biodiversity conservation and sustainable use in drylands. Desert ecosystems across arid Central Asia are severely affected by global change. Understanding the changes in a plant community is an essential prerequisite to revealing the community assembly mechanism, vegetation conservation, and management. The knowledge of large-scale spatial variation in plant community structure in different Central Asian deserts is still limited. In this study, we selected the Taukum (TD, Kazakhstan) and the Gurbantunggut (GD, China) deserts as the research area, with similar latitudes despite being nearly 1000 km apart. Thirteen and 15 sampling plots were set up and thoroughly investigated. The differences in community structure depending on multiple plant attributes (individual level: plant height, canopy diameter, and plant volume, and community level: plant density, total cover, and total volume) were systematically studied. TD had a better overall environmental status than GD. A total of 113 species were found, with 68 and 74 in TD and GD, respectively. The number of species and plant attributes was unequally distributed across different families and functional groups between deserts. The values of several plant attributes, such as ephemerals, annuals, dicotyledons, and shrubs with assimilative branches in GD, were significantly lower than those in TD. The Motyka indices of six plant attributes (26.18-38.61%) were higher between the two deserts than the species similarity index (20.4%), indicating a more robust convergence for plant functional attributes. The community structures in the two deserts represented by different plant attribute matrices demonstrated irregular differentiation patterns in ordination diagrams. The most variance in community structure was attributed to soil and climatic factors, while geographic factors had the smallest proportion. Consequently, the community structures of the two distant deserts were both different and similar to an extent. This resulted from the long-term impacts of heterogeneous environments within the same region. Our knowledge is further deepened by understanding the variation in community structure in different deserts on a large spatial scale. This therefore provides valuable insights into conserving regional biodiversity in Central Asia.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.

Interannual and seasonal relationships between photosynthesis and summer soil moisture in the Ili River basin, Xinjiang, 2000-2018

Soil moisture (SM) is essential for controlling terrestrial carbon uptake, as it directly provides moisture for photosynthesis, especially in arid and semiarid regions. We selected the arid and semiarid Ili River basin (IRB) of Xinjiang as the study area, and investigated the spatial and temporal characteristics and interrelationships with SM and photosynthesis from 2000 to 2018 using the ERA5 products and solar-induced chlorophyll fluorescence (SIF). SM and photosynthesis showed a decreasing trend during the study period. Compared with those in spring and autumn, the variation of summer SM and SIF was more consistent with the interannual variation. Anomaly analysis showed that negative SM anomalies were most profound in 2012-2015, 2008, and 2014. Additionally, we quantified the effect of seasonal SM deficits on photosynthesis by performing model-based experiments. The results indicated that the gross primary productivity (GPP) simulated by the P-model could capture the characteristics of photosynthesis in the IRB, which had a high correlation with SIF (R² = 0.82, p < 0.001). In 2012-2015, 2008, and 2014, SM deficits caused more GPP reduction in the summers than in the springs or the autumns. The trends were mainly visible in the northern IRB, where GPP was below 40 % of the multi-year mean, and SM was below 23 %. GPP decreased more significantly in grassland than in the forest under the influence of SM deficit. This study reveals seasonal differences in the effects of SM deficit on photosynthesis and emphasizes that the summer SM deficit was the main factor responsible for decreases in GPP in the IRB during the study period. These findings contribute to a better understanding of the relationships between photosynthesis and environmental factors, and provide a reference for an accurate assessment of the regional carbon cycle.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Distribution Characteristics and Restoration Application of Vegetation in Chengcun Bay Surrounding Areas of Yangjiang City

In recent years, global warming and sea level rise have further aggravated the risk of coastal erosion. Coastal vegetation plays an important role in resisting storm surges and alleviating coastal erosion. Therefore, screening plant species for the purpose of constructing ecological seawalls to protect or repair damaged coastal zones has become a hot issue. In this paper, a field survey was conducted to investigate the vegetation in Chengcun Bay surrounding areas of Yangjiang City by combining a line survey and sample plot survey. By understanding the vegetation types, distribution and community structure in the bay's surrounding areas and analyzing the restricting environmental factors of those plants, we put forward some countermeasures for coastal vegetation restoration in difficult site conditions from the aspects of plant species selection, vegetation configuration and restoration technology, so as to provide reference for ecological vegetation restoration in similar locations.

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.

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Dryland ecosystems are fragile to climate change due to harsh environmental conditions. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT0), 5 °C (DT5), 10 °C (DT10), 20 °C (DT20), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT0, DT5, DT10, and DT20 exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT0, DT5, DT10, DT20 with the annual Normalized Difference Vegetation Index (NDVI) increased with increasing bio-temperature thresholds. In particular, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT20, as increased DT20 exacerbated ecosystem drought. Moreover, SGS exhibited a significantly advanced trend at a rate of −0.261 days/year for the regional scale, while EGS experienced a significantly delayed trend at a rate of 0.164 days/year. Because of changes in SGS and EGS, LGS across the GLRCA was extended at a rate of 0.425 days/year, which was mainly attributed to advanced SGS. In addition, our study revealed that about 53.6% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. Overall, the results of this study will help predict the response of vegetation to future climate change in the GLRCA, and support decision-making for implementing effective ecosystem management in arid and semi-arid regions.

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.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.