Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors.

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

Monitoring vegetation sensitivity to drought events in China.

The frequency and severity of drought events have increased over the decades under the influence of global warming. Continued drought increases the risk of vegetation degradation. Many studies have investigated the responses of vegetation to drought but rarely from the perspective of drought events. Moreover, the spatial distributions of vegetation sensitivity to drought events are not well understood in China. Thus, the spatiotemporal patterns of drought events were quantified based on the run theory at different time-scales in this study. The relative importance of drought characteristics for vegetation anomalies during drought events were calculated by using the BRT model. Then, the sensitivity of vegetation anomalies and vegetation phenology was quantified by dividing standardized anomalies of vegetation parameters (NDVI and phenological metrics) and SPEI during drought events for different regions in China. The results show that Southern Xinjiang and Southeast China experienced relatively higher values of drought severity, especially at the 3-month and 6-month scales. Most arid areas experienced more drought events but of low severity, while some humid zones underwent few drought events but of high severity. Notable negative NDVI anomalies appeared in the Northeast China and Southwest China, while positive NDVI anomalies were observed in Southeast China and Northern central region. Drought interval, intensity and severity contributed approximately 80 % of the model's explained vegetation variance in most regions. The sensitivity of vegetation anomalies to drought events (VASD) varied regionally in China. The Qinghai-Tibet Plateau and Northeast China tended to exhibit higher sensitivity to drought events. Vegetation in these regions with high sensitivity faced a high risk of degradation and could function as warning signals of vegetation degradation. Drought events at high timescales had a greater impact on vegetation sensitivity in dry zones, while they had a smaller impact on humid areas. With the increase in drought degree of climate zones and the decrease in vegetation coverage, VASD showed a gradual increase. Furthermore, a strong negative correlation between VASD and the aridity index (AI) was observed in all vegetation types. The change in VASD for sparse vegetation was the largest with the change in AI. For vegetation phenology, drought events in most regions delayed the end of the growing season and extended the length of growing season, especially for sparse vegetation. The start of the growing season was advanced in most humid areas, while being delayed in most dry areas during drought events. Knowledge of vegetation sensitivity to drought events will be beneficial to provide decision-making references for the prevention and control of vegetation degradation, especially in the ecological fragile regions.

A Low-Cost Approach for Batch Separation, Identification and Quantification of Microplastics in Agriculture Soil.

An increasing trend of research on microplastics (MPs) pollution in soil requires plenty of accurate data on MPs occurrence in soil samples. Efficient and economical methods of obtaining MP data are in development, especially for film MPs. We focused on MPs originating from agricultural mulching films (AMF) and presented an approach that can separate MPs in batches and identify them quickly. It mainly includes separation by ultrasonic cleaning and centrifugation, digestion of organic matter, and an AMF-MPs identification model. Adding olive oil or n-hexane to saturated sodium chloride constituted the best combination of separation solutions. Controlled experiments proved that the optimized methods improved the efficiency of this approach. The AMF-MPs identification model provides specific characteristics of MPs and can identify MPs efficiently. Evaluation results showed that the mean MP recovery rate reached 95%. The practical application demonstrated that this approach could conduct MPs analysis in soil samples in batches with less time and low cost.

[Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.] / é’è—é«˜åŽŸå ¸åž‹äººå·¥æž—å¹¼æ ‘ç”Ÿç‰©é‡æ¨¡åž‹æž„å»º.

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model (R2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

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.

Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia.

Land degradation has become one of the most critical environmental and socioeconomic issues in the world, particularly in Central Asia. Moreover, the realization of Land Degradation Neutrality (LDN) in Central Asia faces enormous challenges in achieving the global Sustainable Development Goal 15.3 (SDG 15.3). It is critical to monitor land degradation and assess its drivers in Central Asia. In this study, an Optimal Land Degradation Index (OLDI) was established as a new index for monitoring land degradation using a constrained optimization algorithm. The spatiotemporal characteristics of LDN were monitored in Central Asia. Further analysis explored the driving force of land degradation in different areas. The results showed that 7.22% and 15.33% of the total land area exhibited land improvement and land degradation, respectively. According to abrupt change analysis, mutation changes in the OLDI were observed in 2005, 2012 and 2015. At the subnational scale, most regions in Central Asia have not achieved the goal of LDN. The residual analysis highlighted the drivers of spatial differences in land degradation performance in Central Asia. Drought was the main driving force affecting land degradation by the compound effect of decreased precipitation and increased temperature on the Ustyurt Plateau, while 24.01% of the land degradation areas resulted from anthropogenic disturbances and were mainly distributed in the areas surrounding the Aral Sea. The results also indicated that 72.56% of the land improvement areas resulted from human activities and were mainly concentrated in the Balkhash Lake Delta and the Amudarya Delta. In Central Asia, the realization of SDG 15.3 by 2030 remains a severe challenge. Restoration measures should be prioritized in land degradation areas in Central Asia to implement the LDN initiative, especially around the Aral Sea.

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.

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan.

The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad. The overall accuracy (OA) for the image classification of two different datasets for the three cities was between 82% and 93%, and the kappa coefficients (KCs) were approximately 77% and 91%. The Landsat images with other supplementary data showed positive urban growth in all of the cities. The GDP, industrial growth, and urban population growth were driving factors of impervious surface sprawl in these cities from 1993 to 2017.Landscape Expansion Index (LEI) results also provided good evidence for the change of impervious surfaces during the study period. The results emphasize the idea of applying future planning and sustainable urban development procedures for sustainable use of natural resources and their management, which will increase life quality in urban areas and environments.

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta.

Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD's downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran's I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990-2030).

Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes' monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites. After digital image processing, the object-oriented image classification approach was applied. The ANN method with MOLUSCE Plugin was used to simulate the LCLU changes in 2020, 2025, and 2030. GIS has also been used to calculate the distance from the road and water and etc. The simulation results of 2010 and 2015 were validated using classification data with Kappa coefficient. The results showed high accuracy of the classification and prediction as the validation of simulated 2010 and 2015 maps to the referenced maps have high accuracy of Kappa 84 and 88%, respectively. The results revealed that the land cover classes forest-, grass-, wet-, and barren land have been decreased from 50.01, 13.06, 8.24, and 1.06% in 1990 to 32.03, 3.06, 6.26, and 0.97% in 2015, respectively, while the land use classes, crop or farm land, and urban land have been increased almost double from 25.5 and 2.13% in 1990 to 53.71 and 3.86% from the total area in 2015, respectively. For the prediction, forest- and wetlands will loss more than half of their areas by 2030, the grass land will be cleared completely to be only 1.3% from the total study area, while the urban land will be increased to be 4.4% or the double of 1990. These results are attributed to population growth and expanding of agriculture land on the grass land, but the effect of climate was weak as the rainfall increased during the study period. Causes and effects of the LCLU changes were briefly discussed. The output of the study serves as useful tools for policy and decision makers combatting natural resources misused in arid lands.

Identifying climate change impacts on water resources in Xinjiang, China.

Water resources have an important role in maintaining ecological fuctions and sustaining social and economic development. This is especially true in arid and semi-arid areas, where climate change has a large impact on water resources, such as in Xinjiang, China. Using a combination of precipitation and temperature bias correction methods, we analyzed projected changes in different hydrological components in nine high-alpine catchments distributed in Xinjiang using the Soil and Water Assessment Tool (SWAT). The impacts of elevation, area and aspect of the catchments were analyzed. The results suggested an overall warming and wetting trend for all nine catchments in the near future, with the exception of summer precipitation decreasing in some catchments. The total runoff discharge, evapotranspiration and snow/ice melting will generally increase. Warming temperature plays a more important role in the changes of each hydrological component than increasing precipitation. However, northern Xinjiang was more sensitive to predicted precipitation changes than southern Xinjiang. These results also indicate that the overall increases in water resources are not sustainable, and the impacts of climate change are associated with the elevation, area and slope aspect of the catchments.

Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982-2015.

In recent decades, climate change and human activities have severely affected grasslands in Central Asia. Grassland regulation and sustainability in this region require an accurate assessment of the effects of these two factors on grasslands. Based on the abrupt change analysis, linear regression analysis and net primary productivity (NPP), the spatiotemporal patterns of grassland ecosystems in Central Asia during 1982-2015 were studied. Further, the potential NPP (NPPP) was estimated using the Thornthwaite Memorial model and the human-induced NPP (NPPH), which was the difference between NPPP and actual NPP, were used to differentiate the effects of climate change and human activities on the grassland ecosystems, respectively. The grassland NPP showed a slight upward trend during 1982-2015, while two obvious decreasing periods were found before and after the mutation year 1999. Additionally, the main driving forces of the grassland NPP variation for the two periods were different. During 1982-1999, climate change was the main factor controlling grassland NPP increase or decrease, and 84.7% of grasslands experienced NPP reduction, while the regions experiencing an increase represented only 15.3% of the total area. During 1999-2015, the areas of increasing and decreasing grassland NPP represented 41.6% and 58.4% of the total area, respectively. After 1999, human activities became the main driving force of the NPP reduction, whereas climate change facilitated grassland restoration. The five Central Asian countries showed widely divergent relative impacts of climate change and human activities on NPP changes. In Uzbekistan and Turkmenistan, anthropogenic decreases in grassland NPP intensified during 1982-2015, while the negative anthropogenic effects on grassland NPP in Kyrgyzstan and Tajikistan moderated. Further analysis identified precipitation as the major climatic factor affecting grassland variation in most areas of Central Asia and overgrazing as the main form of human activity accelerating grassland degradation. This study improves the understanding of the relative impacts of climate change and human activities on grasslands in Central Asia.

Spatiotemporal transition of institutional and socioeconomic impacts on vegetation productivity in Central Asia over last three decades.

Central Asia experienced substantial institutional and socioeconomic changes during the last few decades, especially the Soviet Union collapse in 1991. It remains unclear how these profound changes impacted vegetation productivity across space and time. This study used the satellite-derived normalized difference vegetation index (NDVI) and gridded climate data to examine the institutional and socioeconomic impacts on vegetation productivity in Central Asia in 1982-2015. The improved Residual Trend (ResTREND) algorithm was used to calculate NDVI residuals (NDVIres) that reflect the impacts of human factors by excluding the influences of multiple climate factors. Our results showed that 45.7% of the vegetated areas experienced significant transitions (p < 0.05) in NDVIres with turning point (TP), of which 83.8% occurred after 1992 except for the Aral Sea Basin. During the pre-TP period, positive NDVIres (i.e., positive impact) and increasing trends (i.e., positive tendency) were predominant, accounting for 31.6% and 16.5% of the vegetated land, respectively. This was attribute to the expanded cultivation due to Virgin Lands Campaign in North Kazakhstan region and the Amu Darya and Syr Darya Basins. However, the institutional and socioeconomic changes largely suppressed vegetation productivity. In the post-TP period, only 7.0% of the vegetated lands experienced an increasing trend in NDVIres, while NDVIres decline accounted for 20.1% of the vegetated areas (p < 0.05), mainly distributed in northern Kazakhstan and large areas in the Amu Darya and Syr Darya Basins. Positive transitions resulted from the changes in crop types, decreases in grazing pressure, and increases in water resources, whereas negative transitions were coincident with areas that saw land abandonment, water resource shortages, and soil salinization due to former intensive cultivation. These findings highlight the spatiotemporal changes of institutional and socioeconomic impacts on vegetation productivity in Central Asian dryland and provide implications for future dryland management and restoration efforts.

Monitoring land sensitivity to desertification in Central Asia: Convergence or divergence?

In Central Asia, desertification risk is one of the main environmental and socioeconomic issues; thus, monitoring land sensitivity to desertification is an extremely urgent issue. In this study, the combination of convergence patterns and desertification risk is advanced from a technical perspective. Furthermore, the environmentally sensitive area index (ESAI) method was first utilized to monitor the risk of desertification in Central Asia. In the study, the spatial and temporal patterns of desertification risk were illustrated from 1992 to 2015 using fourteen indicators, including vegetation, climate, soil and land management quality. The ESAI spatial convergence across administrative subdivisions was explored for three time intervals: 1992-2000, 2000-2008 and 2008-2015. The results indicated that nearly 13.66% of the study area fell into the critical risk of desertification from 1992 to 2008. However, the risk of desertification has improved since 2008, with critical classifications decreasing by 19.70% in 2015. According to the mutation year detection in the ESAI, 25.89% of the pixels with mutation years from 1992 to 2000 were identified, and this value was higher than that during the other time periods. The convergence analysis revealed that the desertification risk for 1992-2000 tended to diverge with a positive convergence coefficient of 0.13 and converge over the 2000-2008 and 2008-2015 time periods with negative convergence coefficients of -0.534 and -0.268, respectively. According to the spatial convergence analysis, we found that the divergence patterns in northern Central Asia from 1992 to 2000 resulted from the effects of the Soviet Union collapse: cropland abandonment in northern Kazakhstan and rangeland abandonment in Tajikistan, Kyrgyzstan and eastern Kazakhstan. In contrast, most areas from 2000 to 2008 experienced increased sensitivity to desertification with the convergence pattern caused by decreased precipitation, especially in northern Central Asia. However, convergence patterns were found in most regions for 2008-2015 with regard to augmented precipitation, which resulted in decreased sensitivity to desertification. Moreover, the low sensitivity areas were more likely to converge under increased precipitation. In this region, the findings of our study suggested that spatial convergence and divergence acted as related predictors of climate change and human activities, respectively. Thus, the ESAI convergence analysis was considered to provide an early warning of potential desertification.

Spatial and temporal characteristics of droughts in Central Asia during 1966-2015.

In drought-prone regions like Central Asia, drought monitoring studies are paramount to provide valuable information for drought risk mitigation. In this paper, the spatiotemporal drought characteristics in Central Asia are analyzed from 1966 to 2015 using the Climatic Research Unit (CRU) dataset. Drought events, as well as their frequency, duration, severity, intensity and preferred season, are studied by using the Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month, 6-month, and 12-month timescales. The Principle Components Analysis (PCA) and the Varimax rotation method, the Sen's slope and the Modified Mann-Kendall method (MMK), as well as the wavelet analysis are adopted to identify the sub-regional drought patterns and to study the drought trend, periodicity and the possible links between drought variation and large-scale climate patterns, respectively. Results show that the drought characteristics in Central Asia vary considerably. The Hexi Corridor region and the southeastern part suffered from more short-term drought occurrences which mostly occurred in summer while the northeastern part experienced fewer droughts with longer duration and higher severity. Central Asia showed an overall wetting trend with a switch to drying trend since 2003. Regionally, the continuous wetting trend is found in north Kazakhstan while a consistent drying in the Aral Sea and Hexi Corridor region is observed in the last half-century. For 2003-2015, a significant drying pattern is detected in most Central Asia, except the northern Kazakhstan. A common significant 16-64-month periodical oscillation can be detected over the six sub-regions. The drought changes in Central Asia are highly associated with ENSO but less related to the Tibetan Plateau pressure. The North Atlantic Oscillation has an influence on drought change in most Central Asia but less for the Hexi Corridor and the drought variation in eastern Central Asia is affected by the strength of the Siberian High.

Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014.

Against the background of global climate change, spatial-temporal variation in net primary productivity (NPP) has attracted much attention. To analyze NPP spatial-temporal variation within the context of changes in hydrothermal conditions, the Vegetation Photosynthesis Model (VPM) is used to elucidate the mathematical relationship between NPP and hydrothermal conditions. Based on this spatial-temporal pattern of NPP and hydrothermal conditions in the Lancang-Mekong River Basin, regression statistics, an empirical model of land evaporation, and the water and thermal product index (K) are used to evaluate correlations between NPP and hydrothermal conditions in terms of their distribution pattern and interaction. The results show the following. (1) From 2000 to 2014, NPP in the Lancang-Mekong River Basin was highest in the central region and gradually decreased toward the southwest and northwest, whereas the annual change rate in NPP showed no significant increasing trend. (2) In the Lancang Basin, the correlation between hydrothermal conditions and NPP was high with respect to their distribution patterns, though this correlation was low in the Mekong Basin. (3) Correlation between K and NPP is high in the region where the effects of water and thermal factors on vegetation growth are similar. (4) K is an effective complement to the correlation between a single hydrothermal factor (temperature or precipitation) and NPP, and the influence of hydrothermal conditions on NPP was positive in the Lancang River and negative in the Mekong River Basin. Our study quantitatively analyzes the spatial-temporal correlation between NPP and hydrothermal conditions. The findings can reflect the vegetation change tendency and provide scientific data for ecological environment development and protection in the study area.

Vegetation dynamics and responses to climate change and human activities in Central Asia.

Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984-2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method. The results show that vegetation pixels significantly decreased for shrubs and sparse vegetation compared with those for the other vegetation types and that the degradation of sparse vegetation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Large Aral Sea than in other regions. The Hurst exponent results indicated that forests are more sustainable than grasslands, shrubs and sparse vegetation. Precipitation is the main factor affecting vegetation growth in the Kazakhskiy Melkosopochnik. Moreover, temperature is a controlling factor that influences the seasonal variation of vegetation greenness in the mountains and the Aral Sea basin. Drought is the main factor affecting vegetation degradation as a result of both increased temperature and decreased precipitation in the Kyzylkum Desert and the northern Ustyurt Plateau. The residual analysis highlighted that sparse vegetation and the degradation of some shrubs in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea were mainly triggered by human activities: the excessive exploitation of water resources in the upstream areas of the Amu Darya basin and oil and natural gas extraction in the southern part of the Karakum Desert and the southern Ustyurt Plateau. The results also indicated that after the collapse of the Soviet Union, abandoned pastures gave rise to increased vegetation in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and abandoned croplands reverted to grasslands in northern Kazakhstan, leading to a decrease in cropland greenness. Shrubs and sparse vegetation were extremely sensitive to short-term climatic variations, and our results demonstrated that these vegetation types were the most seriously degraded by human activities. Therefore, regional governments should strive to restore vegetation to sustain this fragile arid ecological environment.

Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants.

To examine the influence of coal dust from mining on vegetative growth, three typical plants from near an open-pit coalmine in an arid region were selected, and their spectral signals were determined. The present study was conducted near the Wucaiwan open-pit coalmine in the East Junggar Basin in Xinjiang. We extracted nineteen vegetation indices and examined their correlation with the dust flux. The objective was to determine which parameters that quantify vegetation damage could provide a basis for environmental monitoring in arid regions. The results indicate that when coal dust damages vegetation, both chlorophyll and moisture are reduced, and the amount of carotenoids increases with increasing coal dust. The pigment-specific normalized difference (PSNDb), structure-insensitive pigment index (SIPI) and plant water index (PWI) were the most sensitive indices, and sacsaoul was most sensitive to coal-dust pollution.