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Climate change forecasts indicate that the frequency and intensity of extreme climate events will increase in the future; these changes will have important effects on ecosystem stability and function. An important arid region of the world, Central Asia has ecosystems that are extremely vulnerable to extreme weather events. However, few studies have investigated the resistance and resilience of this region's ecosystems to extreme weather events. In this study, first, the extreme drought/wet threshold was calculated based on the 113-year (1901–2013) standardized precipitation–evapotranspiration index (SPEI); second, moderate resolution imaging spectroradiometer (MODIS) remote sensing data were applied to calculate ecosystem water use efficiency (WUE) and quantify ecosystem resistance and resilience after different extreme climate events; and finally, differences in the changes of various ecosystem types before and after climate events were assessed. The results showed the following: (1) The average SPEI was 0.073, and the thresholds of extreme wetness and drought were 0.91 and −0.67, respectively. Central Asia experienced extreme wet periods in 2002 and 2003 and a drought period in 2008. (2) Suitable wetness levels can increase the resistance of an ecosystem; however, continuous wetness reduces ecosystem resistance, as does drought. Wet areas had strong resilience after wet events, and arid areas had strong resilience after drought events. (3) During both wet and drought years, the transition between shrubland and grassland caused changes in ecosystem resistance and resilience. These findings are important for understanding the impact of future climate change on ecosystem stability. [ABSTRACT FROM AUTHOR]

Although understanding the carbon and water cycles of dryland ecosystems in terms of water use efficiency (WUE) is important, WUE and its driving mechanisms are less understood in Central Asia. This study calculated Central Asian WUE for 2001–2021 based on the Google Earth Engine (GEE) platform and analyzed its spatial and temporal variability using temporal information entropy. The importance of atmospheric factors, hydrological factors, and biological factors in driving WUE in Central Asia was also explored using a geographic detector. The results show the following: (1) the average WUE in Central Asia from 2001–2021 is 2.584–3.607 gCkg−1H2O, with weak inter-annual variability and significant intra-annual variability and spatial distribution changes; (2) atmospheric and hydrological factors are strong drivers, with land surface temperature (LST) being the strongest driver of WUE, explaining 54.8% of variation; (3) the interaction of the driving factors can enhance the driving effect by more than 60% for the interaction between most atmospheric factors and vegetation factors, of which the effect of the interaction of temperature (TEM) with vegetation cover (FVC) is the greatest, explaining 68.1% of the change in WUE. Furthermore, the interaction of driving factors with very low explanatory power (e.g., water pressure (VAP), aerosol optical depth over land (AOD), and groundwater (GWS)) has a significant enhancement effect. Vegetation is an important link in driving WUE, and it is important to understand the mechanisms of WUE change to guide ecological restoration projects. [ABSTRACT FROM AUTHOR]

Ecosystem water use efficiency (WUE) and ecosystem photosynthetic efficiency (EPE) are key indicators in studies of the carbon–water cycle in terrestrial ecosystems. Analyses of WUE and EPE can enhance our understanding of the relationship between ecosystem light use efficiency and WUE. Although several studies of individual indexes (i.e., either WUE or EPE) have been conducted, analyses of variation in both WUE and EPE, as well as their relationships, have rarely been conducted. Here, we analyzed spatial and temporal variation in WUE and EPE in Central Asia. Specifically, time trend analysis was conducted to characterize temporal and spatial changes in WUE and EPE in Central Asia from 2001 to 2020 at different altitudes and latitudes. Pearson correlation analysis was used to characterize the effects of precipitation and temperature on WUE and EPE. WUE decreased and EPE increased in Central Asia over the 20-year study period; this might have been due to interannual variations in precipitation and temperature. WUE was highest in August, and EPE was highest in June and July. Substantial spatial heterogeneity in WUE and EPE was observed; WUE was highly variable in Central Asia as well as in western and southern Central Asia. Major changes in EPE were observed in northern, eastern, and southern Central Asia. We also found that both WUE and EPE decreased with the increase in altitude. WUE was positively correlated with temperature and negatively correlated with precipitation, whereas EPE was positively correlated with both temperature and precipitation. The increase in photosynthetic efficiency might be one of the main factors contributing to increases in ecosystem productivity in arid environments. The temporal and spatial variation in WUE and EPE observed in our study will aid ecosystem research, providing a reliable theoretical basis for ecosystem research in areas with scarce large-scale data, integrated water resources management, and ecosystem restoration efforts. Our findings also enhance our understanding of the terrestrial carbon–water cycle and have implications for predicting ecosystem responses to climate change. The results of this study provide insights that will aid studies of the terrestrial carbon–water cycle under the background of climate change. It is of great significance to further study the carbon water cycle in the future. [ABSTRACT FROM AUTHOR]

Aerosols significantly contribute to global and regional climate change by altering the surface solar radiation, thereby affecting plant productivity. Central Asia is a primary source of global dust aerosols. However, the mechanisms of how aerosols affect terrestrial gross primary productivity (GPP), especially in Central Asia, are not clearly understood. In this study, we investigated the spatial variation in aerosol optical depth (AOD) and GPP and the relationship between them during the growing season (April–October) from 2001 to 2018 using remote sensing data from several sources. We created a GWR-SEM model consisting of a geographically weighted model (GWR) coupled with a structural equation model (SEM) to quantify and analyze the effects of AOD on GPP. The results show that AOD decreased slightly at a rate of −0.0002 y−1 during the study period and that there was a tendency towards spatial aggregation. The extent of AOD pollution in the northwest region (around the Aral Sea) was slightly greater than that in the southeast. GPP increased significantly at a rate of 7.2965 g C m−2 y−2, especially in the northern region. There were some differences in the effects of AOD on GPP between different vegetation types; the highest AOD–GPP correlation was found in shrublands and croplands. Analysis of the GWR-SEM model suggested that AOD and two forms of radiation (surface net radiation, SNR, and photosynthetically active radiation, PAR) explained 72.4% (63.4% for 2001, 66.8% for 2018) of the spatial variation in GPP. SNR had the greatest effect on GPP, followed by AOD. Diffuse PAR had the greatest indirect effect on GPP. The findings of this study highlight the importance of aerosol pollution on spatial variation in gross primary productivity, and they provide a methodological framework for investigating the relationship between AOD and GPP in arid areas. [Display omitted] • Spatial and temporal variations of AOD and GPP over Central Asia are evaluated against MODIS products. • The correlation between AOD and GPP over Central Asia are revealed. • A GWR-SEM model is used to quantitatively evaluate aerosol effects on GPP. • The major drivers of GPP change with time and vegetation types. [ABSTRACT FROM AUTHOR]

Aerosols are an important contributor to global atmospheric environmental changes and have critical effects on the global climate system and human health. Central Asia is one of the most important sources of dust aerosols in the world and produces a significant portion of global aerosols. Central Asia is also a scarce aerosol-data area, so it is of great significance to obtain and verify new aerosol data from this region. In this study, based on the aerosol optical depth (AOD) data from remote sensing (MYD04_L2) and ground-based observations (AERONET and Microtops II), the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD in Central Asia over the last 20 years was analyzed by the information entropy method. The results indicate that MAIAC AOD has good application prospects in Central Asia and can effectively compensate for the lack of observational data from Central Asia. MAIAC AOD exhibits excellent spatiotemporal consistency with MYD04 deep blue (DB) AOD and has a better ability than MYD04 DB AOD to describe local fine-scale features. Furthermore, MAIAC AOD demonstrates high consistency with ground-based AOD observations, showing high R (0.737) and low RMSE (0.067) values and having 65.2% of samples falling within the expected error (EE) envelope. When employing the ground-based AOD observations as a bridge, MAIAC exhibits superiority to MYD04 DB in both the richness number of valid high-quality retrievals and the retrieval accuracy of various evaluation indicators. The annual variation in AOD in Central Asia exhibits a unimodal distribution, with AOD being largest in April, followed by March and May, and comparable rangeability. Based on information entropy, interannual variation in AOD exists in most areas of Central Asia, with AOD in the Taklimakan Desert area being significantly increased and that in northern Central Asia (Kazakhstan) showing a downward trend. • MAIAC and MYD04 DB AODs exhibit good spatiotemporal consistency over Central Asia. • A good agreement between the AOD values from MAIAC AOD and AERONET was found. • Spatial and temporal variations are evaluated against MAIAC products. • The AOD annual variation in Central Asia is characterized by a unimodal distribution. [ABSTRACT FROM AUTHOR]

Aerosols are an important contributor to global atmospheric changes and have critical effects on the climate system. Regionally, aerosols in central Asia comprise a significant portion of global aerosols. Based on aerosol optical depth (AOD)Level 2 daily product data and land cover type product data, the long-term AOD characteristics of six major land use/cover types and their relationships with landscape metrics are discussed. Contribution analysis is applied to quantitatively estimate the effects of land use/cover on regional AOD over central Asia. The results show that series of daily AODs over six land uses/covers display remarkable annual cyclic variations and obvious seasonal changes. The annual average AODs for barren land and cropland are highest, followed by regional AODs. There are different frequencies and times of occurrence for high AOD values of various land types. Urban areas are one of the major contributors to the regional atmosphere in winter; grasslands have a great influence on regional AOD decreases. Barren land always has a high contribution to the regional AOD. The land use types affected by anthropogenic activities were smaller contributors to regional aerosols than barren lands affected by climate factors. This paper advances the understanding of relationship between aerosols and land use/cover and facilitates land use decision making. Unlabelled Image • Daily AOD data of six land types are obtained from 2007 to 2017 • Variation characteristics of six major land use/cover types AOD are revealed • Frequencies and times of occurrence for high AOD values vary in diverse land types • Further quantitative calculations show the LUCCs contributors to the regional AOD [ABSTRACT FROM AUTHOR]

It is important to understand the carbon-water cycle, which accurately reflects the temporal and spatial variabilities in ecosystem water use efficiency (WUE). In this study, the Mann-Kendall (MK) test was used to study the variabilities in the spatial patterns of the gross primary production (GPP), evapotranspiration and WUE across Central Asia [the Xinjiang Uyghur Autonomous Region (XJ) in China (CHN), Kazakhstan (KAZ), Turkmenistan (TKM), Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK)] from 2000 to 2014. We compared the change results by country, land cover type, population density, and human influence. In addition, the results of GPP, evapotranspiration (ET), and WUE parameter tests were combined and classified to analyse the causes of the changes in WUE. The results showed that (1) the time series of GPP, ET and WUE exhibited no significant changes. The spatial distribution of the WUE exhibited significant increases in the northern part of KAZ, the Ili Valley and the alpine region in KGZ and exhibited decreases in south Xinjiang and the irrigated area of UZB. (2) The main land cover types that exhibited changes in WUE were farmlands and grasslands, and areas with a medium population density exhibited large WUE changes. (3) The increased WUE resulted from an increased GPP and decreased ET. The increased GPP was because of increased precipitation and the Green for Grain Project, and the decreased ET was due to the response of vegetation to drought stress; the decreased WUE was mainly caused by changes in the crops planted and unreasonable water use practices in the irrigated agricultural areas in Central Asia. This study, which is based on the variabilities in WUE spatial patterns, should provide a theoretical basis for ecosystems in arid land areas. • The spatial-temporal pattern of WUE was examined in Central Asia using multiple remote sensing data. • A new WUE classification method using Mann-Kendall non-parametric estimation. • Farmland and grassland, and the medium population density areas had the great changes of WUE. • The decreased WUE were due to the shifts in crop type and unreasonable water use practices. [ABSTRACT FROM AUTHOR]