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Aerosol optical depth (AOD) is an important physical variable used to characterize atmospheric turbidity for the management and control of air pollution. This study aims to analyze the factors influencing the spatial and temporal variability in AOD across the arid region of Northwest China (ARNC) using MODIS Aqua C006 aerosol product data from 2008 to 2017. In terms of natural and socioeconomic factors, the correlation coefficient (R) was used to identify the most influential factor in the AOD changes. The results show that AOD values in spring and summer were much higher than those in autumn and winter, especially in spring. In general, AOD had an insignificant decreasing trend, with a small overall changing range. Spatial analysis revealed a significantly decreasing trend, mostly across the Gobi Desert area, which is located in the western region of the ARNC. From the perspective of natural factors, AOD was positively correlated with air temperature (AT), wind speed (WP), land surface temperature (LST), and the digital elevation model (DEM) and negatively correlated with precipitation, relative humidity (RH), and the normalized difference vegetation index (NDVI). The greatest positive correlation, with a maximum R value of 0.8, was found between AOD and wind speed. By contrast, AOD and relative humidity had the strongest negative correlation, with R values of −0.77. In terms of anthropogenic factors, gross domestic product (GDP), secondary industry, and population density were the three major anthropogenic factors that influenced the changes in AOD changes in this region. In general, the effects of anthropogenic factors on AOD are more significant in areas with high urban population densities. [ABSTRACT FROM AUTHOR]

This study develops a convolutional recurrent deep learning model to accurately predict fine‐resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation contributes to 61.8% of the total increase in grass cover (130.6 km2) in the study area (40,423 km2) over 2005–2015, nearly triple the contribution of local climate change (+23.0%). The transboundary hydrological impact (+32.4%) and interactions between drivers (−17.2%) are also significant. In an intermediate future climate change scenario, we found no statistically significant trend for the total grass‐covered area due to the counteracting effects among different drivers. The study findings suggest that timely, adaptive and spatially heterogeneous ecological flow management is crucial for addressing grassland degradation in arid regions. This study provides a promising approach to land surface modeling under climate change and human disturbance and expands the existing understanding of the global greening process. Plain Language Summary: The Earth's recent greening (i.e., vegetation increase) has boosted the terrestrial carbon sink, intensified the hydrologic cycle, and contributed to mitigating global warming. CO2 fertilization has been identified as the primary cause at the global scale, while climate change, land use changes, and nitrogen deposition also contribute. However, there are still many gaps in our understanding of how grasslands in arid areas are being affected. Studies in Central Asia identified precipitation as the dominant factor affecting grassland change and grazing as the main influence from humans. Based on satellite data, we employ a novel deep learning (DL) approach to emulate spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2 in the Gobi Desert in northwestern China. Our study identified previously overlooked mechanisms contributing to the increase in grass coverage, including ecological flow management and transboundary hydrological impact. Ecological flow management was found to be responsible for almost three times the increase in grass coverage over 2005–2015 than that of local climate changes. Our findings suggest that timely, adaptive, and spatially targeted ecological flow management is urgently needed to prevent further grassland degradation in arid regions, and DL‐based artificial intelligence can offer valuable management suggestions. Key Points: Deep learning (DL) precisely emulates spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2DL interprets ecological flow regulation (EFR) as the leading cause of grassland greening in the Gobi DesertTimely, adaptive and spatially heterogeneous ecological flow management is desired to prevent grassland degradation in arid regions [ABSTRACT FROM AUTHOR]

Using data observed at a photovoltaic (PV) power plant at the edge of the Gurbantünggüt Desert and at an undeveloped site in the Gobi desert in the summers of 2019 and 2020, we compared and analyzed the variations of radiation and surface albedo in various wavelength bands. Components of the solar radiation received by the surface of the arid regions of China were ranked infrared (IR) > visible (VIS) > ultraviolet (UV). The observed surface albedo of the PV site was significantly lower than that at the undeveloped desert site. The mean surface albedo values for shortwave broadband (SW), IR, VIS, and UV at the PV site were 0.14, 0.15, 0.13, and 0.08, respectively, and the corresponding values for the undeveloped site were 0.22, 0.23, 0.23, and 0.10, respectively. We also developed a parametric formula for the surface albedo at the PV plant given data observed on typical sunny days. We then validated the parametric formula using observations taken in the summers of 2019 and 2020. The results showed that the parametric formula could precisely simulate variations of the surface albedo and reflected radiation of the PV plant. [ABSTRACT FROM AUTHOR]

Located in the interior of Eurasia, the Mongolian Plateau (MP) is extremely sensitive to global warming and become a critical area for studying precipitation patterns. Based on the monthly data of 135 meteorological stations during 1976–2017, we analyze the spatiotemporal change in precipitation and discuss its response to atmospheric circulation. The results show that: (1) Precipitation shows increasing trends in spring, autumn, and winter, but a decreasing trend at a rate of 5.3 mm/decade in summer. The annual precipitation also shows an overall slight decreasing trend. (2) The spatial distribution is uneven, the annual precipitation in the northern Great Khingan Mountains is more, but it gradually decreases at the rate of 10–30 mm/decade, showing a trend of "wet gets dry"; while there is less in the southwest Gobi Desert region, but it gradually increases with the rate of 10–20 mm/decade, showing a trend of "dry gets wet". (3) Over decades, the East Asian summer monsoon (EASM) and westerly circulation show a seesaw change in MP. Affected by the weakening of the EASM, the area of arid regions has gradually expanded. The results also demonstrate that the EASM has a higher impact on the annual precipitation change pattern, particularly in the southeastern MP. The conclusion indicated that the variation in the position and orientation between EASM and the westerly circulation may be an explanation for the spatiotemporal precipitation pattern, providing a new viewpoint to the question of circulation mechanisms behind climate change in MP in recent 40 years. [ABSTRACT FROM AUTHOR]

Global drylands host more than USD 1 trillion in resource extraction investments, which serve to reconfigure communities and landscapes. In Mongolia's Gobi Desert mega-mining brings social challenges and environmental changes that question if nomadic herding and mining can co-exist. Whilst company and community conflict are common, nascent frameworks and mediation models suggest alternate ways to resolve the mining–community conundrum. Here we investigate environmental transformations that herders encounter in the presence of the Oyu Tolgoi mega-mine in Mongolia's Khanbogd soum (district). Using socio-economic and physical data collected through interviews, field studies and climate records, we assessed local engagement and adaptation to large-scale mining. Combining qualitative and quantitative methods enabled us to examine the implications of mining for herder lives and lands in an integrated way. This study presents a holistic assessment of the roles of herders, governments and mines in reshaping pastoralism. In our chosen case study, we find that—contrary to common narratives—mining and herding can, and do, coexist in Khanbogd soum, though ongoing challenges exist which deserve critical attention. [ABSTRACT FROM AUTHOR]

Dust storm originates from arid and semiarid areas and could cause worldwide lasting effects along varying trajectories. As an extreme weather hazard, dust storm is usually induced by strong winds. In this paper, an aerodynamic parameter, defined as the ratio between dust storm frequency (DSF) and strong wind frequency (SWF), was calculated for quantifying dust storm susceptibility (DSS). The characteristic value and temporal change of DSS in five typical land surface areas, including cultivated land, grassland, sand desert, Gobi desert, and salt crust, were analyzed with dust storm and strong wind records at 15 weather stations. The spatial distribution of DSS in the arid and semiarid regions of northern China was described using dust storm and strong wind records at 102 meteorological stations. There are positive correlations between SWF and DSF for any identical land surface type. Land surface conditions have a great influence on dust storm susceptibility. According to DSS value, the five land surface types can be divided into two categories: (1) Cultivated land and sand desert, with DSS between 0.64 and 0.53, about 2.6–10 times higher than that of the other three land surfaces, are prone to forming dust storm under strong wind conditions; (2) Gobi desert, grassland and salt crust, with average DSS from 0.06 to 0.2, exhibit lower probability of dust storm occurrence under strong wind conditions. The DSF in the surrounding areas of the arid Taklimakan desert in Xinjiang are much larger than SWF, and dust storms take place readily even at normal erosive wind conditions. The areas with high DSF are not spatially consistent with SWF distribution but more coincide with high DSS areas where sand deserts and farmland are concentrated. In the nearly four past decades, the DSS showed an overall increasing trend on different ground surfaces which may be related to local human activities and climatic warming. • There is positive correlation between strong wind and dust storm frequency • Dust storm susceptibility is the ratio between dust storm and strong wind frequency • Land surface conditions have great influences on dust storm susceptibility • Farmland, sand desert have higher dust storm susceptibility than other land surface • Dust storm frequency is spatially consistent with dust storm susceptibility [ABSTRACT FROM AUTHOR]