The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO₂ globally. In this paper, we use the AIRS CO₂ product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO₂ and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO₂ concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO₂ emission areas. The areas with high mid-tropospheric CO₂ concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO₂ emissions. The global mid-tropospheric CO₂ concentrations increased gradually (the annual average rate of increase in CO₂ concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO₂ and related mechanisms.

As the main anthropogenic greenhouse gas that contributes most to global warming, CO₂ plays an important role in climate changes in Central Asia. Due to the lack of studies of near-surface CO₂ in this region, we first confirmed the applicability of the near-surface Greenhouse Gases Observing Satellite (GOSAT) CO₂ data in Central Asia using atmospheric CO₂ concentration data from nine ground-based station observations. We then analyzed the temporal and spatial distributions of the near-surface CO₂ concentrations in Central Asia and their controlling factors using statistical analysis methods. The results show that the near-surface CO₂ concentrations are high in the western part of this region and low in the east. From June 2009 to May 2013, the near-surface CO₂ concentrations increased gradually, with the highest value being in spring and the lowest in autumn. The temporal distribution of CO₂ concentrations is mainly affected by photosynthesis, respiration, and heating. The combined effect of terrestrial ecosystems and CO₂ diffusion by wind is responsible for the higher near-surface CO₂ concentration in the northern, western, and southwestern areas of the five Central Asian countries compared to the central, eastern, and southern areas, and energy consumption and wind are the major factors that affect the heterogeneity of the spatial distribution of the CO₂ concentrations in Xinjiang.

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T_s and Normalized Difference Vegetation Index NDVI ( $T_{s_{hot/NDVI}},T_{s_{cold/NDVI}}$). Results were comprehensively validated with flux components and actual ET (ET_a) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: – desert ecosystem ET_a $\to$ R² = 0.94; oasis ecosystem ET_a $\to$ R² = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ET_a variability, with average estimates ranging from 1.24 mm ${\mathrm{d}}^{-1}$to 6.98 mm ${\mathrm{d}}^{-1}$. Additionally, instantaneous ET to NDVI (ET_ins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands.

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities.

Water users in the Amudarya River Basin in Uzbekistan are suffering severe water use competition and uneven water allocation, which seriously threatens ecosystems, as shown, for example, in the well-known Aral Sea catastrophe. This study explores the optimized water allocation schemes in the study area at the provincial level under different incoming flow levels, based on the current water distribution quotas among riparian nations, which are usually ignored in related research. The optimization model of the inexact two-stage stochastic programming method is used, which is characterized by probability distributions and interval values. Results show that (1) water allocation is redistributed among five different sectors. Livestock, industrial, and municipality have the highest water allocation priority, and water competition mainly exists in the other two sectors of irrigation and ecology; (2) water allocation is redistributed among six different provinces, and allocated water only in Bukhara and Khorezm can satisfy the upper bound of water demand; (3) the ecological sector can receive a guaranteed water allocation of 8.237–12.354 km³; (4) under high incoming flow level, compared with the actual water distribution, the total allocated water of four sectors (except for ecology) is reduced by 3.706 km³ and total economic benefits are increased by USD 3.885B.