The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the "Aral Sea ecological crisis". The ecological problems of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries (Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index (NDVI), land cover, and aerosol optical depth (AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. We divided the Aral Sea into 7 first-level ecological restoration subregions (North Aral Sea catchment area in the downstream of the Syr Darya River (Subregion I); artificial flood overflow area in the downstream of the Aral Sea (Subregion II); physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea (Subregion III); physical/chemical remediation area of severe salinization in the central part of the South Aral Sea (Subregion IV); existing water surface and potential restoration area of the South Aral Sea (Subregion V); Aral Sea vegetation natural recovery area (Subregion VI); and vegetation planting area with slight salinization in the South Aral Sea (Subregion VII)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion I and Subregion II with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion III and Subregion IV have severe salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion V located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion VI and Subregion VII where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person's correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data were slightly lower than the best precipitation product (CMFD), the overall performance of DBMA was more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

The isotope composition in precipitation has been widely considered as a tracer of monsoon activity. Compared with the coastal region, the monsoon margin usually has limited precipitation with large fluctuation and is usually sensitive to climate change. The water resource management in the monsoon margin should be better planned by understanding the composition of precipitation isotope and its influencing factors. In this study, the precipitation samples were collected at five sampling sites (Baiyin City, Kongtong District, Maqu County, Wudu District, and Yinchuan City) of the monsoon margin in the northwest of China in 2022 to analyze the characteristics of stable hydrogen (δD) and oxygen (δ¹⁸O) isotopes. We analyzed the impact of meteorological factors (temperature, precipitation, and relative humidity) on the composition of precipitation isotope at daily level by regression analysis, utilized the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)-based backward trajectory model to simulate the air mass trajectory of precipitation events, and adopted the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) to analyze the water vapor sources. The results showed that compared with the global meteoric water line (GMWL), the slope of the local meteoric water line (LMWL; δD=7.34δ¹⁸O-1.16) was lower, indicating the existence of strong regional evaporation in the study area. Temperature significantly contributed to δ¹⁸O value, while relative humidity had a significant negative effect on δ¹⁸O value. Through the backward trajectory analysis, we found eight primary locations that were responsible for the water vapor sources of precipitation in the study area, of which moisture from the Indian Ocean to South China Sea (ITSC) and the western continental (CW) had the greatest influence on precipitation in the study area. The hydrogen and oxygen isotopes in precipitation are significantly influenced by the sources and transportation paths of air mass. In addition, the results of PSCF and CWT analysis showed that the water vapor source areas were primarily distributed in the south and northwest direction of the study area.

Land surface temperature (LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones (the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami (Tuha) Basin, and Pakakuna Mountain Group) using geographical detector (Geodetector) and geographically weighted regression (GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24°C/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09°C/10a in autumn, and showed a warming trend in other seasons. Digital elevation model (DEM), latitude, wind speed, precipitation, normalized difference vegetation index (NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover (LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Improving cultivated land use eco-efficiency (CLUE) can effectively promote agricultural sustainability, particularly in developing countries where CLUE is generally low. This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model. In addition, given the undesirable outputs of agricultural production, we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure (GB-US-SBM) model, which combines global benchmark technology, undesirable output, super efficiency, and slacks-based measure. The results indicated that there was an upward trend in CLUE in China from 2000 to 2020, with an increase rate of 2.62%. The temporal evolution of CLUE in China could be classified into three distinct stages: a period of fluctuating decrease (2000-2007), a phase of gradual increase (2008-2014), and a period of rapid growth (2015-2020). The major grain-producing areas (MPAs) had a lower CLUE than their counterparts, namely, non-major grain-production areas (non-MPAs). The spatial agglomeration effect followed a northeast-southwest strip distribution; and the movement path of barycentre revealed a "P" shape, with Luoyang City, Henan Province, as the centre. In terms of influencing factors of CLUE, investment in science and technology played the most vital role in improving CLUE, while irrigation index had the most negative effect. It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs. Therefore, relevant departments should formulate policies to enhance the level of science and technology, improve irrigation condition, and promote sustainable utilization of cultivated land.

In the Loess Plateau of China, land-use pattern is a major factor in controlling underlying biological processes. Additionally, the process of land-use pattern was accompanied by abandoned lands, potentially impacting soil microbe. However, limited researches were conducted to study the impacts of land-use patterns on the diversity and community of soil microorganisms in this area. The study aimed to investigate soil microbial community diversity and composition using high-throughput deoxyribonucleic acid (DNA) sequencing under different land-use patterns (apricot tree land, apple tree land, peach tree land, corn land, and abandoned land). The results showed a substantial difference (P<0.050) in bacterial alpha-diversity and beta-diversity between abandoned land and other land-use patterns, with the exception of Shannon index. While fungal beta-diversity was not considerably impacted by land-use patterns, fungal alpha-diversity indices varied significantly. The relative abundance of Actinobacteriota (34.90%), Proteobacteria (20.65%), and Ascomycota (77.42%) varied in soils with different land-use patterns. Soil pH exerted a dominant impact on the soil bacterial communities' composition, whereas soil available phosphorus was the main factor shaping the soil fungal communities' composition. These findings suggest that variations in land-use pattern had resulted in changes to soil properties, subsequently impacting diversity and structure of microbial community in the Loess Plateau. Given the strong interdependence between soil and its microbiota, it is imperative to reclaim abandoned lands to maintain soil fertility and sustain its function, which will have significant ecological service implications, particularly with regards to soil conservation in ecologically vulnerable areas.

One of the goals of grazing management in the desert steppe is to improve its ecosystem. However, relatively little is known about soil microbe communities in the desert steppe ecosystem under grazing management. In this study, we investigated the diversity and aboveground biomass of Caragana korshinskii Kom. shrub communities in long-term fencing and grazing areas, combined with an analysis of soil physical-chemical properties and genomics, with the aim of understanding how fence management affects plant-soil-microbial inter-relationships in the desert steppe, China. The results showed that fence management (exclosure) increased plant diversity and aboveground biomass in C. korshinskii shrub area and effectively enhanced soil organic carbon (233.94%), available nitrogen (87.77%), and available phosphorus (53.67%) contents. As well, the Shannon indices of soil bacteria and fungi were greater in the fenced plot. Plant-soil changes profoundly affected the alpha- and beta-diversity of soil bacteria. Fence management also altered the soil microbial community structure, significantly increasing the relative abundances of Acidobacteriota (5.31%-8.99%), Chloroflexi (3.99%-5.58%), and Glomeromycota (1.37%-3.28%). The soil bacterial-fungal co-occurrence networks under fence management had higher complexity and connectivity. Based on functional predictions, fence management significantly increased the relative abundance of bacteria with nitrification and nitrate reduction functions and decreased the relative abundance of bacteria with nitrate and nitrite respiration functions. The relative abundances of ecologically functional fungi with arbuscular mycorrhizal fungi, ectomycorrhizal fungi, and saprotrophs also significantly increased under fence management. In addition, the differential functional groups of bacteria and fungi were closely related to plant-soil changes. The results of this study have significant positive implications for the ecological restoration and reconstruction of dry desert steppe and similar areas.

Nitrogen (N) deposition is a significant aspect of global change and poses a threat to terrestrial biodiversity. The impact of plant-soil microbe relationships to N deposition has recently attracted considerable attention. Soil microorganisms have been proven to provide nutrients for specific plant growth, especially in nutrient-poor desert steppe ecosystems. However, the effects of N deposition on plant-soil microbial community interactions in such ecosystems remain poorly understood. To investigate these effects, we conducted a 6-year N-addition field experiment in a Stipa breviflora Griseb. desert steppe in Inner Mongolia Autonomous Region, China. Four N treatment levels (N0, N30, N50, and N100, corresponding to 0, 30, 50, and 100 kg N/(hm²•a), respectively) were applied to simulate atmospheric N deposition. The results showed that N deposition did not significantly affect the aboveground biomass of desert steppe plants. N deposition did not significantly reduce the alfa-diversity of plant and microbial communities in the desert steppe, and low and mediate N additions (N30 and N50) had a promoting effect on them. The variation pattern of plant Shannon index was consistent with that of the soil bacterial Chao1 index. N deposition significantly affected the beta-diversity of plants and soil bacteria, but did not significantly affect fungal communities. In conclusion, N deposition led to co-evolution between desert steppe plants and soil bacterial communities, while fungal communities exhibited strong stability and did not undergo significant changes. These findings help clarify atmospheric N deposition effects on the ecological health and function of the desert steppe.