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The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.

Lake ice phenology is an indicator of past and present climate, it is sensitive to regional and global climate change. In the past few decades, the climate of Central Asia has changed significantly due to global warming and anthropogenic activities. However, there are few studies on the lake ice phenology in Central Asia. In this study, the lake ice phenology of 53 lakes in Central Asia were extracted using MODIS daily LST products from 2002 to 2020. The results show that MODIS-extracted lake ice phenology is generally consistent with Landsat-extracted and AVHRR-extracted lake ice phenology. Generally, lakes in Central Asia start to freeze from October to December. The trends in the lake ice phenology show strong regional differences. Lakes distributed along the Kunlun Mountains show overall delayed trends in all lake ice phenology variables, while lakes located in southwestern Central Asia show clear advancing trends in the freeze-up start dates (7.06 days) and breakup end dates (6.81 days). Correlations between the phenology of lake ice and local and climatic factors suggest that the ice breakup process and the duration of its complete coverage depend more on heat, while precipitation mainly affects the freezing time of the ice. Wind speed mainly affects the time of completely frozen of ice. In general, the breakup process is more susceptible to climatic factors, while local factors have strong influences on the freeze-up process.

Inland river runoff variations in arid regions play a decisive role in maintaining regional ecological stability. Observation data of inland river runoff in arid regions have short time series and imperfect attributes due to limitations in the terrain environment and other factors. These shortages not only restrict the accurate simulation of inland river runoff in arid regions significantly, but also influence scientific evaluation and management of the water resources of a basin in arid regions. In recent years, research and applications of machine learning and in-depth learning technologies in the hydrological field have been developing gradually around the world. However, the simulation accuracy is low, and it often has over-fitting phenomenon in previous studies due to influences of complicated characteristics such as “unsteady runoff”. Fortunately, the circulation layer of Long-Short Term Memory (LSTM) can explore time series information of runoffs deeply to avoid long-term dependence problems. In this study, the LSTM algorithm was introduced and improved based on the in-depth learning theory of artificial intelligence and relevant meteorological factors that were monitored by coupling runoffs. The runoff data of the Yarkant River was chosen for training and test of the LSTM model. The results demonstrated that Mean Absolute Error (MAE) and Root Mean Square error (RMSE) of the LSTM model were 3.633 and 7.337, respectively. This indicates that the prediction effect and accuracy of the LSTM model were significantly better than those of the convolution neural network (CNN), Decision Tree Regressor (DTR) and Random Forest (RF). Comparison of accuracy of different models made the research reliable. Hence, time series data was converted into a problem of supervised learning through LSTM in the present study. The improved LSTM model solved prediction difficulties in runoff data to some extent and it applied to hydrological simulation in arid regions under several climate scenarios. It not only decreased runoff prediction uncertainty brought by heterogeneity of climate models and increased inland river runoff prediction accuracy in arid regions, but also provided references to basin water resource management in arid regions. In particular, the LSTM model provides an effective solution to runoff simulation in regions with limited data.

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.

Shortfalls in regular evapotranspiration (ET) monitoring and evaluation pose a huge challenge to agricultural water resource distribution in arid Central Asia (CA). In this study, a first detailed regional assessment of GLEAM, ERA5, MERRA2, CLSM and NOAH ET products in CA was performed by systematically implementing the triple collocation (TC) method, in which about 36,936 grid cells for each ET data (within a six-triplet design) were collocated, at 0.25° and with monthly resolutions during 2003–2020. The reliability of the strategy adopted was confirmed in four arid biomes using standard evaluation metrics (R, RMSE and BIAS), and by spatiotemporal cross-validation of the six ET triplets across CA. Results show that the systematic TC method produced more robust ET product assessment metrics with reduced RMSEs compared to the initial ET product validation using in-situ, which showed weak-positive correlation and high negative bias-range (i.e., −21.02 ≤ BIAS < 16 mm) in the four arid biomes of CA. The spatial cross-validation by TC showed that the magnitude of ET random errors significantly varies, and confirms the systematic biases with site-scale measurements. The highest ET uncertainties by CLSM (27.43%), NOAH (29.16%), MERRA2 (38.28%), ERA5 (36.75), and GLEAM (41%) were more evident in the shrubland, cropland, grassland, cropland again, and desert biomes, respectively. Moreover, error magnitudes in high altitudes (Tianshan Mountain range) are generally lower than in plain-desert areas. All ET products spatially captured ET dynamics over CA, but none simultaneously outperformed the other. These findings are invaluable in the utilization of the assessed ET products in supporting regional water resource management, particularly in CA.

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems.

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.

The accelerated global warming and heterogeneous change in precipitation have been resulting in climate system shifts, which plays a key role in the stability of ecosystem and social economic development. Central Asia is account 80% of the temperate desert, characterized by fragile ecosystem; however, it has experienced the fastest warming in recent decades and projected warming in future. The Köppen-Geiger climate classification is a useful tool to assess the potential impacts of climate change on regional ecosystem. The spatial shift and temporal evolution of each climatic zone based on Köppen-Geiger climate classification are analyzed in historical and future period under different scenarios (RCP2.6, RCP4.5 and RCP8.5), high risk regions that might experience more frequent climatic zone shifts are delimited in this study, which could provide the useful information for developing mitigate strategies in coping with the warming threat. The hotter and dryer subtypes of arid climatic zone and warmer subtypes of temperate climatic zone expanded their coverage in Central Asia, corresponding to the tundra climatic, cooler subtype of arid and temperate climatic zone contracted. Based on a method defining the climate-sensitivity, high risk regions are mainly distributed in northern Kazakhstan and Tianshan Mountains region.

The rapid oasis expansion and urbanization that occurred in Xinjiang province (China) in the last decades have greatly modified the land surface energy balance and influenced the local circulation under the arid mountains-plain background system. In this study, we first evaluated the ALARO regional climate model coupled to the land surface scheme SURFEX at 4 km resolution using 53 national climatological stations and 5 automatic weather stations. We found that the model correctly simulates daily and hourly variation of 2 m temperature and relative humidity. A 4-day clear sky period has been chosen to study both local atmospheric circulations and their mutual interaction. Observations and simulations both show that a low-level divergence over oasis appears between 19:00 and 21:00 Beijing Time when the background mountain-plain wind system is weak. The model simulates a synergistic interaction between the oasis-desert breeze and urban-rural breeze from 16:00 until 22:00 with a maximum effect at 20:00 when the downdraft over oasis (updraft over urban) areas increases by 0.8 (0.4) Pa/s. The results show that the oasis expansion decreases the nocturnal urban heat island in the city of Urumqi by 0.8 °C, while the impact of urban expansion on the oasis cold island is negligible.

Agricultural oasis expansion and intensive management practices have occurred in arid and semiarid regions of China during the last few decades. Accordingly, regional carbon and water budgets have been profoundly impacted by agroecosystems in these regions. Therefore, study on the methods used to accurately estimate energy, water, and carbon exchanges is becoming increasingly important. Process-based models can represent the complex processes between land and atmosphere among agricultural ecosystems. However, before the models can be applied they must be validated under different environmental and climatic conditions. In this study, a process-based agricultural ecosystem model (Agro-IBIS) was validated for maize crops using 3 years of soil and biometric measurements at Wulanwusu agrometeorological site (WAS) located in the Shihezi oasis in Xinjiang, northwest China. The model satisfactorily represented leaf area index (LAI) during the growing season, simulating its peak values within the magnitude of 0–10%. The total biomass carbon was overestimated by 15%, 8%, and 16% in 2004, 2005, and 2006, respectively. The model satisfactorily simulated the soil temperature (0–10 cm) and volumetric water content (VWC) (0–25 cm) of farmland during the growing season. However, it overestimated soil temperature approximately by 4 °C and VWC by 15–30% during the winter, coinciding with the period of no vegetation cover in Xinjiang. Overall, the results indicate that the model could represent crop growth, and seems to be applicable in multiple sites in arid oases agroecosystems of Xinjiang. Future application of the model will impose more comprehensive validation using eddy covariance flux data, and consider including dynamics of crop residue and improving characterization of the final stage of leaf development.

Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1–31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations.

The oases in the mountain-basin systems of Central Asia are extremely fragile. Investigating oasis effects and oasis-desert interactions is important for understanding the ecological stability of oases. However, previous studies have been performed only in oasis-desert environments and have not considered the impacts of mountains. In this study, oasis effects were explored in the context of mountain effects in the northern Tianshan Mountains (NTM) using the Weather Research and Forecasting (WRF) model. Four numerical simulations are performed. The def simulation uses the default terrestrial datasets provided by the WRF model. The mod simulation uses actual terrestrial datasets from satellite products. The non-oasis simulation is a scenario simulation in which oasis areas are replaced by desert conditions, while all other conditions are the same as the mod simulation. Finally, the non-mountain simulation is a scenario simulation in which the elevation values of all grids are set to a constant value of 300 m, while all other conditions are the same as in the mod simulation. The mod simulation agrees well with near-surface measurements of temperature, relative humidity and latent heat flux. The Tianshan Mountains exert a cooling and wetting effects in the NTM region. The oasis breeze circulation (OBC) between oases and the deserts is counteracted by the stronger background circulation. Thus, the self-supporting mechanism of oases originating from the OBC plays a limited role in maintaining the ecological stability of oases in this mountain-basin system. However, the mountain wind causes the “cold-wet’’ island effects of the oases to extend into the oasis-desert transition zone at night, which is beneficial for plants in the transition region.

Identifying the effects of grazing is critical for the conservation, protection and sustainable use of arid grassland ecosystems. However, research regarding the ecological effects of grazing along mountainous elevation gradients is limited in arid areas, particularly at the regional scale. Using the Biome-BGC grazing model, we explored the effects of grazing on grassland net primary productivity (NPP), evapotranspiration (ET) and water use efficiency (WUE) from 1979 to 2012 along an elevation gradient in the northern Tianshan Mountains. The NPP, ET and WUE values were generally lower under the grazing scenario than under the ungrazed scenario; the differences between the grazing and ungrazed scenarios showed increasing trends over time; and distinct spatial heterogeneity in these differences was observed. Distinct decreases in NPP and WUE under the grazing scenario mainly occurred in regions with high livestock consumption. The decrease in ET was greater in mountainous areas with high grazing intensity due to decreased transpiration and increased surface runoff. This study contributes to a better understanding of the ecological effects of grazing along an elevation gradient in the northern Tianshan Mountains and provides data to support the scientific management of grassland ecosystems.

Arid grassland ecosystems are widely distributed across Central Asia. However, there is a lack of research and observations of the land–atmosphere exchange of water and heat in the arid grasslands in this region, particularly over complex surfaces. In this study, systematic observations were conducted from 2013 to 2015 using an HL20 Bowen ratio and TDR300 and WatchDog1400 systems to determine the characteristics of these processes during the growing season (April–October) of the arid mountainous grasslands of this region. (1) The latent heat flux (Le) was lower than the sensible heat flux (He) overall, and a small transient decrease in Le was observed before its daytime maximum; daily comparative variations in both fluxes were closely related to vegetation growth. (2) Evapotranspiration (ET) showed substantial variation across different years, seasons and months, and monthly variations in ET were closely related to vegetation growth. Water condensation (Q) was low and relatively stable. Relatively high levels of soil water were measured in spring followed by a decreasing trend. The land–atmosphere exchange of water and heat during the growing season in this region was closely associated with phenology, available precipitation and terrain. This study provides data support for the scientific management of arid mountainous grasslands.

In this work, rain totals from 1960–2014, obtained during the warm season (May to October) from 52 meteorological stations, over Xinjiang, China were classified as either light, moderate, or heavy rain in two sub-regions (northern and southern). Spatial and temporal trends for rain amounts and days for the three rain classes were determined. All light, moderate, and heavy rain amounts displayed increasing trends over the two sub-regions. Furthermore, heavy rain amounts contributed the most to changes in total rain amounts. Light rain days in northern Xinjiang significantly decreased, in contrast to increasing light rain days in southern Xinjiang and moderate and heavy rain days within two sub-regions. Results obtained from correlation and relative weights analyses implied that lower-tropospheric specific humidity was the main factor responsible for light rain day trends in Xinjiang. Increasing temperatures were not found to have a significant effect.