The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world. Under the impacts of climate change and human activities, desertification is becoming increasingly severe on the Mongolian Plateau. Understanding the vegetation dynamics in this region can better characterize its ecological changes. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) images, we calculated the kernel normalized difference vegetation index (kNDVI) on the Mongolian Plateau from 2000 to 2023, and analyzed the changes in kNDVI using the Theil-Sen median trend analysis and Mann-Kendall significance test. We further investigated the impact of climate change on kNDVI change using partial correlation analysis and composite correlation analysis, and quantified the effects of climate change and human activities on kNDVI change by residual analysis. The results showed that kNDVI on the Mongolian Plateau was increasing overall, and the vegetation recovery area in the southern region was significantly larger than that in the northern region. About 50.99% of the plateau showed dominant climate-driven effects of temperature, precipitation, and wind speed on kNDVI change. Residual analysis showed that climate change and human activities together contributed to 94.79% of the areas with vegetation improvement. Appropriate human activities promoted the recovery of local vegetation, and climate change inhibited vegetation growth in the northern part of the Mongolian Plateau. This study provides scientific data for understanding the regional ecological environment status and future changes and developing effective ecological protection measures on the Mongolian Plateau.

Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas. This study took Gansu Province, China, a typical area with complex terrain and variable climate, as the research subject. Based on Google Earth Engine, we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022, and quantitatively analyzed the main causes of regional differences in surface water area. The findings revealed that surface water area in Gansu Province expanded by 406.88 km² from 1985 to 2022. Seasonal surface water area exhibited significant fluctuations, while permanent surface water area showed a steady increase. Notably, terrestrial water storage exhibited a trend of first decreasing and then increasing, correlated with the dynamics of surface water area. Climate change and human activities jointly affected surface hydrological processes, with the impact of climate change being slightly higher than that of human activities. Spatially, climate change affected the 'source' of surface water to a greater extent, while human activities tended to affect the 'destination' of surface water. Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted. Therefore, we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water 'supply-demand' balance strategies. The study not only sheds light on the dynamics of surface water area in Gansu Province, but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.

The Ili River Delta (IRD) is an ecological security barrier for the Lake Balkhash and an important water conservation area in Central Asia. In this study, we selected the IRD as a typical research area, and simulated the water yield and water conservation from 1975 to 2020 using the water yield module of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We further analyzed the temporal and spatial variations in the water yield and water conservation in the IRD from 1975 to 2020, and investigated the main driving factors (precipitation, potential evapotranspiration, land use/land cover change, and inflow from the Ili River) of the water conservation variation based on the linear regression, piecewise linear regression, and Pearson's correlation coefficient analyses. The results indicated that from 1975 to 2020, the water yield and water conservation in the IRD showed a decreasing trend, and the spatial distribution pattern was "high in the east and low in the west"; overall, the water conservation of all land use types decreased slightly. The water conservation volume of grassland was the most reduced, although the area of grassland increased owing to the increased inflow from the Ili River. At the same time, the increased inflow has led to the expansion of wetland areas, the improvement of vegetation growth, and the increase of regional evapotranspiration, thus resulting in an overall reduction in the water conservation. The water conservation depth and precipitation had similar spatial distribution patterns; the change in climate factors was the main reason for the decline in the water conservation function in the delta. The reservoir in the upper reaches of the IRD regulated runoff into the Lake Balkhash, promoted vegetation restoration, and had a positive effect on the water conservation; however, this positive effect cannot offset the negative effect of enhanced evapotranspiration. These results provide a reference for the rational allocation of water resources and ecosystem protection in the IRD.

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development. Based on land use data of the Weihe River basin in 2000, 2010, and 2020, with the support of Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS), GeoDa, and other technologies, this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk. Results showed that land use structure of the Weihe River basin has changed significantly, with the decrease of cropland and the increase of forest land and construction land. In the past 20 a, cropland has decreased by 7347.70 km², and cropland was mainly converted into forest land, grassland, and construction land. The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved, and land use pattern became more concentrated. Meanwhile, landscape ecological risk of the Weihe River basin has been improved. Severe landscape ecological risk area decreased by 19,177.87 km², high landscape ecological risk area decreased by 3904.35 km², and moderate and low landscape ecological risk areas continued to increase. It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious, especially in the contiguous areas of high ecological risk, such as Tianshui, Pingliang, Dingxi areas and some areas of Ningxia Hui Autonomous Region. Landscape ecological risk showed obvious spatial dependence, and high ecological risk area was concentrated. Among the driving factors, population density, precipitation, normalized difference vegetation index (NDVI), and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin. The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin, providing crucial insights for sustainable management in the region.

Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity. This study considered six sub-basins in the upper reaches of the Yangtze River basin, China, to reveal the trend of the runoff evolution and clarify the driving factors of the changes during 1956-2020. Linear regression, Mann-Kendall test, and sliding t-test were used to study the trend of the hydrometeorological elements, while cumulative distance level and ordered clustering methods were applied to identify mutation points. The contributions of climate change and human disturbance to runoff changes were quantitatively assessed using three methods, i.e., the rainfall-runoff relationship method, slope variation method, and variable infiltration capacity (Budyko) hypothesis method. Then, the availability and stability of the three methods were compared. The results showed that the runoff in the upper reaches of the Yangtze River basin exhibited a decreasing trend from 1956 to 2020, with an abrupt change in 1985. For attribution analysis, the runoff series could be divided into two phases, i.e., 1961-1985 (baseline period) and 1986-2020 (changing period); and it was found that the rainfall-runoff relationship method with precipitation as the representative of climate factors had limited usability compared with the other two methods, while the slope variation and Budyko hypothesis methods had highly consistent results. Different factors showed different effects in the sub-basins of the upper reaches of the Yangtze River basin. Moreover, human disturbance was the main factor that contributed to the runoff changes, accounting for 53.0%-82.0%; and the contribution of climate factors to the runoff change was 17.0%-47.0%, making it the secondary factor, in which precipitation was the most representative climate factor. These results provide insights into how climate and anthropogenic changes synergistically influence the runoff of the upper reaches of the Yangtze River basin.

The effect of global climate change on vegetation growth is variable. Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation, and even regional protection of ecological environments. In this study, we constructed a new drought index (i.e., Vegetation Drought Condition Index (VDCI)) based on precipitation, potential evapotranspiration, soil moisture and Normalized Difference Vegetation Index (NDVI) data, to monitor vegetation drought in the nine major river basins (including the Songhua River and Liaohe River Basin, Haihe River Basin, Yellow River Basin, Huaihe River Basin, Yangtze River Basin, Southeast River Basin, Pearl River Basin, Southwest River Basin and Continental River Basin) in China at 1-month-12-month (T1-T12) time scales. We used the Pearson's correlation coefficients to assess the relationships between the drought indices (the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI) and Self-calibrating Palmer Drought Severity Index (scPDSI)) and the NDVI at T1-T12 time scales, and to estimate and compare the lag times of vegetation response to drought among different drought indices. The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1-T6 time scales. Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales. Potential evapotranspiration shows a higher degree of positive influence on vegetation, and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins. The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin, Haihe River Basin, Yellow River Basin, Huaihe River Basin and Yangtze River Basin at T1-T4 time scales. In general, the VDCI is more sensitive (with shorter lag time of vegetation response to drought) than the traditional drought indices (SPEI, scPDSI and SSMI) in monitoring vegetation drought, and thus it could be applied to monitor short-term vegetation drought. The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate, and can be applied in other fields of vegetation drought monitoring with complex mechanisms.

Proposed agroforestry options should begin with the species that farmers are most familiar with, which would be the native multipurpose trees that have evolved under smallholder farms and socioeconomic conditions. The African birch (Anogeissus leiocarpa (DC.) Guill. & Perr.) and pink jacaranda (Stereospermum kunthianum Cham.) trees are the dominant species in the agroforestry parkland system in the drylands of Tigray, Ethiopia. Smallholder farmers highly value these trees for their multifunctional uses including timber, firewood, charcoal, medicine, etc. These trees also could improve soil fertility. However, the amount of soil physical and chemical properties enhanced by the two species must be determined to maintain the sustainable conservation of the species in the parklands and to scale up to similar agro- ecological systems. Hence, we selected twelve isolated trees, six from each species that had similar dendrometric characteristics and were growing in similar environmental conditions. We divided the canopy cover of each tree into three radial distances: mid-canopy, canopy edge, and canopy gap (control). At each distance, we took soil samples from three different depths. We collected 216 soil samples (half disturbed and the other half undisturbed) from each canopy position and soil depth. Bulk density (BD), soil moisture content (SMC), soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AP), available potassium (AK), pH, electrical conductivity (EC), and cation exchange capacity (CEC) were analysed. Results revealed that soil physical and chemical properties significantly improved except for soil texture and EC under both species, CEC under A. leiocarpus, and soil pH under S. kunthianum, all the studied soils were improved under both species canopy as compared with canopy gap. SMC, TN, AP, and AK under canopy of these trees were respectively 24.1%, 11.1%, 55.0%, and 9.3% higher than those soils under control. The two parkland agroforestry species significantly enhanced soil fertility near the canopy of topsoil through improving soil physical and chemical properties. These two species were recommended in the drylands with similar agro-ecological systems.

Ecological stability is a core issue in ecological research and holds significant implications for humanity. The increased frequency and intensity of drought and wet climate events resulting from climate change pose a major threat to global ecological stability. Variations in stability among different ecosystems have been confirmed, but it remains unclear whether there are differences in stability within the same terrestrial vegetation ecosystem under the influence of climate events in different directions and intensities. China's grassland ecosystem includes most grassland types and is a good choice for studying this issue. This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify the directions and intensities of different types of climate events, and based on Normalized Difference Vegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30 consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measure stability). Based on a traditional regression model, standardized methods were integrated to analyze the impacts of the intensity and duration of drought and wet events on vegetation stability. The results showed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had the lowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at an intermediate level. Regarding the impact of the duration and intensity of climate events on vegetation ecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainly affected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppe was mainly affected by intensity in wet environments, and duration had no significant impact. Our conclusions can provide decision support for the future grassland ecosystem governance.

Reclamation of lands abandoned after mining in mountain areas is critical to erosion control, safety from landslides, and ecological protection of mountain ecosystems. However, little is known about alpine coal mine reclamation using the soil seed bank as a potential source for revegetation. We collected samples of persistent soil seed bank for germination experiments from nine reclaimed sites with different soil cover thicknesses and from six control sites in the Qilian Mountains of China. Soil properties of each site were determined (including soil water content, soil available potassium, soil available phosphorus, soil total nitrogen, pH, soil organic matter, soil total phosphorus, and soil total potassium, and soil alkali-hydrolyzable nitrogen), and the relationships of the characteristics of the soil seed bank with soil cover thickness and soil properties were examined. The results showed that the density, number of species, and diversity of the topsoil seed bank were significantly correlated with soil cover thickness, and all increased with the increment of soil cover thickness. Soil cover thickness controlled the soil seed bank by influencing soil properties. With the increase in soil cover thickness, soil properties (e.g., soil organic matter, soil total nitrogen, etc.) content increased while soil pH decreased. The soil seed bank had the potential to restored the pre-mining habitat at reclaimed sites with approximately 20-cm soil cover thickness. Soil properties of reclaimed sites were lower than that of natural sites. The relationship between the soil seed bank and soil cover thickness determined in this study provides a foundation for improving reclamation measures used in coal mines, as well as for the management and monitoring of reclaimed areas.

Since the 1950s, numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin (LRB), China. While these measures have protected the upstream soil and water ecological environment, they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment. Therefore, it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes. Using the Soil and Water Assessment Tool (SWAT) models and sensitivity analyses based on the Budyko hypothesis, this study quantitatively evaluated the effects of climate change, direct water withdrawal, and soil and water conservation measures on runoff in the LRB during different periods, including different responses to runoff discharge, hydrological regime, and flood processes. The runoff series were divided into a baseline period (1956-1969) and two altered periods, i.e., period 1 (1970-1999) and period 2 (2000-2020). Human activities were the main cause of the decrease in runoff during the altered periods, contributing 86.03% (-29.61 mm), while the contribution of climate change was only 13.70% (-4.70 mm). The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season. Analysis of two flood cases indicated a 66.00%-84.00% reduction in basin runoff capacity due to soil and water conservation measures in the upstream area. Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98% and 55.16%, respectively, even with nearly double the precipitation. The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area. These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

The Turpan-Hami (Tuha) Basin in Xinjiang Uygur Autonomous Region of China, holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative, necessitating a holistic understanding of the spatiotemporal evolution of land use/land cover (LULC) to foster sustainable planning that is tailored to the region's unique resource endowments. However, existing LULC classification methods demonstrate inadequate accuracy, hindering effective regional planning. In this study, we established a two-level LULC classification system (8 primary types and 22 secondary types) for the Tuha Basin. By employing Landsat 5/7/8 imagery at 5-a intervals, we developed the LULC dataset of the Tuha Basin from 1990 to 2020, conducted the accuracy assessment and spatiotemporal evolution analysis, and simulated the future LULC under various scenarios via the Markov-Future Land Use Simulation (Markov-FLUS) model. The results revealed that the average overall accuracy values of our LULC dataset were 0.917 and 0.864 for the primary types and secondary types, respectively. Compared with the seven mainstream LULC products (GlobeLand30, Global 30-meter Land Cover with Fine Classification System (GLC_FCS30), Finer Resolution Observation and Monitoring of Global Land Cover PLUS (FROM_GLC PLUS), ESA Global Land Cover (ESA_LC), Esri Land Cover (ESRI_LC), China Multi-Period Land Use Land Cover Change Remote Sensing Monitoring Dataset (CNLUCC), and China Annual Land Cover Dataset (CLCD)) in 2020, our LULC data exhibited dramatically elevated overall accuracy and provided more precise delineations for land features, thereby yielding high-quality data backups for land resource analyses within the basin. In 2020, unused land (78.0% of the study area) and grassland (18.6%) were the dominant LULC types of the basin; although cropland and construction land constituted less than 1.0% of the total area, they played a vital role in arid land development and primarily situated within oases that form the urban cores of the cities of Turpan and Hami. Between 1990 and 2020, cropland and construction land exhibited a rapid expansion, and the total area of water body decreased yet resurging after 2015 due to an increase in areas of reservoir and pond. In future scenario simulations, significant increases in areas of construction land and cropland are anticipated under the business-as-usual scenario, whereas the wetland area will decrease, suggesting the need for ecological attention under this development pathway. In contrast, the economic development scenario underscores the fast-paced expansion of construction land, primarily from the conversion of unused land, highlighting the significant developmental potential of unused land with a slowing increase in cropland. Special attention should thus be directed toward ecological and cropland protection during development. This study provides data supports and policy recommendations for the sustainable development goals of Tuha Basin and other similar arid areas.

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.

Gross primary productivity (GPP) of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought. Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks, aiding efforts to mitigate the detrimental effects of climate change. In this study, we utilized the precipitation and temperature data from the Climatic Research Unit, the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the simulated vegetation GPP using the eddy covariance-light use efficiency (EC-LUE) model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018. The main findings indicated that vegetation GPP decreased in 50.53% of the plateau, mainly in its northern and northeastern parts, while it increased in the remaining 49.47% area. Specifically, meadow steppe (78.92%) and deciduous forest (79.46%) witnessed a significant decrease in vegetation GPP, while alpine steppe (75.08%), cropland (76.27%), and sandy vegetation (87.88%) recovered well. Warming aridification areas accounted for 71.39% of the affected areas, while 28.53% of the areas underwent severe aridification, mainly located in the south and central regions. Notably, the warming aridification areas of desert steppe (92.68%) and sandy vegetation (90.24%) were significant. Climate warming was found to amplify the sensitivity of coniferous forest, deciduous forest, meadow steppe, and alpine steppe GPP to drought. Additionally, the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased. The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months. The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

The characteristics of drought in Xinjiang Uygur Autonomous Region (Xinjiang), China have changed due to changes in the spatiotemporal patterns of temperature and precipitation, however, the effects of temperature and precipitation—the two most important factors influencing drought—have not yet been thoroughly explored in this region. In this study, we first calculated the standard precipitation evapotranspiration index (SPEI) in Xinjiang from 1980 to 2020 based on the monthly precipitation and monthly average temperature. Then the spatiotemporal characteristics of temperature, precipitation, and drought in Xinjiang from 1980 to 2020 were analyzed using the Theil-Sen median trend analysis method and Mann-Kendall test. A series of SPEI-based scenario-setting experiments by combining the observed and detrended climatic factors were utilized to quantify the effects of individual climatic factor (i.e., temperature and precipitation). The results revealed that both temperature and precipitation had experienced increasing trends at most meteorological stations in Xinjiang from 1980 to 2020, especially the spring temperature and winter precipitation. Due to the influence of temperature, trends of intensifying drought have been observed at spring, summer, autumn, and annual scales. In addition, the drought trends in southern Xinjiang were more notable than those in northern Xinjiang. From 1980 to 2020, temperature trends exacerbated drought trends, but precipitation trends alleviated drought trends in Xinjiang. Most meteorological stations in Xinjiang exhibited temperature-dominated drought trend except in winter; in winter, most stations exhibited precipitation-dominated wetting trend. The findings of this study highlight the importance of the impact of temperature on drought in Xinjiang and deepen the understanding of the factors influencing drought.

Infiltration is an important part of the hydrological cycle, and it is one of the main abstractions accounted for in the rainfall-runoff modeling. The main purpose of this study is to compare the infiltration models that were used to assess the infiltration rate of the Mitidja Plain in Algeria. Field infiltration tests were conducted at 40 different sites using a double ring infiltrometer. Five statistical comparison criteria including root mean squared error (RMSE), normalized root mean squared error (NRMSE), coefficient of correlation (CC), Nash-Sutcliffe efficiency (NSE), and Kling-Gupta efficiency (KGE) were used to determine the best performing infiltration model and to confirm anomalies between predicted and observed values. Then we evaluated performance of five models (i.e., the Philip model, Kostiakov model, Modified Kostiakov model, Novel model, and Horton model) in simulating the infiltration process based on the adjusted performance parameters cited above. Results indicated that the Novel model had the best simulated water infiltration process in the Mitidja Plain in Algeria. However, the Philip model was the weakest to simulate the infiltration process. The conclusion of this study can be useful for estimating infiltration rate at various sites using a Novel model when measured infiltration data are not available and are useful for planning and managing water resources in the study area.

Grassland degradation can alter the structure and function of ecosystem and soil seed bank. Therefore, estimating the role of soil seed bank in vegetation regeneration of degraded grasslands is crucial. We selected grasslands with three levels of degradation, namely non-degraded (ND), mildly degraded (MD), and heavily degraded (HD) to analyze the effect of grassland degradation on soil seed bank, as well as the role of soil seed bank on vegetation regeneration of the alpine grasslands, China. Soil samples from each level were collected in May, before seedling emergence, in August, after completion of transient seed bank germination, and in December, after seed dispersal, to determine the seed density and species composition through germination experiment. Result showed that a total of 35 plant species was identified, including 15 species observed in both soil seed bank and above-ground vegetation. A total of 19, 15, and 14 species of soil seed bank were identified in December, May, and August, respectively. The most abundant species in soil seed bank were Compositae (5 species), followed by Poaceae (4 species), and Cyperaceae (3 species). Degradation level has no significant impact on species richness and Shannon- Wiener index of soil seed bank. In addition, sampling month and grassland degradation affected soil seed bank density, in which December>May>August, and ND>MD>HD, indicating that density of transient seed bank was greater than persistent seed bank. Soil seed bank density of surface layer (0-5 cm) accounting for 42%-72% of the total density, which was significantly higher than that of deep layer (5-10 cm). Similarity of species composition between vegetation and soil seed bank was low, and it increased with degradation level (ranged from 0.14 to 0.69). We concluded that grassland degradation affects soil seed bank density more than species diversity, and soil seed bank contributed slightly to vegetation regeneration of degraded alpine grassland. Therefore, it is unlikely that degraded alpine meadow can be restored solely through soil seed bank.

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.

Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation, i.e., the moisture recycling ratio, but various isotope-based models usually lead to different results, which affects the accuracy of local moisture recycling. In this study, a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio. Among the three vapor sources including advection, transpiration, and surface evaporation, the advection vapor usually played a dominant role, and the contribution of surface evaporation was less than that of transpiration. When the abnormal values were ignored, the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9% for transpiration, 0.2% for surface evaporation, and -1.1% for advection, respectively, and the medians were 0.5%, 0.2%, and -0.8%, respectively. The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied, and the contribution of advection was relatively larger. The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios. Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input, and it was important to accurately estimate the isotopes in precipitation vapor. Generally, the Bayesian mixing model should be recommended instead of a linear model. The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.

Methane (CH₄) is a potent greenhouse gas that has a substantial impact on global warming due to its substantial influence on the greenhouse effect. Increasing extreme precipitation events, such as drought, attributable to global warming that caused by greenhouse gases, exert a profound impact on the intricate biological processes associated with CH₄ uptake. Notably, the timing of extreme drought occurrence emerges as a pivotal factor influencing CH₄ uptake, even when the degree of drought remains constant. However, it is still unclear how the growing season regulates the response of CH₄ uptake to extreme drought. In an effort to bridge this knowledge gap, we conducted a field manipulative experiment to evaluate the impact of extreme drought on CH₄ uptake during early, middle, and late growing stages in a temperate steppe of Inner Mongolia Autonomous Region, China. The result showed that all extreme drought consistently exerted positive effects on CH₄ uptake regardless of seasonal timing. However, the magnitude of this effect varied depending on the timing of season, as evidenced by a stronger effect in early growing stage than in middle and late growing stages. Besides, the pathways of CH₄ uptake were different from seasonal timing. Extreme drought affected soil physical-chemical properties and aboveground biomass (AGB), consequently leading to changes in CH₄ uptake. The structural equation model showed that drought both in the early and middle growing stages enhanced CH₄ uptake due to reduced soil water content (SWC), leading to a decrease in NO₃^--N and an increase in pmoA abundance. However, drought in late growing stage primarily enhanced CH₄ uptake only by decreasing SWC. Our results suggested that seasonal timing significantly contributed to regulate the impacts of extreme drought pathways and magnitudes on CH₄ uptake. The findings can provide substantial implications for understanding how extreme droughts affect CH₄ uptake and improve the prediction of potential ecological consequence under future climate change.

Land use change affects the balance of organic carbon (C) reserves and the global C cycle. Microbial residues are essential constituents of stable soil organic C (SOC). However, it remains unclear how microbial residue changes over time following afforestation. In this study, 16-, 23-, 52-, and 62-year-old Mongolian pine stands and 16-year-old cropland were studied in the Horqin Sandy Land, China. We analyzed changes in SOC, amino sugar content, and microbial parameters to assess how microbial communities influence soil C transformation and preservation. The results showed that SOC storage increased with stand age in the early stage of afforestation but remained unchanged at about 1.27−1.29 kg/m² after 52 a. Moreover, there were consistent increases in amino sugars and microbial residues with increasing stand age. As stand age increased from 16 to 62 a, soil pH decreased from 6.84 to 5.71, and the concentration of total amino sugars increased from 178.53 to 509.99 mg/kg. A significant negative correlation between soil pH and the concentration of specific and total amino sugars was observed, indicating that the effects of soil acidification promote amino sugar stabilization during afforestation. In contrast to the Mongolian pine plantation of the same age, the cropland accumulated more SOC and microbial residues because of fertilizer application. Across Mongolian pine plantation with different ages, there was no significant change in calculated contribution of bacterial or fungal residues to SOC, suggesting that fungi were consistently the dominant contributors to SOC with increasing time. Our results indicate that afforestation in the Horqin Sandy Land promotes efficient microbial growth and residue accumulation in SOC stocks and has a consistent positive impact on SOC persistence.

Land use and cover change (LUCC) is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface, with significant impacts on the environment and social economy. Rapid economic development and climate change have resulted in significant changes in land use and cover. The Shiyang River Basin, located in the eastern part of the Hexi Corridor in China, has undergone significant climate change and LUCC over the past few decades. In this study, we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991, 1995, 2000, 2005, 2010, 2015, and 2020 based on Landsat images. We validated the land use and cover data in 2015 from the random forest classification results (this study), the high-resolution dataset of annual global land cover from 2000 to 2015 (AGLC-2000-2015), the global 30 m land cover classification with a fine classification system (GLC_FCS30), and the first Landsat-derived annual China Land Cover Dataset (CLCD) against ground-truth classification results to evaluate the accuracy of the classification results in this study. Furthermore, we explored and compared the spatiotemporal patterns of LUCC in the upper, middle, and lower reaches of the Shiyang River Basin over the past 30 years, and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural (evapotranspiration, precipitation, temperature, and surface soil moisture) and anthropogenic (nighttime light, gross domestic product (GDP), and population) factors. The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015, GLC_FCS30, and CLCD datasets in both overall and partial validations. Moreover, the classification results in this study exhibited a high level of agreement with the ground truth features. From 1991 to 2020, the area of bare land exhibited a decreasing trend, with changes primarily occurring in the middle and lower reaches of the basin. The area of grassland initially decreased and then increased, with changes occurring mainly in the upper and middle reaches of the basin. In contrast, the area of cropland initially increased and then decreased, with changes occurring in the middle and lower reaches. The LUCC was influenced by both natural and anthropogenic factors. Climatic factors and population contributed significantly to LUCC, and the importance values of evapotranspiration, precipitation, temperature, and population were 22.12%, 32.41%, 21.89%, and 19.65%, respectively. Moreover, policy interventions also played an important role. Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years, with the ecological environment improving in the last 10 years. This suggests that governance efforts in the study area have had some effects, and the government can continue to move in this direction in the future. The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Rain-on-snow (ROS) events involve rainfall on snow surfaces, and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China (ARNC). In this study, using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre, we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC. The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains, Tianshan Mountains, Ili River Valley, Tacheng Prefecture, and Altay Prefecture, with the Ili River Valley, Tacheng City, and Altay Mountains exhibiting the most occurrences. Based on the intensity of ROS events, the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains, Ili River Valley, Tacheng City, and Altay Mountains. The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015, mainly influenced by air temperature and the number of rainfall days. However, due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC, snowpack changes exerted slight impact on ROS events, which is a temporary phenomenon. Furthermore, elevation imposed lesser impact on ROS events in the ARNC than other factors. In the ARNC, the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year, while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter. This may lead to more ROS events in winter in the future. These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.

To comprehensively evaluate the alterations in water ecosystem service functions within arid watersheds, this study focused on the Bosten Lake Basin, which is situated in the arid region of Northwest China. The research was based on land use/land cover (LULC), natural, socioeconomic, and accessibility data, utilizing the Patch-level Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to dynamically assess LULC change and associated variations in water yield and water conservation. The analyses included the evaluation of contribution indices of various land use types and the investigation of driving factors that influence water yield and water conservation. The results showed that the change of LULC in the Bosten Lake Basin from 2000 to 2020 showed a trend of increasing in cultivated land and construction land, and decreasing in grassland, forest, and unused land. The unused land of all the three predicted scenarios of 2030 (S1, a natural development scenario; S2, an ecological protection scenario; and S3, a cultivated land protection scenario) showed a decreasing trend. The scenarios S1 and S3 showed a trend of decreasing in grassland and increasing in cultivated land; while the scenario S2 showed a trend of decreasing in cultivated land and increasing in grassland. The water yield of the Bosten Lake Basin exhibited an initial decline followed by a slight increase from 2000 to 2020. The areas with higher water yield values were primarily located in the northern section of the basin, which is characterized by higher altitude. Water conservation demonstrated a pattern of initial decrease followed by stabilization, with the northeastern region demonstrating higher water conservation values. In the projected LULC scenarios of 2030, the estimated water yield under scenarios S1 and S3 was marginally greater than that under scenario S2; while the level of water conservation across all three scenarios remained rather consistent. The results showed that Hejing County is an important water conservation function zone, and the eastern part of the Xiaoyouledusi Basin is particularly important and should be protected. The findings of this study offer a scientific foundation for advancing sustainable development in arid watersheds and facilitating efficient water resource management.

Groundwater quality assessment is important to assure safe and durable water use. In semi-arid areas of Algeria, groundwater represents the main water resource for drinking water supply of the rural population as well as for irrigation of agricultural lands. Groundwater samples from wells and springs were collected from the Gargaat Tarf and Annk Djemel sub-watersheds of the Oum El Bouaghi, Algeria, and were analyzed and compared with the World Health Organization (WHO) standards. Results showed that most of the measured physical and chemical parameters exceeded the quality limits according to the WHO standards. Groundwater had a slightly alkaline water pH (7.00-7.79), electrical conductivity>1500 µS/cm, chloride>500 mg/L, calcium>250 mg/L, and magnesium>155 mg/L. Water quality index (WQI) results showed that 68% of the area had excellent water quality, 24% of the samples fell into good category, and only 8% were of poor quality and unsuitable for human consumption. Six wells in the area showed bacterial contamination. Total coliforms (453.9 (±180.3) CFU (colony-forming units)/100 mL), fecal coliforms (243.2 (±99.2) CFU/100 mL), and fecal streptococci (77.9 (±32.0) CFU/100 mL) loads were above the standard limits set by the WHO. These results confirmed that water resources in the study area were strongly influenced by anthropogenic activities and were not recommended for consumption as drinking water.

Land use and cover change (LUCC) is important for the provision of ecosystem services. An increasing number of recent studies link LUCC processes to ecosystem services and human well-being at different scales recently. However, the dynamic of land use and its drivers receive insufficient attention within ecological function areas, particularly in quantifying the dynamic roles of climate change and human activities on land use based on a long time series. This study utilizes geospatial analysis and geographical detectors to examine the temporal dynamics of land use patterns and their underlying drivers in the Hedong Region of the Gansu Province from 1990 to 2020. Results indicated that grassland, cropland, and forestland collectively accounted for approximately 99% of the total land area. Cropland initially increased and then decreased after 2000, while grassland decreased with fluctuations. In contrast, forestland and construction land were continuously expanded, with net growth areas of 6235.2 and 455.9 km², respectively. From 1990 to 2020, cropland was converted to grassland, and both of them were converted to forestland as a whole. The expansion of construction land primarily originated from cropland. From 2000 to 2005, land use experienced intensified temporal dynamics and a shift of relatively active zones from the central to the southeastern region. Grain yield, economic factors, and precipitation were the major factors accounting for most land use changes. Climatic impacts on land use changes were stronger before 1995, succeeded by the impact of animal husbandry during 1995-2000, followed by the impacts of grain production and gross domestic product (GDP) after 2000. Moreover, agricultural and pastoral activities, coupled with climate change, exhibited stronger enhancement effects after 2000 through their interaction with population and economic factors. These patterns closely correlated with ecological restoration projects in China since 1999. This study implies the importance of synergy between human activity and climate change for optimizing land use via ecological patterns in the ecological function area.

Globally, groundwater contamination by nitrate is one of the most widespread environmental problems, particularly in arid and semiarid areas, which are characterized by low amounts of rainfall and groundwater recharge. The stable isotope composition of groundwater (δ²H-H₂O and δ¹⁸O-H₂O) and dissolved nitrate (δ¹⁵N-NO₃^- and δ¹⁸O-NO₃^-) and factor analysis (FA) were applied to explore groundwater provenance, pollution, and chemistry evolution in the northwestern part of the Amman-Al Zarqa Basin, Jordan. In this study, we collected 23 samples from the Lower Ajloun aquifer in 2021, including 1 sample from a groundwater well and 22 samples from springs. These samples were tested for electrical conductivity, total dissolved solids, pH, temperature, dissolved oxygen, the concentration of major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-, and NO₃^-), and the stable isotope composition of groundwater and dissolved nitrate. The results revealed that groundwater in the study area is mainly Ca-Mg-HCO₃ type and can be classified as fresh water, hard water, and very hard water. The range and average concentration of NO₃^- were 3.5-230.8 and 50.9 mg/L, respectively. Approximately 33% of the sampling points showed NO₃^- levels above the maximum allowable concentration of 50.0 mg/L set by the World Health Organization (WHO) guidelines for drinking water quality. The values of δ¹⁸O-H₂O and δ²H-H₂O showed that groundwater in the study area is part of the current water cycle, originating in the Mediterranean Sea, with significant evaporation, orographic, and amount effects. The values of the stable isotope composition of NO₃^- corresponded to δ¹⁵N-NO₃^- and δ¹⁸O-NO₃^- values produced by the nitrification process of manure or septic waste and soil NH₄⁺. The FA performed on the hydrochemical parameters and isotope data resulted in three main factors, with Factor 1, Factor 2, and Factor 3, accounting for 50%, 21%, and 11% of the total variance, respectively. Factor 1 was considered human-induced factor, named "pollution factor", whereas Factor 2, named "conservative fingerprint factor", and Factor 3, named "hardness factor", were considered natural factors. This study will help local researchers manage groundwater sustainably in the study area and other similar arid and semiarid areas in the world.

The maintenance of sand-fixing vegetation is important for the stability of artificial sand-fixing systems in which seed dispersal plays a key role. Based on field wind tunnel experiments using 11 common plant species on the southeastern edge of the Tengger Desert, China, we studied the secondary seed dispersal in the fixed and semi-fixed sand dunes as well as in the mobile dunes in order to understand the limitations of vegetation regeneration and the maintenance of its stability. Our results indicated that there were significant variations among the selected 11 plant species in the threshold of wind speed (TWS). The TWS of Caragana korshinskii was the highest among the 11 plant species, whereas that of Echinops gmelinii was the lowest. Seed morphological traits and underlying surface could generally explain the TWS. During the secondary seed dispersal processes, the proportions of seeds that did not disperse (no dispersal) and only dispersed over short distance (short-distance dispersal within the wind tunnel test section) were significantly higher than those of seeds that were buried (including lost seeds) and dispersed over long distance (long-distance dispersal beyond the wind tunnel test section). Compared with other habitats, the mobile dunes were the most difficult places for secondary seed dispersal. Buried seeds were the easiest to be found in the semi-fixed sand dunes, whereas fixed sand dunes were the best sites for seeds that dispersed over long distance. The results of linear mixed models showed that after controlling the dispersal distance, smaller and rounder seeds dispersed farther. Shape index and wind speed were the two significant influencing factors on the burial of seeds. The explanatory power of wind speed, underlying surface, and seed morphological traits on the seeds that did not disperse and dispersed over short distance was far greater than that on the seeds that were buried and dispersed over long distance, implying that the processes and mechanisms of burial and long-distance dispersal are more complex. In summary, most seeds in the study area either did not move, were buried, or dispersed over short distance, promoting local vegetation regeneration.

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.

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage (ECS), particularly in ecologically sensitive areas with arid and semi-arid climate. In this study, we calculated the ECS in the Ningxia Section of Yellow River Basin, China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model based on land use data. We further predicted the spatial distribution of ECS in 2050 under four land use scenarios: natural development scenario (NDS), ecological protection scenario (EPS), cultivated land protection scenario (CPS), and urban development scenario (UDS) using the patch-generating land use simulation (PLUS) model, and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector (Geodetector). Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×10⁶ t in 2010, followed by a decreasing trend to 2050. The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area, and low values in the western and northern parts. Between 1985 and 2020, land use changes occurred mainly through the expansion of cultivated land, woodland, and construction land at the expense of unused land. The total ECS in 2050 under different land use scenarios (ranked as EPS>CPS>NDS>UDS) would be lower than that in 2020. Nighttime light was the largest contributor to the spatial differentiation of ECS, with soil type and annual mean temperature being the major natural driving factors. Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Analysing runoff changes and how these are affected by climate change and human activities is deemed crucial to elucidate the ecological and hydrological response mechanisms of rivers. The Indicators of Hydrologic Alteration and the Range of Variability Approach (IHA-RVA) method, as well as the ecological indicator method, were employed to quantitatively assess the degree of hydrologic change and ecological response processes in the Yellow River Basin from 1960 to 2020. Using Budyko's water heat coupling balance theory, the relative contributions of various driving factors (such as precipitation, potential evapotranspiration, and underlying surface) to runoff changes in the Yellow River Basin were quantitatively evaluated. The results show that the annual average runoff and precipitation in the Yellow River Basin had a downwards trend, whereas the potential evapotranspiration exhibited an upwards trend from 1960 to 2020. In approximately 1985, it was reported that the hydrological regime of the main stream underwent an abrupt change. The degree of hydrological change was observed to gradually increase from upstream to downstream, with a range of 34.00%-54.00%, all of which are moderate changes. However, significant differences have been noted among different ecological indicators, with a fluctuation index of 90.00% at the outlet of downstream hydrological stations, reaching a high level of change. After the mutation, the biodiversity index of flow in the middle and lower reaches of the Yellow River was generally lower than that in the base period. The research results also indicate that the driving factor for runoff changes in the upper reach of the Yellow River Basin is mainly precipitation, with a contribution rate of 39.31%-54.70%. Moreover, the driving factor for runoff changes in the middle and lower reaches is mainly human activities, having a contribution rate of 63.70%-84.37%. These results can serve as a basis to strengthen the protection and restoration efforts in the Yellow River Basin and further promote the rational development and use of water resources in the Yellow River.

Wolfberry (Lycium barbarum L.) is important for health care and ecological protection. However, it faces problems of low productivity and resource utilization during planting. Exploring reasonable models for water and nitrogen management is important for solving these problems. Based on field trials in 2021 and 2022, this study analyzed the effects of controlling soil water and nitrogen application levels on wolfberry height, stem diameter, crown width, yield, and water (WUE) and nitrogen use efficiency (NUE). The upper and lower limits of soil water were controlled by the percentage of soil water content to field water capacity (θ_f), and four water levels, i.e., adequate irrigation (W0, 75%-85% θ_f), mild water deficit (W1, 65%-75% θ_f), moderate water deficit (W2, 55%-65% θ_f), and severe water deficit (W3, 45%-55% θ_f) were used, and three nitrogen application levels, i.e., no nitrogen (N0, 0 kg/hm²), low nitrogen (N1, 150 kg/hm²), medium nitrogen (N2, 300 kg/hm²), and high nitrogen (N3, 450 kg/hm²) were implied. The results showed that irrigation and nitrogen application significantly affected plant height, stem diameter, and crown width of wolfberry at different growth stages (P<0.01), and their maximum values were observed in W1N2, W0N2, and W1N3 treatments. Dry weight per plant and yield of wolfberry first increased and then decreased with increasing nitrogen application under the same water treatment. Dry weight per hundred grains and dry weight percentage increased with increasing nitrogen application under W0 treatment. However, under other water treatments, the values first increased and then decreased with increasing nitrogen application. Yield and its component of wolfberry first increased and then decreased as water deficit increased under the same nitrogen treatment. Irrigation water use efficiency (IWUE, 8.46 kg/(hm²·mm)), WUE (6.83 kg/(hm²·mm)), partial factor productivity of nitrogen (PFPN, 2.56 kg/kg), and NUE (14.29 kg/kg) reached their highest values in W2N2, W1N2, W1N2, and W1N1 treatments. Results of principal component analysis (PCA) showed that yield, WUE, and NUE were better in W1N2 treatment, making it a suitable water and nitrogen management mode for the irrigation area of the Yellow River in the Gansu Province, China and similar planting areas.

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern, and drought directly impacts urban site selection, growth processes, and future scenarios. Spatial simulation and projection based on cellular automata (CA) models is important to achieve sustainable urban development in arid areas. We developed a new CA model using bat algorithm (BA) named bat algorithm-probability-of-occurrence-cellular automata (BA-POO-CA) model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas, located in Xinjiang Uygur Autonomous Region, China. We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data, and finally projected its urban scenarios in 2030. The results showed that BA-POO-CA model yielded overall accuracy of 97.70% and figure-of-merits (FOMs) of 35.50% in 2010, and 97.70% and 26.70% in 2020, respectively. The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs, with increases of 5.50% in 2010 and 7.90% in 2020 than the model excluding drought intensity factor. This suggested that the urban growth of Urumqi City was affected by drought, and therefore taking drought intensity factor into account would contribute to simulation accuracy. The BA-POO-CA model including drought intensity factor was used to project two possible scenarios (i.e., business-as-usual (BAU) scenario and ecological scenario) in 2030. In the BAU scenario, the urban growth dominated mainly in urban fringe areas, especially in the northern part of Toutunhe District, Xinshi District, and Midong District. Using exceptional and extreme drought areas as a spatial constraint, the urban growth was mainly concentrated in the "main urban areas-Changji-Hutubi" corridor urban pattern in the ecological scenario. The results of this research can help to adjust urban planning and development policies. Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.

Understanding the distribution and dynamics of glaciers is of great significance to the management and allocation of regional water resources and socio-economic development in arid regions of Northwest China. In this study, based on 36 Landsat images, we extracted the glacier boundaries in the Manas River Basin, Northwest China from 2000 to 2020 using eCognition combined with band operation, GIS (geographic information system) spatial overlay techniques, and manual visual interpretation. We further analyzed the distribution and variation characteristics of glacier area, and simulated glacial runoff using a distributed degree-day model to explore the regulation of runoff recharge. The results showed that glacier area in the Manas River Basin as a whole showed a downward trend over the past 21 a, with a decrease of 10.86% and an average change rate of -0.54%/a. With the increase in glacier scale, the number of smaller glaciers decreased exponentially, and the number and area of larger glaciers were relatively stable. Glacier area showed a normal distribution trend of increasing first and then decreasing with elevation. About 97.92% of glaciers were distributed at 3700-4800 m, and 48.11% of glaciers were observed on the northern and northeastern slopes. The retreat rate of glaciers was the fastest (68.82%) at elevations below 3800 m. There was a clear rise in elevation at the end of glaciers. Glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope. Glacial runoff in the basin showed a fluctuating upward trend in the past 21 a, with an increase rate of 0.03×10⁸ m³/a. The average annual glacial runoff was 4.80×10⁸ m³, of which 33.31% was distributed in the ablation season (June-September). The average annual contribution rate of glacial meltwater to river runoff was 35.40%, and glacial runoff accounted for 45.37% of the total runoff during the ablation season. In addition, precipitation and glacial runoff had complementary regulation patterns for river runoff. The findings can provide a scientific basis for water resource management in the Manas River Basin and other similar arid inland river basins.

The Three-River Source Region (TRSR) in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level. On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022, we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types. During the growing season, the spatiotemporal variations of normalized difference vegetation index (NDVI) for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI data and meteorological data from 2000 to 2022. In addition, the response characteristics of vegetation to temperature, precipitation, and human activity were assessed using trend analysis, partial correlation analysis, and residual analysis. Results indicated that, after in-depth research, from 2000 to 2022, the TRSR's average NDVI during the growing season was 0.3482. The preliminary ranking of the average NDVI for different vegetation types was as follows: shrubland (0.5762)>forest (0.5443)>meadow (0.4219)>highland vegetation (0.2223)>steppe (0.2159). The NDVI during the growing season exhibited a fluctuating growth trend, with an average growth rate of 0.0018/10a (P<0.01). Notably, forests displayed a significant development trend throughout the growing season, possessing the fastest rate of change in NDVI (0.0028/10a). Moreover, the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions, with significant increases accounting for 95.23% and 93.80%, respectively. The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation. By contrast, steppes, meadows, and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations. A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022, particularly in its northwestern areas, accounting for 85.05% of the total area. Meanwhile, human activity played a notable positive role in the southwestern and southeastern areas of the TRSR, covering 62.65% of the total area. Therefore, climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.

Vapor pressure deficit (VPD) plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation, second only to carbon dioxide (CO₂). As a robust indicator of atmospheric water demand, VPD has implications for global water resources, and its significance extends to the structure and functioning of ecosystems. However, the influence of VPD on vegetation growth under climate change remains unclear in China. This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit (CRU) Time-Series version 4.06 (TS4.06) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA-5). Vegetation growth status was characterized using three vegetation indices, namely gross primary productivity (GPP), leaf area index (LAI), and near-infrared reflectance of vegetation (NIRv). The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test. Furthermore, the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model. The results indicated an overall negative correlation between VPD and vegetation indices. Three VPD intervals for the correlations between VPD and vegetation indices were identified: a significant positive correlation at VPD below 4.820 hPa, a significant negative correlation at VPD within 4.820-9.000 hPa, and a notable weakening of negative correlation at VPD above 9.000 hPa. VPD exhibited a pronounced negative impact on vegetation growth, surpassing those of temperature, precipitation, and solar radiation in absolute magnitude. CO₂ contributed most positively to vegetation growth, with VPD offsetting approximately 30.00% of the positive effect of CO₂. As the rise of VPD decelerated, its relative contribution to vegetation growth diminished. Additionally, the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China. This research provides a theoretical foundation for addressing climate change in China, especially regarding the challenges posed by increasing VPD.

Vegetation restoration through artificial plantation is an effective method to combat desertification, especially in arid and semi-arid areas. This study aimed to explore the ecological effect of the plantation of Sabina vulgaris on soil physical and chemical properties on the southeastern fringe of the Mu Us Sandy Land, China. We collected soil samples from five depth layers (0-20, 20-40, 40-60, 60-80, and 80-100 cm) in the S. vulgaris plantation plots across four plantation ages (4, 7, 10, and 16 years) in November 2019, and assessed soil physical (soil bulk density, soil porosity, and soil particle size) and chemical (soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK), cation-exchange capacity (CEC), salinity, pH, and C/N ratio) properties. The results indicated that the soil predominantly consisted of sand particles (94.27%-99.67%), with the remainder being silt and clay. As plantation age increased, silt and very fine sand contents progressively rose. After 16 years of planting, there was a marked reduction in the mean soil particle size. The initial soil fertility was low and declined from 4 to 10 years of planting before witnessing an improvement. Significant positive correlations were observed for the clay, silt, and very fine sand (mean diameter of 0.000-0.100 mm) with SOC, AK, and pH. In contrast, fine sand and medium sand (mean diameter of 0.100-0.500 mm) showed significant negative correlations with these indicators. Our findings ascertain that the plantation of S. vulgaris requires 10 years to effectively act as a windbreak and contribute to sand fixation, and needs 16 years to improve soil physical and chemical properties. Importantly, these improvements were found to be highly beneficial for vegetation restoration in arid and semi-arid areas. This research can offer valuable insights for the protection and restoration of the vegetation ecosystem in the sandy lands in China.

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.

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.