The loess plains cover approximately 2000.00 km² of the northern Negev Desert, accounting for about 9% of Israel's total land area. As elsewhere, the loess in the Negev Desert is composed of wind-transported dust and sand particles that have been deposited in sink sites. The loess deposits are characteristically covered by biocrusts, which constitute a substantial share of the region's primary productivity. The biocrusts regulate the vascular vegetation communities, including herbaceous and woody plants, many of which are endemic and/or endangered plant species. Throughout history, the region's main land-uses have been based on extensive livestock grazing and runoff-harvesting agriculture, which both still exist to some extent. These land-uses did not challenge the sustainability of the geo-ecosystems over centuries and millennia. At present, predominant land-uses include intensive rangelands (1016.81 km², encompassing 51% of the loess plains' area), croplands (encompassing both rainfed and irrigated cropping systems: 930.92 km², 47% of the loess plains' area), and afforestation lands (158.75 km²). These current land-uses impose substantial challenges to the functioning of the loess plains. Further, urban and rural settlements have expanded considerably in the last decades (158.45 km²), accompanied by mass construction of infrastructures. Altogether, these new land-uses have caused widespread soil erosion, soil structure deformation, depletion of soil organic carbon, environmental contamination, native vegetation removal, invasion of plant species, and habitat fragmentation. Recent climate change has intensified these stressors, exacerbating adverse impacts and forming feedback loops that intensify land degradation and desertification. The declining ecosystem functioning over recent decades emphasizes the urgent need for passive and active restoration schemes. While some of these efforts have proven to be successful, other have failed. Therefore, proactive policy making and environmental legislation are needed to plan and develop schemes aimed at halting land degradation, while simultaneously maximizing nature conservation and restoration of degraded lands across the loess plains. Such actions are expected to increase the regions' capacity for climate change mitigation and adaptation.

Scientifically constructing an ecological security pattern (ESP) is an important spatial analysis approach to improve ecological functions in arid areas and achieve sustainable development. However, previous research methods ignored the complex trade-offs between ecosystem services in the process of constructing ESP. Taking the mainstream of the Tarim River Basin (MTRB), China as the study area, this study set seven risk scenarios by applying Ordered Weighted Averaging (OWA) model to trade-off the importance of the four ecosystem services adopted by this study (water conservation, carbon storage, habitat quality, and biodiversity conservation), thereby identifying priority protection areas for ecosystem services. And then, this study identified ecological sources by integrating ecosystem service importance with eco-environmental sensitivity. Using circuit theory, the ecological corridors and nodes were extracted to construct the ESP. The results revealed significant spatial heterogeneity in the four ecosystem services across the study area, primarily driven by hydrological gradients and human activity intensity. The ESP of the MTRB included 34 ecological sources with a total area of 1471.38 km², 66 ecological corridors with a length of about 1597.45 km, 11 ecological pinch points, and 13 ecological barrier points distributed on the ecological corridors. The spatial differentiation of the ESP was obvious, with the upper and middle reaches of the MTRB having a large number of ecological sources and exhibiting higher clustering of ecological corridors compared with the lower reaches. The upper and middle reaches require ecological protection to sustain the existing ecosystem, while the lower reaches need to carry out ecological restoration measures including desertification control. Overall, this study makes up for the shortcomings of constructing ESP simply by spatial superposition of ecosystem service functions and can effectively improve the robustness and stability of ESP construction.

Coal mining predisposes soils to heavy metal (HM) accumulation, which adversely affects the ecological environment and human health, particularly in extremely arid and vulnerable areas. In this study, soil samples were gathered from the Black Mountain Open Pit Coal Mine in Turpan City, Northwest China to determine the health risk of heavy metals (HMs). Results showed that positive matrix factorization model divided the sources of soil HMs into four categories, i.e., natural and animal husbandry (43.46%), industrial transportation (22.87%), fossil fuel combustion (10.64%), and atmospheric deposition and domestic pollution (23.03%). All kinds of pollution evaluation indices showed that Cd (cadmium) and Pb (plumbum) pollution was evident. The Monte Carlo simulated health risk assessment results showed that 4.00% non-carcinogenic risk and 12.00% carcinogenic risk were posed to children, and the positive matrix factorization-based health risk assessment showed that fossil fuel combustion had the highest contribution to the health risks to adults and children, while industrial transportation was the lowest. In this study, the risks of HMs in the soil of mining area were analyzed using source analysis, which not only provides reliable data support for the prevention and control of HM pollution in the soil of this arid mining area, but also provides a theoretical basis for subsequent regional research.

Intense evaporation in areas with loess-like sulfate saline soils has resulted in significant ecological challenges that include water shortages and soil salinization. Investigating evaporation rate in loess-like sulfate saline soils under varying salt contents carries crucial implications for understanding regional water loss processes, predicting soil salinization advancement, and formulating effective ecological management strategies. Therefore, this study sampled the loess-like sulfate saline soil that is widely distributed in western China as experimental materials and investigated the impact of different initial salt contents (0.00%, 0.50%, 1.50%, 3.00%, and 5.00%) on the evaporation rate, water content, and temperature of soil. The results showed that the evaporation rate decreased with increasing initial salt content. After a salt accumulation layer formed on the soil surface, the water content of the surface soil fluctuated. An increase in the initial salt content resulted in a corresponding increase in the surface temperature. Considering the evaporation characteristics of loess-like sulfate saline soil and the impact of an anomalous increase in surface soil water content on soil surface resistance, this study proposed a modified evaporation model on the basis of Fujimaki's evaporation model of saline soil by introducing a correction coefficient β to modify the soil surface resistance. A comparison of the calculated evaporation rates before and after the modification with the measured evaporation rates revealed a significant improvement in the calculation accuracy of the modified model, indicating that the modified model is capable of more accurately simulating the evaporation rate of sulfate saline soil with different initial salt contents. This paper proposes an effective method for calculating the evaporation rate of loess-like sulfate saline soils, providing a theoretical basis for evaporation research in saline soil.

Precipitation is scarce in semi-arid areas, which results in serious drought. Occurrence of flash drought is quite often in these areas, and flash drought may also cause significant disasters. However, monitoring flash drought is still weak and remains a challenge. This study aims to identify, evaluate, and monitor flash drought events that occurred from 1961 to 2020 in reservoirs of the Ceará State, Brazil. The Christian's method, standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and evaporative demand drought index (EDDI) were used to assess the severity and persistence of flash drought. Moreover, analyses conducted in 2001, 2008, 2011, 2012, 2016, and 2020 revealed the complexity and interaction of flash drought with environmental and meteorological factors. The results indicated that in dry years such as 2001, 2012, and 2016, drought indices pointed to the intensification of drought conditions, with impacts on major reservoirs in the area, such as Banabuiú, Castanhão, and Orós. Low precipitation, associated with high evaporative demand, intensified water stress, reducing water availability for the population and local ecosystems. In wet years such as 2008, 2011, and 2020, SPEI and EDDI indicated higher moisture levels and drought relief, favoring the recovery of reservoirs. It was also observed that most flash drought episodes evolved into conventional droughts, highlighting their persistence and potential long-term impact. Moreover, the months of May and November presented a higher frequency of flash drought during the wet and dry periods, respectively, negatively impacting most of the studied reservoirs. These findings underscore the need for effective drought monitoring and mitigation strategies to reduce its impacts on agriculture and water resources in the semi-arid area. Early detection and analysis of flash drought are important for improving water resource management and for continuous adaptation to changing drought conditions.

Grassland is a key component of the ecosystem in the Qinghai Lake Basin, China. Understanding the effects of climate change and human activities on grassland productivity significantly improves ecological conservation and promotes sustainable vegetation growth in this area. Based on the net primary productivity (NPP) products of MOD17A3HGF (a moderate-resolution imaging spectroradiometer (MODIS) product that provides annual NPP at 500 m resolution) and meteorological data, we analyzed the spatial and temporal evolution of grassland NPP and its interaction with climate factors in the Qinghai Lake Basin from 2001 to 2022 via partial correlation and trend analysis methods. We also used the deflecting trend residual method and scenario analysis method to quantitatively assess the relative contributions of climatic factors and human activities to grassland NPP. The results revealed that: (1) during the past 22 a, grassland NPP increased considerably, with a gradient change from the northwest to the southeast of the study area; (2) sunshine duration, precipitation, and temperature positively influenced grassland NPP, with sunshine duration exerting a stronger effect on grassland NPP than precipitation and temperature; and (3) 98.47% of the grassland in the study area was restored, with an average contribution of 65.00% from human activities and 35.00% from climatic alterations. Compared with climate change, human-induced factors had a greater effect on grassland NPP in this area. The results of the study not only provide important scientific support for ecological restoration and sustainable development of the basin but also offer new ideas for research on similar ecologically fragile areas.

Research on grassland carrying capacity (GCC) and forage-livestock balance is of great significance for promoting the harmonious development of human and grassland. However, the lack of understanding of GCC and forage-livestock balance in the agro-pastoral transition zone of northern China has limited the grassland sustainable development. Here, the spatial and temporal characteristics of GCC and forage-livestock balance in the grassland of agro-pastoral transition zone of northern China from 2000 to 2022 were analyzed using meteorological data and remote sensing data. Geographical detectors and geographically weighted regression were also used to identify the driving factors and their interactions with GCC changes. Moreover, future GCC trends were predicted using the Coupled Model Intercomparison Project Phase 6 dataset. Results revealed that: (1) GCC showed an overall upward trend from 2000 to 2022 but with significant inter-annual fluctuations. Its spatial distribution decreased gradually from north to south and from east to west. Precipitation, temperature, and cumulative solar radiation were the main drivers of the inter-annual variation of GCC, and the interaction between precipitation and temperature was the main influencing factor of the spatial distribution of GCC; (2) the forage-livestock balance was in an overloaded state in most years, but its index remained basically stable. Spatially, grazing overloading was mainly distributed in northeastern area and the severe overloading was mainly distributed in northwestern area; and (3) future projections indicated a downward trend in potential GCC. Under shared socioeconomic pathway (SSP)2-4.5 scenario, the potential GCC had a ranged of 1.38×10⁷-1.86×10⁷ standard sheep unit (SHU) and a mean of 1.60×10⁷ SHU. Meanwhile, the potential GCC under SSP5-8.5 scenario had a range of 1.18×10⁷-1.69×10⁷ SHU and a mean of 1.49×10⁷ SHU. These results indicated that although GCC of the agro-pastoral transition zone of northern China showed an overall increasing trend from 2000 to 2022, the forage-livestock balance index remained basically stable. The GCC was predicted to show a decreasing trend in the future. The findings provide a scientific basis for the sustainable development of grassland and the optimization of grazing management policies in this area.

The Qinghai-Xizang Plateau of China faces challenges like thaw slumping, threatening slope stability and infrastructure. Understanding the mechanical properties of the roots of the dominant herbaceous plant species in the alpine meadow layer of the permafrost regions on the Qinghai-Xizang Plateau is essential for evaluating their role in enhancing soil shear strength and mitigating slope deformation in these fragile environments. In this study, the roots of four dominant herbaceous plant species—Kobresia pygmaea, Kobresia humilis, Carex moorcroftii, and Leontopodium pusillum—that are widely distributed in the permafrost regions of the Qinghai-Xizang Plateau were explored to determine their mechanical properties and effects in enhancing soil shear strength. Through indoor single root tensile and root group tensile tests, we determined the root diameter, tensile force, tensile strength, tensile ratio, and strength frequency distributions. We also evaluated their contributions to inhibiting slope deformation and failure during the formation and development of thermal thaw slumps in the alpine meadow. The results showed that the distribution of the root diameter of the dominant plant species is mostly normal, while the tensile strength tends to be logarithmically normally distributed. The relationship between the root diameter and root tensile strength conforms to a power function. The theoretical tensile strength of the root group was calculated using the Wu-Waldron Model (WWM) and the Fiber Bundle Model (FBM) under the assumption that the cumulative single tensile strength of the root bundle is identical to the tensile strength of the root group in the WWM. The FBM considers three fracture modes: FBM-D (the tensile force on each single root is proportional to its diameter relative to the total sum of all the root diameters), FBM-S (the cross-sectional stress in the root bundle is uniform), and FBM-N (each tensile strength test of individual roots experiences an equal load). It was found that the model-calculated tensile strength of the root group was 162.60% higher than the test value. The model-derived tensile force of the root group from the FBM-D, FBM-S, and FBM-N was 73.10%, 28.91%, and 13.47% higher than the test values, respectively. The additional cohesion of the soil provided by the roots was calculated to be 25.90-45.06 kPa using the modified WWM, 67.05-38.15 kPa using the FBM-S, and 57.24-32.74 kPa using the FBM-N. These results not only provide a theoretical basis for further quantitative evaluation of the mechanical effects of the root systems of herbaceous plant species in reinforcing the surface soil but also have practical significance for the effective prevention and control of thermal thaw slumping disasters in the permafrost regions containing native alpine meadows on the Qinghai-Xizang Plateau using flexible plant protection measures.

Given that Xinjiang Uygur Autonomous Region of China possesses exceptionally abundant solar radiation resources that can be harnessed to develop clean energy, accurately characterizing their spatiotemporal distribution is crucial. This study investigated the applicability of the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product downward surface shortwave radiation dataset (DSSR_CER) under clear-sky conditions in Xinjiang. By integrating multi-source data and utilizing techniques like multivariate fitting and model simulation, we established a two-layer aerosol model and developed a clear-sky downward surface shortwave radiation (DSSR) retrieval model specific to Xinjiang using the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. We further explored the spatiotemporal distribution characteristics of DSSR under clear-sky conditions in Xinjiang from 2017 to 2019 based on the localized DSSR retrieval model. Our findings revealed a significant discrepancy in DSSR_CER under clear-sky conditions at the Xiaotang station in Xinjiang. By comparing, screening, and correcting core input parameters while incorporating the two-layer aerosol model, we achieved a more accurate SBDART simulated DSSR (DSSR_SBD) compared to DSSR_CER. The annual mean DSSR exhibited a distinct distribution pattern with high values in mountainous regions such as the Altay Mountains, Kunlun Mountains, and Tianshan Mountains and significantly lower values in adjacent lowland areas, including the Tarim River Basin and Junggar Basin. In the four typical administrative regions in northern Xinjiang, the annual mean DSSR (ranging from 551.60 to 586.09 W/m²) was lower than that in the five typical administrative regions in southern Xinjiang (ranging from 522.10 to 623.62 W/m²). These spatial variations stem from a complex interplay of factors, including latitude, altitude, solar altitude angle, and sunshine duration. The variations in seasonal average DSSR aligned closely with variations in the solar altitude angle, with summer (774.76 W/m²) exhibiting the highest values, followed by spring (684.86 W/m²), autumn (544.76 W/m²), and winter (422.74 W/m²). The monthly average DSSR showed a unimodal distribution, peaking in June (792.94 W/m²) and reaching its lowest level in December (363.06 W/m²). Overall, our study findings enhance the current understanding of the spatiotemporal distribution characteristics of DSSR in Xinjiang and provide certain references for the management of clean energy development in this region.

Recent years have witnessed increasingly frequent extreme precipitation events, especially in desert steppes in the semi-arid and arid transition zone. Focusing on a desert steppe in western-central Inner Mongolia Autonomous Region, China, this study aimed to determine the principle time-varying pattern of extreme precipitation and its dominant climate forcings during the period 1988-2017. Based on the generalized additive models for location, scale, and shape (GAMLSS) modeling framework, we developed the best time-dependent models for the extreme precipitation series at nine stations, as well as the optimized non-stationary models with large-scale climate indices (including the North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation (SO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and North Pacific Oscillation (NPO)) as covariates. The results indicated that extreme precipitation remained stationary at more than half of the stations (Hailisu, Wuyuan, Dengkou, Hanggin Rear Banner, Urad Front Banner, and Yikewusu), while linear and non-linear time-varying patterns were quantitatively identified at the other stations (Urad Middle Banner, Linhe, and Wuhai). These non-stationary behaviors of extreme precipitation were mainly reflected in the mean value of extreme precipitation. The optimized non-stationary models performed best, indicating the significant influences of large-scale climate indices on extreme precipitation. In particular, the NAO, NPO, SO, and AMO remained as covariates and significantly influenced the variations in the extreme precipitation regime. Our findings have important reference significance for gaining an in-depth understanding of the driving mechanism of the non-stationary behavior of extreme precipitation and enable advanced predictions of rainstorm risks.

Agriophyllum squarrosum (L.) Moq., commonly known as sandrice, is an annual medicinal plant prevalent in the dunes across China's deserts. A garden trial revealed that flavonoid content varies among sandrice ecotypes due to long-term local adaptation to water variability. To investigate how sandrice responds to drought stress through the molecular metabolic regulation of flavonoids, we employed transcriptomic and metabolomic analyses during a 9-d ambient drought stress, examining three ecotypes along a precipitation gradient. The three ecotypes located in Dengkou (DK) County, Dulan (DL) County, and Aerxiang (AEX) village of northern China, which had 137, 263, and 485 mm precipitation, respectively. Soil moisture content was 4.04% after drought stress, causing seedlings of the three sandrice ecotypes to display collapsed structures, yellowing leaves, wilting, and curling. Among these, DL exhibited superior drought tolerance, in which plant height increase (PHI) and leaf area (LA) were significantly higher than those of DK and AEX. Flavonoid-targeted metabolomics identified that rutin, isoquercitrin, and astragalin constituted over 95.00% of the 15 flavonoid metabolites detected. A total of 12 differentially accumulated flavonoids (DAFs) were found, with rutin being the most abundant (1231.57-2859.34 ng/100 mg fresh weight (FW)), showing a gradual increase along the precipitation gradient. Transcriptomic analysis revealed 14 common differentially expressed genes (DEGs) associated with flavonoid synthesis among the three ecotypes. Integrative analysis of DEGs and DAFs indicated that sandrice adapts to drought stress by activating different flavonoid synthesis pathways. In DK, the dihydrokaempferol-dihydroquercetin pathway, regulated by flavonoid 3'-monooxygenase (CYP75B1), likely enhances drought adaptation. In AEX, transcriptional repression by O-methylatransferase (OMT) shifts the metabolic flux from the quercetin-isorhamnetin pathway to the quercetin-isoquercetin-rutin pathway in response to drought. DL, the most drought- tolerant ecotype, appears to activate the naringenin-apigenin-luteolin route and employs a unique flavonoid accumulation pattern in response to drought stress. Our data reveal that flavonoid synthesis in sandrice is fine-tuned among ecotypes to cope with drought, offering valuable germplasm resources and evaluation methods for sandrice acclimation and providing insights into drought response in non-model plants.

Understanding how different vegetation-based restoration practices alter soil chemical and microbial characteristics is crucial, as restoration practices influence phosphorus (P) transformation and fractions and modify P adsorption behavior during the restoration process of degraded land. This study investigated the impacts of vegetation-based restoration practices on soil chemical and microbial parameters, P fractions, and patterns of P adsorption and desorption, and highlighted the combined influence on P availability. To evaluate the impact of vegetation-based restoration practices on P fractions and adsorption behavior in the semi-arid degraded land in India, this study compared three distinct tree-based restoration systems, including Leucaena leucocephala (Lam.) de Wit-based silviculture system (SCS), Acacia nilotica (L.) Willd. ex Delile-based silvopasture system (SPS), and Emblica officinalis Gaertn-based hortipasture system (HPS), with a natural grassland system (NGS) and a degraded fallow system (FS) as control. The soil samples across various soil depths (0-15, 15-30, and 30-45 cm) were collected. The findings demonstrated that SCS, SPS, and HPS significantly improved soil organic carbon (SOC) and nutrient availability. Moreover, SCS and SPS resulted in increased microbial biomass phosphorus (MBP) content and phosphatase enzyme activity. The P fractionation analysis revealed that ferrum-associated phosphorus (Fe-P) was the major P fraction, followed by aluminum-associated phosphorus (Al-P), reflecting the dominance of ferrum (Fe) and aluminum (Al) oxides in the semi-arid degraded land. Compared with FS, vegetation-based restoration practices significantly increased various P fractions across soil depths. Additionally, P adsorption and desorption analysis indicated a lower adsorption capacity in tree-based restoration systems than in FS, with FS soils adsorbing higher P quantities in the adsorption phase but releasing less P during the desorption phase. This study revealed that degraded soils responded positively to ecological restoration in terms of P fraction and desorption behavior, influencing the resupply of P in restoration systems. Consequently, litter rich N-fixing tree-based restoration systems (i.e., SCS and SPS) increased total phosphorus (TP) stock for plants and sustained the potential for long-term P supply in semi-arid ecosystems. With the widespread adoption of restoration practices across degraded landscapes, SCS and SPS would significantly contribute to soil restoration and improve productivity by maintaining the soil P supply in semi-arid ecosystems in India.

Climate change and human activities have led to desertification and decreased land productivity, significantly affecting human livelihoods in desert regions. Identifying suitable areas for cultivating economic and native plants based on ecological capacity, biological restoration, and risk management can be valuable tools for combating desertification. In this study, we identified suitable areas for the growth of economic and medicinal Moringa peregrina trees in desert regions of Sistan and Baluchestan Province, southern Iran, using library research and field methods. We also assessed the economic involvement of local communities in areas under different topographic conditions (namely flat area, undulating area, rolling area, moderately sloping area, and steep area) in the study area. Financial indicators such as the net present value (NPV), benefit-cost ratio (BCR), internal rate of return (IRR), and return on investment (ROI) were calculated for areas under various topographic conditions in the study area. The rolling area with results of NPV (6142.75 USD), IRR (103.38), BCR (5.38), and ROI (in the 3^rd year) was the best region for investing and cultivating M. peregrina. The minimum economic level varied from 0.80 hm² in the flat area to 21.60 hm² in the steep area. Also, approximately 5,314,629.51 hm² of desert lands in the study area were deemed suitable for M. peregrina cultivation, benefiting around 1,743,246 households in the study area. Cultivating M. peregrina in southern Iran can positively affect local communities and help preserve land from erosion. Our study will provide theoretical support for planting native species in other degraded desert regions to enhance ecosystem services and the well-being of indigenous populations.

Salt-tolerant bacteria associated with halophytes enhance plant resistance and adaptation to environmental stress. The purpose of this study was to investigate the diversity and plant-beneficial traits of bacteria associated with three halophytes in an arid land, Northwest China. The bacterial strains were isolated from the roots, shoots, rhizosphere, and bulk soil of three halophytes, i.e., Salicornia europaea L., Kalidium foliatum (Pall.) Moq., and Suaeda aralocaspica (Bunge) Freitag & Schütze, collected from the saline soils near to the Wujiaqu City, Xinjiang, Northwest China. A total of 567 strains were isolated and identified from these three halophytes belonging to 4 phyla, 6 classes, 25 orders, 36 families, and 66 genera, including 147 potential novel species. A total of 213 strains exhibited one or more plant growth- promoting properties, while 20 strains demonstrated multiple in vitro plant growth-promoting activities, including phosphate solubilization, nitrogen fixation, siderophore production, and production of hydrolytic enzymes such as protease and cellulase. Our findings showed that halophytes in the arid land harbor diverse bacteria with the potential to enhance plant growth and adaptability under challenging environmental conditions.

Understanding the impact of meteorological and topographical factors on snow cover fraction (SCF) is crucial for water resource management in the Qilian Mountains (QLM), China. However, there is still a lack of adequate quantitative analysis of the impact of these factors. This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer (MODIS) SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree (HGBRT) model. The results indicated that the monthly distribution of SCF exhibited a bimodal pattern. The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions. Overall, the SCF showed a decreasing trend during 2000-2021. The decrease in SCF occurred at higher elevations, while an increase was observed at lower elevations. At the annual scale, the SCF showed a downward trend in the western regions affected by westerly (52.84% of the QLM). However, the opposite trend was observed in the eastern regions affected by monsoon (45.73% of the QLM). The SCF displayed broadly similar spatial patterns in autumn and winter, with a significant decrease in the western regions and a slight increase in the central and eastern regions. The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF. Furthermore, compared with meteorological factors, a variation of 46.53% in spring surface runoff can be attributed to changes in spring SCF. At the annual scale, temperature and relative humidity were the most important drivers of SCF change. An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF, with a maximum decrease of 0.22%/a. An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF (about 0.06%/a). The impacts of slope and aspect were found to be minimal. At the seasonal scale, the primary factors impacting SCF change varied. In spring, precipitation and wind speed emerged as the primary drivers. In autumn, precipitation and temperature were identified as the primary drivers. In winter, relative humidity and precipitation were the most important drivers. In contrast to the other seasons, slope exerted the strongest influence on SCF change in summer. This study facilitates a detailed quantitative description of SCF change in the QLM, enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.

Biochar and animal manure application can improve crop yields in salt-affected soil. Previous studies have primarily applied biochar and animal manure either alone or at fixed ratios, while their combined effects with varying combination proportions are still unclear. To address this knowledge gap, we performed a 2-a experiment (2023-2024) in a salinized cotton field in Wensu County of Xinjiang Uygur Autonomous Region of China with the following 6 treatments: control; application of biochar (10 t/hm²) alone (BC100%); application of cow manure (10 t/hm²) alone (CM100%); application of 70% biochar (7 t/hm²) combined with 30% cow manure (3 t/hm²) (BC70%+CM30%); application of 50% biochar (5 t/hm²) combined with 50% cow manure (5 t/hm²) (BC50%+CM50%); and application of 30% biochar (3 t/hm²) combined with 70% cow manure (7 t/hm²) (BC30%+CM70%). By measuring soil pH, electrical conductivity, soil organic matter, available phosphorus, available potassium, and available nitrogen at 0-20 and 20-40 cm depths, as well as yield components and cotton yield in 2023 and 2024, this study revealed that soil nutrients in the 0-20 cm depth were more sensitive to the treatment. Among all the treatments, BC50%+CM50% treatment had the highest value of soil pH (9.63±0.07) but the lowest values of electrical conductivity (161.9±31.8 μS/cm), soil organic matter (1.88±0.27 g/kg), and available potassium (42.72±8.25 mg/kg) in 2024. Moreover, the highest cotton yield (5336.63±467.72 kg/hm²) was also observed under BC50%+CM50% treatment in 2024, which was 1.9 times greater than that under the control treatment. In addition, cotton yield in 2023 was jointly determined by yield components (density and number of cotton bolls) and soil nutrients (available phosphorus and available potassium), but in 2024, cotton yield was only positively related to yield components (density, number of cotton bolls, and single boll weight). Overall, this study highlighted that in salt-affected soil, the combination of biochar and cow manure at a 1:1 ratio is recommended for increasing cotton yield and reducing soil salinity stress.

Snow cover plays a critical role in global climate regulation and hydrological processes. Accurate monitoring is essential for understanding snow distribution patterns, managing water resources, and assessing the impacts of climate change. Remote sensing has become a vital tool for snow monitoring, with the widely used Moderate-resolution Imaging Spectroradiometer (MODIS) snow products from the Terra and Aqua satellites. However, cloud cover often interferes with snow detection, making cloud removal techniques crucial for reliable snow product generation. This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms. Using real-time field camera observations from four stations in the Tianshan Mountains, China, this study assessed the performance of these datasets during three distinct snow periods: the snow accumulation period (September-November), snowmelt period (March-June), and stable snow period (December-February in the following year). The findings showed that cloud-free snow products generated using the Hidden Markov Random Field (HMRF) algorithm consistently outperformed the others, particularly under cloud cover, while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction (STAR) demonstrated varying performance depending on terrain complexity and cloud conditions. This study highlighted the importance of considering terrain features, land cover types, and snow dynamics when selecting cloud removal methods, particularly in areas with rapid snow accumulation and melting. The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning, multi-source data fusion, and advanced remote sensing technologies. By expanding validation efforts and refining cloud removal strategies, more accurate and reliable snow products can be developed, contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.

The western alpine region is an important freshwater supply and water conservation area for China and its surrounding areas. As ecological civilization construction progresses, the ecohydrology of the western alpine region in China, which is a crucial ecological barrier, has undergone significant changes. In this study, we collected 1077 sampling points and presented a comprehensive overview of research results pertaining to the hydrochemistry of river water, meltwater, groundwater, and precipitation in the western alpine region of China using piper diagram, end-member diagram, and hydrological process indication. Water resources in the western alpine region of China were found to be weakly alkaline and have low total dissolved solids (TDS). The mean pH values for river water, meltwater, groundwater, and precipitation are 7.92, 7.58, 7.72, and 7.32, respectively. The mean TDS values for river water, meltwater, groundwater, and precipitation are 280.99, 72.48, 544.41, and 67.68 mg/L. The hydrochemical characteristics of the water resources in this region exhibit significant spatial and temporal variability. These characteristics include higher ion concentrations during the freezing period and higher ion concentrations in inland river basins, such as the Shule River Basin and Tarim River Basin. The principal hydrochemical type of river water and meltwater is HCO₃^-•SO₄^2--Ca²⁺, whereas the principal cations in groundwater are Mg²⁺ and Ca²⁺, and the principal anions are HCO₃^- and SO₄^2-. In terms of precipitation, the principal hydrochemical type is SO₄^2--Ca²⁺. The chemical ions in river water and groundwater are primarily influenced by rock weathering and evaporation-crystallization, whereas the chemical ions in meltwater are mainly affected by rock weathering and atmospheric precipitation, and the chemical ions in precipitation are derived primarily from terrestrial sources. The main forms of water input in the western alpine region of China are precipitation and meltwater, and mutual recharge occurs between river water and groundwater. Hydrochemical characteristics can reflect the impact of human activities on water resources. By synthesizing the regional hydrochemical studies, our findings provide insights for water resources management and ecological security construction in the western alpine region in China.

The release of essential nutrients from soil minerals for plant growth in calcareous soils, facilitated by organic extractants, is critical in semi-arid areas, particularly for elements affected by high soil pH. This study aims to investigate the release of calcium (Ca), magnesium (Mg), and phosphorus (P) through the application of wood vinegar extract in surface calcareous soils in Borojerd City, Lorestan Province, Iran. The experiment was conducted using a completely randomized design with three replications. The treatments included soils from three different land uses: vineyard, wheat field, and rangeland, each treated with 1.00% wood vinegar solution. Cumulative measurements of the specified elements were recorded over 10 consecutive 0.5 h intervals. The release data were analyzed using four various kinetic models (Elovich equation, parabolic diffusion law, power function equation, and zero-order kinetics). The highest concentrations recorded were for Ca (39,500.00 mg/kg), Mg (5880.00 mg/kg), and P (5.00 mg/kg) in grape cultivation. The findings revealed a significant difference in Ca release between grape cultivation and rangeland (P<0.01), while the Mg release showed a significant difference between both grape cultivation and rangeland and wheat cultivation (P<0.01). Additionally, the cumulative release of P showed significant differences between grape cultivation and both wheat and rangeland (P<0.01). The results indicated that the zero-order kinetics provided the best fit for the data (R²=0.99). The maximum initial release amount was observed in grape cultivation when applying the zero-order kinetics, while the highest release rate was achieved using the parabolic diffusion law across three applications. Wood vinegar had the capacity to degrade various clay minerals, including vermiculite, smectite, palygorskite, and, to some extent, illite, resulting in the release of associated elements. Consequently, it can be concluded that wood vinegar can be effectively utilized in grape cultivation as an agent for reducing soil acidity, thereby enhancing the availability of soil nutrients and decreasing reliance on chemical fertilizers.

Cotton, as one of important economic crops, is widely planted in the saline-alkaline soil of southern Xinjiang, China. Moreover, in order to control the saline-alkaline content for seed germination and seedlings survive of cotton, farmers always adopt salt leaching during winter and spring seasons. However, excessive amount of salt leaching might result in the waste of water resources and unsuitable irrigation seasons might further increase soil salinization. In this study, a field experiment was conducted in the saline-alkaline soil in 2020 and 2021 to determine the effects of leaching amount and period on water-salinity dynamics and cotton yield. Five leaching amounts (0.0 (W0), 75.0 (W1), 150.0 (W2), 225.0 (W3), and 300.0 (W4) mm) and three leaching periods (seedling stage (P1), seedling and squaring stages (P2), and seedling, squaring, flowering, and boll setting stages (P3)) were used. In addition, a control treatment (CK) with a leaching amount of 300.0 mm in spring was performed. The soil water-salt dynamics, cotton growth, seed cotton yield, water productivity (WP), and irrigation water productivity (WPI) were analyzed. Results showed that leaching significantly decreased soil electrical conductivity (EC), and W3P2 treatment reduced EC by 11.79% in the 0-100 cm soil depth compared with CK. Plant height, stem diameter, leaf area index, and yield under W3 and W4 treatments were greater than those under W1 and W2 treatments. Compared with W3P1 and W3P3 treatments, seed cotton yield under W3P2 treatment significantly enhanced and reached 6621 kg/hm² in 2020 and 5340 kg/hm² in 2021. Meanwhile, WP and WPI under W3P2 treatment were significantly higher than those under other leaching treatments. In conclusion, the treatment of 225.0 mm leaching amount and seedling and squaring stages-based leaching period was beneficial for the salt control, efficient water utilization, and yield improvement of cotton in southern Xinjiang, China.

Surface soil cracking in alpine meadows signifies the transition of degradation from quantitative accumulation to qualitative deterioration. Quantitative research remains insufficient regarding changes in the mechanical properties of degraded meadow soils and the mechanical thresholds for cracking initiation. This study explored the relationships between surface cracking and the physical properties, tensile strength, and matrix suction of root-soil composites in alpine meadow sites with different stages of degradation (undegraded (UD), lightly degraded (LD), moderately degraded (MD), and heavily degraded (HD)) under different water gradients (high water content (HWC), medium water content (MWC), and low water content (LWC)) corresponding to different drying durations at a constant temperature of 40.0°C. The Huangcheng Mongolian Township in Menyuan Hui Autonomous County, Qinghai Province, China was chosen as the study area. The results indicated that as the degradation degree of alpine meadow intensified, both water content of root-soil composite and the fine grain content of soil decreased. In contrast, the root-soil mass ratio and root area ratio initially increased and then decreased with progressive degradation. Under a consistent water content, the tensile strength of root-soil composite followed a pattern of MD>HD>LD>UD. The peak displacement of tensile strength also decreased as the degradation degree of alpine meadow increased. Both the tensile strength and matrix suction of root-soil composite increased as root-soil water content decreased. A root-soil water content of 30.00%-40.00% was found to be the critical threshold for soil cracking in alpine meadows. Within this range, the matrix suction of root-soil composite ranged from 50.00 to 100.00 kPa, resulting in the formation of linear cracks in the surface soil. As the root-soil water content continued to decrease, liner cracks evolved into branch-like and polygonal patterns. The findings of this study provide essential data for improving the mechanical understanding of grassland cracking and its development process.

Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation. The Mu Us Sandy Land (MUSL), which is situated in the semi-arid zone of northwestern China adjacent to the Loess Plateau, has been at the forefront of desertification and oasis formation over the past two millennia. This study is based on the synthesis of the Normalized Difference Vegetation Index (NDVI) data from MOD13A3 data in the MODIS (Moderate-Resolution Imaging Spectroradiometer) dataset (2002-2021) and climate data (temperature and precipitation) at annual and monthly scales from the National Earth System Science Data Center. A range of analytical methods, including univariate linear regression, Theil-Sen trend analysis and Mann-Kendall significance test, correlation analysis, residual analysis, and Hurst index, were used to explore the response mechanisms of the NDVI to climate change and human activities and to predict the future trends of the NDVI in the MUSL. The results showed that through the method of correlation analysis, in terms of both spatially averaged correlation coefficients and area proportion, the NDVI was positively correlated with temperature and precipitation in 97.59% and 96.51% of the study area, respectively, indicating that temperature has a greater impact on the NDVI than precipitation. Residual analysis quantified the contributions of climate change and human activities to the NDVI changes, revealing that climate change and human activities contribute up to 30.00% and 70.00%, respectively, suggesting that human activities predominantly affect the NDVI changes in the MUSL. The Hurst index was used to categorize the future trend of the NDVI into four main directions of development: continuous degradation (0.05% of the study area), degradation in the past but improvement in the future (54.45%), improvement in the past but degradation in the future (0.13%), and continuous improvement (45.36%). In more than 50.00% of the regions that have been degraded in the past but were expected to improve in the future, the NDVI was expected to exhibit a stable trend of anti-persistent improvement. These findings provide theoretical support for future ecological protection, planning, and the implementation of ecological engineering in the MUSL, and also offer a theoretical basis for the planning and execution of construction projects, environmental protection measures, and the sustainable development of vegetation.

The Loess Plateau (LP), one of the most ecologically fragile regions in China, is affected by severe soil erosion and environmental degradation. Despite large-scale ecological restoration efforts made by Chinese government in recent years, the region continues to face significant ecological challenges due to the combined impact of climate change and human activities. In this context, we developed a kernal Remote Sensing Ecological Index (kRSEI) using Moderate Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine (GEE) platform to analyze the spatiotemporal patterns and trends in ecological environmental quality (EEQ) across the LP from 2000 to 2022 and project future trajectories. Then, we applied partial correlation analysis and multivariate regression residual analysis to further quantify the relative contributions of climate change and human activities to EEQ. During the study period, the kRSEI values exhibited significant spatial heterogeneity, with a stepwise degradation pattern in the southeast to northwest across the LP. The maximum (0.51) and minimum (0.46) values of the kRSEI were observed in 2007 and 2021, respectively. Trend analyses revealed a decline in EEQ across the LP. Hurst exponent analysis predicted a trend of weak anti-persistent development in most of the plateau areas in the future. A positive correlation was identified between kRSEI and precipitation, particularly in the central and western regions; although, improvements were limited by a precipitation threshold of 837.66 mm/a. A moderate increase in temperature was shown to potentially benefit the ecological environment within a certain range; however, temperature of -1.00°C-7.95°C often had a negative impact on the ecosystem. Climate change and human activities jointly influenced 65.78% of LP area on EEQ, primarily having a negative impact. In terms of contribution, human activities played a dominant role in driving changes in EEQ across the plateau. These findings provide crucial insights for accurately assessing the ecological state of the LP and suggest the design of future restoration strategies.

Challenges in land use and land cover (LULC) include rapid urbanization encroaching on agricultural land, leading to fragmentation and loss of natural habitats. However, the effects of urbanization on LULC of different crop types are less concerned. The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region, Mexico, from 1994 to 2024, and predicted the LULC in 2034 using remote sensing data, with the goals of sustainable land management and climate resilience strategies. Despite increasing urbanization and drought, the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region. Using Landsat imagery, we assessed crop attributes through indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and vegetation condition index (VCI), alongside watershed delineation and spectral features. The random forest model was applied to classify LULC, providing insights into both historical and future trends. Results indicated a significant decline in vegetation cover (109.13 km²) from 1994 to 2024, accompanied by an increase in built-up land (75.11 km²) and bare land (67.13 km²). Projections suggested a further decline in vegetation cover (41.51 km²) and continued urban land expansion by 2034. The study found that paddy crops exhibited the highest values, while common bean and maize performed poorly. Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024, highlighting the increasing vulnerability of agriculture to climate change. The study concludes that sustainable land management, improved water resource practices, and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area. These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.

Artificial cyanobacteria crusts are formed by inoculating soil with cyanobacteria. These crusts help prevent soil erosion and restore soil functionality in degraded croplands. However, how fast the artificial cyanobacteria crusts can be formed is a key issue before their practical application. In addition, the effects of artificial cyanobacteria crusts on soil nutrients and plant growth are not fully explored. This study analyzed the effect of inoculation of cyanobacteria from local biological soil crusts on soil nutrients and Pak-choi (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee; Chinese cabbage) growth in a cropland, northern China through field experiments by comparing with no fertilizer. The results showed that artificial cyanobacteria crusts were formed on the 18^th d after inoculation with a coverage of 56.13%, a thickness of 3.74 mm, and biomass of 22.21 μg chla/cm². Artificial cyanobacteria crusts significantly improved the soil organic matter (SOM), NO₃^--N, total nitrogen (TN) contents, and the activities of sucrase, alkaline phosphatase, urease, and catalase enzymes of plants on the 50^th d after inoculation. Additionally, artificial cyanobacteria crusts led to an increase in plant biomass, improved root morphology, and raised the phosphorus and potassium contents in the plants. Furthermore, the biomass of plant grown with artificial cyanobacteria crusts was comparable with that of grown with chemical fertilizer. The study suggested that, considering plant biomass and soil nutrients, it is feasible to prevent wind erosion in the cropland of arid and semi-arid areas by inoculating cyanobacteria crusts. This study provides new perspectives for the sustainable development and environmental management of cropland in arid and semi-arid areas.

Diversity of soil microorganisms in different habitats of arid and semi-arid areas plays an important role in the soil texture and nutrient, promoting the growth of vegetation in those areas. To clarify the response of soil bacterial community diversity to the changes of environmental factors in different habitats, this study collected soil samples under the canopies of Tamarix ramosissima Ledeb. in oasis, transition zone, and desert habitats in the upper reaches of the Tarim River, Northwest China. High-throughput sequencing technology and PICRUSt2 software were used to explore the composition and function of soil bacterial communities in different habitats of T. ramosissima. The results showed that: (1) soil environmental factors under the canopy of T. ramosissima in the three habitats differed significantly, with soil moisture and nutrient conditions being better in the oasis; (2) Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria, and Gemmatimonadetes were the major bacterial communities in the three habitats; (3) soil bacterial community composition under the canopy of T. ramosissima varied greatly, and the richness was significantly different among the three habitats; (4) redundancy analysis indicated that soil water content and available phosphorous were the most important environmental factors influencing the composition of soil bacterial community; and (5) 6 primary functions and 21 secondary functions were obtained by PICRUSt2 function prediction, with metabolism being the most dominant function. This study revealed the response of soil bacterial community composition to habitat changes and their driving factors in the upper reaches of the Tarim River, which could improve the understanding of ecological sensitivity of soil microorganisms in arid and semi-arid areas, and provide a theoretical foundation for improving soil quality and ecological protection.

Integrating sprinkler with deficit irrigation system is a new approach to improve crop water productivity and ensure water and food security in arid areas of India. This study undertook a field experiment of sprinkler-irrigated cumin (variety GC-4) with a mini-lysimeter setup at an experimental research farm in Jodhpur, India during 2019-2022. Four irrigation treatments T₁, T₂, T₃, and T₄ were designed at irrigation water/cumulative pan evaporation (IW/CPE) of 1.0, 0.8, 0.6, and 0.4, respectively, with three replications. Daily actual crop evapotranspiration (ET_c) was recorded and weekly soil moisture was monitored over the crop growth period. Quantities of applied water and drainage from mini-lysimeters were also measured at every irrigation event. Yield of cumin was recorded at crop maturity. Furthermore, change in farmer's net income from 1-hm² land was computed based on the cost of applying irrigation water and considering yield variations among the treatments. Results indicated the highest mean seasonal actual ET_c (371.7 mm) and cumin yield (952.47 kg/hm²) under T₁ (with full irrigation). Under T₂, T₃, and T₄, the seasonal actual ET_c decreased by 10.4%, 27.6%, and 41.3%, respectively, while yield declined by 5.0%, 28.4%, and 50.8%, respectively, as compared to the values under T₁. Furthermore, crop water productivity of 0.272 (±0.068) kg/m³ under T₂ was found relatively higher in comparison to other irrigation treatments, indicating that T₂ can achieve improved water productivity of cumin in arid areas at an optimum level of deficit irrigation. The results of cost-economics indicated that positive change in farmer's net income from 1-hm² land was 108.82 USD under T₂, while T₃ and T₄ showed net losses of 5.33 and 209.67 USD, respectively. Moreover, value of yield response factor and ratio of relative yield reductions to relative ET_c deficits were found to be less than 1.00 under T₂ (0.48), and more than 1.00 under T₃ (1.07) and T₄ (1.23). This finding further supports that T₂ shows the optimized level of deficit irrigation that saves 20.0% of water with sacrificing 5.0% yield in the arid areas of India. Findings of this study provide useful strategies to save irrigation water, bring additional area under irrigation, and improve crop water productivity in India and other similar arid areas in the world.

Soil moisture (SM) is a critical variable in terrestrial ecosystems, especially in arid and semi-arid areas where water sources are limited. Despite its importance, understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient. This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation (ETC), Mann-Kendall test, Theil-Sen estimator, ridge regression analysis, and other relevant methods. The following findings were obtained: (1) at the pixel scale, the long-term monthly SM data from the European Space Agency Climate Change Initiative (ESA CCI) exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error (RMSE) of 0.014 m³/m³; (2) from 2000 to 2022, the study area experienced significant increase in annual average SM, with a rate of 0.408×10^-3 m³/(m³•a). Moreover, higher altitudes showed a notable upward trend, with SM increasing rates at 0.210×10^-3 m³/(m³•a) between 1000 and 2000 m, 0.530×10^-3 m³/(m³•a) between 2000 and 4000 m, and 0.760×10^-3 m³/(m³•a) at altitudes above 4000 m; (3) land surface temperature (LST), root zone soil moisture (RSM) (10-40 cm depth), and normalized difference vegetation index (NDVI) were identified as the primary factors influencing annual average SM, which accounted for 34.37%, 24.16%, and 22.64% relative contributions, respectively; and (4) absolute contribution of LST was more significant in subareas at higher altitudes, with average absolute contributions of 0.800×10^-3 m³/(m³•a) between 2000 and 4000 m and 0.500×10^-2 m³/(m³•a) above 4000 m. This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas, highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas, providing valuable insights for ecological research and water resource management in these areas.

Precipitation isotopes (δ¹⁸O and δ²H) are closely related to meteorological conditions for precipitation generation and the initial state of water vapor source areas, and are essential to the study of the regional hydrological cycle. The deuterium excess (d-excess) indicates deviation in isotope fractionation during evaporation and can trace water vapor sources. This study analyzed 443 precipitation samples collected from the Gannan Plateau, China in 2022 to assess precipitation isotope variations and their driving factors. Water vapor sources were evaluated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Concentration Weighted Trajectory (CWT), and Potential Source Contribution Factor (PSCF) models. Results showed that precipitation isotope values showed significant spatial and temporal variations on the Gannan Plateau. Temporally, precipitation isotope values peaked in June (when evaporation dominated) and minimized in March (depletion effect of air masses in the westerly wind belt). Spatially, the isotope values showed a distribution pattern of "high in the east and low in the west", which was mainly regulated by the differences in altitude and local meteorological conditions. Compared with the global meteoric water line (GMWL) with equation of δ²H=8.00δ¹⁸O+10.00, the slope and intercept of local meteoric water line (LMWL) for precipitation on the Gannan Plateau were smaller (7.49 and 7.63, respectively), reflecting the existence of a stronger secondary evaporation effect under the clouds in the region. The sources of water vapor on the Gannan Plateau showed significant seasonality and spatial heterogeneity. Specifically, the westerly belt and monsoon were the main water vapor transport paths at each sampling point, with Central Asian continental water vapor dominating in spring (53.49%), Indian Ocean water vapor dominating in summer (52.53%), Atlantic Ocean water vapor dominating in autumn (46.74%), and Atlantic Ocean and Mediterranean Sea water vapor dominating in winter (42.30% and 33.68%, respectively). Changes in the intensity of convective activity and Outgoing Longwave Radiation (OLR) affected the enrichment of isotopic values, which exhibited the same change trends as δ¹⁸O. During the precipitation process, the δ¹⁸O value first decreased and then increased. During the initial and final stages of precipitation process, precipitation was mainly influenced by continental air masses, while during the middle stage, it was controlled by marine air masses. The systematic research on precipitation isotopes and water vapor sources is important for climate change research and extreme precipitation prediction on the Gannan Plateau and other similar areas.

Vegetation plays a major role in soil protection against erosion effects, and studies have also highlighted its importance in retaining sediments from roadside slopes. Yet, hydro-sedimentological studies under natural precipitation conditions are still scarce in semi-arid areas due to difficulties in monitoring the few and very concentrated precipitation events. Quantifying sediment connectivity and yield at watershed scale, often highly impacted by the erosion of unpaved roads, is necessary for management plans. This study aims to evaluate the efficiency of native vegetation on roadside slope segments in Caatinga biome in retaining sediments and conserving the soil in a semi-arid area of Brazil. Surface runoff, sediment concentration, and yield measurements were measured from 34 natural precipitation events in four years on two slopes with and without vegetation. The runoff coefficients of the plot with no vegetation varied from 3.0% to 58.0%, while in the vegetated plot, they showed variation from 1.0% to 21.0%. The annual specific sediment yield ranged from 4.6 to 138.7 kg/(hm²•a) for the vegetated plot and from 34.9 to 608.5 kg/(hm²•a) for the unvegetated one. These results indicate a 4 to 12 times higher soil loss on the unvegetated slope in relation to the vegetated one and demonstrate that natural Caatinga vegetation acts as an effective barrier against surface-transported sediments. Moreover, natural Caatinga vegetation present on the slope plays an important role in breaking connectivity between sediment flows from unpaved roads and the watershed drainage system. These findings indicate that investments in unpaved road and roadside slope restoration, not only enhance road infrastructure but also promote environmental gains by reducing the impact of erosion.

Understanding plant community assembly is crucial for effective ecosystem conservation and restoration. The ecological filter framework describes community assembly as a process shaped by dispersal, environmental, and biotic filters. Additionally, functional traits and phylogenetic relationships are increasingly recognized as important factors influencing species coexistence and community structure. However, both the ecological filter framework and the roles of functional traits and phylogeny in community assembly remain underexplored in the Algerian steppes—particularly in the El Bayadh region, where ongoing vegetation degradation threatens ecosystem stability. This study applied Hierarchical Modeling of Species Communities (HMSC) as an integrative approach to assess how ecological filters influence plant community assembly in the El Bayadh steppe and to evaluate the roles of functional traits and phylogenetic relationships in this process. Environmental data—including soil properties, topography, precipitation, and land use types (grazing and exclosure)—were collected across 50 plots in April and October, 2023, along with functional traits from 24 species. These traits include root length, leaf area, specific leaf area, clonality, life history, and seed mass. HMSC results revealed that soil properties and precipitation were the primary drivers of community structure, while sand height and elevation had a moderate influence. In contrast, competition and grazing played relatively minor roles. Species responses to environmental covariates were heterogeneous: soil fertility and texture had mixed effects, benefiting some species while limiting others; sand encroachment and precipitation variability generally had negative impacts, whereas grazing exclusion favored many species. A weak phylogenetic signal was recorded, indicating that community assembly was driven more by environmental filtering than by shared evolutionary history. Functional trait responses to environmental variation reflected plant strategies that balanced resource acquisition and conservation. Specifically, seed mass, leaf area, and root length increased under higher soil moisture and nutrient availability but declined in response to salinity, precipitation variability, and sand height. Clonality and perennial life history traits enhanced the survival of plant species under harsh conditions. Overall, this study provides a holistic understanding of community assembly processes in the El Bayadh steppe and offers valuable insights for ecosystem management and restoration in arid and degraded ecosystem environments.

Flash drought is characterized by a period of rapid drought intensification with impacts on agriculture, water resources, ecosystems, and human environment. In the Qilian Mountains, northwestern China, flash droughts are becoming more frequently due to the global climate warming. However, the spatiotemporal variations and their driving factors of flash droughts are not clear in this region. In this study, the European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v5-Land (ERA5-Land) dataset was utilized to identify two types of flash drought events (heatwave-induced and water scarcity-induced flash drought events) that occurred in the growing season (April‒September) during 1981-2020 in this area. The results showed that the frequency of heatwave-induced flash droughts has decreased since 2010, while the frequency of water scarcity-induced flash droughts has declined markedly. Spatially, heatwave-induced flash droughts were predominantly concentrated in the western Qilian Mountains, whereas water scarcity-induced flash droughts were primarily concentrated in the central and eastern Qilian Mountains. A significantly increasing temporal trend in both types of flash droughts in the eastern Qilian Mountains was found. Meanwhile, there was a decreasing temporal trend of heatwave-induced flash droughts in the southwestern part of the region. Additionally, the influence of two major atmospheric modes, i.e., the El Niño‒Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), on these two types of flash droughts was explored by the Superposed Epoch Analysis. The ENSO mainly influences flash droughts in the central and eastern parts of the Qilian Mountains by altering the strength of the East Asian monsoon, while the NAO mainly affects flash droughts in the entire parts of the Qilian Mountains by inducing anomalous westerlies activity. Our findings have important implications for predicting the evolution of flash drought events in the Qilian Mountains region under continued climate warming.

The escalating global demand for sustainable agriculture necessitates the development of effective biological alternatives to conventional chemical fertilizers, particularly those addressing phosphorus (P) use efficiency. This study focused on the isolation and detailed characterization of phosphate-solubilizing fungi from soil or compost to evaluate their impact and potential for use as biofertilizers. Fungal isolation was performed using serial dilution from various sources, followed by molecular and morphological characterization to identify promising strains. Four strains were ultimately selected and identified using morphological, biochemical, and molecular techniques: Aspergillus flavus (CM1), Penicillium crustosum (C3), Penicillium fellutanum (C4), and Metarhizium robertsii (J1). The most active strain was initially tested in liquid and solid media supplemented with synthetic P (Ca₃(PO₄)₂) and was evaluated by measuring fungal biomass and P titration. This strain demonstrated good growth and activity, supporting an optimal temperature of 25°C, a pH of 3, an ammonium concentration of 1.5 g/L, and a glucose addition of 25.0 g/L. The biofertilization potential of the selected strains was then comprehensively evaluated through controlled experiments, including the optimization of growing conditions, quanti fication of soluble P under hermetic storage in soil, and measurement of soil fungal populations to assess their impact. P transformation experiments conducted in hermetic jars showed that CM1 had the highest CO₂ release (approximately 7115.30 mg CO₂/100 g soil) and the highest soluble P levels at the final sampling time (78.85 mg/L), thus outperforming the other strains. Furthermore, in soil hermetic jars, CM1 (reaching up to 26×10⁴ CFU (colony forming units)/g soil) and C4 significantly enhanced soil microbial activity and P bioavailability. These results clearly highlight the potential of the selected fungal strains as biofertilizers to improve P availability and boost crop productivity in P-deficient soils.

Land use in arid and semi-arid regions has a substantial effect on climate, environment, and biodiversity, thereby projecting the spatiotemporal changes in land use and the subsequent effects. This study employed the locally calibrated Future Land Use Simulation (FLUS) model, which coupled system dynamics with cellular automata and integrated an artificial neural network algorithm and a roulette wheel selection mechanism. We projected future land use (2020-2100) dynamics of Lanzhou, a typical river valley city in Northwest China, under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The simulation results were validated and subsequently reclassified using the International Geosphere Biosphere Programme (IGBP) system to produce a dataset suitable for driving climatic and environmental models. Under the SSP1-2.6 scenario, urban and built-up land expanded consistently, whereas irrigated cropland and pasture as well as grassland contracted continuously. Conversely, the SSP5-8.5 scenario was characterized by a contraction of urban and built-up land, and relative stability of irrigated cropland and pasture as well as grassland. The SSP2-4.5 scenario presented a more complex trade-off, where urban and built-up land and grassland increased first and then decreased, whereas irrigated cropland and pasture followed an opposite trajectory. A significant inverse relationship between urban and built-up land and irrigated cropland and pasture was observed under all scenarios, underscoring the fundamental spatial competition that prevailed in this land-constrained valley city. Furthermore, the negative correlation of grassland with urban and built-up land, coupled with the positive correlation of grassland with irrigated cropland and pasture under both the SSP1-2.6 and SSP5-8.5 scenarios, indicated an evolution from broad confrontation to intricate internal trade-offs within the urban-agricultural-ecological system. This study underscored the critical influence of regional topographic and hydrological constraints on land-use evolution in arid regions, providing guidance for water resource management and ecosystem protection in Lanzhou, with applications for sustainable land-use planning in other arid and semi-arid river valley cities.

Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification, yet where net primary productivity (NPP) degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China. We integrated multi-source remote sensing with the Carnegie-Ames-Stanford Approach (CASA) model to estimate NPP during 2000-2020, assessed trend persistence using the Hurst exponent, and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020, while regional mean NPP rose from 138.97 to 160.69 g C/(m²•a). Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall. Although NPP increased across 64.11% of the region during 2000-2020, persistence analysis suggested that 53.93% of the Tianshan Mountains was prone to continued NPP decline, including 36.41% with significant projected decline and 17.52% with weak projected decline; these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains. In contrast, potential improvement was limited (strong persistent improvement: 4.97%; strong anti-persistent improvement: 0.36%). Driver attribution indicated that land use dominated NPP variability (mean absolute SHAP value=29.54%), followed by precipitation (16.03%) and temperature (11.05%). SHAP dependence analyses showed that precipitation effects stabilized at 300.00-400.00 mm, and temperature exhibited an inverted U-shaped response with a peak near 0.00°C. These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion, highlighting the need for threshold-informed, spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.

Central Asia is characterized by an arid climate and widespread desert distribution, with its sustainable development severely constrained by dust events. An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area. Based on the meteorological observations from 2000 to 2020, we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries (Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan) via Theil-Sen trend analysis and Geodetector modeling method, quantitatively revealing the influence of environmental factors, such as temperature, precipitation, and vegetation, on the frequency of dust weather. The results showed that: (1) dust weather in Central Asia was mainly distributed in a large ''dust belt'' extending from west to east from northern part of the Caspian lowland desert, and concentrated in basins, plains, and other low-altitude areas. Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia, with a maximum annual frequency of 21.9%; (2) strong dust weather in Central Asia has fluctuated and slightly decreased since 2001. The highest frequency (1.1%) occurred in spring (from March to June); (3) from 2000 to 2020, changes such as spot shifting and shrinking occurred in the four main source areas (north of the Aral Sea, Kyzylkum Desert, Karakum Desert, and Garabogazköl Bay region), where sandstorms occurred in Central Asia, and northern Caspian lowland desert became the most important low-emission dust source in Central Asia; and (4) the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia. This study provides a theoretical basis for sand prevention and sand control in Central Asia. In the future, Central Asia should focus on the rational utilization of land and water resources, and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.

The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets. Conventional vegetation indices, specifically the Normalized Difference Vegetation Index (NDVI), perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils. This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index (MSAVI) for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery (30 m resolution). A spectral index-based classification framework was developed, combining MSAVI with the Normalized Difference Water Index (NDWI) and Salinity Index 1 (SI1) to reduce spectral confusion between vegetation, saline soils, and surface water. The MSAVI-based classification achieved an overall accuracy of 77.96% (Kappa coefficient=0.71), supported by 313 field-collected validation points from 2023. While the multi-index approach enabled finer discrimination of ecologically important classes, particularly separating salt pans from solonchak soils, it resulted in a lower overall accuracy (73.80%), highlighting a trade-off between class separability and classification performance. Land cover change analysis revealed a highly dynamic landscape, with 52.96% of the study area transitioning between classes over the decade. Transformed areas (16,893 km²) exceeded stable zones (15,004 km²), driven primarily by rapid desiccation and salinization. Solonchak soils increased at an annual rate of 5.58%, while surface water bodies declined by 4.83% per year. Concurrently, sparse or distressed vegetation increased by 1.43% annually, reflecting ongoing afforestation efforts. This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail. The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.

In the northern Tarim River Basin, the Weigan River Basin is a critical endorheic system characterized by extreme aridity, where drought poses a major natural hazard to agricultural production and ecological stability. This study assessed the future evolution of drought under climate change by employing the standardized moisture anomaly index (SZI) on the basis of multi-model the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations under historical conditions (1970-2014) and future scenarios (shared socioeconomic pathway (SSP)1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2015-2100). The results show that precipitation-evapotranspiration anomalies are projected to first decline but then increase over time, with increased fluctuations and uncertainty under high-emission scenarios (SSP5-8.5). These trends indicate intensifying drought risks and reveal a strong influence of emission pathways on regional water cycling. Temporal analysis of SZI indicates a transition from wetting to drying under low- and medium-emission pathways (SSP1-2.6 and SSP2-4.5), whereas high-emission scenarios are characterized by persistent drying and increased variability. The significant lower-tail dependence (0.271) observed under SSP2-4.5 and SSP5-8.5 suggests that extreme droughts may be subject to nonlinear co-amplification across scenarios. The frequency of moderate and more severe drought events is expected to increase substantially, especially under SSP5-8.5, where drought occurrence is predicted to extend into spring and autumn and become more evenly distributed throughout the year. Spatially, drought duration shows significant positive autocorrelation across all scenarios, with hot spots consistently concentrated in the southern and southeastern regions of the basin. Random forest analysis, interpreted as association-based pattern attribution, indicates that meteorological variables (precipitation and potential evapotranspiration (PET)) make the greatest contributions to the hot spot pattern, followed by topography and soil moisture. Among land use categories, farmland generally shows higher drought sensitivity than other land use types, as reflected by its relative contribution patterns across scenarios. The spatial pattern of drought is statistically structured by climatic forcing, surface conditions, and soil moisture status, reflecting their coupled associations with hot spot occurrence. In addition, a drought spatial uncertainty index was constructed from multi-scenario hot spot maps, revealing spatially heterogeneous structural variability throughout the basin. Correlation analysis further highlights strong internal couplings among environmental variables (e.g., elevation-linked hydroclimatic gradients and grassland-bare soil contrasts). These findings offer a scientific basis for developing region-specific drought monitoring and adaptation strategies under future climate change conditions.

Arid and semi-arid ecosystems are prone to extensive fires due to specific climatic conditions, sparse vegetation cover, and high density of fine fuels. Understanding the flammability characteristics of land covers is essential for fire management and designing land restoration programs in arid and semi-arid ecosystems. This study provided a new approach to evaluate the flammability of shrublands and woodlands using flammability indices (FIs) including time to ignition (TI), duration of combustion (DC), and flame height (FH) of plant species and their relative frequencies in the Dalfard Basin of southeastern Iran. The results showed that there was a significant difference in FIs between land covers. Shrublands had higher flammability potential compared with woodlands. Plant moisture content had a negative relationship with TI (P<0.010) and no significant relationship with DC and FH (P>0.050). Artemisia spp., Astragalus gossypinus Fischer, Amygdalus scoparia Spach, and Cymbopogon jwarancusa (Jones) Schult. had the highest FI. Tree species such as Rhazya stricta Decne., and Pistacia atlantica Desf. showed greater resistance to fire. Using principal component analysis, the relationship between species and FIs was examined, and TI of wet fuel was the most important FI in relation to species. Structural equation model showed that life form (P<0.001) was the most important flammability driver. Precipitation (P<0.010) and legume species (P<0.010) were significantly related to the flammability in arid land. This study emphasizes the importance of managing high-risk species and using resistant species in vegetation restoration and shows that combining species FIs with their abundance is an effective tool for assessing fire risk and fuel management at the plant community scale.

Fire is a fundamental ecological driver shaping natural vegetation patterns. In the semi-arid southern Espinal-Monte ecotone of Argentina, the spatiotemporal patterns of fire occurrence related to and modulated by climatic gradients and antecedent conditions are not well researched. This study examined fire occurrence in the semi-arid southern Espinal-Monte ecotone (southeastern La Pampa, northeastern Río Negro, and southwestern Buenos Aires with an area of 68×10³ km²) of Argentina, a key environmental transition zone with pronounced climatic and vegetation gradients. The objective was to identify the spatiotemporal patterns of fire occurrence and their relationship with climatic variables. Thermal anomaly (TA) data from the MODIS (Moderate Resolution Imaging Spectrometer; MOD14) sensor (November 2000-March 2020) with confidence levels >65.0% were analyzed. Climatic variables (rainfall isohyets and aridity indices) were obtained from the WorldClim datasets, and annual meteorological conditions (rainfall and potential evapotranspiration) were calculated using the climatic research unit (CRU) database. Monthly data and moving averages of rainfall and aridity indices from distinct periods (two and three years preceding fire events) were integrated. Spatial analysis was conducted using kernel density estimation on a 10 km×10 km grid to correlate TA with climatic gradients, while linear regression examined relationships between summer TA and meteorological variables over different periods. Results showed that the highest fire occurrence was recorded in summer, with peaks in December and January. Spatially, 55.0% of TA occurred in areas with annual rainfall of 300-400 mm, and 64.5% in areas with an aridity index of 0.3-0.4, forming an arc-like distribution in the center of the ecotone. The highest TA densities were observed in southeastern La Pampa and northeastern Río Negro, decreasing toward southwestern Buenos Aires. Significant correlations (R²>0.700) were found among TA accumulation, aridity index values, and cumulative rainfall from previous two and three years, at both vegetation unit and provincial levels. Summer was the critical season for fire occurrence, with spatial distribution primarily determined by the interaction between climatic conditions and woody biomass availability. The lower fire incidence in southwestern Buenos Aires was linked to sparse woody vegetation and agricultural expansion, which reduced fuel load. These findings reinforce that fuel availability, modulated by climatic conditions from previous years, is a key limiting factor for fire dynamics in this area, and that human activities such as agriculture and grazing alter fire regimes by affecting fuel structure and continuity.