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.

The implementation of long-term shelterbelt programs in the middle reaches of the Yellow River (MRYR), China not only has improved the overall ecological environment, but also has led to the changes of land use pattern, causing carbon storage exchanges. However, the relationship between carbon storage and land use change in the MRYR is not concerned, which results in the uncertainty in the simulation of carbon storage in this area. Land use changes directly affect the carbon storage capacity of ecosystems, and as an indicator reflecting the overall state of land use, land use degree has an important relationship with carbon storage. In this study, land use data and the integrated valuation of ecosystem services and trade-offs (InVEST) model were used to assess the trends in land use degree and carbon storage in the MRYR during 1980-2020. The potential impact index and the standard deviation ellipse (SDE) algorithm were applied to quantify and analyze the characteristics of the impact of land use changes on carbon storage. Subsequently, land use transitions that led to carbon storage variations and their spatial variations were determined. The results showed that: (1) the most significant periods of carbon storage changes and land use transitions were observed during 1990-1995 and 1995-2020, with the most changed areas locating in the east of Fenhe River and in northwestern Henan Province; (2) the positive impact of land use degree on carbon storage may be related to the environmental protection measures implemented along the Yellow River, while the negative impact may be associated with the expansion of construction land in plain areas; and (3) the conversion of other land use types to grassland was the primary factor affecting carbon storage changes during 1980-2020. In future land use planning, attention should be given to the direction of grassland conversion, and focus on reasonably limiting the development of construction land. To enhance carbon storage, it will be crucial to increase the area of high-carbon-density land types, such as forest land and grassland under the condition that the area of permanent farmland does not decrease.

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.

In floristic research, the grid mapping method is a crucial and highly effective tool for investigating the flora of specific regions. This methodology aids in the collection of comprehensive data, thereby promoting a thorough understanding of regional plant diversity. This paper presents findings from a grid mapping study conducted in the Surkhan-Sherabad botanical-geographic region (SShBGR), acknowledged as one of the major floristic areas in southwestern Uzbekistan. Using an expansive dataset of 14,317 records comprised of herbarium specimens and field diary entries collected from 1897 to 2023, we evaluated the stages and seasonal dynamics of data accumulation, species richness (SR), and collection density (CD) within 5 km×5 km grid cells. We further examined the taxonomic and life form composition of the region's flora. Our analysis revealed that the grid mapping phase (2021-2023) produced a significantly greater volume of specimens and taxonomic diversity compared with other periods (1897-1940, 1941-1993, and 1994-2020). Field research spanned 206 grid cells during 2021-2023, resulting in 11,883 samples, including 6469 herbarium specimens and 5414 field records. Overall, fieldwork covered 251 of the 253 grid cells within the SShBGR. Notably, the highest species diversity was documented in the B198 grid cell, recording 160 species. In terms of collection density, the E198 grid cell produced 475 samples. Overall, we identified 1053 species distributed across 439 genera and 78 families in the SShBGR. The flora of this region aligned significantly with the dominant families commonly found in the Holarctic, highlighting vital ecological connections. Among our findings, the Asteraceae family was the most polymorphic, with 147 species, followed by the continually stable and diverse Poaceae, Fabaceae, Brassicaceae, and Amaranthaceae. Besides, our analysis revealed a predominance of therophyte life forms, which constituted 52% (552 species) of the total flora. The findings underscore the necessity for continual data collection efforts to further enhance our understanding of the biodiversity in the SShBGR. The results of this study demonstrated that the application of grid-based mapping in floristic studies proves to be an effective tool for assessing biodiversity and identifying key taxonomic groups.

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.

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.

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.

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.

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.

Water is essential for agricultural production; however, climate change has exacerbated drought and water stress in arid and semi-arid areas such as Iran. Despite these challenges, irrigation water efficiency remains low, and current water management schemes are inadequate. Consequently, Iranian crops suffer from low water productivity, highlighting the urgent need for enhanced productivity and improved water management strategies. In this study, we investigated irrigation management conditions in the Hamidiyeh farm, Khuzestan Province, Iran and used the calibrated AquaCrop and WinSRFR (a surface irrigation simulation model) models to reflect these conditions. Subsequently, we examined different management scenarios using each model and evaluated the results from the second year. The findings demonstrated that combining simulation of the AquaCrop and WinSRFR models was highly effective and could be employed for irrigation management in the field. The AquaCrop model accurately simulated wheat yield in the first year, being 2.6 t/hm², which closely aligned with the measured yield of 3.0 t/hm². Additionally, using the WinSRFR model to adjust the length of existing borders from 200 to 180 m resulted in a 45.0% increase in efficiency during the second year. To enhance water use efficiency in the field, we recommended adopting borders with a length of 180 m, a width of 10 m, and a flow rate of 15 to 18 L/s. The AquaCrop and WinSRFR models accurately predicted border irrigation conditions, achieving the highest water use efficiency at a flow rate of 18 L/s. Combining these models increased farmers' average water consumption efficiency from 0.30 to 0.99 kg/m³ in the second year. Therefore, the results obtained from the AquaCrop and WinSRFR models are within a reasonable range and consistent with international recommendations. This adjustment is projected to improve the water use efficiency in the field by approximately 45.0% when utilizing the border irrigation method. Therefore, integrating these two models can provide comprehensive management solutions for regional farmers.

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.

In recent decades, global climate change and overgrazing have led to severe degradation of alpine meadows. Understanding the changes in soil characteristics and vegetation communities in alpine meadows with different degrees of degradation is helpful to reveal the mechanism of degradation process and take the remediation measures effectively. This study analyzed the changes in vegetation types and soil characteristics and their interrelationships under three degradation degrees, i.e., non-degradation (ND), moderate degradation (MD), and severe degradation (SD) in the alpine meadows of northeastern Qinghai-Xizang Plateau, China through the long-term observation. Results showed that the aggressive degradation changed the plant species, with the vegetation altering from leguminous and gramineous to forbs and harmful grasses. The Pielou evenness and Simpson index increased by 24.58% and 7.01%, respectively, the Shannon-Wiener index decreased by 17.52%, and the species richness index remained constant. Soil conductivity, soil organic matter, total potassium, available potassium, and porosity declined. However, the number of vegetation species increased in MD. Compared with ND, the plant diversity in MD enhanced by 8.33%, 8.69%, and 7.41% at family, genus, and species levels, respectively. In conclusion, changes in soil properties due to degradation can significantly influence the condition of above-ground vegetation. Plant diversity increases, which improves the structure of belowground network. These findings may contribute to designing better protection measures of alpine meadows against global climate change and overgrazing.

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.

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.

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.

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.

The Qinghai-Xizang Plateau (QXP) serves as a crucial ecological barrier in China and Asia, exerting profound influences on global climate and biodiversity conservation. Gannan Tibetan Autonomous Prefecture (hereinafter referred as Gannan Prefecture), located on the northeastern edge of the QXP, represents a fragile alpine ecosystem in which land use change significantly impacts ecosystem services (ESs). This study established a comprehensive framework, utilizing the Patch-generating Land-Use Simulation (PLUS) model coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to predict land use patterns under the natural development scenario, cultivated land protection scenario, and ecological protection scenario for Gannan Prefecture by 2030 and evaluated four critical ESs: habitat quality (HQ), water yield (WY), soil retention (SR), and carbon storage (CS). The primary aim is to elucidate the impacts of dynamic land use change on ESs. The results revealed that, from 2000 to 2020, HQ exhibited minimal variation, whereas CS experienced a slight decline. Conversely, WY and SR showed significant improvements. Under the natural development scenario, construction land was projected to increase by 4247.74 hm², primarily at the expense of forest land. The cultivated land protection scenario anticipated an increase in farmland by 2634.36 hm², which was crucial for maintaining food security. The ecological protection scenario predicted a notable expansion of forest land, accompanied by a restrained development rate of construction land. The ecological protection scenario also showed an increase in the ecosystem service index (ESI), encompassing 26.07% of the region. Forest land and grassland emerged as the primary contributors to ESs, while construction land substantially impacted WY. Water bodies exhibited minimal contribution to ESs. This study enhanced the understanding of land use change impacts on ESs in fragile and high-altitude ecosystems, offering essential theoretical frameworks and practical direction for forthcoming ecological policy and regional planning endeavors.

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.

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.

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.

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.

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.

Understanding the ecological evolution is of great significance in addressing the impacts of climate change and human activities. However, the ecological evolution and its drivers remain inadequately explored in arid and semi-arid areas. This study took the Helan Mountain, a typical arid and semi-arid area in China, as the study area. By adopting an Enhanced Remote Sensing Ecological Index (ERSEI) that integrates the habitat quality (HQ) index with the Remote Sensing Ecological Index (RSEI), we quantified the ecological environment quality of the Helan Mountain during 2010-2022 and analyzed the driving factors behind the changes. Principal Component Analysis (PCA) was used to validate the composite ERSEI, enabling the extraction of key features and the reduction of redundant information. The results showed that the contributions of first principal component (PC1) for ERSEI and RSEI were 80.23% and 78.72%, respectively, indicating that the ERSEI can provide higher precision and more details than the RSEI in assessing ecological environment quality. Temporally, the ERSEI in the Helan Mountain exhibited an initial decline followed by an increase from 2010 to 2022, with the average value of ERSEI ranging between 0.298 and 0.346. Spatially, the ERSEI showed a trend of being higher in the southwest and lower in the northeast, with high-quality ecological environments mainly concentrated in the western foothills at higher altitudes. The centroid of ERSEI shifted northeastward toward Helan County from 2010 to 2022. Temperature and digital elevation model (DEM) emerged as the primary drivers of ERSEI changes. This study highlights the necessity of using comprehensive monitoring tools to guide policy-making and conservation strategies, ensuring the resilience of fragile ecosystems in the face of ongoing climatic and anthropogenic pressures. The findings offer valuable insights for the sustainable management and conservation in arid and semi-arid ecosystems.

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.

The evolution of land use patterns and the emergence of urban heat islands (UHI) over time are critical issues in city development strategies. This study aims to establish a model that maps the correlation between changes in land use and land surface temperature (LST) in the Mashhad City, northeastern Iran. Employing the Google Earth Engine (GEE) platform, we calculated the LST and extracted land use maps from 1985 to 2020. The convolutional neural network (CNN) approach was utilized to deeply explore the relationship between the LST and land use. The obtained results were compared with the standard machine learning (ML) methods such as support vector machine (SVM), random forest (RF), and linear regression. The results revealed a 1.00°C-2.00°C increase in the LST across various land use categories. This variation in temperature increases across different land use types suggested that, in addition to global warming and climatic changes, temperature rise was strongly influenced by land use changes. The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C, while forest lands experienced the smallest increase of 1.19°C. The developed CNN demonstrated an overall prediction accuracy of 91.60%, significantly outperforming linear regression and standard ML methods, due to the ability to extract higher level features. Furthermore, the deep neural network (DNN) modeling indicated that the urban lands, comprising 69.57% and 71.34% of the studied area, were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030, respectively. In conclusion, the LST predictioin framework, combining the GEE platform and CNN method, provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

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.

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.

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.

Under current climate warming, the growth resilience of plantation forests after extreme droughts has garnered increasing attention. Platycladus orientalis Linn. is an evergreen tree species commonly used for afforestation, and the stability of P. orientalis plantation forests in the Loess Hilly region directly affects the ecological and environmental security of the entire Loess Plateau of China. However, systematic analyses of the growth resilience of P. orientalis plantation forests after extreme droughts along precipitation gradients remain scarce. In this study, we collected tree ring samples of P. orientalis along a precipitation gradient (255, 400, and 517 mm) from 2021 to 2023 and used dendroecological methods to explore the growth resilience of P. orientalis to drought stress on the Loess Plateau. Our findings revealed that the growth resilience of P. orientalis increased with increasing precipitation, enabling the trees to recover to the pre-drought growth levels. In regions with low precipitation (255 mm), the plantation forests were more sensitive to extreme droughts, struggling to recover to previous growth levels, necessitating conditional artificial irrigation. In regions with medium precipitation (400 mm), the growth of P. orientalis was significantly limited by drought stress and exhibited some recovery ability after extreme droughts, therefore warranting management through rainwater harvesting and conservation measures. Conversely, in regions with high precipitation (517 mm), the impacts of extreme droughts on P. orientalis plantation forests were relatively minor. This study underscored the need for targeted strategies tailored to different precipitation conditions rather than a "one-size-fits-all" approach to utilize precipitation resources effectively and maximize the ecological benefits of plantation forests. The findings will help maintain the stability of plantation forests and improve their ecosystem service functions in arid and semi-arid areas.

The Tarim River Basin (TRB) is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China. In the context of climate change, however, the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production. To better understand how unfavorable climatic conditions affect crop production, we explored the relationship of extreme weather and climate events with crop yields and phenology. In this research, ten indicators of extreme weather and climate events (consecutive dry days (CDD), min Tmax (TXn), max Tmin (TNx), tropical nights (TR), warm days (Tx90p), warm nights (Tn90p), summer days (SU), frost days (FD), very wet days (R95p), and windy days (WD)) were selected to analyze the impact of spatial and temporal variations on the yields of major crops (wheat, maize, and cotton) in the TRB from 1990 to 2020. The three key findings of this research were as follows: extreme temperatures in southwestern TRB showed an increasing trend, with higher extreme temperatures at night, while the occurrence of extreme weather and climate events in northeastern TRB was relatively low. The number of FD was on the rise, while WD also increased in recent years. Crop yields were higher in the northeast compared with the southwest, and wheat, maize, and cotton yields generally showed an increasing trend despite an earlier decline. The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices (TNx, Tn90p, TR, and FD), extreme daytime temperature indices (TXn, Tx90p, and SU), extreme precipitation indices (CDD and R95p), and extreme wind (WD). By using Random Forest (RF) approach to determine the effects of different extreme weather and climate events on the yields of different crops, we found that the importance of extreme precipitation indices (CDD and R95p) to crop yield decreased significantly over time. As well, we found that the importance of the extreme nighttime temperature (TR and TNx) for the yields of the three crops increased during 2005-2020 compared with 1990-2005. The impact of extreme temperature events on wheat, maize, and cotton yields in the TRB is becoming increasingly significant, and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.

Amid global climate change, rising levels of nitrogen (N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation (a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange (NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model (SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE. However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.

The Liaohe River Basin (LRB) in Northeast China, a critical agricultural and industrial zone, has faced escalating water resource pressures in recent decades due to rapid urbanization, intensified land use changes, and climate variability. Understanding the spatiotemporal dynamics of water yield and its driving factors is essential for sustainable water resource management in this ecologically sensitive region. This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to quantify the spatiotemporal patterns of water yield in the LRB (dividing into six sub-basins from east to west: East Liaohe River Basin (ELRB), Taizi River Basin (TRB), Middle Liaohe River Basin (MLRB), West Liaohe River Basin (WLRB), Xinkai River Basin (XRB), and Wulijimuren River Basin (WRB)) from 1993 to 2022, with a focus on the impacts of climate change and land use cover change (LUCC). Results revealed that the LRB had an average annual precipitation of 483.15 mm, with an average annual water yield of 247.54 mm, both showing significant upward trend over the 30-a period. Spatially, water yield demonstrated significant heterogeneity, with higher values in southeastern sub-basins and lower values in northwestern sub-basins. The TRB exhibited the highest water yield due to abundant precipitation and favorable topography, while the WRB recorded the lowest water yield owing to arid conditions and sparse vegetation. Precipitation played a significant role in shaping the annual fluctuations and total volume of water yield, with its variability exerting substantially greater impacts than actual evapotranspiration (AET) and LUCC. However, LUCC, particularly cultivated land expansion and grassland reduction, significantly reshaped the spatial distribution of water yield by modifying surface runoff and infiltration patterns. This study provides critical insights into the spatiotemporal dynamics of water yield in the LRB, emphasizing the synergistic effects of climate change and land use change, which are pivotal for optimizing water resource management and advancing regional ecological conservation.

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.

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.

Salt stress might be an important factor that decreases the emergence of seedlings and reduces plants' growth, causing their endangered status. However, the effects of salt stress on the germination of the Mediterranean species are less concern. Limonium strictissimum (Salzm.) Arrigoni, part of the group related to Limonium articulatum (Loisel.) Kuntze, is a Cyrno-Sardinian endemism, which is included in the International Union for Conservation of Nature (IUCN) Red Lists as Endangered (EN). Fresh seeds of L. strictissimum harvested from the only known population in Sardinia and a population in southeastern Corsica were used with the aims to study the effects of temperature and salinity on the seed germination and to evaluate the ability to recover their germination after exposure to salt stress. In both populations, empty fruits with a lower percentage were observed in Sardinia than in Corsica. The seeds showed a high germination capacity, which was not influenced by temperature and did not even differ between the two populations. Compared with non-saline condition, the presence of salt in the substrate, independently of the concentration tested, inhibited seed germination in both populations, with germination percentages never exceeding 40%. The Sardinian population showed a recovery capacity after exposure to high salt stress, always above 50% regardless of the salt concentration and incubation temperature considered. The seeds previously exposed to 125 mM NaCl at 30°C had the lowest recovery percentage (56%), while the highest recovery percentage (84%) was recorded at 25°C with a concentration of 250 mM NaCl. Differently, the Corsican population recorded a higher recovery percentage (54%) in seeds previously exposed to 500 mM NaCl at 20°C, while for the concentration of 125 mM NaCl, the best recovery percentage (11%) was recorded at 30°C. The ability of L. strictissimum to recover germination after salt exposure can be interpreted as an adaptation to the coastal habitats in which it grows. This study provides new insights into the ecophysiology of L. strictissimum seed germination, which could help preserve and implement effective conservation measures for this endangered species with restricted populations.

The net primary productivity (NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau (QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index (NDVI) and the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI_3g datasets to obtain a NDVI dataset (1982-2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach (CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m²•a), and 217.52 g C/(m²•a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982-2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m²•a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation (R99p), simple daily intensity index (SDII), and hottest day (TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index (CSDI) and warm spell duration index (WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.

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.