Search Results (2,239)

Aeolian sand hazards severely constrain highway safety and operation in arid regions. To support targeted mitigation along Highway S315 in the Gobi Desert of northern China, this study integrates meteorological observations with sand removal records to quantify wind regimes and classify sand hazard intensity. Event thresholds were objectively identified using change points in semi-logarithmic distributions of daily sand removal volumes, and spatial hazard severity was graded based on annual sand removal per unit road length. The results showed that (1) the study area was subject to intense aeolian activity, with a mean annual sand-driving wind frequency of 23.98%, an annual drift potential of 344.91 vector units (VU), and a resultant sand transport direction of 129.88° (east–southeast). (2) Based on inflection point characteristics, sand hazard events were classified into three intensity levels, namely, slight (<800 m³), moderate (800–3000 m³), and severe (>3000 m³), accounting for 13.0%, 76.1%, and 10.9% of all events along Highway S315, respectively. (3) Spatial grading criteria for sand hazard severity were defined as slight (<3 × 10³ m³ km⁻¹ yr⁻¹), moderate (3 × 10³–1.0 × 10⁴ m³ km⁻¹ yr⁻¹), and severe (>1.0 × 10⁴ m³ km⁻¹ yr⁻¹). Application of these criteria to a representative road section (K9+000–K30+600; 21.6 km) indicated that severe, moderate, and slight sand hazard segments extend over 6.0 km, 9.1 km, and 6.5 km, respectively, thereby delineating priority zones for targeted mitigation measures. This study proposes a quantitative framework that couples regional wind-driven sand dynamics with highway hazard severity, enabling targeted mitigation and offering a transferable reference for sand risk management in arid and desert regions. Full article

This study systematically evaluated the response mechanisms of water and sediment processes in the Kuye River Basin to climate change and human activities from 2023 to 2053 by integrating multi-source climate scenarios (CMIP5 models), land-use change projections (based on the Markov chain model), and a distributed hydrological model (SWAT model). The results indicate that under the RCP8.5 high-emission scenario, annual precipitation in the basin shows a non-significant increasing trend but with intensified interannual variability. Spatially, precipitation exhibits a pattern of increasing from northwest to southeast, with a marked decadal transition occurring around 2043. Land-use structure undergoes significant transformation, with construction land projected to account for 30.54% of the total basin area by 2050, while grassland and cropland continue to decline. Water and sediment processes display distinct phased characteristics: a fluctuating adjustment phase (2023–2033), a relatively stable phase (2034–2043), and a sharp growth phase (2044–2053). Parameter sensitivity analysis identifies the curve number (CN2) and soil bulk density (SOL_BD) as key regulatory parameters, revealing the synergistic mechanism by which land-use changes amplify climatic effects through alterations in surface properties. Based on the findings, an adaptive watershed management framework is proposed, encompassing dynamic water resource regulation, spatial zoning, targeted erosion control, and iterative scientific management. Particular emphasis is placed on addressing hydrological transition risks around 2043 and promoting low-impact development practices in high-erosion areas. This study provides a scientific basis for the integrated management of water and soil resources in the context of ecological conservation and high-quality development in the Yellow River Basin. The methodology developed herein offers a valuable reference for predicting water and sediment processes and implementing adaptive management in similar semi-arid basins. Full article

Xinjiang is an arid and semi-arid region where ecosystems are fragile, and monitoring how its ecology changes over time is critical for its sustainable development. In this study, a Remote Sensing Ecological Index (RSEI) was established for Xinjiang from 2000 to 2025. To understand temporal and spatial changes in ecological quality, we conducted spatial autocorrelation analysis, Theil–Sen median trend analysis, a Mann–Kendall trend test, and Hurst exponent analysis. We also used Geodetector to determine which factors affect the RSEI. The main results were as follows: (1) The RSEI in Xinjiang remained low, with a mean value between 0.285 and 0.336. Mountainous areas had higher values, basins had lower values, and spatial clustering was strong (Moran’s I index: 0.81–0.86). (2) H-H clusters expanded and then shrank, while L-L clusters grew after 2015. Areas with excellent ecological grades increased, but so did areas with poor grades, indicating that improvement and degradation both exist. (3) Most areas were stable, but 19.13% showed persistent degradation, indicating that these areas need more attention. (4) Land surface temperature (q = 0.624) and land cover (q = 0.576) were the main driving factors, and factor interactions showed enhanced effects. The results of this study could provide a scientific basis for ecosystem protection and restoration in Xinjiang. Full article

Soil phosphorus (P) deficiency is an important factor limiting plant growth in the semi-arid Loess Plateau region in China. The topsoils in this area undergo repeated drying–rewetting (DRW) cycles, which can influence soil P availability, a process that may become more pronounced due to climate change. However, little is known about how soil P availability responds to DRW cycles under different land-use types. To investigate this issue, we conducted three 120-day soil culture experiments to investigate the direction and magnitude of soil available P and the responses of its influencing factors to repeated DRW cycles and their frequency and intensity under three typical land-use types (cropland, grassland, and shrubland) in Gansu Province, North-western China. The results showed that the available P concentration of cropland, grassland, and shrubland soils after repeated DRW cycles significantly decreased by 8.9%, 11.5%, and 14.2%, respectively, compared with a constant humidity control. With increasing intensity of the DRW cycles, the available P concentration of grassland and shrubland soils significantly increased by 14.3% and 15.5%, respectively, while in cropland soil P significantly decreased by 10.4%. Compared with low-frequency DRW cycles, high-frequency DRW cycles significantly reduced the available P concentration by 6.4% in grassland soil and increased it by 9.8% in shrubland soil but had no significant effect in cropland soil. Overall, the responses of soil P availability to repeated DRW cycles vary among different land-use types, and the magnitude of the soil P availability response to repeated DRW cycles depended strongly on soil microorganism biomass, phosphatase activity, and the initial soil properties, being more pronounced in grassland and shrubland soils than in cropland soils. It is therefore essential to consider land-use type when studying the effects of DRW on soil P cycling in semi-arid regions, especially in the context of climate change. Full article

Ensuring household food security through climate resilient and sustainable crop production continues to be a central challenge for rural farming households in Kenya. Therefore, the adoption of adaptation strategies to a changing climate is crucial in maize-growing regions. A multivariate probit model was deployed to understand determinants of the adoption of climate adaptation strategies and drought-tolerant maize varieties among 819 smallholder farmers in arid and semi-arid areas. The survey was conducted in four major maize-growing counties in Kenya. Results show that most climate change adaptation strategies implemented by maize-dependent smallholders are complementary. Multivariate logistic coefficients showed a significant inverse relationship between marital status and the adoption of soil and water conservation strategy in Machakos (−2.321; p = 0.01). Secondary education was significantly associated with the adoption of water harvesting in Machakos (2.538; p = 0.001), while it was associated with soil and water conservation in Homa Bay (2.208; p = 0.0001) and Migori (1.538; p = 0.01), respectively. Unemployment was positively (21.726; p = 0.01) linked with the adoption of water harvesting in Machakos, with the probability of a farmer adopting water harvesting strategies in Machakos (1.460; p = 0.01). Remarkably, soil and water conservation strategies in Machakos (1.807; p = 0.001) and Migori (2.458; p = 0.0001) positively correlated with food insecurity. Incidentally, only farmers in Migori County had a significant (1.024; p = 0.01) likelihood of adopting drought-tolerant maize varieties with increasing land size. In the same county, the source of maize variety was positively associated with the adoption of drought-tolerant varieties. There is a need to promote policies like informal and formal education and awareness creation to enhance smallholder farmers’ capacity to adopt multiple sustainable climate-smart adaptation strategies that can promote the continued adoption of drought-tolerant maize varieties. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

Reduced soil moisture (SM) together with elevated vapor pressure deficit (VPD) suppresses gross primary productivity (GPP) and thus weakens the capacity of the terrestrial carbon pool. Against the backdrop of global climate change, soil and atmospheric drought exert a more profound impact on vegetation growth, and their combined impacts remain unclear. Based on multi-source remote sensing observations and reanalysis datasets, three vegetation remote sensing indices, GPP, SIF, and NDVI (collectively referred to as Vegetation Remote Sensing Indices, VSI), are employed in this study to assess the relative impacts of soil and atmospheric drought on terrestrial vegetation. First, Copula-based conditional probabilities are applied to identify which factor (reduced SM or high VPD) plays a dominant role under conditions of declining vegetation productivity and to determine their corresponding thresholds. Furthermore, the underlying driving mechanisms are elucidated by utilizing Structural Equation Modeling (SEM) for path analysis to clarify how climatic factors indirectly affect vegetation productivity by influencing SM and VPD. The results suggest that vegetation growth in China’s different climatic zones is affected by distinct factors. Specifically, SM is the primary factor influencing vegetation productivity, dominating 71.16% of the nation’s vegetated areas. Its influence is particularly pronounced in arid and semi-arid regions. In contrast, the impact of VPD is predominantly concentrated in semi-humid plain regions. Furthermore, the critical thresholds for SM in different climate zones are identified: the threshold averages approximately 0.33 m³/m³ in humid and plateau regions and 0.13 m³/m³ in arid and semi-arid regions. The SEM analysis further reveals the complex pathways by which climatic variables influence vegetation growth. In SM-dominated regions, higher SM directly promotes vegetation growth; in VPD-dominated regions, drier air imposes a stronger suppression on vegetation growth. Nonetheless, the plateau temperate semi-arid zone demonstrates distinct hydrometeorological characteristics. Attributed to the region’s unique hydrometeorological conditions, the negative effects of higher VPD are generally outweighed by the favorable conditions for photosynthesis with which it co-occurs. These findings clarify the intricate impacts of SM and VPD on vegetation productivity, providing a foundational framework for the development of tailored ecological management strategies and drought early warning systems. Full article

In semi-arid areas, water loss from small agricultural water storage facilities is significant, owing to evaporation. A longitudinal study was conducted between 2019 and 2022 at the Agricultural Research Station, Ananthapuramu, located in the semi-arid climate of Peninsular India, which compared 12 distinct treatments designed to reduce evaporation. These treatments included bamboo sheets, agricultural residues, Azolla (Azolla pinnata), monomolecular alcohol films, and oil-based films, along with an untreated control. Evaporation rates and meteorological data were measured using the depth loss method and automatic weather station. Results indicated substantial treatment effects, such as bamboo sheets decreasing evaporation by 88%, reducing daily loss from 5.2 mm to 0.8 mm, while Azolla achieved a 62% reduction (2.8 mm). Organic residues decreased evaporation by 37–47%, and chemical monolayers and oils by 21–42%. Ridge regression models demonstrated strong performance (R² = 0.789–0.808), with bamboo sheets exhibiting the lowest Root Mean Square Error (0.127 mm day⁻¹). Economic analysis revealed annual water savings of 4700–4800 m³ ha⁻¹ for bamboo sheets and 2300–2500 m³ ha⁻¹ for less effective covers. Assuming a baseline water value of 0.20 US$ m⁻³, annual net benefits ranged from 250 to 900 US$ ha⁻¹, with Net Present Values spanning from 7000 to 160,000 US$ ha⁻¹ across various scenarios. Overall, bamboo sheets and Azolla were identified as the most effective and economically viable options for mitigating evaporation in semi-arid smallholder water systems. Maximum air temperature (T_max) was a key meteorological variable used to model daily evaporation, together with wind speed, followed by relative humidity and sunshine duration. Full article

Forest cover condition is a key determinant of ecosystem functioning and ecological resilience, yet its spatial variability across large and environmentally heterogeneous regions remains insufficiently understood. Leaf area index (LAI) provides a continuous and physically meaningful indicator of forest canopy condition, reflecting variations in canopy density associated with climate and landscape structure. Here, we develop a spatially explicit and interpretable analytical framework to diagnose the dominant climatic and landscape controls on forest cover condition across mainland China during 2000–2020. By integrating machine-learning modelling with SHapley Additive exPlanations, GeoDetector interaction analysis, and nonlinear dependence diagnostics, we quantify the relative contributions and interactions of precipitation, temperature, topography, and forest landscape structure to spatial patterns in forest LAI. The results reveal pronounced spatial heterogeneity in forest cover control regimes. Precipitation dominates forest cover condition in humid regions but exhibits nonlinear saturation, whereas forest fragmentation strongly constrains canopy development and moderates climate-LAI relationships in arid and semi-arid forested landscapes. In high-elevation regions, topographic and thermal factors exert primary control. Overall, the findings demonstrate that forest cover condition reflects climate-conditioned and landscape-dependent control regimes, providing a transparent basis for large-scale forest cover assessment and ecological monitoring. Full article

Water scarcity in arid/semi-arid regions restricts agricultural sustainability systems and hinders the achievement of regional sustainable development goals, especially in northwest China’s extremely arid areas, where acute water supply–demand conflicts and inefficient traditional practices intensify competition for water between agricultural and ecological sectors. This study aims to verify the effectiveness of an intelligent automatic irrigation system in mitigating water scarcity pressures and enhancing agricultural sustainability in the Shule River Basin of northwestern China, a region where traditional irrigation methods not only yield suboptimal crop outputs but also undermine long-term water resource sustainability. A smart irrigation module, integrating “sensing–decision–execution” processes, was embedded within a digital twin platform to enable precise, resource-efficient water management that aligns with sustainable development principles. Sunflower (Helianthus annuus L.), the most popular cash crop in the area, was used as the test crop, with three soil moisture-based irrigation levels compared against traditional farmer practices. Key indicators including leaf area index (LAI), dry biomass, grain yield, and irrigation water use efficiency (IWUE) were systematically evaluated. The results showed that (1) LAI increased from the seedling to flowering stage, with smart irrigation treatments significantly outperforming farmer practices in both crop growth and water-saving effects, laying a foundation for sustainable yield improvement; (2) total dry biomass at maturity was positively correlated with irrigation amount but smart irrigation optimized the allocation of water resources to avoid waste, balancing productivity and sustainability; (3) grain yield peaked within 70–89% field capacity (fc), with further increases leading to diminishing returns and unnecessary water consumption that impairs sustainable water use; (4) IWUE followed a parabolic trend, reaching its maximum under the same optimal irrigation range, indicating that smart irrigation can maximize water productivity while preserving water resources for ecological and future agricultural needs. The digital twin-driven smart irrigation system enhances both crop yield and water productivity in arid regions, providing a scalable model for precision water management in water-stressed agricultural zones. The results provide a key empirical basis and technical approach for sustainably using irrigation water, optimizing water–energy–food–ecology synergy, and advancing sustainable agriculture in arid regions of Northwest China, which is crucial for achieving regional sustainable development objectives amid worsening water scarcity. Full article

Drought is one of the most limiting abiotic stresses for agricultural production, especially in horticultural crops grown in arid and semi-arid areas. In the present study, we evaluated the potential of bacterial isolates obtained from coastal environments in Chile to improve drought tolerance in Lagenaria siceraria, a plant species increasingly used as a rootstock for cucurbit cropping. Rhizosphere bacteria were isolated from Sicyos baderoa, the only native cucurbit species of the Chilean coast, from which four isolates with plant growth-promoting traits, such as indole-3-acetic acid production, phosphorus solubilization, nitrogen fixation, and siderophore production, were selected. These isolates were inoculated on two L. siceraria genotypes, Illapel and Osorno, under both normal irrigation and water deficit conditions. The results showed that Peribacillus frigoritolerans showed a clearer positive effect on biomass and net photosynthesis under water deficit in the Illapel genotype, increasing shoot biomass by up to ~75% and restoring net photosynthetic rates by up to ~260% relative to non-inoculated drought-stressed plants. In contrast, responses associated with Staphylococcus succinus and those observed in the Osorno genotype were mainly expressed as trait- and tissue-specific adjustments, consistent with a more stabilizing response rather than broad growth stimulation. Additionally, malondialdehyde levels were reduced by up to ~25%, while free proline accumulation increased by more than 100% under water deficit. In contrast, total phenolic compounds showed more variable responses, indicating genotype- and strain-specific adjustment of antioxidant metabolism. Overall, the observed responses were heterogeneous and strongly dependent on the specific strain–genotype–trait combination and, therefore, should be interpreted as preliminary evidence supporting the potential value of native rhizobacteria for improving early drought-related traits in cucurbit rootstocks. Among the tested strains, Peribacillus frigoritolerans emerged as the most promising candidate for enhancing early drought tolerance in responsive genotypes such as Illapel, while highlighting the need for follow-up studies under replicated nursery and field conditions, including grafted plants, multiple drought intensities and combined inoculant strategies. Full article

Cotton (Gossypium spp.) is the most important fiber crop for the textile industry globally. Abiotic stresses, including drought, have become prevalent in affecting cotton production worldwide. There is a shortage of studies on the use of biochar as a soil amendment in the semi-arid and arid Southwest and West U.S. Cotton Belt to alleviate drought stress. This study was conducted to examine the effects of biochar at four application rates (0, 6.25, 12.5, and 25.0 t ha⁻¹) on cotton yield and yield components using six tetraploid cotton genotypes, including one Pima (G. barbadense L.) and five Upland cottons (G. hirsutum L.), under well-watered (WW) and drought stress (DS) conditions in an arid region of New Mexico, USA. The six cotton genotypes consistently showed that DS at the flowering stage significantly decreased boll number (BN), boll weight (BW), and lint percentage (LP), and thereby seed cotton weight (SCW) per plant and lint weight (LW) per plant. However, Pima DP 359 RF had the lowest reduction (23–33%) in BN, SCW, and LW due to drought, while DP 2020 B3XF was the most sensitive to drought, with a 45–48% reduction in the traits. Under DS conditions, biochar at the rate of 12.5 t ha⁻¹ had the highest SCW and LW, and the lowest reduction in BN, BW, SCW, and LW due to drought, which was significantly different from the non-biochar control, and no genotype × biochar interaction was detected. However, biochar had no positive effects on cotton productivity under non-drought conditions. This study has demonstrated the positive effects of biochar on cotton yield and yield components in alleviating drought stress, laying the foundation for more follow-up studies toward its utility in cotton production in semi-arid and arid areas. Full article

Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, SAVI-based, and EVI-based methods—applied to atmospherically corrected Sentinel-2 Level-2A imagery (10 m spatial resolution) over a 0.045 km² urban–agricultural polygon in the Tashkent region, Uzbekistan. Multi-temporal observations acquired during the 2023 growing season (June–August) were used to examine intra-seasonal vegetation dynamics. In the absence of field-measured LAI, a Random Forest regression model was implemented as an inter-method consistency analysis to assess agreement among index-derived LAI estimates rather than to perform external validation. Statistical comparisons revealed highly systematic and practically significant differences between methods, with the EVI-based approach producing the highest and most dynamically responsive LAI values (mean LAI = 1.453) and demonstrating greater robustness to soil background and atmospheric effects. Mean LAI increased by 66.7% from June to August, reflecting irrigation-driven crop phenology in the semi-arid study area. While the results indicate that EVI provides the most reliable relative LAI estimates for small urban–agricultural patches, the absence of ground-truth data and the influence of mixed pixels at 10 m resolution remain key limitations. This study offers a transferable methodological framework for comparative LAI assessment in data-scarce urban environments and provides a basis for future integration with field measurements, higher-resolution imagery, and LiDAR-based 3D vegetation models. Full article

Soil erosion poses a significant environmental challenge in semi-arid and Mediterranean regions, jeopardizing the sustainability of land and water resources. This study explores the spatio-temporal dynamics of water erosion within the Tafna watershed in Algeria, which encompasses an area of approximately 7200 km². Utilizing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS), the assessment relies on bias-corrected simulated rainfall data that offers consistent spatial coverage over the past four decades (1983–2023). Additionally, the rainfall asymmetry coefficient (Cs) was calculated to evaluate the impact of temporal rainfall variability on soil loss. The results indicate significant spatial and temporal variability; average erosion rates vary from less than 6 t/ha/year in stable areas to 23–27 t/ha/year in steep, sparsely vegetated regions. Overall, soil erosion has increased by approximately 16% during the study period, driven by heightened rainfall aggressiveness and an intensification of erosive potential. Correlation analysis underscores the intricate relationships between rainfall, topography, and erosive dynamics, highlighting the exacerbating effect of irregular rainfall patterns (Cs). These findings underscore the Tafna watershed’s high vulnerability to both natural and human-induced pressures, reinforcing the necessity for differentiated land management and targeted soil and water conservation strategies. The methodology developed in this study provides a transferable approach for assessing water erosion in other semi-arid and Mediterranean watersheds facing similar data limitations and hydro-climatic variability. Full article

Arid and semi-arid regions are highly vulnerable to drought and depend heavily on rainfed agriculture. To minimize the impact of drought, a transition from crisis management to risk management is necessary, which requires a comprehensive risk assessment that accounts for not only drought hazard but also drought vulnerability and population exposure. However, integrated studies that account for socio-economic, agricultural, demographic, and climate factors are currently lacking in Iran. The objective of this study is to comprehensively assess the spatio-temporal changes in drought risk from 2000 to 2019 across Iran. We used the standardized precipitation evapotranspiration index (SPEI) and multiple socio-economic and demographic data to compute drought risk. In particular, we used the SPEI to map drought hazard, an analytical hierarchical process method to assess drought vulnerability, and population density data to compute population exposure. Drought risk increased in 57% of the area of Iran, mainly in the northwest, west, and central regions, at a rate of up to 10% per year. In 21% of the area of Iran, drought risk declined by up to 10% per year, predominantly in the northern and southern regions of the Alborz Mountains, encompassing the provinces of Tehran, Gilan, Mazandaran, and Khorasan Razavi. Our results show that the spatial patterns of drought risk vary across Iran and are modulated by the interaction between climatic and socio-economic factors. The results of this study provide useful information for drought risk management and intervention in Iran. Full article