Understanding how ecohydrological processes shape soil-plant-water interactions across various ecosystem types is vital for elucidating vegetation water-use strategies in cold, arid regions. This study focused on mountain ecosystems along the northeastern margin of the Qinghai-Tibet Plateau. By comprehensively collecting data on soil profiles, soil moisture and salinity characteristics, and plant leaf functional traits, we systematically evaluated the mechanisms by which these factors influence plant water-use efficiency (WUE). Our findings revealed that soil carbon, nitrogen, phosphorus, and moisture were markedly enriched in the surface layer (0-20cm), with spatial heterogeneity largely controlled by ecosystem type. Patterns of water-salt interactions followed two distinct regimes: either moisture deficit accompanied by salt accumulation or simultaneous supplementation of water and salt, depending strongly on the hydrological and evapotranspiration context. Moreover, under water-limited conditions, WUE was mainly driven by photosynthetic and water-related traits, whereas in environments with ample water and nutrients, nutrient availability and metabolic traits played a dominant role. These results suggest that ecosystem-specific resource environments shape distinct adaptive strategies for plant water use. This study reveals the mechanism by which ecosystem types regulate water WUE through the coordinated effects of soil moisture patterns and plant functional traits. It provides a theoretical basis for understanding vegetation water adaptation strategies and guiding resource management in cold alpine mountain ecosystems.

Groundwater resources in arid, semi-arid, and coastal regions are of vital importance due to the scarcity or complete absence of reliable surface water sources. Port Sudan city is now serving as the administrative capital following the country's political instability. As a result, the city has witnessed a massive influx of internally displaced people, placing pressure on its already fragile water resources. The region is underlain by Precambrian basement terrains, restricting groundwater occurrence to structurally controlled aquifers and alluvial deposits. This study integrates gravity data analysis with the analytical hierarchy process (AHP) to delineate potential groundwater zones in the area. Structural features were extracted from gravity data using edge detection techniques, including vertical and horizontal derivatives, tilt angle derivative, and analytical signal. A density map of the identified structures was generated and integrated with other groundwater recharge-controlling factors including geology, rainfall, land use, slope, and drainage density within an AHP framework. The multi-criteria evaluation resulted in a groundwater potential map delineating three distinct zones: low (41.5%), moderate (13%), and high potential (45.5%). These zones were validated using 2D gravity inverse modeling constrained by boreholes data along two profiles. This integrated approach provided a preliminary yet effective tool for groundwater exploration in complex basement terrains and supports decision-making for further detailed hydrogeological and geophysical investigations in Port Sudan and similar arid environments. Incorporating more detailed geophysical analyses could further enhance subsurface characterization and improve groundwater potential assessments.

Accurate cotton yield estimation in arid oasis regions faces challenges from landscape fragmentation and the conflict between monitoring precision and computational costs. To address this, we developed a robust integrated framework combining multi-source remote sensing, spatiotemporal fusion, and data assimilation. To resolve spatiotemporal data gaps, the existing Agricultural Fusion (Agri-Fuse) algorithm was validated and employed to generate high-resolution time-series data, which achieved superior spectral fidelity (Root Mean Square Error, RMSE = 0.041) compared to traditional methods like Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Subsequently, high-precision Leaf Area Index (LAI) time series retrieved via the eXtreme Gradient Boosting (XGBoost) algorithm (c = 0.97) were integrated into the Ensemble Kalman Filter (EnKF)-assimilated World Food Studies (WOFOST) model. This approach significantly corrected simulation biases, improving the yield estimation accuracy (R2 = 0.86, RMSE = 171 kg/ha) compared to the open-loop model. Crucially, we systematically evaluated the trade-off between assimilation frequency and efficiency. Findings identified the 3-day fusion interval as the optimal operational strategy, maintaining high accuracy (R2 = 0.83, RMSE = 181 kg/ha) while reducing computational costs by 66.5% compared to daily assimilation. This study establishes a scalable, cost-effective benchmark for precision agriculture in complex arid environments.

Abstract. This study assessed the potential of the ASCAT SSM 6.25 km product for agricultural drought monitoring across Mexico. The drought monitoring in Mexico currently relies on precipitation-based indices, without considering the use of in situ soil moisture observations and satellite-derived soil moisture products. To evaluate the performance, cross-validation was performed between ASCAT, ERA5-Land, and ESA CCI soil moisture products for assessing correlations under varying climatic and land cover conditions. Furthermore, drought indicators, including SMAPI and SPEI, were calculated and compared against the SPI from the Mexican Drought Monitor to evaluate the assessment during drought events. Results showed strong potential of ASCAT SSM in central and northeastern Mexico, while arid regions exhibited low correlation due to subsurface scattering effects. Otherwise, soil moisture anomalies derived from ASCAT aligned well with precipitation anomalies outside arid zones. In conclusion, ASCAT SSM 6.25 km demonstrates significant potential for integration into Mexico’s drought monitoring systems as drought indicators based on ASCAT effectively captured precipitation deficits and drought conditions, mirroring SPI patterns reported by national monitoring agencies.

Abstract The Middle East hosts a high diversity of terrestrial and freshwater biota. However, this biodiversity is under threat from climate change, overexploitation and land‐use change—particularly through irrigated agriculture and urbanization. Biodiversity data for the region remain fragmented and sparse, creating significant knowledge gaps that hinder effective conservation planning. We investigated how land‐use types and water availability influence multiple dimensions of freshwater biodiversity, using Odonata (dragonflies and damselflies) as bioindicators. We analysed 12,770 occurrence records for 144 Odonata species across the Middle East to estimate taxonomic (TD), phylogenetic (PD) and functional diversity (FD), applying incidence‐based Hill numbers with rarefaction and extrapolation. Our results show that the availability of long‐lasting water bodies significantly enhances all facets of Odonata diversity, underscoring the importance of stable aquatic habitats. Urbanization had consistently negative effects across all biodiversity dimensions, likely due to the limited availability of suitable aquatic and terrestrial habitats. Agricultural land use had a positive effect on TD and PD, possibly explained by the creation of artificial irrigation water bodies, but showed no effect on FD—suggesting a homogenization of functional traits and the dominance of species with similar ecological roles. While agricultural expansion may enhance regional biodiversity, its potential to drive functional homogenization raises concerns about ecosystem resilience. Moreover, its long‐term sustainability is uncertain due to accelerating groundwater depletion and salinization across the region. These findings highlight the urgent need for region‐specific research and integrated water management strategies to balance development with biodiversity conservation in arid regions.

Abstract Pecans [ Carya illinoinensis (Wangenh.) K. Koch] are widely cultivated in the semi‐arid and arid regions of New Mexico and Texas, where irrigation relies heavily on the Rio Grande River and brackish groundwater. This study evaluated the impact of these water sources on soil physicochemical properties, nutrient availability, and pecan tree performance across six orchards along the Rio Grande in southern New Mexico and western Texas over two growing seasons. Soil samples were analyzed for texture, ion concentrations, sodium adsorption ratio (SAR), electrical conductivity (EC), and pH. Pecan performance was assessed using stem water potential (SWP) and leaf and kernel nutrient concentrations. Soil texture significantly influenced magnesium (Mg), calcium (Ca), and sodium (Na). The highest SAR (11.75) and EC (6.21 dS/m) were observed in loamy soil at Fabens 2, with pH ranging from 7.3 to 7.5. SWP values ranged from −12 to −14 bar in clayey soils and −10 to −12.5 bar in sandy soils. Leaf and kernel nutrient concentrations varied by location, with the highest zinc (Zn) levels in Fabens 2 (leaf: 160 mg/kg) and Derry (kernel: 120 mg/kg), and peak phosphorus (P) in Derry (leaf: 1195 mg/kg) and Las Cruces (kernel: 2858 mg/kg). Loamy soils with higher EC supported elevated Zn, Na, and potassium (K) in leaves, while sandy loams promoted higher Mg and kernel nutrient accumulation. In leaf, Zn decreased with Mg and K, while Na was strongly antagonistic to Ca and Mg. In the kernel, P, Mg, Ca, and K increased together. Zn tended to decline as P and K were raised. Seasonal variations showed greater Mg, Ca, and Na in leaves in October, while P and Ca in kernels peaked in 2015. A massive increase in nutrients from soil to leaf, then a decrease in the kernel. These findings underscore the need for site‐specific nutrient management and regular soil and tissue testing to optimize fertilization and mitigate imbalances.

ABSTRACT Grassland shrub encroachment is a widespread issue in global arid and semi-arid regions, posing a great threat to ecological environments and livestock production. Accurate shrub cover estimation is crucial for assessing encroachment extent and dynamics, particularly in severely impacted typical steppes. However, monitoring this process using coarse-resolution satellite sensors is challenging due to the typically low stature of shrubs and their intermixed growth with herbaceous vegetation. To address this, the Typical Steppe Shrub Encroachment Index (SSEI) was developed by mathematically transforming and integrating optical and Synthetic Aperture Radar (SAR) remote sensing features sensitive to shrub coverage. The SSEI leverages phenological differences between shrubs and herbaceous vegetation to identify the optimal monitoring window, effectively enhancing the weak signal of shrubs. Results show that the SSEI, achieves a significantly higher correlation (R = 0.76, p < 0.01, n = 758) and lower bias (RMSE = 3.5%, MAE = 2.75%) with shrub cover than individual remote sensing features and exhibits better sensitivity to changes in shrub cover. This index enables accurate discrimination between shrub-encroached grasslands and shrub-free grasslands when shrub cover reaches ≥10%. Furthermore, the relative vegetation volume scattering q-factor was introduced to complement the SSEI, effectively distinguishing shrub-encroached areas from haymaking fields via a simple threshold. This study demonstrates the value of fusing optical and microwave data for shrub cover extracting in heterogeneous landscapes at coarse resolutions. Requiring no training samples or complex parameterization, the SSEI provides a practical tool for large-scale monitoring of grassland shrub encroachment.

The conventional method of flue gas desulfurization gypsum (FGDG) application, i.e., blending with flood irrigation, is hindered by low water efficiency and significant amendment loss due to runoff and uncontrolled leaching, particularly in arid and semi-arid regions in which water scarcity is a major constraint. This study aimed to evaluate a novel integration of FGDG band application with drip irrigation to enhance targeting and resource efficiency. A laboratory-scale experiment investigated the effects of two FGDG application methods (band and blend application) and drip rates (0.3 and 0.6 L h−1) on soil water movement and chemical properties. Band application significantly accelerated initial wetting front advancement by up to 44.9 cm h−1 near the emitter and sustained horizontal propagation, while blend application promoted a more uniform water distribution. Chemically, band application created localized zones of reduced pH (7.57–8.62) and elevated water-soluble Ca2+ (up to 492.2 mmol kg−1), facilitating a 79.1% reduction in exchangeable Na+ near the emitter. In contrast, blend application resulted in broader but shallower amendment distribution, reducing exchangeable sodium percentage uniformly to 1.99–4.16% across the soil profile. The combination of banded FGDG and drip irrigation achieves targeted amelioration, with superior Na+/Ca2+ exchange and favorable moisture dynamics resulting from the synergy between amendment placement and water delivery. This approach is a viable strategy for precision reclamation in arid regions.

Introduction. The study of suspension uplift processes (e. g., particles of dust, sand, soil, etc.) by wind gusts in the surface layer is aimed at fundamentally understanding the mechanisms of wind erosion, dust storm formation, pollutant transport, and related phenomena. This area of scientific research has significant practical importance for combating desertification, erosion, drought, as well as for increasing crop yields and preserving natural ecosystems. Predicting these processes allows for the assessment and timely response to negative effects associated with them. The objective of this work is to propose and implement a mathematical model that enables numerical experiments with various scenarios of suspension uplift by wind gusts. Materials and Methods. The paper presents a continuous mathematical model of multicomponent air medium motion in the atmospheric surface layer. The model accounts for factors such as turbulent mixing, variable density, Archimedes’ force, tangential stress at media interfaces, etc. A distinctive feature of the mathematical model is the presence of suspension particles (their composition and aggregate state) in the air medium, as well as the influence of anthropogenic factors — suspension sources. The approach based on mathematical modelling aims to ensure the universality of the numerical implementation. Results. The mathematical model has been implemented as a software package. Numerical experiments simulating the uplift of suspension by wind gusts in computational domains have been conducted. Discussion. The results of this work can be in demand for a wide range of tasks related to human health protection, environmental safety, and land-use planning in arid and steppe regions of the country. Conclusion. Further research by the authors may be directed towards modelling the movement of dust-laden air flows for natural landscapes containing forest plantations.

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments.

The ESTIMET (Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration algorithm) model provides continuous, spatially distributed daily ET, essential for model calibration in data-scarce environments where conventional hydrological monitoring is unavailable. The challenge of applying SWAT in arid regions without ground observations, this study proposes a remote-sensing-based calibration approach using ESTIMET to overcome data scarcity. Daily satellite-derived evapotranspiration (ET) data to assess the performance of the Soil and Water Assessment Tool (SWAT) was used to evaluate the performance of the SWAT in a desertified watershed in Brazil, aiming to assess ESTIMET’s effectiveness in supporting SWAT calibration, quantify sediment yield, and examine the influence of land-use changes on environmental quality over 21-years period. The results highlight a distinct hydrological response in SWAT initially underestimated ET, contrasting with patterns typically observed in other semi-arid applications and demonstrating that desertified environments require distinct calibration strategies. Performance indicators showed strong agreement between observed and simulated ET (R2 = 0.94; NSE = 0.76), supporting satellite-based ET as a valuable source for improving SWAT performance in watersheds where empirical hydrometeorological data are sparse or unevenly distributed. Sediment yield was generally low to moderate, with degradation concentrated in bare-soil areas associated with deforestation.

This study investigates the effects of saline reclaimed water recharge on soil salt accumulation and water migration in Xinjiang, China, aiming to provide scientific guidance for the sustainable utilization of reclaimed water in arid regions. Indoor vertical infiltration simulation experiments were conducted using reclaimed water with varying salinity levels (0, 1, 2, 3, and 4 g L−1) to evaluate their impacts on soil water–salt distribution and infiltration dynamics. Results showed that irrigation with saline reclaimed water increased soil pH and significantly enhanced both the infiltration rate and wetting front migration velocity, while causing only minor changes in the moisture content of the wetted zone. When the salinity was 2 g L−1, the observed improvement effect was the most significant. Specifically, the cumulative infiltration increased by 22.73% after 180 min, and the time required for the wetting peak to reach the specified depth was shortened by 21.74%. At this salinity level, the soil’s effective water storage capacity reached 168.19 mm, with an average moisture content increase of just 6.20%. Soil salinity increased with the salinity of the irrigation water, and salts accumulated at the wetting front as water moved downward, resulting in a characteristic distribution pattern of desalination in the upper layer and salt accumulation in the lower layer. Notably, reclaimed water recharge reduced soil salinity in the 0–30 cm layer, with salinity in the 0–25 cm layer decreasing below the crop salt tolerance threshold. When the salinity of the reclaimed water was ≤2 g L−1, the salt storage in the 0–30 cm layer was less than 7 kg ha−1, achieving a desalination rate exceeding 60%. Reclaimed water with a salinity of 2 g L−1 enhanced infiltration (wetting front depth increased by 27.78%) and desalination efficiency (&gt;60%). These findings suggest it is well suited for urban greening and represents an optimal choice for the moderate reclamation of saline-alkali soils in arid environments. Overall, this study provide a reference for the water quality threshold and parameters of reclaimed water for urban greening, farmland irrigation, and saline land improvement.

Fermented beverages made from cactus fruits hold deep historical, cultural, and nutritional significance in arid and semi-arid regions of Mexico and the United States. Despite their longstanding role in local diets and food systems, these beverages remain understudied in ethnobiological research. One example is colonche, a traditional beverage made by open fermentation of prickly pear fruits, particularly Opuntia streptacantha , in the Central Mexican Plateau. Similarly, the Comcaac ( Seri ) people of the Sonoran Desert prepare imám hamaax (pitahaya wine) from Pachycereus pringlei fruit. This study combines ethnographic and microbiological approaches to investigate the production of these two beverages and their associated microbial communities. We documented traditional knowledge about harvesting, preparation, and fermentation practices in both regions. Forty-three yeast isolates were obtained from these fermented beverages, representing 12 species from 10 genera. These included both Saccharomyces and non- Saccharomyces yeast species, such as Kluyveromyces marxianus , Zygosaccharomyces bailii , Torulaspora delbrueckii , Meyerozyma guilliermondii , and Rhodotorula mucilaginosa . We observed regional differences in yeast composition, reflecting local ecological conditions and fermentation practices. This study contributes to the ethnobiology of traditional fermentations by documenting the microbial and cultural diversity of cactus-based beverages and highlighting the depth of the traditional ecological knowledge that sustains them. Our findings suggest that these fermentation systems provide valuable insights into microbial management, signs of domestication, biocultural resilience, and food heritage in arid environments.

Salinization is a growing global problem, particularly in arid and semi-arid areas, where salt concentration interferes with the soil structure, altering natural cycling, decreasing agricultural outputs, and threatening food security. Although many soil amendments have been studied, there is still a limited understanding of their interaction with soil after mixture application and the geochemical processes and long-term sustainability that govern their effects. To address this knowledge gap, this review elucidated the effectiveness and sustainability of soil amendments, biochar, humic substances, and mineral additives in restoring saline and sodic soils of arid and semi-arid region to explore the geochemical processes that underlie their impact. A systematic search of 174 peer-reviewed studies was conducted across multiple databases (Web of Science, Google Scholar, and Scopus) using relevant keywords and the findings were converted into quantitative values to evaluate the effects of biochar, gypsum, zeolite, and humic substances on key soil properties. Biochar significantly improved cation exchange capacity, nutrient retention, microbial activity, and water retention by enhancing soil porosity and capillarity, thereby increasing plant-available water. Gypsum improved phosphorus availability, while zeolite facilitated the removal of sodium and supported microbial activity. Humic substances enhanced soil porosity, water retention, and aggregate stability. When applied together, these amendments improved soil health by regulating salinity, enhancing nutrient cycling, while also stabilizing soil conditions and ensuring long-term sustainability through improved geochemical balance and reduced environmental impacts. The findings highlight the critical role of multi-functional amendments in promoting climate-resilient agriculture and long-term soil health restoration in saline-degraded regions. Further research and field implementation are crucial to optimize their effectiveness and ensure sustainable soil management across diverse agricultural environments.

ABSTRACT During scientific expeditions across the Qinghai-Tibetan Plateau, a group of Buellia species were frequently collected from alpine arid regions. These were characterized by having whitish chalky thalli, a surface covered with thick coarse pruina, a reddish-brown hypothecium, and the presence of xanthones. Detailed observations were made of specimen morphology and anatomy. Phylogenetic analysis was conducted based on four loci: the internal transcribed spacer of the rDNA (ITS), partial large subunit nuc rDNA region (28S), RNA polymerase II second largest subunit gene (rpb2), and β-tubulin gene (tubb). Three species, which formed a new distinct lineage within Buellia s.l. were confirmed as new to science: Buellia plana, B. elevata, and B. tibetana. This lineage is here referred to as the pruinocalcarea-group because of a species previously described that belongs to this group: B. pruinocalcarea. Species included in this group are closely related to Tetramelas; the group also has affinities to the epigaea-group and subalbula-group. Detailed descriptions, photographs of the new species, and a key to the species are provided.

Saline soils typically exhibit poor engineering properties, including low strength and instability, which severely compromising the structural stability of subgrade in arid and semi-arid regions. Based on this, a method for improving subgrade in saline soil areas using a composite of steel slag powder and lime was proposed in this study. Through orthogonal experimental design, the effects of total content, steel slag proportion, moisture content, and curing age on the unconfined compressive strength of saline soil were systematically analysed. Shear performance parameters of the modified soil were obtained in conjunction with direct shear tests. The mechanism of modification was elucidated using Scanning Electron Microscope-Energy Dispersive Spectroscopy (SEM-EDS) and X-ray Diffraction (XRD). The results indicate that the optimal improvement scheme is a total admixture content of 15% (comprising 75% steel slag and 25% lime), the moisture content is 4.48%, and the curing age is 28 days. At this point, the unconfined compressive strength reached 403.50 kPa, approximately 3.8 times that of the unmodified soil; the cohesion increased significantly from 11.8 kPa to 54.2 kPa, while the angle of internal friction showed no significant change. Microstructural analysis indicates that the composite modification of steel slag powder and lime generates calcium silicate hydrate gel and calcium carbonate, significantly reducing the porosity of saline soil. This enhances intergranular bonding, resulting in a more compact and stable structure. The study has validated the high efficiency and sustainability of this method, providing reliable technical support for the remediation of subgrade defects in road engineering projects within saline soil regions.

Abstract. This study explores changes in agricultural drought event characteristics in projections of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different future scenarios based on three Shared Socioeconomic Pathways (SSP). To quantify the intensity of agricultural droughts, the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6) with a 65-year reference period is applied to the simulations of 18 ESMs. In a first step, these ESMs are evaluated based on performance metrics and pattern correlations of drought related variables including precipitation and approximated reference evapotranspiration with reanalysis datasets including ERA5 and CRU. With this we extend the model benchmarking performed in the third chapter of the IPCC AR6 by 15 years and additional variables. In a second step we analyze global and regional projected SPEI6 distributions to estimate and characterize the changes in agricultural drought in the future based on multi-model means of change rates, distributions and relative area covered by specific events. We quantify the change of drought index values for 42 IPCC AR6 WG1 reference regions individually with a focus on those with most harvest area and find negative trends in water budget and SPEI for higher emission scenarios in most of them, particularly in the Mediterranean and other arid regions. This agrees with other recent studies. Increasing reference evapotranspiration emerges as the dominant driver for drier conditions in these regions. What is considered as the driest 2.3 % months during 1950–2014 is projected to be the new normal or moderate condition in arid regions by 2100, following a high emission future scenario (SSP5-8.5). For this scenario, 40 % of the harvest regions surface is considered to be under extreme drought conditions during Northern Hemisphere autumn. Under a low emission scenario (SSP1-2.6) with an expected global warming of 1.8 °C it would be less than 10 %. Our results show a significant difference between future scenarios regarding distribution shifts and spatial extent of extreme drought conditions in harvesting regions and can serves as a foundation for further impact and mitigation studies.

Agricultural soils are the largest anthropogenic source of nitrous oxide (N2O), primarily due to excessive nitrogen (N) fertilization and inefficient N management. Mitigating N2O emissions from croplands without compromising productivity is therefore a major global challenge for climate and environmental sustainability. A three-year split-plot field experiment was conducted in an arid maize production region of northwestern China to examine how green manure intercropping combined with reduced chemical N input regulates N2O emissions and soil N residues. The main plots comprised maize monoculture (M), maize intercropped with common vetch (M/V), and maize intercropped with rape (M/R), while subplots consisted of local conventional N application (N1: 360 kg N ha−1) and a 25% reduced rate (N2: 270 kg N ha−1). Results indicated that intercropping with green manure can offset the reduction in maize grain yield caused by a 25% decrease in N supply. Green manure intercropping significantly decreased cumulative N2O emissions compared with monoculture maize, and the mitigation effect was further strengthened under reduced N input. The M/V system under reduced N input exhibited the strongest mitigation effect, reducing N2O emissions per unit of grain yield by 9.2–11.5% compared with the M/R system. This reduction was driven by the ability of M/V to stabilize soil mineral N availability. Notably, the independent maize growth stage contributed 52.6–66.9% of total seasonal N2O emissions, emphasizing it as a critical period for emission mitigation. Overall, integrating green manure intercropping with reduced chemical N input effectively mitigates N2O emissions while maintaining maize productivity in arid regions, providing a practical strategy for sustainable and environmentally responsible agricultural intensification.

The water yield (WY) service is a critical ecosystem service in arid regions, and understanding its spatiotemporal heterogeneity and controls is important for sustainable watershed management. Annual water yield (WY) in the Aksu River Basin (ARB), China, from 2000 to 2020 was simulated using the InVEST model, with validation against observed runoff (NSE = 0.840, R2 = 0.846, RMSE = 1.787). The results revealed a decline in WY from 66.49 mm in 2000 to 43.15 mm in 2015, while retaining a clear north–south gradient, with higher values in the north. Areas showing decreasing and increasing trends accounted for 45.34% and 3.14% of the basin, respectively. WY exhibited strong spatial autocorrelation (global Moran’s I = 0.912–0.941), with high-value clusters in the north and low-value clusters in the south. GeoDetector identified precipitation, temperature, and potential evapotranspiration as key drivers (q = 0.889, 0.880, and 0.832, respectively), with precipitation-related interactions generally exceeding 0.9, indicating enhanced explanatory power through multi-factor coupling. After variable screening and collinearity control, MGWR revealed spatially varying effects of drivers and significant spatial non-stationarity. Overall, despite the declining trend, WY in the ARB maintained a relatively stable spatial structure, with its heterogeneity primarily driven by the coupling of climatic forcing and topographic constraints, providing a scientific basis for zonal water resource management in arid river basins.

Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI-IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.