Search Results (10,110)

Global warming is reshaping the global dry–wet pattern, yet its future trajectory remains uncertain, with important implications for sustainable water resources. China, influenced by both the monsoon system and the mid-latitude westerlies, requires an integrated assessment linking net water balance (precipitation minus evaporation, PME) to moisture transport. Here we use precipitation, evaporation, and air temperature records for 1981–2023, together with Lagrangian moisture tracking and precipitation recycling diagnostics, to quantify changes in PME across China and to identify the underlying mechanisms. We further assess future evolution under different warming levels (1.5 °C, 2 °C, and 3–4 °C) for 2024–2099 using a CMIP6 multi-model ensemble. China experienced a pronounced warming during the historical period, while precipitation declined overall and evaporation remained nearly stable. As a result, reduced moisture supply strengthened drought sensitivity. Spatially, warming-driven drying is concentrated in the eastern and southern monsoon regions. In contrast, the inland arid and semi-arid Northwest and parts of high-elevation transition zones show a relative shift toward warmer and wetter conditions. Moisture transport diagnostics indicate that China’s moisture supply is jointly sustained by the mid- to high-latitude westerlies and low-latitude oceanic monsoon pathways. These pathways form a continuous transition from the Northwest to the Southeast. Land–atmosphere recycling is stronger in the Southeast, whereas the Northwest depends more on imported moisture, with plateau topography further reshaping the main transport corridors. In the future, PME continues to decline under 1.5 °C warming. Under 2 °C warming, PME enters a transitional state with patchy regional patterns. Under 3–4 °C warming, PME shifts to an overall increase, but uncertainty becomes larger. These results identify a critical turning window at around 2–3 °C warming for China’s PME response, providing a physical basis for sustainable water-resource management and adaptation planning. Full article

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

To address water-heat constraints and environmental risks associated with plastic film mulching in winter wheat production in the semi-arid region of Northwest China, a two-year field experiment (2021–2023) was conducted in Tongwei County, Gansu Province. A single-factor randomized block design was applied, with full plastic film mulching (PM) and bare land (CK) as controls, to evaluate the effects of 3-row (S3), 4-row (S4), and 5-row (S5) corn stalk strip mulching on soil hydrothermal conditions, active carbon fractions, and yield under rainfed conditions. Results showed that straw mulching significantly enhanced soil water retention, particularly in the 0–40 cm layer, where moisture content increased by 7.70–19.28% compared with CK (p < 0.05), with S3 performing best. Treatment S5 achieved the highest accumulated temperature and reduced the soil diurnal temperature range by 20.73–35.62% (p < 0.05). Active carbon fractions were also significantly improved, especially during the jointing–grain-filling stage (BBCH 31–87). In terms of yield, S5 exhibited the greatest increase, with a 15.88% higher two-year average grain yield than CK (p < 0.05), reaching over 90% of PM. Overall, S5 demonstrated optimal synergistic regulation of water, heat, and carbon, indicating strong potential as a sustainable alternative to plastic film mulching. Full article

Sandy calcareous soils in arid regions suffer from low phosphorus (P) availability due to high fixation rates, limiting crop productivity. This study investigates a novel hybrid smart fertilizer (BN) composed of olive pomace biochar (BC) and nano-hydroxyapatite (nHAP). BN was synthesized and characterized using XRD, FTIR, SEM/TEM, and zeta potential analysis. Its P release kinetics were modeled, and its agronomic performance was assessed on faba bean (Vicia faba L.) in a pot experiment under sandy soil conditions with and without wood vinegar (WV). The 1:1 BC:nHAP formulation showed a two-stage release profile: a rapid initial burst (Higuchi model, R² = 0.86) followed by sustained zero-order release (R² = 0.80). In the pot experiment, BN combined with WV significantly increased plant height by 36%, shoot fresh weight by 232%, and available soil P by 39% compared to conventional SSP (p < 0.05). This synergistic treatment also improved root nodulation and nutrient (N, P, K) uptake. The BC-nHAP hybrid coupled with WV acts as an efficient P delivery system, improving soil fertility in arid environments based on circular economy principles, aligning with SDGs 2, 12, and 15. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article

Aiming at the problems of uneven radiation distribution and difficult regulation in double-film multi-span greenhouses in the Gobi Desert, a spatiotemporal simulation model of the radiation environment based on the coupling of Rhino–Grasshopper and Radiance was constructed in this study. Parametric simulation and multi-objective optimization were adopted to significantly improve the solar radiation capture and distribution uniformity inside the greenhouse, providing a scientific basis for greenhouse design in the Gobi area. The results show that the model has high accuracy (R² > 0.98), and the radiation inside the greenhouse presents a distribution pattern of “higher in the northeast, lower in the southwest, higher in the upper layer and lower in the lower layer”. The optimal orientation is 1° west of south, and the optimal configuration is 8 m span, 5 m eave height, and 30° roof slope. This study can provide quantitative support for the structural design, planting layout and energy-saving regulation of double-film multi-span greenhouses in arid desert areas, and has important practical value for promoting the efficient and sustainable development of facility agriculture in the Gobi Desert. Full article

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants and remains a major threat to food security and rural livelihoods across Africa, the Middle East, and Asia. In the Mediterranean, uneven outbreak reporting and intense spatial clustering hinder the identification of regions where environmental and anthropogenic conditions favor disease occurrence. This study applied an interpretable machine-learning ecological niche modeling framework to characterize PPR spatial suitability in North Africa. A merged outbreak dataset (n = 744) was compiled from the Food and Agriculture Organization (FAO) EMPRES-i and the World Animal Health Information System (WAHIS) databases for 2005–2026. Outbreak locations were linked to environmental and anthropogenic predictors, spatially thinned, and paired with randomly sampled pseudo-absences at a 1:1 ratio. After correlation-based screening and Boruta feature selection, four classifiers were compared under five-fold spatial block cross-validation: a generalized linear model (GLM), a support vector machine (SVM), Random Forest (RF), and extreme gradient boosting (XGBoost). All models showed good discriminatory performance. Random Forest (RF) and extreme gradient boosting (XGBoost) yielded the highest area under the receiver operating characteristic curve value (AUC = 0.94). Random Forest achieved the highest specificity, XGBoost achieved the highest sensitivity, and the support vector machine showed the most even sensitivity–specificity tradeoff among the machine-learning classifiers. Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were consistently the dominant drivers. Predicted PPR suitability based on reported outbreaks was concentrated along the North African coastal belt and low across most arid inland regions. These findings suggest that passive surveillance is likely to be most informative in coastal production systems where host density, environmental suitability, and reporting opportunity overlap. At the same time, areas of lower reported-outbreak suitability should not be interpreted as disease-free and may require complementary active surveillance approaches. Full article

This study assesses the impacts of climate change (CC) on maize production in Bosnia and Herzegovina, comparing ten maize-producing municipalities and using Gradiška as a case study. Agroclimatic indicators and ISAREG-based soil water balance simulations were used to evaluate regional suitability for future maize production. Projections indicate substantial increases in average temperatures of 2 to 6 Celsius by the end of the century, depending on the RCP scenario, together with important reductions in accumulated mean precipitation, particularly during summer. Rising temperatures accelerate maize phenology, shortening growth cycles and enabling double-cropping opportunities for short-season cycles. Medium-season cycles may become feasible in most regions, while long-season cycles remain constrained in high-altitude areas due to thermal requirements. Rainfed maize in Gradiška is expected to face increased relative evapotranspiration deficits under future ‘hot & dry’ conditions, with potential relative yield losses due to water deficit of up to 12%. Irrigated maize shows a variation in irrigation requirements from −26% to +8% relative to the baseline, which reflects the combined effect of a shortened crop growth cycle under higher temperatures and increased evapotranspiration demand under drier conditions. Regions with high soil water-holding capacity are the most resilient, while areas with shallow soils or Mediterranean climates are more vulnerable under future conditions. The findings underscore the need for agronomic adaptation measures to the projected CC impacts, including supplemental irrigation, drought-tolerant cultivars, and potential adjustment of sowing. Full article

Climate change and population growth are intensifying water scarcity in arid regions, yet previous analyses focusing on a single driver may not fully capture the compounded effects of climatic and anthropogenic factors. This study integrates water-balance analysis, trend analysis, and correlation-based statistical analysis to examine the combined effects of hydroclimatic anomalies and socioeconomic activities on water resource dynamics in ecologically vulnerable Northwest China. Our results show that despite increasing precipitation, warming-associated increases in evapotranspiration, together with irrigation-based water use accounting for 89.8% of total consumption, have offset the potential runoff gains, suggesting that agricultural water use is a major anthropogenic contributor to regional water stress. Based on these findings and a comparative review of representative arid-region practices in Israel, Australia, and Saudi Arabia, we propose a technology-market-institution tripartite governance framework for Northwest China. This framework is intended to support more proactive adaptation in regional water management and to provide a context-specific reference for advancing SDG 6 and SDG 13 in dryland regions. Full article

In arid and semi-arid regions, the cultivation of artificial grasslands commonly suffers from low productivity due to insufficient water supply. The rational application of water-retaining agents is an important approach to alleviating production constraints in artificial grasslands facing resource-based water scarcity. This study investigated two types of water-retaining agents [starch-grafted acrylate water-retaining agent (B1) and polyacrylamide water-retaining agent (B2)] and four application rates [0 kg·hm⁻² (CK), 30 kg·hm⁻² (T1), 60 kg·hm⁻² (T2), 90 kg·hm⁻² (T3)], systematically analyzing their effects on the growth, osmotic adjustment substances, antioxidant enzyme activities, and yield of alfalfa. The results showed that alfalfa plant height, stem diameter, leaf area, branch number, soluble sugar (SS), soluble protein (SP), and proline (Pro) all exhibited a decreasing trend with increasing cutting times. The activities of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) in alfalfa leaves initially increased and then decreased with increasing application rates of water-retaining agents, while malondialdehyde (MDA) content showed a decreasing trend. Under the B2T2 treatment, both alfalfa yield and water-use efficiency (WUE) reached their highest values, recorded as 4931.97 kg·hm⁻² (2022), 6021.44 kg·hm⁻² (2023) and 2.19 kg·m⁻³ (2022), 2.39 kg·m⁻³ (2023), respectively. Based on the principal component analysis for comprehensive evaluation, the B2T2 treatment (polyacrylamide water-retaining agent applied at 60 kg·hm⁻²) achieved the highest comprehensive score in both years and could synergistically improve alfalfa yield and water-use efficiency. However, its applicability in the Yellow River irrigation region of Gansu Province and similar ecological areas still requires further verification through field trials. Full article

Root exudates are key drivers of rhizosphere microbial assembly, yet their effects across coexisting plant species with different functional roles remain unclear. We examined the effects of root exudates from five desert steppe species in Inner Mongolia: one constructive species, two dominant species, and two accompanying species. Exudates were collected hydroponically and applied to bulk soil in a three-week incubation experiment. Microbial communities were analyzed using high-throughput sequencing, functional prediction, and co-occurrence network analysis. Exudate addition significantly altered bacterial community composition, reducing bacterial richness, while fungal communities showed weaker responses. Exudates from constructive and dominant species enriched Actinobacteria, including Rubrobacter, Arthrobacter, and Solirubrobacter, and increased functional groups linked to chemoheterotrophy and nitrogen transformation. In contrast, exudates from accompanying species induced distinct microbial assemblages without promoting Actinobacteria dominance. Exudate addition also increased bacterial network complexity, suggesting enhanced microbial interactions. Soil pH decreased and available nitrogen and phosphorus increased, strongly correlating with bacterial community shifts. Overall, root exudates mediate species-specific microbial assembly and functional reorganization in desert steppe soils, driven mainly by plant functional roles rather than taxonomic relatedness. This study provides new insights into how plant-derived substrates regulate microbial communities and nutrient cycling in arid ecosystems. Full article

Soil erosion is a significant environmental concern in arid regions, particularly in dam-regulated watersheds, where intermittent flows from sprinkler irrigation can exacerbate land degradation. This study assesses soil erosion susceptibility in the Sidi Aich watershed using a combined approach of the Revised Universal Soil Loss Equation (RUSLE) and the Analytic Hierarchy Process (AHP), enabling the integration of both regional characteristics and expert-driven weighting. The RUSLE model accounts for natural and human-induced factors, whereas AHP provides a hierarchical weighting system that highlights rainfall erosivity and the local impacts of dam-regulated discharges. Results show that 26.12% of the area falls into the very high susceptibility category, 25.45% into high, 23.91% into moderate, and 24.51% into low susceptibility. Model validation demonstrates satisfactory predictive performance, with Area Under the Curve (AUC) values of 0.85 for AHP and 0.78 for RUSLE. Overall, the findings emphasize the critical role of dam-controlled releases in increasing soil vulnerability, a factor that may not be fully captured when using RUSLE alone. By combining RUSLE and AHP, this research provides a more realistic and regionally tailored assessment of erosion risk, offering valuable guidance for watershed management and erosion mitigation strategies in arid environments. Full article

Semantic segmentation of land use and land cover (LULC) in arid regions remains challenging due to severe class imbalance, fragmented spatial distributions, and high spectral similarity among different land cover types. These characteristics often lead to an information bottleneck in deep segmentation networks and hinder the extraction of discriminative semantic representations. To address these issues, we propose SDS-Former, a lightweight semantic segmentation network specifically designed for remote sensing imagery in arid environments. SDS-Former incorporates an SSM-inspired Lightweight Semantic Enhancement (LSE) module to strengthen contextual modeling and alleviate the loss of discriminative information in deep features. To tackle scale variations, a Dynamic Selective Feature Fusion (DSFF) module is employed in the decoder to adaptively weight and fuse high-level semantics with low-level spatial details. Furthermore, a Feature Refinement Head (FRH) is introduced to enhance boundary localization and improve the recognition of small-scale and sparsely distributed land cover objects. Extensive ablation and comparative experiments demonstrate that SDS-Former consistently outperforms representative semantic segmentation methods across multiple evaluation metrics. On the Tarim Basin dataset, the proposed network achieves a mean Intersection over Union (mIoU) of 82.51% and an F1 score of 86.47%, indicating its superior effectiveness and robustness. Qualitative results further verify that SDS-Former exhibits clear advantages in distinguishing spectrally similar land cover types and preserving the spatial continuity of ground objects in complex arid-region scenes. Full article

Under the combined pressures of climate change and irrigated cropland expansion, groundwater tables are declining rapidly across arid regions, thereby intensifying water limitation in riparian ecosystems. However, the mechanisms by which dominant riparian tree species coordinate multiple functional traits to maintain carbon–water balance remains poorly understood. This study investigated coordinated ecophysiological trait shifts of Populus euphratica Oliv. along a groundwater-depth gradient (2.19, 4.88, and 7.45 m) in the middle reaches of the Tarim River (China), hereafter referred to as shallow, middle, and deep groundwater depths, respectively. We quantified photosynthetic, hydraulic, stomatal, leaf anatomical and nutrient traits, and estimated long-term intrinsic water-use efficiency (WUE_i) from foliar δ¹³C. As the groundwater table declined, (1) photosynthetic capacity and photochemical performance decreased, whereas WUE_i increased markedly from 38.5 ± 2.9 to 54.2 ± 1.0 μmol mmol⁻¹, accompanied by the lowest transpiration rate at the deep groundwater depth (4.6 ± 0.5 mmol m⁻² s⁻¹); (2) stomatal and anatomical adjustments consistent with water-loss reduction were observed, including a significant decline in stomatal density from 93.5 ± 14.5 to 79.3 ± 17.4 pores mm⁻², and reduced stomatal size and stomatal area fraction (−20.3% and −32.7%, respectively); (3) the percentage loss of hydraulic conductivity increased, whereas sapwood-specific hydraulic conductivity declined, accompanied by greater sapwood investment relative to leaf area, with Huber value rising from 0.06 ± 0.02 to 0.11 ± 0.04 mm² cm⁻² at deep water depth; and (4) chlorophyll concentrations and leaf water content declined, whereas structural investment increased, as reflected by higher specific leaf mass and leaf dry matter content, and leaf nutrients were enriched, with total nitrogen and total phosphorus increasing by 67.1% and 42.0%, respectively. Trait-WUE_i relationships further indicated that WUE_i covaried most strongly with leaf anatomical and nutrient traits. These results demonstrate that increasing groundwater depth was associated with coordinated shifts in carbon assimilation, water-use regulation, hydraulic function, and nutrient allocation in P. euphratica. Such trait coordination may help explain how this species persists under chronic water limitation in arid riparian forests. Full article