Search Results (12,836)

Efficient water management for irrigation is critical for sustaining plant production in arid and hyper-arid regions, where optimizing emitter type, burial depth, and irrigation scheduling can significantly enhance water-use efficiency and yield. This study evaluated the effects of continuous and intermittent subsurface irrigation using porous (PRP) and emitting (GRP) pipes at two installation depths (25 and 35 cm) on soil water distribution, potato germination, and yield under arid conditions in Saudi Arabia. Soil water content was monitored using volumetric sampling, EnviroSCAN sensors, and HYDRUS modeling, with strong agreement observed among methods (R² ≥ 0.92). Results showed that shallow emitter placement (25 cm) combined with intermittent irrigation (five pulses, WF5C) maximized soil water retention in the root zone, reducing deep percolation losses. The GRP_25cm treatment improved soil water content by up to 140.7% at 30 cm depth and achieved the highest germination (74–83%) and yields (164.5–171.7 kg). In contrast, deeper installations (35 cm) consistently underperformed. Overall, intermittent irrigation enhanced water distribution and plant performance compared with continuous flow, leading to a 40–49% yield increase. These findings highlight the importance of emitter type, placement depth, and irrigation scheduling in optimizing water-use efficiency and plant productivity. The study provides practical recommendations for sustainable irrigation strategies in arid and hyper-arid regions facing increasing water scarcity. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

Severe leaf galling by the jujube gall midge Dasineura jujubifolia (Diptera: Cecidomyiidae) compromises photosynthesis and yield in arid-zone jujube orchards, yet Xinjiang-specific evidence to guide biological control has been scarce. Here we provide the first systematic characterization in Xinjiang (Hami, China) of D. jujubifolia and its parasitoid complex, integrating region-specific field surveys with gall dissection and laboratory assays. We documented five parasitoid wasps, including two species newly recorded in China—Pseudotorymus samsatensis (Hymenoptera: Torymidae) and Baryscapus adalia (Hymenoptera: Eulophidae). In Hami, the host completed 4–5 generations per year with a 19–24-day generation time. Functional roles were partitioned: P. samsatensis (dominant), Systasis parvula (Hymenoptera: Pteromalidae), and B. adalia were larval ectoparasitoids, whereas Aprostocetus sp. (Hymenoptera: Eulophidae) and Synopeas sp. (Hymenoptera: Platygastridae) were endoparasitoids. Time-series data revealed tight temporal synchrony between P. samsatensis and host peaks. Controlled experiments quantified daily emergence rhythms, diet-dependent adult longevity, and sex ratios, providing parameters to inform release timing and conservation in biological control programs. Collectively, these findings establish management-ready baselines for D. jujubifolia and its parasitoids in arid jujube systems and support conservation-oriented, reduced-pesticide integrated pest management (IPM). Full article

Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article

In arid environments such as the Namib Desert, non-rainfall water sources—including dew and fog—constitute indispensable yet understudied components of the regional hydrological cycle. These moisture inputs play a critical role in sustaining ecological functionality and biogeochemical processes, but remain among the least quantified facets of desert ecohydrology. The present study investigates multi-year trends in morning dew formation within the Namib Desert, utilizing observations from the Gobabeb–Namib Research Institute between 2015 and 2022. Meteorological data from the Southern African Science Service Centre for Climate and Adaptive Land Management (SASSCAL), in conjunction with direct field observations of dew, were used to develop an empirical equation to estimate dew occurrence. A sensitivity analysis verified the robustness of this formulation, and subsequent validation using field data confirmed its reliability (84.84% accuracy). During this eight-year period, the annual number of days with morning dew decreased from 170 in 2015 to 140 in 2022, representing an overall decline of approximately 18%. However, the total daily dew occurrence across 24 h remained relatively constant, indicating that the observed decline is confined primarily to morning condensation events. Dew formation was most prevalent during the wet season (December–May). Both monthly and annual analyses revealed a discernible declining trend in morning dew occurrence across this hyperarid ecosystem (p < 0.05). This decline corresponded with a gradual increase in both air and soil temperatures (approximately +0.03 °C yr⁻¹) and a slight but consistent decrease in relative humidity (approximately −0.26% yr⁻¹) between 2015 and 2022. The principal drivers of this decline include rising soil and air temperatures and decreasing atmospheric humidity. The analysis further identified an inverse relationship between air temperature and dew formation, implying that climatic warming intensifies evaporative demand and thereby suppresses dew condensation. Random forest analysis identified soil temperature, air temperature, and relative humidity as the most important predictors influencing dew occurrence, whereas wind speed and direction played lesser roles. Collectively, these findings underscore the vulnerability of dew-dependent ecosystems to anthropogenic climate change and highlight the imperative to continue investigating non-rainfall moisture dynamics in desert environments. Full article

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The ordinary kriging were spatially interpolated to analyze As, Hg, Cd, Cr, and Pb heavy-metal pollution spatial patterns. Pollution was evaluated using the Nemerow and geoaccumulation (I_(geo)) indices, and sources quantified via Pearson correlations, PCA (Principal Component Analysis), and PMF (Positive Matrix Factorization). The results indicated that Hg and Cd posed the highest ecological risks. The overall mean concentrations (mg.kg⁻¹) of Hg, Cd, As, Pb, and Cr were 0.04, 0.27, 9.91,23.81, and 57.34, respectively. Compared with the background values, they were 1.90, 2.41, 0.83, 1.14, 2.74 times higher, respectively. Geospatially, regions with higher pollution probabilities for Cd, Cr, Pb, Hg, and As were concentrated in the northern and central parts of Ningxia, whereas the southern region exhibited lower pollution probabilities. pH significantly influenced the accumulation and spatial distribution of heavy metals in soil. Source apportionment identified three primary contributors: transportation and natural parent materials (As, Pb, Cr), industrial activities (Hg), and agricultural practices (Cd). Hg and Cd were identified as the key risk elements requiring prioritized management. These results enhance understanding of the pollution levers of heavy metals in Ningxia cultivated soils, and also provide foundation for developing more scientific and precise soil risk control policies, offering significant practical value for environmental risk management. Full article

Landscape character assessment (LCA) is a systematic approach used to classify, describe, and analyze the physical and cultural attributes that define the landscape. The traditional approaches to LCA are fundamentally subjective and descriptive, relying on human evaluations of aesthetic value, and they often show inconsistencies in results when assessed by different observers for the same landscape. This research aims to establish a spatial and quantitative methodology through GIS for evaluating the landscape character of King Khalid University (KKU)’s campus in the Southern Province of Saudi Arabia, which is considered crucial for designing a sustainable and context-sensitive landscape. To identify the feasible developed areas and their sustainable characteristics, three key landscape variables were measured and spatially expressed, subsequently averaged to categorize landscape character. The variables include land use and land cover, which were obtained from Sentinel 2 remote sensing data through supervised classification, as well as landforms and hydrological settings derived from a digital elevation model (DEM) utilizing GIS functionalities. The findings revealed three distinct landscape characters, each characterized by quantifiable landscape attributes. The landscapes exhibiting the most significant character encompass approximately 20% (1074 ha) of the study area, whereas those with the least significance account for 6.5% (342 ha). The remaining 73.5% (3884 ha) is classified as landscapes with an average significance character. The results provide a solid scientific basis for choosing locations in the campus’s study area that promote environmentally friendly and sustainable landscape development. This method improves objectivity in LCA and offers a reproducible framework for implementation in arid and semi-arid areas. Full article

Investigating ecological irrigation risks associated with mine water utilization is of great significance for alleviating water resource shortages in arid mining regions of western China, thereby supporting efficient coal extraction and coordinated ecological development. In this study, a representative mining area in Xinjiang was investigated to reveal the evolution patterns of mine water quality under arid geo-environmental conditions in western China and to systematically assess environmental risks induced by ecological irrigation. Surface water, groundwater, and mine water samples were collected to study ion ratio coefficients, hydrochemical characteristics, and evolution processes. Based on this, a multi-index analysis was employed to evaluate ecological irrigation risks and establish corresponding risk control measures. The results show that the total dissolved solids (TDS) of mine water in the study area are all greater than 1000 mg/L. The evolution of mine water quality is mainly controlled by water–rock interaction and is affected by evaporation and concentration. The main ions Na⁺, Cl⁻, Ca²⁺, and SO₄²⁻ originate from the dissolution of halite, gypsum, and anorthite. If the mine water is directly used for irrigation without treatment, the soluble sodium content, sodium adsorption ratio, salinity hazard, and magnesium adsorption ratio will exceed the limits, leading to the accumulation of Na⁺ in the soil, affecting plant photosynthesis, and posing potential threats to the groundwater environment. Given the evolution process of mine water quality and the potential risks of direct use for irrigation, measures can be taken across three aspects: nanofiltration combined with reverse osmosis desalination, adoption of drip irrigation and intermittent irrigation technologies, and selection of drought-tolerant vegetation. These measures can reduce the salt content of mine water, decrease the salt accumulation in the soil layer, and lower the risk of groundwater pollution, thus reducing the environmental risks of ecological irrigation with mine water. The research will provide an important theoretical basis for the scientific utilization and management of mine water resources in arid areas by revealing the evolution law of mine water quality in arid areas and clarifying its ecological irrigation environmental risks. Full article

The Bactrian camel exemplifies mammalian adaptation to deserts, but the spatial organization of its innate immune system remains uncharacterized. This study integrated transcriptomes from 110 samples across 11 major tissues and organs to resolve tissue-specific gene expression and innate immune modularity. Through differential expression analysis, Tau specificity index (τ > 0.8), and machine learning validation (Random Forest F1-score = 0.86 ± 0.11), we identified 4242 high-confidence tissue-specific genes (e.g., LIPE/PLIN1 in adipose). Weighted gene co-expression network analysis (WGCNA) of 1522 innate immune genes revealed 11 co-expression modules, with six exhibiting significant tissue associations (FDR < 0.01): liver-specific (r = 0.96), spleen-adipose-enriched (r = 0.88), muscle-associated (r = 0.82), and blood-specific (r = 0.80) modules. These networks demonstrated multifunctional coordination of immune pathways—including Pattern Recognition, Cytokine Signaling, and Phagocytosis—rather than isolated functions. Our results establish that camel innate immunity is organized into spatially modular networks tailored to tissue microenvironments, providing the first systems-level framework for understanding immune resilience in desert-adapted mammals and may inform strategies for enhancing livestock resilience in arid regions. Full article

The marginal arid region encompassing the western Nafud in the east to Wadi Tabuk in the west has only been subject to limited archaeological survey. This paper reports on data from a systematic remote sensing survey of the region as part of the Endangered Archaeology in the Middle East and North Africa project, using the results to produce preliminary models of settlement, occupation, and land-use, and contextualising within the broader archaeological landscapes of northern Arabia. It also provides datasets that can be used to outline broad trends in modern disturbances and threats to these sites, in part demonstrating the effectiveness of this approach for producing a cost-effective baseline dataset for the management of heritage sites at a landscape level. While confirming that long-term settlement and agriculture were largely confined to the Wadi Tabuk region from the later prehistoric period onwards, including the identification of a significant new fortified settlement south of Tabuk, it also demonstrates evidence of a broader complex landscape of pastoralism, funerary monuments, and other monumental structures across much of the survey area. Most notably, this area may mark a border zone when geographically distinct distributions of Neolithic-adjacent kites and mustatil meet with minimal overlap. Full article

Wildfire risk assessment requires integrating heterogeneous geospatial data to capture complex environmental dynamics. This study develops a hierarchical multimodal fusion framework combining high-resolution aerial imagery, historical fire data, topography, meteorology, and vegetation indices within Google Earth Engine. We introduce three progressive fusion levels: a single-modality baseline (NAIP-WHP), fixed-weight fusion (FIXED), and a novel geographically adaptive dynamic-weight approach (FUSED) that adjusts feature contributions based on regional characteristics like human activity intensity or aridity. Machine learning benchmarking across 49 U.S. regions reveals that Support Vector Machines (SVM) applied to the FUSED dataset achieve optimal performance, with an AUC-ROC of 92.1%, accuracy of 83.3%, and inference speed of 1.238 milliseconds per sample. This significantly outperforms the fixed-weight fusion approach, which achieved an AUC-ROC of 78.2%, and the single-modality baseline, which achieved 73.8%, representing relative improvements of 17.8% and 24.8%, respectively. The 10 m resolution risk heatmaps demonstrate operational viability, achieving an 86.27% hit rate in Carlsbad Caverns, NM. SHAP-based interpretability analysis reveals terrain dominance and context-dependent vegetation effects, aligning with wildfire ecology principles. Full article

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R² values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Sand dune migration, as a typical dynamic process of aeolian geomorphology in arid regions, directly influences regional ecological security and infrastructure development. Focusing on the western edge of the Kumtag Desert, this study uses remote sensing imagery and field investigations, combined with multi-factor meteorological observations and CMIP6 climate scenarios, to quantitatively analyze the migration characteristics and influencing factors of representative dunes, and to construct a predictive model for future migration trends. The dominant migration direction is W–WNW–NW, which closely matches the composite resultant drift potential. The average annual migration speed is 12.86 m·a⁻¹, classifying these dunes as fast-moving; small to medium dunes migrate faster (13.84 m·a⁻¹) than large dunes (11.27 m·a⁻¹). Wind speed, sand-moving wind frequency, drift potential (DP), Vegetation Fractional Cover (FVC), and precipitation significantly affect migration speeds; wind speed is the primary driver (single-factor R² = 0.41), while precipitation (R² = 0.26) and FVC (R² = 0.27) exert a suppressing effect, particularly on small to medium dunes. Based on stepwise multiple regression analysis combined with CMIP6 multi-model predictions, under the SSP8.5 scenario, characterized by significant temperature increases, drastic fluctuations in precipitation patterns, and notable increases in wind speed, the average annual sand dune migration speed is projected to reach 18.59 m·a⁻¹ by the end of this century, an increase of 5.78 m·a⁻¹ compared to the current speeds; whereas under the SSP1–2.6 and SSP2–4.5 scenarios, changes are projected to be minor and overall relatively stable. The findings of this study provide a scientific basis for regional infrastructure and engineering planning, as well as for the renovation and protection of existing oil and power transmission lines. Full article

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article