Search Results (172)

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments. Full article

Freshwater scarcity is a critical global challenge, particularly in arid and semi-arid regions like southern Iraq. This study evaluates the thermal and distillate performance of a conventional single-slope solar still under extreme summer conditions in Basrah, Iraq. The objective is to analyze and validate a coupled theoretical–experimental model for predicting temperature fields and freshwater productivity. The model incorporates transient energy and mass balance equations with temperature- and salinity-dependent thermophysical properties. Experiments were conducted using brackish water from the Shatt al-Arab River (salinity: 5.2 g/kg), and measured temperatures and productivity were compared against simulations over a 24-h period. Strong agreement was achieved between experimental and theoretical results, with $R^2>0.90$ for temperature predictions and $R^2=0.985$ for hourly productivity. Maximum hourly yield reached $0.46\mathrm{L}/{\mathrm{m}}^2$, with a total daily productivity of $3.5\mathrm{L}/{\mathrm{m}}^2$, The daily thermal efficiency was found to be 26.90% experimentally and 28.20% theoretically. A positive linear relation between the thermal gradient $(T_{\mathrm{w}}-T_{\mathrm{g}})$ and hourly productivity was also established. The findings confirm the reliability of the developed model and highlight the potential of solar distillation as a sustainable freshwater source for high-temperature regions. Full article

This study evaluates the quality and irrigation suitability of drainage water in the Al-Jouf Region, Saudi Arabia, where water scarcity necessitates the reuse of nonconventional resources. Eighteen drainage water samples were analyzed for physicochemical parameters and irrigation indices, including electrical conductivity (EC), sodium percentage (Na⁺%), sodium adsorption ratio (SAR), magnesium hazard (MH), Kelly’s ratio (KR), permeability index (PS), and irrigation water quality index (IWQI). Multivariate statistical tools were applied to identify dominant hydrogeochemical processes. Inverse Distance Weighting (IDW) interpolation in ArcGIS Desktop 10.8 was employed to map significant physicochemical data and irrigation indicators. Results revealed that while EC values indicated low to moderate salinity (0.74–25.2 μS/cm), most samples showed high Na⁺%, SAR, and KR, classifying them as doubtful to unsuitable for irrigation. The IWQI ranged from 84.47 to 1617.87, indicating poor to inferior quality due to evaporation, fertilizer leaching, and sodium accumulation. Furthermore, the results highlight the importance of precise geographic modeling in determining whether drainage water is suitable for long-term agricultural use in arid regions such as Al-Jouf. Sustainable reuse of such drainage water requires freshwater blending, gypsum application, and the cultivation of salt-tolerant crops, aligning with Saudi Vision 2030 objectives for sustainable water management in arid regions. Full article

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress. Full article

In arid regions, the challenges posed by rainfall data availability, missing data, and limited historical records significantly affect hydrological modeling studies and climate change assessments. For various hydrology applications, it is essential to implement advanced techniques in order to obtain a complete dataset series. This study explores the implementation of multiple machine learning techniques to address the complexity of filling daily rainfall data for 88 rainfall stations in the Al-Batinah region of Oman, covering the period from 1993 to 2024. The machine learning models applied in this study include Multiple Linear Regression (MLR), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Gradient-Boosting Trees (GBT). A non-clustering approach is used as well as a clustering approach as part of the methodology. In the first method, rainfall stations are not clustered, while in the second method, optimal cluster numbers are calculated using K-means clustering. The target station utilizes the nearby rainfall station data located within a 50 km radius with the highest correlation coefficients. A novel Ensemble Fusion Model has been applied to improve the efficacy of multiple predictive models, including the RF Fusion Model (RF) and Multi-Model Super Ensemble Fusion Model (MMSE). The estimation approaches are further enhanced and evaluated by Bayesian optimization of hyperparameters, dataset imputation utilizing Multiple Imputation by Chained Equations (MICE), and Leave-One-Year-Out (LOYO) cross-validation. Based on the results, it can be concluded that the GBT model performs the best in both cluster and non-cluster approaches. A further benefit of applying Ensemble Fusion Models to rainfall gap-filling methods is that the coefficient of determination (R²) for clustering and non-clustering approaches increases to 22.5% and 22.2%, respectively. Full article

Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R² up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R² ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article

This study assessed six tomato (Solanum lycopersicum L.) cultivars within an integrated solar-powered closed hydroponic system in Al Dhaid, UAE (25°16′11.2″ N, 55°55′52.2″ E). The system combined an insect-proof net house, closed hydroponics, root-zone cooling, ultra-low-energy drip irrigation, and a cost-effective solar-powered reverse osmosis (RO) desalination unit to address salinity constraints. The cultivars, selected for their adaptability to controlled environments in the UAE, were evaluated for yield, water-use efficiency (WUE), and fertilizer-use efficiency (FUE). Among them, Torcida recorded the highest mean yield (0.619 kg/m²/harvest), WUE (27.1 kg/m³), FUE (26.5 kg fruit/kg fertilizer), and marketable fruit ratio (66.3%), followed by Roenza, Eviva, and SV 4129 TH; Lamina was intermediate, while Saley, a bushy type, produced the lowest yield. The top cultivars achieved cumulative yields exceeding 7 kg/m²—surpassing regional open-field benchmarks (4–5 kg/m²; 3–6 kg/m³). Compared with conventional cooled hydroponic greenhouses (3.5 kg/plant; 8 kg/m³), the system demonstrated similar productivity using three times less water. The RO unit produced water at baseline 1.05 USD/m³—58–68% below regional tariffs—while minimizing reliance on grid electricity and mechanical cooling. Overall, the integrated solar-powered hydroponic–RO model proved technically reliable, resource-efficient, and economically viable, offering a scalable solution for sustainable vegetable production in hyper-arid regions. Full article

Nitrous oxide (N₂O), as one of the important greenhouse gases in the atmosphere, has a significant impact on global climate change. Its emissions are significantly regulated by land use changes, especially in ecologically fragile semi-arid areas. However, there is still a lack of systematic analysis on the key biotic and abiotic factors through which different land use patterns affect N₂O emissions. Therefore, this study focuses on four typical land use types in the Loess Plateau of central Gansu: Picea asperata (PA), Medicago sativa (MS), Abandoned land (AL), and Wheat field (WF). Static box gas chromatography was used to monitor soil N₂O flux in situ, and multidimensional analysis was conducted based on soil physicochemical properties, microbial community structure, and nitrogen cycling functional genes. Based on the observational data from the 2024 growing season (April to October), Research findings show that the cumulative emissions of N₂O from wheat fields increased significantly by 26.4%, 19.4%, and 39.8% compared to medicago sativa, abandoned land, and picea asperata, respectively. Mechanism analysis reveals that picea asperata promote nitrogen fixation and absorption in soil through higher soil water content and organic carbon content, as well as enrichment of Proteobacteria and high expression of nrfA and napA genes, thereby inhibiting N₂O production and emissions. The wheat fields, on the other hand, have significantly increased N₂O emissions due to the increased abundance of amoA_B, nxrB, and nirK functional genes and enhanced urease activity, which promote nitrification and denitrification processes. The Partial Least Squares Path Model (PLS-PM) further confirmed that nitrification functional genes are key driving factors for N₂O emissions. This study systematically reveals the microbial and biochemical pathways involved in regulating N₂O emissions through land use in semi-arid regions, providing a theoretical basis for regional nitrogen cycle management and climate mitigation. Full article

Representatives of the family Moinidae (Crustacea: Cladocera) are well-adapted to life in temporary waters. Different species are characteristic of the Arid Belt of Eurasia. We aimed to compare the moinid species composition and genetic diversity found in Djibouti (with extreme and uniform environments) with neighboring Ethiopia (a relatively large country with diverse environmental conditions). Any cladocerans were found in only four localities in Djibouti from Ecoregion 527 (Western Red Sea Drainages) according to Abell et al. (2008). The moinids belonged to two taxa: M. cf. micrura and M. heilongjiangensis. In Ethiopia, moinids were found in 28 water bodies from four other Ecoregions (522, 525, 526 and 528). They belonged to M. micrura and M. belli. A genetic study based on full mitogenomes, sequences of the mitochondrial COI and nuclear ITS1 loci demonstrated that M. micrura from Djibouti and Ethiopia belong to distant lineages. Our genetic analysis revealed a very contrasting moinid fauna in two neighboring countries of the African Horn: there was no single haplotype, clade or even species sharing these territories. We have revealed unexpectedly small genetic distances between Chinese (type locality) and Djiboutian populations of M. heilongjiangensis; the question of the invasive status of the latter could therefore be raised. Moreover, the status of M. micrura populations from the Rift Valley also needs to be checked; they could be non-indigenous, as they belong to “European” M. micrura s. str. Finally, we have demonstrated that M. cf. micrura is not a monophyletic clade. Full article

Sea level rise (SLR) is a global phenomenon impacting coastlines worldwide, with its effects varying according to local geophysical and climatic conditions. The Arabian Gulf, characterized by hyper-arid conditions and low-lying coastal zones, is particularly vulnerable to SLR. This includes the eastern Arabian Peninsula, where densely populated cities and critical infrastructure in countries such as Iraq, Kuwait, Saudi Arabia, Bahrain, Qatar, and the United Arab Emirates (UAE) face increasing risk. This study assesses the potential impact of SLR on Qatar’s coastline using CVI, which integrates both physical and socio-economic parameters. The analysis separately calculates the Physical Vulnerability Index (PVI) and the Socio-Economic Vulnerability Index (SVI), which are then combined to produce the final CVI score. Each variable is assigned a semi-quantitative score on a scale from 1 to 5, representing a gradient from very low to very high vulnerability. To determine the relative importance of each variable, the AHP is employed as a weighting method. The findings reveal that the majority of Qatar’s coastline falls within the high to very high vulnerability categories, with the exception of Doha, which is classified as low risk due to extensive coastal modifications and protective infrastructure. In contrast, areas such as Al Khor and Ras Laffan in the north and northeast, as well as Dukhan and Al Zubarah in the west, exhibit considerably higher vulnerability. These results highlight the urgent need for continued assessment of SLR impacts and the development of targeted adaptation and resilience strategies to safeguard Qatar’s coastal zones. Full article

Vast areas of natural rangelands in the Kingdom of Saudi Arabia (KSA) suffer from deterioration due to the scarcity of vegetation cover and poor soil quality. Assessing soil quality in rangelands is crucial to identifying degraded lands and to implementing proper sustainable management practices. In this study, a total data set (TDS) containing 27 physical and chemical soil indicators was generated for three rangelands (Al-Fahyhyl, Al-Sahwa, and Al-Tamryate) in KSA. Principal component analysis (PCA) and analytic hierarchy process (AHP) analysis were employed to establish a minimum data set (MDS) and to evaluate key physical and chemical properties affecting soil quality, along with the associated weight factor for each indicator. Results indicated that the MDS represented ≥70% of the total variability of the TDS and accurately estimated the soil quality index (SQI) based on determined physical and chemical soil properties in the study regions. Linear regression indicated high correlation between SQI-TDS and SQI-MDS, with the R² ranging between 0.51–0.87. On the surface layer (0–30 cm), the MDS contained seven soil indicators (sand, dispersion ratio (DR), mean weight diameter (MWD), bulk density (BD), total organic carbon (TOC), available phosphorus (Pa), and available potassium (Ka)), whereas in the sub-surface layer it contained six indicators (sand, DR, MWD, BD, TOC, Pa, and Ka). In all regions, sand had the largest weight factor (0.4514–0.4835), followed by TOC (0.2441–0.2512). Under the arid climate present in all the study sites, sand and TOC levels are crucial for nutrient retention, soil structure, and water retention. Most of the study areas had very low and low SQI (Al-Fahyhyl, 74.4%; Al-Sahwa, 61.8%; and Al-Tamryate, 81.7%), indicating an immediate need for suitable agricultural practices such as reduced tillage, increased organic amendments, and proper water management. The outcomes of this study offer valuable insights for land managers, legislators, and agricultural stakeholders to pinpoint regions in need of development, conduct comprehensive and continuous monitoring of SQI in rangeland areas, and implement land management plans for rangeland rehabilitation and environmental sustainability. Full article

Understanding long-term rainfall variability is essential for addressing Saudi Arabia’s growing challenges of water scarcity, climate resilience, and sustainable resource management in its arid to hyper-arid environment. This study analyzes the spatiotemporal variations and long-term rainfall trends across the 13 administrative regions of the Kingdom of Saudi Arabia (KSA) using four decades of observed data (1982–2021) from the National Center for Meteorology (NCM). The non-parametric Mann–Kendall (M–K) test and Sen’s slope estimator were applied to detect and quantify rainfall trends. Results reveal that 10 of the 13 regions show statistically significant negative trends, excluding the Eastern, Mecca, and Tabuk regions, with declines ranging from −4 to −16 mm/yr. The most pronounced decreases occurred in Hail, Al-Qassim, Riyadh, Medina, and Asir, while Mecca and Tabuk exhibited weak positive signals during the last decade, likely linked to Red Sea Trough dynamics. Seasonal analysis indicates the largest declines during winter and spring, crucial periods for groundwater recharge and agriculture, whereas summer rainfall remains localized in the southwestern highlands with a slight decreasing trend. Overall, rainfall variability in Saudi Arabia reflects both long-term drying and short-term oscillations. The findings provide a robust rainfall baseline to support water security, climate adaptation, and sustainable management strategies in one of the world’s driest regions. Full article