Search Results (143)

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

Red sandstones of the Cambrian age are globally distributed and represent an important sedimentation phase during this critical time interval. Their sedimentology and geochemistry can provide key information about the sedimentation style, paleoclimatic conditions, and weathering trends during the Cambrian. In the Salt Range of Pakistan, the Khewra Sandstone constitutes the Lower Cambrian strata and consists of red–maroon sandstones with minor siltstone and shale in the basal part. Cross-bedding, graded bedding, ripple marks, parallel laminations, load casts, ball and pillows, desiccation cracks, and bioturbation are the common sedimentary features of the formation. The sandstones are fine to medium to coarse-grained with subangular to subrounded morphology and display an overall coarsening upward trend. Petrographic analysis indicates that the sandstones are sub-arkose and sub-lithic arenites, and dolomite and calcite are common cementing materials. X-ray Diffraction (XRD) analysis indicates that the main minerals in the formation are quartz, feldspars, kaolinite, illite, mica, hematite, dolomite, and calcite. Geochemical analysis indicates that SiO₂ is the major component at a range of 53.3 to 88% (averaging 70.4%), Al₂O₃ ranges from 3.1 to 19.2% (averaging 9.2%), CaO ranges from 0.4 to 25.3% (averaging 7.4%), K₂O ranges from 1.2 to 7.4% (averaging 4.8%), MgO ranges from 0.2 to 7.4% (averaging 3.5%), and Na₂O ranges from 0.1 to 0.9% (averaging 0.4%), respectively. The results of the combined proxies indicate that the sedimentation occurred in fluvial–deltaic settings under overall arid to semi-arid paleoclimatic conditions with poor to moderate chemical weathering. The Khewra Sandstone represents the red Cambrian sandstones on the NW Indian Plate margin of Gondwana and can be correlated with contemporaneous red sandstones in the USA, Europe, Africa, Iran, and Turkey (Türkiye). Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

The Aptian–Albian interval represents a significant cooling phase within the Cretaceous “hothouse” climate, marked by dynamic climatic fluctuations. High-resolution continental records are essential for reconstructing terrestrial climate and ecosystem evolution during this period. This study examines a lacustrine-dominated succession of the Shahezi Formation (Lishu Rift Depression, Songliao Basin, NE Asia) to access paleo-weathering intensity and paleoclimate variability between the Middle Aptian and Early Albian (c. 118.2–112.3 Ma). Multiple geochemical proxies, including the Chemical Index of Alteration (CIA), were applied within a sequence stratigraphic framework covering four stages of lake evolution. Our results indicate that a hot and humid subtropical climate predominated in the Lishu paleo-lake, punctuated by transient cooling and drying events. Periods of lake expansion corresponded to episodes of intense chemical weathering, while two distinct intervals of aridity and cooling coincided with phases of a reduced lake level and fan delta progradation. To address the impact of potassium enrichment on CIA values, we introduced a rectangular coordinate system on A(Al₂O₃)-CN(CaO* + Na₂O)-K(K₂O) ternary diagrams, enabling more accurate weathering trends and CIA corrections (CIA_corr). Uncertainties in CIA correction were evaluated by integrating geochemical and petrographic evidence from deposits affected by hydrothermal fluids and external potassium addition. Importantly, our results show that metasomatic potassium addition cannot be reliably inferred solely from deviations in A-CN-K diagrams or the presence of authigenic illite and altered plagioclase. Calculations of “excess K₂O” and CIA_corr values should only be made when supported by robust geochemical and petrographic evidence for external potassium enrichment. This work advances lacustrine paleoclimate reconstruction methodology and highlights the need for careful interpretation of weathering proxies in complex sedimentary systems. Full article

The occurrence of toxic metal(loid)s in surface freshwater is a global concern due to its impacts on human and ecosystem health. Conceptual and quantitative metal(loid) models are needed to assess the impact of metal(loid)s in watersheds affected by acid rock drainage. Few case studies have focused on arid and semiarid headwaters, with scarce hydrological and hydrochemical information. This work reports the use of WASP8 (US EPA) to model Al, Fe, As, Cu, and SO₄²⁻ concentrations in the Upper Elqui River watershed in north–central Chile. Calibrated model performance for total concentrations was “good” (25.9, RRMSE; 0.7, R²-d) to “very good” (0.8–0.9, R²-d). The dissolved concentrations ranged between “acceptable” (56.3, RRMSE), “good” (28.6, RRMSE; 0.7 d), and “very good” (0.9, R²-d). While the model validation achieved mainly “very good” (0.8–0.9, R²-d) predictions for total concentrations, the predicted dissolved concentrations were less accurate for all indicators. Sensitivity analysis showed that the partition coefficient is a sensitive constant for estimating dissolved concentrations, and that integrating sorption and sediment interaction reduces the model error. This work highlights the need for detailed and site-specific information on the reactive and hydrodynamic properties of suspended solids, which directly impact the partition coefficient, sedimentation, and resuspension velocity calibration. Full article

In arid and semi-arid areas, soil is blown up by the wind because of its loose structure. Wind erosion causes soil quality and fertility loss, land degradation, air pollution, disruption of ecological balance, and agricultural and livestock losses. Consequently, there is an immediate imperative for methods to mitigate the impacts of wind erosion. SICP (soybean urease-induced carbonate precipitation) has emerged as a promising biogeotechnical technology in mitigating wind erosion in arid and semi-arid regions. To enhance bio-cementation efficacy and treatment efficiency of SICP, aluminum chloride (AlCl₃) was employed as an additive to strengthen the SICP process. Multiple SICP treatment cycles with AlCl₃ additive were conducted on Tengger Desert sand specimens, with the specimens treated without AlCl₃ as the control group. The potential mechanisms by which AlCl₃ enhances SICP may have two aspects: (1) its flocculation effect accelerates the salting-out of proteinaceous organic matter in the SICP solution, retaining these materials as nucleation sites within soil pores; (2) the highly charged Al³⁺ cations adsorb onto negatively charged sand particle surfaces, acting as cores to attract and coalesce free CaCO₃ in solution, thereby promoting preferential precipitation at particle surfaces and interparticle contacts. This mechanism enhances CaCO₃ cementation efficiency, as evidenced by 2.69–3.89-fold increases in penetration resistance at the optimal 0.01 M AlCl₃ concentration, without reducing CaCO₃ production. Wind erosion tests showed an 88% reduction in maximum erosion rate (from 1142.6 to 135.3 g·m⁻²·min⁻¹), directly correlated with improved microstructural density observed via SEM (spherical CaCO₃ aggregates at particle interfaces). Economic analysis revealed a 50% cost reduction due to fewer treatment cycles, validating the method’s sustainability. These findings highlight AlCl₃-modified SICP as a robust, cost-effective strategy for wind erosion control in arid zones, with broad implications for biogeotechnical applications. Full article

Over the past four decades, Wadi Fatimah in western Saudi Arabia has undergone significant environmental changes that have contributed to desertification. High-resolution spatial and temporal analyses are essential for monitoring the extent of desertification and understanding its driving factors. This study aimed to assess the spatial distribution of desertification in Wadi Fatimah using satellite and climate data. Landsat imagery from 1984 to 2022 was employed to derive land surface temperature (LST) and assess vegetation trends using the Normalized Difference Vegetation Index (NDVI). Climate variables, including precipitation and evapotranspiration (ET), were sourced from the gridded TerraClimate dataset (1980–2022). LST estimates were validated using MOD11A2 products (2001–2022), while TerraClimate precipitation data were evaluated against observations from four local rain gauge stations: Wadi Muharam, Al-Seal Al-Kabeer, Makkah, and Baharah Al-Jadeedah. A Desertification Index (DI) was developed based on four variables: NDVI, LST, precipitation, and ET. Five regression models—ridge, lasso, elastic net, polynomial regression (degree 2), and random forest regression—were applied to evaluate the predictive capacity of these variables in explaining desertification dynamics. Among these, Random Forest and Polynomial Regression demonstrated superior predictive performance. The classification accuracy of the desertification map showed high overall accuracy and a strong Kappa coefficient. Results revealed extensive land degradation in the central and lower sub-basins of Wadi Fatimah, driven by both climatic stressors and anthropogenic pressures. LST exhibited a clear upward trend between 1984 and 2022, especially in the lower sub-basin. Precipitation and ET analysis confirmed the region’s arid climate, characterized by limited rainfall and high ET, which exacerbate vegetation stress and soil moisture deficits. Validation of LST with MOD11A2 data showed reasonable agreement, with RMSE values ranging from 2 °C to 6 °C and strong correlation coefficients across most years. Precipitation validation revealed low correlation at Al-Seal Al-Kabeer, moderate at Baharah Al-Jadeedah, and high correlations at Wadi Muharam and Makkah stations. These results highlight the importance of developing robust validation methods for gridded climate datasets, especially in data-sparse regions. Promoting sustainable land management and implementing targeted interventions are vital to mitigating desertification and preserving the environmental integrity of Wadi Fatimah. Full article

Conservation of ecologically important freshwater mussels is high on the international agenda, but there is only limited knowledge about the status of rare unionid species in arid and semi-arid areas which are particularly vulnerable. One such example concerns Anodonta vescoiana which was recognized as one of the few endemic species of unionid mussels from Iraq and was restricted to the marshes of southern Mesopotamia and its connected river systems. The last confirmed report of A. vescoiana was in 2009 from the Al-Ezz River. We conducted extensive field surveys during the years 2021 and 2022 at approximately 20 freshwater sites, but we failed to observe any live or dead specimens, suggesting a probable extirpation or severe decline. In contrast, we documented the invasive Sinanodonta woodiana at numerous sites across the Tigris–Euphrates basin including the Al-Ezz River. This documentation of S. woodiana indicates successful establishment of the species and colonization of freshwater systems modified by anthropogenic practices, which include alterations of hydrological dynamics and ecological conditions. Here, we compile existing evidence of the global ecological impacts and development of S. woodiana invasion, while also highlighting Iraq as an important example of the displacement of native unionid mussel species by invasive alien unionids. We assessed the factors that contributed to the disappearance of A. vescoiana in Iraq including biological competition (with S. woodiana), salinity stress, habitat fragmentation, and pollution. The time window to act and prevent the further decline of rare unionid species in Iraq, as well as other arid and semi-arid areas which face similar threats, is short. Urgent actions include systematic monitoring to identify remnant populations, implementing biosecurity policies (for fisheries or habitats), and restoration (of habitats) to secure the long-term persistence of remaining unionid diversity. Full article

This study focuses on the upper section of the Zhujiang Formation in the Enping Sag of the Zhujiangkou Basin in the South China Sea, investigating the mechanisms by which astronomical orbital cycles drive paleoclimate, sea-level fluctuations, and sedimentary development. In this study, a cyclic stratigraphic analysis was performed using natural gamma-ray logging data and geochemical proxies (Chemical Index of Alteration (CIA); Al₂O₃ content) in combination with depositional noise modeling (DYNOT Dynamic Orbital Tuning Model and ρ1 noise factor). High-resolution time series analysis revealed three key findings: (1) a 15.98–19.09 Ma astronomical timescale was established through the identification of Milankovitch cycles including 405 kyr eccentricity, 100 kyr eccentricity, 40 kyr obliquity, and 20 kyr precession; (2) sea-level changes exhibited 405 kyr eccentricity-driven cyclicity, with high-eccentricity phases corresponding to warm-humid climates and transgressive mudstone deposition, and low-eccentricity phases reflecting arid conditions and regressive sandstone development; and (3) orbital-scale precession modulation regulated sediment source-to-sink processes through climate–sea level coupling. This work provides a quantitative framework for predicting astronomical cycle-controlled reservoirs, offering critical insights for deepwater hydrocarbon exploration in the Zhujiangkou Basin. Full article

Acacia species are important trees in arid ecosystems due to their diverse ecological roles, such as providing vegetation cover, community structures, food resources for animals, soil stabilization, and erosion prevention. However, in the Arabian Peninsula, Acacia species are declining due to climate change, overgrazing, and fuelwood harvesting. This study evaluates the effectiveness of various pre-sowing treatments—sulfuric acid soaking and tap and hot water soaking—on breaking seed dormancy to enhance germination in nine Acacia species native to the Al-Baha region of Saudi Arabia. The key germination indicators assessed were the mean germination time (MGT), germination percentage (GP), and germination index (GI). Sulfuric acid treatments for 10–15 min reduced the MGT and increased the GP for A. etbaica, A. hamoulosa, and A. tortilis, while A. origena responded best to 1 min of hot water soaking. Conversely, A. asak, A. ehrenbergiana, and A. johnwoodii showed little to no germination improvement with treatment and A. oerfota and A. gerrardii showed no germination improvement, indicating the need for alternative methods. These findings indicate that the seed germination requirements vary within Acacia spp. from the same geographic region and similar climatic conditions. Further work is required for five of the species tested to develop better seed germination techniques, given the potential utility of Acacia spp., in ecological restoration and sustainable land management in arid regions. Full article

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems. Full article

Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R² = 0.773), followed by the ratio (B5/B7) model (R² = 0.489), while the inverse log transformation (1/log (B5/B7), R² = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems. Full article

Soil salinity is a growing threat to agricultural sustainability, particularly in arid and semi-arid regions. Understanding how salinity affects soil organic matter (OM) is critical for improving land management and maintaining soil health. This study addresses these challenges by exploring the molecular-level impact of salinity on OM dynamics. Salinity exerts a depth-dependent influence on lignin and microbial lipid biomarkers, which are used to trace plant inputs and microbial activity, respectively. For lignin biomarkers, in the surface layer (0–20 cm), higher salinity levels are associated with increased Syringyl/Vanillyl (S/V) and Cinnamyl/Vanillyl (C/V) ratios, suggesting enhanced preservation of syringyl (S) and cinnamyl (C) units. In the middle layer (−20 to −60 cm), higher salinity correlates with elevated SVC (total lignin phenols), Acid/aldehyde (Ad/Al) ratios, and other markers of selective lignin degradation. For lipid biomarkers, salinity modulates microbial adaptation and turnover, as seen in variations in i17 (iso-C17), a17 (anteiso-C17), and unsaturation indices such as C16:1/C16, reflecting Gram-positive and Gram-negative bacterial activity. These trends indicate that salinity stress alters microbial lipid profiles, leading to reduced turnover and enhanced preservation in deeper, more anoxic environments. Principal Component Analysis (PCA) revealed depth- and salinity-driven patterns that distinguish between surface microbial transformations and deep-layer molecular preservation. Correlation analysis of Principal Components (PCs) with salinity revealed that higher salinity favored molecular stability in deeper layers, while lower salinity was associated with microbial transformations in surface layers. These findings underscore salinity’s critical role in OM stabilization and turnover, and provide a molecular framework to guide sustainable management of saline soils. Full article