Arabian Journal of Geosciences最新文献

There has been an increase in recent studies revealing the relationship between global warming, El Niño – Southern Oscillation (ENSO) and global climatological events. The onset of the El- Niño event in June 2023, was associated with a significant positive sea surface temperature anomaly in the Niño 3.4 region of the tropical Pacific Ocean, exceeding + 2.0° C relative to the long-term climatological mean. This study documents five distinct climatological events (Events 1–5) that occurred in Mexico, India, United Arab Emirates and China that met the condition of “notable extreme rainfall events” over a short period of time. This study indicates the severity of these catastrophes, that were exacerbated through record-breaking precipitation rates which surpassed the average rainfall of the respective regions, thereby highlighting the influence of global warming and the ENSO conditions through microclimate alterations. As a result, our work abetted to raise public awareness, thus creating a prerequisite for enhancing existing climate mitigation and acclimatization of the humankind for the future.

Denoising well log signals is a crucial task in the field of hydrocarbon exploration and reservoir characterization. Wavelet decomposition has become an essential tool in recent years for effectively reducing noise while preserving essential geological information in well log data. This work mainly focuses on the development of unconventional methods for signal denoising using Symlet (Sym8), biorthogonal (bior6.8), Daubechies (db8) and Haar wavelets to analyze lithology in the Jharia coalfield region. The well log data includes gamma ray log (GR), resistivity log (shallow resistivity log, medium resistivity log), density log, sonic log, and neutron log (NPHI) are used as the signal on which de-noising has been applied. Different parameters like the ‘heursure’ soft thresholding and 8-level decomposition are exercised to denoise the signal. This study demarcates the lithology of the Jharia coalfield region using the wavelet denoising effect and power spectrum analysis of the denoised signal. Lithology identification from de-noised signals is used to delineate three lithologies such as sand, shale, and coal to understand the performance of each wavelet decomposition method. Lithology information obtained from the denoised signal using the ‘db8’ wavelet is more applicable to distinguish the reservoir properties, stratigraphic sequence, and sedimentary facies of the Jharia coalfield region.

Urban environments are increasingly characterized by the intensification of urban heat islands (UHIs), where localized areas experience higher temperatures than their rural surroundings. These temperature disparities are influenced by a complex interplay of geological and built environmental factors. The role of geological features such as topography, soil composition and underlying bedrock in shaping urban climate patterns is crucial in understanding urban heat distribution. Geologically diverse landscapes can influence heat absorption, retention and dissipation, affecting microclimates within cities. Furthermore, the interaction between these natural geological elements and urban infrastructure such as roads, buildings and green spaces creates unique thermal conditions. Recognizing the interplay between geology and urban heat can help create more resilient and comfortable urban environments in the face of rising global temperatures. This paper explores the geological influences that contribute to UHI and the distribution of heat within urban spaces, highlighting how the physical landscape interacts with urban development to shape local climates. By examining these factors, the study aims to provide insights into effective strategies for urban planning and climate adaptation, ultimately helping cities to mitigate the impacts of rising temperatures and create more sustainable urban environments.

Graphical Abstract

Reliable slope stability assessment under uncertain geotechnical and hydrogeological conditions remains a major challenge in geotechnical engineering because conventional deterministic approaches often exhibit limited capability in representing nonlinear interactions and uncertainty propagation. To address this limitation, the present study proposes an integrated uncertainty-aware analytical framework combining artificial neural network (ANN) prediction, Bayesian belief network (BBN) probabilistic inference, and exploratory partial least squares structural equation modeling (PLS-SEM) interpretation for slope stability assessment. A benchmark dataset consisting of 10,000 slope cases was utilized to evaluate the framework under varying geotechnical, geometric, and hydrogeological conditions. The ANN component was employed to capture nonlinear relationships between governing variables and factor of safety, whereas the Bayesian framework quantified probabilistic dependency behavior and uncertainty propagation among slope stability parameters. PLS-SEM was incorporated as a supplementary exploratory interpretability mechanism within the integrated analytical workflow. The ANN model demonstrated strong predictive capability with testing performance metrics of R² = 0.9304, RMSE = 0.0363, and MAE = 0.0291, indicating stable nonlinear learning behavior with minimal overfitting. Sensitivity analysis identified cohesion, internal friction angle, and slope angle as the dominant predictive variables. Bayesian probabilistic analysis further revealed that cohesion exhibited the highest uncertainty contribution, followed by internal friction angle and pore water pressure ratio. Scenario-based inference demonstrated that elevated pore water pressure and reduced shear strength conditions substantially increased posterior instability probability. The integrated ANN–BBN framework provides a structured and interpretable methodology for combining predictive modeling with probabilistic reasoning in uncertainty-aware slope stability assessment under complex geotechnical conditions.

Rapid urbanization substantially alters watershed hydrology by increasing impervious surfaces, resulting in higher runoff volumes, greater peak discharges, and shorter runoff response times. The objective of this study was to investigate the influence of total subarea imperviousness (TSIA), effective subarea imperviousness (ESIA), and directly connected subarea imperviousness (DCSIA) on total runoff depth (Q_t), peak runoff depth (Q_p) and lag time (T_lag) under varying storm return periods, peak ratios, and rainfall durations using the Storm Water Management Model (SWMM), for three urban development scenarios (2016–2020, 2021–2025, and 2026–2030) in the Bidkin industrial catchment of the Delhi–Mumbai Industrial Corridor (DMIC), Maharashtra, India.

The study catchment (3179.1 ha.) was divided into 42 subcatchments with six drainage outlets for detailed hydrological simulation. Spatial variations in urbanization were quantified using three imperviousness indicators, and three runoff variables. Regression analysis demonstrated strong relationships between imperviousness indicators and runoff variables, with coefficient of determination (R²) values ranging from 0.952 to 0.997 for total runoff depth and from 0.20 to 0.60.

for peak runoff depth. The results suggest that, in the selected subareas, the average TSIA values were 31.65%, 40.78%, and 25.57% for Phases I, II, and III, respectively, indicating phase-wise variations in imperviousness. Higher TSIA was generally associated with greater total and peak runoff, whereas higher ESIA was associated with shorter lag times. Peak runoff and lag time also varied with rainfall peak ratio and storm duration. The phase-wise variations in imperviousness performance showed peak runoff and lag time generally increased across the entire catchment. The proposed SWMM–imperviousness framework provides a practical decision-support tool for industrial catchment planning, stormwater infrastructure design, and urban runoff mitigation across three development-phase scenarios.

Rainfall-induced failures of reinforced earth (RE) walls pose significant risks to highway infrastructure in monsoon-prone regions, especially where permeable embankment fills overlie low-permeability soft foundations. This study addresses the geological significance of the laterite soft clay stratigraphic interface as a dominant hydro-geological control in such failures. This forensic study examines the basal sliding and outward rotational collapse of an RE wall along National Highway 66 (NH-66) at Kooriyad, Kerala, during intense monsoon rainfall in May 2025, following a record 702.8 mm of pre-monsoon precipitation (+ 132% above normal) and a concentrated 220 mm deluge over 72 h. Integrating field reconnaissance, laboratory testing, and coupled transient seepage slope stability analyses, the investigation reconstructs the failure mechanism. Evidence reveals a strong hydraulic contrast between the lateritic embankment fill and underlying soft clay that promoted deep-seated basal instability rather than surficial erosion or overtopping, with shallow groundwater conditions. Transient seepage modeling demonstrates rapid matric suction loss in the fill and persistent excess pore-water pressure buildup at the fill–foundation interface due to poor drainage through the clay (foundation permeability kₛ = 1.0 × 10⁻⁷ m/s), with excess pore-water pressures reaching 120–160 kPa at the fill–foundation interface. Time-dependent slope stability analyses show progressive effective stress reduction and shear resistance loss, causing basal sliding and outward rotation of the RE wall. The global factor of safety declined by ~ 13% during the event, reaching a critical minimum of 0.37 at approximately 48 h (from an initial 0.42), aligning with the observed collapse timing, aligning with the observed collapse timing. A novel dimensionless Hydro-Mechanical Degradation Index (HMDI) is introduced to quantify rainfall-driven hydro-mechanical degradation, incorporating pore-pressure buildup, permeability contrast, and reinforcement effects. HMDI shows strong inverse correlation with factor of safety and effectively signals instability onset, with HMDI exceeding 5.6 during the critical 48–72 h failure period. The findings underscore the critical influence of hydraulic contrast and transient groundwater response on RE wall performance on soft ground. This framework enables back-analysis of similar basal stability failures in high-rainfall areas and informs performance-based design and remediation for embankments in high-rainfall areas.

A warming trend across the globe, primarily driven by anthropogenic factors, has affected densely populated developing nations selectively, based on their resilience capacity and economic strength. Bihar is primarily an agrarian state of eastern India, where regional impacts of climate change have manifested themselves in altering the thermo-hydrological regime in terms of intensity and seasonality. The present study examines the spatial and temporal trends in temperature (1940–2024) in the state of Bihar. The analysis was conducted on both annual and seasonal scales using ERA5 reanalysis data (1940–2024) at 0.25° x 0.25° resolution for all 38 districts of Bihar. It also investigates temporal patterns and trends in actual and potential evapotranspiration based on gap-filled Terra-MODIS 8-day composite data for the period 2002–2024. The study employed a suite of analytical tools, including descriptive statistics (mean, standard deviation, coefficient of variation), trend detection methods (Modified Mann–Kendall test, Sen’s slope estimator), and spatial mapping to investigate long-term temperature averages, variability, and trends across Bihar. Findings reveal that southwestern districts such as Arwal, Aurangabad, Gaya, and Patna consistently record the highest annual mean temperatures and exhibit the most pronounced warming trajectories. In contrast, northeastern districts like Kishanganj, Araria, and Supaul remain relatively cooler and show minimal warming. Seasonal breakdowns highlight a decline in winter cold extremes, increased pre-monsoon variability in eastern regions, and a steady rise in monsoon temperatures across the southwest. Collectively, the data confirm a clear warming trend in Bihar, impacting the soil moisture and evapotranspiration trends. The water balance is tightly coupled to the monsoon cycle, with surplus, while the highest crop water stress occurs in the pre-monsoon phase, making irrigation indispensable. These climatic shifts threaten Bihar’s agriculture by altering crop phenology, increasing evapotranspiration, and intensifying seasonal water stress, thereby affecting food and water security. They also escalate the state’s vulnerability to recurrent floods, pre-monsoon droughts, public health risks, and intensify socio-economic instability. This study recommends that Bihar develop its own climate adaptation plans to address these hydrological challenges.

The Bidou prospect is a mildly‒known gold district in the Nyong Complex (Cameroon) with active artisanal alluvial mining operations. There is limited and disputed research on the origin of this placer gold deposit. Integrated alluvial gold morphology and microchemistry, and bulk stream sediments geochemistry were investigated through combined scanning electron microscopy (SEM)‒energy dispersive spectrometry (EDS), electron microprobe analysis (EMPA) and inductively coupled plasma mass spectrometry (ICP‒MS) techniques respectively, to preliminarily decrypt the gold deposit type and prospective sources in the Bidou area. The heavy mineral assemblage in the stream sediments comprises rutile, ilmenite, ilmenorutile, zircon, garnet, kyanite, monazite and gold. The grains morphology ranges from angular, folded, hooked, elongated to sub‒rounded shapes, indicating limited transport distances and proximal source rocks. The gold grains are associated with primary quartz, pyrite, rutile, K‒feldspar and kyanite inclusions. The gold grains are characterized by extremely high Au (97.367 ‒ 100 wt%) and fineness (995 ‒ 1000) values, and low Ag (0 ‒ 0.502 wt%), Cu (0 ‒ 0.23 wt%) and Ni (0 ‒ 0.002 wt%) contents, within limited ranges, suggesting a homogeneous or limited primary sources. The gold composition, primary mineral inclusions and Ag‒Au‒Cu x 10 discrimination plot collectively suggest a potential primary hydrothermal‒orogenic (Archean lode) gold mineralization in the Bidou prospect. Quartz, pyrite and rutile inclusions, coupled with Cr and Ni in the gold could preliminarily imply a quartz‒sulphide vein‒type gold, potentially emplaced by the interaction between gold‒rich hot reducing hydrothermal ore fluids with mafic lithologies in the Bidou area.

Aims: This study analyzed the spatiotemporal distribution, interannual variability, and recurrence of burned areas across land-use and land-cover (LULC) classes in the Brazilian Cerrado from 2019 to 2024. The main objective was to evaluate how fire regimes interact with natural and human-modified landscapes to support territory-oriented management strategies. Methods: Using MapBiomas Fire (Col. 4.1) and LULC (Col. 10.1) datasets, we quantified burned proportions across LULC types using R (terra package) and QGIS. Statistical analyses included descriptive statistics, the non-parametric Mann-Kendall test for temporal trends, interannual variance, and pixel-based binary summation to map spatial fire recurrence. Results: Burned areas predominated in native vegetation: savannas (~ 52.8%) and wetlands (~ 19.5%), followed by grasslands (~ 9.1%), while forest formations and pasturelands each accounted for approximately 5.5% of the total burned area. Fire increased significantly in Soybean, Rice, Cotton, and Mosaics of Uses, with a sharp spike in 2024 for savannas and forests, coinciding with extreme dry-season thermal anomalies. High-recurrence hotspots were concentrated in ecotonal zones, particularly in the Ilha do Bananal and surrounding Indigenous Lands, driven by high fuel accumulation and anthropogenic ignitions. Conclusions: Fire in the Cerrado is structural but increasingly threatens fire-sensitive ecosystems like forests and wetlands. Mapping fire recurrence directly operationalizes Integrated Fire Management (IFM) by prioritizing early prescribed burns, optimizing firebreak and brigade placement, aligning intercultural burning calendars with indigenous communities, and establishing protection protocols for vulnerable habitats.

Trace metal accumulation in marine sediments is a significant environmental concern due to their prolonged residence times, toxicity, and potential ecological impacts. This study investigated the distribution, pollution status, ecological risk, and source apportionment of trace metals in shelf surface sediments off the southeast coast of India. A set of 35 surface sediments was analysed for sediment texture, organic matter (OM), and concentrations of Fe, Cr, Co, Zn, Ni, Pb, Cd, Cu, Ba, V, and Mn. The sediments were predominantly muddy sand to slightly muddy sand in texture, and OM ranged from 1.05% to 2.65%. The minimum and maximum concentrations of Fe: 1.38–6.33 wt%, Cr (7.24-169.95 ppm), Co (2.79–29.64 ppm), Zn (2.85–44.89 ppm), Ni (13.17–53.74 ppm), Pb (12.20-38.25 ppm), Cd (0.22–4.10 ppm), Cu (22.04–80.48 ppm), Ba (18.71-567.15 ppm), V (15.87-121.39 ppm), and Mn (75.24-1239.49 ppm). Average concentrations of Pb, Cu, and Cd were higher than the upper continental crust (UCC) values. Various contamination indices (Contamination factor, degree of contamination, modified degree of contamination, and geoaccumulation index) indicate substantial Cd contamination, moderate Pb contamination, and no Cu contamination. Ecological risk indices further revealed that Cd posed an extremely high ecological risk, whereas Pb and Cu posed a moderate risk, and the remaining metals showed negligible risk. The Pollution Load Index (PLI) is < 1, indicating a baseline level of pollution. Pearson’s correlation revealed that fine-grained sediments primarily control metal concentrations, whereas OM exerted a secondary influence. However, three potential sources: lithogenic (54%), mixed lithogenic-anthropogenic (25%), and maritime-related activities (21%) were identified using principal component analysis with absolute principal component scores (PCA-APCS). The PCA findings are broadly consistent with Pearson’s correlation and Q-mode cluster analyses.