The Mahadek Formation represents a Cretaceous sedimentary succession developed within a tectonically active basin in north-eastern India. This study integrates grain-size statistics, discriminant function analysis, and field observations to reconstruct depositional processes and basin evolution. 40 representative samples have been collected from the Mahadek Formation and analysed for grain-size statistics using Folk and Ward (1957) statistical parameters. The mean grain size ranges from 0.23 to 2.47 ɸ, standard deviation from 0.88 to 1.71 ɸ, skewness from − 0.25 to + 0.43, and kurtosis from 0.58 to 2.11. The sediments are dominated by medium- to coarse-grained sandstones with subordinate fine and very coarse fractions, indicating variable hydrodynamic conditions and fluctuating flow competence. The predominance of poorly sorted and fine-skewed samples suggests fluctuating depositional energy and episodic deposition of finer sediment. The discriminant-function results indicate variable environmental signatures, and their integration with sedimentary structures, lithological characteristics and fossil evidence suggests a predominantly fluvial system with intermittent shallow-marine influence. These characteristics indicate a shift from high-energy fluvial environments to transitional and shallow marine conditions. However, the grain-size and discriminant-function data do not independently constrain specific climatic phases, eustatic sea-level fluctuations or tectonic subsidence. The depositional characteristics of the Mahadek Formation are compatible with the broader Late Cretaceous regional and global climatic and eustatic framework, but these factors are considered as regional context rather than directly demonstrated controls. The study highlights the importance of integrating sedimentological data with the climate and eustatic controls to understand the evolution of fluvio-shallow marine depositional systems in the sedimentary basins.
Remote sensing and GIS significantly enhance the possibilities for groundwater assessment, monitoring, and management. This study aims to utilise these tools to illustrate urban growth over the past three decades in Guwahati City, India and to decipher the Groundwater Potential Zones (GWPZ), thereby facilitating effective groundwater management. Landsat images were used to classify land use and land cover (LULC). The thematic layers were appropriately selected and assigned weights employing multi-criteria decision analysis, Analytical Hierarchy Process (AHP). Urban expansion has occurred extensively over three decades, with 210
Artificial Neural Networks (ANN) and multiple regression (MLR) models have been widely used to predict soil erosion. Few investigations have, however, explored the prediction accuracy of soil detachment capacity in rills (Dc). This study has tested whether ANN and MLR can predict Dc in forestlands affected by deforestation and fire, and subjected to soil conservation techniques as anti-erosive measures. We used a database (partly published in recent articles) of more than 1000 measurements of Dc under variable soil slopes (S) and flow discharges (Q) by flume experiments on soil samples collected in Northern Iran. S and Q, the shear stress (τ) or stream power (Ω) together with a ‘dummy’ categorical variable related to the soil condition were adopted as predictors of Dc. The modelling exercise showed a low prediction capacity of ANN (coefficients of efficiency of Nash and Sutcliffe, NSE, < 0.48). In contrast, the MLR equations were reliable and accurate models (NSE up to 0.88 for calibration and to 0.87 for validation) to simulate Dc. S and Q were better predictors of Dc compared to τ or Ω. The MLR equations accurately predicted the mean Dc with errors lower than 5
In this paper, a modified version of Tree Grass Differentiation Index (TGDI) is calculated by using image processing algorithms, based on Canny edge detection and brightness. Calculated value of TGDI is utilized for thresholding to generate a binary mask of tree canopies in the image. The U-NET convolutional neural network (CNN) architecture is employed to train the semantic segmentation model for tree canopies using binary mask generated from the TGDI thresholding process. The process was repeated by training a UNET model without TGDI as a feature, and its performance has been compared with the model trained with TGDI as a feature. Furthermore, Residual UNET architecture has been explored to compare the performance of the model trained with and without TGDI as a feature. To perform this study, Boston, USA area is considered and the dataset is taken from the US Tree Map website. The raster has a resolution of 60 cm and has three bands, Red, Green and Blue, with height 12,810 and width 9966. The performance of the models was evaluated based on accuracy, Dice coefficient, and F1 score. U-NET with TGDI as feature achieved the pixel accuracy of 96.7
Tight gas reservoirs in structurally complex areas, such as the Eastern Sichuan Basin, generally exhibit moderate porosity but extremely low permeability (usually < 0.1 mD ), together with heterogeneous lithofacies and abundant natural fractures. The mechanical and petrophysical complexity of these formations poses significant challenges for conventional logging techniques, potentially resulting in up to a 30 ^2 of 0.89 for TOC prediction (vs. 0.71), and the processing time was reduced by 92-94
Afghanistan is becoming more water insecure, given that water resources rely on winter precipitation, snowpack, glacier melt, seasonal river flow and shallow groundwater systems. Based on the review of peer-reviewed literature, institutional reports and hydrological datasets, this review presents evidence regarding the effects of climate change on water security in Afghanistan from 2000 to 2026. It examines observed and projected changes in the areas of temperature, precipitation, droughts and floods, glacier and snowpack conditions, river flow, groundwater recharge, and human pressures on water resources. The evidence reviewed indicates that temperatures have been rising in Afghanistan, precipitation has become more variable, there have been more droughts and floods, glaciers in the Hindu Kush region have been retreating, the reliability of snow cover has decreased, groundwater recharge has been decreasing, and there has been an increasing mismatch between water supply and demand. Such changes are significant in Kabul, Helmand, Amu Darya, Hari Rod – Murghab, and Northern river basins. The review further reveals that population growth, expansion of irrigation, over-extraction of groundwater, dam construction and limited institutional capacity exacerbate water stress caused by climate change. The study helps fill important knowledge gaps in the field of climate-resilient pathways by pulling together recent quantitative evidence and proposing a range of climate-resilient pathways that include integrated water resources management, better hydrometeorological monitoring, sustainable groundwater management, more efficient irrigation, water storage development and regional cooperation.
Greenhouse gas (GHG) emissions are a significant driver of global climate change, leading to widespread environmental, social, and economic impacts. GHG emissions occur primarily due to human activities such as burning fossil fuels for energy, industrial processes, deforestation, agriculture, and waste management. This leads to an overall warming of the Earth’s surface and atmosphere, causing climate change. The consequences of GHG emissions and climate change are far-reaching and include intense extreme weather events, disruptions to ecosystems and biodiversity, and impacts on human health, agriculture, and economies. In this review article, we examine the trends and patterns of GHG emissions, assess their environmental and human health impacts, and explore a range of mitigation strategies to reduce emissions and mitigate climate change. Through a comprehensive analysis of existing literature and case studies, we highlight the importance of concerted local, national, and global efforts to address GHG emissions and transition to a low-carbon economy. Overall, this article is a valuable resource for policymakers, researchers, and stakeholders seeking to understand the complexities of GHG emissions and identify pathways toward a sustainable, resilient future.
The Tamiraparani River, originating in the southern Western Ghats, is a tropical fluvial system that drains the high-grade metamorphic and granitoid rocks of the Southern Granulite Terrain in India. This study investigates downstream variations in grain size, framework petrography, and heavy-mineral assemblages to characterize sediment transport processes, provenance, and sediment dispersal patterns within the river system. The sediments are predominantly poorly to moderately sorted and exhibit variable skewness and kurtosis, reflecting fluctuations in depositional energy and hydraulic conditions during transport. Framework petrography and ternary compositional analyses indicate derivation primarily from high-grade metamorphic and granitoid source rocks, accompanied by progressive textural and compositional maturity downstream. Heavy-mineral assemblages are dominated by zircon, tourmaline, and rutile, with subordinate hornblende and chlorite, and yield moderate to high ZTR index values indicate sediment maturity and varying degrees of sediment recycling. The integrated sedimentological and mineralogical data suggest that sediment characteristics are primarily controlled by source-rock composition, sediment supply, and seasonal hydrodynamic processes operating within a tropical fluvial environment. The observed downstream variations provide valuable insights into sediment transport, provenance characteristics, and source-to-sink relationships in short tropical river systems. These findings contribute to a better understanding of sediment dispersal processes in tropical fluvial environments and provide a useful modern comparative framework for interpreting sedimentary successions in comparable tropical fluvial settings.
The Nongpoh granitoids of the Meghalaya Plateau provide insight into the thermal and petrogenetic evolution of granitic magmatism along the northeastern margin of the Indian Shield. These granitoid suites are characterized by high SiO2 contents, enriched in large-ion lithophile elements, and fractionated rare earth element (REE) patterns with negative Eu anomalies consistent with evolved calc-alkaline compositions. Whole-rock Zr concentrations range from 171 to 936 ppm, with an average of 607 ppm. The zircon saturation temperature calibrated yields mean values between 870 and 920 °C. Although the zircon saturation thermometry reflects conditions at zircon crystallization rather than peak magma temperature, the observed values suggest that zircon was crystallized from relatively Zr-enriched melts with elevated melt temperatures during magma evolution. The trace-element characteristics indicate a predominantly crustal signature, even though crustal melting may have been facilitated by heat input, possibly related to mafic underplating at depth. The integrated geochemical and thermometric data point to the generation of the Nongpoh granitoids through high-temperature crustal anatexis within an active continental-margin setting.
Northeast Nigeria, particularly the Yola region, remains an underexplored frontier for radioactive minerals despite rising global demand. This study integrates airborne radiometric data and Landsat-derived spectral indices within Oasis Montaj, ArcGIS, ENVI, and Surfer to delineate hydrothermal systems and prioritize exploration targets for potassium (K), thorium (Th), and uranium (U) mineralization. Radiometric analyses reveal strong lithogeochemical controls, with K concentrations ranging from − 1.47
As climate change progresses, natural hazards are becoming more frequent, more intense, and increasingly complex in their spatial behaviour, placing growing pressure on traditional hazard assessment frameworks. This review examines recent developments between 2019 and 2025, with particular attention to the expanding use of Geographic Information Systems (GIS), remote sensing, and artificial intelligence (AI) in modelling climate-related hazards such as floods, droughts, wildfires, and coastal processes. A structured screening of peer-reviewed literature retrieved from Scopus and Web of Science, supplemented by searches in Google Scholar, resulted in the retention of 121 peer-reviewed journal articles for analysis, covering floods, droughts, wildfires, and coastal hazards. The review identifies a pronounced methodological transition from conventional statistical and GIS-overlay approaches toward machine learning, deep learning, and hybrid modelling frameworks better suited to capturing nonlinear and dynamic hazard interactions. While these approaches often enhance predictive performance, several challenges remain, including data scarcity, spatial autocorrelation bias, validation constraints, and limited transferability across diverse climatic contexts. Moreover, although AI-based models frequently report higher predictive accuracy, their interpretability and integration into operational planning and risk management systems remain uneven, particularly in data-constrained environments. Using Morocco as an illustrative case of climatic heterogeneity and uneven monitoring infrastructure, this review underscores the importance of developing climate-aware, transparent, and transferable modelling strategies that integrate remote sensing time series, physical process understanding, and rigorous validation practices. Strengthening these dimensions is essential to ensure that methodological advancements translate into effective risk reduction and adaptive planning.
This study presents the first comprehensive model-comparison framework integrating the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) for GPZ mapping in the Purulia district of West Bengal, India, situated on the eastern fringe of the Chota Nagpur Plateau. Fifteen hydrogeologically significant conditioning factors were prepared from multi-source geospatial data (ALOS PALSAR DEM, Sentinel-2, Geological Survey of India, Central Ground Water Board), and 66 verified groundwater well observations were partitioned into training (60.6
To enhance the practical applicability of the proposed framework, approximate methods for estimating the required input parameters can be provided. Mineral content may be estimated through rapid petrographic assessment of fresh rock surfaces. Porosity can be approximated from bulk density measurements by comparing the measured bulk density with the weighted average mineral density derived from the estimated mineral composition. Grain size may be obtained using image-based analysis of rock surface photographs, providing a rapid first-order characterization of the rock fabric. For this, a comprehensive database was compiled from published experimental studies encompassing igneous, metamorphic, and sedimentary rocks subjected to compression testing. The dataset incorporated predictor variables including mean grain size, mineralogical composition (plagioclase feldspar, alkali feldspar, quartz, calcite, clay, mica, and amphibole contents), density, porosity, elastic modulus, and Poisson’s ratio, while experimentally measured CI and CD thresholds were used as target outputs. Separate datasets were developed for CI and CD prediction and partitioned into training and testing subsets for independent validation. Two ensemble-based supervised learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were implemented and comparatively evaluated using coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and prediction accuracy metrics. The developed ML models successfully exhibited prediction accuracies of approximately 78–80
Climate change is expected to significantly alter hydrological regimes and runoff variability in mountainous watersheds across South Korea. This study evaluated the long-term impacts of climate change on streamflow dynamics within the Chuncheon watershed, a major basin supplying the Soyang, Chuncheon, and Uiam multipurpose dams. The Soil and Water Assessment Tool (SWAT) was integrated with CMIP6 climate projections under two Shared Socioeconomic Pathway (SSP) scenarios, SSP126 and SSP585, to simulate future hydrological responses during 2020–2100. Bias correction and statistical downscaling were performed using the simple quantile mapping (SQM) method, while hydrological variability was analyzed using the Mann–Kendall test, cumulative anomaly analysis, and two-sample t-test. The SWAT model showed satisfactory performance during calibration and validation, with R² values ranging from 0.57 to 0.87, NSE values from 0.51 to 0.82, and PBIAS values between 4
This study examines the petrographic and geochemical characteristics of organic black shale from western portion of the Paleogene N’kapa Formation, in order to assess source rock potential, organic matter and the oil-prone potentials. The organic black shales are typically dominated by the vitrinite (average 54.56
Soil erosion and reservoir sedimentation are major environmental challenges in Ethiopian highland watersheds, contributing to land degradation and increased sediment loads in downstream reservoirs. However, spatially explicit assessments that jointly evaluate soil erosion, sediment delivery, and conservation priority areas remain limited in data-scarce mountainous environments. This study applied an integrated geospatial framework to assess soil erosion, sediment delivery, and conservation priority areas in the Genale Dawa-3 Hydropower Watershed, southeastern Ethiopia. Google Earth Engine (GEE) was used to derive land-use/land-cover and vegetation information from multi-temporal Sentinel-2 imagery, while GIS-based RUSLE-Sediment Delivery Ratio (SDR) modelling estimated soil erosion and sediment delivery. Topographic parameters were derived from a 12.5-m ALOS PALSAR DEM, and rainfall and soil data were obtained from ground observations. The results show substantial land degradation between 2016 and 2025. Basin-averaged estimated soil loss increased by 60.2
Mineral dust plays a critical role in modulating the Earth’s radiation budget by scattering and absorbing solar radiation; however, uncertainties remain due to limited knowledge of particle morphology, chemical composition, mixing state, and internal structure. To address these gaps, mineral dust (PM5; Dia. ≤5 μm) particles were collected during two consecutive sand and dust storms (SDS) [ 4th May 2022 (SDSa) and 6th May 2022 (SDSb)] over Jhunjhunu region, in the vicinity of the Thar Desert. Advanced analytical techniques like Electron Paramagnetic Resonance Spectroscopy (EPR), Focused Ion Beam Scanning Electron Microscopy coupled with Energy Dispersive Spectroscopy (FIB-SEM-EDS), and Atomic Force Microscopy (AFM) were utilized for in-depth analysis of physico-chemical characteristics of mineral dust at the bulk and individual particle level. FIB-SEM-EDS revealed internally heterogeneous particles with distinct core-shell structures during both SDS, indicating atmospheric aging and complex mixing processes. EPR analysis confirmed the presence of paramagnetic Fe³⁺ ions during both events aligning with the g-factor values reported in the literature for Fe3+ ions. AFM highlighted pronounced surface morphological irregularities, with SDSa particles exhibiting more surface roughness (46, 55, and 77 nm) being more intense compared to SDSb (26, 41, and 44 nm).
Coal gangue soil (CGS) samples include trace components, some of which are highly carcinogenic and may cause a danger to the environment. Additionally, they have the capacity to bioaccumulation, which poses a significant barrier to the beneficiation or safe disposal of coal by-products (CGS). It becomes crucial to understand the chromium (Cr) content in CGS samples in order to carry out thorough risk assessments for developing coal power plants. The aim of this work is to determine Cr content of different CGS samples by using a BCR sequential extraction scheme. A time-saving shaking device known as ultrasonic-assisted single-step extraction (UA-SSE), which uses the same experimental parameters and extracting solutions as the BCR sequential extraction scheme, was used to shorten the 51-hour length of the BCR sequential scheme for Cr associated with different chemical phases for CGS samples up to 2 h. The UA-SSE extraction method resulted in higher Cr contents in the acid-soluble fractions of CGS samples (> 1
The Lesser Himalaya preserves a record of Proterozoic sedimentation and metamorphism overprinted by Paleozoic felsic magmatism, providing key insights into pre-Himalayan crustal reworking along the northern Indian margin. This study presents integrated petrographic observations and whole-rock major, trace element and rare earth element (REE) geochemistry of Mesoproterozoic Salkhala Formation and the intruding Kaplas and Jamotha granites. The metapelites are characterized by elevated Al₂O₃, Th and LREE contents, together with high Al₂O₃/TiO₂ and Th/Sc ratios, indicating derivation from an evolved felsic upper continental crustal source. The Kaplas and Jamotha granites are weakly to moderately peraluminous, show S-type affinity, and exhibit pronounced negative Eu anomalies, consistent with feldspar-controlled melt evolution and derivation through partial melting of metasedimentary protoliths. Trace-element systematics and tectonic discrimination diagrams suggest post-collisional felsic magmatism related to Paleozoic tectonothermal reactivation and intracrustal anatexis. The overall geochemical coherence between granites and host metapelites supports a shared crustal source and highlights the role of sediment recycling and metamorphic–magmatic coupling in the long-term evolution of the Lesser Himalaya.
The Wadi Queih area, Central Eastern Desert of Egypt, forms part of the northern Arabian–Nubian Shield and records a complex Neoproterozoic tectono-magmatic evolution related to the Pan-African orogeny. This study integrates detailed field mapping, petrography, Energy-dispersive X-ray (EDX), and whole-rock geochemistry to constrain the lithological evolution of the area and to assess the nature and controls of gold mineralization. The exposed rock units comprise island-arc metavolcanics, continental margin (Dokhan) volcanics, Hammamat molasse-type sediments, and post-collisional within-plate granites represented mainly by syenogranites and quartz–feldspar porphyries. Petrographic investigations indicate greenschist-facies metamorphism accompanied by widespread hydrothermal alteration. Geochemical data indicates sub-alkaline arc related affinities for metavolcanic rocks, whereas granitic intrusions display within-plate affinities consistent with post-collisional tectonic settings. Gold mineralization occurs as structurally controlled quartz veins and hydrothermal alteration zones associated with quartz–feldspar porphyry intrusions. Gold occurs both as free grains and in close association with sulfide minerals, particularly pyrite, within hydrothermally altered metavolcanic host rocks. Integrated structural, petrographic, and geochemical evidence suggests that mineralization developed as part of a deformation-related hydrothermal system comparable to orogenic gold deposits elsewhere in the Arabian–Nubian Shield.
