Geosystems and Geoenvironment最新文献

Land use and land cover (LULC) changes are important indicators of environmental and socio-economic changes made by the natural and anthropogenic sources. The present study is based on the Cellular Automata (CA) Markov model for predicting the LULC changes in the Tawi Basin. To decipher the spatio-temporal distributions of LULC, the Landsat images of 2010 and 2020 were used to analyse the LULC classification. Further, CA Markov model simulations of various scenarios of eight decades (2030 to 2100) were generated based on LULC of 2010 and 2020 data to know the LULC perspective changes in the Tawi Basin, which has witnessed the enormous developmental activities such as growth in settlement, population, and agriculture sector over the years. The model predicts that a population explosion leading to rapid urbanization and rural expansions.

Settlement is expected to increase from 5.29% of the total area in 2020 to 13.975% in the year 2100. The CA–Markov model results paint a picture of significant changes in land use and settlement patterns in the Tawi Basin. The study serves as a crucial tool for guiding future planning efforts, urging environmentalists, planners, and decision-makers to prioritize sustainable practices and make informed decisions for the well-being of the region.

Explanations for major catastrophes in the history of life commonly focus on their time-spans. Less biotic crises are worth attention as well, however, which requires their investigation in a longer-lasting context. The present study relates the Taghanic (Givetian) and mid-Carboniferous (Serpukhovian) biotic crises to some long-term changes in their environmental developments. The trends in these developments are interpreted on the basis of changes of the global sea level, the global average temperatures, the total surface area of exposed land, the total number of lithospheric plates, and the concentration of atmospheric oxygen. It is found that the Taghanic and mid-Carboniferous biotic crises can be related directly or indirectly to some long-term environmental changes.

Peripheral volcano sedimentary belts around the nucleus of Singhbhum-Granite-Complex are a part of one of the oldest continental crusts, known as the Singhbhum Craton (SC). Deciphering mutual relationship among these volcano-sedimentary packages offers considerable challenges. Badampahar-Gorumahisani Belt (BG Belt), Tomka Daitari Belt (TD Belt) and Bonai-Kendujhar Belt (BK Belt) draping Singhbhum-Granite-Complex as long linear belts from east, south and west, have some certain difference, primarily in terms of lithology. While the sedimentaries of BG Belt is mainly chemogenic, BK Belt is dominated by terrestrial sediments and TD Belt contains the both. Available data suggests the younger age of BK Belt than the rest couple of belts. Between TD Belt and BG Belt, another volcano sedimentary belt, commonly known as Hadgarh Belt, is present and it is less studied. The present study aims to characterize the Hadgarh Belt based on lithology and structure, which indicates its similarity with TD Belt. Almost identical lithologies are manifested by these two volcano sedimentary sequences barring the fact that the Hadgarh Belt has minor dominance of metasediments over metavolcanics, which is in subequal proportion in Tomka-Daitari and sensustricto BIF bands are absent in Hadgarh Belt. Both the belts have undergone uniform polyphase deformation and metamorphism. In both the belts, volcano sedimentary sequence of IOG is overlain by less deformed younger Mahagiri Quartzites and they are separated by an angular unconformity, marked by an impersistent conglomerate horizon. The intermediate area also sustains the similarities in depositional and deformational history with respect to the TD and Hadgarh belts on either side. Petrological studies also invoke similar mineral assemblage in the two belts, which is also in corroboration with the petrochemistry of the litho-units concerned. All the data thus generated, shows that the area, in totality, was evolved in an island arc setting varying from deep to shallow marine environments and sequence of deformations, intrusion of ultramafics followed by granite are also similar. In a nut shell, Hadgarh Belt can be referred as an extended part of the TD Belt.

This study investigates the evolving dry-wet climate patterns in southern India during 2020–2023, focusing on the impact of climate change. Spanning all 30 districts of Tamil Nadu, our analysis employs the HadGEM3-GC31-LL climate model, projecting a significant increase in humidity levels from 2021 to 2100. Key findings reveal consistently higher post-monsoon aridity indices compared to the monsoon season, exceeding 0.65 and raising concerns about potential flash floods. Regions most affected include Kanniyakumari, Nilgiris, Chennai and others. To address these challenges, the study recommends urgent policy interventions, emphasizing water conservation through initiatives like farm pond construction. Tailored policies are crucial to shield farmers and dairy producers from economic fallout, with an emphasis on integrating indigenous knowledge for effective climate change adaptation. In summary, this research highlights the need for immediate action, advocating for comprehensive strategies such as water conservation and tailored policies to enhance resilience and mitigate the impact of climate change in the studied regions.

The Bhavani Suture Zone, a region in the Southern Granulite Terrane (SGT), is where the Neoarchean Madurai Block and the Southern Madurai Block in western Tamil Nadu have been joined. The Mettupalayam Mafic-Ultramafic Complex, located in the eastern section of the BSZ, is made up of metagabbros, metadiorites, amphibolites and mafic granulites. The mafic-ultramafic outcrops are viewed and mafic-ultramafic rocks collected from Nellimalai, Togamalai, Sakkitiyan Karadu, Karudamalai, Karattur, Odhimalai, Tenkalmalai, Ramakavundanur hills. This study methodology is composed of three perspectives such as remote sensing study, laboratory spectral signature study and geochemical study. Firstly this study applied on Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Sentinel-2A used as a tool for mapping mafic-ultramafic rocks and applied the remote sensing techniques like color composites, BR (band ratio), PCA (Principal Component Analysis), MNF (Minimum Noise Fraction), SAM (Spectral Angle Mapper) and SVM (Support Vector Machine). Secondly laboratory spectral signature study is conducted for the 19 samples with ASD FieldSpec Pro® spectroradiometer was used to get the reflectance spectra in the 350–2500 nm spectral region. For different host rocks, the continuum-removed reflectance spectra offer diagnostic absorption features. Critical analysis was done on how the rock samples' elemental composition and related important minerals affected the absorption bands. The major and minor elements geochemical compositions of the BMUC rock samples identified by XRF method. The aim of this research is to characterize the BMUC using remote sensing studies, spectral signature study and geochemical analysis. The Sentinel-2A showing discriminating the lithology well than ASTER data and the spectral signatures absorptions are indicating presence of Fe and Mg contents. The rock samples are falling the series of tholeiitic to calc alkaline characteristics in geochemistry.

Globally, the quality of groundwater has proven to have been affected by some natural and human activities in recent years. To ensure there is good drinking water (Sustainable Development Goal 6.3, there is a need to elucidate the groundwater quality status of the area of interest. The groundwater in the northwestern parts of Ghana is not yet well characterized. Hence, this study employed a multi-method approach of hydrochemistry, water quality index (WQI), multivariate statistics, and machine models: multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), and artificial neural network (ANN), are combined in the characterization and prediction of the water quality in the area. They are robust in providing conclusions on groundwater assessment that can be relied upon for decision-making processes regarding groundwater usage and monitoring. Except for NO₃⁻ and TDS exceeding their standard levels in 22 and 2 locations, respectively, the other physicochemical parameters are within acceptable limits. The groundwater is generally good for domestic usage based on the WQI, with 79.2% of excellent to good waters. The groundwater evolved from Na-type, Cl-type, and Cl(SO₄)-Ca(Mg) facies. Agricultural activities are the main source of human impact on the groundwater. Silicate mineral dissolution and ion exchange processes are the natural processes that affect groundwater mineralization, with mineral dissolution being the dominant process. Based on the performance metrics: MAE, MSE and RMSE of the ML methods considered in the WQI forecasting, the order of performance of the models is ANN > RFR > DTR > MLR, with the following respective R² values 0.9974, 0.9193, 0.8966 and 0.8886.

The Periyar Plateau in central Kerala is characterized by a strong negative Bouguer gravity field around three major islands of a strongly negative field of the value of -150 mGal. Detailed gravity studies reveal that these islands of strong negative field are of a shallow nature and are not traceable in the 1/4° average gravity grids. Spectral analysis of Bouguer anomaly map and 2D gravity modelling of Periyar Plateau region reveal the average depth of the shallow source layer is as shallow as 700-1000 m, which is correlatable with the layer of lateritic/lithomargic soil cover and the underlying weathered country rock having a density of 2.2 g/cc. Such a strong influence on the regional gravity field is due to the wide contrast in the densities of the lateritic cover and the primary rocks of the basement. These results underline the need to recognize the strong influence of such alteration zones on the regional gravity fields, especially the tropics, a factor not always considered.

Landslides are frequent natural hazards in mountainous regions, and harshly upset people's lives and livelihoods. In the present study, we have carried out an analysis of seven GIS-based machine-learning techniques; and asses their performance for landslide susceptibility mapping (LSM) in the Bhilangana Basin, Garhwal Himalaya. A landslide inventory consisting of 423 polygons was prepared using repeated field investigations, and multi-dated satellite images for the periods between 2000 and 2022. The landslide dataset was classified into two groups: training (70%) and test dataset (30%), and 12 predictive variables were used for the LSM. The methods used to produce LSM are boosted regression tree (BRT), Fisher discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), model-architect analysis (MDA), random forest (RF) and support vector machine (SVM). The sensitivity and performance of these models to predict landslide susceptible areas were carried out using the area under the curve (AUC) method. The RF model (AUC = 0.988) has given the highest precision indicating the best performance. Though MARS (0.974), SVM (0.965) and MDA (0.952) models have also performed adequately for the LSM (all have AUC values above 0.95), however, it is recommended that the RF model is highly suitable for LSM in the mountainous region.

Flood vulnerability assessment is crucial for developing effective flood management and mitigation strategies. The present study aims to understand the flood vulnerability of the Kallada River Basin (KRB) and identify suitable locations for safe flood sheltering facilities in the basin. As part of this, morphometric analysis of KRB was carried out by dividing the basin into fifty-eight 4^th-order sub-basins to understand the sub-basin-wise terrain characteristics and the degree of vulnerability to flooding. GIS tools were used to assess various morphometrical parameters, such as drainage frequency, texture ratio, ruggedness number, basin relief, bifurcation ratio, length of overland flow, drainage density, circularity ratio and area, and geo-environmental factors such as sand percent, rainfall, and mean slope of these basins. The morphometric parameters exhibited distinct spatial trends, with higher values primarily concentrated in the east and northeast parts for certain parameters and in the western parts for others. Using hierarchical cluster analysis, the sub-basins were categorized into six clusters, revealing that 51% were vulnerable to floods, 26% moderately vulnerable, and 22% not vulnerable. Sub-basins in the central and western KRB were found to be highly vulnerable to flooding, while those in the eastern parts showed moderate vulnerability or were not vulnerable. Flood vulnerability mapping was validated using flood data of 2018 and 2019. Additionally, the weighted overlay method identified suitable areas for flood shelters in moderately vulnerable and vulnerable sub-basins and the areas were categorized into highly suitable, suitable, moderately suitable, and not suitable areas. Most areas of SB53 and SB55 were found highly suitable, emphasizing their potential for flood shelter locations. The findings of this study can be used by competent authorities to initiate flood mitigation and to develop targeted flood preparedness measures in similar river basins, particularly in the context of increasing flood events.

Flood is considered to be a serious environmental hazard, especially in the tropical countries owing to its devastating consequence on human life. Tripura, a small hilly state of northeast India has faced large scale flood events over the last few decades. The present study is an attempt to identify flood hazard zones along the lower course of the Dhalai River flowing through the Dhalai district of Tripura State. An integrated approach of remote sensing and GIS coupled together with Analytical Hierarchy Process (AHP) was applied to identify the flood hazard zones of the study area and nine parameters were selected for this purpose. Thematic maps of the parameters were reclassified after assigning ranks to different classes. A pair-wise comparison matrix among all the parameters was prepared using AHP to determine the relative weight of each parameter. Finally, flood hazard zoning (FHZ) map of the study area was prepared by multiplying the reclassified values with the weighted values using raster calculator of Arc GIS 10.1. The outcome of the study revealed that 109.69 km² (27.65%) of the study area fall under low flood risk category. At the same time, around 114.46 km² (28.85%) and 90.43 km² (22.80%) areas fall under moderate and high flood risk zone respectively. The study also disclosed that the high risk zone has maximum concentration of agricultural land (68.63%) and settled area (9.77%) in comparison to the other two zones which has increased vulnerability of flood hazard. The results validated the efficiency of AHP technique in generating fast and cost effective information regarding flood hazard assessment, especially in no data regions. Hence, the derived information could be very much helpful for the planners to prepare proper strategies to reduce the vulnerability of this hazard.