Geological Journal最新文献

{"title":"Advancing Geohazard Monitoring: Sentinel-1 InSAR Observations of Land Subsidence in Northern and Central Bangladesh","authors":"Gavin D. Middleton,&nbsp;Nahid D. Gani,&nbsp;M. Royhan Gani","doi":"10.1002/gj.5206","DOIUrl":"https://doi.org/10.1002/gj.5206","url":null,"abstract":"<div>\u0000 \u0000 <p>Bangladesh in the Bengal Delta faces complex environmental issues, including sea-level rise, coastal flooding, high population density, and widespread poverty. These factors lead to severe land loss, saltwater intrusion, water scarcity, and biodiversity decline, further exacerbated by climate change. These challenges significantly risk groundwater availability and increase the likelihood of natural hazards such as subsidence, landslides, and flooding. This study quantitatively maps the spatial distribution of subsidence in urban and agricultural settings by utilising Differential Interferometric Synthetic Aperture Radar (DInSAR) and Persistent Scatter Interferometric Synthetic Aperture Radar (PSI) techniques with ascending Sentinel-1 satellite data. We analysed 55 pairs of images with DInSAR and 142 pairs with PSI from March 2017 to October 2022, focusing on five target locations for DInSAR and urban Dhaka for PSI. Findings reveal consistent subsidence in urban Dhaka at an average rate of 16 mm/year, along with semi-seasonal subsidence variability in five agricultural locations. Specific rates are 7 mm/year in Dhaka, 8 mm/year in both Rajshahi and Mymensingh, and 9 mm/year in Rangpur. Sylhet subsides at a rate of 5 mm/year, potentially linked to the fold and thrust belt and the Dauki Fault. Our research highlights the significant environmental impacts of human activities like groundwater withdrawal and land-use changes, which contribute to subsidence and groundwater depletion via the Bengal Water Machine. While further study is required to comprehensively understand the relationship between LOS indicated subsidence rates, geological factors, and geomorphological changes, our findings offer crucial insights into the current impacts of climate change and ongoing environmental degradation in the region.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1106-1128"},"PeriodicalIF":1.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue “Climate Change and Geohazards”: An Introduction","authors":"Magdalena Radulescu,&nbsp;Uday Chatterjee,&nbsp;Asish Saha,&nbsp;Shouraseni Sen Roy,&nbsp;Sujoy Kumar Jana","doi":"10.1002/gj.5208","DOIUrl":"https://doi.org/10.1002/gj.5208","url":null,"abstract":"<p>Almost all disasters are weather-related, including drought, wildfires, pollution and floods. They affect lives and represent a significant burden on societies, economies and the environment. Floods, tornadoes and devastating wildfires became common phenomenons in many countries during the last decade because of the climate change. These risks can be properly managed to increase eco-system resilience by using smart technologies for geohazard detection and prediction.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1025-1028"},"PeriodicalIF":1.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Revolutionary Hybridised MCDM Approach on Geographic Information System for Evaluation of Flood Risk in Subarnarekha River Basin, India","authors":"Sipra Mophapatra,&nbsp;Dillip Kumar Ghose,&nbsp;Deba Prakash Satapathy","doi":"10.1002/gj.5196","DOIUrl":"https://doi.org/10.1002/gj.5196","url":null,"abstract":"<div>\u0000 \u0000 <p>Determining and characterising locations vulnerable to flooding can help in reducing damage and the number of fatalities. During the monsoon season, East India's Subarnarekha River frequently floods to a significant degree. In current work, we suggest a unique hybrid strategy for preparing the entire catchment's Flood Susceptibility Mapping (FSM). The study area's FSM was conducted by considering 10 flood conditioning factors utilising the Best-Worst Method (BWM) and a multi-parametric Analytical Hierarchy Process (AHP) as per expert knowledge. Meanwhile, the proposed strategy incorporates a Decision Making Trial and Evaluation Laboratory (DEMATEL) for examining causal linkages and dependencies between different elements affecting the flooding process. Several statistical matrices were used to compare the suggested strategy of BWM and AHP. Based on our findings, we concluded that the integration of DEMATEL with AHP and BWM (ID BWM, ID AHP) was more effective than alternative strategies. The findings show that out of 10 flood conditioning factors, slope, elevation, distance from the river, drainage density, Topographic wetness Index (TWI), Land Use Land Cover (LULC), Normalised Difference Vegetation Index (NDVI), precipitation, soil texture, and curvature, factors that have the biggest effects on the local flooding phenomenon are elevation, slope, precipitation, and distance from the river. For validating the efficacy of the flood susceptibility map, Area under the Receiver Operating Characteristic Curve (AUC-ROC) was adopted and demonstrated, showing a pretty high accuracy of (0.92 or 92% and 0.94 or 94%) for ID AHP and ID BWM, respectively. Our research findings provide a highly affordable and useful answer to the flooding problems of basin Subarnarekha.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1046-1064"},"PeriodicalIF":1.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilising Machine Learning Approaches for Enhanced Landslide Susceptibility Mapping in Sikkim, India","authors":"Sujit Kumar Roy,&nbsp;Sumon Dey,&nbsp;Jayanta Das,&nbsp;Billal Hossen,&nbsp;Swarup Das,&nbsp;Md. Mahmudul Hasan,&nbsp;Pratik Mojumder","doi":"10.1002/gj.5198","DOIUrl":"https://doi.org/10.1002/gj.5198","url":null,"abstract":"<div>\u0000 \u0000 <p>Landslides pose significant hazards in the mountainous region of Sikkim, India, necessitating accurate susceptibility mapping to mitigate risks. This study applies four machine learning models: Boosted Tree (BT), Gradient Boosting Machine (GBM), K-Nearest Neighbour (KNN), and Multilayer Perceptron (MLP) to develop a detailed landslide susceptibility map. Feature selection was performed using correlation analysis, the Boruta model, and multicollinearity tests, which identified 13 key landslide conditioning factors based on 1456 landslide inventory points. The GBM model demonstrated the highest predictive performance with an AUC of 0.99, followed by BT (AUC: 0.965), MLP (AUC: 0.940), and KNN (AUC: 0.895) in the testing dataset. The confusion matrix validation confirmed that GBM outperformed other models, achieving the highest F1 score (0.894) and accuracy (89.4%), followed by BT with an F1 score of 0.874 and accuracy of 87.8%. KNN and MLP displayed lower performance, with KNN showing an F1 score of 0.724 and accuracy of 72.6%, and MLP significantly underperforming with an F1 score of 0.096 and accuracy of 48.6%. Statistical significance testing using the Wilcoxon Signed-Rank Test revealed significant differences between BT and MLP (<i>p</i> = 0.018), while other model pairs exhibited no statistically significant performance differences. Additionally, the variable importance analysis highlighted Diurnal Temperature Range (DTR) as the most critical factor influencing landslide occurrence (43.99%), followed by elevation (21.59%). These findings provide valuable insights for policymakers and government authorities, enabling them to take necessary measures for effective landslide management in the vulnerable areas of Sikkim, confirming the efficacy of machine learning models for geohazard assessments.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1150-1169"},"PeriodicalIF":1.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long Term and Short Term Spatio-Temporal Characterisation of Rainfall Variability and Dynamicity Over the Westernmost Part of West Bengal, India Using Modified Mann-Kendal Test and Innovative Trend Analysis","authors":"Suman Mukherjee,&nbsp;Edris Alam,&nbsp;Manas Mondal,&nbsp;Subrata Haldar,&nbsp;Subhasis Bhattacharya,&nbsp;Md Kamrul Islam,&nbsp;Suman Paul","doi":"10.1002/gj.5187","DOIUrl":"https://doi.org/10.1002/gj.5187","url":null,"abstract":"<div>\u0000 \u0000 <p>It has become important to understand the dynamic nature of hydro-meteorological phenomena, especially rainfall, as rainfall is considered to be the principal source of water in the hydrological cycle. Purulia, the westernmost district of West Bengal, India, is part of the Chotanagpur Plateau fringe with its undulating topography, varying slope, hard rock aquifer, limited surface and subsurface water resources, and sub-humid dry climate. Drought has been a recurring phenomenon for years, and the majority of its residents practise rain-fed agriculture, solely relying on the monsoonal rain. Therefore, it is crucial to study the nature and pattern of annual and seasonal rainfall. The objective of the study is to bring out the long-term nature of the rainfall trend along with the short term and to understand the characteristics of the rainfall over the region. This study has used the India Meteorological Department (IMD) provided daily gridded rainfall dataset for 1961 to 2020, non-parametric Mann-Kendal (M-K) test, Modified version of M-K test, and Sen's slope estimator to determine the trend of rainfall in long-term and short-term time series; the recently developed approach, i.e., innovative trend analysis (ITA) is also applied to determine the underlying trend and its stability in the long-term time series. For the purpose of change point identification, this paper has applied the sequential version of M-K test (SQMK). Both the long term (1961–2020) and two short term (1961–1990 and 1991–2020) time series have been analysed annually and seasonally. To understand the long-term variation in the character of rainfall temporally and spatially, three indices, i.e., precipitation concentration index (PCI), rainfall deviation index (RDI) and modified Fournier index (MFI) have also been implemented. The ITA approach provides a better understanding of the trend as it can determine the trend whilst the M-K test failed to determine it in some cases. In contrast to the long-term (1961–2020) and first-half (1961–1990) series, the second half of the time step (1991–2020) had the largest falling trend; i.e., 90% of the total stations have recorded a downward trend during the monsoon season. PCI and RDI, as well as SSE, identified the western half of the district as being the driest, and MFI revealed that the eastern section of the district has high rainfall intensity. This study may help the planners and policymakers to frame policies for its people and their livelihood; the comprehension of the previous hints will be used to predict the future.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1065-1092"},"PeriodicalIF":1.4,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Tropical Cyclone Yaas on Coastal Regions of Odisha and West Bengal, India: An Assessment Using Sentinel Datasets","authors":"Bahadur Das,&nbsp;Dipanwita Dutta,&nbsp;Ratnadeep Ray","doi":"10.1002/gj.5153","DOIUrl":"https://doi.org/10.1002/gj.5153","url":null,"abstract":"<div>\u0000 \u0000 <p>Coastal areas are increasingly susceptible to frequent cyclones driven by climate change. This leads to severe flooding, habitat loss, economic damage and community displacement, necessitating urgent adaptation measures. In this context, the present study aims to assess the impact of tropical cyclone Yaas on the coastal districts of Odisha and West Bengal combining SAR and optical satellite data. The Sentinel-1 data was used for flood inundation analysis, allowing for the identification and mapping of areas affected by the cyclonic flooding. In addition, Sentinel-2 data was employed for land use and land cover (LULC) analysis, enabling the evaluation of the cyclone's impact on various land cover classes. A fuzzy Analytic Hierarchy Process (AHP) was applied to analyse the changes in forest canopy cover. By integrating these diverse datasets and analyses, the study provides a holistic understanding of the cyclone's impact on the coastal region's environment and land cover. The findings reveal that the Yaas cyclonic flood affected an area of 2528.70 sq. km, accounting for 6.8% of the total region. In the coastal areas of West Bengal, more than 24% of cropland was affected particularly in the districts of Purba Medinipur, North 24 Parganas, Hoogly, Howrah and South 24 Parganas. In Odisha state, the most affected cropland areas were Bhadrak (945.11 sq. km) and Kendrapara (557.90 sq. km), while the districts of Bhadrak, Balasore, Jajpur, Khordha and Cuttack experienced the greatest impact on built-up areas. The findings of this comprehensive study contribute to a deeper understanding of the magnitude and extent of tropical cyclone Yaas's impacts. This study can be useful for the development of effective mitigation and adaptation strategies that is, restoration of mangrove forests, introduction of salt-tolerant crops and upgrading of existing embankments and levees to enhance the resilience of the coastal communities.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1029-1045"},"PeriodicalIF":1.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial Assessment and Mapping Landslide Susceptibility for the Garo Hills Division, Meghalaya, India","authors":"Naveen Badavath,&nbsp;Smrutirekha Sahoo","doi":"10.1002/gj.5166","DOIUrl":"https://doi.org/10.1002/gj.5166","url":null,"abstract":"<div>\u0000 \u0000 <p>Creating accurate and effective Landslide Susceptibility (LS) maps can aid disaster prevention and mitigation efforts and provide sufficient public safety. The primary aim of this study is to develop an LS map for the Garo Hills region in Meghalaya, India, using the weight of evidence (WoE), frequency ratio (FR), and Shannon entropy (SE) methods. A comprehensive landslide inventory catalogued 98 events from 2000 to 2023 for the analysis, and nine key geographical and environmental parameters were prepared. Conducted multicollinearity and correlation analysis to identify and mitigate collinearity issues between factors. The model's performance was analysed through the area under the curve (AUC) value of receiver operating characteristic (ROC) curves and three recent landslides. The results showed that FR method achieved the highest accuracy, with successive rate curve (SRC) AUC and predictive rate curve (PRC) AUC values of 0.860 and 0.940, respectively, and classified susceptibility at three sites as high, moderate, and low. The WoE method effectively identified three landslides site in high and very high susceptibility zones, achieving SRC AUC and PRC AUC values of 0.844 and 0.915, respectively. The SE method showed robust performance in predicting landslide-prone areas, with PRC AUC comparable to other methods (0.913), though its SRC AUC (0.771) was lower. Developed maps revealed that high and very high susceptibility zones account for approximately 10% and 3% of the study area, predominantly near roads, steep slopes, and higher elevations. The information in this study is valuable for civilians and the government authorities involved in hazard monitoring and management.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 5","pages":"1184-1201"},"PeriodicalIF":1.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continental Geodynamics and Earth's Environment: Introduction","authors":"P. G. Athira,&nbsp;Bo Hui,&nbsp;Yirang Jang,&nbsp;Cheng-Xue Yang,&nbsp;M. Santosh,&nbsp;M. Satish-Kumar","doi":"10.1002/gj.5161","DOIUrl":"https://doi.org/10.1002/gj.5161","url":null,"abstract":"","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 2","pages":"237-239"},"PeriodicalIF":1.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving Mechanism of Synergistic Efficiency in Reducing Pollution and Carbon: Evidence From 249 Green Parks","authors":"Chuang Li,&nbsp;Keke Li,&nbsp;Liping Wang","doi":"10.1002/gj.5133","DOIUrl":"https://doi.org/10.1002/gj.5133","url":null,"abstract":"<div>\u0000 \u0000 <p>The green park serves as a significant spatial carrier for China's strategy to become a manufacturing powerhouse and promote industrial transformation and upgrading. It is a crucial platform for implementing and driving the transformation and development of green manufacturing, promoting green development and harmonious coexistence between man and nature in the current era. The main focus of this study is a total of 249 green parks announced by the Ministry of Industry and Information Technology, and it employs the multi-time point PSM-DID method to investigate 280 prefecture-level cities. The results show that: (1) The coefficient of the green park certification policy is estimated to have a significantly negative impact at the 1% significance level. (2) The green park certification facilitates the integration of pollution reduction and carbon reduction efforts by leveraging green technology innovation and government support. (3) The promoting effect of green industrial park certification has regional consistency and resource heterogeneity. Therefore, it is of great significance to study the effects and pathways of green park certification on the synergies of carbon and pollution reduction.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 6","pages":"1431-1452"},"PeriodicalIF":1.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Sets of Unconformity Migration Capacity and Their Influence on Hydrocarbon Accumulation in the Bozhong Area of the Bohai Bay Basin","authors":"Lei Pang,&nbsp;Yonghe Sun,&nbsp;Ming Hu,&nbsp;Tongwen Sun,&nbsp;Shangming Shi","doi":"10.1002/gj.5129","DOIUrl":"https://doi.org/10.1002/gj.5129","url":null,"abstract":"<div>\u0000 \u0000 <p>The Bohai Bay Basin is one of China's largest basins in terms of discovered hydrocarbon reserves. In this basin, unconformities serve as key pathways for the lateral migration of hydrocarbons, with the T8, T5 and T2 unconformities being the main ones developed here. Studying how these three unconformities differ in migration capacity is therefore essential for understanding hydrocarbon accumulation. By using logging data, along with measurements of porosity, permeability and sedimentary facies distribution, we analysed and compared the structures, physical properties and continuity of these unconformities. Based on this analysis, we linked hydrocarbon reserves per unit area to migration probability and developed a model for migration range. The results show that: (1) Although the T8 unconformity has poorer porosity and permeability compared to T5, it provides better continuity for migration channels, making T8 the main pathway in depressions, while T5 is more discontinuous and thus likely to form lithologic reservoirs in these areas. (2) T8 and T5 overlap gradually along the uplift belts, where both the porosity-permeability and thickness of these unconformities improve, and T2 has both good continuity and physical properties, facilitating lateral migration on the uplift belts. (3) Compared with other unconformities, T2 is the primary migration pathway above the uplift areas.</p>\u0000 </div>","PeriodicalId":12784,"journal":{"name":"Geological Journal","volume":"60 6","pages":"1394-1408"},"PeriodicalIF":1.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}