Natural Hazards Research最新文献

{"title":"Exploring GIS-based modeling for assessing social vulnerability to Ganga Riverbank erosion, India","authors":"Md Hasanuzzaman ,&nbsp;Biswajit Bera ,&nbsp;Aznarul Islam ,&nbsp;Pravat Kumar Shit","doi":"10.1016/j.nhres.2024.08.001","DOIUrl":"10.1016/j.nhres.2024.08.001","url":null,"abstract":"<div><div>Riverside communities along the lower Ganges in India face significant threats like riverbank erosion, floods, and climate change impacts. Despite extensive research on riverbank erosion in the region, a key gap remains in understanding how erosion and climate change jointly affect local communities. Additionally, research prioritizing village-level studies and strategies is urgently needed for effective management of the study area. This study aimed to compute a GIS-based Social Vulnerability Index (SociVI) by assessing 10 components and 31 sub-components at the village level. We used spatial analysis techniques like Moran's I and Getis-Ord G∗ to identify hotspots and clustering patterns among variables and SociVI values. Principal component analysis (PCA) and multi-correlation statistics determined the most significant component. Our fieldwork involved surveying 1641 households, 547 focus group discussions, and 12 key informant interviews across 547 villages. The SociVI analysis revealed that residents on the left bank of the river, particularly in the upper section of the Farakka barrage, and those living in the char villages were highly susceptible to social vulnerability. Scores ranged from 0.67 to 0.88, with 34 villages (6.22%) on the left bank and 8 villages (1.46%) on the right bank showing notably high SociVI values. Furthermore, our hot spot analysis identified 51 villages (9.32%) as hot spots with 99% confidence, 7.13% of which were located on the left bank and 2.19% on the right bank. According to the PCA results, demographics (PC1), riverbank calamities (PC2), displacement of households (PC3), and climatic variability (PC4) emerged as the most significant factors. This study's findings are crucial, highlighting critical areas and villages requiring focused efforts to reduce local vulnerability and bolster adaptation capacities amid these challenges.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 134-147"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of seismic potential impacts of an Mw 8.4 hypothetical earthquake in central Nepal province","authors":"Siddam Reddy Vineetha ,&nbsp;Chenna Rajaram","doi":"10.1016/j.nhres.2024.10.006","DOIUrl":"10.1016/j.nhres.2024.10.006","url":null,"abstract":"<div><div>The national capital of Nepal is situated on a lacustrine sediment basin. The country has had major seismic events that have resulted in significant damage to structures, human casualties, and substantial economic losses. Mitigating seismic risk is a challenging problem in Nepal due to poor construction practices, no enforcement of seismic safety guidelines, and a lack of awareness in the public. Seismic risk mitigation is essential in improving seismic resistance of buildings, and in reducing the economic loss and casualties in the forthcoming seismic events. The scientific results of earthquake loss estimation studies will lead to improve the policies towards seismic resilience.</div><div>The current research uses the SELENA (Seismic Loss Estimation using a Logic Tree Approach) tool to explore the seismic damage to buildings, human loss, and seismic risk in the 11 districts due to a scenario earthquake. The seismic risk of the study region due to the scenario earthquake is determined through fragility functions. The expected economic losses vary from 0.1 to 0.6 million dollars, and the possible casualties range from 1000 to 5000. The outcome of the study will be helpful for the local authorities and policymakers to take mitigation measures for the existing buildings.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 209-218"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide detection based on deep learning and remote sensing imagery: A case study in Linzhi City","authors":"Yutong Wang ,&nbsp;Hong Gao ,&nbsp;Shuhao Liu ,&nbsp;Dayi Yang ,&nbsp;Aixuan Liu ,&nbsp;Gang Mei","doi":"10.1016/j.nhres.2024.07.001","DOIUrl":"10.1016/j.nhres.2024.07.001","url":null,"abstract":"<div><div>Landslides result in serious damage to economic and land resources. Automated landslide detection over a wide area for the study and prevention of geohazards is important. Linzhi is located in the southeastern part of the Tibetan Plateau, one of the most landslide-prone regions in China. In this paper, we utilize a deep learning approach in combination with remote sensing images to detect landslides in Linzhi City. SHAP-based interpretability analysis and exponential Weighted Method and Technique for Order Preference by Similarity to Ideal Solution (EWM-TOPSIS) method are employed to investigate the catastrophic factors that affect landslides and results of landslide detection in Linzhi City. The obtained results indicate that the model is basically accurate in landslide detection in the Linzhi area, and most of the evaluation indexes of the model training are above 80%. Moreover, vegetation cover and rainfall are important causal factors triggering landslides in Linzhi City. Our research will provide a reference for landslide detection in similar areas.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 95-108"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation and ranking of CMIP6 global climate models over upper blue nile (abbay) basin of Ethiopia","authors":"Jemal Ali Mohammed","doi":"10.1016/j.nhres.2024.06.004","DOIUrl":"10.1016/j.nhres.2024.06.004","url":null,"abstract":"<div><div>The use of Global Climate Models (GCMs) data is the most practical way to conduct studies on climate science. However, performance evaluation and the selection of appropriate GCMs are vital. In this research, the effectiveness of eight selected CMIP6 GCMs in simulating the annual and seasonal rainfall observed over the Ethiopian Upper Blue Nile Basin from 1988 to 2014 was assessed. Five performance metrics (PMs) were used in the study: the correlation coefficient, root mean square error, bias percentage, Kling-Gupta efficiency and Nash-Sutcliffe efficiency. The scores of the various PMs were then combined into one, and the CMIP6 GCMs were ranked using Compromised Programming (CP). The findings from the CP were verified using a spatial, Taylor Diagram (TD), and areal average annual and seasonal evaluations. Even though the PMs produced some contradicting results, the study exhibited that CP was capable to evaluate the CMIP6 GCMs consistently. A regional evaluation of the CMIP6 GCMs relative to the observed data revealed that the best-ranked CMIP6 GCMs by using CP were capable to more accurately replicate the observed annual and seasonal rainfall. The lowest-ranking CMIP6 GCMs were found to have either spatially overvalued or undervalued the amount of rainfall over the basin. The best three CMIP6 GCMs for annual rainfall, according to the results of the CP method, are BCC-CSM2-MR, MIROC6, and NorESM2-MM; for the <em>Kiremt</em> season, the best CMIP6 GCMs are BCC-CSM2-MR, GISS-E2-2-G, and EC-Earth3. INM-CM5-0, MIROC6, and MRI-ESM2-0 ranked highest for <em>Bega</em> season, and EC-Earth3, BCC-CSM2-MR, and MRI-ESM2-0 for <em>Belg</em> season. It is recommended using the above-ranked CMIP6 GCMs to predict the characteristics of rainfall in the UBNB. Furthermore, results suggest that the CMIP6 GCMs be evaluated with a range of PMs across the whole temporal scales and that techniques such as CP be used to identify the best-performing CMIP6 GCMs.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 61-74"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change induced risks assessment of a coastal area: A “socioeconomic and livelihood vulnerability index” based study in coastal Bangladesh","authors":"Kishwar Jahan Chowdhury ,&nbsp;Md Rahmat Ali ,&nbsp;Md Arif Chowdhury ,&nbsp;Syed Labib Ul Islam","doi":"10.1016/j.nhres.2024.06.005","DOIUrl":"10.1016/j.nhres.2024.06.005","url":null,"abstract":"&lt;div&gt;&lt;div&gt;While climate change impacts the entire world, the people of Bangladesh bear a disproportionately heavy burden. Situated at the forefront of extreme climatic events such as cyclone, flood, saltwater intrusion, drought, and heavy rainfall, they face severe vulnerabilities. Coastal communities have been facing climate change impacts and livelihood threats for some time now. Hatiya – a coastal Upazila (sub-district) of the Noakhali District in Bangladesh faced extreme climatic and socio-economic challenges in the recent past. To understand the climate change-induced risks and vulnerabilities of Hatiya Upazila, it is vital to understand the socioeconomic and livelihood vulnerability index of this area. In this study, the Livelihood Vulnerability Index (LVI), Socioeconomic Vulnerability Index (SeVI) and Livelihood Vulnerability Index-Inter-Governmental Panel on Climate Change ​(LVI-IPCC) vulnerability index have been analyzed to evaluate the impacts of climate change on the livelihood and socioeconomic profile of the affected communities of Hatiya. A total of 150 household surveys and 11 Focus Group Discussions have been conducted in Hatiya Upazila for this purpose following purposive random sampling. The collected data included livelihood strategies, social network &amp; communications, food, health, water, social, economic, physical, and climatic disaster &amp; variability. All these vulnerability indicators were divided into 7 sub-components of LVI, and 5 subcomponents of SeVI, forming indicators to measure the desired vulnerability index. The index was formed by three IPCC endorsed climate change vulnerability indicators i.e., exposure, sensitivity, and adaptive capacity. The LVI value of Hatiya Upazila was found to be 0.495, which indicated that Hatiya has a medium vulnerability in terms of livelihood. Based on the weighted average scores, Hatiya was found to be the most vulnerable due to natural hazards (0.729), while indicators within this domain revealed that the highest percentage (64.6%) of households lost their property and other resources during natural hazards. In addition, Hatiya possessed a high level of socio-economic vulnerability (0.704). Livelihood Strategies become less diversified with the increased deterioration rate of natural resources such as fishing, agriculture, forest resources, etc. Most of the households were found to have weak Social Network &amp; Communications as they did not go to the local government or others for any kind of help, so the score for these components (0.722) was in the highly vulnerable range of LVI. However, the LVI-IPCC value of the study area was 0.027, indicating medium vulnerability. The SeVI index value for Hatiya Upazila was 0.704 which indicated high vulnerability and social, and economic vulnerability mostly influenced by natural hazards. The average indexed values of the three LVI-IPCC climate change contributing factors such as adaptive capacity, exposure, and sensitivity of Hatiya","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 75-87"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141396360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of potentially susceptible flooding areas leveraging geospatial technology with multicriteria decision analysis in Edo State, Nigeria","authors":"Kesyton Oyamenda Ozegin ,&nbsp;Stephen Olubusola Ilugbo","doi":"10.1016/j.nhres.2024.07.002","DOIUrl":"10.1016/j.nhres.2024.07.002","url":null,"abstract":"<div><div>Floods have claimed lives and devastated societal and ecological systems. Because of their catastrophic tendency and the financial and fatalities they cause, floods have become more and more significant on a global scale in recent years. In Edo State, Nigeria, flooding is a frequent threat that happens annually and seriously damages both lives and property. While the potential of flooding cannot entirely be eliminated, geospatial-based technologies can greatly lessen its effects. In Nigeria's flood-prone Edo State, the study's objectives are to identify inundated places and provide nuanced mapping of the flood risk. To facilitate the determination of the flood risk index (FRI), the study's fundamental flood-predictive features were determined by taking into consideration elevation, slope, distance from the river, rainfall intensity, land use/land cover, soil texture, topographic roughness index, topographic wetness index, normalized difference vegetation index (NDVI), runoff coefficient, aspect, drainage capacity, flow accumulation, the sediment transport index, and the stream power index. The significance of each predictive factor in the analytic hierarchy procedure (AHP) was determined by gathering expert views and perspectives from public entities. A flood threat map was created by processing the gathered data using the AHP and the ArcGIS 10.5 framework. The multicollinearity (MC) estimation was applied to assess the model's predictability. The results of the FRI showed that there were high and extremely severe flood risk zones that affected roughly 26 and 9% of the area, respectively. Flood risks have been identified as predominant in the Edo south region of the study area, which is characterized by low elevation and slope, high drainage capacity, distance from the river, topographic wetness, and index. It showed that the model's resultant vulnerability to flooding maps agreed with past flood occurrences that were previously experienced in the research area, demonstrating the technique's efficacy in locating and mapping locations plagued by flooding. Linear regression (R²) analysis was further conducted on the FRI to evaluate the scientific reliability of the utilized methodology; this shows 0.816 (81.6%) dependability. Consequently, frequent and long-lasting implementation of flooding predictions, warning systems, and mitigation strategies may be achieved.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 109-133"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global impact of urbanization on ecosystems: A comprehensive bibliometric analysis","authors":"Himadri Soni,&nbsp;Rajiv Kant Yadav,&nbsp;Suresh Kumar Patra","doi":"10.1016/j.nhres.2024.04.001","DOIUrl":"10.1016/j.nhres.2024.04.001","url":null,"abstract":"<div><div>This paper demonstrates a comprehensive assessment of bibliometric analysis of published research on the global impact of urbanization on environmental consequences. Scopus database was used to collect data on the global impact of urbanization on ecosystem research from 2000 to 2023 and scrutinizes the progression of publications, keyword analysis, co-citation of authors, citation of documents, co-authorship of authors, and most efficient and influential authors, countries, and institutions. A total of 4322 research papers (journals) were published during the period (2000–2023). This bibliometric study was carried out with the Vos Viewer software. The study's findings demonstrate that the number of publications has continuously increased between 2000 and 2023. In 2020 and 2022, there were 396 and 635 publications, respectively. The top three influential journals in terms of citations are Remote Sensing of Environment (2415 citations), Ecological Economics (1456 citations), and PLoS (1410). From 2000 to 2023, the most often appearing terms were urbanization (3028), urban area (1111), people (853), land use (769), environmental monitoring (594), environmental effect (570), sustainable development (567), and climate change (534). We created a time series with four periods (2000–2005, 2006–2011, 2012–2017, and 2018–2023) based on the periodic changes, which also shows the trend. In more specific terms, this study demonstrates not just a logical structure but also the development of keywords throughout time. The empirical evidence also shows that while this field initially focused on a few themes, it has since broadened to include the many aspects of urban sustainability. This analysis also identifies the most efficient and influential authors, institutions, and nations in terms of total publishing (TP), proportion of cited publications (PCP), total citation (TC), h-index, and g-index. Yang K is the most prominent author, China and the United States are the most efficient nations, and the Chinese Academy of Science is the most powerful organization. The top two nations by co-authorship are China (40 ​695 citations and 1527 publications) and the United States (41 ​119 citations and 833 publications). Additionally, it is noted that while environmental concerns continue to dominate, basic socioeconomic problems like equality, justice, and public participation are underrepresented. Sustainable development indicators, energy, ecologically friendly infrastructure, water, the use of land, and urban planning are prominent subject topics, with the first three exerting a larger influence in shaping the field's growth. This research may be utilized as a resource for people intrigued in learning more about the evaluation of urban sustainability and its advancement.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 21-35"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution laws of landslides and theirs influencing factors in the Qiaojia segment of Jinsha River, China","authors":"Liu Chaohai ,&nbsp;Renmao Yuan ,&nbsp;Wang Ying","doi":"10.1016/j.nhres.2024.06.002","DOIUrl":"10.1016/j.nhres.2024.06.002","url":null,"abstract":"<div><div>The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a typical southwest mountainous area, and geological disasters such as landslides are very serious. A total of 3573 landslides were identified after a combination of image interpretation and field investigation in an area of 8.4 ​km².This paper conducted a spatial analysis to reveal the distribution laws of landslides and analyzed the relationship between landslide and 13 influencing factors such as elevation, slope, slope aspect, topographic relief, soil, land use, lithology, annual average rainfall, ground peak ground acceleration (PGA). It can be concluded that the landslide showed the characteristics of non-uniformity and zonal distribution. A statistics analysis indicates that landslides are significantly correlated with elevation, slope gradient, slope direction, distance from faults, lithology, rivers, highways and so on. Therefore, when constructing engineering in alpine and canyon areas, it is essential to avoid the areas with steep slopes, large height difference, active faults, and the area being distributed by soft or broken hard rock masses to reduce disaster risks.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 48-60"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141394888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing vulnerability of fishermen communities in coastal Bangladesh: A “climate vulnerability index”- based study in Assasuni Upazila, Satkhira, Bangladesh","authors":"Imtiaz Ahmed ,&nbsp;Md. Arif Chowdhury ,&nbsp;Rashed Uz Zzaman ,&nbsp;Syed Labib Ul Islam ,&nbsp;Shamsun Nahar ,&nbsp;Sujit Kumar Roy","doi":"10.1016/j.nhres.2023.12.018","DOIUrl":"10.1016/j.nhres.2023.12.018","url":null,"abstract":"<div><div>Climate Vulnerability Index (CVI) is developed to measure the susceptibility of communities to climate change using a case study. The index includes factors for each of the three aspects of vulnerability, including ‘Exposure’, ‘Sensitivity’, and ‘Adaptive Capability’. Sensitivity is determined by “Health”, “Food”, and “Water”, Adaptive Capability is characterized by “Socio-demographic profile,” “Livelihood strategies,” and “Social networks”, and Exposure is identified by “Natural Disaster” and “Climate Variability”. A study was conducted to investigate the vulnerability of fishermen in Assasuni Upazila, Satkhira, Bangladesh. The study involved individual surveys of randomly identified 100 fishermen from three groups: Gher-based, Ocean-based, and River-based. The findings indicate that the Gher-based fishing community exhibits higher levels of adaptive capacity (0.39), sensitivity (0.57), and exposure (0.74) in comparison to the other two communities. The sub-indicator about the migration of individuals for Gher-based livelihoods exhibits a relatively higher value of 0.85, in contrast to the relatively lower values of 0.23 and 0.11 for river and ocean-based livelihoods, respectively. The utilization of index-based output observations may aid policymakers from national to local levels in identifying and implementing the appropriate adaptation practices that prioritize the welfare of fishing communities residing in the coastal regions of Bangladesh.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 562-572"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach","authors":"Subhra Halder,&nbsp;Suddhasil Bose","doi":"10.1016/j.nhres.2024.01.002","DOIUrl":"10.1016/j.nhres.2024.01.002","url":null,"abstract":"<div><div>This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 4","pages":"Pages 573-578"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}