Natural Hazards Research最新文献

{"title":"Atami landslides 2021 Japan: Landfill issues, elderly casualties, key lessons and challenges","authors":"Namita Poudel ,&nbsp;Guo Chi,&nbsp;Cao Yuqiu,&nbsp;Rajib Shaw","doi":"10.1016/j.nhres.2024.06.006","DOIUrl":"10.1016/j.nhres.2024.06.006","url":null,"abstract":"<div><div>Landslides are a common problem worldwide, significantly impacting human societies. Japan is particularly susceptible to multiple hazards, including landslides. The Atami landslide in 2021 raised concerns about Japan's disaster management and evacuation processes. In this context, this research aims to compare the Atami landslide with previous landslides occurring between 2013 and 2021, focusing on their causes and impacts, particularly on elderly people. A comparative method is used to analyze two or more similar types of disasters. To accomplish the objectives of the paper, pertinent reports, government papers, and articles are reviewed. The findings indicate that the Atami landslide was distinct due to secondary causes, specifically illegal landfill management, where the landfill's height was increased beyond permissible limits. During the monsoon season, heavy rainfall led to flash floods in Atami city, resulting in human casualties and property loss. The study also found that the number of elderly casualties was high, similar to previous landslides, highlighting deficiencies in the evacuation system. The research suggests implementing a combined digital and community network-based early warning system and immediate follow-up inspections of other landfill sites as additional measures to improve existing disaster management strategies for future preparedness.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 88-94"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725730","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":"Improving social resilience to forest fire from community perspective","authors":"Yafang Wen,&nbsp;Ariyaningsih,&nbsp;Chi Guo,&nbsp;Anuska Ray,&nbsp;Rajib Shaw","doi":"10.1016/j.nhres.2024.08.004","DOIUrl":"10.1016/j.nhres.2024.08.004","url":null,"abstract":"<div><div>Recently, terms like social and community resilience have provided new ideas in reducing disaster risks especially in forest fire. However, a comprehensive and in-depth review of community social resilience concerning forest fires is lacking. There is little research investigate whether certain social or community resilience factors can initiate forest fires or whether forest fire prevention positively be influenced by them. To fill this gap, this paper aims to identify and discuss the factors influencing the occurrence of forest fires in the scope of community social resilience. It also provides recommendations for forest fire prevention and enhancing community social resilience to forest fires. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) framework were used to do the systematic review. The results show that there are 4 main factors concerning the social resilience to forest fire such as, social capital, forest fire cultural, community economic, and community characteristics. In addition, this research also suggests future recommendations for preventing forest fires and improving community resilience to forest fires.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 166-174"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725896","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":"Spatial-temporal assessment of soil erosion using the RUSLE model in the upstream Inaouène watershed, Northern Morocco","authors":"Chakir Hamouch ,&nbsp;Jamal Chaaouan ,&nbsp;Charaf eddine Bouiss","doi":"10.1016/j.nhres.2024.08.002","DOIUrl":"10.1016/j.nhres.2024.08.002","url":null,"abstract":"<div><div>This study aims to assess the risk of soil erosion in two different years (1984 and 2022) to gain insights into the extent of soil loss risk in the study area spatially and temporally. Using the Revised Universal Soil Loss Equation (RUSLE), which evaluates the soil loss rate, focusing primarily on erosivity of rainfall \"R,\" soil erodibility \"K,\" vegetation cover \"C,\" topography \"LS,\" and anti-erosion practices \"P.\" To achieve this, we incorporated various factors of the equation into a Geographic Information System (GIS) and spatial remote sensing. By overlaying these factors, we obtained a quantitative map of soil losses in our watershed. The results of this work show that the upstream Inaouène experienced a strong erosion dynamic in both 1985 and 2022, with a notable decrease in the amount of soil loss in the last year. Soil degradation in 1985 had an average of about 68 (T/ha/year), with maximum and minimum losses between 2162 and 0.067 ​T/ha/year, while losses in 2022 recorded an average of 52.4 (T/ha/year), with a maximum of 1850 (T/ha/year). The study area represents very high quantities of losses in both periods compared to several studies conducted in this region using the same model. This is due to the fact that the study area is located in a region characterized by very favorable natural and human conditions and factors to trigger and promote significant soil losses, including concentrated and intense rainfall, the predominance of fragile rocks, steep slopes, low vegetation cover in the eastern and southeastern part of the terrain, in addition to irrational human interference with the land.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 148-156"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725898","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":"Research on rescue priority based on high spatiotemporal resolution mobile positioning data","authors":"Na Gao ,&nbsp;Jingjing Liu,&nbsp;Lijuan Yuan","doi":"10.1016/j.nhres.2024.08.003","DOIUrl":"10.1016/j.nhres.2024.08.003","url":null,"abstract":"<div><div>Estimate the key rescue areas of earthquake accurately, which is of great significance for deploying rescue forces and implementing rescue activities in post-earthquake scientifically.This paper based on the idea of first zoning, then classification, and then prioritizing rescue, taking the core area of Tangshan City as the study area, based on urban road data and mobile positioning data, combined with GIS methods to achieve street level rescue zoning, k-means clustering analysis is used to classify rescue sectors, and personnel burial model is used to conduct rescue priority classification.The results indicate that rescue priority is closely related to the time of earthquake occurrence. When the earthquake occurs between 18pm and 7pm in the next day, the number of priority rescue sector at level I and II is the highest. When the earthquake occurs between 8am and 11am on weekends, the number of priority rescue sector in residential areas increases, while the number of priority rescue zone decreases in workspace areas. This study provides refined rescue zoning and priority grading in the early stages of disaster relief with the absence of disaster information, which will help to assist in decision-making for professional force dispatch.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 157-165"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725996","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":"A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context","authors":"Roquia Salam ,&nbsp;Filiberto Pla ,&nbsp;Bayes Ahmed ,&nbsp;Marco Painho","doi":"10.1016/j.nhres.2024.09.001","DOIUrl":"10.1016/j.nhres.2024.09.001","url":null,"abstract":"<div><div>Detecting rainfall-induced shallow landslides in data-sparse regions has become increasingly important for effective landslides disaster management. Previous studies have predominantly focused on automated methods for deep-seated, earthquake-triggered landslides. This study addresses this gap by employing a U-net Convolutional Neural Network (CNN) model to detect rainfall-induced shallow landslides using multi-temporal, high-resolution PlanetScope (3m spatial resolution), medium-resolution Sentinel-2 (10m spatial resolution) imagery, and ALOS-PALSAR-provided digital elevation model (DEM). Four datasets were created: Datasets A and B using PlanetScope, and Datasets C and D using Sentinel-2, with Datasets B and D also including DEM data. A total of 181 manually delineated landslide polygons were used as ground truth masks. Each dataset was tested using repeated stratified hold-out validation. Performance metrics included precision, recall, F1 score, loss, and accuracy. Results indicated that Datasets A and B outperformed the others; however, integrating DEM with Dataset B did not enhance model accuracy. The best mean precision, recall, F1 score, loss, and accuracy were 1, 0.625, 0.625, 0.380, and 0.999, respectively, for both Datasets A and B. This study demonstrates the U-net model's potential for detecting rainfall-induced shallow landslides in various geographic and temporal contexts globally.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 175-186"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725897","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":"Effects of microplastics on soil physical, chemical and biological properties","authors":"Monisha Mondol ,&nbsp;Prodipto Bishnu Angon ,&nbsp;Arpita Roy","doi":"10.1016/j.nhres.2024.02.002","DOIUrl":"10.1016/j.nhres.2024.02.002","url":null,"abstract":"<div><div>Pollution from microplastics (MPs) is recognized as a significant new global change factor that may have an impact on ecosystem services and functions. Although it is known that soil ecosystems, especially agricultural land, are a significant source of MPs, little is known about the effects of MPs on soil ecosystems, such as those above and below ground. As a major secondary source of microplastics (MPs) in various environmental media, the soil environment is where microplastics aggregate. To evaluate the effects of MP contamination on arable land, residential land areas (due to primary and secondary MPs), and the development and reproduction of soil fauna, we performed a global analysis in this study. This study sought to determine whether MP contamination exists in soil and how it influences the physical, chemical, and biological properties of the soil. To examine the causes, impacts, mitigation, and global perspective of MP pollution of soil, several research databases about its identification, occurrences, and consequences were combed for pertinent data and citations. The academic literature is collected using search engines such as Google Scholar, Springer Link, Elsevier, and Frontiers. Through this study, it is possible to evaluate how these qualities, MPs in landfill leachate, and the route of contamination from primary and secondary MPs to the soil affect soil toxicity and its consequential effects on physical, chemical, and biological properties as well as living organisms. This work also addresses the laws, rules, and numerous state-of-the-art treatment strategies for reducing the consequences of MPs. Significant gaps in knowledge require further thorough research.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 14-20"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465269","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":"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":"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":"<div><div>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}