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.
{"title":"Spatial-temporal assessment of soil erosion using the RUSLE model in the upstream Inaouène watershed, Northern Morocco","authors":"Chakir Hamouch , Jamal Chaaouan , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.08.003
Na Gao , Jingjing Liu, Lijuan Yuan
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.
{"title":"Research on rescue priority based on high spatiotemporal resolution mobile positioning data","authors":"Na Gao , Jingjing Liu, 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.09.001
Roquia Salam , Filiberto Pla , Bayes Ahmed , Marco Painho
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.
{"title":"A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context","authors":"Roquia Salam , Filiberto Pla , Bayes Ahmed , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.02.002
Monisha Mondol , Prodipto Bishnu Angon , Arpita Roy
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.
{"title":"Effects of microplastics on soil physical, chemical and biological properties","authors":"Monisha Mondol , Prodipto Bishnu Angon , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.07.001
Yutong Wang , Hong Gao , Shuhao Liu , Dayi Yang , Aixuan Liu , Gang Mei
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.
{"title":"Landslide detection based on deep learning and remote sensing imagery: A case study in Linzhi City","authors":"Yutong Wang , Hong Gao , Shuhao Liu , Dayi Yang , Aixuan Liu , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.08.001
Md Hasanuzzaman , Biswajit Bera , Aznarul Islam , Pravat Kumar Shit
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.
印度恒河下游的河边社区面临着河岸侵蚀、洪水和气候变化影响等重大威胁。尽管对该地区的河岸侵蚀进行了广泛的研究，但在了解侵蚀和气候变化如何共同影响当地社区方面仍然存在一个关键差距。此外，为了有效地管理研究区域，迫切需要优先考虑村一级的研究和战略。本研究旨在通过对村庄层面的10个组成部分和31个子组成部分进行评估，计算基于gis的社会脆弱性指数（SociVI）。我们使用Moran's I和Getis-Ord G *等空间分析技术来识别变量和社会价值之间的热点和聚类模式。主成分分析（PCA）和多相关统计确定了最显著成分。我们的实地工作包括在547个村庄调查1641个家庭，进行547个焦点小组讨论，并对12个关键信息提供者进行访谈。SociVI的分析显示，河流左岸的居民，特别是法拉卡拦河坝上游的居民，以及居住在char村的居民，极易受到社会脆弱性的影响。得分范围为0.67 ~ 0.88，其中左岸34个村（6.22%）和右岸8个村（1.46%）的SociVI值显著较高。此外，我们的热点分析确定51个村庄（9.32%）为热点，置信度为99%，其中7.13%位于左岸，2.19%位于右岸。根据PCA结果，人口统计（PC1）、河岸灾害（PC2）、家庭流离失所（PC3）和气候变率（PC4）是最显著的影响因素。这项研究的发现是至关重要的，它突出了需要集中努力减少当地脆弱性和加强应对这些挑战的适应能力的关键地区和村庄。
{"title":"Exploring GIS-based modeling for assessing social vulnerability to Ganga Riverbank erosion, India","authors":"Md Hasanuzzaman , Biswajit Bera , Aznarul Islam , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.10.006
Siddam Reddy Vineetha , Chenna Rajaram
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.
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.
{"title":"Assessment of seismic potential impacts of an Mw 8.4 hypothetical earthquake in central Nepal province","authors":"Siddam Reddy Vineetha , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.06.004
Jemal Ali Mohammed
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 Kiremt 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 Bega season, and EC-Earth3, BCC-CSM2-MR, and MRI-ESM2-0 for Belg 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.
{"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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.06.005
Kishwar Jahan Chowdhury , Md Rahmat Ali , Md Arif Chowdhury , Syed Labib Ul Islam
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 & communications, food, health, water, social, economic, physical, and climatic disaster & 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 & 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
{"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 , Md Rahmat Ali , Md Arif Chowdhury , 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 & communications, food, health, water, social, economic, physical, and climatic disaster & 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 & 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}
Pub Date : 2025-03-01DOI: 10.1016/j.nhres.2024.07.002
Kesyton Oyamenda Ozegin , Stephen Olubusola Ilugbo
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.
{"title":"Evaluation of potentially susceptible flooding areas leveraging geospatial technology with multicriteria decision analysis in Edo State, Nigeria","authors":"Kesyton Oyamenda Ozegin , 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 (R<sup>2</sup>) 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}
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