Pub Date : 2025-01-01DOI: 10.1016/j.pdisas.2024.100393
Nutchapon Prasertsoong , Nattapong Puttanapong
This study introduces a novel approach to monitoring floods and estimating socioeconomic impacts in Thailand. The approach leverages advancements in geospatial data, employing two web-based applications developed on the Google Earth Engine platform. These tools provide user-friendly access to a vast array of satellite data at the provincial level, including flooded areas, nighttime-light density, drought index, rainfall, cropland, and urban areas. The study also merges these satellite-based indices with official provincial GDP data from 2018 to 2022 to empirically analyze socioeconomic impacts using four machine learning algorithms. The result obtained from Random Forest (RF) demonstrates the highest predictive power for GDP forecasting (r-squared value of 0.912). Feature analysis methods identified the proportion of flooded urban areas as one of the most significant variables in predicting provincial GDP. The RF prediction model was also employed to conduct counterfactual simulations for the period 2018–2022, hypothesizing a scenario devoid of flood events. This approach facilitated the determination of a theoretical GDP value in the absence of floods, thereby enabling the calculation of flood-related economic losses, which averaged 0.945 % of GDP. The study's analytical framework, notable for its cost-effectiveness, leverages openly accessible data and open-source software packages, making it highly applicable to various developing countries.
{"title":"An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand","authors":"Nutchapon Prasertsoong , Nattapong Puttanapong","doi":"10.1016/j.pdisas.2024.100393","DOIUrl":"10.1016/j.pdisas.2024.100393","url":null,"abstract":"<div><div>This study introduces a novel approach to monitoring floods and estimating socioeconomic impacts in Thailand. The approach leverages advancements in geospatial data, employing two web-based applications developed on the Google Earth Engine platform. These tools provide user-friendly access to a vast array of satellite data at the provincial level, including flooded areas, nighttime-light density, drought index, rainfall, cropland, and urban areas. The study also merges these satellite-based indices with official provincial GDP data from 2018 to 2022 to empirically analyze socioeconomic impacts using four machine learning algorithms. The result obtained from Random Forest (RF) demonstrates the highest predictive power for GDP forecasting (r-squared value of 0.912). Feature analysis methods identified the proportion of flooded urban areas as one of the most significant variables in predicting provincial GDP. The RF prediction model was also employed to conduct counterfactual simulations for the period 2018–2022, hypothesizing a scenario devoid of flood events. This approach facilitated the determination of a theoretical GDP value in the absence of floods, thereby enabling the calculation of flood-related economic losses, which averaged 0.945 % of GDP. The study's analytical framework, notable for its cost-effectiveness, leverages openly accessible data and open-source software packages, making it highly applicable to various developing countries.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100393"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153110","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 : 2025-01-01DOI: 10.1016/j.pdisas.2025.100404
Sivasakthy Selvakumaran , Iain Rolland , Luke Cullen , Rob Davis , Joshua Macabuag , Charbel Abou Chakra , Nanor Karageozian , Amir Gilani , Christian Geiβ , Miguel Bravo-Haro , Andrea Marinoni
This work investigates the application of remote sensing technologies within the specific operational context of emergency urban search and rescue (USAR) efforts post-disaster. We thoroughly investigate two innovative methodologies, each tailored to meet distinct operational goals in a USAR setting. The first employs a belief propagation framework that is designed to provide prompt and robust initial damage assessments using sparse data, with the capability to incorporate additional on-site information as it becomes available. The second methodology introduces a modified graph convolutional network to quantify the uncertainty levels inherent in damage classification tasks. Three case studies were considered, using damage assessment data from the 2020 Beirut explosion, the 2021 Haiti earthquake and the 2023 Türkiye-Syria earthquake. Our experimental results demonstrate the potential of these approaches to achieve operational objectives, particularly in terms of robustness and scalability in disaster scenarios. The versatility offered by graph-based methodologies establishes a solid foundation for addressing these dynamic challenges, suggesting a promising direction for continued research in this field.
{"title":"Improving operational use of post-disaster damage assessment for Urban Search and Rescue by integrated graph-based multimodal remote sensing data analysis","authors":"Sivasakthy Selvakumaran , Iain Rolland , Luke Cullen , Rob Davis , Joshua Macabuag , Charbel Abou Chakra , Nanor Karageozian , Amir Gilani , Christian Geiβ , Miguel Bravo-Haro , Andrea Marinoni","doi":"10.1016/j.pdisas.2025.100404","DOIUrl":"10.1016/j.pdisas.2025.100404","url":null,"abstract":"<div><div>This work investigates the application of remote sensing technologies within the specific operational context of emergency urban search and rescue (USAR) efforts post-disaster. We thoroughly investigate two innovative methodologies, each tailored to meet distinct operational goals in a USAR setting. The first employs a belief propagation framework that is designed to provide prompt and robust initial damage assessments using sparse data, with the capability to incorporate additional on-site information as it becomes available. The second methodology introduces a modified graph convolutional network to quantify the uncertainty levels inherent in damage classification tasks. Three case studies were considered, using damage assessment data from the 2020 Beirut explosion, the 2021 Haiti earthquake and the 2023 Türkiye-Syria earthquake. Our experimental results demonstrate the potential of these approaches to achieve operational objectives, particularly in terms of robustness and scalability in disaster scenarios. The versatility offered by graph-based methodologies establishes a solid foundation for addressing these dynamic challenges, suggesting a promising direction for continued research in this field.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100404"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153141","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 : 2025-01-01DOI: 10.1016/j.pdisas.2024.100395
Azin Al Kajbaf , Christina Gore , Jennifer F. Helgeson , Jarrod Loerzel
Paycheck protection program (PPP) loans were established during the COVID-19 pandemic to help U.S-based businesses continue paying their employees. PPP loans were meant to help businesses recover from the disruptions caused by the COVID-19 pandemic; however, pre-existing socioeconomic stressors and the impacts of concurrent or previous climate and weather disasters could also amplify the impacts experienced by businesses. The concurrence of these interrupting acute shocks and chronic stressors creates a complex event that can result from combinations of natural, biological, and human-made causes. It is recognized that complex events tend to create an impact that is greater than the sum of their parts. The objective of this study is to evaluate whether the PPP loan allocations are correlated with prior community (i.e., county-level) experience of climate and weather disasters. This analysis seeks to understand whether past experience improves or diminishes the ability of businesses, and the communities in which they function, to respond to disruptive events. The heterogeneity in the correlations is investigated by examining climate and weather disaster occurrence across time, severity of impacts, and county-level characteristics. Our analysis results show a strong association between counties that previously experienced natural hazard events and the first wave of PPP loans from April 3rd to April 16th, 2020; however, the direction of association is different based on the extent of experience. Furthermore, counties that had increased levels of economic risk, including measures of community resilience and relatively greater unemployment rates, received less PPP loan allocations. We believe that the results of this study can potentially be helpful in decision-making regarding the allocation of recovery grants in future and start an important conversation about the structure of support offered to business during and following disaster events.
{"title":"Analysis of natural disasters and COVID-19 pandemic complex impacts on distribution of PPP loans","authors":"Azin Al Kajbaf , Christina Gore , Jennifer F. Helgeson , Jarrod Loerzel","doi":"10.1016/j.pdisas.2024.100395","DOIUrl":"10.1016/j.pdisas.2024.100395","url":null,"abstract":"<div><div>Paycheck protection program (PPP) loans were established during the COVID-19 pandemic to help U.S-based businesses continue paying their employees. PPP loans were meant to help businesses recover from the disruptions caused by the COVID-19 pandemic; however, pre-existing socioeconomic stressors and the impacts of concurrent or previous climate and weather disasters could also amplify the impacts experienced by businesses. The concurrence of these interrupting acute shocks and chronic stressors creates a complex event that can result from combinations of natural, biological, and human-made causes. It is recognized that complex events tend to create an impact that is greater than the sum of their parts. The objective of this study is to evaluate whether the PPP loan allocations are correlated with prior community (i.e., county-level) experience of climate and weather disasters. This analysis seeks to understand whether past experience improves or diminishes the ability of businesses, and the communities in which they function, to respond to disruptive events. The heterogeneity in the correlations is investigated by examining climate and weather disaster occurrence across time, severity of impacts, and county-level characteristics. Our analysis results show a strong association between counties that previously experienced natural hazard events and the first wave of PPP loans from April 3rd to April 16th, 2020; however, the direction of association is different based on the extent of experience. Furthermore, counties that had increased levels of economic risk, including measures of community resilience and relatively greater unemployment rates, received less PPP loan allocations. We believe that the results of this study can potentially be helpful in decision-making regarding the allocation of recovery grants in future and start an important conversation about the structure of support offered to business during and following disaster events.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100395"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153145","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 : 2025-01-01DOI: 10.1016/j.pdisas.2025.100406
Tanmoy Mazumder, Md. Mustafa Saroar
This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. Lightning, especially prevalent during April to June, is a significant threat in Bangladesh, causing fatalities, injuries, and economic losses. By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. Findings indicate a downward trend in lightning strikes but not necessarily in fatalities, revealing the complexity of contributing factors. Northern Bangladesh experiences more lightning strikes, whereas the northeast has higher casualty rates. Correlation analysis indicates that lightning fatalities are influenced by multiple factors, with high correlations to cropland area (0.69), agricultural population (0.61), and lightning flashes (0.45). The Random Forest model has appeared to be the best model to predict [with high accuracy] the influence of lightning vulnerability factors. The most significant predictors of lightning vulnerability are cropland area (32 %) followed by literacy rate (19 %), rural population (18 %), lightning flashes (16 %), and water area (15 %) in Bangladesh. The extensive presence of croplands and rural populations increases exposure to lightning during peak farming seasons, while low literacy rates exacerbate risks by limiting awareness of safety measures. Additionally, large water bodies influence local microclimates and pose risks to those working or travelling in and around these wetlands such as agriculture laborers and fishermen. Changes in lightning flash frequencies due to climate variability, combined with socio-economic disparities and infrastructure deficits, further amplify vulnerabilities. A district-level vulnerability map developed in this study provides actionable insights for geographically/area-based targeted policy interventions to address these interlinked factors driving vulnerability. This comprehensive, data-driven approach marks a significant advancement in our understanding of lightening vulnerability and offrrs valuable insight for strategy developed to combat the fatalities of lightning in Bangladesh.
{"title":"Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach","authors":"Tanmoy Mazumder, Md. Mustafa Saroar","doi":"10.1016/j.pdisas.2025.100406","DOIUrl":"10.1016/j.pdisas.2025.100406","url":null,"abstract":"<div><div>This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. Lightning, especially prevalent during April to June, is a significant threat in Bangladesh, causing fatalities, injuries, and economic losses. By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. Findings indicate a downward trend in lightning strikes but not necessarily in fatalities, revealing the complexity of contributing factors. Northern Bangladesh experiences more lightning strikes, whereas the northeast has higher casualty rates. Correlation analysis indicates that lightning fatalities are influenced by multiple factors, with high correlations to cropland area (0.69), agricultural population (0.61), and lightning flashes (0.45). The Random Forest model has appeared to be the best model to predict [with high accuracy] the influence of lightning vulnerability factors. The most significant predictors of lightning vulnerability are cropland area (32 %) followed by literacy rate (19 %), rural population (18 %), lightning flashes (16 %), and water area (15 %) in Bangladesh. The extensive presence of croplands and rural populations increases exposure to lightning during peak farming seasons, while low literacy rates exacerbate risks by limiting awareness of safety measures. Additionally, large water bodies influence local microclimates and pose risks to those working or travelling in and around these wetlands such as agriculture laborers and fishermen. Changes in lightning flash frequencies due to climate variability, combined with socio-economic disparities and infrastructure deficits, further amplify vulnerabilities. A district-level vulnerability map developed in this study provides actionable insights for geographically/area-based targeted policy interventions to address these interlinked factors driving vulnerability. This comprehensive, data-driven approach marks a significant advancement in our understanding of lightening vulnerability and offrrs valuable insight for strategy developed to combat the fatalities of lightning in Bangladesh.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100406"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378059","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}
This study emphasizes the need for a critical review of existing literature to identify the enablers and barriers to social modeling. Rather than solely focusing on vulnerability, it seeks to deconstruct and redefine resilience, particularly in the context of livelihood systems within communities that have been underexplored in current research. Through a qualitative approach, the study combines critical and constructivist paradigms to develop social modeling that enhances the resilience of disaster-prone communities via their livelihood systems. The goal is to create an innovative, participatory, and sustainable model for rural community livelihoods that can withstand challenges. Central to this model is the accumulation of both capital and social capital. The study offers strategic and practical recommendations for stakeholders and communities in disaster-prone areas to rebuild more robust livelihood systems by harnessing ecological, social, economic, and cultural potentials. It has significant implications for the analytical framework of community livelihood systems and the strategic and operational planning needed to address livelihoods in disaster-affected areas. Social modeling is a critical strategy for planning and implementing social protection and economic mitigation in such communities.
{"title":"Resilience rising: Redefining livelihood systems in disaster-prone rural communities","authors":"Reza Amarta Prayoga , Eko Wahyono , Nuzul Solekhah , Fatwa Nurul Hakim , Siti Fatimah , Lis Purbandini , Djoko Puguh Wibowo , Rachmini Saparita","doi":"10.1016/j.pdisas.2024.100391","DOIUrl":"10.1016/j.pdisas.2024.100391","url":null,"abstract":"<div><div>This study emphasizes the need for a critical review of existing literature to identify the enablers and barriers to social modeling. Rather than solely focusing on vulnerability, it seeks to deconstruct and redefine resilience, particularly in the context of livelihood systems within communities that have been underexplored in current research. Through a qualitative approach, the study combines critical and constructivist paradigms to develop social modeling that enhances the resilience of disaster-prone communities via their livelihood systems. The goal is to create an innovative, participatory, and sustainable model for rural community livelihoods that can withstand challenges. Central to this model is the accumulation of both capital and social capital. The study offers strategic and practical recommendations for stakeholders and communities in disaster-prone areas to rebuild more robust livelihood systems by harnessing ecological, social, economic, and cultural potentials. It has significant implications for the analytical framework of community livelihood systems and the strategic and operational planning needed to address livelihoods in disaster-affected areas. Social modeling is a critical strategy for planning and implementing social protection and economic mitigation in such communities.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100391"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163532","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 : 2024-12-01DOI: 10.1016/j.pdisas.2024.100375
Nerea Martín-Raya, Jaime Díaz-Pacheco, Pedro Dorta Antequera, Abel López-Díez
{"title":"Corrigendum to “Identifying urban prone areas to flash floods: The case of Santa Cruz de Tenerife” [Progress in Disaster Science Volume 24 (2024), 100372]","authors":"Nerea Martín-Raya, Jaime Díaz-Pacheco, Pedro Dorta Antequera, Abel López-Díez","doi":"10.1016/j.pdisas.2024.100375","DOIUrl":"10.1016/j.pdisas.2024.100375","url":null,"abstract":"","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100375"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163467","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 : 2024-12-01DOI: 10.1016/j.pdisas.2024.100389
Fabiha Rahman , Shampa , Sonia Binte Murshed , Mashfiqus Salehin , Faisal Mahmud Sakib , Erin Coughlan de Perez
Forecast-based financing (FbF) is gaining traction globally in the humanitarian sector as a critical tool for reducing disaster risk. Global and national literature on FbF mainly shed light on the effectiveness in reducing household-level vulnerability without a particular focus on women's specific vulnerability. This study uses qualitative methods to analyze rural Bangladeshi women's riverine flood challenges and whether the FbF has reduced their vulnerability to flooding. The study was conducted in two distinct geographical settings in northern Bangladesh: Charland (river island) and Mainland, without flood embankment protection. The findings reveal that FbF cash assistance primarily aided rural women in reducing the financial vulnerability of their households. Spending the cash assistance on buying food and boat evacuation directly benefits women and men alike and reduces the need for taking loans. However, no spending was made on women's personal utility and safety needs before, during, and after the flood. Charland and Mainland females faced barriers to basic utility and hygiene services, with Charland women faring slightly better. Our findings suggest that existing rural socio-cultural norms, cash disbursement timing, and other factors influenced women's cash aid anticipatory action choices, and the humanitarian actors and recipients should coordinate to improve the situation.
{"title":"Does forecast-based financing (FbF) lower women's vulnerability to flooding?","authors":"Fabiha Rahman , Shampa , Sonia Binte Murshed , Mashfiqus Salehin , Faisal Mahmud Sakib , Erin Coughlan de Perez","doi":"10.1016/j.pdisas.2024.100389","DOIUrl":"10.1016/j.pdisas.2024.100389","url":null,"abstract":"<div><div>Forecast-based financing (FbF) is gaining traction globally in the humanitarian sector as a critical tool for reducing disaster risk. Global and national literature on FbF mainly shed light on the effectiveness in reducing household-level vulnerability without a particular focus on women's specific vulnerability. This study uses qualitative methods to analyze rural Bangladeshi women's riverine flood challenges and whether the FbF has reduced their vulnerability to flooding. The study was conducted in two distinct geographical settings in northern Bangladesh: <em>Charland</em> (river island) and Mainland, without flood embankment protection. The findings reveal that FbF cash assistance primarily aided rural women in reducing the financial vulnerability of their households. Spending the cash assistance on buying food and boat evacuation directly benefits women and men alike and reduces the need for taking loans. However, no spending was made on women's personal utility and safety needs before, during, and after the flood. <em>Charland</em> and Mainland females faced barriers to basic utility and hygiene services, with <em>Charland</em> women faring slightly better. Our findings suggest that existing rural socio-cultural norms, cash disbursement timing, and other factors influenced women's cash aid anticipatory action choices, and the humanitarian actors and recipients should coordinate to improve the situation.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100389"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746996","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}
This paper attempts to measure the role of the mangrove ecosystem in minimizing coastal exposure using the InVEST (V3.9.0) Coastal Vulnerability Assessment (CVA) model in Tamil Nadu, India. The result depicts that the exposure value of the Tamil Nadu coastal stretch varies from 1.71 to 4.78 on a five-point scale. More than half of the coastal segments in Tamil Nadu have high to very high exposure, whereas nearly 10 % of the coastal segments are recorded under very low exposure. The model demonstrated that having the existing mangrove patches in the Pichavaram and Muthpet regions significantly reduces the exposure value from 3.47 to 2.80 and 4.78 to 2.10, respectively. Further, the present study modelled the impact of future Sea Level Rise (SLR) on the mangrove ecosystems using a static inundation modelling approach under different Representative Concentration Pathways (RCPs). Results depict a significant loss of mangrove habitats from 9.55 % to 58.33 % and 20.88 % to 48.02 % for both the Pichavaram and Muthupet mangrove regions, respectively, by the end of this century (2100). Since the coastal hazards are expected to intensify, our results can benefit policymakers by highlighting the prioritized areas and location-specific interventions for fostering Ecosystem-based Disaster Risk Reduction (Eco-DRR) strategies.
{"title":"An integrated coastal exposure modelling approach to assist mangrove ecosystem based disaster risk reduction (Eco-DRR) in Tamil Nadu, India","authors":"Pankaj Singha , Rajarshi Dasgupta , Shizuka Hashimoto , Bijon Kumer Mitra","doi":"10.1016/j.pdisas.2024.100388","DOIUrl":"10.1016/j.pdisas.2024.100388","url":null,"abstract":"<div><div>This paper attempts to measure the role of the mangrove ecosystem in minimizing coastal exposure using the InVEST (V3.9.0) Coastal Vulnerability Assessment (CVA) model in Tamil Nadu, India. The result depicts that the exposure value of the Tamil Nadu coastal stretch varies from 1.71 to 4.78 on a five-point scale. More than half of the coastal segments in Tamil Nadu have high to very high exposure, whereas nearly 10 % of the coastal segments are recorded under very low exposure. The model demonstrated that having the existing mangrove patches in the Pichavaram and Muthpet regions significantly reduces the exposure value from 3.47 to 2.80 and 4.78 to 2.10, respectively. Further, the present study modelled the impact of future Sea Level Rise (SLR) on the mangrove ecosystems using a static inundation modelling approach under different Representative Concentration Pathways (RCPs). Results depict a significant loss of mangrove habitats from 9.55 % to 58.33 % and 20.88 % to 48.02 % for both the Pichavaram and Muthupet mangrove regions, respectively, by the end of this century (2100). Since the coastal hazards are expected to intensify, our results can benefit policymakers by highlighting the prioritized areas and location-specific interventions for fostering Ecosystem-based Disaster Risk Reduction (Eco-DRR) strategies.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100388"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163466","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 : 2024-12-01DOI: 10.1016/j.pdisas.2024.100390
Mohammad Zaher Serdar, Fatima-Zahra Lahlou, Tareq Al-Ansari
The growing global demand for energy, water, and food presents a challenge to sustainable development. This challenge is further intensified by the expanding human population and the rapid pace of industrialization, which together exert intense pressure on these essential resources. Recent trends indicate a significant increase in energy usage, and it is expected that water and food requirements will also increase. Such pressures highlight the critical need to understand and manage the interconnected nature and associated risks, including cascading risks, within the energy, water, and food sectors. This study delves into the relationship between climate change risks and the Energy-Water-Food (EWF) nexus, assessing the diverse impacts of risk categories on distinct sectors. Furthermore, it expands on the concept of cascading risks, an area often overlooked, across two primary dimensions: hazards, which can evolve into cascading and compound hazards; and systems, where failures can propagate between interdependent systems or even within their own boundaries. Building on the reviewed concepts, a framework is proposed to quantify cascading performance within EWF nexus considering the dynamic interactions of the involved elements to reinforce their resilience.
{"title":"Enhancing resilience to climate change: Addressing cascading risks within the energy, water and food nexus","authors":"Mohammad Zaher Serdar, Fatima-Zahra Lahlou, Tareq Al-Ansari","doi":"10.1016/j.pdisas.2024.100390","DOIUrl":"10.1016/j.pdisas.2024.100390","url":null,"abstract":"<div><div>The growing global demand for energy, water, and food presents a challenge to sustainable development. This challenge is further intensified by the expanding human population and the rapid pace of industrialization, which together exert intense pressure on these essential resources. Recent trends indicate a significant increase in energy usage, and it is expected that water and food requirements will also increase. Such pressures highlight the critical need to understand and manage the interconnected nature and associated risks, including cascading risks, within the energy, water, and food sectors. This study delves into the relationship between climate change risks and the Energy-Water-Food (EWF) nexus, assessing the diverse impacts of risk categories on distinct sectors. Furthermore, it expands on the concept of cascading risks, an area often overlooked, across two primary dimensions: hazards, which can evolve into cascading and compound hazards; and systems, where failures can propagate between interdependent systems or even within their own boundaries. Building on the reviewed concepts, a framework is proposed to quantify cascading performance within EWF nexus considering the dynamic interactions of the involved elements to reinforce their resilience.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100390"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163465","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 : 2024-11-17DOI: 10.1016/j.pdisas.2024.100387
Alexander Fekete , Udo Nehren
In Central Europe, climate change is increasing the frequency and intensity of extreme events and weather variability. We need to better understand the interrelations between natural hazards and related extreme events and their impacts on gray, blue, and green infrastructures. According to literature research, a broad spectrum of hazard impacts can lead to transmission line and tower failures in Germany. A spatial assessment in a case study area in western Germany, using a geographic information system reveals the spatial exposure of forests, settlements, roads, rail, and waterways to transmission line failure. The main purpose of this study is to map this spatial exposure risk. In some districts, there is a higher risk of forest fires ignited by dropping transmission lines and impacts of winter storms or earthquakes.
The result indicates that better integration of climate change and other natural, technical, and man-made hazards is required and needs to be researched. We also need to better understand the linkages with critical infrastructure, such as emergency management, and the different cascades of impact on primary, secondary, and tertiary infrastructure.
The findings can inform fellow scientists, planners, and practitioners on better capturing and applying interconnected risks through spatial assessments. Moreover, the results can also inform operators and emergency managers on preparing for rare and unexpected risks.
{"title":"Climate change increased risk of forest fire, winter storm and technical failure risks related to power transmission lines – a spatial GIS risk assessment at Cologne district, Germany","authors":"Alexander Fekete , Udo Nehren","doi":"10.1016/j.pdisas.2024.100387","DOIUrl":"10.1016/j.pdisas.2024.100387","url":null,"abstract":"<div><div>In Central Europe, climate change is increasing the frequency and intensity of extreme events and weather variability. We need to better understand the interrelations between natural hazards and related extreme events and their impacts on gray, blue, and green infrastructures. According to literature research, a broad spectrum of hazard impacts can lead to transmission line and tower failures in Germany. A spatial assessment in a case study area in western Germany, using a geographic information system reveals the spatial exposure of forests, settlements, roads, rail, and waterways to transmission line failure. The main purpose of this study is to map this spatial exposure risk. In some districts, there is a higher risk of forest fires ignited by dropping transmission lines and impacts of winter storms or earthquakes.</div><div>The result indicates that better integration of climate change and other natural, technical, and man-made hazards is required and needs to be researched. We also need to better understand the linkages with critical infrastructure, such as emergency management, and the different cascades of impact on primary, secondary, and tertiary infrastructure.</div><div>The findings can inform fellow scientists, planners, and practitioners on better capturing and applying interconnected risks through spatial assessments. Moreover, the results can also inform operators and emergency managers on preparing for rare and unexpected risks.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100387"},"PeriodicalIF":2.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698317","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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