Articles in press have
been peer-reviewed and accepted, which are not yet assigned to
volumes /issues, but are citable by Digital Object Identifier
(DOI).
Display Method:
2023, 14(5):
713-721.
doi: 10.1007/s13753-023-00494-x
Abstract:
keep_len="250">The concepts of business continuity management, operational resilience, and organizational resilience each refer to actions that businesses and organizations can take in anticipating and responding to disruptions. However, the existing definitions and usages are difficult to differentiate due to overlapping objectives, implementation processes, and outcomes. This article examines definitions and approaches for these three concepts and suggest a framework to operationalize methods and tools relevant to each. These definitions emphasize three dyads: risk versus resilience; organizational processes versus assets; and normal operating conditions versus crisis conditions. Using these dyads to differentiate the concepts of business continuity management, operational resilience, and organizational resilience can support planners in clarifying objectives and identifying which approach will be most beneficial as businesses or organizations plan for and encounter disruptions. This article evaluates these concepts by examining illustrative examples of disruptions and responses. The concepts of business continuity management, operational resilience, and organizational resilience each refer to actions that businesses and organizations can take in anticipating and responding to disruptions. However, the existing definitions and usages are difficult to differentiate due to overlapping objectives, implementation processes, and outcomes. This article examines definitions and approaches for these three concepts and suggest a framework to operationalize methods and tools relevant to each. These definitions emphasize three dyads: risk versus resilience; organizational processes versus assets; and normal operating conditions versus crisis conditions. Using these dyads to differentiate the concepts of business continuity management, operational resilience, and organizational resilience can support planners in clarifying objectives and identifying which approach will be most beneficial as businesses or organizations plan for and encounter disruptions. This article evaluates these concepts by examining illustrative examples of disruptions and responses.
keep_len="250">The concepts of business continuity management, operational resilience, and organizational resilience each refer to actions that businesses and organizations can take in anticipating and responding to disruptions. However, the existing definitions and usages are difficult to differentiate due to overlapping objectives, implementation processes, and outcomes. This article examines definitions and approaches for these three concepts and suggest a framework to operationalize methods and tools relevant to each. These definitions emphasize three dyads: risk versus resilience; organizational processes versus assets; and normal operating conditions versus crisis conditions. Using these dyads to differentiate the concepts of business continuity management, operational resilience, and organizational resilience can support planners in clarifying objectives and identifying which approach will be most beneficial as businesses or organizations plan for and encounter disruptions. This article evaluates these concepts by examining illustrative examples of disruptions and responses. The concepts of business continuity management, operational resilience, and organizational resilience each refer to actions that businesses and organizations can take in anticipating and responding to disruptions. However, the existing definitions and usages are difficult to differentiate due to overlapping objectives, implementation processes, and outcomes. This article examines definitions and approaches for these three concepts and suggest a framework to operationalize methods and tools relevant to each. These definitions emphasize three dyads: risk versus resilience; organizational processes versus assets; and normal operating conditions versus crisis conditions. Using these dyads to differentiate the concepts of business continuity management, operational resilience, and organizational resilience can support planners in clarifying objectives and identifying which approach will be most beneficial as businesses or organizations plan for and encounter disruptions. This article evaluates these concepts by examining illustrative examples of disruptions and responses.
2023, 14(5):
722-735.
doi: 10.1007/s13753-023-00515-9
Abstract:
keep_len="250">Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management. However, few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters. This article provides a qualitative analysis of these forensic studies, focusing on five main issues: (1) the methodologies applied; (2) the forensic approaches used in the disaster risk management phases; (3) the hazards addressed; (4) if the methodologies involve social participation, and using what types of participation; and (5) if there are references to urban planning in the scientific studies analyzed. Our results showed a predominance of the Forensic Investigations of Disasters (FORIN) and Post-Event Review Capability (PERC) methodologies used in isolation or combination. There is a need for methodologies that engage people in participatory FORIN, fostering the co-production of knowledge and action research approaches. Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management. However, few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters. This article provides a qualitative analysis of these forensic studies, focusing on five main issues: (1) the methodologies applied; (2) the forensic approaches used in the disaster risk management phases; (3) the hazards addressed; (4) if the methodologies involve social participation, and using what types of participation; and (5) if there are references to urban planning in the scientific studies analyzed. Our results showed a predominance of the Forensic Investigations of Disasters (FORIN) and Post-Event Review Capability (PERC) methodologies used in isolation or combination. There is a need for methodologies that engage people in participatory FORIN, fostering the co-production of knowledge and action research approaches.
keep_len="250">Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management. However, few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters. This article provides a qualitative analysis of these forensic studies, focusing on five main issues: (1) the methodologies applied; (2) the forensic approaches used in the disaster risk management phases; (3) the hazards addressed; (4) if the methodologies involve social participation, and using what types of participation; and (5) if there are references to urban planning in the scientific studies analyzed. Our results showed a predominance of the Forensic Investigations of Disasters (FORIN) and Post-Event Review Capability (PERC) methodologies used in isolation or combination. There is a need for methodologies that engage people in participatory FORIN, fostering the co-production of knowledge and action research approaches. Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management. However, few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters. This article provides a qualitative analysis of these forensic studies, focusing on five main issues: (1) the methodologies applied; (2) the forensic approaches used in the disaster risk management phases; (3) the hazards addressed; (4) if the methodologies involve social participation, and using what types of participation; and (5) if there are references to urban planning in the scientific studies analyzed. Our results showed a predominance of the Forensic Investigations of Disasters (FORIN) and Post-Event Review Capability (PERC) methodologies used in isolation or combination. There is a need for methodologies that engage people in participatory FORIN, fostering the co-production of knowledge and action research approaches.
2023, 14(5):
736-750.
doi: 10.1007/s13753-023-00519-5
Abstract:
keep_len="250">Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics. Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics.
keep_len="250">Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics. Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics.
2023, 14(5):
751-767.
doi: 10.1007/s13753-023-00514-w
Abstract:
keep_len="250">Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk. Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk.
keep_len="250">Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk. Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk.
2023, 14(5):
768-781.
doi: 10.1007/s13753-023-00512-y
Abstract:
keep_len="250">Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake. Turkey is one of the most at-risk countries for earthquake disasters worldwide. Therefore, it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales. In this study, a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed. With the aim of representing the vulnerability as a continuum across space, buildings were set as the smallest unit of analysis. The study area is in İzmit City of Turkey, with the exposed human and structural elements falling inside the most hazardous zone of seismicity. The index was represented by the building vulnerability, socioeconomic vulnerability, and vulnerability of the built environment. To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from, an extension of the catastrophe progression method for the objective weighing of indicators was proposed. Earthquake vulnerability index and components were mapped, a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability, and the study quantitatively revealed an estimate of people at risk. With its objectivity and straightforward implementation, the method can aid decision support for disaster risk reduction and emergency management. Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake. Turkey is one of the most at-risk countries for earthquake disasters worldwide. Therefore, it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales. In this study, a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed. With the aim of representing the vulnerability as a continuum across space, buildings were set as the smallest unit of analysis. The study area is in İzmit City of Turkey, with the exposed human and structural elements falling inside the most hazardous zone of seismicity. The index was represented by the building vulnerability, socioeconomic vulnerability, and vulnerability of the built environment. To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from, an extension of the catastrophe progression method for the objective weighing of indicators was proposed. Earthquake vulnerability index and components were mapped, a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability, and the study quantitatively revealed an estimate of people at risk. With its objectivity and straightforward implementation, the method can aid decision support for disaster risk reduction and emergency management.
keep_len="250">Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake. Turkey is one of the most at-risk countries for earthquake disasters worldwide. Therefore, it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales. In this study, a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed. With the aim of representing the vulnerability as a continuum across space, buildings were set as the smallest unit of analysis. The study area is in İzmit City of Turkey, with the exposed human and structural elements falling inside the most hazardous zone of seismicity. The index was represented by the building vulnerability, socioeconomic vulnerability, and vulnerability of the built environment. To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from, an extension of the catastrophe progression method for the objective weighing of indicators was proposed. Earthquake vulnerability index and components were mapped, a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability, and the study quantitatively revealed an estimate of people at risk. With its objectivity and straightforward implementation, the method can aid decision support for disaster risk reduction and emergency management. Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake. Turkey is one of the most at-risk countries for earthquake disasters worldwide. Therefore, it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales. In this study, a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed. With the aim of representing the vulnerability as a continuum across space, buildings were set as the smallest unit of analysis. The study area is in İzmit City of Turkey, with the exposed human and structural elements falling inside the most hazardous zone of seismicity. The index was represented by the building vulnerability, socioeconomic vulnerability, and vulnerability of the built environment. To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from, an extension of the catastrophe progression method for the objective weighing of indicators was proposed. Earthquake vulnerability index and components were mapped, a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability, and the study quantitatively revealed an estimate of people at risk. With its objectivity and straightforward implementation, the method can aid decision support for disaster risk reduction and emergency management.
2023, 14(5):
782-794.
doi: 10.1007/s13753-023-00517-7
Abstract:
keep_len="250">In this study, we set out to develop a new social vulnerability index (SVI). In doing so, we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability. To make the entanglement of socio-spatial inequalities visible, we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg, Germany. This kind of high-resolution analysis was not previously available, but is increasingly requested by political decision makers. We can thus show hot spots of social vulnerability (SV) in Hamburg, considering the effects of social welfare, education, and age. In doing so, we defined SV as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change’s (IPCC) concepts of risk and vulnerability. Our SVI consists of two subcomponents: sensitivity and coping capacity. Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city. Areas with high SVI are distributed over the entire city, notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe, as well as in the densely populated inner city areas of Eimsbüttel and St. Pauli. As a map at a detailed scale, our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards. We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning. In this study, we set out to develop a new social vulnerability index (SVI). In doing so, we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability. To make the entanglement of socio-spatial inequalities visible, we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg, Germany. This kind of high-resolution analysis was not previously available, but is increasingly requested by political decision makers. We can thus show hot spots of social vulnerability (SV) in Hamburg, considering the effects of social welfare, education, and age. In doing so, we defined SV as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change’s (IPCC) concepts of risk and vulnerability. Our SVI consists of two subcomponents: sensitivity and coping capacity. Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city. Areas with high SVI are distributed over the entire city, notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe, as well as in the densely populated inner city areas of Eimsbüttel and St. Pauli. As a map at a detailed scale, our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards. We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning.
keep_len="250">In this study, we set out to develop a new social vulnerability index (SVI). In doing so, we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability. To make the entanglement of socio-spatial inequalities visible, we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg, Germany. This kind of high-resolution analysis was not previously available, but is increasingly requested by political decision makers. We can thus show hot spots of social vulnerability (SV) in Hamburg, considering the effects of social welfare, education, and age. In doing so, we defined SV as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change’s (IPCC) concepts of risk and vulnerability. Our SVI consists of two subcomponents: sensitivity and coping capacity. Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city. Areas with high SVI are distributed over the entire city, notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe, as well as in the densely populated inner city areas of Eimsbüttel and St. Pauli. As a map at a detailed scale, our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards. We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning. In this study, we set out to develop a new social vulnerability index (SVI). In doing so, we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability. To make the entanglement of socio-spatial inequalities visible, we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg, Germany. This kind of high-resolution analysis was not previously available, but is increasingly requested by political decision makers. We can thus show hot spots of social vulnerability (SV) in Hamburg, considering the effects of social welfare, education, and age. In doing so, we defined SV as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change’s (IPCC) concepts of risk and vulnerability. Our SVI consists of two subcomponents: sensitivity and coping capacity. Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city. Areas with high SVI are distributed over the entire city, notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe, as well as in the densely populated inner city areas of Eimsbüttel and St. Pauli. As a map at a detailed scale, our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards. We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning.
2023, 14(5):
795-812.
doi: 10.1007/s13753-023-00520-y
Abstract:
keep_len="250">Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management. Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management.
keep_len="250">Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management. Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management.
2023, 14(5):
813-821.
doi: 10.1007/s13753-023-00518-6
Abstract:
keep_len="250">The damaging effects of extreme weather is concerning for many countries across the globe. Though the impact of these events on the housing market has been studied extensively, one aspect that remains unexplored is the value of mortgages. Further, there is no clarity on the impact of the specific types of homes. This study analyzed mortgages and apartment values and included residential land sale prices over 16 years for different localities across Jamaica. The analysis revealed that mortgages are adversely affected by excess rainfall while apartment sale prices are reduced by hurricanes but increased by excess rain. However, residential land prices remain unaffected by both events. The results point to the importance of climate adaptation for the local real estate market and property investment. The damaging effects of extreme weather is concerning for many countries across the globe. Though the impact of these events on the housing market has been studied extensively, one aspect that remains unexplored is the value of mortgages. Further, there is no clarity on the impact of the specific types of homes. This study analyzed mortgages and apartment values and included residential land sale prices over 16 years for different localities across Jamaica. The analysis revealed that mortgages are adversely affected by excess rainfall while apartment sale prices are reduced by hurricanes but increased by excess rain. However, residential land prices remain unaffected by both events. The results point to the importance of climate adaptation for the local real estate market and property investment.
keep_len="250">The damaging effects of extreme weather is concerning for many countries across the globe. Though the impact of these events on the housing market has been studied extensively, one aspect that remains unexplored is the value of mortgages. Further, there is no clarity on the impact of the specific types of homes. This study analyzed mortgages and apartment values and included residential land sale prices over 16 years for different localities across Jamaica. The analysis revealed that mortgages are adversely affected by excess rainfall while apartment sale prices are reduced by hurricanes but increased by excess rain. However, residential land prices remain unaffected by both events. The results point to the importance of climate adaptation for the local real estate market and property investment. The damaging effects of extreme weather is concerning for many countries across the globe. Though the impact of these events on the housing market has been studied extensively, one aspect that remains unexplored is the value of mortgages. Further, there is no clarity on the impact of the specific types of homes. This study analyzed mortgages and apartment values and included residential land sale prices over 16 years for different localities across Jamaica. The analysis revealed that mortgages are adversely affected by excess rainfall while apartment sale prices are reduced by hurricanes but increased by excess rain. However, residential land prices remain unaffected by both events. The results point to the importance of climate adaptation for the local real estate market and property investment.
2023, 14(5):
822-839.
doi: 10.1007/s13753-023-00516-8
Abstract:
keep_len="250">Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI. Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.
keep_len="250">Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI. Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.
2023, 14(5):
840-857.
doi: 10.1007/s13753-023-00513-x
Abstract:
keep_len="250">Socioeconomic development, subsidence, and climate change have led to high flood risks in coastal cities, making the vulnerable, especially elderly people, more prone to floods. However, we mostly do not know how the accessibility of life-saving public resources for the elderly population will change under future scenarios. Using Shanghai as a case, this study introduced a new analytical framework to fill this gap. We integrated for the first time models of coastal flooding, local population growth, and medical resource supply-demand estimation. The results show that under an extreme scenario of coastal flooding in the year 2050, in the absence of adaptation, half of the elderly population may be exposed to floods, the supply of medical resources will be seriously insufficient compared to the demand, and the accessibility of emergency medical services will be impaired by flooding. Our methodology can be applied to gain insights for other vulnerable coastal cities, to assist robust decision making about emergency responses to flood risks for elderly populations in an uncertain future. Socioeconomic development, subsidence, and climate change have led to high flood risks in coastal cities, making the vulnerable, especially elderly people, more prone to floods. However, we mostly do not know how the accessibility of life-saving public resources for the elderly population will change under future scenarios. Using Shanghai as a case, this study introduced a new analytical framework to fill this gap. We integrated for the first time models of coastal flooding, local population growth, and medical resource supply-demand estimation. The results show that under an extreme scenario of coastal flooding in the year 2050, in the absence of adaptation, half of the elderly population may be exposed to floods, the supply of medical resources will be seriously insufficient compared to the demand, and the accessibility of emergency medical services will be impaired by flooding. Our methodology can be applied to gain insights for other vulnerable coastal cities, to assist robust decision making about emergency responses to flood risks for elderly populations in an uncertain future.
keep_len="250">Socioeconomic development, subsidence, and climate change have led to high flood risks in coastal cities, making the vulnerable, especially elderly people, more prone to floods. However, we mostly do not know how the accessibility of life-saving public resources for the elderly population will change under future scenarios. Using Shanghai as a case, this study introduced a new analytical framework to fill this gap. We integrated for the first time models of coastal flooding, local population growth, and medical resource supply-demand estimation. The results show that under an extreme scenario of coastal flooding in the year 2050, in the absence of adaptation, half of the elderly population may be exposed to floods, the supply of medical resources will be seriously insufficient compared to the demand, and the accessibility of emergency medical services will be impaired by flooding. Our methodology can be applied to gain insights for other vulnerable coastal cities, to assist robust decision making about emergency responses to flood risks for elderly populations in an uncertain future. Socioeconomic development, subsidence, and climate change have led to high flood risks in coastal cities, making the vulnerable, especially elderly people, more prone to floods. However, we mostly do not know how the accessibility of life-saving public resources for the elderly population will change under future scenarios. Using Shanghai as a case, this study introduced a new analytical framework to fill this gap. We integrated for the first time models of coastal flooding, local population growth, and medical resource supply-demand estimation. The results show that under an extreme scenario of coastal flooding in the year 2050, in the absence of adaptation, half of the elderly population may be exposed to floods, the supply of medical resources will be seriously insufficient compared to the demand, and the accessibility of emergency medical services will be impaired by flooding. Our methodology can be applied to gain insights for other vulnerable coastal cities, to assist robust decision making about emergency responses to flood risks for elderly populations in an uncertain future.
2023, 14(5):
858-867.
doi: 10.1007/s13753-023-00522-w
Abstract:
keep_len="250">In 2018, the Emergency, Disasters and Ambulatory Transportation General Department at the Ministry of Health in Saudi Arabia established hospital emergency planning and preparation units (HEPPUs) to improve hospitals’ ability to respond to disasters. This study aimed to gain an in-depth understanding of the functioning of HEPPUs within hospitals in the western region of Saudi Arabia. Qualitative research methodology and semistructured interviews with emergency managers were employed. Four key themes emerged from the data: establishment and evolution, roles and responsibilities, communication and coordination, and challenges and limitations. The findings emphasize the importance of interdisciplinary collaboration, effective communication, and responses to challenges in enhancing healthcare resilience and disaster management. This study contributes insights and offers practical recommendations for improving the preparedness and performance of HEPPUs within Saudi Arabian hospitals. In 2018, the Emergency, Disasters and Ambulatory Transportation General Department at the Ministry of Health in Saudi Arabia established hospital emergency planning and preparation units (HEPPUs) to improve hospitals’ ability to respond to disasters. This study aimed to gain an in-depth understanding of the functioning of HEPPUs within hospitals in the western region of Saudi Arabia. Qualitative research methodology and semistructured interviews with emergency managers were employed. Four key themes emerged from the data: establishment and evolution, roles and responsibilities, communication and coordination, and challenges and limitations. The findings emphasize the importance of interdisciplinary collaboration, effective communication, and responses to challenges in enhancing healthcare resilience and disaster management. This study contributes insights and offers practical recommendations for improving the preparedness and performance of HEPPUs within Saudi Arabian hospitals.
keep_len="250">In 2018, the Emergency, Disasters and Ambulatory Transportation General Department at the Ministry of Health in Saudi Arabia established hospital emergency planning and preparation units (HEPPUs) to improve hospitals’ ability to respond to disasters. This study aimed to gain an in-depth understanding of the functioning of HEPPUs within hospitals in the western region of Saudi Arabia. Qualitative research methodology and semistructured interviews with emergency managers were employed. Four key themes emerged from the data: establishment and evolution, roles and responsibilities, communication and coordination, and challenges and limitations. The findings emphasize the importance of interdisciplinary collaboration, effective communication, and responses to challenges in enhancing healthcare resilience and disaster management. This study contributes insights and offers practical recommendations for improving the preparedness and performance of HEPPUs within Saudi Arabian hospitals. In 2018, the Emergency, Disasters and Ambulatory Transportation General Department at the Ministry of Health in Saudi Arabia established hospital emergency planning and preparation units (HEPPUs) to improve hospitals’ ability to respond to disasters. This study aimed to gain an in-depth understanding of the functioning of HEPPUs within hospitals in the western region of Saudi Arabia. Qualitative research methodology and semistructured interviews with emergency managers were employed. Four key themes emerged from the data: establishment and evolution, roles and responsibilities, communication and coordination, and challenges and limitations. The findings emphasize the importance of interdisciplinary collaboration, effective communication, and responses to challenges in enhancing healthcare resilience and disaster management. This study contributes insights and offers practical recommendations for improving the preparedness and performance of HEPPUs within Saudi Arabian hospitals.
2023, 14(5):
868-872.
doi: 10.1007/s13753-023-00511-z
Abstract:
keep_len="250">The United Nations Office for Disaster Risk Reduction and the World Meteorological Organization launched in 2022 the executive plan of the world program “Early Warning Systems for All” to be implemented from 2023 to 2027. This program champions an investment of USD 3.1 billion into the four pillars of warning systems and calls for multi-hazard and people-centered warning systems (PCWS). However, there is a scientific gap concerning interdisciplinary approaches to promoting them. Motivated by the call for action of “Early Warning Systems for All” and warning research gaps on the lack of interdisciplinarity, a workshop series “Interdisciplinary Approaches for Advancing People-Centered Warning Systems” was held in early 2023. This short article shares the preliminary findings and recommendations of this research, which involved a transnational virtual dialogue between one scientific organization in Brazil and one from the United States. The findings and recommendations discussed in three virtual sessions and one collective working paper were centered on three aspects: promoting interdisciplinary integration in research; the need to discuss the characteristics of a PCWS; and promoting problem- and solution-based programs with people to integrate them at all phases of the warning system. The United Nations Office for Disaster Risk Reduction and the World Meteorological Organization launched in 2022 the executive plan of the world program “Early Warning Systems for All” to be implemented from 2023 to 2027. This program champions an investment of USD 3.1 billion into the four pillars of warning systems and calls for multi-hazard and people-centered warning systems (PCWS). However, there is a scientific gap concerning interdisciplinary approaches to promoting them. Motivated by the call for action of “Early Warning Systems for All” and warning research gaps on the lack of interdisciplinarity, a workshop series “Interdisciplinary Approaches for Advancing People-Centered Warning Systems” was held in early 2023. This short article shares the preliminary findings and recommendations of this research, which involved a transnational virtual dialogue between one scientific organization in Brazil and one from the United States. The findings and recommendations discussed in three virtual sessions and one collective working paper were centered on three aspects: promoting interdisciplinary integration in research; the need to discuss the characteristics of a PCWS; and promoting problem- and solution-based programs with people to integrate them at all phases of the warning system.
keep_len="250">The United Nations Office for Disaster Risk Reduction and the World Meteorological Organization launched in 2022 the executive plan of the world program “Early Warning Systems for All” to be implemented from 2023 to 2027. This program champions an investment of USD 3.1 billion into the four pillars of warning systems and calls for multi-hazard and people-centered warning systems (PCWS). However, there is a scientific gap concerning interdisciplinary approaches to promoting them. Motivated by the call for action of “Early Warning Systems for All” and warning research gaps on the lack of interdisciplinarity, a workshop series “Interdisciplinary Approaches for Advancing People-Centered Warning Systems” was held in early 2023. This short article shares the preliminary findings and recommendations of this research, which involved a transnational virtual dialogue between one scientific organization in Brazil and one from the United States. The findings and recommendations discussed in three virtual sessions and one collective working paper were centered on three aspects: promoting interdisciplinary integration in research; the need to discuss the characteristics of a PCWS; and promoting problem- and solution-based programs with people to integrate them at all phases of the warning system. The United Nations Office for Disaster Risk Reduction and the World Meteorological Organization launched in 2022 the executive plan of the world program “Early Warning Systems for All” to be implemented from 2023 to 2027. This program champions an investment of USD 3.1 billion into the four pillars of warning systems and calls for multi-hazard and people-centered warning systems (PCWS). However, there is a scientific gap concerning interdisciplinary approaches to promoting them. Motivated by the call for action of “Early Warning Systems for All” and warning research gaps on the lack of interdisciplinarity, a workshop series “Interdisciplinary Approaches for Advancing People-Centered Warning Systems” was held in early 2023. This short article shares the preliminary findings and recommendations of this research, which involved a transnational virtual dialogue between one scientific organization in Brazil and one from the United States. The findings and recommendations discussed in three virtual sessions and one collective working paper were centered on three aspects: promoting interdisciplinary integration in research; the need to discuss the characteristics of a PCWS; and promoting problem- and solution-based programs with people to integrate them at all phases of the warning system.
2016, 7(2): 111-122.
doi: 10.1007/s13753-016-0090-9
摘要：
Although social vulnerability has recently gained attention in academic studies, Brazil lacks frameworks and indicators to assess it for the entire country. Social vulnerability highlights differences in the human capacity to prepare for, respond to, and recover from disasters. It varies over space and time, and among and between social groups, largely due to differences in socioeconomic and demographic characteristics. This article provides a social vulnerability index (SoVI®) replication study for Brazil and shows how SoVI® concepts and indicators were adapted to the country. SoVI® Brazil follows the place-based framework adopted in the Social Vulnerability Index initially developed for the United States. Using a principal component analysis (PCA), 45 city-level indicators were reduced to 10 factors that explain about 67 % of the variance in the data. Clearly identified spatial patterns showed a concentration of the most socially vulnerable cities in the North and Northeast regions of Brazil, as well as the social vulnerability of metropolitan areas and state capitals in the South and Southeast regions. The least vulnerable cities are mainly concentrated in the inland regions of the Southeast. Although different factors contribute to the social vulnerability in each city, the overall results confirm the social and economic disparities among Brazilian’s regions and reflect a differential vulnerability to natural hazards at local to regional scales.
Although social vulnerability has recently gained attention in academic studies, Brazil lacks frameworks and indicators to assess it for the entire country. Social vulnerability highlights differences in the human capacity to prepare for, respond to, and recover from disasters. It varies over space and time, and among and between social groups, largely due to differences in socioeconomic and demographic characteristics. This article provides a social vulnerability index (SoVI®) replication study for Brazil and shows how SoVI® concepts and indicators were adapted to the country. SoVI® Brazil follows the place-based framework adopted in the Social Vulnerability Index initially developed for the United States. Using a principal component analysis (PCA), 45 city-level indicators were reduced to 10 factors that explain about 67 % of the variance in the data. Clearly identified spatial patterns showed a concentration of the most socially vulnerable cities in the North and Northeast regions of Brazil, as well as the social vulnerability of metropolitan areas and state capitals in the South and Southeast regions. The least vulnerable cities are mainly concentrated in the inland regions of the Southeast. Although different factors contribute to the social vulnerability in each city, the overall results confirm the social and economic disparities among Brazilian’s regions and reflect a differential vulnerability to natural hazards at local to regional scales.
2019, 10(3): 386-403.
doi: 10.1007/s13753-019-00233-1
摘要：
Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures-Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.
Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures-Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.
2016, 7(1): 1-29.
doi: 10.1007/s13753-016-0081-x
摘要：
The first international conference for the post-2015 United Nations landmark agreements (Sendai Framework for Disaster Risk Reduction 2015–2030, Sustainable Development Goals, and Paris Agreement on Climate Change) was held in January 2016 to discuss the role of science and technology in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030. The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 aimed to discuss and endorse plans that maximize science’s contribution to reducing disaster risks and losses in the coming 15 years and bring together the diversity of stakeholders producing and using disaster risk reduction (DRR) science and technology. This article describes the evolution of the role of science and technology in the policy process building up to the Sendai Framework adoption that resulted in an unprecedented emphasis on science in the text agreed on by 187 United Nations member states in March 2015 and endorsed by the United Nations General Assembly in June 2015. Contributions assembled by the Conference Organizing Committee and teams including the conference concept notes and the conference discussions that involved a broad range of scientists and decision makers are summarized in this article. The conference emphasized how partnerships and networks can advance multidisciplinary research and bring together science, policy, and practice; how disaster risk is understood, and how risks are assessed and early warning systems are designed; what data, standards, and innovative practices would be needed to measure and report on risk reduction; what research and capacity gaps exist and how difficulties in creating and using science for effective DRR can be overcome. The Science and Technology Conference achieved two main outcomes: (1) initiating the UNISDR Science and Technology Partnership for the implementation of the Sendai Framework; and (2) generating discussion and agreement regarding the content and endorsement process of the UNISDR Science and Technology Road Map to 2030.
The first international conference for the post-2015 United Nations landmark agreements (Sendai Framework for Disaster Risk Reduction 2015–2030, Sustainable Development Goals, and Paris Agreement on Climate Change) was held in January 2016 to discuss the role of science and technology in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030. The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 aimed to discuss and endorse plans that maximize science’s contribution to reducing disaster risks and losses in the coming 15 years and bring together the diversity of stakeholders producing and using disaster risk reduction (DRR) science and technology. This article describes the evolution of the role of science and technology in the policy process building up to the Sendai Framework adoption that resulted in an unprecedented emphasis on science in the text agreed on by 187 United Nations member states in March 2015 and endorsed by the United Nations General Assembly in June 2015. Contributions assembled by the Conference Organizing Committee and teams including the conference concept notes and the conference discussions that involved a broad range of scientists and decision makers are summarized in this article. The conference emphasized how partnerships and networks can advance multidisciplinary research and bring together science, policy, and practice; how disaster risk is understood, and how risks are assessed and early warning systems are designed; what data, standards, and innovative practices would be needed to measure and report on risk reduction; what research and capacity gaps exist and how difficulties in creating and using science for effective DRR can be overcome. The Science and Technology Conference achieved two main outcomes: (1) initiating the UNISDR Science and Technology Partnership for the implementation of the Sendai Framework; and (2) generating discussion and agreement regarding the content and endorsement process of the UNISDR Science and Technology Road Map to 2030.
