Pub Date : 2025-04-06DOI: 10.1016/j.ijdrr.2025.105468
Mohammad Suleiman Awwad
This study aimed to investigate the relationship between sociodemographic characteristics and psychological distress within the context of the Arab culture, specifically Jordan, during the Coronavirus disease 2019 (COVID-19) pandemic. A cross-sectional survey was conducted to assess the stress levels among Jordanian citizens in rural and urban areas. This study revealed interesting findings. In accordance with the literature, females experienced higher stress levels than males, and higher education led to less stress. However, contrary to the literature, unmarried individuals experience less stress, higher income does not lead to less stress, younger age groups experience higher stress, and unemployed individuals experience higher stress than employed individuals do. There was no significant difference in stress levels between urban and rural areas. This study demonstrated the significant role of cultural context in the mechanics of the relationship between demographic and social characteristics and psychological distress by challenging dominant perspectives, especially in foreign cultures compared with Arab cultures.
{"title":"Decoding health-related disasters through sociodemographic characteristics: Does Arab cultural context matter? Lessons from COVID-19","authors":"Mohammad Suleiman Awwad","doi":"10.1016/j.ijdrr.2025.105468","DOIUrl":"10.1016/j.ijdrr.2025.105468","url":null,"abstract":"<div><div>This study aimed to investigate the relationship between sociodemographic characteristics and psychological distress within the context of the Arab culture, specifically Jordan, during the Coronavirus disease 2019 (COVID-19) pandemic. A cross-sectional survey was conducted to assess the stress levels among Jordanian citizens in rural and urban areas. This study revealed interesting findings. In accordance with the literature, females experienced higher stress levels than males, and higher education led to less stress. However, contrary to the literature, unmarried individuals experience less stress, higher income does not lead to less stress, younger age groups experience higher stress, and unemployed individuals experience higher stress than employed individuals do. There was no significant difference in stress levels between urban and rural areas. This study demonstrated the significant role of cultural context in the mechanics of the relationship between demographic and social characteristics and psychological distress by challenging dominant perspectives, especially in foreign cultures compared with Arab cultures.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105468"},"PeriodicalIF":4.2,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-04DOI: 10.1016/j.ijdrr.2025.105448
Mahdi Suleimany, Tandis Sulaimani
This study aims to investigate the spatio-temporal patterns of countries' vulnerability to extreme heat, a critical consequence of climate change that threatens communities' environmental, infrastructural, economic, and social systems. Recognizing a significant theoretical gap regarding comprehensive country-scale assessments, this research develops a Composite Vulnerability Index (CVI) by employing the hybrid F’ANP model to integrate ten indicators, considering heat exposure, sensitivity, and adaptive capacity. Analyzing data from 156 countries within seven regions over the 2001 to 2020 period, the study reveals notable regional disparities in heat vulnerability levels and fluctuation. Key findings indicate that while East Asia & Pacific and Latin America & the Caribbean regions underwent non-stationary CVI trends, Sub-Saharan Africa is the most vulnerable region due to inadequate infrastructure and economic challenges. Europe & Central Asia, the Middle East & North Africa, and also North American countries exhibit increasing vulnerability, attributed to rising land surface temperatures and the frequency of extreme heat events. Conversely, the South Asia region demonstrates a marked decline in CVI, reflecting improvements in adaptive capacity. This research underscores the need for targeted policy interventions and international collaboration to alleviate countries' heat vulnerability, emphasizing continuous monitoring and informed risk management for mitigating climate change impacts.
{"title":"Spatio-temporal analysis of countries' vulnerability to extreme heat, using the hybrid F’ANP model","authors":"Mahdi Suleimany, Tandis Sulaimani","doi":"10.1016/j.ijdrr.2025.105448","DOIUrl":"10.1016/j.ijdrr.2025.105448","url":null,"abstract":"<div><div>This study aims to investigate the spatio-temporal patterns of countries' vulnerability to extreme heat, a critical consequence of climate change that threatens communities' environmental, infrastructural, economic, and social systems. Recognizing a significant theoretical gap regarding comprehensive country-scale assessments, this research develops a Composite Vulnerability Index (CVI) by employing the hybrid F’ANP model to integrate ten indicators, considering heat exposure, sensitivity, and adaptive capacity. Analyzing data from 156 countries within seven regions over the 2001 to 2020 period, the study reveals notable regional disparities in heat vulnerability levels and fluctuation. Key findings indicate that while East Asia & Pacific and Latin America & the Caribbean regions underwent non-stationary CVI trends, Sub-Saharan Africa is the most vulnerable region due to inadequate infrastructure and economic challenges. Europe & Central Asia, the Middle East & North Africa, and also North American countries exhibit increasing vulnerability, attributed to rising land surface temperatures and the frequency of extreme heat events. Conversely, the South Asia region demonstrates a marked decline in CVI, reflecting improvements in adaptive capacity. This research underscores the need for targeted policy interventions and international collaboration to alleviate countries' heat vulnerability, emphasizing continuous monitoring and informed risk management for mitigating climate change impacts.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105448"},"PeriodicalIF":4.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1016/j.ijdrr.2025.105457
Nadia Matarazzo , Rosa Coluzzi , Vito Imbrenda , Maria Lanfredi , Michele Galella , Dionisia Russo Krauss
The first wave of COVID-19 arrived in Europe in February 2020, firstly impacting Italy, especially in the most economically advanced areas of the country, mainly located in the northern-central part of the peninsula. In general, the effects of pandemic in Italy outlined sharp differences across a latitudinal gradient. This paper focuses on Basilicata, an inner region of Southern Italy, connecting its peripherality, according to the SNAI (National Strategy for Inner Areas) classification, with its involvement in the first wave of the COVID-19 pandemic. Through the analysis of the number of infected people and deaths and the investigation of socio-economic and environmental data, we observed a low impact of the contagion in the first wave, supporting the thesis that some territorial and socio-economic features of this inner area (such as the specific settlement morphology and environmental conditions or the sparse infrastructural fabric, as well as the social model for the care of frail people) have somehow acted as a barrier for the spread of the virus. Our results suggest that the SNAI scheme could be overly rigid in certain cases due to the significance of highly local factors. Furthermore, while connectivity is valued in its own right, the observation of pandemic spread underscores the need to promote new territorial structures that not only foster environmental balance but also transform structural vulnerabilities into protective assets.
{"title":"The role of peripherality in the spread of pandemic: evidence from Basilicata (Southern Italy) during the first wave of COVID-19","authors":"Nadia Matarazzo , Rosa Coluzzi , Vito Imbrenda , Maria Lanfredi , Michele Galella , Dionisia Russo Krauss","doi":"10.1016/j.ijdrr.2025.105457","DOIUrl":"10.1016/j.ijdrr.2025.105457","url":null,"abstract":"<div><div>The first wave of COVID-19 arrived in Europe in February 2020, firstly impacting Italy, especially in the most economically advanced areas of the country, mainly located in the northern-central part of the peninsula. In general, the effects of pandemic in Italy outlined sharp differences across a latitudinal gradient. This paper focuses on Basilicata, an inner region of Southern Italy, connecting its peripherality, according to the SNAI (National Strategy for Inner Areas) classification, with its involvement in the first wave of the COVID-19 pandemic. Through the analysis of the number of infected people and deaths and the investigation of socio-economic and environmental data, we observed a low impact of the contagion in the first wave, supporting the thesis that some territorial and socio-economic features of this inner area (such as the specific settlement morphology and environmental conditions or the sparse infrastructural fabric, as well as the social model for the care of frail people) have somehow acted as a barrier for the spread of the virus. Our results suggest that the SNAI scheme could be overly rigid in certain cases due to the significance of highly local factors. Furthermore, while connectivity is valued in its own right, the observation of pandemic spread underscores the need to promote new territorial structures that not only foster environmental balance but also transform structural vulnerabilities into protective assets.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105457"},"PeriodicalIF":4.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1016/j.ijdrr.2025.105455
Jamal Dabbeek , Helen Crowley , Vitor Silva , Sevgi Ozcebe
Occupancy patterns are known to strongly affect the number of people killed by earthquakes. Existing exposure models for Europe based on housing census do not account for the daily movement of the population between the place of residence (residential occupancy) and places of economic activity (non-residential occupancy), or the seasonal patterns due to tourism. This study presents a framework to upgrade exposure models from static to 'dynamic', i.e., allowing the input population to change in time and space based on daily and monthly population movement patterns. Open-source population data is used to disaggregate and rescale occupants inside residential, commercial and industrial buildings of 28 European countries, resulting in 24 occupancy categories: two times (i.e., day and night) x 12 months at 30 arc-seconds resolution. The static vs dynamic exposure models are compared using the number and distribution of fatalities resulting from loss calculations for a stochastic set of earthquakes generated from the European Seismic Hazard model (ESHM20). The results demonstrate that the spatiotemporal patterns of population can significantly impact earthquake mortality rates and should not be neglected in scenario loss assessment. The results also demonstrate that the worst occurrence time depends on both the distribution of indoor population between building occupancies and the earthquake rupture characteristics. The ability to capture population distribution during the day and night or seasonal changes (e.g., winter vs summer) is a feature that can advance the ongoing rapid damage/loss assessment services in Europe and consequently support emergency response planning.
{"title":"Impact of population spatiotemporal patterns on earthquake human losses","authors":"Jamal Dabbeek , Helen Crowley , Vitor Silva , Sevgi Ozcebe","doi":"10.1016/j.ijdrr.2025.105455","DOIUrl":"10.1016/j.ijdrr.2025.105455","url":null,"abstract":"<div><div>Occupancy patterns are known to strongly affect the number of people killed by earthquakes. Existing exposure models for Europe based on housing census do not account for the daily movement of the population between the place of residence (residential occupancy) and places of economic activity (non-residential occupancy), or the seasonal patterns due to tourism. This study presents a framework to upgrade exposure models from static to 'dynamic', i.e., allowing the input population to change in time and space based on daily and monthly population movement patterns. Open-source population data is used to disaggregate and rescale occupants inside residential, commercial and industrial buildings of 28 European countries, resulting in 24 occupancy categories: two times (i.e., day and night) x 12 months at 30 arc-seconds resolution. The static vs dynamic exposure models are compared using the number and distribution of fatalities resulting from loss calculations for a stochastic set of earthquakes generated from the European Seismic Hazard model (ESHM20). The results demonstrate that the spatiotemporal patterns of population can significantly impact earthquake mortality rates and should not be neglected in scenario loss assessment. The results also demonstrate that the worst occurrence time depends on both the distribution of indoor population between building occupancies and the earthquake rupture characteristics. The ability to capture population distribution during the day and night or seasonal changes (e.g., winter vs summer) is a feature that can advance the ongoing rapid damage/loss assessment services in Europe and consequently support emergency response planning.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105455"},"PeriodicalIF":4.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.ijdrr.2025.105438
C. Negulescu , J. Vieille , P. Gehl , N. Taillefer , K. Trevlopoulos , S. Auclair
The magnitude Mw 4.9 earthquake of November 11, 2019 strongly affected the municipality of Le Teil, Rhone Valley, France. A quarter of around 2,800 exposed buildings were damaged, which led to safety evacuation orders. BRGM, the French Geological Survey, was commissioned by the French Ministry of Ecological Transition and Territorial Cohesion to collect quantitative data about the progress of the reconstruction. This work focuses on field monitoring of certain resilience parameters, such as the time for reconstruction and repairs. On the basis of emergency post-earthquake building safety inspections done by the French Association for Earthquake Engineering (AFPS), the reconstruction of buildings in the municipality of Le Teil is studied at different scales: building, infra-communal and communal. Interpreting damage observations and modelling reconstruction requires multiple sources of information, hence the importance of having data at these three different scales. Based on their reference state in the damaged buildings database, and in order to assess the progress of the reconstruction, the buildings are assigned a reconstruction stage based on annual visual inspections conducted from street level. In addition, a set of indicators has been employed to provide quantitative information evolving over time, such as the number of evacuation orders (issued and lifted) or the number of buildings whose reconstruction process has begun. Moreover, this study includes a detailed classification of the buildings, estimations for their vulnerability and predictive risk scenario calculations depending on assumptions about the level of structural reinforcement. These elements raise important questions about the reconstruction, particularly from the point of view of design standards. Finally, we highlight the importance of physical vulnerability and recovery capacities, two main components of the reconstruction process. This work constitutes a pioneering effort in terms of collecting records and observations of post-earthquake reconstruction in France, over a multi-year timespan.
{"title":"Follow-up of the Post-seismic reconstruction in Le Teil from the November 11th 2019 seismic event to now: Insights and zoom over building rehabilitation","authors":"C. Negulescu , J. Vieille , P. Gehl , N. Taillefer , K. Trevlopoulos , S. Auclair","doi":"10.1016/j.ijdrr.2025.105438","DOIUrl":"10.1016/j.ijdrr.2025.105438","url":null,"abstract":"<div><div>The magnitude Mw 4.9 earthquake of November 11, 2019 strongly affected the municipality of Le Teil, Rhone Valley, France. A quarter of around 2,800 exposed buildings were damaged, which led to safety evacuation orders. BRGM, the French Geological Survey, was commissioned by the French Ministry of Ecological Transition and Territorial Cohesion to collect quantitative data about the progress of the reconstruction. This work focuses on field monitoring of certain resilience parameters, such as the time for reconstruction and repairs. On the basis of emergency post-earthquake building safety inspections done by the French Association for Earthquake Engineering (AFPS), the reconstruction of buildings in the municipality of Le Teil is studied at different scales: building, infra-communal and communal. Interpreting damage observations and modelling reconstruction requires multiple sources of information, hence the importance of having data at these three different scales. Based on their reference state in the damaged buildings database, and in order to assess the progress of the reconstruction, the buildings are assigned a reconstruction stage based on annual visual inspections conducted from street level. In addition, a set of indicators has been employed to provide quantitative information evolving over time, such as the number of evacuation orders (issued and lifted) or the number of buildings whose reconstruction process has begun. Moreover, this study includes a detailed classification of the buildings, estimations for their vulnerability and predictive risk scenario calculations depending on assumptions about the level of structural reinforcement. These elements raise important questions about the reconstruction, particularly from the point of view of design standards. Finally, we highlight the importance of physical vulnerability and recovery capacities, two main components of the reconstruction process. This work constitutes a pioneering effort in terms of collecting records and observations of post-earthquake reconstruction in France, over a multi-year timespan.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105438"},"PeriodicalIF":4.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.ijdrr.2025.105451
Chao Fan , Fangsheng Wu , Ali Mostafavi
Online social networks are increasingly being utilized for collective sense-making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechanisms of collective information processing in human networks during five threat cases including airport power outage, hurricanes, wildfire, and blizzard, considering the temporal and spatial dimensions. Using the 13 MM Twitter data generated by 5 MM online users during these threats, we examined human activities, communication structures and frequency, social influence, information flow, and medium response time in social networks. The results show that the activities and structures are stable in growing networks, which lead to a stable power-law distribution of the social influence in networks. These temporally invariant patterns are not affected by people's memory and ties' strength. In addition, spatially localized communication spikes and global transmission gaps in the networks. The findings could inform about network intervention strategies to enable a healthy and efficient online environment, with potential long-term impact on risk communication and emergency response.
{"title":"Dynamics of collective information processing for risk encoding in social networks during crises","authors":"Chao Fan , Fangsheng Wu , Ali Mostafavi","doi":"10.1016/j.ijdrr.2025.105451","DOIUrl":"10.1016/j.ijdrr.2025.105451","url":null,"abstract":"<div><div>Online social networks are increasingly being utilized for collective sense-making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechanisms of collective information processing in human networks during five threat cases including airport power outage, hurricanes, wildfire, and blizzard, considering the temporal and spatial dimensions. Using the 13 MM Twitter data generated by 5 MM online users during these threats, we examined human activities, communication structures and frequency, social influence, information flow, and medium response time in social networks. The results show that the activities and structures are stable in growing networks, which lead to a stable power-law distribution of the social influence in networks. These temporally invariant patterns are not affected by people's memory and ties' strength. In addition, spatially localized communication spikes and global transmission gaps in the networks. The findings could inform about network intervention strategies to enable a healthy and efficient online environment, with potential long-term impact on risk communication and emergency response.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105451"},"PeriodicalIF":4.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.ijdrr.2025.105452
Anshuka Anshuka , Xinyu Fu , Iain White
Flooding is becoming more frequent and severe in urban areas under climate change, with profound implications for the real estate markets. Understanding how flood risk is priced in housing markets—particularly how public hazard information influences buyer behavior—is essential for hazard risk management and has therefore gained increasing attention. This review synthesizes the global empirical research to conceptualize flood risk pricing dynamics. We identify five key risk signals affecting property values, distinguishing between institutional signals (e.g., regulatory maps, insurance premiums) and societal signals (e.g., past flood events, perceived security from protective measures, and fears linked to sea-level rise). While existing studies generally agree on the price effects of these risk signals, variations exist due to varying regulatory and market contexts across locations. These risk signals are also typically considered in isolation; however, we argue that these signals function as an interconnected system, where shifts in one can dynamically influence others and, in turn, market behavior. This review underscores the need to move beyond analyzing individual risk signals and instead consider their interplay within a systems-based approach, offering new insights into how institutional and societal signals can be leveraged for more effective flood risk management.
{"title":"High water, high stakes: A global review of flood risk and housing price effects","authors":"Anshuka Anshuka , Xinyu Fu , Iain White","doi":"10.1016/j.ijdrr.2025.105452","DOIUrl":"10.1016/j.ijdrr.2025.105452","url":null,"abstract":"<div><div>Flooding is becoming more frequent and severe in urban areas under climate change, with profound implications for the real estate markets. Understanding how flood risk is priced in housing markets—particularly how public hazard information influences buyer behavior—is essential for hazard risk management and has therefore gained increasing attention. This review synthesizes the global empirical research to conceptualize flood risk pricing dynamics. We identify five key risk signals affecting property values, distinguishing between institutional signals (e.g., regulatory maps, insurance premiums) and societal signals (e.g., past flood events, perceived security from protective measures, and fears linked to sea-level rise). While existing studies generally agree on the price effects of these risk signals, variations exist due to varying regulatory and market contexts across locations. These risk signals are also typically considered in isolation; however, we argue that these signals function as an interconnected system, where shifts in one can dynamically influence others and, in turn, market behavior. This review underscores the need to move beyond analyzing individual risk signals and instead consider their interplay within a systems-based approach, offering new insights into how institutional and societal signals can be leveraged for more effective flood risk management.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105452"},"PeriodicalIF":4.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ijdrr.2025.105447
Xiaoyue Wang , Yunyan Du , Jiawei Yi , jiale Qian , Nan Wang , Sheng Huang , Wenna Tu
Given global climate change and rapid urbanization, China is having diverse types of rainfall and frequent extreme rainfall events. Utilizing semantic information from geotagged social media dataset, to study the rainfall perception patterns under different rainfall types and identify the rainfall types that cause public disaster perceptions is of great significance for urban management and disaster response. In this study, we extracted over 780,000 rainfall related microblogs from 210 million microblogs and studied urban public perception of various rainfall types in China, rather than focusing on a single event or city. The contributions of this study are: (1) Identify the spatial and thematic patterns perceived by the public in 213 cities (for example, ML, MH and HH are the most sensitive rainfall types on microblogging), and comprehensively understand how rainfall characteristics (duration and intensity) affect public attention. (2) Reveal the types of rainfall causing disaster perception (such as HH and MH) and their specific contents, which are different from the officially defined rainstorm events. 33.2 % of cities also perceive disaster under non rainstorm events. (3) Determine the rainfall types that lead to disasters and disaster impacts through public perception, provide feasible insights for urban management, and enhance the decision-making of disaster risk reduction and infrastructure planning.
{"title":"Research on the urban public perception of different rainfall types in China","authors":"Xiaoyue Wang , Yunyan Du , Jiawei Yi , jiale Qian , Nan Wang , Sheng Huang , Wenna Tu","doi":"10.1016/j.ijdrr.2025.105447","DOIUrl":"10.1016/j.ijdrr.2025.105447","url":null,"abstract":"<div><div>Given global climate change and rapid urbanization, China is having diverse types of rainfall and frequent extreme rainfall events. Utilizing semantic information from geotagged social media dataset, to study the rainfall perception patterns under different rainfall types and identify the rainfall types that cause public disaster perceptions is of great significance for urban management and disaster response. In this study, we extracted over 780,000 rainfall related microblogs from 210 million microblogs and studied urban public perception of various rainfall types in China, rather than focusing on a single event or city. The contributions of this study are: (1) Identify the spatial and thematic patterns perceived by the public in 213 cities (for example, ML, MH and HH are the most sensitive rainfall types on microblogging), and comprehensively understand how rainfall characteristics (duration and intensity) affect public attention. (2) Reveal the types of rainfall causing disaster perception (such as HH and MH) and their specific contents, which are different from the officially defined rainstorm events. 33.2 % of cities also perceive disaster under non rainstorm events. (3) Determine the rainfall types that lead to disasters and disaster impacts through public perception, provide feasible insights for urban management, and enhance the decision-making of disaster risk reduction and infrastructure planning.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105447"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ijdrr.2025.105449
Jose George , P. Athira
The effects of climate change are felt differently on regional scales, necessitating region specific analysis. Prediction of climate change and its impacts is riddled with uncertainties, which is exacerbated when moving to finer scale analysis. Climate change impact predictions on regional scales, when used for policy making or in design procedure should consider the uncertainty in the projected result. Ignoring the uncertainty can lead to poor policy decisions and inadequate structures. A major limitation in combining uncertainties in policy action is in how the uncertainty is communicated by the scientific community to policy makers and stakeholders. Simple graphical approaches have been found to be effective in communicating research outcomes to the public. The present study proposes a graphical approach for reporting regional scale climate change impacts and their associated uncertainty from an ensemble projection of regional extreme events. The concept of risk, which combines the information of event magnitude, frequency and regional vulnerabilities, is used to convey the impacts of extreme events over a catchment. The risk is defined as an index to facilitate comparison between different magnitude events, across different time periods, and across multiple scenarios. The uncertainty is represented as the range of risk predicted for each event and a level of confidence is developed based on the ensemble prediction. The projected risks of multiple extreme events are plotted in comparison with calculated risk of historical events that occurred in the region, to enable a policy maker to relate the index with actual consequences.
{"title":"Graphical representation of climate change impacts and associated uncertainty to enable better policy making in hydrological disaster management","authors":"Jose George , P. Athira","doi":"10.1016/j.ijdrr.2025.105449","DOIUrl":"10.1016/j.ijdrr.2025.105449","url":null,"abstract":"<div><div>The effects of climate change are felt differently on regional scales, necessitating region specific analysis. Prediction of climate change and its impacts is riddled with uncertainties, which is exacerbated when moving to finer scale analysis. Climate change impact predictions on regional scales, when used for policy making or in design procedure should consider the uncertainty in the projected result. Ignoring the uncertainty can lead to poor policy decisions and inadequate structures. A major limitation in combining uncertainties in policy action is in how the uncertainty is communicated by the scientific community to policy makers and stakeholders. Simple graphical approaches have been found to be effective in communicating research outcomes to the public. The present study proposes a graphical approach for reporting regional scale climate change impacts and their associated uncertainty from an ensemble projection of regional extreme events. The concept of risk, which combines the information of event magnitude, frequency and regional vulnerabilities, is used to convey the impacts of extreme events over a catchment. The risk is defined as an index to facilitate comparison between different magnitude events, across different time periods, and across multiple scenarios. The uncertainty is represented as the range of risk predicted for each event and a level of confidence is developed based on the ensemble prediction. The projected risks of multiple extreme events are plotted in comparison with calculated risk of historical events that occurred in the region, to enable a policy maker to relate the index with actual consequences.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"122 ","pages":"Article 105449"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-31DOI: 10.1016/j.ijdrr.2025.105444
Bin Zhou , Qiang Zou , Hu Jiang , Tao Yang , Wentao Zhou , Siyu Chen , Zihao Zeng
Debris flows pose a significant hazard in the Himalayas due to the region's diverse climatic conditions and complex topography. However, previous studies have predominantly focused on individual debris flow types, often neglecting the multi-triggering mechanisms that influence their occurrence. This limitation has reduced the accuracy of probability assessments and hindered the development of effective risk management strategies for vulnerable areas. To address this gap, we developed an indicator system that incorporates multi-triggering mechanisms and applied three hybrid machine learning models to comprehensively assess debris flow probability. These models generated probability maps for Rainfall-Triggered Debris Flow (RTDF), Glacier Debris Flow (GDF), Glacial Lake Outburst Debris Flow (GLODF), and multi-type debris flows. The results indicate that high RTDF probability is concentrated in the Yarlung Zangbo River Valley, the Indus River Valley, and the southern slope. High GDF probability is primarily located in the Western Himalayas, while high GLODF probability is predominantly distributed along the Central and Eastern Himalayan ridge. Notably, 52.98 % of catchments are vulnerable to at least one type of debris flow, with 2.04 % at risk from all three types. This study addresses a critical gap in debris flow probability assessment by integrating multi-triggering mechanisms, offering valuable insights to improve risk management and enhance resilience strategies in the Himalayas.
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