Abstract:
keep_len="250">In this article we propose that all countries that are striving to become a Weather-Ready Nation (WRN) would benefit greatly from including El Niño-Southern Oscillation (ENSO)-related research findings into their decision-making processes, not only when an El Niño or a La Niña forecast has been issued quasi-periodically. For an aspiring WRN, to benefit from ENSO information, such as disruptive or beneficial changes that could be foreseeably expected to occur in seasonal flow and in sub-seasonal hydrometeorological anomalies, requires its continuous mainstreaming about the status of the ENSO process into a WRN's decision-making activities. The ENSO process provides a bridge between sub-seasonal weather anomalies and a sub-decadal climate phenomenon as well as a bridge between coping with weather extremes today and preparing for climate change-related hydrometeorological hazards in the future. ENSO extremes every few years provide a chance to evaluate a nation's strategic and tactical responses to hydrometeorological hazard forecasts and disasters. Each successive ENSO extreme and its Neutral phase tests previously designed best practices. Involvement of today's youth and young professionals on climate, water, and weather issues has been increasing and will do so in coming decades. Shifting awareness and attention to ENSO and away from ENSO extremes is crucial. The heightened urgency for understanding the full ENSO "cycle" especially by youth and young professionals today is because they will soon be in professional positions that enable them to advise decision makers about climate policy issues. Their understanding of the ENSO cycle is critically needed, as global warming is expected to continue to increase for the rest of the twenty-first century. In this article we propose that all countries that are striving to become a Weather-Ready Nation (WRN) would benefit greatly from including El Niño-Southern Oscillation (ENSO)-related research findings into their decision-making processes, not only when an El Niño or a La Niña forecast has been issued quasi-periodically. For an aspiring WRN, to benefit from ENSO information, such as disruptive or beneficial changes that could be foreseeably expected to occur in seasonal flow and in sub-seasonal hydrometeorological anomalies, requires its continuous mainstreaming about the status of the ENSO process into a WRN's decision-making activities. The ENSO process provides a bridge between sub-seasonal weather anomalies and a sub-decadal climate phenomenon as well as a bridge between coping with weather extremes today and preparing for climate change-related hydrometeorological hazards in the future. ENSO extremes every few years provide a chance to evaluate a nation's strategic and tactical responses to hydrometeorological hazard forecasts and disasters. Each successive ENSO extreme and its Neutral phase tests previously designed best practices. Involvement of today's youth and young professionals on climate, water, and weather issues has been increasing and will do so in coming decades. Shifting awareness and attention to ENSO and away from ENSO extremes is crucial. The heightened urgency for understanding the full ENSO "cycle" especially by youth and young professionals today is because they will soon be in professional positions that enable them to advise decision makers about climate policy issues. Their understanding of the ENSO cycle is critically needed, as global warming is expected to continue to increase for the rest of the twenty-first century.

Abstract:
keep_len="250">Social vulnerability, as one of the risk components, partially explains the magnitude of the impacts observed after a disaster. In this study, a spatiotemporally comparable assessment of social vulnerability and its drivers was conducted in Portugal, at the civil parish level, for three census frames. The first challenging step consisted of the selection of meaningful and consistent variables over time. Data were normalized using the Adjusted Mazziotta-Pareto Index (AMPI) to obtain comparable adimensional-normalized values. A joint principal component analysis (PCA) was applied, resulting in a robust set of variables, interpretable from the point of view of their self-grouping around vulnerability drivers. A separate PCA for each census was also conducted, which proved to be useful in analyzing changes in the composition and type of drivers, although only the joint PCA allows the monitoring of spatiotemporal changes in social vulnerability scores and drivers from 1991 to 2011. A general improvement in social vulnerability was observed for Portugal. The two main drivers are the economic condition (PC1), and aging and depopulation (PC2). The remaining drivers highlighted are uprooting and internal mobility, and daily commuting. Census data proved their value in the territorial, social, and demographic characterization of the country, to support medium- and long-term disaster risk reduction measures. Social vulnerability, as one of the risk components, partially explains the magnitude of the impacts observed after a disaster. In this study, a spatiotemporally comparable assessment of social vulnerability and its drivers was conducted in Portugal, at the civil parish level, for three census frames. The first challenging step consisted of the selection of meaningful and consistent variables over time. Data were normalized using the Adjusted Mazziotta-Pareto Index (AMPI) to obtain comparable adimensional-normalized values. A joint principal component analysis (PCA) was applied, resulting in a robust set of variables, interpretable from the point of view of their self-grouping around vulnerability drivers. A separate PCA for each census was also conducted, which proved to be useful in analyzing changes in the composition and type of drivers, although only the joint PCA allows the monitoring of spatiotemporal changes in social vulnerability scores and drivers from 1991 to 2011. A general improvement in social vulnerability was observed for Portugal. The two main drivers are the economic condition (PC1), and aging and depopulation (PC2). The remaining drivers highlighted are uprooting and internal mobility, and daily commuting. Census data proved their value in the territorial, social, and demographic characterization of the country, to support medium- and long-term disaster risk reduction measures.

Abstract:
keep_len="250">A natural hazard-related disaster event often causes a series of secondary disasters, forming a disaster chain. Modeling the evolution of disaster chains in multi-hazard scenarios is crucial for risk governance and urban resilience. However, existing multi-hazard models are limited by complex model design and fixed disaster types, making it impossible to ensure flexible reactions to complex and diverse scenarios. This study presents a disaster management system for disaster model service chain (DMSC) to implement interoperable multi-hazard modeling. To achieve efficient model interaction in the DMSC, a management module is designed to normalize heterogeneous single-hazard models based on disaster system theory and the Open Geospatial Consortium standards, enabling them to be accessible, reusable, and interoperable. The normalized models are then adaptively orchestrated through an orchestration module to establish optimal executable DMSCs for different multi-hazard scenarios. Taking an earthquake disaster chain as a case study, we demonstrate that the disaster management system shows stable and flexible performance for multi-hazard modeling. A natural hazard-related disaster event often causes a series of secondary disasters, forming a disaster chain. Modeling the evolution of disaster chains in multi-hazard scenarios is crucial for risk governance and urban resilience. However, existing multi-hazard models are limited by complex model design and fixed disaster types, making it impossible to ensure flexible reactions to complex and diverse scenarios. This study presents a disaster management system for disaster model service chain (DMSC) to implement interoperable multi-hazard modeling. To achieve efficient model interaction in the DMSC, a management module is designed to normalize heterogeneous single-hazard models based on disaster system theory and the Open Geospatial Consortium standards, enabling them to be accessible, reusable, and interoperable. The normalized models are then adaptively orchestrated through an orchestration module to establish optimal executable DMSCs for different multi-hazard scenarios. Taking an earthquake disaster chain as a case study, we demonstrate that the disaster management system shows stable and flexible performance for multi-hazard modeling.

Abstract:
keep_len="250">The purpose of this article is to analyze disaster risk governance through assemblage theory, identifying how-during the altered political context of a military regime with a centralized disaster risk management as in the case of Chile in 1985-new actors emerge during the disaster response phase as a de/reterritorialization effect that is influenced by their agencies and relationships, disfiguring the edges of the assemblage. Based on this conceptualization, it is possible to investigate the interactions between the different actors, their power relations, and their reconfigurations in the governance exercise. For this purpose, we reviewed the response phase of the 1985 San Antonio earthquake that affected the central zone of Chile, where strategic functions, institutions, and forms of power are concentrated. To describe and visualize the actors during the response phase in the disaster risk governance framework, a map of actors was developed that identifies the existing relationships and their different weights. The central scale proved to be dominant and occupied a political space that was transfigured by its overrepresentation-enforced by allies such as the banking system and business associations-enhancing a neoliberal agenda. The leaps in scale from the central scale to the local scale cancel agency of the last, destabilizing its capacity to deal with the effects of the earthquake and isolating it from the decision-making processes. Consequently, delays in providing aid demonstrate that authoritarian governments do not provide better management in the disaster response phase. The purpose of this article is to analyze disaster risk governance through assemblage theory, identifying how-during the altered political context of a military regime with a centralized disaster risk management as in the case of Chile in 1985-new actors emerge during the disaster response phase as a de/reterritorialization effect that is influenced by their agencies and relationships, disfiguring the edges of the assemblage. Based on this conceptualization, it is possible to investigate the interactions between the different actors, their power relations, and their reconfigurations in the governance exercise. For this purpose, we reviewed the response phase of the 1985 San Antonio earthquake that affected the central zone of Chile, where strategic functions, institutions, and forms of power are concentrated. To describe and visualize the actors during the response phase in the disaster risk governance framework, a map of actors was developed that identifies the existing relationships and their different weights. The central scale proved to be dominant and occupied a political space that was transfigured by its overrepresentation-enforced by allies such as the banking system and business associations-enhancing a neoliberal agenda. The leaps in scale from the central scale to the local scale cancel agency of the last, destabilizing its capacity to deal with the effects of the earthquake and isolating it from the decision-making processes. Consequently, delays in providing aid demonstrate that authoritarian governments do not provide better management in the disaster response phase.

Abstract:
keep_len="250">As countries continue to deal with the global COVID-19 pandemic and its consequences, policymakers recognize that science, technology, and innovation (STI) practices offer a means of addressing many of the health problems that arise from the ongoing pandemic. Such recognition has given rise to many STI policy initiatives across various areas of science and policy, leading to a better understanding of coronavirus and the development of COVID-19 vaccines, treatments, and diagnostics. However, the practical implementation of vaccine and treatment strategies within local communities extends well beyond the laboratory. This study explored how misinformation and trust amplify or attenuate coronavirus and COVID-19 vaccine perceptions of those from ethnic minority groups deemed more susceptible to the impacts of the virus. Primary data in this study were collected in Scotland through semistructured interviews with 26 expert and nonexpert members from Scotland's minority ethnic communities. The study findings show that risk perception is fluid and dependent on the information and evidential environment in which people find themselves. Misinformation, fake news, conspiracies, and trust or distrust (from prior experiences and historic practices) influence the perception of coronavirus and how risk messages are received, including the acceptance of coronavirus vaccines. This article reflects on Scotland's approach to building trust and COVID-19 vaccine confidence and engagement based on the findings of this study, identifying areas of strength and areas for further improvement or research. The authors believe, as shown by our research, that vaccine engagement will be more impactful if developed by and with the public, and reflects public values, concerns, and priorities. As countries continue to deal with the global COVID-19 pandemic and its consequences, policymakers recognize that science, technology, and innovation (STI) practices offer a means of addressing many of the health problems that arise from the ongoing pandemic. Such recognition has given rise to many STI policy initiatives across various areas of science and policy, leading to a better understanding of coronavirus and the development of COVID-19 vaccines, treatments, and diagnostics. However, the practical implementation of vaccine and treatment strategies within local communities extends well beyond the laboratory. This study explored how misinformation and trust amplify or attenuate coronavirus and COVID-19 vaccine perceptions of those from ethnic minority groups deemed more susceptible to the impacts of the virus. Primary data in this study were collected in Scotland through semistructured interviews with 26 expert and nonexpert members from Scotland's minority ethnic communities. The study findings show that risk perception is fluid and dependent on the information and evidential environment in which people find themselves. Misinformation, fake news, conspiracies, and trust or distrust (from prior experiences and historic practices) influence the perception of coronavirus and how risk messages are received, including the acceptance of coronavirus vaccines. This article reflects on Scotland's approach to building trust and COVID-19 vaccine confidence and engagement based on the findings of this study, identifying areas of strength and areas for further improvement or research. The authors believe, as shown by our research, that vaccine engagement will be more impactful if developed by and with the public, and reflects public values, concerns, and priorities.

Abstract:
keep_len="250">There exist certain behaviors that people tend to do in disaster situations before evacuation. Such behaviors include warning confirmation behavior (for example, seeking information) and family-oriented behavior (for example, contacting one's family). Identifying factors that affect these behaviors is of particular importance in building a better understanding of why people often fail to respond quickly to evacuation orders. For this purpose, the present study employed some of the established factors affecting evacuation behavior as predictor variables along with the timing of an evacuation order. A total of 518 participants took part in a 12-item online questionnaire survey that contained a hypothetical disaster scenario. The results of ordinal logistic regression analyses revealed that only risk area residence and disaster preparedness were associated with warning confirmation behavior, while gender, age, disaster preparedness, and risk perception had some associations with family-oriented behavior. Also, the participants were not more likely to engage in these behaviors in the morning and the afternoon than the evening in the hypothetical scenario. These findings imply the possibility that people engage in warning confirmation behavior and family-oriented behavior before evacuation regardless of individual characteristics and the circumstances surrounding them. There exist certain behaviors that people tend to do in disaster situations before evacuation. Such behaviors include warning confirmation behavior (for example, seeking information) and family-oriented behavior (for example, contacting one's family). Identifying factors that affect these behaviors is of particular importance in building a better understanding of why people often fail to respond quickly to evacuation orders. For this purpose, the present study employed some of the established factors affecting evacuation behavior as predictor variables along with the timing of an evacuation order. A total of 518 participants took part in a 12-item online questionnaire survey that contained a hypothetical disaster scenario. The results of ordinal logistic regression analyses revealed that only risk area residence and disaster preparedness were associated with warning confirmation behavior, while gender, age, disaster preparedness, and risk perception had some associations with family-oriented behavior. Also, the participants were not more likely to engage in these behaviors in the morning and the afternoon than the evening in the hypothetical scenario. These findings imply the possibility that people engage in warning confirmation behavior and family-oriented behavior before evacuation regardless of individual characteristics and the circumstances surrounding them.

Abstract:
keep_len="250">On 11 March 2011, the Tohoku tsunami hit the northeastern region of Japan, causing massive damage to people and property. The tsunami was bigger than any other in Japan's recorded history, but the damage varied by community. This research addressed the effects of socioeconomic status and demographic composition on mortality in the 2011 Tohoku tsunami using community-level data. These effects were estimated using regression analysis, taking into account a variety of potential contributing aspects at the community level, including strength of the tsunami, population characteristics, gender, age, education, household composition, evacuation methods, and occupation. It was found that the height of the tsunami and the shares of three-generation households and employees in the manufacturing industry are all positively correlated with tsunami mortality. The impacts of these factors on mortality are particularly large for the older adults. On 11 March 2011, the Tohoku tsunami hit the northeastern region of Japan, causing massive damage to people and property. The tsunami was bigger than any other in Japan's recorded history, but the damage varied by community. This research addressed the effects of socioeconomic status and demographic composition on mortality in the 2011 Tohoku tsunami using community-level data. These effects were estimated using regression analysis, taking into account a variety of potential contributing aspects at the community level, including strength of the tsunami, population characteristics, gender, age, education, household composition, evacuation methods, and occupation. It was found that the height of the tsunami and the shares of three-generation households and employees in the manufacturing industry are all positively correlated with tsunami mortality. The impacts of these factors on mortality are particularly large for the older adults.

Abstract:
keep_len="250">With the acceleration of urbanization in South China, rainstorms and floods are threatening the safety of people in urban areas. The 11 April 2019 (4·11 hereafter) rainstorm in Shenzhen City was a typical pre-rainy season rainstorm that caused great damage, yet such pre-rainy season events have not attracted sufficient attention in research. Risk perception of the public may indirectly affect their disaster preparedness, which is important for disaster management. In this study, we conducted a questionnaire survey that considered demographic factors and the level of risk perception, knowledge of risk, impact of the 4·11 rainstorm event on public risk perception, and degree of trust in the government. We used a two-factor model of risk perception to evaluate the factors that influenced public risk perception of the 4·11 rainstorm in Shenzhen. The main conclusions are:The 4·11 rainstorm improved public awareness of both risk and impact through the medium term, but the public's perceived low probability of disaster occurrence and lack of knowledge of the pre-rainy season rainstorm phenomenon led to serious losses during this event. Although the public has high trust in the Shenzhen government, the management of rainstorm disasters in the pre-rainy season needs to be further improved. With the acceleration of urbanization in South China, rainstorms and floods are threatening the safety of people in urban areas. The 11 April 2019 (4·11 hereafter) rainstorm in Shenzhen City was a typical pre-rainy season rainstorm that caused great damage, yet such pre-rainy season events have not attracted sufficient attention in research. Risk perception of the public may indirectly affect their disaster preparedness, which is important for disaster management. In this study, we conducted a questionnaire survey that considered demographic factors and the level of risk perception, knowledge of risk, impact of the 4·11 rainstorm event on public risk perception, and degree of trust in the government. We used a two-factor model of risk perception to evaluate the factors that influenced public risk perception of the 4·11 rainstorm in Shenzhen. The main conclusions are:The 4·11 rainstorm improved public awareness of both risk and impact through the medium term, but the public's perceived low probability of disaster occurrence and lack of knowledge of the pre-rainy season rainstorm phenomenon led to serious losses during this event. Although the public has high trust in the Shenzhen government, the management of rainstorm disasters in the pre-rainy season needs to be further improved.

Abstract:
keep_len="250">This article presents the findings of a pilot project to test the large-scale rollout of smoke alarms in an informal community in Cape Town, South Africa. The work provides novel insight into the effectiveness and challenges associated with using smoke detectors in low-income communities. Technical details and detector considerations are also provided that will assist in enhancing future interventions. The project installed 1200 smoke detection devices in TRA informal settlement in the suburb of Wallacedene, in the City of Cape Town, and monitored their effectiveness for a period of 12 months. The monitoring showed that there were 11 real activations, where the presence of the devices likely saved lives and homes. The project also identified a series of challenges, especially in relation to nuisance alarms, where everyday household emissions, dust, and insect ingress caused false alarms, leading some participants to uninstall devices. The findings of the pilot study suggest that although smoke detectors could provide a valuable tool for reducing the frequency and impact of informal settlement fires in South Africa and elsewhere, they need to be adapted to meet the specific needs and conditions encountered in informal dwellings. Modifications, such as adjusting device sensitivity, preventing dust and insect ingress and tailoring devices to everyday conditions, will be essential to make smoke alarms more suitable and effective in the future. Smoke alarms could become an important component of low-income community fire safety if such challenges can be addressed. This article presents the findings of a pilot project to test the large-scale rollout of smoke alarms in an informal community in Cape Town, South Africa. The work provides novel insight into the effectiveness and challenges associated with using smoke detectors in low-income communities. Technical details and detector considerations are also provided that will assist in enhancing future interventions. The project installed 1200 smoke detection devices in TRA informal settlement in the suburb of Wallacedene, in the City of Cape Town, and monitored their effectiveness for a period of 12 months. The monitoring showed that there were 11 real activations, where the presence of the devices likely saved lives and homes. The project also identified a series of challenges, especially in relation to nuisance alarms, where everyday household emissions, dust, and insect ingress caused false alarms, leading some participants to uninstall devices. The findings of the pilot study suggest that although smoke detectors could provide a valuable tool for reducing the frequency and impact of informal settlement fires in South Africa and elsewhere, they need to be adapted to meet the specific needs and conditions encountered in informal dwellings. Modifications, such as adjusting device sensitivity, preventing dust and insect ingress and tailoring devices to everyday conditions, will be essential to make smoke alarms more suitable and effective in the future. Smoke alarms could become an important component of low-income community fire safety if such challenges can be addressed.

Abstract:
keep_len="250">Disaster clusters refer to major disasters that cluster in space and time without any linkage, resulting in large direct damage and economic ripple effects (EREs). However, the cumulative EREs caused by a disaster cluster may not be equal to the summation EREs of the individual disasters within a cluster. We constructed a global economic ripple input-output model suitable for the analysis of disaster clusters and demonstrated the extent of this difference with the example of two typical catastrophes that occurred in 2011 (the Great East Japan Earthquake and the Great Thailand Flood), within an interval of only 136 days. The results indicate that:(1) The EREs suffered by 11 of the 35 countries affected (30%) are "1+1>2", and "1+1<2" for 24 of the 35 countries affected (70%). This indicates that there is a significant difference between the cumulative and the summation losses. The difference is related to factors such as trade distance, economic influence of disaster-affected sectors, and trade ties; (2) The EREs are more than two times the direct loss and have an industrial dependence, mostly aggregated in key sectors with strong industrial influence and fast trade times in the industrial chain; and (3) Additional EREs due to the extension of the recovery period will be aggregated in countries with close trade ties to the disaster-affected country, further magnifying the difference. Disaster clusters refer to major disasters that cluster in space and time without any linkage, resulting in large direct damage and economic ripple effects (EREs). However, the cumulative EREs caused by a disaster cluster may not be equal to the summation EREs of the individual disasters within a cluster. We constructed a global economic ripple input-output model suitable for the analysis of disaster clusters and demonstrated the extent of this difference with the example of two typical catastrophes that occurred in 2011 (the Great East Japan Earthquake and the Great Thailand Flood), within an interval of only 136 days. The results indicate that:(1) The EREs suffered by 11 of the 35 countries affected (30%) are "1+1>2", and "1+1<2" for 24 of the 35 countries affected (70%). This indicates that there is a significant difference between the cumulative and the summation losses. The difference is related to factors such as trade distance, economic influence of disaster-affected sectors, and trade ties; (2) The EREs are more than two times the direct loss and have an industrial dependence, mostly aggregated in key sectors with strong industrial influence and fast trade times in the industrial chain; and (3) Additional EREs due to the extension of the recovery period will be aggregated in countries with close trade ties to the disaster-affected country, further magnifying the difference.

Abstract:
keep_len="250">Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to (1) compare the observed versus expected frequency (chi-square) of permit issuance before and after the EF5 2011 tornado; (2), determine if significant space-time clusters of permits existed using the SaTScan^TM cluster analysis program (version 9.7); and (3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event, and one (residential) showed significance for nine of the 10 years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path. Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to (1) compare the observed versus expected frequency (chi-square) of permit issuance before and after the EF5 2011 tornado; (2), determine if significant space-time clusters of permits existed using the SaTScan^TM cluster analysis program (version 9.7); and (3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event, and one (residential) showed significance for nine of the 10 years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.

Abstract:
keep_len="250">Based on station precipitation observations, radar quantitative precipitation estimates (QPE), and radar fusion data during Typhoon Fitow (2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China, as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%. (2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details. (3) One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems. Based on station precipitation observations, radar quantitative precipitation estimates (QPE), and radar fusion data during Typhoon Fitow (2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China, as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%. (2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details. (3) One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Abstract:
keep_len="250">Against the background of global climate change, the increasing heat health risk from the combined effect of changes in high temperature, exposure, vulnerability, and other factors has become a growing concern. Yet the low number of temperature observation stations is insufficient to represent the complex changes in urban heatwaves, and subdistrict-scale (town, township, neighborhood committee, and equivalent) heat health risk and adaptability assessments are still limited. In this study, we built daytime and nighttime high-temperature interpolation models supported by data from 225 meteorological stations in Beijing. The models performed well at interpolating the cumulative hours of high temperature and the interpolation quality at night was better than that during the day. We further established a methodological framework for heat health risk and adaptability assessments based on heat hazard, population exposure, social vulnerability, and adaptability at the subdistrict scale in Beijing. Our results show that the heat health risk hotspots were mainly located in the central urban area, with 81 hotspots during the day and 76 at night. The average value of the heat health risk index of urban areas was 5.60 times higher than that of suburban areas in the daytime, and 6.70 times higher than that of suburban areas in the night. Greater population density and higher intensity of heat hazards were the main reasons for the high risk in most heat health risk hotspots. Combined with a heat-adaptive-capacity evaluation for hotspot areas, this study suggests that 11 high-risk and low-adaptation subdistricts are priority areas for government action to reduce heat health risk in policy formulation and urban development. Against the background of global climate change, the increasing heat health risk from the combined effect of changes in high temperature, exposure, vulnerability, and other factors has become a growing concern. Yet the low number of temperature observation stations is insufficient to represent the complex changes in urban heatwaves, and subdistrict-scale (town, township, neighborhood committee, and equivalent) heat health risk and adaptability assessments are still limited. In this study, we built daytime and nighttime high-temperature interpolation models supported by data from 225 meteorological stations in Beijing. The models performed well at interpolating the cumulative hours of high temperature and the interpolation quality at night was better than that during the day. We further established a methodological framework for heat health risk and adaptability assessments based on heat hazard, population exposure, social vulnerability, and adaptability at the subdistrict scale in Beijing. Our results show that the heat health risk hotspots were mainly located in the central urban area, with 81 hotspots during the day and 76 at night. The average value of the heat health risk index of urban areas was 5.60 times higher than that of suburban areas in the daytime, and 6.70 times higher than that of suburban areas in the night. Greater population density and higher intensity of heat hazards were the main reasons for the high risk in most heat health risk hotspots. Combined with a heat-adaptive-capacity evaluation for hotspot areas, this study suggests that 11 high-risk and low-adaptation subdistricts are priority areas for government action to reduce heat health risk in policy formulation and urban development.