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Abstract
The southern United States is no stranger to hazard and disaster events. Intense hurricanes, drought, flooding, and other climate-sensitive hazards are commonplace and have outnumbered similar events in other areas of the United States annually in both scale and magnitude by a ratio of almost 4:1 during the past 10 years. While losses from climate-sensitive hazards are forecast to increase in the coming years, not all of the populations residing within these hazard zones have the same capacity to prepare for, respond to, cope with, and rebound from disaster events. The identification of these vulnerable populations and their location relative to zones of known or probably future hazard exposure is necessary for the development and implementation of effective adaptation, mitigation, and emergency management strategies. This paper provides an approach to regional assessments of hazards vulnerability by describing and integrating hazard zone information on four climate-sensitive hazards with socioeconomic and demographic data to create an index showing both the areal extent of hazard exposure and social vulnerability for the southern United States. When examined together, these maps provide an assessment of the likely spatial impacts of these climate-sensitive hazards and their variability. The identification of hotspots—counties with elevated exposures and elevated social vulnerability—highlights the distribution of the most at risk counties and the driving factors behind them. Results provide the evidentiary basis for developing targeted strategic initiatives for disaster risk reduction including preparedness for response and recovery and longer-term adaptation in those most vulnerable and highly impacted areas.
Abstract
The southern United States is no stranger to hazard and disaster events. Intense hurricanes, drought, flooding, and other climate-sensitive hazards are commonplace and have outnumbered similar events in other areas of the United States annually in both scale and magnitude by a ratio of almost 4:1 during the past 10 years. While losses from climate-sensitive hazards are forecast to increase in the coming years, not all of the populations residing within these hazard zones have the same capacity to prepare for, respond to, cope with, and rebound from disaster events. The identification of these vulnerable populations and their location relative to zones of known or probably future hazard exposure is necessary for the development and implementation of effective adaptation, mitigation, and emergency management strategies. This paper provides an approach to regional assessments of hazards vulnerability by describing and integrating hazard zone information on four climate-sensitive hazards with socioeconomic and demographic data to create an index showing both the areal extent of hazard exposure and social vulnerability for the southern United States. When examined together, these maps provide an assessment of the likely spatial impacts of these climate-sensitive hazards and their variability. The identification of hotspots—counties with elevated exposures and elevated social vulnerability—highlights the distribution of the most at risk counties and the driving factors behind them. Results provide the evidentiary basis for developing targeted strategic initiatives for disaster risk reduction including preparedness for response and recovery and longer-term adaptation in those most vulnerable and highly impacted areas.
When Do Losses Count?
Six Fallacies of Natural Hazards Loss Data
Current global and national databases that monitor losses from natural hazards suffer from a number of limitations, which in turn lead to misinterpretation and fallacies concerning the “truthfulness” of hazard loss data. These biases often go undetected by end users and are generally a product of the type of information stored in loss databases and how they are constructed. This paper highlights some common shortcomings and root causes for data misinterpretation by asking what biases are present in existing databases and how these then manifest themselves in actual loss figures. For illustrative purposes, four widely used, nonproprietary, Web-based hazard databases are examined: the international Emergency Events Database (EM-DAT), the international Natural Hazards Assessment Network (NATHAN), the Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the National Weather Service's Storm Events. We identify six general biases: hazard bias, temporal bias, threshold bias, accounting bias, geographic bias, and systemic bias. To achieve resilient and sustainable communities, we need systematic and comprehensive inventories at the national as well as international level, and data that are temporally and geographically comparable.
Current global and national databases that monitor losses from natural hazards suffer from a number of limitations, which in turn lead to misinterpretation and fallacies concerning the “truthfulness” of hazard loss data. These biases often go undetected by end users and are generally a product of the type of information stored in loss databases and how they are constructed. This paper highlights some common shortcomings and root causes for data misinterpretation by asking what biases are present in existing databases and how these then manifest themselves in actual loss figures. For illustrative purposes, four widely used, nonproprietary, Web-based hazard databases are examined: the international Emergency Events Database (EM-DAT), the international Natural Hazards Assessment Network (NATHAN), the Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the National Weather Service's Storm Events. We identify six general biases: hazard bias, temporal bias, threshold bias, accounting bias, geographic bias, and systemic bias. To achieve resilient and sustainable communities, we need systematic and comprehensive inventories at the national as well as international level, and data that are temporally and geographically comparable.
Abstract
This study investigates evacuation behaviors associated with Hurricane Matthew in October of 2016. It assesses factors influencing evacuation decisions and evacuation departure times for Florida, Georgia, and South Carolina from an online survey of respondents. Approximately 62% of the Florida sample, 77% of the Georgia sample, and 67% of the South Carolina sample evacuated. Logistic regression analysis of the departures in the overall time period identified variability in evacuation timing, primarily dependent on prior experience, receipt of an evacuation order, and talking with others about the evacuation order. However, using four logistic regressions to analyze differences in departure times by day shows that the only significant variable across the three main days of evacuation was our proxy variable for evacuation-order times. Depending on the day, other variables of interest include number of household vehicles, previous hurricane experience, and receipt of an evacuation order. Descriptive results show that many variables are considered in the decision to evacuate, but results from subsequent analyses, and respondents’ comments about their experiences, highlight that evacuation orders are the primary triggering variable for when residents left.
Abstract
This study investigates evacuation behaviors associated with Hurricane Matthew in October of 2016. It assesses factors influencing evacuation decisions and evacuation departure times for Florida, Georgia, and South Carolina from an online survey of respondents. Approximately 62% of the Florida sample, 77% of the Georgia sample, and 67% of the South Carolina sample evacuated. Logistic regression analysis of the departures in the overall time period identified variability in evacuation timing, primarily dependent on prior experience, receipt of an evacuation order, and talking with others about the evacuation order. However, using four logistic regressions to analyze differences in departure times by day shows that the only significant variable across the three main days of evacuation was our proxy variable for evacuation-order times. Depending on the day, other variables of interest include number of household vehicles, previous hurricane experience, and receipt of an evacuation order. Descriptive results show that many variables are considered in the decision to evacuate, but results from subsequent analyses, and respondents’ comments about their experiences, highlight that evacuation orders are the primary triggering variable for when residents left.
The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.
The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.
