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Compound flooding events are a threat to many coastal regions and can have widespread socio-economic implications. However, their frequency of occurrence, underlying flood drivers, and direct link to past socio-economic losses are largely unknown despite being key to supporting risk and adaptation assessments. Here, we present an impact-based analysis of compound flooding for 203 coastal counties along the U.S. Gulf and East coasts by combining data from multiple flood drivers and socio-economic loss information from 1980 to 2018. We find that ~80% of all flood events recorded in our study area were compound rather than univariate. In addition, we show that historical compound flooding events in most counties were driven by more than two flood drivers (hydrological, meteorological, and/or oceanographic) and distinct spatial clusters exist that exhibit variability in the underlying driver of compound flood events. Furthermore, we find that in more than 80% of the counties, over 80% of recorded property and crop losses were linked to compound flooding. Nearly 80% of counties have a higher median loss from compound than univariate events. For these counties, the median property loss is over 26 times greater, and the median crop loss is over 76 times greater for compound events on average. Our analysis overcomes some of the limitations of previous compound-event studies based on pre-defined flood drivers and offers new insights into the complex relationship between hazards and associated socio-economic impacts. 

Household power outage experiences vary based on outage characteristics and the household's ability to cope with a disruption. While disaster management scholarship has produced methods to predict where the most significant impacts of a hazard may occur, these methods do not anticipate secondary effects, such as those from power outages. This research is necessary as the expected risks associated with power outages will increase in the United States due to climate change, increasing electricity demand, and aging infrastructure. To understand households' power outage experiences, we collected 896 surveys from three cities in the United States: Detroit, MI; Miami, FL; and Phoenix, AZ. Participants were recruited through Amazon's Mechanical Turk (MTurk) service to complete a survey. We hypothesized that racial/ethnic minority groups, specifically non-white households and lower-income households experienced more frequent and prolonged power outages. We also hypothesized that the same groups were more likely to have experienced more significant adverse effects, such as throwing away perishable food and not receiving assistance. We found that non-white households in Phoenix and Detroit were more likely to experience longer outages than white households; however, this association was not present in Miami and was not statistically significant in any city. Income was not a major factor in predicting food waste or assistance received during the longest self-reported outage. Further assessments in varying geographical contexts and more generalizable samples are necessary to increase understanding of how households experience power outages. 

The consensus that disasters do not cause an increase in crime rates is receiving renewed attention. In recent years, research has emerged that challenges this consensus by positing that crime rates and the type of crime may vary depending on the phase of the emergency. To address this, this research utilizes comprehensive crime data from the National Incident-Based Reporting System and hazard event data from the Spatial Hazard Events and Losses Database for the United States. Employing regression discontinuity design principles, swaths of linear regression models across different time scales are fitted, yielding nearly 200 statistically significant coefficients. The findings reveal correlations between certain natural hazard types and changes in crime rates. For instance, a correlation between winter weather hazard events and a subsequent drop in crime rates is observed whereas severe thunderstorms were associated with an increase in crime rates. Additionally, an increase in crime rates following natural hazard events that were observed in the shorter time scales (e.g., hail, tornadoes) did not persist into the longer time scale, where, in fact, negative treatment effects and a negative change in trend were found. These results shed light on the complex relationship between natural hazards and crime rates, providing valuable insights for policymakers, law enforcement agencies, and other stakeholders. Given that the intensity and frequency of natural hazards are on the rise, a better understanding of these dynamics can increase the efficiency of resource allocation for public safety and target the deployment of law enforcement more effectively. 

Natural hazards such as hurricanes, floods, and wildfires cause devastating socio-economic impacts on communities. In South Florida, most of these hazards are becoming increasingly frequent and severe because of the warming climate, and changes in vulnerability and exposure, resulting in significant damage to infrastructure, homes, and businesses. To better understand the drivers of these impacts, we developed a bottom-up impact-based methodology that takes into account all relevant drivers for different types of hazards. We identify the specific drivers that co-occurred with socio-economic impacts and determine whether these extreme events were caused by single or multiple hydrometeorological drivers (i.e., compound events). We consider six types of natural hazards: hurricanes, severe storm/thunderstorms, floods, heatwaves, wildfire, and winter weather. Using historical, socio-economic loss data along with observations and reanalysis data for hydrometeorological drivers, we analyze how often these drivers contributed to the impacts of natural hazards in South Florida. We find that for each type of hazard, the relative importance of the drivers varies depending on the severity of the event. For example, wind speed is a key driver of the socio-economic impacts of hurricanes, while precipitation is a key driver of the impacts of flooding. We find that most of the high-impact events in South Florida were compound events, where multiple drivers contributed to the occurrences and impacts of the events. For example, more than 50% of the recorded flooding events were compound events and these contributed to 99% of total property damages and 98% of total crop damages associated with flooding in Miami-Dade County. Our results provide valuable insights into the drivers of natural hazard impacts in South Florida and can inform the development of more effective risk reduction strategies for improving the preparedness and resilience of the region against extreme events. Our bottom-up impact-based methodology can be applied to other regions and hazard types, allowing for more comprehensive and accurate assessments of the impacts of compound hazards. 

Abstract As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to this literature, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects of power outages, how power outage impacts seasonally vary, and the different durations of power outage impacts. 

Louisiana is one of the most hazard-prone states in the U.S., and many of its people are engaged directly or indirectly in agricultural activities that are impacted by an array of weather hazards. However, most hazard impact research on agriculture to date, for Louisiana and elsewhere, has focused on floods and hurricanes. This research develops a method of future crop loss risk assessment due to droughts, extreme low and high temperatures, hail, lightning, and tornadoes, using Louisiana as a case study. This approach improves future crop risk assessment by incorporating historical crop loss, historical and modeled future hazard intensity, cropland extent, population, consumer demand, cropping intensity, and technological development as predictors of future risk. The majority of crop activities occurred and will continue to occur in south-central and northeastern Louisiana along the river basins. Despite the fact that cropland is decreasing across most of the state, weather impacts to cropland are anticipated to increase substantially by 2050. Drought is by far the costliest among the six hazards, accounting for $56.1 million of $59.2 million (∼95%) in 2050-projected crop loss, followed by extreme cold ($1.4 million), extreme heat ($1.0 million), tornadoes ($0.4 million), hail ($0.2 million), and lightning ($0.05 million), respectively. These findings will assist decision-makers to minimize risk and enhance agricultural resilience to future weather hazards, thereby strengthening this economically-important industry in Louisiana and enhancing food security. 

Research on the societal costs of disaster-related US school closures has focused, and due to COVID-19 will likely continue to focus, on pandemics, with very limited research on closures from natural hazards. This is surprising given that school closures occur frequently to protect children, teachers, and staff pre-event as well as post-disaster to convert facilities into emergency shelters, etc. This study investigates the secondary effects from post-flooding, temporary school closures after the catastrophic 2015 flash flood in Richland County, South Carolina. Lost productivity from school closures was quantified using the Human Capital Method. Out of the 208 completed surveys, 75% of households had children that missed school. Post-stratifying survey results on race produced an average of $437 in lost productivity due to school closures and an overall $2175 in lost productivity due to flood-related impacts in general. Expressed in FEMA benefit-cost analysis terms, our study shows that schools have a standard value of $215 per household and per day for the unweighted sample ($180 for the race-weighted sample). Furthermore, households' disutility for a late start is almost as great as their disutility for a school closure. These exploratory findings suggest that unplanned school closures should be minimized, and community characteristics carefully considered to avoid unintended socioeconomic consequences. 

Background: Individuals affected by disasters are at risk for adverse mental health sequelae. Individuals living in the US Gulf Coast have experienced many recent major disasters, but few studies have explored the cumulative burden of experiencing multiple disasters on mental health. Objective: The objective of this study was to evaluate the relationship between disaster burden and mental health. Methods: We used data from 9278 Gulf Long-term Follow-up Study participants who completed questionnaires on perceived stress, anxiety, depression, and post-traumatic stress disorder (PTSD) in 2011-2013. We linked 2005-2010 county-level data from the Spatial Hazard Events and Losses Database for the United States, a database of loss-causing events, to participant's home address. Exposure measures included total count of loss events as well as severity quantified as property/crop losses per capita from all hazards. We used multilevel modeling to estimate odds ratios (OR) and 95% confidence intervals (CI) for each exposure-outcome relationship. Results: Total count of loss events was positively associated with perceived stress (ORQ4:1.40, 95% CI:1.21-1.61) and was inversely associated with PTSD (ORQ4:0.66, 95% CI:0.45-0.96). Total duration of exposure was also associated with stress (ORQ4:1.16, 95% CI:1.01-1.33) but not with other outcomes. Severity based on cumulative fatalities/injuries was associated with anxiety (ORQ4:1.31, 95% CI:1.05-1.63) and stress (ORQ4:1.34, 95% CI:1.15-1.57), and severity based on cumulative property/crop losses was associated with anxiety (ORQ4:1.42, 95% CI:1.12-1.81), depression (ORQ4:1.22, 95% CI:0.95-1.57) and PTSD (ORQ4:1.99, 95% CI:1.44-2.76). Keywords: PTSD; disasters; mental health; natural hazards.