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Communicating risks and mitigation benefits associated with natural hazards such as wind to the general public is challenging given the location-dependency of parameters and the complexity of the problem. Web tools play a crucial role in educating residents, decision-makers, and stakeholders regarding potential wind hazard losses to, for example, residential buildings.However, a notable gap exists on the practical incorporation of mitigation actions within these tools. This gap hampers the collective awareness and understanding among stakeholders, communities, and citizens regarding the tangible advantages of mitigation strategies in reducing wind-related risks. Furthermore, there exists a need to elucidate the functionality and objectives of these tools in a more accessible manner. This study aims to present and outline the wind risk and mitigation calculator tool (WRMCT) within the Hazardaware platform, which is an address-based risk assessment tool. This tool, developed for 196 counties in the Gulf of Mexico coastal area, facilitates users' education of potential risks and benefits associated with mitigation strategies. WRMCT enables users to access location-specific wind risk and interactively suggests potential mitigation actions along with economic savings to support informed decisions and residential risk reduction. WRMCT intends to enhance users’ ability to make informed decisions, take proactive measures in mitigating wind hazards, and contribute to the development of resilient, residential communities.

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.

An ongoing debate in academic and practitioner communities, centers on the measurement similarities and differences between social vulnerability and community resilience. More specifically, many see social vulnerability and community resilience measurements as conceptually and empirically the same. Only through a critical and comparative assessment can we ascertain the extent to which these measurement schemas empirically relate to one another. This paper uses two well-known indices—the social vulnerability index (SoVI) and the Baseline Resilience Indicators for Communities (BRIC) to address the topic. The paper employs spatio-temporal correlations to test for differences or divergence (negative associations) and similarities or convergence (positive associations), and the degree of overlap. These tests use continental U.S. counties, two timeframes (2010 and 2015), and two case study sub-regions (to identify changes in measurement associations going from national to regional scales given the place-based nature of each index). Geospatial analytics indicate a divergence with little overlap between SoVI and BRIC measurements, based on low negative correlation coefficients (around 30%) for both time periods. There is some spatial variability in measurement overlap, but less than 2% of counties show hot spot clustering of correlations of more than 50% in either year. The strongest overlap and divergence in both years occurs in few counties in California, Arizona, and Maine. The degree of overlap in measurements at the regional scale is greater in the Gulf Region (39%) than in the Southeast Atlantic region (21% in 2010; 28% in 2015) suggesting more homogeneity in Gulf Coast counties based on population and place characteristics. However, in both study areas SoVI and BRIC measurements are negatively associated. Given their inclusion in the National Risk Index, both social vulnerability and resilience metrics are needed to interpret the local community capacities in natural hazards risk planning, as a vulnerable community could be highly resilient or vice versa.

Disaster recovery spending for major flood events in the United States is at an all-time high. Yet research examining equity in disaster assistance increasingly shows that recovery funding underserves vulnerable populations. Based on a review of academic and grey literature, this article synthesizes empirical knowledge of population disparities in access to flood disaster assistance and outcomes during disaster recovery. The results identify renters, low-income households, and racial and ethnic minorities as populations that most face barriers accessing federal assistance and experience adverse recovery outcomes. The analysis explores the drivers of these inequities and concludes with a focus on the performance of disaster programs in addressing unmet needs, recognition of intersectional social vulnerabilities in recovery analysis, and gaps in data availability and transparency.

Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network, which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, unreliable from some aspects, comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically analyzed within these papers are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems.