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Historically, dense vegetation cover near buildings has caused power disruptions during weather phenomena. These types of severe storms impact the coast of Florida each year. However, challenges exist for obtaining both power outage data and calculating the impact of tree cover. NASA's Nighttime Lights, Black Marble, VNP46 product is utilized to analyze the natural and built environments. One aspect of the built environment that can be mapped with the Black Marble data is the megawatts of electricity used by the electrical power grid based on the magnitude of emitted nighttime light energy. This paper discusses using Black Marble data and other landscape variables within a probabilistic model to examine spatial patterns and map electricity outages with Bayesian networks. The research results indicate a high probability of a significant power outage when dense vegetation is present, but nuances in our natural and built environments like electric substations and land cover type alter the chance of reducing energy emissions. [ABSTRACT FROM AUTHOR]

This study contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to the COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study: (1) the linear regression model, (2) the spatial autoregressive model (SAR), and (3) the spatial error model (SEM). For the model estimation, traffic volume data for 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models offered a good fit and intuitive parameter estimates. The significant value of spatial autocorrelation coefficients in the SEM supports our hypothesis that common unobserved factors affect traffic volumes in neighboring detectors. The model results clearly indicate a disruption in normal traffic demand due to the increased transmission rate of COVID-19. The traffic demand for recreational areas, especially on holidays, was found to have declined after March 2020. In addition, change in daily COVID-19 cases was found to have a larger impact on South Florida (District 6)'s freeway demand on weekdays compared to other parts of the state. Further, the gradual increase of demand due to rapid vaccination was also demonstrated in this study. The model system will help transportation researchers and policy makers understand the changes in freeway volume during the COVID-19 pandemic as well as its spatiotemporal recovery. [ABSTRACT FROM AUTHOR]

In addition to measuring forecast accuracy in terms of errors in a tropical system’s forecast track and other meteorological characteristics, it is important to measure the impact of those errors on society. With this in mind, the authors designed a coupled natural–human modeling framework with high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using the model, this article begins exploring how tropical cyclone forecast errors impact evacuations and, in doing so, builds toward the development of new verification approaches. Specifically, the authors implement track errors representative of 2007 and 2022, and create situations with unexpected rapid intensification and/or rapid onset, and evaluate their impact on evacuations across real and hypothetical forecast scenarios (e.g., Hurricane Irma, Hurricane Dorian making landfall across east Florida). The results provide first-order evidence that 1) reduced forecast track errors across the 2007–22 period translate to improvements in evacuation outcomes across these cases and 2) unexpected rapid intensification and/or rapid onset scenarios can reduce evacuation rates, and increase traffic, across the most impacted areas. In exploring these relationships, the results demonstrate how experiments with coupled natural–human models can offer a societally relevant complement to traditional metrics of forecast accuracy. In doing so, this work points toward further development of natural–human models and associated methodologies to address these types of questions and improve forecast verification across the weather enterprise. [ABSTRACT FROM AUTHOR]

Many coastal urban areas are experiencing impacts of accelerated chronic and episodic flooding on the built environment and people's livelihoods and quality of life. These impacts sometimes exceed the households' adaptive and coping capacities to deal with flooding, prompting residents to consider relocation. It is unclear how urban dwellers living in flood-prone locations perceive this adaptation strategy and under what flood-driven circumstances they would consider permanently moving. This paper provides empirical evidence on relocation preferences among urban residents along the U.S. East Coast. It further explores how this decision is influenced by socioeconomic determinants, experiences with flood exposure, comprehensive concerns with flooding, and preferences for relocation destinations. We administered an online survey to 1450 residents living in flood-prone urban areas across multiple states, from New York to Florida, and analyzed the results using descriptive and inferential statistics. Results show that almost half of respondents would consider relocating due to coastal flooding, with only 13 percent declining this option. The results show that age and race, several determinants of place attachment, problem-solving capacity, and flood-related household- and community-level concerns play a significant role in willingness to relocate. • Accelerated flooding in coastal urban areas will increase the need for relocation. • Empirical evidence about coastal relocation drivers is limited. • Survey responses indicate that many urban coastal residents are willing to relocate. • Willingness to relocate is affected by age and race, and other personal concerns. • Crime, future flooding, community support, and living costs predict relocation. [ABSTRACT FROM AUTHOR]

Flooding is the most costly type of natural disaster, as well as the most frequent. To provide risk-based flood insurance, providers must be able to accurately determine an asset's risk of flooding. Additionally, after a flooding event, providers need to quickly determine the direct damages that occurred to verify insurance claims and assist affected communities. Many current approaches to flood risk and flood damage estimation use data or statistical extrapolation that add various sources of uncertainty into the final damage estimate. To reduce uncertainties in flood risk analyses, the objective of this research is to outline an approach to flood damage estimation conducted on a statewide scale while still estimating flood risk and damage on a structure-by-structure basis. This approach uses the observed flooding extent during and after Hurricane Irma in Florida, extracted from a collection of satellite images over the course of 8 days. The results show that residential structures had the largest exposure to flooding during Hurricane Irma, with estimates ranging from $300 million to $2 billion per county, for the three counties that were studied in depth. For agriculture, fruit crops were estimated to have a potential at-risk revenue of $38.2 million, and vegetables were estimated to have a value at risk of $940 million across all crops. With improvements in the data used, this approach can offer a quick and accurate assessment of flood damages directly after a flood hazard, which could reduce the recovery time and economic impacts to the affected communities. [ABSTRACT FROM AUTHOR]