Joseph Harris

The Priority Risk Index is increasingly used as a methodology for quantifying jurisdictional risk for hazard mitigation planning purposes, and it can evolve to meet specific community needs. The index incorporates probability, impact, spatial extent, warning time, and duration when assessing each hazard, but it does not explicitly integrate a vulnerability and consequence analysis into its final scoring. To address this gap, a new index was developed-the Enhanced Priority Risk Index (EPRI). The new index adds a sixth category, vulnerability, calculated from a vulnerability and consequence analysis of the impacts on seven sectors identified in Standard 4.1.2 of the Emergency Management Accreditation Program (EMAP). To obtain a vulnerability score, impacts are ranked by sector from low (1) to very high (4), then a weighting factor is applied to each sector. The vulnerability score is added to the EPRI and provides risk levels based on the number of exploitable weaknesses and countermeasures identified within a specific jurisdiction. The vulnerability score and resulting EPRI are scalable and can be applied across jurisdictions, providing a transferable methodology that improves the hazard identification and risk assessment process and provides an approach for meeting EMAP accreditation standards.

Southeast Louisiana is susceptible to the impact of subsidence due to natural and anthropogenic processes including sediment compaction and loading, fluid withdrawal, and faulting. Subsidence rates in Southeast Louisiana are higher than anywhere else in the United States, and the impact of subsidence rates on industrial complexes has not been studied. Spatial interpolation methods were analyzed to determine the best fit for subsidence rates and to create a predictive surface for the lower Mississippi River Industrial corridor (LMRIC). Empirical Bayesian kriging, ordinary kriging, universal kriging, and inverse distance weighted interpolation methods were applied to the 2004 National Oceanic and Atmospheric Administration (NOAA) published Technical Report #50 dataset and cross-validation methods were utilized to determine the accuracy of each method. The mean error and root mean square error were calculated for each interpolation method, then used to detect bias and compare the predicted value with the actual observation value. Cross-validation estimates are comparable for each method statistically and visually; however, the results indicate the empirical Bayesian kriging interpolation method is the most accurate of the methods using the lowest mean error and root mean square error scores. Digital elevation models for the years 2025, 2050, and 2075 were developed based on the predictive surface of subsidence rates using the results from the empirical Bayesian kriging interpolation method. Results indicate that by 2025, 31.4% of landmass in the LMRIC will be below 0 m NAVD88, with 40.4% below 0 m NAVD88 by 2050, and 51.8% by 2075. Subsidence rates in the LMRIC range from approximately 16 mm to less than one mm per year. Nine of the 122 industrial complexes located in the LMRIC are estimated to be below 0 m NAVD88 by the year 2075. Limited economic impacts can be inferred based on the number of facilities impacted; however, service disruptions due to subsidence impacting infrastructure surrounding these industrial complexes would have catastrophic economic impacts on a regional, state, and national level.

The mosquito Aedes aegypti has long been a vector for human illness in the Southeastern United States. In the past, it has been responsible for outbreaks of dengue, chikungunya, and yellow fever and, very recently, the Zika virus that has been introduced to the region. Multiple studies have modeled the geographic distribution of Ae. aegypti as a function of climate factors; however, this ignores the importance of humans to the anthropophilic biter. Furthermore, Ae. aegypti thrives in areas where humans have created standing water sites, such as water storage containers and trash. As models are developed to examine the potential impact of climate change, it becomes increasingly important to include the most comprehensive set of predictors possible. ResultsThis study uses Maxent, a species distribution model, to evaluate the effects of adding poverty and population density to climate-only models. Performance was evaluated through model fit statistics, such as AUC, omission, and commission, as well as individual variable contributions and response curves. Models which included both population density and poverty exhibited better predictive power and produced more precise distribution maps. Furthermore, the two human population characteristics accounted for much of the model contribution—more so than climate variables. Conclusions Modeling mosquito distributions without accounting for their dependence on local human populations may miss factors that are very important to niche realization and subsequent risk of infection for humans. Further research is needed to determine if additional human characteristics should be evaluated for model inclusion.