Wildfire is an important but understudied natural hazard. Research on wildfire, as with other natural hazards, is all too often conducted at a spatial scale that is too broad to identify local or even regional patterns. This study addresses these research gaps by examining the current and future wildfire risk, considering projections of population and property value, at the census-block level in Louisiana, a U.S. state with relatively dense population and abundant timber resources that would be vulnerable to loss from this hazard. Here wildfire risk is defined as the product of vulnerability to the hazard (which is itself defined as the product of burn probability, damage probability, and percent damaged) and exposure to the hazard, the latter of which is represented here by property value. Historical data (1992-2015) suggest that the highest risk is in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. Based on existing climate and environmental model output, this research assumes that wildfire will increase by 25 percent by 2050 in Louisiana from current values. When combined with projections of population and property value, it is determined that the geographic distribution of risk by 2050 will remain similar to that today-with highest risk in southwestern inland Louisiana and east-central Louisiana. However, the magnitude of risk will increase across the state, especially in those areas. These results will assist environmental planners in preparing for and mitigating a substantial hazard that often goes underestimated.

Flood depth grids from U.S. Federal Emergency Management Agency (FEMA) provide model-output estimates of the depth of water that can, on average, be expected to occur at various return periods for localized areas. However, use of these depth grids can be limited by spurious data and an insufficient number of return periods for certain planning applications. This research proposes a new method for estimating flood depth grids to address these shortcomings. The Gumbel distribution is used to characterize the flood depth-return period relationship for grid cells for which the data are plausible. Then the Gumbel parameters of slope (α) and intercept (u) are used to project flood elevations for extreme return periods for which an entire area can be assumed to be submerged. Spatial interpolation methods are then used to impute the flood elevations for spurious or missing grid cells. Then, the flood depth is recomputed from the flood elevations, once they are re-calculated at the shorter return periods. Validation of this technique for a Metairie, Louisiana, U.S.A. study area suggests that the cokriging spatial interpolation technique provides the most suitable estimates of flood depth, provided that the FEMA-generated model output is assumed to provide the “correct” results. These methods may assist engineers, developers, planners, and others in mitigating the world’s most widespread and expensive natural hazard.

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.

The United States Federal Emergency Management Agency (FEMA) provides model-output localized flood grids that are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA ─ areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher-return-period flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards for U.S. properties that are located both inside and outside the SFHA using existing annual exceedance probability (AEP) surfaces. Flood hazards are characterized by the Gumbel extreme value distribution to project extreme flood event elevations for which an entire area is assumed to be submerged. Spatial interpolation techniques impute flood elevation values and are used to estimate flood hazards for areas outside the SFHA. The proposed method has the potential to improve the assessment of flood risk for properties located both inside and outside the SFHA and therefore to improve the decision-making process regarding flood insurance purchases, mitigation strategies, and long-term planning for enhanced resilience to one of the world’s most ubiquitous natural hazards.

Proper assessment of the economic risk from hazards is an important prerequisite toward enhancing resilience and is often overlooked or underestimated in importance. This research describes a method of assessing risk due to extreme cold temperature, hail, lightning, and tornado in 2050, utilizing projections from well-respected model output, with Louisiana as a case study. Our approach improves upon previous hazard risk assessments by considering the magnitude of the exposed population in weighing the property loss. This makes the approach her preferable over previous risk assessments. Furthermore, the present research uses current model projections to estimate changes in future conditions of the hazard presence. Finally, our use of downscaled data to the census-block circumvents the complications of examining hazards at the county level, particularly in cases for which population is unevenly distributed in the county. Results suggest that extreme cold temperature and tornado are by far the costliest of the four hazards in terms of property loss, although tornado loss is inherently difficult to project due to the unpredictable nature of individual tornado paths. Both extreme temperatures and hail are projected to decrease in loss as temperatures warm, especially in the New Orleans area, where population may decrease. The lightning hazard, while small and likely underestimated due to assignment of lightning damage to the phenomena in which it is embedded, is projected to increase, both on an absolute and per capita basis. Our results can assist environmental planners in protecting life and property, while also promoting hazard resilience and environmental, economic, and social sustainability.

Louisiana is among the most vulnerable places on Earth to coastal flooding, for many reasons. Tropical-cyclone-induced storm surge, shoreline erosion accelerated by eustatic sea level rise, tidal influences, minimization of river sediment nourishment due to the presence of levees, and land subsidence caused by compaction of marsh lands and underground resource extraction all contribute to the flood hazard. In addition, increasing frequency and intensity of natural hazards under climate change scenarios are expected to exacerbate the coastal flood risk. Many studies focus on flood risk assessment and mitigation strategies both for the present and future, and other research has analyzed future flood risk considering climate change and sea level rise. Yet few studies consider all of these factors in concert. This research represents a comprehensive approach that considers coastal subsidence, eustatic sea level rise, and tropical cyclone storm surge variability under climate change scenarios, to evaluate future flood risk at the individual building level in Grand Isle, Louisiana. Results suggest that on average, the 100-year flood depth will increase by 37 cm at the individual building level in Grand Isle by 2050, with subsidence contributing over 80 percent of this increase. Subsidence is projected to increase structure and content losses by approximately 18 percent above modeled losses at present, while eustatic sea level rise may contribute approximately one percent of additional losses. A 100-year storm surge event amid a “low” scenario of environmental change would increase the structure and content losses at Grand Isle by 68–74 percent of today’s value in ten years, 141–149 percent in 25 years, and 346–359 percent in 50 years. Even more menacingly, “high” scenarios of environmental change are expected to increase the 100-year storm surge losses by approximately 85–91 percent of today’s value in ten years, 199–218 percent in 25 years, and 407–415 percent in 50 years. Outcomes from this study will fill the gap in the current literature by implementing a more realistic risk assessment model and will direct flood risk managers, property owners, and other stakeholders to build a comprehensive framework to minimize future flood risk in one of the most vulnerable sites in the USA to coastal flooding.

Recent previous research has established the “sharpest gradient” approach to defining the circumpolar vortex and has identified correlations of the area and circularity of the Northern Hemisphere’s circumpolar vortex (NHCPV) to important atmospheric-oceanic teleconnections. However, because geographical shifts in the NHCPV, independent of area or circularity changes, could affect surface environmental conditions, this research addresses the question of the extent to which the NHCPV centroid undergoes such shifts, both intra- and inter-annually. Results show that during the 1979–2017 period, the centroid has moved less on a daily basis in more recent years, perhaps indicative of a stabilization in circulation, with semi-annual and seasonal periodicities in the daily distance moved. A consistent preference toward the Eastern Hemisphere is evident by the displacement of the centroids toward the Pacific basin throughout the study period. Collectively, these results indicate the mid-tropospheric response to the near-surface warming.

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.