Census-Block-Level Property Risk Estimation Due to Extreme Cold Temperature, Hail, Lightning, and Tornadoes in Louisiana, United States

Rohli

et al. 2021

Front. Environ. Sci.

Self Cite

Sinkholes (or dolines) are an often-overlooked environmental hazard. The processes that lead to their formation are slow and insidious, which encourage a lack of awareness or concern for the potential danger, until the sudden, climactic formation leads to unexpected property damage and possibly human casualties. This research identifies the risk to residential properties to the sinkhole hazard, using Louisiana, United States as a case study. Risk is defined as the product of the hazard intensity and the loss to structure and contents within the building resulting from the hazard-related disaster. Results suggest that risk is highly scale-dependent. Although the risk due to sinkholes is small on a per capita basis statewide, especially when compared to the per capita risk of other natural hazards, the property risk for census tracts or census blocks partially or completely overlying a salt dome is substantial. At finer scales, Terrebonne Parish, in coastal southeastern Louisiana, has the greatest concentration of salt domes, while Madison Parish, which is east of Monroe, has the highest percentage of area at risk for sinkhole formation, and St. Mary Parish—immediately west of Terrebonne—has the greatest risk of property loss. An Acadia Parish census tract has the maximum annual property losses in 2050 projected at $40,047 (2010$), and the highest projected annual per building ($43) and per capita ($18) property loss are in the same St. Mary Parish census tract. At the census block level, maximum annual property loss ($7,040) is projected for a census block within Cameron Parish, with maximum annual per building loss ($85 within West Baton Rouge Parish), and maximum per capita annual property loss ($120 within Plaquemines Parish). The method presented in this paper is developed generally, allowing application for risk assessment in other locations. The results generated by the methodology are important to local, state, and national emergency management efforts. Further, the general public of Louisiana, and other areas where the developed method is applied, may benefit by considering sinkhole risk when purchasing, remodeling, and insuring a property, including as a basis of comparison to the risk from other types of hazard.

Section: Discussionmentioning

confidence: 99%

Section: Population Projectionmentioning

confidence: 99%

Section: Population Projectionmentioning

confidence: 99%

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Property Risk Assessment of Sinkhole Hazard in Louisiana, U.S.A

Rohli

et al. 2021

Front. Environ. Sci.

Self Cite

“…The technique for population (P) projection follows that of Mostafiz et al (2020a). Specifically, because the U.S. Census Bureau does not provide annual P estimates by census-block (i), the process begins with annual P growth rate calculations at the parish (i.e., county) (j) scale.…”

Section: Population Projectionmentioning

confidence: 99%

Property Risk Assessment for Expansive Soils in Louisiana

Rohli

et al. 2021

Front. Built Environ.

Self Cite

The physical properties of soil can affect the stability of construction. In particular, soil swelling potential (a term which includes swelling/shrinking) is often overlooked as a natural hazard. Similar to risk assessment for other hazards, assessing risk for soil swelling can be defined as the product of the probability of the hazard and the value of property subjected to the hazard. This research utilizes past engineering and geological assessments of soil swelling potential, along with economic data from the U.S. Census, to assess the risk for soil swelling at the census-block level in Louisiana, a U.S. state with a relatively dense population that is vulnerable to expansive soils. Results suggest that the coastal parts of the state face the highest risk, particularly in the areas of greater population concentrations, but that all developed parts of the state have some risk. The annual historical property loss, per capita property loss, and per building property loss are all concentrated in southeastern Louisiana and extreme southwestern Louisiana, but the concentration of wealth in cities increases the historical property loss in most of the urban areas. Projections of loss by 2050 show a similar pattern, but with increased per building loss in and around a swath of cities across southwestern and south-central Louisiana. These results may assist engineers, architects, and developers as they strive to enhance the resilience of buildings and infrastructure to the multitude of environmental hazards in Louisiana.

“…Property loss (Mostafiz et al 2020a;Mostafiz et al 2020b) estimation from floods is important for assessing risk and therefore improving planning efforts to mitigate this hazard. However, such loss estimation from flooding is extremely complicated for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Neighborhood-Scale, Residential Property Flood-Loss Assessment Methodologies

Rahman

et al. 2021

Front. Environ. Sci.

Self Cite

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.