Assessing community-level flood risk at the micro-scale by owner/occupant type and first-floor height

Assi, Ayat Al; Mostafiz, Rubayet Bin; Friedland, Carol J.; Rahim, Md Adilur; Rohli, Robert V.

doi:10.1002/essoar.10511940.1

Cited by 6 publications

(9 citation statements)

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“…The overarching goal of this research is to characterize flood hazards at locations both inside and outside the SFHA. More specifically, the research addresses the question, “If no modeled flood data exist for some or all return periods, what are the flood characteristics?” To that end, this research introduces a method for describing flood hazards whereby the flood is characterized using the Gumbel extreme value distribution (Waylen and Woo, 1982 ; Nadarajah and Kotz, 2004 ; Al Assi et al, 2022b ), and flood elevations are projected at higher return periods (Mostafiz et al, 2021c ). The gaps in flood surfaces due to limited data are filled by spatial interpolation techniques.…”

Section: Introductionmentioning

confidence: 99%

A data-driven spatial approach to characterize the flood hazard

et al. 2022

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Model output of localized flood grids 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 [i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)] flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards that are located both inside and outside the SFHA using existing 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.

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Section: Introductionmentioning

confidence: 99%

A data-driven spatial approach to characterize the flood hazard

et al. 2022

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“…First, synthetic flood parameters are generated based on shaded X Zone properties. Second, AAL is quantified using the computational framework developed by Al Assi et al (2022). In that approach, AAL is partitioned to homes (I = 1 through n) separately for building, contents, and use, with the AAL reduction calculated for M increases of increment J in firstfloor height above the 𝐹𝐹𝐻 0 (Al Assi et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Flood risk is assessed as the product of flood occurrence probability and the associated consequences (Šugareková & Zeleňáková, 2021). Average annual loss (AAL) has been used in past research to represent flood risk (Armal et al, 2020;Bowers et al, 2022;Hallegatte et al, 2013;Mostafiz et al, 2022a;Rahim et al, 2021Rahim et al, , 2022Wing et al, 2022;Yildirim & Demir, 2022) in terms of costs associated with direct building loss, direct contents loss, and indirect losses such as use loss while the building is being renovated (Al Assi et al, 2022). AAL is calculated as the integral of flood loss as a known function of the flood probability (or flood return period), and the Gumbel distribution function is one of the most widely accepted probability functions (Patel, 2020;Singh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…These are used here by hypothetical type of single-family homes in the U.S.A. (i.e., one vs. two-plus stories, with vs. without basement) as input to the framework methodology presented in Al Assi et al (2022). The results of two case studies are compared with the results generated from the Gumbel regression parameters produced using Mostafiz et al's (2021Mostafiz et al's ( , 2022b method, who extrapolated the Gumbel parameters in the shaded X Zone using spatial interpolation, to confirm the results of this method for a range of 500year flood depths in inland and coastal areas.…”

Section: Introductionmentioning

confidence: 99%

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Homeowner Flood Risk and Risk Reduction from Home Elevation between the 100- and 500-year Floodplains

Assi

Mostafiz

Friedland

et al. 2022

Preprint

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Floods inflict significant damage even outside the 100-year floodplain. Thus, restricting flood risk analysis to the 100-year floodplain (special flood hazard area (SFHA) in the U.S.A.) is misleading. Flood risk outside the SFHA is often underestimated because of minimal floodrelated insurance requirements and regulations and sparse flood depth data. This study proposes a systematic approach to predict flood risk for a single-family home using average annual loss (AAL) in the shaded X Zonethe area immediately outside the SFHA (i.e., the 500-year floodplain), which lies between the 1.0-and 0.2-percent annual flood probability. To further inform flood mitigation strategy, annual flood risk reduction with additional elevation above an initial first-floor height (𝐹𝐹𝐻 0 ) is estimated. The proposed approach generates synthetic flood parameters, quantifies AAL for a hypothetical slab-on-grade, single-family home with varying attributes and scenarios above the slab-on-grade elevation, and compares flood risk for two areas using the synthetic flood parameters vs. an existing spatial interpolation-estimated flood parameters. Results reveal a median AAL in the shaded X Zone of 0.13 and 0.17 percent of replacement cost value (𝑉 𝑅 ) for a one-story, single-family home without and with basement, respectively, at 𝐹𝐹𝐻 0 and 500-year flood depth < 1 foot. Elevating homes one and four feet above 𝐹𝐹𝐻 0 substantially mitigates this risk, generating savings of 0.07-0.18 and 0.09-0.23 percent of 𝑉 𝑅 for a one-story, single-family home without and with basement, respectively. These results enhance understanding of flood risk and the benefits of elevating homes above 𝐹𝐹𝐻 0 in the shaded X Zone.

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“…The building AAL (𝐴𝐴𝐿 ) is estimated using the method presented in Gnan (2021) and Gnan et al (2022a). Flood depths derived from Monte Carlo simulations (e.g., Brodie, 2013;Hennequin et al, 2018;Kind, 2014;Kind et al, 2020;Qi et al, 2013;Rahim et al, 2021Rahim et al, , 2022aRahman et al, 2002;Taghinezhad et al, 2020;Yu et al, 2013) with the fitted Gumbel extreme value distribution (e.g., Al Assi et al, 2022;Bhat et al, 2019;Gnan et al, 2022b;Kim & Lee, 2021;Manfreda et al, 2021;Mostafiz et al, 2021a;2022b;Rahim et al, 2022b;Singh et al, 2018) are translated to building loss percentages using the U.S. Army Corps of Engineers (USACE; 2000) depth-damage function (DDF) designed for the home's attributes (e.g., onestory or two-or-more stories, with or without basement). The loss percentages are then multiplied by the structure replacement cost (i.e., building value, 𝐵𝑉), and the average of the resulting losses of all Monte Carlo-simulated flooding events is the AAL.…”

Section: Building Aalmentioning

confidence: 99%

Freeboard Life-Cycle Benefit-Cost Analysis of a Rental Single-family Residence for Landlord, Tenant, and Insurer

Gnan

Mostafiz

Rahim

et al. 2022

Preprint

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Abstract. Flood risk to single-family rental housing remains poorly understood, leaving a large and increasing population underinformed to protect themselves, including regarding insurance. This research introduces a life-cycle benefit-cost analysis for the landlord, tenant, and insurer (i.e., National Flood Insurance Program (NFIP)) to optimize freeboard (i.e., additional first-floor height above the base flood elevation (BFE)) selection for a rental single-family home. Flood insurance premium; apportioned flood risk among the landlord, tenant, and NFIP by insurance coverage and deductible; rental loss; moving and displacement costs; freeboard construction cost; and rent increase upon freeboard implementation are considered in estimating net benefit (NB) by freeboard. For a 2,500 square-foot case study home in Metairie, Louisiana, a two-foot freeboard optimizes the combined savings for landlord and tenant, with joint life-cycle NB of $23,658 and $14,978, for a 3 % and 7 % real discount rate, respectively. Any freeboard up to 2.5 feet benefits the tenant and NFIP, while the landlord benefits for freeboards up to 4.0 feet. Collectively, results suggest that at the time of construction, even minimal freeboard provides substantial savings for the landlord, tenant, and NFIP. The research provides actionable information, supporting the decision-making process for landlords, tenants, and others, thereby enhancing investment and occupation decisions.

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Assessing community-level flood risk at the micro-scale by owner/occupant type and first-floor height

Cited by 6 publications

References 0 publications

A data-driven spatial approach to characterize the flood hazard

A data-driven spatial approach to characterize the flood hazard

Homeowner Flood Risk and Risk Reduction from Home Elevation between the 100- and 500-year Floodplains

Freeboard Life-Cycle Benefit-Cost Analysis of a Rental Single-family Residence for Landlord, Tenant, and Insurer

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