A document by Rubayet Bin Mostafiz. Click on the document to view its contents.

Freeboardelevation of a structure above the base flood elevation (BFE)is a critical component in mitigating or avoiding flood losses. However, the unrevealed benefits and savings of freeboard installation have prevented communities from adopting this approach. To improve decision-making for flood-vulnerable communities and enhance flood risk mitigation strategies, this study presents the methodology underlying a new webtool, FloodSafeHome, that estimates comprehensively the economic benefits and savings of freeboard installation for new construction of residential buildings. Specifically, the proposed evaluation framework has been designed to calculate monthly savings for individual buildings by assessing freeboard cost, insurance savings per year, and expected annual flood loss. This new evaluation method is built into a web-based, decision-making tool for use by the public and community leaders in three southeastern Louisiana parishes, to identify expected future benefits of building residences with freeboard and enhance their decision-making processes with interactive risk/benefit analysis features. For example, results indicate the levels of freeboard that optimize the costbenefit ratio for flood-insured homes in the study area. This approach is expected to improve long-term flood resilience and provide cost-efficient flood mitigation strategies particularly in disaster vulnerable regions.

United States Federal Emergency Management Agency 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). But these flood grids are often unavailable or fail to include return periods for particular applications, such as understanding flood risk of properties during the 70-year useful building life cycle. Furthermore, 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 the flood hazard for U.S. properties that are located both inside and outside the SFHA. The flood hazard is characterized by the Gumbel extreme value distribution to project flood elevations to extreme flood events for which an entire area is assumed to be submerged. Spatial interpolation techniques impute elevation values in the extreme flood elevation surfaces and therefore can estimate the flood hazard for areas outside the SFHA. The proposed method can improve the assessment of flood risk for properties located in both inside and outside the SFHA and therefore, 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.

Construction with freeboard – vertical height of a structure above the minimum required – is commonly accepted as a sound investment for flood hazard mitigation. However, determining the optimal height of freeboard poses a major decision problem. This research introduces a life-cycle benefit-cost analysis (LCBCA) approach for optimizing freeboard height for a new, single-family residence, while incorporating uncertainty, and, in the case of insured homes, considering the costs from losses, insurance, and freeboard (if any) to the homeowner and National Flood Insurance Program (NFIP) separately. Using a hypothetical, case study home in Metairie, Louisiana, results show that adding 2 ft. of freeboard at the time of construction might be considered the optimal option given that it yields the highest net benefit, but the highest net benefit-cost ratio occurs for the 1 ft. freeboard. Even if flood loss reduction is not considered when adding freeboard, the savings in annual insurance premiums alone are sufficient to recover the construction costs paid by the homeowner if at least one foot of freeboard is included at construction. Collectively, these results based on conservative assumptions suggest that at the time of construction, even a small amount of freeboard provides a huge savings for the homeowner and (especially) for the financially-strapped NFIP. For community planners, the results suggest that wise planning with reasonable expectations on the front end makes for a more sustainable community.

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.

Evaluating flood risk is an essential component of understanding and increasing community resilience. A robust approach for quantifying flood risk in terms of average annual loss (AAL) in dollars at the community level is needed to provide valuable information for stakeholder decision-making. This research develops a computational framework to evaluate AAL at the community level by owner/occupant type (i.e., homeowner, landlord, and tenant) for increasing first-floor heights. The AAL values are calculated here by numerically integrating loss-exceedance probability distributions to represent economic annual flood risk to the building, contents, and use. A case study for a census block in Jefferson Parish, Louisiana, reveals that homeowners bear a mean AAL of $4,390 at the 100-year flood elevation (E_100), compared with $2,960, and $1,590 for landlords and tenants, respectively, because the homeowner incurs losses to building, contents, and use, rather than only two of the three, as for the landlord and tenant. The results of this case study show that increasing first-floor heights reduces AAL proportionately for each owner/occupant type, and that two feet of additional elevation above E_100 may provide the most economically advantageous benefit. The modeled results suggest that Hazus Multi-Hazard (Hazus-MH) output underestimates the AAL by 11% for building and 15% for contents. Application of this technique to the community level while partitioning the owner/occupant types will improve planning for improved resilience and assessment of impacts attributable to the costly flood hazard.

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 flood-related 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 Zone – the 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 () 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 () for a one-story, single-family home without and with basement, respectively, at and 500-year flood depth < 1 foot. Elevating homes one and four feet above substantially mitigates this risk, generating savings of 0.07–0.18 and 0.09–0.23 percent of 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 in the shaded X Zone.

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.