Uncertainty and Sensitivity Analysis of the HAZUS-MH Flood Model

Tate, Eric; Muñoz, Cristina; Suchan, Jared

doi:10.1061/(asce)nh.1527-6996.0000167

Cited by 66 publications

(70 citation statements)

References 43 publications

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“…HAZUS-MH is a GIS-based loss estimation model that combines flood depth grids, building characteristics, and depth-damage relationships to estimate monetary losses under varying user-designed scenarios (Scawthorn et al 2006a, b). The resulting flood loss estimates are useful first approximations, but can also be associated with a significant degree of uncertainty (Ding et al 2008;Tate et al 2014). To develop more reliable estimates, we replaced HAZUS-MH default data with inputs of higher resolution and quality describing the flood hazard, building characteristics, and depth-damage functions.…”

Section: Modeling Flood Lossesmentioning

confidence: 99%

Flood recovery and property acquisition in Cedar Rapids, Iowa

et al. 2015

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Voluntary property acquisitions are playing an increasingly prominent role in the aftermath of US flood disasters, as policy tools for community recovery and hazard mitigation. Following historic flooding in 2008, the City of Cedar Rapids, Iowa, instituted a federally supported program for the acquisition of over 1300 damaged properties. Using Cedar Rapids as a case study, this article investigates post-flood property acquisition from the perspectives of cost effectiveness and social equity. To assess economic viability, a benefit-cost analysis was performed at the parcel scale. Social equity was assessed using a social vulnerability index tailored to flood recovery. The results indicate that the property acquisitions are cost effective based on the avoidance of future flood losses, and prioritize socially vulnerable neighborhoods. The dual economic and social analysis sheds light on the capacity of federally supported buyouts to support holistic post-disaster planning and decision-making.

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Section: Modeling Flood Lossesmentioning

confidence: 99%

Flood recovery and property acquisition in Cedar Rapids, Iowa

et al. 2015

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“…This measure is computed through the following equation:

RIL = \frac{x_{U} - x_{L}}{\overset{false\sim}{normalx}}

where x L and x U are the lower and upper limits, respectively; and

\overset{x}{true}

is the median. Here, 5th and 95th percentiles are primarily used as the lower and upper limits aligned with many other studies (e.g., Tate et al, ). This is called RIL hereafter.…”

Section: Methodsmentioning

confidence: 99%

A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates

Ahmadisharaf

Kalyanapu

2019

J Flood Risk Management

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Reliable estimation of flood loss is crucial for flood management. Since this estimation relies on models that are prone to uncertainty, it is vital to clearly understand how the uncertainties affect the appraised flood loss. This paper presents a coupled probabilistic hydrologic and hydraulic modelling framework to estimate the uncertainty of anticipated loss. A hydrologic model performs rainfall‐runoff transformation and a two‐dimensional unsteady hydraulic model simulates flood inundation. The outputs of the latter are fed into a loss estimation tool. Two sources of uncertainty—rainfall depth and antecedent moisture condition—are used to illustrate the framework on the Swannanoa River watershed in North Carolina, United States. The impact of the uncertainty of these two sources is tracked over the hydrologic model, hydraulic model and loss estimation tool. Our case study results illustrate that the estimations on the percent affected people can be four times more uncertain than the rainfall depth and two times more than the flood extent, but its uncertainty is comparable to hydrograph attributes. The appraised structural damages are nearly two times more uncertain than the affected people. These findings, however, may only be valid for a case study with hilly topography and should be cautiously extrapolated to other areas.

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“…Loss estimates from flood models are uncertain due to variations in input hazard, exposure, and damage parameters (Tate et al 2015). HAZUS-MH uses the AGDAM methodology to compute agricultural damage (FEMA 2012).…”

Section: P29mentioning

confidence: 99%

Flood Risk Reduction from Agricultural Best Management Practices

Antolini

Tate

Dalzell

et al. 2019

J American Water Resour Assoc

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Research Impact Statement: Agricultural best management practices (BMPs) can reduce flood risk, providing a co-benefit to nutrient reduction.ABSTRACT: Best management practices (BMPs) play an important role in improving impaired water quality from conventional row crop agriculture. In addition to reducing nutrient and sediment loads, BMPs such as fertilizer management, reduced tillage, and cover crops could alter the hydrology of agricultural systems and reduce surface water runoff. While attention is devoted to the water quality benefits of BMPs, the potential cobenefits of flood loss reduction are often overlooked. This study quantifies the effects of selected commonly applied BMPs on expected flood loss to agricultural and urban areas in four Iowa watersheds. The analysis combines a watershed hydrologic model, hydraulic model outputs, and a loss estimation model to determine relationships between hydrologic changes from BMP implementations and annual economic flood loss. The results indicate a modest reduction in peak discharge and economic loss, although loss reduction is substantial when urban centers or other high-value assets are located downstream in the watershed. Among the BMPs, wetlands, and cover crops reduce losses the most. The research demonstrates that watershed-scale implementation of agricultural BMPs could provide benefits of flood loss reduction in addition to water quality improvements.(

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Uncertainty and Sensitivity Analysis of the HAZUS-MH Flood Model

Cited by 66 publications

References 43 publications

Flood recovery and property acquisition in Cedar Rapids, Iowa

Flood recovery and property acquisition in Cedar Rapids, Iowa

A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates

Flood Risk Reduction from Agricultural Best Management Practices

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