Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Noh, Seong Jin; Lee, Jun Hak; Lee, Seungsoo; Seo, Dong‐Jun

doi:10.3390/w11030597

Cited by 20 publications

(25 citation statements)

References 24 publications

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“…Enhanced computing power has enabled large‐scale flood modeling applications ranging from regional to national scales (Sampson et al, 2015; Wing et al, 2017; Wing et al, 2019). However, although higher‐resolution (i.e., 10 m or finer spatial resolution) flood modeling studies are being reported in the recent literature (Noh, Lee, Lee, & Seo, 2019; Nyaupane et al, 2018), there are various open challenges for large‐scale flood simulation. Some of the challenges in a large‐scale coupled hydrologic‐hydraulic modeling framework are (a) high computational cost for model tuning and calibration (model parameterization), even with the support of high‐performance computing (HPC) (Dung, Merz, Bárdossy, Thang, & Apel, 2011; Getirana et al, 2017; Peña & Nardi, 2018), (b) inaccuracy in digital elevation models (DEMs) (Bhuyian, Kalyanapu, & Nardi, 2014; Casas, Benito, Thorndycraft, & Rico, 2006; Cook & Merwade, 2009; McKean, Tonina, Bohn, & Wright, 2014), and (c) difficulty in addressing reservoir operation and regulation (Fleischmann, Collischonn, Paiva, & Tucci, 2019; Mateo et al, 2014; Shin, Pokhrel, & Miguez‐Macho, 2017).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Hurricane Harvey flood event through coupled hydrologic‐hydraulic models: Challenges and next steps

Dullo

Gangrade

Morales-Hernändez

et al. 2021

J Flood Risk Management

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Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large‐scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar‐based hourly rainfall data, a series of hydrologic‐hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D‐GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km2, and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high‐danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high‐danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high‐performance computing hydrodynamic large‐scale flood simulations.

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Section: Introductionmentioning

confidence: 99%

Simulation of Hurricane Harvey flood event through coupled hydrologic‐hydraulic models: Challenges and next steps

Dullo

Gangrade

Morales-Hernändez

et al. 2021

J Flood Risk Management

View full text Add to dashboard Cite

show abstract

“…The MAE increased to 0.92 m from 0.63 m across considering 48 total gages representative of both pluvial and pluvial‐fluvial forcing vs. 41 gages only affected by pluvial forcing. Use of a spatially distributed runoff coefficient specified according to landcover based on values reported by Noh et al. (2019) significantly improved hindcast accuracy compared a uniform runoff coefficient of unity. The median MAE based on hourly stage across all gages was reduced from 1.18 to 0.84 m, and the median NSE was increased from 0.25 to 0.51. Bathmetric DEM corrections contributed to improved low flow accuracy and channel shape corrections reduced non‐physical constriction of channel flow.…”

Section: Discussionmentioning

confidence: 97%

“…Scenarios 1 and 2 represent hindcast scenarios with flooding driven by hourly ST4 precipitation data, and an upscale factor of 10. Scenario 1 uses a uniform runoff coefficient of unity, which assumes that all rainfall is converted to runoff, while Scenario 2 uses spatially distributed runoff coefficients (Noh et al., 2019). Scenarios 3–5 represent forecast scenarios with flooding driven by 6 hourly QPF data and varying degrees of upscaling.…”

Section: Methodsmentioning

confidence: 99%

A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale

Schubert

Luke²,

AghaKouchak

et al. 2022

Water Resources Research

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Flooding has emerged as a widespread threat to human development, health, and safety as a result of land use change, aging infrastructure, population growth, and more extreme precipitation (Galloway et al., 2018; NASEM, 2019). Floods damage property and infrastructure, disrupt business activity, degrade ecosystems with the mobilization and transport of toxics, and adversely impact populations (especially low-income populations) with job losses, displacement, stress, and health impacts (Galloway et al., 2018; Hino & Nance, 2021). Over the past several decades, flood damages have escalated from increasing exposure and vulnerability (Gall et al., 2011;White et al., 2001) and more intense precipitation associated with global warming (Davenport et al., 2021) and urbanization (Zhang et al., 2018). Indeed, research now makes clear that urbanization magnifies the flooding threat through multiple pathways including increased precipitation from altered storm dynamics, increased runoff from changes in infiltration and runoff, and increased exposure and vulnerability (Galloway et al., 2018;Zhang et al., 2018).Recent years have been marked by a string of disastrous floods. The U.S. was hit particularly hard in 2017 with flooding from several hurricanes including Harvey, Irma, and Maria, and extreme weather across the Midwest

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“…This often requires visits to the pictured site and painstaking photo interpretation and data entry procedures (e.g., Macchione et al 2019). Nonetheless, photographic high-water mark (HWM) data provide value as shown by Noh et al (2019), Yu et al (2016), Xing et al (2019), Blumberg et al (2015), Fohringer et al (2015), Kutija et al (2014), andMcDougall andTemple-Watts (2012).…”

Section: Introductionmentioning

confidence: 99%

Validation of Urban Flood Inundation Models Applied Using Nationally Available Data Sets: Novel Analyses of Observed High Water Information

et al. 2021

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This paper presents novel spatial analysis techniques to evaluate simulations of urban flood inundation from two hydrologic models applied using nationally available datasets. These techniques account for differences in model computational element size and discretization when comparing simulated flood depths to surveyed high-water marks. To complement direct evaluations of predicted depths at high-water marks, our techniques provide five additional metrics to assess modeled depths in areal sectors between the surveyed high-water mark and the stream channel. Our study also demonstrates a novel technique to evaluate flood predictions at damaged structures and crowdsourced observations of flooded locations. The work in this study is part of a more complete evaluation of two hyper-resolution hydrologic models to generate street-level flood inundation predictions. We used the 679-km 2 Sugar Creek watershed above USGS Gage 02146800, near Fort Mill, SC, which contains the Charlotte, NC, municipal area. We assessed model performance at 172 surveyed high-water marks, 373 locations of flooded structures, and nearly 2,000 crowd-sourced observations of flooded locations. Results indicate that the analyses techniques help distinguish model performance and identify model deficiencies.

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Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Cited by 20 publications

References 24 publications

Simulation of Hurricane Harvey flood event through coupled hydrologic‐hydraulic models: Challenges and next steps

Simulation of Hurricane Harvey flood event through coupled hydrologic‐hydraulic models: Challenges and next steps

A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale

Validation of Urban Flood Inundation Models Applied Using Nationally Available Data Sets: Novel Analyses of Observed High Water Information

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