2019
DOI: 10.3390/w11030597
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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

Abstract: Hurricane Harvey was one of the most extreme weather events to occur in Texas, USA; there was a huge amount of urban flooding in the city of Houston and the adjoining coastal areas. In this study, we reanalyze the spatiotemporal evolution of inundation during Hurricane Harvey using high-resolution two-dimensional urban flood modeling. This study’s domain includes the bayou basins in and around the Houston metropolitan area. The flood model uses the dynamic wave method and terrain data of 10-m resolution. It is… Show more

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Cited by 20 publications
(25 citation statements)
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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%
“…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%
“…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%
“…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%