Open AccessSimulation of Hurricane Harvey flood event through coupled hydrologic‐hydraulic models: Challenges and next steps
Author(s) -
Dullo Tigstu T.,
Gangrade Sudershan,
MoralesHernández Mario,
Sharif Md Bulbul,
Kao ShihChieh,
Kalyanapu Alfred J.,
Ghafoor Sheikh,
Evans Katherine J.
Publication year - 2021
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12716
Subject(s) - flood myth , hydrology (agriculture) , environmental science , hydrological modelling , bathymetry , digital elevation model , routing (electronic design automation) , scale (ratio) , flood forecasting , 100 year flood , water level , geology , climatology , computer science , remote sensing , geography , oceanography , geotechnical engineering , cartography , computer network , archaeology
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 65km 2 , 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.