Using search-constrained inverse distance weight modeling for near real-time riverine flood modeling: Harris County, Texas, USA before, during, and after Hurricane Harvey

Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simp...

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Bibliographic Details
Published inNatural hazards (Dordrecht) Vol. 105; no. 1; pp. 277 - 292
Main Authors Berens, Andrew S., Palmer, Tess, Dutton, Nina D., Lavery, Amy, Moore, Mark
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.01.2021
Springer Nature B.V
Subjects
Civil Engineering
Digital Elevation Models
Discharge measurement
Distance
Earth and Environmental Science
Earth Sciences
Emergency preparedness
Emergency response
Environmental Management
Flooding
Floods
Gauges
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Health hazards
High water mark
Hurricanes
Hydrogeology
Hydrologic data
Imagery
Modelling
Natural Hazards
Original Paper
Parameters
Public health
Real time
Rivers
Weight
United States > US
Texas
Harris County
United states
Hurricane harvey
Inverse distance weighting
Flood modeling
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Summary:Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models’ outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash–Sutcliffe efficiency of in excess of .6 for all parameterizations relative to a Nash–Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.
Bibliography:Author contributions Andrew Berens was the project leader and lead author. Tess Palmer, Nina Dutton, and Amy Lavery contributed to the data acquisition, analysis, validation of models, and writing of the paper. Mark Moore accessed and prepared the stream gauge data for analysis and offered a county-level perspective to the manuscript development. All authors read and approved the final manuscript.
ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-020-04309-w