Abstract

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 crowd-sourced 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-km2 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.

Highlights

  • Validation of urban flood prediction models requires accurate observations of flood extents and depths

  • The yellow triangle outlined in red is the point on the NHD+ stream network vector that is closest to the surveyed high-water mark (HWM)

  • We computed maximum depths in each computational element to compare to the total of 172 surveyed HWMs and over 2,000 flooddamage/flooded-street observations

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Summary

Introduction

Validation of urban flood prediction models requires accurate observations of flood extents and depths. Social-media data and news reports/photos are growing sources of data for validation and have the potential to provide vast volumes of flood-related information. The information in the photos and reports must be converted into useable forms, such as relating the pictured flood level to a local depth at a specific location and time of occurrence. This often requires visits to the pictured site and painstaking photo interpretation and data entry procedures (e.g., Macchione et al 2019). 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), and McDougall and Temple-Watts (2012)

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