Preprints
https://doi.org/10.5194/nhess-2021-275
https://doi.org/10.5194/nhess-2021-275

  14 Oct 2021

14 Oct 2021

Review status: this preprint is currently under review for the journal NHESS.

Gridded Flood Depth Estimates from Satellite Derived Inundations

Seth Bryant1,2, Heather McGrath3, and Mathieu Boudreault4 Seth Bryant et al.
  • 1GFZ German Research Centre for Geosciences, Section 4.4. Hydrology, Potsdam, Germany
  • 2Institute for Environmental Sciences and Geography, University of Potsdam, Germany
  • 3Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, Canada
  • 4Department of Mathematics, Université du Québec à Montréal, Montréal, Canada

Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

Seth Bryant et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-275', Guy J.-P. Schumann, 06 Nov 2021
  • RC2: 'Comment on nhess-2021-275', Anonymous Referee #2, 10 Nov 2021

Seth Bryant et al.

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Short summary
The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here addresses this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
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