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Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

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  • Rebecca M Diehl
  • Jesse D Gourevitch
  • Stephanie Drago
  • Beverley C Wemple

Abstract

As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.

Suggested Citation

  • Rebecca M Diehl & Jesse D Gourevitch & Stephanie Drago & Beverley C Wemple, 2021. "Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
  • Handle: RePEc:plo:pone00:0248683
    DOI: 10.1371/journal.pone.0248683
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    References listed on IDEAS

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    1. Blair William Gerald Scriven & Heather McGrath & Emmanuel Stefanakis, 2021. "GIS derived synthetic rating curves and HAND model to support on-the-fly flood mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1629-1653, November.
    2. Zhouyayan Li & Jerry Mount & Ibrahim Demir, 2022. "Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 977-1004, May.

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