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Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping Applications for the U.S. National Water Model
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  • Fernando Aristizabal,
  • Fernando Renzo Salas,
  • Gregory Petrochenkov,
  • Trevor Grout,
  • Brian Avant,
  • Bradford Bates,
  • Ryan Spies,
  • Nick Chadwick,
  • Zachary Wills,
  • Jasmeet Judge
Fernando Aristizabal
Lynker, Lynker

Corresponding Author:fernando.aristizabal@noaa.gov

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Fernando Renzo Salas
Office of Water Prediction (NOAA/NWS), Office of Water Prediction (NOAA/NWS)
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Gregory Petrochenkov
United States Geological Survey, United States Geological Survey
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Trevor Grout
National Oceanic and Atmospheric Administration, National Oceanic and Atmospheric Administration
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Brian Avant
Lynker, Lynker
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Bradford Bates
Lynker, Lynker
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Ryan Spies
Lynker Technologies, Lynker Technologies
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Nick Chadwick
National Oceanic and Atmospheric Administration Office of Water Prediction, National Oceanic and Atmospheric Administration Office of Water Prediction
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Zachary Wills
Cooperative Institute for Satellite Earth System Studies, Cooperative Institute for Satellite Earth System Studies
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Jasmeet Judge
University of Florida, University of Florida
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Height Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach-averaged synthetic rating curves. We highlight here that HAND is limited to producing inundation only when sourced from its nearest drainage line, thus lacks the ability to source inundation from multiple fluvial sources. A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton-Strahler stream order known as level paths (LP). The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks. A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage-discharge relationship by increasing the size of the catchments. We observe that the improvement of this method applied at 4-5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4-5% of the network. This novel contribution is framed in a new open-source implementation that utilizes the latest combination of hydro-conditioning techniques to enforce drainage and counter limitations in the input data.