ABSTRACT:

This repository includes data and software for the paper titled: "Effects of High-Quality Elevation Data and Explanatory Variables on the Accuracy of Flood Inundation Mapping via Height Above Nearest Drainage" submitted to HESS June 2023.

ABSTRACT:

Flood inundation mapping and evaluation software configured to work with U.S. National Water Model.

Please see noaa-owp/inundation-mapping on Github for the latest official version.

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 flowpath, 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.

ABSTRACT:

Floods are one of the most significant natural disasters and having near-realtime (NRT) or retrospective information on inundation can help first responders, forecasters, engineers, the general public, and other stakeholders better manage these devastating events to reduce threats to life and property. This manuscript is a detailed examination of how hydrologically relevant terrain data known as height above nearest drainage (HAND) can be used to enhance satellite based C-band synthetic aperture radar (SAR) riverine flood inundation mapping in areas with a variety of land covers. Previous work with C-band SAR has listed numerous difficulties with detecting surface water under thick vegetation and urban areas. While HAND has been used to assist SAR in several capacities, it has not been utilized as a feature for inundation mapping with advanced machine learning classification algorithms.