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.

The National Water Model (NWM) currently requires the post-processing of forecast discharges to produce forecast flood inundation maps (FIM) to support the National Weather Service’s mission of protecting life and property. Height Above Nearest Drainage (HAND) is a means of detrending digital elevation models (DEM) by normalizing elevations to the nearest, relevant drainage line (creeks, rivers, etc). It’s worthy of producing high-resolution FIMs at large spatial scales and frequent time steps using reach-averaged synthetic rating curves. However, HAND based FIMs suffer from a known limitation caused by independent catchments that lack the ability to cross catchment boundaries and ridgelines. To counter this constraint, a version of HAND known as Generalized Mainstems (GMS) is proposed that reduces the Horton-Strahler stream order of the stream network. To demonstrate skill enhancement, we constructed HAND derived at three different stream resolutions including the NWM full resolution (FR), the NWM mainstems (MS) resolution, and the NWM GMS resolution stream networks. The FR stream network contains all NWM forecast locations and the MS resolution stream network contains all river segments at or downstream of NWS river forecast points. GMS contains all segments within the FR stream resolution but instead of deriving HAND by accounting for all river segments at once, it is derived independently at the level path (LP) scale. LPs are unique identifiers propagated upstream from a sub-basin’s outlet along the direction of maximum flow distance and repeated recursively until all segments are assigned LP identifiers. These serve as processing units for HAND dataset production and FIMs are made at the LP scale. These FIMs are then mosaiced together, effectively turning the stream network into discrete groups of homogenous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill on the order of 2% points of Critical Success Index for MS and 2% points more for GMS is demonstrated by comparing to HEC-RAS FIMs. Additionally, both Probability of Detection and False Alarm Ratio improve which can be partly explained by a positive correlation of stream order with river stage at fixed discharge values within the synthetic rating curves produced by HAND.

The National Water Center (NWC), part of the National Weather Service’s (NWS) Office of Water Prediction (OWP), seeks to provide forecast flood inundation maps (FIM) as services at the national scale along 2.7+ million river reaches. The National Water Model (NWM) forecasts stream discharges along these reaches at varying time horizons. Building on the methodology of previous versions, OWP FIM 3.0 seeks to map NWM stream discharges to inundation extents with a modified version of the Height Above Nearest Drainage method for mapping stages. FIM 3.0 produces required datasets for generation of FIMs, which are relative elevations, reach-level catchments, rating curves and crosswalk tables, to convert the NWM discharge forecasts to inundation extents as a post-processing step. Computational tests estimate that producing these FIM required datasets for the continental U.S. will be around 600 total CPU hours. Only publicly available input datasets from the NWM or NHDPlusHR are required. The latest methods leveraged within FIM 3.0 include the use of consistent reach lengths and deriving FIM outputs at lower stream densities (downstream of River Forecast Center forecast points) to mitigate catchment boundary limitations. FIM 3.0 is primarily parallelized across Hydrologic Unit Codes (HUC) 4, 6, or 8 as processing units which can be selected by the user at run time. FIM capability improvements were measured by comparisons to engineering scale maps using contingency statistics. Future versions will support NWM geospatial dataset production, usage of Lidar elevations, and more advanced hydrological methods. FIM 3.0 is highly configurable, modular, and portable due to its software architecture and containerization. All software dependencies are open-source including GDAL/OGR, TauDEM, and RichDEM. The source code and Docker container are expected to be made available publicly for community review and development.

The National Water Model (NWM) currently requires the post-processing of forecast discharges to produce forecast flood inundation maps (FIM) that support the National Weather Service’s mission of protecting life and property. Height Above Nearest Drainage (HAND) is a means of detrending digital elevation models (DEM) by normalizing elevations to the nearest, relevant drainage line (creeks, rivers, etc). It’s worthy of producing high-resolution FIMs at large spatial scales and frequent time steps using reach-averaged synthetic rating curves. Current operational capabilities support 10 meter (1/3 arc-second) spatial resolution DEMs sourced from the National Elevation Dataset (NED). The 3D Elevation Program (3DEP) managed by the United States Geological Survey (USGS) publishes a variety of gridded elevation datasets at 1 m, 3 m (1/3 arc-second), 5 m, and 10 m (1/9 arc-second) among others. While the 1/3 arc-second product provides seamless coverage across CONUS, the remaining products lack full spatial support with respect to that of the NWM. However, 3DEP is actively publishing additional data with national coverage scheduled for 2023. We seek to investigate the efficacy of assimilating higher resolution 1 m and 3 m (1/3 arc-second) data derived from light detection and ranging sensors (Lidar). These Lidar derived datasets not only represent higher horizontal resolution but also have improved vertical accuracy when compared to the NED. We seek to utilize Py3DEP from the HyRiver ecosystem of tools to retrieve 3DEP data. HAND derived FIMs will be evaluated against high-fidelity HEC-RAS 1D inundation maps for 100 year and 500 year events. Possible skill enhancements can be observed from having terrain information that better agrees with those of the benchmark HEC-RAS datasets. Lidar terrain data can better resolve fine scale features that flood inundation extents may be very sensitive to. Additionally, we would investigate mosaicing techniques to deal with processing units (hydrologic unit codes) of heterogeneous data availability. This can involve resampling DEM’s to create seamless rasters within units. Lastly, we can investigate the effect of Lidar data on synthetic rating curves as well as consider the latest hydro-conditioning techniques from GeoFlood for stream line delineation on Lidar data.