This is an information based (no code) repository for describing the creation of NHD-level roughness estimates to support terrain based synthetic rating curves using the Height Above Nearest Drainage (HAND) Approach. It hopes to support ongoing work by NOAA for operational continental scale flood forecasting and documents the technical methods presented in the in-review paper:
Johnson, J.M., Eyelade D., Clarke K.C, Singh-Mohudpur, J. (2021) “Characterizing Reach-level Empirical Roughness Along the National Hydrography Network: Developing DEM-based Synthetic Rating Curves.”
The gradient boosted machine approach used is documented
here,
and the predictions can be accessed with the USGS-nhdplusTools package
install.packages("nhdplusTools")library(nhdplusTools)
#> USGS Support Package: https://owi.usgs.gov/R/packages.html#support
roughness = get_vaa("roughness")
tail(roughness)
#> comid roughness
#> 2691334 209993 0.3170
#> 2691335 210053 0.1003
#> 2691336 210055 0.1122
#> 2691337 210057 0.1245
#> 2691338 942110021 0.1437
#> 2691339 942110034 0.1236In the first iteration of the NFIE, and the following Continental Flood Inundation Mapping framework (CFIM), a global roughness value of 0.05 was used for all NHDPlus reaches. Zheng, Tarboton, et al. (2018) found a global roughness produced SRCs with variable accuracy, but that accurate depth estimates could be achieved for the Tar River Watershed by calibrating roughness to a streamflow-stage relation produced from HEC-RAS modeling. Zheng, Maidment, et al., (2018) implemented the HAND approach with a LiDAR based DEM, calling the approach ‘GeoFlood’. This approach proved capable of capturing the Federal Emergency Management Agency flood plain coverage with 60–90% accuracy but was dependent on adjusting the Manning’s n value. As part of that work, the authors highlighted that extreme sensitivity of SRC estimates to even small variations in roughness.
Other studies have evaluated the skill of SRCs indirectly by comparing HAND-based inundation maps to remotely sensed flood products or aerial imagery (Garousi‐Nejad et al., 2019; Johnson et al., 2019). In these studies, the assignment of roughness was also identified as the principal limiting factor in accurate flood prediction and the success of the continental flood mapping framework. It is promising that in most cases, it appears roughness can be calibrated to achieve accurate results while the downside is that this requirement removes the primary advantage of the SRCs approach, namely its applicability across large scales and in ungauged basins.
Our objective in this work is to define a national set of reach-level empirical roughness values suitable to theoretical rating curve techniques that can support ongoing efforts to improve operational flood prediction and activities relaying on estimated roughness.
Zheng, X., Tarboton, D. G., Maidment, D. R., Liu, Y. Y., & Passalacqua, P. (2018). River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage. JAWRA Journal of the American Water Resources Association, 54(4), 785–806. https://doi.org/10.1111/1752-1688.12661
Zheng, X., Maidment, D. R., Tarboton, D. G., Liu, Y. Y., & Passalacqua, P. (2018). GeoFlood: Large-Scale Flood Inundation Mapping Based on High-Resolution Terrain Analysis. Water Resources Research, 54(12), 10,013-10,033. https://doi.org/10.1029/2018WR023457
Johnson, J. M., Munasinghe, D., Eyelade, D., & Cohen, S. (2019). An Integrated Evaluation of the National Water Model (NWM) Height Above Nearest Drainage (HAND) Flood Mapping Methodology. https://doi.org/10.5194/nhess-19-2405-2019
Garousi‐Nejad, Irene, et al. “Terrain analysis enhancements to the height above nearest drainage flood inundation mapping method.” Water Resources Research 55.10 (2019): 7983-8009. https://doi.org/10.1029/2019WR024837