Preprints
https://doi.org/10.5194/hess-2021-552
https://doi.org/10.5194/hess-2021-552
 
03 Dec 2021
03 Dec 2021
Status: this preprint is currently under review for the journal HESS.

Evaluation of a New Observationally Based Channel Parameterization for the National Water Model

Aaron Heldmyer1, Ben Livneh1,2, James McCreight3, Laura Read3, Joseph Kasprzyk1, and Toby Minear2 Aaron Heldmyer et al.
  • 1Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, 80309, USA
  • 2Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, 80309, USA
  • 3National Center for Atmospheric Research, Boulder, 80305, USA

Abstract. Accurate representation of channel properties is important for forecasting in hydrologic models, as it affects height, celerity, and attenuation of flood waves. Yet, considerable uncertainty in the parameterization of channel geometry and hydraulic roughness (Manning’s n) exists within the NOAA National Water Model (NWM), due largely to data scarcity: only ~2,800 out of the 2.7 million river reach segments in the NWM have measured channel properties. In this study, we seek to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry (HyG) dataset of approximately 48,000 gages. We begin with a Sobol’ sensitivity analysis of channel geometry parameters for 12 small semi-natural basins across the continental U.S., which reveals an outsized sensitivity of simulated flow to Manning’s n relative to channel geometry parameters. We then develop and evaluate a set of regression-based regionalizations of channel parameters estimated using the HyG dataset. Finally, we compare the model output generated from updated channel parameter sets to observations and the current NWM v2.1 parameterization. We find that, while the NWM land surface model holds the most influence over flow given its control over total volume, the updated channel parameterization leads to improvements in simulated streamflow performance relative to observed flows, with a statistically significant mean R2 increase from 0.479 to 0.494 across approximately 7,400 gage locations. HyG-based channel geometry and roughness provide a substantial overall improvement in channel representation over the default parameterization, updating the previous set value for most reaches of Manning’s n = 0.060 to a new range between 0.006 and 0.537 (median 0.077). This research provides a more representative, observationally based channel parameter dataset for the NWM routing module, as well as new insight into the influence of the routing module within the overall modeling framework.

Aaron Heldmyer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-552', Anonymous Referee #1, 03 Jan 2022
    • AC1: 'Reply on RC1', Aaron Heldmyer, 27 May 2022
  • RC2: 'Comment on hess-2021-552', Anonymous Referee #2, 04 May 2022
    • AC2: 'Reply on RC2', Aaron Heldmyer, 27 May 2022

Aaron Heldmyer et al.

Aaron Heldmyer et al.

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Short summary
Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM), yet are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished dataset of approximately 48,000 gages. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.