Abstract

The National Water Model (NWM) provides continental-scale streamflow predictions but exhibits bias and reduced skill in highly regulated and snow-driven basins. In contrast, River Forecast Center (RFC) models incorporate decades of local calibration and regulatory knowledge but are limited in spatial coverage to specific points of interest. This study evaluates whether hydrologic knowledge embedded in RFC-calibrated simulations can be transferred to improve NWM performance at unmonitored locations using the Streamflow Analysis for Bias Estimation and Reduction (SABER) framework. Using Colorado Basin River Forecast Center (CBRFC) simulations as surrogate reference data, we applied SABER to the NWM 44-year retrospective model simulation across the Colorado River Basin. We regionalized bias-correction parameters using standardized flow-duration curves and watershed clustering. Model performance was assessed at 84 CBRFC-modeled locations using bootstrap validation. Performance was evaluated with KGE, NSE, ME, MAE, and RMSE. Results indicate that SABER improves NWM performance relative to the CBRFC reference. Median KGE increased from -0.02 to 0.78, median NSE improved from -0.98 to 0.63, and median Mean Error decreased from 44 cfs to 0 cfs. Overall, SABER demonstrated effective transfer of CBRFC calibrated hydrologic knowledge to unmonitored reaches, improving NWM performance while identifying remaining limitations in the underlying model. This approach provides a scalable pathway for integrating regional operational expertise, such as often existing in RFC models, into large-scale hydrologic modeling systems.

Degree

MS

College and Department

Ira A. Fulton College of Engineering; Civil and Construction Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2026-04-13

Document Type

Thesis

Keywords

bias correction, SABER, national water model, NWM, river forecast center, Colorado basin, surrogate gauge, hydrologic modeling, CBRFC

Language

english

Included in

Engineering Commons

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