Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations. Issue 5 (19th May 2020)

• Record Type: Journal Article
• Authors: Andreadis, K. M.; Brinkerhoff, C. B.; Gleason, C. J.
• Is Part Of: Water resources research. Volume 56:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-19
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026611
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The Surface Water Ocean Topography (SWOT) satellite mission expected to launch in 2021 will offer a unique opportunity to map river discharge at an unprecedented spatial resolution globally from observations of water surface elevation, width, and slope. Because river discharge will not be directly observed from SWOT, a number of algorithms of varying complexity have been developed to estimate discharge from SWOT observables. Outstanding issues include the lack of accurate prior information and parameter equifinality. We developed a new data assimilation discharge algorithm that aimed to overcome these limitations by integrating a data‐driven approach to estimate priors with a model informed by hydraulic geometry relations. A comprehensive simulated dataset of 18 rivers was used to evaluate the algorithm and four different configurations (rectangular channel, generic channel, and geomorphologically classified channel with and without regularization) to assess the impact of progressively adding hydraulic geometry constraints to the estimation problem. The algorithm with the full set of constraints outperformed the other configurations with median Nash‐Sutcliffe coefficients of 0.77, compared with −0.46, 0.31 and 0.66, while other error metrics showed similar improvement. Results from this study show the promise of this hybrid data‐driven approach to estimating river discharge from SWOT observations, although a number of enhancements need to be tested to improve the operational applicability of the algorithm. Key Points: Data assimilation of SWOT observations can retrieve large‐river discharge even with a simple steady‐state model A data‐driven approach led to a plausible estimation of prior probability distributions of hydraulic variables Hydraulic geometry constraints improved river discharge estimation accuracy across all metrics