Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates

Abstract

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50-100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a "worst case" benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.Plain Language Summary Measurements of river flow are essential for the allocation of water resources, flood and drought forecast and mitigation efforts, and others. Access to local measurements is not ubiquitous and is particularly difficult for rivers flowing in remote locations or across country borders. Measurements taken by satellites such as the upcoming Surface Water and Ocean Topography (SWOT) mission will offer freely available global data and methods to estimate discharge using such data have been in development. We conducted a comprehensive assessment of the accuracy and precision of five SWOT discharge inversion algorithms under three conditions: (a) ideal, that is if the measurements were available once a day and contained no error; (b) with no measurement error but changing how frequently the measurements were taken, and (c) under different levels of measurement error. We found that the methods consistently improved over the initial estimates of discharge and we identified river hydraulic properties that increase the chances of successful parameter estimation. We also found that despite the use of very similar discharge equations, the subtle differences in equations FRASSON ET AL.

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

“…Intermittent satellite-based discharge estimates can be derived from optical and altimeter data (Abdalla et al, 2021;Tarpanelli et al, 2021). The Surface Water and Ocean Topography (SWOT) mission will soon enable frequent spaceborne observations of river stage for large rivers to allow inference of discharge (Biancamaria et al, 2016;Frasson et al, 2021). Currently, no mission is specifically dedicated to ET measurements (Fisher et al, 2017), and ET is most typically inferred from satellite-observed surface or skin temperature (related to sensible heat) as the residual of a simple energy balance model (Anderson et al, 2021), or indirectly obtained via soil moisture and VOD DA in a land surface model (Martens et al, 2017).…”

Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite

“…16 of these rivers are then additionally validated under a "measurement-error" scenario that more closely mimics expected SWOT by adding realistic radar errors and sampling along the satellite's future ground track. SWOT river error modeling was developed by Durand et al (2020) and then applied to these 16 rivers by Frasson et al (2021). This error modeling is non-trivial and computationally expensive, and so…”

“…(2019) (https://doi.org/10.1029/2019GL084529), Frasson et al. (2021) (https://zenodo.org/record/3817817), Churchill et al. (1964) (https://pubs.usgs.gov/pp/0737/report.pdf), Owens et al.…”

“…This error modeling is non‐trivial and computationally expensive, and so Frasson et al. (2021) were limited to only 16 test cases with SWOT measurement errors. Likewise, we stick to these 16 rivers for the same reason.…”

“…These datasets are created using standard hydraulic models forced with known inflows and measured bathymetry to model the hydraulic response of the rivers, and then those terms visible to SWOT are extracted to produce hydraulically realistic synthetic observations. These data were published by Frasson et al (2021) and Durand et al (2016). See Figure S3 in Supporting Information S1 for a map of these river's locations along the global SWOT river network (Altenau et al, 2021).…”