Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates

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 using e.g. the Manning-Strickler flow law, complementing the in-situ network of streamgages – if unobserved flow law parameters such as the friction coefficient and river bathymetry can be adequately estimated. Several discharge inversion algorithms designed to compute flow law parameters have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess performance of five discharge inversion algorithms developed for SWOT; algorithm inputs include synthetic SWOT observations produced using hydraulic models, and an a priori estimate of mean annual flow (Qwbm). Considering daily sampling without SWOT measurement error, 4/5 algorithms estimated discharge with median normalized root mean square errors below 60% and correlation coefficient above 0.94, despite high Qwbm biases, which after normalization showed a median value of 46% and ranged from 0.3% to 887%. We find that prior accuracy was an important control on algorithm performance, but algorithm estimates generally improved on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than the accuracy of the prior Qwbm, hydraulic variability among reaches, and flow law accuracy, in governing discharge algorithm performance. This study lays the groundwork to develop predictive power of algorithm performance, and thus map global distribution of expected SWOT discharge accuracy.