Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment

Abstract

The Surface Water and Ocean Topography (SWOT) mission aims to provide essential data on river width, height and slope in order to estimate worldwide river discharge accurately. This mission offers a powerful tool for monitoring river discharge in dynamic coastal areas, like the Saigon-Dongnai estuary in Southern Vietnam. However, estimating discharge of tidally-influenced rivers using SWOT measurements can be challenging when hydraulic variables have the same order of magnitude as SWOT measurement errors. In this paper we present a methodology to enhance discharge estimation accuracy from SWOT measurements based on simulated SWOT products at the 200 meter node resolution and varying river reach size. We assess measurement error variability and its impact on discharge estimation by employing a Monte Carlo analysis. Our approach significantly improved discharge estimation in the Saigon tidal river, reducing RMSE from 1400 m3/s to 180 m3/s and increasing R² from 0.31 to 0.95. Notably, the percentage of Monte Carlo particles meeting the 30% rRMSE threshold rose from 0% to 79%. This study underscores the feasibility of obtaining reliable discharge estimates from SWOT data in complex coastal areas where hydraulic variables are of the same order of magnitude as SWOT errors. Additionally, the proposed methodology to improve discharge estimation from SWOT measurements is widely adaptable as it can be applied to similar regions and can be combined with any discharge estimation method.