SWOT, river slope, and discharge: a case study of the Rio Petrohue

Abstract

The Surface Water and Ocean Topography (SWOT) satellite mission offers novel global water surface elevation (WSE) and discharge estimates, providing new opportunities for river measurements in ungauged and data-limited regions. However, accurately estimating river slopes from WSEs remains a challenge, especially in steep and complex terrains. As river slopes are non-linear, an approach that integrates this complexity is needed. Traditional methods, such as ordinary least squares (OLS) regression, are sensitive to outliers, which can lead to biased slope estimates and inaccurate discharge predictions. This field-based study and accompanying analyses introduce a novel quantile regression filtering approach to address these challenges by systematically identifying and removing slope-influencing outliers. Using in situ GNSS measurements along the Rio Petrohue (-42.3°, -72.4°), a river with highly variable slopes, we validate the performance of SWOT-derived slopes and assess the stability of WSE trends. Our approach highlights the limitations of current regression techniques and demonstrates how slope-based quantile filtering enhances slope estimation accuracy between GNSS and SWOT measurements. This filtering method establishes a framework for future automated algorithms, with positive implications for hydrological modeling, flood prediction, and water resource management.