Hydrological Insights From SWOT: Comparative Analysis of Water Surface Elevation and Area Time Series From Hydrocron API

Abstract

The Surface Water and Ocean Topography (SWOT) mission plays an essential role in enhancing the monitoring and management of inland water bodies by providing high-resolution global observations of surface water dynamics. A critical tool in leveraging SWOT data is the Hydrocron API (Application Programming Interface), which facilitates access to temporally consistent SWOT-derived hydrological datasets. In this study, SWOT's Lake data "L2_HR_LakeSP" time series data retrieved from Hydrocron was utilized to evaluate water surface elevation (WSE) and surface area dynamics across six distinct lake locations around the world. To quantify the accuracy of SWOT, error metrics including Symmetric Mean Absolute Percentage Error (SMAPE), Absolute Percentage Error (APE), and Normalized Root Mean Square Error as a percentage (NRMSE%) were computed for both WSE and surface area estimates. The results indicated that the highest WSE error, with a SMAPE of 3.83 %, was observed in the lake characterized by the smallest surface area, suggesting a sensitivity of SWOT measurements to spatial scale. Conversely, the greatest error in surface area estimation occurred in the shallowest lake with SMAPE and APE values of 19.56 % and 22.01 %, respectively, highlighting the influence of bathymetric complexity on SWOT's detection capabilities. Despite these localized variances, the overall performance of SWOT data was found to be highly promising, demonstrating strong potential for operational hydrological applications and long-term water resource monitoring. The integration of SWOT observations with hydrological models via platforms such as Hydrocron underscores the mission's potential in advancing the understanding of inland water dynamics at both regional and global scales.