Angelica Tarpanelli (Research Institute for Geo-Hydrological Protection, National Research Council, Italy); Alessio Domeneghetti (DICAM, School of Civil Engineering, University of Bologna, Italy)

This study advances the evaluation of the Surface Water and Ocean Topography (SWOT) mission for monitoring of riverine hydrodynamics in Italy. Given the recent launch of the SWOT mission, its application to riverine hydrology remains relatively new and requires thorough validation. To date, only a limited number of studies have systematically assessed its capability to observe riverine dynamics. This study evaluates SWOT Water Surface Elevation (WSE) and slope data over its entire operational period, by comparing them with in-situ measurements and hydrodynamic model simulations, over a 165-km stretch of the Po River in Northern Italy, starting from Boretto gauging station. A 1D/2D HEC-RAS model, incorporating cross-sections extracted from the most recent LiDAR and bathymetric surveys, is used to dynamically simulate hydrodynamic variables along the river.
Preliminary results from the analysis of SWOT River Single Pass product reveal systematic errors in its observations. In addition, when compared with model simulations, the findings underscore the critical importance of quality-aware filtering for the reliable use of SWOT observations. Good-quality WSE data generally show strong agreement with both in-situ measurements and hydrodynamic model simulations, typically within ±0.5–0.8 meters. In contrast, lower-quality observations exhibit significantly larger discrepancies, with deviations reaching several meters. However, only a small fraction of the available data is classified as good quality, presenting challenges for comprehensive analysis. Moreover, results suggest that SWOT accuracy is spatially correlated with geophysical and orbital factors.
To address these limitations, the study expands to use other quality indicators, including bitwise flags, enables the extraction of data with improved quality and coverage. Additionally, complementary SWOT products such as Pixel Cloud datasets are leveraged. These products provide higher spatial detail but being less processed, require thorough preprocessing, such as spatial filtering and noise/outlier removal, to derive reliable hydrodynamic information. Where systematic biases are observed, statistical corrections are applied to enhance SWOT data consistency. The study also explores the spatial and temporal performance of SWOT observations in relation to a range of influencing factors, including: (i) distance from nadir track, (ii) satellite pass orientation, (iii) river planform geometry (e.g., straight vs. meandering), and (iv) flow regime (e.g., rising limb, peak, recession, or low flow).
Although based on a single case study, the analysis contributes to illustrating both the potential and limitations of SWOT products for riverine applications. It highlights the importance of quality-aware data integration with physically based models to support operational hydrology, long-term water monitoring, and decision-making for flood and drought risk mitigation in inland-to-coastal environments. The methodology is also extendable to other riverine systems for inter-basin comparative assessments under varying hydraulic regimes.