Investigating SWOT observations for river hydrodynamics: Evidences from the Po River

This study presents an evaluation of the Surface Water and Ocean Topography (SWOT) mission for monitoring riverine hydrodynamics, using the Po River (Northern Italy) as a test case. Given the recent launch of SWOT, its application to river hydraulics remains relatively unexplored and requires thorough validation against in-situ observations and specially, physically based models. We assess SWOT Water Surface Elevation (WSE), water surface slope, and recently released discharge products during the science phase, by comparing them with gauge measurements and hydrodynamic simulations over a ~300 km reach of the Po River, including the delta section (a coupled 1D/2D HEC-RAS model is employed to dynamically simulate river hydraulics).

The analysis integrates multiple SWOT products, including the Level-2 High-Rate Pixel Cloud (SWOT_L2_HR_PIXC) and Level-2 River Single-Pass Vector Product (SWOT_L2_HR_RiverSP - RiverSP), explicitly accounting for quality flags and performance under different flow regimes.

Results highlight the critical importance of quality-aware filtering for reliable use of SWOT observations. Good- and degraded-flagged WSE data generally show strong agreement with both in-situ measurements and model simulations, whereas suspect and bad flagged observations exhibit significantly larger discrepancies, with deviations reaching several meters. The analysis also reveals spatial patterns in SWOT performance linked to geophysical and orbital factors.

Comparison of RiverSP WSE node data against 10 in-situ stations shows biases up to ~10 cm for good and degraded data, with mean Kling–Gupta Efficiency (KGE) values of 0.92 and 0.82 for high-flow and low-flow regimes, respectively. Across all 114 analysed SWOT passes (each representing a longitudinal river WSE profile), on average, 68% of node observations per pass, are flagged as good or degraded, while 12 passes contain no usable data. Nevertheless, approximately 82% of the profiles show high agreement with the hydrodynamic model (KGE ≥ 0.8), highlighting the strong performance of SWOT in reproducing river WSE profiles. Profile-based comparisons also reveal orbit-dependent performance variability among different SWOT passes.

To address data limitations and improve spatial coverage, complementary Pixel Cloud products are leveraged for their higher spatial resolution, although these require extensive preprocessing, including spatial filtering and noise/outlier removal.

The study further explores the spatial and temporal performance of SWOT observations in relation to (i) distance from nadir track, (ii) satellite pass orientation, (iii) river planform geometry (e.g. straight vs. meandering reaches), and (iv) flow regime (e.g. rising limb, peak, recession, low flow). Although based on a single case study, the results illustrate both the potential and current limitations of SWOT products for riverine applications. The findings emphasize the importance of integrating quality-controlled satellite observations with physically based hydrodynamic models to support operational hydrology, long-term monitoring, and decision-making for flood and drought risk mitigation in inland-to-coastal environments. The proposed methodology is readily transferable to other river systems for inter-basin comparative analyses under diverse hydraulic conditions.