Fabrice Papa (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France , France); Charlotte Blondel (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France , France); Rodrigo Paiva (Instituto de Pesquisas Hidráulicas IPH, Universidade Federal do Rio Grande do Sul UFRGS, Brazil, Brazil); Vinícius Siqueira (Instituto de Pesquisas Hidráulicas IPH, Universidade Federal do Rio Grande do Sul UFRGS, Brazil, Brazil); Ayan Fleischmann (Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil , Brazil); Sylvain Biancamaria (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France , France); Adrien Paris (Hydro Matters, 1 Chemin de la Pousaraque, 31460 Le Faget, France, France); Stéphane Calmant (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France , France)

The Amazon Basin, the world’s largest hydrological system, plays a critical role in regulating the global water cycle, climate feedbacks, and biogeochemical fluxes such as carbon and methane. However, it remains one of the most challenging regions to model hydrologically due to sparse in-situ data and complex floodplain dynamics typical of low-gradient terrains. In our recent study (Wongchuig et al., 2024), we introduced a large-scale hydrological-hydrodynamic modeling framework using the MGB model ("Modelo de Grandes Bacias") combined with a Multi-Observation Local Ensemble Kalman Filter (MoLEnKF). This approach enables the simultaneous assimilation of satellite-derived variables, including soil moisture, terrestrial water storage, flood extent, and water surface elevation, into model state variables. This integration improves the accuracy of discharge and water level estimates across the Amazon Basin, surpassing the performance of referenced hydrologic-hydrodynamic models within this region. This study builds on previous experiments (Wongchuig et al., 2020) using synthetic SWOT observations, in which the ability of SWOT-like data to reduce model uncertainty and improve hydrologic estimates was demonstrated under uncalibrated global model settings. The integration of real SWOT data, which is now available from nearly two full hydrological years since the satellite launch in December 2022, signifies the subsequent phase in this progression.
The SWOT mission provides high-resolution measurements of river surface elevation, inundation extent, and slope for rivers wider than 100 meters. However, the efficacy of its discharge retrieval algorithms is constrained by the presence of uncertainties associated with bathymetry, roughness, and river network topology. In this context, the incorporation of SWOT data into conceptual-physically-based models such as MGB through data assimilation offers a robust alternative, blending physical principles with observational constraints. The development of the SWORD database further supports this integration by providing a standardized, satellite-compatible river network. As noted by Altenau et al. (2021), aligning SWOT-derived reaches with model structures is key to effective assimilation.
As emphasized by Häfliger et al. (2019) and Andreadis et al. (2020), SWOT’s global perspective is essential for closing data gaps and advancing predictions in ungauged basins worldwide. The objective of this study is to demonstrate the feasibility and added value of integrating real SWOT data (e.g., water surface elevation anomaly and river slope) and complementary satellite/in-situ data into a large-scale hydrological-hydrodynamic model through multi-observation data assimilation. In addressing this objective, the study will also examine key challenges such as observation uncertainty and river network inconsistency. Beyond the Amazon, this scalable approach has the potential to improve predictions in other data-scarce tropical basins such as the Congo and Mekong. The integration of SWOT’s high-resolution observations with dynamic modeling enhances our capacity to monitor, predict, and manage freshwater systems in the face of accelerating environmental change.

References :
Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang, X., Frasson, R. P. de M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. Water Resources Research, 57(7). https://doi.org/10.1029/2021WR030054
Andreadis, K. M., Brinkerhoff, C. B., & Gleason, C. J. (2020). Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations. Water Resources Research, 56(5), 1–38. https://doi.org/10.1029/2019WR026611
Häfliger, V., Martin, E., Boone, A., Ricci, S., & Biancamaria, S. (2019). Assimilation of synthetic SWOT river depths in a regional hydrometeorological model. Water (Switzerland), 11(1). https://doi.org/10.3390/w11010078
Wongchuig, S., Cauduro Dias de Paiva, R., Biancamaria, S., & Collischonn, W. (2020). Assimilation of future SWOT-based river elevations, surface extent observations and discharge estimations into uncertain global hydrological models. Journal of Hydrology, 590, 125473–125473. https://doi.org/10.1016/j.jhydrol.2020.125473
Wongchuig, S., Paiva, R