Global scale river discharge and mean depth from radar altimetry and model
Abstract
Rivers are vital for water security, yet scalable freshwater monitoring and global knowledge of river mean depth and discharge remains limited by sparse in situ data, satellite observations of river surface only and global river flow estimations relying on models with insufficient hydraulic information. Without a robust global product for river bathymetry derived from satellite altimetry, large-scale hydrodynamic assessments remain limited. Here we present a near-global dataset of discharge rating curves, and river mean depths at 26 000+ river locations, integrating satellite water elevation observations with a global discharge monthly reanalysis over 30 years. Climatic anomaly-filtering and Bayesian calibration provide robust river elevation-discharge relationships yielding median KGE between rated and modeled discharge of 0.41. We produce reliable discharge estimates, validated against public discharge databases, and river mean depth derived from multiple satellite altimetry missions across diverse hydroclimates and flow conditions, along with uncertainty. These results offer a scalable, transferable framework for deriving hydraulic geometry relationships from satellite-model synergies. The approach is likely to improve with the emergence of future high-resolution missions, as well as advancements in global hydrology models and datasets.
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BibTeX
@article{paris2026global,
title = {{Global scale river discharge and mean depth from radar altimetry and model}},
author = {Adrien Paris and Pierre-André Garambois and Laetitia Gal and Arnaud Cerbelaud and Kévin Larnier and Cédric H. David and Rômulo Augusto Jucá Oliveira and Sly Wongchuig Correa and Mohammad J. Tourian and Stéphane Calmant},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78275-5},
url = {https://www.nature.com/articles/s41467-026-78275-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Global scale river discharge and mean depth from radar altimetry and model
AU - Adrien Paris
AU - Pierre-André Garambois
AU - Laetitia Gal
AU - Arnaud Cerbelaud
AU - Kévin Larnier
AU - Cédric H. David
AU - Rômulo Augusto Jucá Oliveira
AU - Sly Wongchuig Correa
AU - Mohammad J. Tourian
AU - Stéphane Calmant
PY - 2026
DA - 2026/10/05
JO - Nature Communications
DO - 10.1038/s41467-026-78275-5
UR - https://www.nature.com/articles/s41467-026-78275-5
AB - Rivers are vital for water security, yet scalable freshwater monitoring and global knowledge of river mean depth and discharge remains limited by sparse in situ data, satellite observations of river surface only and global river flow estimations relying on models with insufficient hydraulic information. Without a robust global product for river bathymetry derived from satellite altimetry, large-scale hydrodynamic assessments remain limited. Here we present a near-global dataset of discharge rating curves, and river mean depths at 26 000+ river locations, integrating satellite water elevation observations with a global discharge monthly reanalysis over 30 years. Climatic anomaly-filtering and Bayesian calibration provide robust river elevation-discharge relationships yielding median KGE between rated and modeled discharge of 0.41. We produce reliable discharge estimates, validated against public discharge databases, and river mean depth derived from multiple satellite altimetry missions across diverse hydroclimates and flow conditions, along with uncertainty. These results offer a scalable, transferable framework for deriving hydraulic geometry relationships from satellite-model synergies. The approach is likely to improve with the emergence of future high-resolution missions, as well as advancements in global hydrology models and datasets.
SN - 2041-1723
ER -