Paris et al. (2026) Altimetric Rating Curves (ARCs) parameters from "Global Scale River Discharge and Mean Depth from Radar Altimetry and Model"

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-03-30
• Authors: Adrien Paris, Pierre-Andre Garambois, Laëtitia Gal, Arnaud Cerbelaud, Kévin Larnier, C. H. David, Rômulo Augusto Jucá Oliveira, Sly Correa Wongchuig, Mohammad J. Tourian, Stéphane Calmant
• DOI: 10.5281/zenodo.19236472

Research Groups

• Research groups associated with the authors (Paris, Garambois, Gal, Cerbelaud, Larnier, David, Juca Oliveira, Wongchuig, Tourian, Calmant)
• Hydroweb-Next database initiative
• MeanDRS reanalysis development team (Collins et al., 2024)
• CHIRPS v2 precipitation data providers
• Global Runoff Data Centre (GRDC)
• Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations (SCHAPI)

Short Summary

This study generates a global dataset of calibrated power-law rating curve parameters and associated discharge statistics for over 32,000 river virtual stations by combining satellite altimetry water surface elevation time series with a 30-year global monthly river discharge reanalysis, enabling the first near-global satellite-derived map of mean river depth.

Objective

• To develop and provide a global dataset of river rating curves and derived mean depths by combining nadir satellite altimetry water surface elevation time series with a global monthly river discharge reanalysis.

Study Configuration

• Spatial Scale: Global, covering over 32,000 river virtual stations across six continents, spanning a wide range of river sizes and hydroclimatic regimes.
• Temporal Scale: Utilizes a 30-year global monthly river discharge reanalysis and CHIRPS v2 precipitation data from 1981–2009 for reference, with altimetry time series varying per station.

Methodology and Data

• Models used:
  • Power-law rating curve: Q = a⋅(WSE - z₀)b
  • Bayesian Markov Chain Monte Carlo (MCMC) for parameter estimation.
  • Monthly Mean Rating Curve (MMRC) and Quantile-Quantile Rating Curve (QQRC) calibration approaches.
• Data sources:
  • Nadir satellite altimetry Water Surface Elevation (WSE) time series from the Hydroweb-Next database.
  • 30-year global monthly river discharge reanalysis (MeanDRS; Collins et al., 2024), bias-corrected through long-term inverse routing at approximately 1,000 gauging stations.
  • CHIRPS v2 precipitation data (1981–2009 reference period) for identifying anomalous hydroclimatic years.
  • In situ streamflow records from global and national gauge networks (GRDC, SCHAPI, and others) for independent validation.

Main Results

• A dataset providing calibrated power-law rating curve parameters (a, b, z₀) and associated discharge statistics for over 32,000 river virtual stations globally.
• Parameter estimation was performed via Bayesian MCMC, yielding optimal values and uncertainty estimates (standard deviations) for each parameter.
• Two calibration approaches were used: Monthly Mean Rating Curve (MMRC) when WSE and discharge records overlap, and Quantile-Quantile Rating Curve (QQRC) for the majority (~93%) of cases without temporal overlap.
• Anomalous hydroclimatic years were identified and removed using CHIRPS v2 precipitation data to reduce the influence of extreme ENSO-driven events.
• The effective mean river depth (heq = Z̄(t) − z₀) can be approximated at each station, constituting the first near-global satellite-derived map of mean river depth.
• Performance metrics (KGE, NSE, nRMSE, pBIAS, R²) are provided, reflecting goodness of fit against MeanDRS for calibration and independent validation accuracy against in situ gauges.
• Performance is generally stronger in large to very large rivers (discharge > 100 m³/s) and in basins where MeanDRS has been bias-corrected, with lower scores in arid/semi-arid regions, areas with strong human regulation, and non-perennial rivers.

Contributions

• Provides the first near-global satellite-derived map of mean river depth.
• Generates a comprehensive global dataset of calibrated power-law rating curves for over 32,000 virtual stations, combining satellite altimetry and a global hydrological reanalysis.
• Offers a robust methodology for deriving river discharge and depth at a global scale, particularly valuable in data-scarce regions.
• Includes both optimal parameter values and their uncertainty estimates, enhancing the reliability of the dataset.
• Addresses the common challenge of non-overlapping altimetry and discharge records through the innovative Quantile-Quantile Rating Curve (QQRC) approach.

Funding

Funding information for this specific research is not provided in the given text.

Citation

@article{Paris2026Altimetric,
  author = {Paris, Adrien and Garambois, Pierre-Andre and Gal, Laëtitia and Cerbelaud, Arnaud and Larnier, Kévin and David, C. H. and Oliveira, Rômulo Augusto Jucá and Wongchuig, Sly Correa and Tourian, Mohammad J. and Calmant, Stéphane},
  title = {Altimetric Rating Curves (ARCs) parameters from "Global Scale River Discharge and Mean Depth from Radar Altimetry and Model"},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19236472},
  url = {https://doi.org/10.5281/zenodo.19236472}
}