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Global patterns in river water storage dependent on residence time
Abstract
Accurate assessment of global river flows and stores is critical for informing water management practices, but current estimates of global river flows exhibit substantial spread and estimates of river stores remain sparse. Estimates of river flows and stores are hampered by uncertainties in land runoff, an unobserved quantity that provides water input to rivers. Here we leverage global river flow observations and an ensemble of land surface models to generate a globally gauge-corrected monthly river flow and storage dataset. We estimate a global river storage mean (± monthly variability) of 2,246 ± 505 km 3 and a global continental flow of 37,411 ± 7,816 km 3 yr −1 . Our global river water storage time series demonstrates that flow wave residence time is a fundamental driver that can double or halve river water stores and their variability. We also reconcile the wide range in previous estimates of monthly variability in global river flows. We identify previously underappreciated freshwater sources to the ocean from the Maritime Continent (Indonesia, Malaysia and Papua New Guinea) amounting to 1.6 times the Congo River and illustrate our capability of detecting severe anthropogenic water withdrawals.
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“Here, we generate a near global-scale set of stage-discharge parametric rating curves, building on the spatial coverage of the Hydroweb-Next nadir altimetry database and on MeanDRS, a monthly river hydrology model unbiased by long-term inverse routing50.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [50]
In the citing paper
“The Mean Discharge Runoff and Storage database (MeanDRS;50) provides global estimates of monthly discharge, which are bias-corrected using in situ data from 1000 global gauges over the 1980-2009 period using the long-term inverse routing method (except for Antarctica).”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [50]
In the citing paper
“By synergizing 30 years of nadir altimetry data with the global bias-corrected MeanDRS river discharge dataset50, we construct continuous, long-term discharge time series that transcend the limitations of individual satellite missions.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [50]
In the citing paper
“We rigorously validated our rated discharge estimates using independent in situ databases from regional and international sources, providing 3,378 gauged river points for comparison to evaluate the rating curves independently of the input hydrological model50.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [50]
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“The low bias may reflect good correlation, potentially driven by higher-quality altimetry in larger rivers and cummulative effect of the bias correction method employed by ref. 50 leading to unbiased model estimates at downstream stations of large, integrative basins.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [50]
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Cited by
Global scale river discharge and mean depth from radar altimetry and model
Paris, A., Garambois, P.-A., Gal, L., Cerbelaud, A., Larnier, K., David, C. H., Jucá Oliveira, R. A., Correa, S. W., Tourian, M. J., & Calmant, S. (2026) · Nature Communications · doi:10.1038/s41467-026-78275-5
Self-citation
Metadata
- Authors
- Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M.
- ORCID
- Venue
- Nature Geoscience
- Published
- 2024-04-22
- Volume
- 17
- Issue
- 5
- Pages
- 433-439
- ISSN
- 1752-0894
- 1752-0908
- Cited by
- 46
- References
- 0
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- NASA | Jet Propulsion Laboratory (NNH17ZDA001N-THP, NNH20ZDA001N-EUSPI:A.30)
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