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Extending global river gauge records using satellite observations
Abstract
Long-term, continuous, and real-time streamflow records are essential for understanding and managing freshwater resources. However, we find that 37% of publicly available global gauge records ( N = 45 837) are discontinuous and 77% of gauge records do not contain real-time data. Historical periods of social upheaval are associated with declines in gauge data availability. Using river width observations from Landsat and Sentinel-2 satellites, we fill in missing records at 2168 gauge locations worldwide with more than 275 000 daily discharge estimates. This task is accomplished with a river width-based rating curve technique that optimizes measurement location and rating function (median relative bias = 1.4%, median Kling-Gupta efficiency = 0.46). The rating curves presented here can be used to generate near real-time discharge measurements as new satellite images are acquired, improving our capabilities for monitoring and managing river resources.
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“This empirical method relies on simultaneous in situ measurements of water level and flow, which are labor-intensive and increasingly scarce as global monitoring networks decline7,8.”
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Yang, X., Pavelsky, T. M., Allen, G. H., & Donchyts, G. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2920225 · Cited by 156
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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
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Metadata
- Authors
- Riggs, R. M., Allen, G. H., Wang, J., Pavelsky, T. M., Gleason, C. J., David, C. H., & Durand, M.
- ORCID
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2023-05-26
- Volume
- 18
- Issue
- 6
- Pages
- 064027
- ISSN
- 1748-9326
- Cited by
- 43
- References
- 1
- Licence
- creativecommons.org/licenses/by/4.0 (vor)
- iopscience.iop.org/info/page/text-and-data-mining (tdm)
- Funders
- NASA’s Terrestrial Hydrology Program (NNH17ZDA001N-THP)
- NASA SWOT Science Team (NNH19ZDA001N-SWOTST)
- NASA SWOT Project Office
- Texas A&M Presidential Excellence Fund
- Texas Space Grant Consortium
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