Remote sensing-based extension of GRDC discharge time series : a monthly product with uncertainty estimates

dc.contributor.authorElmi, Omid
dc.contributor.authorTourian, Mohammad J.
dc.contributor.authorSaemian, Peyman
dc.contributor.authorSneeuw, Nico
dc.date.accessioned2025-07-16T08:23:55Z
dc.date.issued2024
dc.date.updated2025-01-27T16:05:16Z
dc.description.abstractThe Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km 3 /month.en
dc.description.sponsorshipDeutsche Forschungsgemeinschaft
dc.description.sponsorshipProjekt DEAL
dc.identifier.issn2052-4463
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-168030de
dc.identifier.urihttps://elib.uni-stuttgart.de/handle/11682/16803
dc.identifier.urihttps://doi.org/10.18419/opus-16784
dc.language.isoen
dc.relation.uridoi:10.1038/s41597-024-03078-6
dc.rightsCC BY
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titleRemote sensing-based extension of GRDC discharge time series : a monthly product with uncertainty estimatesen
dc.typearticle
dc.type.versionpublishedVersion
ubs.fakultaetLuft- und Raumfahrttechnik und Geodäsie
ubs.institutGeodätisches Institut
ubs.publikation.noppnyesde
ubs.publikation.seiten12
ubs.publikation.sourceScientific data 11 (2024), No. 240
ubs.publikation.typZeitschriftenartikel

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