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dc.contributor.authorMiao, C
dc.contributor.authorGou, J
dc.contributor.authorFu, B
dc.contributor.authorTang, Q
dc.contributor.authorDuan, Q
dc.contributor.authorChen, Z
dc.contributor.authorLei, H
dc.contributor.authorChen, J
dc.contributor.authorGuo, J
dc.contributor.authorBorthwick, Alistair
dc.contributor.authorDing, W
dc.contributor.authorDuan, X
dc.contributor.authorLi, Y
dc.contributor.authorKong, D
dc.contributor.authorGuo, X
dc.contributor.authorWu, J
dc.date.accessioned2022-05-23T11:18:41Z
dc.date.available2022-05-23T11:18:41Z
dc.date.issued2022-03
dc.identifier.issn2095-9273
dc.identifier.issn2095-9281
dc.identifier.urihttp://hdl.handle.net/10026.1/19252
dc.description.abstract

Reconstruction of natural streamflow is fundamental to the sustainable management of water resources. In China, previous reconstructions from sparse and poor-quality gauge measurements have led to large biases in simulation of the interannual and seasonal variability of natural flows. Here we use a well-trained and tested land surface model coupled to a routing model with flow direction correction to reconstruct the first high-quality gauge-based natural streamflow dataset for China, covering all its 330 catchments during the period from 1961 to 2018. A stronger positive linear relationship holds between upstream routing cells and drainage areas, after flow direction correction to 330 catchments. We also introduce a parameter-uncertainty analysis framework including sensitivity analysis, optimization, and regionalization, which further minimizes biases between modeled and inferred natural streamflow from natural or near-natural gauges. The resulting behavior of the natural hydrological system is represented properly by the model which achieves high skill metric values of the monthly streamflow, with about 83% of the 330 catchments having Nash-Sutcliffe efficiency coefficient (NSE) > 0.7, and about 56% of the 330 catchments having Kling-Gupta efficiency coefficient (KGE) > 0.7. The proposed construction scheme has important implications for similar simulation studies in other regions, and the developed low bias long-term national datasets by statistical postprocessing should be useful in supporting river management activities in China.

dc.format.extent547-556
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectLand surface model
dc.subjectNatural streamflow
dc.subjectParameter uncertainty analysis
dc.subjectReconstruction
dc.subjectRivers
dc.subjectComputer Simulation
dc.subjectWater Resources
dc.subjectHydrology
dc.subjectChina
dc.titleHigh-quality reconstruction of China’s natural streamflow
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/36546176
plymouth.issue5
plymouth.volume67
plymouth.publication-statusPublished
plymouth.journalScience Bulletin
dc.identifier.doi10.1016/j.scib.2021.09.022
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeNetherlands
dcterms.dateAccepted2021-09-08
dc.identifier.eissn2095-9281
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.scib.2021.09.022
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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