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Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina

Kokkonen, Teemu; Jakeman, Anthony; Young, Peter C; Koivusalo, Harri

Description

Regionalization approaches to daily streamflow prediction are investigated for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters). Model parameters are considered to represent the dynamic response characteristics (DRCs) of a catchment. It is demonstrated that all catchments within the region cannot be assumed to have a similar hydrological behaviour, and thence a regionalization approach considering differences in...[Show more]

dc.contributor.authorKokkonen, Teemu
dc.contributor.authorJakeman, Anthony
dc.contributor.authorYoung, Peter C
dc.contributor.authorKoivusalo, Harri
dc.date.accessioned2015-12-13T23:12:14Z
dc.date.available2015-12-13T23:12:14Z
dc.identifier.issn0885-6087
dc.identifier.urihttp://hdl.handle.net/1885/87954
dc.description.abstractRegionalization approaches to daily streamflow prediction are investigated for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters). Model parameters are considered to represent the dynamic response characteristics (DRCs) of a catchment. It is demonstrated that all catchments within the region cannot be assumed to have a similar hydrological behaviour, and thence a regionalization approach considering differences in physical catchment descriptors (PCDs) is required. Such a regionalization approach can be regarded as a top-down method, in the sense that factors controlling parameter variability are identified first within the entire region under study, and then such information is exploited to predict runoff in a smaller sub-region. Regionalization results reveal that consideration of interrelations between dependent variables, which here are the parameters of the rainfall-runoff model, can improve performance of regression as a regionalization method. Breaking the parameter correlation structure inherent in the model, and exploiting merely relationships between model parameters and PCDs (no matter how weakly related they are), can result in a significant decrease in regionalization performance. Also, high significance of regression between values of PCDs and DRCs does not guarantee a set of parameters with a good predictive power. When there is a reason to believe that, in the sense of hydrological behaviour, a gauged catchment resembles the ungauged catchment, then it may be worthwhile to adopt the entire set of calibrated parameters from the gauged catchment instead of deriving quantitative relationships between catchment descriptors and model parameters.
dc.publisherJohn Wiley & Sons Inc
dc.sourceHydrological Processes
dc.subjectKeywords: Calibration; Hydrology; Rain; Regression analysis; Stream flow; Gauged catchments; Catchments; catchment; hydrological modeling; parameterization; prediction; rainfall-runoff modeling; regionalization; streamflow; United States Hydrological modelling; Rainfall-runoff; Regionalization
dc.titlePredicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume17
dc.date.issued2003
local.identifier.absfor050299 - Environmental Science and Management not elsewhere classified
local.identifier.ariespublicationMigratedxPub17450
local.type.statusPublished Version
local.contributor.affiliationKokkonen, Teemu, Helsinki University of Technology
local.contributor.affiliationJakeman, Anthony , College of Medicine, Biology and Environment, ANU
local.contributor.affiliationYoung, Peter C, Lancaster University
local.contributor.affiliationKoivusalo, Harri, Helsinki University of Technology
local.bibliographicCitation.startpage2219
local.bibliographicCitation.lastpage2238
local.identifier.doi10.1002/hyp.1329
dc.date.updated2015-12-12T08:30:50Z
local.identifier.scopusID2-s2.0-0042231195
CollectionsANU Research Publications

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