Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

Almeida, Susana, Le Vine, Nataliya, McIntyre, Neil, Wagener, Thorsten and Buytaert, Wouter (2016) Accounting for dependencies in regionalized signatures for predictions in ungauged catchments. Hydrology and Earth System Sciences, 20 2: 887-901. doi:10.5194/hess-20-887-2016


Author Almeida, Susana
Le Vine, Nataliya
McIntyre, Neil
Wagener, Thorsten
Buytaert, Wouter
Title Accounting for dependencies in regionalized signatures for predictions in ungauged catchments
Journal name Hydrology and Earth System Sciences   Check publisher's open access policy
ISSN 1607-7938
1027-5606
Publication date 2016-02-26
Year available 2016
Sub-type Article (original research)
DOI 10.5194/hess-20-887-2016
Open Access Status DOI
Volume 20
Issue 2
Start page 887
End page 901
Total pages 15
Place of publication Goettingen, Germany
Publisher Copernicus
Language eng
Abstract A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall–runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall–runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.
Keyword Geosciences, Multidisciplinary
Water Resources
Geology
Water Resources
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID SFRH/BD/65522/2009
NE/J017450/1
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Centre for Water in the Minerals Industry
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