Imperial College London
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ProfessorWouterBuytaert

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor in Hydrology and Water Resources
 
 
 
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Contact

 

+44 (0)20 7594 1329w.buytaert Website

 
 
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Assistant

 

Miss Judith Barritt +44 (0)20 7594 5967

 
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Location

 

403ASkempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Almeida:2016:10.5194/hess-20-887-2016,
author = {Almeida, S and Le, Vine N and McIntyre, N and Wagener, T and Buytaert, W},
doi = {10.5194/hess-20-887-2016},
journal = {Hydrology and Earth System Sciences},
pages = {887--901},
title = {Accounting for dependencies in regionalized signatures for predictions in ungauged catchments},
url = {http://dx.doi.org/10.5194/hess-20-887-2016},
volume = {20},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Almeida,S
AU  - Le,Vine N
AU  - McIntyre,N
AU  - Wagener,T
AU  - Buytaert,W
DO  - 10.5194/hess-20-887-2016
EP  - 901
PY  - 2016///
SN  - 1607-7938
SP  - 887
TI  - Accounting for dependencies in regionalized signatures for predictions in ungauged catchments
T2  - Hydrology and Earth System Sciences
UR  - http://dx.doi.org/10.5194/hess-20-887-2016
UR  - http://hdl.handle.net/10044/1/32460
VL  - 20
ER  -
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