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Volume 20, issue 2 | Copyright
Hydrol. Earth Syst. Sci., 20, 887-901, 2016
https://doi.org/10.5194/hess-20-887-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Feb 2016

Research article | 26 Feb 2016

Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

Susana Almeida1,2, Nataliya Le Vine2, Neil McIntyre2,3, Thorsten Wagener1,4, and Wouter Buytaert2 Susana Almeida et al.
  • 1Department of Civil Engineering, University of Bristol, Bristol, UK
  • 2Department of Civil and Environmental Engineering, Imperial College London, London, UK
  • 3Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia
  • 4Cabot Institute, University of Bristol, Bristol, UK

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.

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The absence of flow data to calibrate hydrologic models may reduce the ability of such models to reliably inform water resources management. To address this limitation, it is common to condition hydrological model parameters on regionalized signatures. In this study, we justify the inclusion of larger sets of signatures in the regionalization procedure if their error correlations are formally accounted for and thus enable a more complete use of all available information.
The absence of flow data to calibrate hydrologic models may reduce the ability of such models to...
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