Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

Susana Almeida, Nataliya Le Vine, Neil McIntyre, Thorsten Wagener, Wouter Buytaert

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
283 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)887-901
JournalHydrology and Earth System Sciences
Issue number2
Publication statusPublished - 26 Feb 2016


  • Hydrology
  • Rainfall runoff modeling

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