Ensemble evaluation of hydrological model hypotheses

Tobias Krueger, Jim Freer, John N. Quinton, Christopher J. A. Macleod, Gary S. Bilotta, Richard E. Brazier, Patricia Butler, Philip M. Haygarth

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)

Abstract

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error.
Original languageEnglish
Article numberW07516
JournalWater Resources Research
Volume46
Issue number7
DOIs
Publication statusPublished - Jul 2010

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