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Ensemble evaluation of hydrological model hypotheses
Author(s) -
Krueger Tobias,
Freer Jim,
Quinton John N.,
Macleod Christopher J. A.,
Bilotta Gary S.,
Brazier Richard E.,
Butler Patricia,
Haygarth Philip M.
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2009wr007845
Subject(s) - glue , field (mathematics) , lysimeter , flexibility (engineering) , scale (ratio) , surface runoff , environmental science , ensemble forecasting , computer science , hydrological modelling , econometrics , statistics , hydrology (agriculture) , mathematics , soil science , engineering , geotechnical engineering , machine learning , geology , climatology , ecology , soil water , mechanical engineering , physics , quantum mechanics , biology , pure mathematics
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.