HR: 16:45h
AN: H14A-04    [Abstracts]
TI: Hypothesis Testing by Model Rejection
AU: Freer, J
EM: j.freer@lancaster.ac.uk
AF: Lancaster University, Environmental Science, Lancaster, LA1 4YQ United Kingdom
AU: * Beven, K
EM: k.beven@lancaster.ac.uk
AF: Lancaster University, Environmental Science, Lancaster, LA1 4YQ United Kingdom
AU: Smith, P J
EM: p.j.smith@lancaster.ac.uk
AF: Lancaster University, Environmental Science, Lancaster, LA1 4YQ United Kingdom
AB: Most applications of models in hydrology which make use of calibration against observations do not allow for model rejection but rather accept the optimal model as having value in prediction. The calibration process, including statistical methods of inference, is in fact generally carried out under the implicit assumption that the chosen model is correct. Only rarely are models, as competing hypotheses about how a system under study functions, compared within a framework that allows model rejection. Even rarer are studies reported that reject all the models tried when they show features in their response that do not match the available observations. This has perhaps been considered allowable because of the multiple sources of uncertainty in the modelling process. Even if the model might be correct, the forcing data used to drive it might be in error, the effective parameter values used might be in error, and the observations with which the model is compared might be in error. Thus model evaluation (and rejection) must take account of these different sources of uncertainty. This paper outlines a method for doing so as an extension of the GLUE methodology and demonstrates an application to rainfall-runoff modelling.
DE: 1860 Runoff and streamflow
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting