HR: 10:40h
AN: H12C-02    [Abstracts]
TI: Identifying a Switch in the Catchment State through a Hierarchical Mixture of Experts model
AU: Sharma, A
EM: a.sharma@unsw.edu.au
AF: School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052 Australia
AU: * Marshall, L A
EM: lucy@civeng.unsw.edu.au
AF: School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052 Australia
AU: Nott, D
EM: djn@maths.unsw.edu.au
AF: School of Mathematics, The University of New South Wales, Sydney, NSW 2052 Australia
AB: Despite the abundance of models to describe the rainfall-runoff process, there is not a single model that will perform reliably over the range of possible catchment types and conditions. Evidence exists of the catchment responding differently under different antecedent conditions, so using a model with a rigid structure can lead to significant bias in the modelled hydrograph. Consider for many conceptual models, could it be said that the saturation excess overland flow mechanism is valid for all seasons, for dry and wet antecedent conditions? Could it be possible that the true model is much more complex and fluctuates between two or more model states? An alternative approach to selecting a single model is to combine the results from several hydrological models. Methods based on Bayesian statistical techniques provide a means to compare and combine competing models whilst allowing for model uncertainty. A framework that allows different modelling configurations to apply in multiple hydrologic states is presented. Each model configuration is adopted at a given time with a probability that depends on the current hydrologic "state" of the catchment. This framework is known as a Hierarchical Mixture of Experts (HME). The HME model framework is applied to a range of Australian catchments of varying attributes using an established conceptual model. Results from this application are compared to the alternative where the hydrologic state is assumed to be stationary. The comparison is performed under a Bayesian framework, enabling assessment of the accuracy of each alternative irrespective of the complexity the model contains. Due to the probabilistic basis under which the model exists, Bayesian techniques form an ideal basis to identify the distribution of the parameters.
DE: 1836 Hydrologic budget (1655)
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting