Modelling and understanding the hierarchy in a mixture of experts using multiple catchment descriptors

• Extends specification of a hierarchical mixture of experts (HME) catchment modelling framework.
• Presents basis for choosing a HME gating function knowing only general catchment attributes.
• Validates the HME gating function classification framework using data from 50 gauged catchments.

SummaryModelling with a mixture of experts represent a platform for probabilistic combination of responses from multiple hydrologic models, thereby better expressing the uncertainty associated with the use of a single stand-alone model structure. In hydrology, the mixture of experts framework has previously been applied successfully and demonstrated to address modelling uncertainty and improving the goodness-of-fit with respect to the observed runoff (Jeremiah et al., 2013, Marshall et al., 2006 and Marshall et al., 2007b).The key to successful reduction in model uncertainty through the mixture of experts architecture lies in the specification of the associated gating function. The gating function models calculate the probability of selecting each component using a range of plausible predictor variables. The predictors used in this function include derived or modelled indicators of the catchment state that force the different component model forms to be used. A previous article by the authors assessed the advantages of identifying multiple predictor variables in the gating function. The present study takes this further, by attempting to relate the gating function and associated predictor variables to measurable catchment attributes. This is performed using data for over 50 catchments in Australia, the result being a classification of gating function complexity and formulation as a function of one or more catchment attributes. Formulation of the gating function using this classification enables users to specify the mixture of experts architecture over ungauged models, through a transposition of the model from gauged catchments via the classification proposed.

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