HR: 1340h
AN: H13B-0404    [Abstracts]
TI: Representing nonstationarity in the rainfall-runoff relationship using a Hierarchical Mixture of Experts model
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: 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: Nott, D
EM: djn@maths.unsw.edu.au
AF: School of Mathematics, The University of New South Wales, Sydney, NSW 2052 Australia
AB: The evaluation and comparison of hydrological models has long been a challenge to the practicing hydrological community. With the variety of models available, modelers are faced with the problem of determining which is best for a particular modeling exercise. Here we present an alternative where, instead of choosing a single model, the catchment is allowed to dynamically exist in multiple hydrologic states. Each such state is represented by a unique rainfall-runoff model, and the "switch" from one state to the other occurs probabilistically depending on the catchment antecedent conditions. This new modeling framework, known as Hierarchical Mixture of Experts (HME), is applied to a number of Australian catchments having varying attributes. Results from this application are compared to the alternative where only one dominant hydrologic state is assumed to exist. The proposed alternatives are developed under a Bayesian framework, which is ideally suited here given the probabilistic basis of the HME model. We conclude by discussing how the HME modeling framework can be used for Predictions in Ungauged Basins (PUB). The traditional approach to PUB suffers from several drawbacks including the necessity of using a rigid modeling framework that can be easily parameterized and the requirement that the regionalization is applicable to the ungauged catchment it is intended for use in. We theorise instead that one may use an HME framework with a finite number of states to model multiple catchments, with the selection of each state depending on catchment characteristics and the modeled antecedent conditions.
DE: 1836 Hydrologic budget (1655)
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting