HR: 11:10h
AN: H52A-03    [Abstracts]
TI: Stochastic Models of Long-Term Hydrological Data using a Bayesian Approach: The Challenges of Multi-site Data
AU: * Thyer, M A
EM: mark.thyer@newcastle.edu.au
AF: School of Engineering University of Newcastle, University Drive, Newcastle, NSW 2305 Australia
AU: Frost, A J
EM: a.frost@bom.gov.au
AF: Co-operative Research Centre for Catchment Hydrology, Hydrology Unit Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001 Australia
AU: Kuczera, G A
EM: george.kuczera@newcastle.edu.au
AF: School of Engineering University of Newcastle, University Drive, Newcastle, NSW 2305 Australia
AB: Multi-site stochastic simulations of long-term rainfall and streamflow are used as hydrological inputs for water resource allocation models used to estimate drought risks. A general framework for evaluating the performance of competing multi-site stochastic model parameterisations has been developed. This framework includes a Bayesian approach to quantify parameter uncertainty. Diagnostics used to evaluate model performance are their ability to reproduce important extreme observed statistics and Bayes Factors to calculate model probabilities. Current models included in this framework are the lag-one autoregressive model and the two-state hidden Markov model. The challenges in implementing a Bayesian approach for these multi-site stochastic models will be outlined. The case study used is the hydrological data from Sydney's main water supply catchment; the Warragamba Catchment. The practical impact of evaluating parameter uncertainty is confirmed by illustrating that extreme drought risks are significantly underestimated if parameter uncertainty is ignored.
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2005 Joint Assembly