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A Bayesian Hierarchical Approach for Including Climate Mechanisms into Stochastic Hydrological Models

Congress: 2008
Author(s): Mark A. Thyer


Keyword(s): Bayesian hierarchical, Interdecadal Pacific Oscillation, IPO, climate variability, drought risk, stochastic model
AbstractIntroduction: The impact of climate variability and climate change on water supply drought security is currently the subject of considerable uncertainty. Water resource planners feel ill-equipped to provide reliable estimates of future drought security due to the shortcomings of the current suite of stochastic models. For example, there is considerable evidence that hydrological data are dynamically modulated by long-term climatic processes such as the Interdecadal Pacific Oscillation (IPO) and El Niño Southern Oscillation (ENSO). These processes show hydrological persistence and could be used to improve simulations. However, most of the stochastic models currently used to simulate hydrological data do not have mechanisms to emulate this long-term climate variability. Objective: The aim is to improve the stochastic model’s ability to characterise both the short and long-term variability of hydrological data. Method: A Bayesian hierarchical framework that can utilise climate indices, such as the IPO and Southern Oscillation Index (SOI), to inform a stochastic model of annual hydrological data is presented. Results: The utility of this approach is demonstrated by an improvement in the reproduction of several key long-term observed statistics and rare drought risk estimates for sites in water supply catchments in NSW, Australia. Conclusion: This framework has the capability of utilising data other than hydrological sources to incorporate climate variability on multiple time scales. Such an approach demonstrates the shortcomings of the current suite of stochastic hydrological models and the importance of including climate mechanisms in stochastic modelling in order to better characterise hydrological variability.
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