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Establishing the hierarchy of influence of drivers of seasonal rainfall variability in South Australia to inform seasonal rainfall forecasting

Citation

Tozer, C and Kiem, AS and Verdon-Kidd, D, Establishing the hierarchy of influence of drivers of seasonal rainfall variability in South Australia to inform seasonal rainfall forecasting, Proceedings of the 36th Hydrology and Water Resources Symposium: the art and science of water, 7-10 December 2015, Hobart, Tasmania, pp. 1561-1568. ISBN 9781922107497 (2015) [Refereed Conference Paper]


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Official URL: http://hwrs2015.com.au/

Abstract

Seasonal rainfall forecasts are important for risk management in dryland and irrigated cropping, viticulture, emergency services, natural resource management and many other sectors. A key challenge in the development of skilful seasonal rainfall forecasts is the identification of the atmospheric and oceanic processes that drive the rainfall variability. Seasonal rainfall forecasts for South Australia (SA) currently have low predictive skill and we hypothesise that this is because the key drivers of SAs rainfall variability have yet to be fully identified and therefore are not adequately represented in the forecast models. Previously, much of the focus in Australia has been on determining the causes of seasonal and annual rainfall variability in eastern and Western Australia, with little research conducted on SAs rainfall variability. Therefore the aim of this study is to identify relationships between a host of potential climate drivers and seasonal rainfall across South Australia. First, a threshold method is used that accounts for the inherently non-linear nature of the links between large-scale climate phenomena and hydroclimatic variability. Then, a novel method for climate predictor selection is used to (a) identify the key combination of drivers that explains the most seasonal rainfall variability in different regions of SA and (b) determine the hierarchy of importance of the key drivers. This provides a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and will also lead to the development of improved (or new) statistical forecasting schemes.

Item Details

Item Type:Refereed Conference Paper
Keywords:risk management, rainfall probabilities, precipitation variability, data processing, mathematical models, rainfall variability, South Australia, rainfall forecasting, climate drivers, climate prediction
Research Division:Earth Sciences
Research Group:Atmospheric Sciences
Research Field:Climatology (excl. Climate Change Processes)
Objective Division:Environment
Objective Group:Climate and Climate Change
Objective Field:Climate Variability (excl. Social Impacts)
Author:Tozer, C (Dr Carly Tozer)
ID Code:109734
Year Published:2015
Deposited By:CRC-Antarctic Climate & Ecosystems
Deposited On:2016-06-28
Last Modified:2016-07-20
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