Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones

Nguyen-Huy, Thong and Deo, Ravinesh C. and An-Vo, Duc-Anh and Mushtaq, Shahbaz and Khan, Shahjahan (2017) Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones. Agricultural Water Management, 191. pp. 153-172. ISSN 0378-3774


Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Niño Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Niña event is more evident than during an El Niño event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Niña and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version, in accordance with the copyright policy of the publisher. The project was financed by University of Southern Queensland Postgraduate Research Scholarship (USQPRS 2015–2017), School of Agricultural, Computational and Environmental Sciences and Strategic Research Funding (SRF) Projects (Resilient Landscapes SRF and Computational Models SRF).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 30 Jun 2017 02:16
Last Modified: 23 Apr 2018 01:02
Uncontrolled Keywords: copula-statistical models; seasonal precipitation forecasting; vine copulas; joint distribution; goodness of fit; climate indices
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
Identification Number or DOI: 10.1016/j.agwat.2017.06.010

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