Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression

Nguyen-Huy, Thong ORCID: and Deo, Ravinesh C. ORCID: and Mushtaq, Shahbaz and Khan, Shahjahan ORCID: (2020) Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression. In: Handbook of probabilistic models. Elsevier (Butterworth-Heinemann), Oxford, United Kingdom, pp. 203-227. ISBN 978-0-12-816514-0


Skillful probabilistic seasonal rainfall forecasts play a vital role in supporting water resource users, developing agricultural risk-management plans, and improving decision-making processes. This chapter applies a novel statistical copula-based approach to develop a probabilistic seasonal rainfall forecast model using multiple large-scale oceanic and atmospheric climate indices. Here, a d-vine copula is used to forecast the seasonal cumulative rainfall in 16 weather stations across the Australia's Wheatbelt. These stations span different climate conditions recording historical data for the period 1889–2012. The seasonal rainfalls are forecast in different quantile levels using different climate predictor data sets. The corrected Akaike information criterion (AIC)–conditional log-likelihood is then used to screen the most influential covariates to be additively incorporated into the multivariate probabilistic forecast model, resulting in a parsimonious predictive model. The mutually inclusive correlations between El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices and seasonal rainfall are found to be statistically significant. Therefore, using the climate information, skillful rainfall forecasts can be made three to 6 months ahead. The d-vine copula model is found to outperform the traditional quantile regression methods in forecasting rainfall in the median and the upper levels. The information from lagged, concurrent, and combined climate indices is therefore demonstrated to be a potentially useful predictor for forecasting seasonal rainfall in Australia's Wheatbelt region.

Statistics for USQ ePrint 37214
Statistics for this ePrint Item
Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published chapter, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 22 Oct 2019 23:33
Last Modified: 12 Jun 2020 05:24
Uncontrolled Keywords: climate indices; conditional forecast; quantile regression; rainfall prediction; vine copulas
Fields of Research (2008): 01 Mathematical Sciences > 0104 Statistics > 010404 Probability Theory
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070301 Agro-ecosystem Functionand Prediction
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences

Actions (login required)

View Item Archive Repository Staff Only