Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression
Edited book (chapter)
Nguyen-Huy, Thong, Deo, Ravinesh C., Mushtaq, Shahbaz and Khan, Shahjahan. 2020. "Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression." Samui, Pijush, Bui, Dieu Tien, Chakraborty, Subrata and Deo, Ravinesh C. (ed.) Handbook of probabilistic models. Oxford, United Kingdom. Elsevier. pp. 203-227
| Chapter Title | Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression |
|---|---|
| Book Chapter Category | Edited book (chapter) |
| ERA Publisher ID | 1821 |
| Book Title | Handbook of probabilistic models |
| Authors | Nguyen-Huy, Thong (Author), Deo, Ravinesh C. (Author), Mushtaq, Shahbaz (Author) and Khan, Shahjahan (Author) |
| Editors | Samui, Pijush, Bui, Dieu Tien, Chakraborty, Subrata and Deo, Ravinesh C. |
| Page Range | 203-227 |
| Chapter Number | 8 |
| Number of Pages | 25 |
| Year | 2020 |
| Publisher | Elsevier |
| Place of Publication | Oxford, United Kingdom |
| ISBN | 9780128165140 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/B9780128165140000084 |
| Abstract | 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. |
| Keywords | climate indices; conditional forecast; quantile regression; rainfall prediction; vine copulas |
| ANZSRC Field of Research 2020 | 490506. Probability theory |
| 410499. Environmental management not elsewhere classified | |
| 300402. Agro-ecosystem function and prediction | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
| Centre for Applied Climate Sciences | |
| Institution of Origin | University of Southern Queensland |
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