Storylines: an alternative approach to representing uncertainty in physical aspects of climate change |
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| Authors: | Theodore G Shepherd Emily Boyd Raphael A Calel Sandra C Chapman Suraje Dessai Ioana M Dima-West Hayley J Fowler Rachel James Douglas Maraun Olivia Martius Catherine A Senior Adam H Sobel David A Stainforth Simon F B Tett Kevin E Trenberth Bart J J M van den Hurk Nicholas W Watkins Dimitri A Zenghelis |
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| Institution: | 1.School of Life Sciences, Faculty of Science,University of Technology Sydney,Sydney,Australia;2.NSW Department of Primary Industries,Wagga Wagga Agricultural Institute,Wagga Wagga,Australia;3.Climate Change Research Centre and ARC Centre of Excellence for Climate System Science,University of New South Wales,Sydney,Australia;4.NSW Office of Environment and Heritage,Queanbeyan,Australia;5.Agricultural College,Guangxi University,Nanning,China;6.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Northwest A&F University,Yangling,China;7.College of Resources and Environment,University of Chinese Academy of Science,Beijing,China |
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| Abstract: | Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPIJJA), Sep.–Nov. (SPISON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available. |
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