Using large-scale climate drivers to forecast meteorological drought condition in growing season across the Australian wheatbelt

Puyu Feng; Bin Wang; Jing-Jia Luo; De Li Liu; Cathy Waters; Fei Ji; Hongyan Ruan; Dengpan Xiao; Lijie Shi; Qiang Yu

doi:10.1016/j.scitotenv.2020.138162

Using large-scale climate drivers to forecast meteorological drought condition in growing season across the Australian wheatbelt

Sci Total Environ. 2020 Jul 1:724:138162. doi: 10.1016/j.scitotenv.2020.138162. Epub 2020 Mar 27.

Authors

Puyu Feng¹, Bin Wang², Jing-Jia Luo³, De Li Liu⁴, Cathy Waters⁵, Fei Ji⁶, Hongyan Ruan⁷, Dengpan Xiao⁸, Lijie Shi⁹, Qiang Yu¹⁰

Affiliations

¹ State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, Shaanxi, China; NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.
² NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
³ Institute for Climate and Application Research, Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing, China.
⁴ NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia; Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia.
⁵ NSW Department of Primary Industries, Dubbo, NSW 2830, Australia.
⁶ Department of Planning, Industry and Environment, Queanbeyan, NSW 2620, Australia.
⁷ Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Key Laboratory of Beibu Gulf Environment Change and Resources Use Utilization of Ministry of Education, Nanning Normal University, Nanning 530001, China.
⁸ Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, China.
⁹ NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia; School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia.
¹⁰ State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, Shaanxi, China; School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia; College of Resources and Environment, University of Chinese Academy of Science, Beijing 100049, China. Electronic address: yuq@nwafu.edu.cn.

PMID: 32247977
DOI: 10.1016/j.scitotenv.2020.138162

Abstract

Recurring drought has caused large crop yield losses in Australia during past decades. Long-term drought forecasting is of great importance for the development of risk management strategies. Recently, large-scale climate drivers (e.g. El Niño-Southern Oscillation) have been demonstrated as useful in the application of drought forecasting. Machine learning-based models that use climate drivers as input are commonly adopted to provide drought forecasts as these models are easy to develop and require less information compared to physical-based models. However, few machine learning-based models have been developed to forecast drought conditions during growing season across all Australian cropping areas. In this study, we developed a growing season (Apr.-Nov.) meteorological drought forecasting model for each climate gauging location across the Australian wheatbelt based on multiple lagged (past) large-scale climate indices and the Random Forest (RF) algorithm. The Standardized Precipitation Index (SPI) was used as the response variable to measure the degree of meteorological drought. Results showed that the RF model could provide satisfactory drought forecasts in the eastern areas of the wheatbelt with Pearson's correlation coefficient r > 0.5 and normalized Root Mean Square Error (nRMSE) < 23%. Forecasted drought maps matched well with observed drought maps for three representative periods. We identified NINO3.4 sea surface temperature and Multivariate ENSO Index as the most influential indices dominating growing season drought conditions across the wheatbelt. In addition, lagged impacts of large-scale climate drivers on growing season drought conditions were long-lasting and the indices in previous year could also potentially affect drought conditions during current year. As large-scale climate indices are readily available and can be rapidly used to feed data driven models, we believe the proposed meteorological drought forecasting models can be easily extended to other regions to provide drought outlooks which can help mitigate adverse drought impacts.

Keywords: Climate drivers; Drought forecasting; Machine learning; Random forest.