Under the influence of the Mediterranean climate, South Australia (SA) has significant extreme rainfall and temperature events and annual variability. In order to investigate the relationship between annual daily maximum rainfall (ADMR) and annual daily maximum temperature (ADMT) in SA, a series of statistical tests were carried out. Two kinds of null and alternative hypotheses were established based on the difference of weather stations and related altitudes. After analyzing descriptive statistics and using normality test, Mann-Whitney test, Kruskal-Wallis test and correlation and regression analysis were applied. In different weather stations of SA, ADMR/ADMT data vary significantly. Nonetheless, in different stations of SA with different altitudes, ADMR/ADMT data are not significantly different. Furthermore, there is a weak correlation between selected ADMR and ADMT data in SA. Thus, it is unsuitable to directly predict ADMR based on ADMT in SA, and vice versa. For policymakers of SA, they need to adjust measures to local conditions when making ADMR/ADMT related policies.

Zhuodi Yao. Variability of Annual Daily Maximum Rainfall Data of South Australia. Academic Journal of Environment & Earth Science (2025), Vol. 7, Issue 4: 101-106. https://doi.org/10.25236/AJEE.2025.070413.

[1] Ai, C, Huang, L & Zhang, Z 2020, ‘A Mann–Whitney test of distributional effects in a multivalued treatment’, Journal of Statistical Planning and Inference, vol. 209, pp. 85–100.

[2] Bureau of Meteorology 2018, Bureau of Meteorology, Australian Government, viewed 10 June 2020, <http://www.bom.gov.au/climate/data/?ref=ftr>.

[3] Ashcroft, L, Karoly, DJ & Dowdy, AJ 2019, ‘Historical extreme rainfall events in southeastern Australia’, Weather and Climate Extremes, vol. 25, p. 100210.

[4] Avila, FB, Dong, S, Menang, KP, Rajczak, J, Renom, M, Donat, MG & Alexander, LV 2015, ‘Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia’, Weather and Climate Extremes, vol. 9, pp. 6–16.

[5] Emura, T & Hsu, J-H 2020, ‘Estimation of the Mann–Whitney effect in the two-sample problem under dependent censoring’, Computational Statistics & Data Analysis, vol. 150, p. 106990.

[6] Evans, FH, Guthrie, MM & Foster, I 2020, ‘Accuracy of six years of operational statistical seasonal forecasts of rainfall in Western Australia (2013 to 2018)’, Atmospheric Research, vol. 233, p. 104697.

[7] Google Map 2020, Google Map, Google LLC (United States), viewed 10 June 2020, <https://www.google.com/maps>.

[8] Guo, S, Zhong, S & Zhang, A 2013, ‘Privacy-preserving Kruskal–Wallis test’, Computer Methods and Programs in Biomedicine, vol. 112, no. 1, pp. 135–145.

[9] Kamruzzaman, M, Beecham, S, Metcalfe, AV & Cai, W 2019, ‘Granger causal predictors for maximum rainfall in Australia’, Atmospheric Research, vol. 218, pp. 1–11.

[10] L. & Nicótina 2008, ‘On the impact of rainfall patterns on the hydrologic response’, Water Resources Research.

[11] Li, M & Gençay, R 2017, ‘Tests for serial correlation of unknown form in dynamic least squares regression with wavelets’, Economics Letters, vol. 155, pp. 104–110.

[12] Lim, EP, Hendon, HH, Anderson, DLT, Charles, A & Alves, O 2011, ‘Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall’, Monthly Weather Review, vol. 139, no. 3, pp. 958–975.

[13] Montazerolghaem, M, Vervoort, W, Minasny, B & McBratney, A 2016, ‘Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia’, Weather and Climate Extremes, vol. 13, pp. 1–14.

[14] Pérez, NP, Guevara López, MA, Silva, A & Ramos, I 2015, ‘Improving the Mann–Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography’, Artificial Intelligence in Medicine, vol. 63, no. 1, pp. 19–31.

[15] Ruxton, GD & Beauchamp, G 2008, ‘Some suggestions about appropriate use of the Kruskal–Wallis test’, Animal Behaviour, vol. 76, no. 3, pp. 1083–1087.

[16] Sadras, V, Roget, D & Krause, M 2003, ‘Dynamic cropping strategies for risk management in dry-land farming systems’, Agricultural Systems, vol. 76, no. 3, pp. 929–948.

[17] Sadras, VO, Lawson, C, Hooper, P & McDonald, GK 2012, ‘Contribution of summer rainfall and nitrogen to the yield and water use efficiency of wheat in Mediterranean-type environments of South Australia’, European Journal of Agronomy, vol. 36, no. 1, pp. 41–54.

[18] Web of Science 2020, Web of Science, Clarivate Analytics (United States), viewed 10 June 2020, <https://apps-webofknowledge-com>.

[19] Wu, J & Zhu, L 2011, ‘Testing for serial correlation and random effects in a two-way error component regression model’, Economic Modelling, vol. 28, no. 6, pp. 2377–2386.

[20] Ye, Q & Ahammed, F 2020, ‘Quantification of relationship between annual daily maximum temperature and annual daily maximum rainfall in South Australia’, Atmospheric and Oceanic Science Letters, pp. 1–8.

[21] Yin, Y 2020, ‘Model-free tests for series correlation in multivariate linear regression’, Journal of Statistical Planning and Inference, vol. 206, pp. 179–195.

[22] Zhao, F, Zhang, L, Chiew, FHS, Vaze, J & Cheng, L 2013, ‘The effect of spatial rainfall variability on water balance modelling for south-eastern Australian catchments’, Journal of Hydrology, vol. 493, pp. 16–29.