全球变化知识资源中心

The increasing concern about climate change has increased the necessity for precise figures about deviations in statistical characteristics of rainfall events in both temporal and spatial scales. Many areas across Australia have high variability patterns of rainfall and inadequate rainfall records which has various effects on the availability of food and water. Modelling spatial and temporal rainfall is essential for prediction and simulation purposes in numerous aspects of planning, agriculture, forestry, meteorology and hydrology. Two different models are generally used to describe the two main features of rainfall: the occurrence and the amount. This paper describes the occurrence probability of 5-day average rainfall (successive 5-day rainfall mean) in Australia. Daily accumulated rainfall observations of 105 meteorological stations spanning from 1950 to 2013 were collected from Australian Bureau of Meteorology for the study. Logistic regression is applied to predict rainfall probability of occurrence in all the meteorological stations using nine observational rainfall stations as case studies. Weather conditions such as the 5-day periods which is defined as the consecutive 5-day average rainfall in a year and the annual rainfall which is defined as the annual daily rainfall mean factors were used as the predictors. The fitted logistic regression models predicted the occurrence and non-occurrence 5-day average rainfall events quite well with good accuracies. The predictors significantly affected the fitted models in all stations. Analysis of the levels of each of the predictors showed that the parameters of the 5-day period factors were more influential in all the models, particularly during the rainy season in most stations relative to that of the annual factors. The fitted models could be used to simulate rainfall probabilities for the stations with insufficient rainfall histories and could assist agronomist and various stakeholders in the planning of their various operations.