Temperature Modeling with the Group Method of Data Handling to Inform Projected Rainfall Depth Changes for Extreme Events in Central West, New South Wales, Australia

Abstract

The focus of this research is to introduce the application of the polynomial neural network of the group method of data handling (GMDH) for the first time in the regional area of the New South Wales state of Australia. Within this regional context, temperature data are modeled to assess its projected variation impacts on rainfall depth due to climate change. The study area encompasses six local government areas within the state’s Central West region. Stochastic methods for monotonic trend identification were used to support the modeling. Four established homogeneity tests were also used for assessing data integrity by determining the frequency of breakpoints within the mean of the data. The results of the GMDH modeling returned a coefficient of determination exceeding 0.9 for all stations dominated by an overall upward trend with an average maximum temperature increase of 0.459 °C per decade across the study region. The homogeneity tests found all data categorized as useful within the context of applicability for further climate change studies. By combining the modeled upward temperature trend with the intensity frequency distribution (IFD) design rainfall modification factor, projected depth increases by 2070 are obtained, enabling improved designs for stormwater infrastructure based on classified temperature variation scenarios.