Non-stationary design rainfalls for Australia

Action needs to be taken in response to the changes in future flood risk due to the impact of global warming on the magnitude and frequency of extreme rainfalls. Projected changes in extreme rainfalls can be used to estimate the associated changes in design flood estimates using Intensity-Frequency-Duration (IFD) curves in combination with event-based flood models. IFD curves are estimated from records of historical annual maxima across different storm durations and exceedance probabilities. Past studies investigating changes in extreme rainfall across Australia have been limited in scope as they have focused on single durations, single exceedance probabilities, or limited regional extents. This means that we do not yet have a comprehensive understanding of how projected changes in extreme rainfalls impact on IFD curves.Here, to fill this gap, we investigate the changes in extreme rainfall changes across different storm durations and exceedance probabilities across 42 stations which span the entire continent of Australia. We begin with examining the trend in annual maximum rainfall across 16 different storm durations (6 min to 7 day) using the Theil-Sen slope estimator, testing for statistical significance using the Mann-Kendall test. To extrapolate 1% annual exceedance probability, we fit non-stationary Generalized Extreme Value Distributions (GEVs) at each site. Non-stationarity was assessed by varying the location parameter, varying the scale parameter, and varying both the location and scale parameters as a linear trend in time.We find that the short duration (1 hr and 1 day) show fewer positive trends with some sites exhibiting negative trends. Based on Akaike Information Criteria, the GEV models which varied either the location parameter, or both the scale and location parameters, were found to be superior. However, when changes in quantile estimates were examined for rare exceedance probabilities (up to the 1 in 100 AEP), it was found the GEV model which only varied the location parameter was unable to capture the increased rate of change in extreme rainfalls. Accordingly, we conclude that changes in extreme rainfalls is best represented by non-stationary models that incorporate changes in both location and scale parameters.