Development and Application of Australia-Wide Extreme Storms Database for Hydrologic Risk Assessment

It is now well understood that anthropogenic induced global warming is increasing extreme rainfalls, with the more extreme the rainfall, the greater the intensification. This in turn is increasing the magnitude of rare floods. For floods with annual exceedance probabilities rarer than 1 in 20, the intensification of rainfall offsets any decreases in soil moisture. Less well understood, however, is the changing impact of spatial and temporal patterns of extreme rainfall on flooding under a warming climate. The spatial and temporal distribution of rainfalls during storm events has a significant influence on runoff volumes (and hence water availability) and on flood peaks. Hence, robust datasets are required to model hydrologic risk with changes in storm spatial and temporal patterns.

To this end, we have developed Australia-Wide Extreme Storms Database (or AWESD) which characterises storm patterns with high spatial (12km) and temporal (hourly) resolution for hydrologic risk assessments. Whilst the record length for such high-resolution data is currently 30 years, the availability of information at a high resolution over large homogeneous regions allows the trading of space for time, which has the potential to provide equivalent independent record lengths that are much longer than 30 years. To develop such a database, we first identify and track storms using two data sets: a low resolution (daily) gridded dataset based on observations, and a higher resolution (hourly) reanalysis dataset. Identified storms are then filtered to ensure they are independent in both space and time. Storms identified in the high-resolution reanalysis dataset are checked for consistency with the observation-based data set to ensure a grounding in reality.

Having developed the database, we then created a software for storm selection. Storms are selected based on an input catchment location plus a prespecified buffer region and within a range of prespecified ratios of catchment size. Storms are then transposed to the catchment centre. The final storms selection can then be formatted to facilitate input to event-based flood modelling software.

The development of the extreme storms database and storm selection software facilitates the undertaking of hydrologic risk assessments as storms may be sampled on depth/rarity, spatial homogeneity and temporal homogeneity. For example, it can be used to investigate how spatial and temporal patterns of rainfall may vary with event severity, and this could be used to inform estimates of dam failure risks. Furthermore, it can be used for climate impact assessments by sampling storms based on characteristics associated with a warmer climate e.g. higher depths and shifting spatio-temporal pattern distributions. With this storm database we believe it will enhance hydrological risk assessments performed for both present and future climate scenarios and deepen understanding of the role of spatio-temporal distributions on extremes.