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Detailed Australian Riverine Flood Loss Model

Intelligently Designed. Location and Portfolio Level Intelligence.

For 25 years, Risk Frontiers has been leading the development of natural catastrophe models for the Asia-Pacific region. The latest release of FloodAUS is built upon high resolution riverine flood modelling and data to determine the return period of floodwater depth at individual addresses in Australia for over 500,000 unique simulated events.

Building on our recognised expertise in developing and managing the National Flood Information Database (NFID) for the Insurance Council of Australia, peer reviewed research and on-the-ground post-event surveys, FloodAUS 4.0 has national coverage for modelling the impact of floods on property and businesses.

Ankle Deep

FloodAUS 4.0 takes advantage of Risk Frontiers’ in-house flood data, which supplements the NFID with other flood studies. Risk Frontiers mesh catchment correlation, address-level flood hazard data, exposure locations and types, and region-specific building vulnerability functions for FloodAUS. This results in the seamless integration of data to provide authoritative flood exposure risk information to G-NAF and Geovision building address level.

Flood extents and depths Townsville 2019
Figure 1 : Map of Townsville showing modelled flood extents and depths for the 2019 flooding event. The circles represent the inferred flood depth for a land parcel, with larger and darker circles indicating increased inundation.
NFID and FEZ modelled floodplains.
Figure 2 : Map of NFID and the FEZ modelled floodplains. FloodAUS uses basin, catchment and sub-catchment flood analysis of water depths to account for hydrological processes using high-quality elevation, river network data validated against flood studies and history.

Model Overview

Hazard Resolution

Better than 0.01%

Exposure Resolution

Location Address Level

Event Catalogue

50,000 years of stochastic flood data derived from NFID and Risk Frontiers’ own aggregation and acquisition of definitive flood data

Line of Business


Business Interruption



All properties on mainland Australia and Tasmania. Utilises G-NAF|Geoscape and/or Geovision

Updated Flood Loss Curves & Catchment Correlations

FloodAUS 4.0 utilises enhanced vulnerability curves, in conjunction with an updated loss model which incorporates cross catchment correlation and new flood exposure data. Catchment correlation gives the expectation of flooding across multiple catchments in a single event and our method is based on over 100 years of rain gauge data. Vulnerability functions are constructed using a semi-empirical approach by taking published vulnerability functions for Australia and adjusting using real claims data. They have been validated against claims sets from 5 sources across four events; the 1974 Brisbane (500 claims), 1998 Katherine (270 claims), 2007 Hunter Valley (240 claims) and 2011 Brisbane floods (> 4,000 claims). FloodAUS 4.0 incorporates loss curves for residential buildings and contents, and commercial and industrial buildings and contents.

Using Damage Ratio Variance

Mean damage ratio relative to flood depth

  • Damage ratios for a certain flood depth consider not only the mean but also the variance of damage observed through real flood studies.
  • Floor height is an input parameter for the model and high-rise building losses considers the height of the building.

Bespoke Climate Analysis

Risk Frontiers is implementing a catastrophe loss modelling solution to enable business and community leaders to better understand their exposure to future climate scenarios. In FloodAUS, both projected changes to precipitation-induced flooding and the impact of sea level rise on fluvial flood risk in coastal catchments, is accounted for. We presently offer a future view of flood risk in Australia for 2030s, 2050s and 2090s, under a low, midde and high-emissions scenario.

Figure 3: Projected change in extreme rainfall across Australia under a low emission (left) and high emissions scenario (right)


Dr Ryan Crompton –

Dr James O’Brien –