Risk Management Solutions (RMS) has developed a new flood model allowing New Zealand’s Tower to allocate its home insurance customers with a low, medium or high-risk rating for their property.

The model is based on 50,000 years of continuous simulation of the precipitation cycle and captures the spatial and temporal correlations of flood risk, and all sources of flood – pluvial and fluvial – resulting in a catalogue of 350,000 simulated events.

It is available on the RMS cloud-based software platform, allowing users to run models based on data from the National Institute of Water and Atmospheric Research, Land Information New Zealand, local and regional councils and the Insurance Council of New Zealand. It also includes all publicly available flood defence and mitigation efforts.

“The latest RMS New Zealand Inland Flood HD Model is the world’s first fully probabilistic flood model for the country,” RMS MD for Asia and Europe Vivek Bajaj said. “We are pleased to support Tower’s goal of transparency and clarity.”

Since 1968, flood has accounted for more than half of all loss events in New Zealand, and damage from heavy precipitation or river flooding represented 60% of weather-related losses. RMS says that with no model available for the market, insurers’ understanding of flood risk came “from a patchwork of hazard maps” and pricing and loss accumulation happened on “an inconsistent and incomplete basis”.

The new model uses high-definition framework and fully captures the flood risk with stochastic modelling of all sources of flooding, revealing that neighbouring properties can have very different ratings depending on their land, whether they have a flood wall and other factors.

"One location could have a very different loss profile to its neighbour,” RMS says.

The hazard model has been developed on a 10-metre uniform resolution grid in urban centres, and validated against 60 years of insured losses. Flood defences can be switched on or off within the model to see a defended or undefended view of risk, and modified to reflect local knowledge about a site’s defence level.