Hurricane Harvey raised flood up the cat risk modelling list of priorities
Flood risk modelling is traditionally hard to model. Despite the huge scale of the peril, relative to more dramatic catastrophes like hurricanes and earthquakes, flood has been relatively neglected. Robert Muir-Wood, chief research officer at RMS, is resolved to correct this.
“The whole question of flood modelling is certainly on the agenda we’re pushing,” he told GR. “It’s always been perceived as the third peril behind its sisters, hurricane and ‘quake. However, it’s also more difficult to model it effectively.”
Last year’s Hurricane Harvey caused heavy flooding in the Houston, Texas metropolitan area. There was much more uncertainty about reinsurers’ losses from Harvey than would have been the case for straight-forward wind risk.
“Hurricane Harvey last year has elevated flood risk on the list of priorities,” said Muir-Wood.
Since 2000, cat modelling has delivered a third wave of disruptive technology through flood risk cost data delivered at building resolution, Muir-Wood explained.
In most circumstances a flood can be precisely determined. The height of the flood water is typically consistent among nearby locations. It is the elevation of neighbouring buildings that varies. How this relates to absolute heights adds a layer of complexity to flood models.
How long a flood event lasts is also important to consider, Muir-Wood added. “Once the ground is saturated, it doesn’t take much more rain to get more flooding,” he said.
The revolution in high resolution flood risk modelling is a consequence of the availability of big data sets on daily rainfalls and river flows, allied with the vast outputs of climate models and digital terrain data.