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Arthur T Knackerbracket has found the following story:

An international team of scientists, led by the University of Bristol, has found that current estimates of flood risk rely upon methods for calculating flood damage which are inadequately verified and match poorly with observations.

[...] When calculating flood risk—that is, translating modelled representations of the physical of phenomenon of flooding to its impacts—it is common to apply a 'depth-damage function' or curve, which relates a given water depth to a proportional building loss (for example one metre of water equals 50 per cent loss of building value).

[...] The new study, published today in the journal Nature Communications, used commonly applied curves, developed by various US government agencies, and examined how they compare to millions of actual flood insurance claims made in the US.

[...] It found that universally applied depth-damage curves show low skill in the replication of property-level damages, rendering the results of projects where they have been applied (for example the justification of billions of dollars of infrastructure investment) suspect.

[...] At low inundation depths, most damages are somewhat minimal (<10 per cent of building value) with a very low chance of experiencing maximal (>90 per cent) damage. But as depth increases, the distribution shifts and swings towards greater probability of high (>90 per cent) damage and lower probability of low (<10 per cent) damage.

Lead author, Dr. Oliver Wing from Bristol's School of Geographical Sciences, said: "This relationship can be represented with a beta distribution, meaning future flood risk analyses can employ a function which properly captures the true stochastic relationship between depth and damage."

More information: O. Wing, N. Pinter, P. Bates, C. Kousky. New insights into US flood vulnerability revealed from flood insurance big data.Nature Communications, 2020.

Journal information: Nature Communications

Original Submission