Quantifying the Risk of Shoreline Hazards: The Datasets Needed for Effective Catastrophe Modeling

Model with precision to achieve effective catastrophe modeling

Seventy-five percent of the US population resides in a coastal area. Potential hazards go beyond simple flooding. Erosion, deposition and changes in littoral cells can impact the risks associated with coastline structures. Additional triggers include high winds, flooding due to intense precipitation and storm surges caused by tropical cyclones, tsunami, landslides and tidal waves.

Since Hurricane Katrina, catastrophe modelers are paying even more attention to coastal hazards, looking for new ways to mitigate risks and identify gaps in their underwriting processes, to achieve effective catastrophe modeling.

Today, reliable models start with accurate data. This includes up-to-date datasets for each of the aforementioned triggering factors, plus details on the property in question, such as its type, age and construction. With the right insights, insurers better determine which shoreline properties will undergo constant depreciation and produce more effective models.

What does it take to effectively model the risk of shoreline hazards?

Precisely studied several models developed by shoreline hazard researchers, assessing which datasets would best support probabilistic and deterministic models. Based on that study, we’ve compiled the list of datasets that are most often used by catastrophe modelers in the shoreline hazard domain.

• The primary factor in assessing risk is the distance from the property to the shoreline. Even slight variations in measuring the exact location of a property can lead to losses for both the insurer and the insured. Some firms rely on street-level geocoding, which can increase the chance of over- or under-insuring (See Figure 01). Precisely provides rooftop-level geocoding, which increases the accuracy of all measurements. Our Property Attributes data also contains over 200 attributes, including the age, construction type, number of floors and owner details, for example, which make it easier to model risk.
• A digital elevation model (DEM) can delineate the coast line and the elevation of the coast line. The relative elevation between the coast line and the property is vital to understand the risks associated with tsunami and tidal floods.