Storm surge is one of the most destructive drivers of loss in tropical cyclone events and one of the hardest perils to model well. Getting it right depends less on any single breakthrough and more on the quality of the inputs feeding the model: the terrain, the tides, the storm catalog. KatRisk’s recent update to its US Storm Surge model is a case in point: a change that looks small on paper but shows how much accuracy rides on getting the underlying data right.
Why storm surge is deceptively hard to get right
Unlike wind, which behaves fairly consistently over hundreds of meters, surge is a highly local phenomenon. Water pushed onshore by a storm interacts with bathymetry, tides, and the shape of the land it’s flooding. A few feet of elevation difference between two neighboring properties can be the difference between a total loss and no loss at all.
That means a storm surge model is only as good as three things working together: a large, realistic catalog of storm tracks and intensities, a hydrodynamic engine that can simulate how surge actually propagates and builds inland, and terrain data precise enough to translate a surge height into an actual flood depth at a given address. Miss on any one of these, and the loss estimate can be off in ways that are hard to detect until a real event exposes the gap.
The input that’s easy to overlook: elevation
Of the three, terrain and elevation data tend to get the least attention as it’s not as visible as the storm catalog or the hydrodynamics. However, it’s where a lot of the location-level accuracy actually comes from. Elevation data quality has improved substantially over the past several years as new, higher-resolution surveys have become available in different regions. A model that isn’t kept current with those improvements will keep computing flood depths against outdated ground-level assumptions, even if everything else about it is sound.
KatRisk’s latest US Storm Surge model update addresses this gap. The update refreshes the Digital Terrain Model (DTM) used to compute flood depth above ground level, bringing it in line with current best-in-class elevation inputs, most notably in the Northeast US, where terrain data has seen some of the largest recent improvements. The underlying storm surge methodology itself hasn’t changed; only the terrain it runs on has been upgraded.
The effect is exactly what you’d expect from an elevation-only change: every grid cell shifts to some degree, but overall event losses don’t move dramatically. Early analysis shows median percentage changes ranging from about 0.65% for the 10-to-25-year return period band to -0.12% for events beyond a 10,000-year return period; a reminder that better inputs don’t necessarily mean bigger numbers, just more defensible ones. County and state-level loss differentials are already available for clients who want to see how their own book shifts, and KatRisk is running the updated model in parallel with the current version so clients can compare results before anything changes in their workflow.
Precision compounds
None of this happens in isolation. KatRisk’s storm surge model pairs this kind of terrain precision with a catalog spanning more than 50,000 years of simulated tropical cyclone activity and explicit modeling of climate variability (including El Niño and La Niña teleconnections), so that surge risk can be evaluated across different climate states, not just a single historical average. It also integrates with KatRisk’s tropical cyclone wind and flood models, giving a fuller picture of the combined coastal hazard rather than surge in isolation.
That integration matters because surge rarely arrives alone. A landfalling storm often brings heavy rainfall and river discharge at the same time elevated coastal water levels are backing up drainage, so treating surge as an isolated peril along tidal basins and estuaries can understate the real flood depth. Modeling surge alongside wind and inland flood, rather than as a standalone number, gives a more complete read on where compound flooding drives loss.
Why it matters for insurers
For underwriters and portfolio managers, the takeaway isn’t that any one update transforms a model overnight, it’s that surge accuracy is a compounding function of data quality, and that compounding only works in your favor if the inputs are kept current. An insurer relying on stale terrain data is carrying uncertainty they may not even know about, concentrated exactly in the coastal geographies where it matters most.
It’s also worth remembering that a 10-meter terrain grid only pays off if the exposure data feeding it is precise enough to use it. A location geocoded to a ZIP code centroid can’t take advantage of block-level, let alone building-level, elevation accuracy. Pairing high-resolution hazard data with equally precise location and building footprint information is what actually turns better terrain data into better underwriting decisions.
Storm surge risk isn’t static, and neither are the tools used to measure it. Keeping pace with better elevation data, deeper storm catalogs, and climate-aware modeling is what turns a surge model from a rough approximation into something insurers can actually underwrite against.
To learn more about KatRisk’s Storm Surge model, request a demo.
