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Coastal flooding drives considerable risks to many communities, butprojections of future flood risks are deeply uncertain. The paucity ofobservations of extreme events often motivates the use of statisticalapproaches to model the distribution of extreme storm surge events. A key deepuncertainty that is often overlooked is model structural uncertainty. There iscurrently no strong consensus among experts regarding which class ofstatistical model to use as a best practice. Robust management of coastalflooding risks requires coastal managers to consider the distinct possibilityof non-stationarity in storm surges. This increases the complexity of thepotential models to use, which tends to increase the data required to constrainthe model. Here, we use a Bayesian model averaging approach to analyze thebalance between model complexity sufficient to capture decision-relevant risksand data availability to constrain complex model structures. We characterizedeep model structural uncertainty through a set of calibration experiments.Specifically, we calibrate a set of models ranging in complexity usinglong-term tide gauge observations from the Netherlands and the United States.We find that in both cases, roughly half the model weight is associated withnon-stationary models. Our approach provides a formal framework to integrateinformation across model structures, in light of the potentially sizablemodeling uncertainties. By combining information from multiple models, ourinference sharpens for the projected storm surge 100-year return levels, andestimated return levels increase by several centimeters. We assess the impactsof data availability through a set of experiments with temporal subsets andmodel comparison metrics. Our analysis suggests about 70 years of data arerequired to stabilize estimates of the 100-year return level, for the locationsand methods considered here.

Neglecting Model Structural Uncertainty Underestimates Upper Tails of Flood Hazard