  Coastal flooding drives considerable risks to many communities, but
projections of future flood risks are deeply uncertain. The paucity of
observations of extreme events often motivates the use of statistical
approaches to model the distribution of extreme storm surge events. A key deep
uncertainty that is often overlooked is model structural uncertainty. There is
currently no strong consensus among experts regarding which class of
statistical model to use as a best practice. Robust management of coastal
flooding risks requires coastal managers to consider the distinct possibility
of non-stationarity in storm surges. This increases the complexity of the
potential models to use, which tends to increase the data required to constrain
the model. Here, we use a Bayesian model averaging approach to analyze the
balance between model complexity sufficient to capture decision-relevant risks
and data availability to constrain complex model structures. We characterize
deep model structural uncertainty through a set of calibration experiments.
Specifically, we calibrate a set of models ranging in complexity using
long-term tide gauge observations from the Netherlands and the United States.
We find that in both cases, roughly half the model weight is associated with
non-stationary models. Our approach provides a formal framework to integrate
information across model structures, in light of the potentially sizable
modeling uncertainties. By combining information from multiple models, our
inference sharpens for the projected storm surge 100-year return levels, and
estimated return levels increase by several centimeters. We assess the impacts
of data availability through a set of experiments with temporal subsets and
model comparison metrics. Our analysis suggests about 70 years of data are
required to stabilize estimates of the 100-year return level, for the locations
and methods considered here.
