Simulating extreme total water levels using a time‐dependent, extreme value approach

TLDR

Coastal flood hazard zones and defenses are typically based on maximum recorded water levels or design events such as the 100‑year return level, but short, intermittent, and variable‑quality observational records often preclude accurate and robust estimates of extreme return level events. The study introduces a Monte Carlo total water level full simulation model (TWL‑FSM) that simulates waves, tides, and nontidal residuals while accounting for conditional dependencies among components. The model generates synthetic TWLs by fitting nonstationary extreme‑value distributions that incorporate seasonality and climate variability, enabling empirical extraction of return levels and more robust hazard estimates, as demonstrated along a northern Oregon littoral cell. Simulations show 100‑year TWL return levels up to 90 cm higher than those extrapolated from observational data, implying 30 % more coastal flooding at the Oregon site, and providing more robust estimates and tighter confidence bounds to aid coastal engineers, managers, and emergency planners.

Abstract

Abstract Coastal flood hazard zones and the design of coastal defenses are often devised using the maximum recorded water level or a “design” event such as the 100 year return level, usually projected from observed extremes. Despite technological advances driving more consistent instrumental records of waves and water levels, the observational record may be short, punctuated with intermittent gaps, and vary in quality. These issues in the record often preclude accurate and robust estimates of extreme return level events. Here we present a total water level full simulation model (TWL‐FSM) that simulates the various components of TWLs (waves, tides, and nontidal residuals) in a Monte Carlo sense, taking into account conditional dependencies that exist between the various components. Extreme events are modeled using nonstationary extreme value distributions that include seasonality and climate variability. The resulting synthetic TWLs allow for empirical extraction of return level events and the ability to more robustly estimate and assess present‐day flood and erosion hazards. The approach is demonstrated along a northern Oregon, USA littoral cell but is applicable to beaches anywhere wave and water level records or hindcasts are available. Simulations result in extreme 100 year TWL return levels as much as 90 cm higher than those extrapolated from the “observational” record. At the Oregon site, this would result in 30% more coastal flooding than the “observational” 100 year TWL return level projections. More robust estimates of extreme TWLs and tighter confidence bounds on return level events can aid coastal engineers, managers, and emergency planners in evaluating exposure to hazards.