Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

Abstract

Rising seas coupled with ever increasing coastal populations present the potential for significant social and economic loss in the 21st century. Relatively short records of the full multidimensional space contributing to total water level coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave run-up, etc.) result in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential is presented here as a methodology capable of producing new iterations of the sea-state parameters associated with the present-day Pacific Ocean climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto-regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation, the Madden-Julian Oscillation, seasonality, and the prior two days of weather progression. Joint probabilities of sea-state parameters unique to simulated weather patterns are used to create new time series of the hypothetical components contributing to synthetic total water levels (swells from multiple directions coupled with water levels due to wind setup, temperature anomalies, and tides). The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential reveals the importance of considering the multivariate nature of extreme coastal flooding, while progressing the ability to incorporate large-scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21st century.Plain Language Summary Predicting extreme coastal flooding is a present-day societal need and will only become more relevant as mean water levels increase due to sea level rise. However, the number of processes contributing to such events is too high for relatively short observational records to have measured all of the constructive combinations of waves, surge, wind, and sea level anomalies. We present a framework designed to create hypothetical combinations of relevant flood hazard potential processes by simulating the climate and weather patterns that drive coastal flooding. Including large-scale oceanic and atmospheric patterns as the drivers of coastal hazards reveals the climate a coastal community is most vulnerable to, which will be increasingly more important to understand as the climate changes during the 21st century.

Climatic Drivers of Extreme Sea Level Events Along the Coastline of Western Australia

“…Given the ability to forecast these individual contributions to sea level in advance range from effectively limitless (i.e., tides and the annual cycle) to order days to weeks (i.e., surges that depend on resolving individual storms), these uncertainties in individual contributions will influence uncertainties in predictions of ESLs in very different ways. Recent developments in tools to predict future coastal flooding by ESLs (e.g., Anderson et al, 2019;Vitousek et al, 2017;Vousdoukas et al, 2018) describe promising approaches to integrate deterministic predictions with probabilistic forecasts of individual contributions (each with varying levels of forecast skill). As the present study focused only on drivers of ESLs based on offshore total sea level (still water level) variability, there are also opportunities to consider all contributions to total water level variability at the coastline by incorporating predictions of wave runup (e.g., Melet et al, 2018;Serafin et al, 2017).…”

The Application of Ensemble Wave Forcing to Quantify Uncertainty of Shoreline Change Predictions

“…VITOUSEK ET AL. (Anderson et al, 2019); (c) Run Monte Carlo simulations to obtain wave parameters (e.g., wave height, wave period and wave direction) associated to each synoptic state. To account for the joint probability between variables, we use a multivariate Gaussian copula within each WT (Rueda et al, 2017).…”

“…Figure 1. Methodology leading to 3-hourly realizations of wave climate following Cagigal et al (2020): (a) Define historical daily Weather Types (WTs), based on sea-level pressure fields over the wave generation region, which is determined by the "Evaluating the Source and Travel of the wave Energy reaching a Local Area" (ESTELA)-method of Perez et al (2014); (b) Reproduce sequences of daily WTs using an autoregressive logistic regression (ALR) model(Anderson et al, 2019); (c) Run Monte Carlo simulations to obtain wave parameters (e.g., wave height, wave period and wave direction) associated to each synoptic state. To account for the joint probability between variables, we use a multivariate Gaussian copula within each WT(Rueda et al, 2017).…”

Climate‐Based Emulator of Distant Swell Trains and Local Seas Approaching a Pacific Atoll

“…To account for the modulation on the number of swells produced by ENSO, we use the Annual Weather Types (AWTs) based on sea surface temperature anomalies (SSTA) proposed in Anderson et al. (2019). These AWTs are constructed by averaging the SSTA in the equatorial Pacific and defining Hovmöller diagrams (Hovmöller, 1949) beginning in June and ending the following May to preserve the boreal winter variability.…”