An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
<p>Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the s...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-12-01
|
| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://www.adv-stat-clim-meteorol-oceanogr.net/4/53/2018/ascmo-4-53-2018.pdf |
| Summary: | <p>Projections of coastal storm surge hazard are a basic requirement for
effective management of coastal risks. A common approach for estimating
hazards posed by extreme sea levels is to use a statistical model, which may
use a time series of a climate variable
as a covariate to modulate the statistical model and account for potentially
nonstationary storm surge behavior (e.g., North Atlantic Oscillation index).
Previous works using nonstationary statistical approaches to assess coastal
flood hazard have demonstrated the importance of accounting for many key
modeling uncertainties. However, many assessments have typically relied on a
single climate covariate, which may leave out important processes and lead to
potential biases in the projected flood hazards. Here, I employ a recently
developed approach to integrate stationary and nonstationary statistical
models, and characterize the effects of choice of covariate time series on
projected flood hazard. Furthermore, I expand upon this approach by
developing a nonstationary storm surge statistical model that makes use of
multiple covariate time series, namely, global mean temperature, sea level,
the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a
case study, I show that a storm surge model that accounts for additional
processes raises the projected 100-year storm surge return level by up to
23 cm relative to a stationary model or one that employs a single covariate
time series. I find that the total model posterior probability associated
with each candidate covariate, as well as a stationary model, is about
20 %. These results shed light on how including a wider range of physical
process information and considering nonstationary behavior can better enable
modeling efforts to inform coastal risk management.</p> |
|---|---|
| ISSN: | 2364-3579 2364-3587 |