Typological representation of the offshore oceanographic environment along the Alaskan North Slope
By Craig A. Jones, Ph.D., Managing Principal
Christopher Flanary, Ph.D., Consultant
Publishing Journal: Continental Shelf Research
Erosion and flooding impacts to Arctic coastal environments are intensifying with nearshore oceanographic conditions acting as a key environmental driver. Robust and comprehensive assessment of the nearshore oceanographic conditions require knowledge of the following boundary conditions: incident wave energy, water level, incident wind energy, ocean temperature and salinity, bathymetry, and shoreline orientation. The number of offshore oceanographic boundary conditions can be large, requiring a significant computational investment to reproduce nearshore conditions. This present study develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007–2019) and Future (2020–2040) timespan along the Alaskan North Slope. We used WAVEWATCH III® and Delft3D-FLOW model output from six oceanographic sites located along a constant ∼50 m bathymetric line spanning the Chukchi to Beaufort Seas. K-means clustering was applied to the energy-weighted joint-probability distribution of significant wave height (Hs) and peak period (Tp). Distributions of wave and wind direction, wind speed, and water level associated with location-independent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. Reanalysis data (e.g., ASRv2, ERA5, and GOFS) grounded the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Location-dependence for each site is established through the occurrence joint-probability distribution in the form of unique scaling factors representing the fraction of time that the typology would occupy over a representative year. As anticipated, these typologies show increasingly energetic ocean conditions in the future. They also enable computationally efficient simulation of the nearshore oceanographic environment along the North Slope of Alaska for better characterization of coastal processes (e.g., erosion, flooding, or sediment transport).
- The application of k-means clustering on oceanographic data elucidates a subset of sea states associated with the Arctic Ocean along the Alaskan North Slope.
We associate oceanographic details (wave direction, wind direction/speed, water level, temperature, and salinity) with clustered wave height and period data.
This evaluation demonstrates the changes expected to occur over Historical (2007–2019) and Future (2020–2040) timespans in response to climate change.
The results of our study are relevant to coastal hazard analysis including assessment of flooding and erosion of villages located along the North Slope of Alaska.
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