Title: Gaussian Process Regression for Arctic Coastal Erosion Forecasting,
Abstract: Arctic coastal morphology is governed by multiple factors, many of which are
affected by climatological changes. As the season length for shorefast ice
decreases and temperatures warm permafrost soils, coastlines are more
susceptible to erosion from storm waves. Such coastal erosion is a concern,
since the majority of the population centers and infrastructure in the Arctic
are located near the coasts. Stakeholders and decision makers increasingly need
models capable of scenario-based predictions to assess and mitigate the effects
of coastal morphology on infrastructure and land use. Our research uses
Gaussian process models to forecast Arctic coastal erosion along the Beaufort
Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling
methodology capable of extracting patterns and trends from data-sparse
environments such as remote Arctic coastlines. To train our model, we use
annual coastline positions and near-shore summer temperature averages from
existing datasets and extend these data by extracting additional coastlines
from satellite imagery. We combine our calibrated models with future climate
models to generate a range of plausible future erosion scenarios. Our results
show that the Gaussian process methodology substantially improves yearly
predictions compared to linear and nonlinear least squares methods, and is
capable of generating detailed forecasts suitable for use by decision makers.