UC San Diego

Decreasing sea ice extent, increasing storm activity, and warming weather conditions are driving widespread erosion of Arctic permafrost coasts. This erosion threatens infrastructure, indigenous cultural practices, and regional economic development. It also leads to the influx of carbon and other compounds into the ocean, with implications both for ocean ecology and global carbon cycling. Understanding the drivers of Arctic coastal erosion is crucial for resilience efforts and carbon modeling, but the processes driving erosion are complex and hard to accurately quantify. Airborne and spaceborne lidar provide a way to quantify Arctic shore zone morphology, which is an important factor in modulating local shoreline change. This dissertation focuses on using cross-shore elevation profiles from lidar to capture spatiotemporal variations in coastal morphology and relate different shore types to the dynamic response of the shoreline to spatiotemporal variations in ocean and atmospheric forcings. In Chapter 2, we demonstrate the potential for the satellite ICESat-2 to be combined with multispectral imagery to measure variable shoreline change across three distinct shore types on the Alaskan Beaufort Sea coast. In Chapter 3, we introduce a framework to systematically classify profiles from airborne lidar into physically distinct shore types using k-means clustering, and demonstrate its utility of the Western Chukchi Sea coast. In Chapter 4, we combine these elevation-based shore types with historical shoreline change data, sediment maps, and meteorological datasets to investigate the potential drivers of spatial variability in long-term shoreline trends. We conclude that remotely sensed elevation datasets can be used to meaningfully quantify shoreline morphology in the Arctic, but that this data must be used in conjunction with environmental forcings and other shoreline properties in order to better understand the sensitivity of local shorelines to a changing Arctic.