Over 30% of the world's coastline consists of permafrost and large sections of these coasts are subject to erosion. In the Arctic, unlike with temperate low-latitude coastlines, thermal processes affect erosion mechanisms. There is a lack of long-term predictive capability of mor
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Over 30% of the world's coastline consists of permafrost and large sections of these coasts are subject to erosion. In the Arctic, unlike with temperate low-latitude coastlines, thermal processes affect erosion mechanisms. There is a lack of long-term predictive capability of morphodynamics for Arctic coastlines, which results in a knowledge gap regarding the coastal processes inducing permafrost erosion and how these processes will change under the effects of climate change. Previously developed parametric morphodynamic permafrost models lack generalizability because they either do not include all erosion mechanisms or were calibrated for specific eroding coastlines. Comprehensive morphodynamic models that include permafrost dynamics are too computationally expensive to perform long-term analysis and are thus applied to storm time scales only.
This study implements a novel method that integrates thermodynamics, hydrodynamics, and morphodynamics to predict the morphodynamic evolution of a permafrost-affected coastline. We developed and validated a process-based numerical model for Barter Island (North Slope, Alaska). This model showed skill in predicting the ground temperature distribution and the erosion of a permafrost bluff. Sensitivity analyses indicated that the environmental drivers affected by climate change (i.e., air and sea temperatures, water level) are expected to accelerate the erosion of permafrost-affected coastlines under the effects of climate change, confirming the findings of previous work. Rising temperatures will compound with diminishing sea ice to widen the annual window during which erosion can occur, which will increase the number of storm events that lead to erosion. Lower bluffs composed of finer sands are especially vulnerable.
The low computation costs mean that the model can be used to predict coastal erosion for larger regions, potentially benefiting strategic coastal management and policy-making.
Additionally, the developed model will improve global climate models. It can facilitate the mapping of permafrost degradation and organic carbon release, with the release of organic carbon through permafrost erosion being one of the greatest unknown drivers of global warming. Though further calibration is required, the developed model can be used as a tool to research the quantitative effects of climate change on the erosion of Arctic coastlines and gain a deeper understanding of how climate change affects the processes that ultimately lead to the erosion of permafrost bluffs.