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Institute
Submarine permafrost degradation rates may be determined by a number of interacting processes, including rates of sea level rise and coastal erosion, sea bottom temperature and salinity regimes, geothermal heat flux and heat and mass diffusion within the sediment column. Observations of ice-bearing permafrost in shelf sediments are necessary in order to determine its spatial distribution and to quantify its degradation rate. We tested the use of direct current electrical resistivity to ice-bearing permafrost in Elson Lagoon northeast of Barrow, Alaska (Beaufort Sea). A sharp increase in electrical resistivity was observed in profiles collected perpendicular to and along the coastline and is interpreted to be the boundary between ice-free sediment and underlying ice-bearing submarine permafrost. The depth to the interpreted ice-bearing permafrost increases from <2 m below sea level to over 12 m below sea level with increasing distance from the coastline. The dependence of the saline sediment electrical resistivity on temperature and freezing was measured in the laboratory to provide validation for the field measurements. Electrical resistivity was shown to be effective for detection of shallow ice-bearing permafrost in the coastal zone. Historical coastal retreat rates were combined with the inclination of the top of the ice-bearing permafrost to calculate mean vertical permafrost degradation rates of 1 to 4 cm yr(-1).
Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.
Arctic coastal infrastructure and cultural and archeological sites are increasingly vulnerable to erosion and flooding due to amplified warming of the Arctic, sea level rise, lengthening of open water periods, and a predicted increase in frequency of major storms. Mitigating these hazards necessitates decision-making tools at an appropriate scale. The objectives of this paper are to provide such a tool by assessing potential erosion and flood hazards at Herschel Island, a UNESCO World Heritage candidate site. This study focused on Simpson Point and the adjacent coastal sections because of their archeological, historical, and cultural significance. Shoreline movement was analyzed using the Digital Shoreline Analysis System (DSAS) after digitizing shorelines from 1952, 1970, 2000, and 2011. For purposes of this analysis, the coast was divided in seven coastal reaches (CRs) reflecting different morphologies and/or exposures. Using linear regression rates obtained from these data, projections of shoreline position were made for 20 and 50 years into the future. Flood hazard was assessed using a least cost path analysis based on a high-resolution light detection and ranging (LiDAR) dataset and current Intergovernmental Panel on Climate Change sea level estimates. Widespread erosion characterizes the study area. The rate of shoreline movement in different periods of the study ranges from -5.5 to 2.7 mI double dagger a(-1) (mean -0.6 mI double dagger a(-1)). Mean coastal retreat decreased from -0.6 mI double dagger a(-1) to -0.5 mI double dagger a(-1), for 1952-1970 and 1970-2000, respectively, and increased to -1.3 mI double dagger a(-1) in the period 2000-2011. Ice-rich coastal sections most exposed to wave attack exhibited the highest rates of coastal retreat. The geohazard map combines shoreline projections and flood hazard analyses to show that most of the spit area has extreme or very high flood hazard potential, and some buildings are vulnerable to coastal erosion. This study demonstrates that transgressive forcing may provide ample sediment for the expansion of depositional landforms, while growing more susceptible to overwash and flooding.
Arctic coasts are vulnerable to the effects of climate change, including rising sea levels and the loss of permafrost, sea ice and glaciers. Assessing the influence of anthropogenic warming on Arctic coastal dynamics, however, is challenged by the limited availability of observational, oceanographic and environmental data. Yet, with the majority of permafrost coasts being erosive, coupled with projected intensification of erosion and flooding, understanding these changes is critical. In this Review, we describe the morphological diversity of Arctic coasts, discuss important drivers of coastal change, explain the specific sensitivity of Arctic coasts to climate change and provide an overview of pan-Arctic shoreline change and its multifaceted impacts. Arctic coastal changes impact the human environment by threatening coastal settlements, infrastructure, cultural sites and archaeological remains. Changing sediment fluxes also impact the natural environment through carbon, nutrient and pollutant release on a magnitude that remains difficult to predict. Increasing transdisciplinary and interdisciplinary collaboration efforts will build the foundation for identifying sustainable solutions and adaptation strategies to reduce future risks for those living on, working at and visiting the rapidly changing Arctic coast.
ArcticBeach v1.0
(2022)
In the Arctic, air temperatures are increasing and sea ice is declining, resulting in larger waves and a longer open water season, all of which intensify the thaw and erosion of ice-rich coasts. Climate change has been shown to increase the rate of Arctic coastal erosion, causing problems for Arctic cultural heritage, existing industrial, military, and civil infrastructure, as well as changes in nearshore biogeochemistry. Numerical models that reproduce historical and project future Arctic erosion rates are necessary to understand how further climate change will affect these problems, and no such model yet exists to simulate the physics of erosion on a pan-Arctic scale. We have coupled a bathystrophic storm surge model to a simplified physical erosion model of a permafrost coastline. This Arctic erosion model, called ArcticBeach v1.0, is a first step toward a physical parameterization of Arctic shoreline erosion for larger-scale models. It is forced by wind speed and direction, wave period and height, sea surface temperature, all of which are masked during times of sea ice cover near the coastline. Model tuning requires observed historical retreat rates (at least one value), as well as rough nearshore bathymetry. These parameters are already available on a pan-Arctic scale. The model is validated at three study sites at 1) Drew Point (DP), Alaska, 2) Mamontovy Khayata (MK), Siberia, and 3) Veslebogen Cliffs, Svalbard. Simulated cumulative retreat rates for DP and MK respectively (169 and 170 m) over the time periods studied at each site (2007-2016, and 1995-2018) are found to the same order of magnitude as observed cumulative retreat (172 and 120 m). The rocky Veslebogen cliffs have small observed cumulative retreat rates (0.05 m over 2014-2016), and our model was also able to reproduce this same order of magnitude of retreat (0.08 m). Given the large differences in geomorphology between the study sites, this study provides a proof-of-concept that ArcticBeach v1.0 can be applied on very different permafrost coastlines. ArcticBeach v1.0 provides a promising starting point to project retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations.