Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast

Analyses focused on the ABSC stretching from Utqiaġvik, Alaska to the United States-Canada border (figure 1). For reference, Utqiaġvik is located at 71.2906° N, 156.7886° W. The ABSC is the northern coast of Alaska, open to the Beaufort Sea, which is a marginal sea of the Arctic Ocean. This area has ample source data that characterizes the coastal landscapes from high elevation bluffs to low elevation deltas and lowlands. However, this analysis does not include delta regions because shoreline change rate statistics (Gibbs and Richmond 2017) were not calculated for those areas and these areas lack reliable topographic information. Further, the processes that erode coastal bluffs and high-elevation coasts are fundamentally different than those that cause shoreline retrogradation on a river delta. We therefore focus our analyses on areas included in the Gibbs and Richmond (2017) dataset on shoreline change and exclude river deltas.

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We compiled available datasets and model outputs (supplemental table 1) that describe the ABSC, including historical long-term erosion rate (∼1950–∼2010; Gibbs et al 2017), elevation (3 m vertical accuracy; Alaska DGGS, 2018), modeled mean annual ground temperature (Nicolsky and Romanovsky 2017), geomorphology (Lara et al 2018), land cover (NSSI 2013), near surface lithology and geology, ecological landscape unit, subsidence potential due to permafrost thaw, segregated ice content (e.g. ice lenses), and massive ice content (e.g. ice wedges) (Jorgenson et al 2014). We used the shore type classification of Gibbs et al (2017), which identified the coast as sheltered by a barrier island or exposed to the open ocean. Shorelines delineated from Gibbs et al (2017) (mapped as instantaneous land–water boundaries) were also used to quantify shoreline orientations, as the orientation of the shoreline relative to incoming waves may be an important control on coastal dynamics. While waves are clearly an important factor in coastal erosion processes, our analysis focuses on the characteristics of the land to explore both erosional controls and coastal archetypes that may be useful in understanding the variability in coastal landscape characteristics. Values from all datasets were assigned to points along the ∼2010 shoreline at a 50 m spacing, using the average value from a 30 m buffer around each point. Points that did not have data available from all 12 datasets were excluded from further analysis. The final dataset consisted of 11 546 points covering over 600 km along the ABSC (Piliouras et al 2023).

The shoreline points were first divided into categories of exposed (33%) and sheltered (67%) as a first order control on coastal change rates (figures S1 and S2). Exposed and sheltered datasets were then analyzed separately using python's scikit-learn agglomerative clustering scheme to determine the typologies (figures S3 and S4, supplemental methods). We used only the datasets describing the shoreline change rates, orientations, ground temperatures, and elevations for the clustering and excluded the categorical data (whose integer values are arbitrary) from the algorithm. The final typologies were based on median values of the distributions for the four continuous datasets. We also include the modes for the categorical datasets that were not used in the clustering to provide additional information about each typology (figures S5 and S6, table 1). In the following sections, we use the terms low, intermediate, and high to refer to coastal elevations of ⩽1 m, 1–4 m, and >4 m, respectively.

We found the ABSC was best described by 16 coastal typologies. Table 1 describes finer scale properties of the 16 individual typologies (standard deviations, ranges, and full characteristics are provided in supplemental table ST1), figure 1 shows their geographic distributions, and figure 2 shows a graphical description of each typology using only the four variables used in the clustering analysis.

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The length of coastline represented by each typology varies greatly. Half of the typologies (E1, E4, E5, E6, S3, S5, S7, and S8) each comprised less than 6% (1/16th) of the coast and collectively represent only ∼22% of the coastline. In contrast, the two most extensive typologies (S0–13.6% and S2–16.4%), both sheltered, comprise a combined 30% of the coastline. The region between Drew Point and Harrison Bay contains nearly all the points belonging to E1, E4, E5, and E6, showing that these typologies are spatially limited and are not representative of the broader ABSC. A literature search on Web of Science suggests that papers about erosion at Drew Point comprise between 4% and 52% (depending on exact search terms used, see supplemental material) of the literature on coastal erosion along the ABSC, despite representing only ∼1% of the coastal types.

The site photos shown in figure 1 highlight the varied environments and erosion mechanisms along the ABSC. For example, S0 and S4 show modest erosion of low-lying bluffs and beaches via littoral processes combined with thermo-denudation of moderately ice-rich permafrost. Typology S7 is characterized by faster erosion associated with thermo-abrasion of moderately high, ice-rich permafrost bluffs with no protective fronting beach. Both E0 and E2 are on exposed coasts but are oriented in an easterly to northerly direction and are therefore sheltered from northwesterly storms known to drive large erosion events (Wiseman et al 1973, Reimnitz and Maurer 1979, Erikson et al 2020). The low retreat rate at these sites is likely driven by a combination of thermo-abrasion and thermo-denudation occurring at low intensity. This contrasts with E5, which is largely restricted to a small area near Drew Point, where rapid erosion of very high, ice-rich permafrost bluffs is dominated by thermo-abrasion and block collapse and further modified by thermo-denudation between storms (Wobus et al 2011, Jones et al 2018).

Across all typologies, erosion rates on exposed coasts are nearly three times greater than those of sheltered coasts (table 1, figure S2). When compared at the individual typology level we see a similar difference in erosion rates. E3 and S1 are similar typologies that differ only in their erosion rates and being exposed vs. sheltered (table 1, figure 2). This suggests that the higher erosion rates in E3 are largely due to the exposure to the open ocean. If barrier islands were to drown or otherwise disappear, for example due to sand and gravel mining, erosion rates for points in S1 may more than triple as they approach the higher rates of E3. Further, exposed coasts, except for E3, have also exhibited faster erosion rates in recent decades (ca. 1980–2010) compared to long-term rates (ca. 1940–2010; table 1). In contrast, all sheltered typologies except S7 show slower or roughly constant erosion rates in recent decades compared to the long-term rates. This suggests that changes in wave and ice conditions may be driving higher erosion rates along the exposed coast (Baranskaya et al 2021), making exposed areas even more vulnerable to erosion with changing ocean conditions.

Most oil and gas development on the ABSC has occurred between the Colville and Sagavanirktok Rivers, though a pipeline also extends from the Sagavanirktok River east to the Canning River and there is ongoing development west of the Colville River. Most energy infrastructure is currently located on coasts with sheltered typologies, with most having relatively low long-term erosion rates of <1 m yr⁻¹ (table 1, figure 1).

In addition to the influence of barrier islands on erosion rates, our analyses highlight three large-scale results: (i) the highest long-term erosion rates occur on exposed coasts with intermediate heights and north-facing shorelines (figures 2 and 3); (ii) variability in erosion rates decreases with increasing elevation (figures 3(A) and 4); (iii) no single variable is a good predictor of erosion rate.

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The typologies with the lowest erosion rates all had low coastal elevations. The typology with the highest historical erosion rates, E1 (11.8 m yr⁻¹), had intermediate elevations (between 1 m and 4 m), disrupting the trend of increasing erosion rates with increasing elevation (figure 3(A)). Typologies S3, S7, S8, E1 E4, and E6 all have intermediate elevations, but only E1 shows anomalous rates of shoreline change compared to the overall trend, suggesting the E1 coasts are unique compared to the rest of the ABSC. On higher bluffed coasts, only the lower portion of the bluff is regularly impacted by waves or coastal currents, resulting in erosion via thermo-abrasion that typically leads to undercutting, niche formation, and larger block collapse, explaining the trend in figure 3(A). While Irrgang et al (2022) suggested a possible connection between elevation and erosion rate along the Alaskan and Canadian Beaufort Sea coasts, this analysis helps to quantify this relationship for the ABSC. The lack of separation between exposed and sheltered typologies in figure 3(A) suggests that elevation may have a stronger control on erosion rates than barrier islands, but spatial differences in fetch will also affect shoreline dynamics and complicate this interpretation.

Coasts with northeast-facing shorelines have some of the highest erosion rates, particularly for typologies E1 and E5 (figure 3(B)). Waves in this region come predominantly from the east (Nederhoff et al 2022), partially helping to explain this relationship. However, based on figures 3(B) and (a) northeast-facing shoreline is clearly not always associated with high erosion rates, as many other typologies also contain northeast-facing shorelines with substantially lower erosion rates.

Neither exposure nor elevation provides a robust predictor for erosion rate alone (figures 3(A), S1 and S8), but they are both correlated with variability in erosion rate. The range of erosion rates decreases with increasing elevation (figure 4) and is also smaller for sheltered vs. exposed typologies (figure S1). The lower variability in erosion rates of sheltered locations is likely due to the limited wave-driven processes in these environments. The lower variability in erosion rates of higher elevation areas may be a function of how shorelines were delineated: shorelines at higher elevations may be more consistently measuring true bluff erosion, as opposed to mixed bluff and beach erosion for lower elevation shorelines.

Typologies with warmer permafrost (i.e. relatively higher ground temperatures compared to other locations) also have higher erosion rates (table 1, figure 2), suggesting a thermal control on erosion rates since warmer permafrost requires less heat input to thaw and mechanically erode. However, there is no obvious or consistent trend in erosion rate with ground temperatures (figures 3(B) and S8). The two typologies with the highest median shoreline retreat rates (E1 and E5) differ only in their geomorphic landforms, ground temperatures, and elevations (table S1, figure 2). Therefore, since E1 has a higher median erosion rate than E5, and the two typologies otherwise share so many landscape characteristics, we infer that the warmer permafrost temperatures combined with intermediate elevations are associated with even faster erosion in E1 compared to E5 (figure 2).

Figures 2, 3, 5 and S7 highlight the complexity of developing a relatively simple and meaningful conceptual model of erosion rates on the ABSC. As in past studies (e.g. Jones et al 2018), our findings revealed a complicated set of relationships between historical rates of shoreline change and landscape characteristics. The presence or absence of a barrier island is the clearest control and a primary factor in setting rates of change, with sheltered areas having three times slower erosion rates than exposed areas. Barrier islands are widely known to protect the mainland from waves that abrade and erode the coast, making this an intuitive but nonetheless important result. Elevation is a clear secondary control, with higher elevation bluffs eroding faster, likely due to the undercutting and subsequent block toppling that occurs due to thermoabrasion. However, elevation shows only a weak inverse relationship with rate of shoreline change (figures 3(A) and S8), and the linear fit suggests that elevation explains only 22%–36% of the variability. E1 does not conform to this trend, meaning that other factors may diminish or interfere with the control of elevation on shoreline change rate.

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Aside from barriers and elevation, all remaining variables we examined may contribute to controlling shoreline change rate, though likely with weaker relationships and only in combination with other characteristics. As shown in figures 3 and 5, the highest erosion rates along the ABSC are associated with north- to northeast-facing shorelines, glaciomarine deposits, peaty silty lowlands, peaty pebbly silty deposits, and high ice contents with high thaw settlement potential. Physically, these associations seem reasonable. Waves are predominantly from the east, so northeast-facing shorelines would be subject to more wave attack. Glaciomarine deposits, peaty silty lowlands, and peaty pebbly silty lithology all imply a particular grain size assemblage, suggesting that deposits with a mixture of peat, silt, and gravel may erode faster than deposits that are all mud or especially sandy. Ice content has been previously thought to control coastal dynamics (Nielsen et al 2022). Higher ice contents may require more heat to melt the ice and erode the coast, but assuming sufficient heat is available, the loss of large amounts of ground ice can lead to much higher erosional rates. Thus, while all of these factors can be explained as potential controls, the distributions of shoreline change rates show that none of these conditions on their own is enough to predict a high erosion rate. The distribution of shoreline change rates for glaciomarine deposits, for example, ranges from highly erosional at rates >15 m yr⁻¹ to net aggradational at rates >5 m yr⁻¹ (figure 5). These results therefore suggest that no single variable (outside of barrier island presence) is a strong predictor of erosion rate, though it does identify a specific set of surface and subsurface conditions that in combination may drive higher rates of erosion on permafrost coasts.

Our results highlight that the highest erosion rates are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content on exposed coasts. All else being equal, warmer permafrost temperatures may also contribute to faster erosion rates. Comparing long-term to short-term rates of shoreline change also indicates that exposed coasts have experienced an acceleration in erosion rates while sheltered coasts have had roughly constant rates of change. As the Arctic continues to warm, we expect that rising permafrost temperatures (Biskaborn et al 2019), increasing storminess (Nederhoff et al 2022, Parker et al 2022), and a longer open-water season (Crawford et al 2021) will contribute to even faster erosion rates along exposed permafrost coasts. Coastlines protected by barrier islands may remain protected from the increased wave activity, but will still be subject to increased temperatures and thermodenudation. If barrier islands are drowned by sea level rise or destroyed by sand and gravel mining, the loss of these protective barriers may drive a large increase in erosion rates on previously sheltered permafrost coasts. Finally, the highest erosion rates are currently seen on coasts with high ice content. If widespread permafrost degradation were to occur across the North Slope that melted all ice in the shallow subsurface, then we may expect ice to play an insignificant role in future rates of coastal erosion. Thus, while ice content may currently drive spatial variability in coastal erosion rates, its influence may to decrease in the future as more areas thaw and become ice-free.