📄 earth_science

Model of coastal bluff retreat accounting for complex geology of the Salish Sea

This paper presents a physically based model that integrates wave conditions, site-specific geology, and historical retreat rates to forecast coastal bluff erosion in Puget Sound through 2100, thereby supporting vulnerability assessments and adaptation planning under sea-level rise scenarios.

Original authors: Eric Grossman, Liam Horner, Sean Vitousek, Keeley Chiasson, Callie Little, Andrea MacLennan

Published 2026-09-07

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Eric Grossman, Liam Horner, Sean Vitousek, Keeley Chiasson, Callie Little, Andrea MacLennan

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Coastal bluffs are the steep, earthen cliffs that line many shorelines, holding back the land above while the ocean works to wear them away. These cliffs are not static monuments; they are dynamic systems constantly reshaped by the forces of nature. When rain soaks into the soil, it adds weight and reduces the friction that holds the ground together, making the slope more likely to slide. When waves crash against the base of a cliff, they carve away the support, leaving the rock and soil above to collapse under their own weight. This process, known as bluff retreat, is a natural part of coastal evolution, but it poses significant risks to homes, roads, and ecosystems built near the edge. As the climate changes, rising sea levels and more intense storms are expected to accelerate this erosion, threatening communities that have long relied on the stability of these coastlines. Understanding exactly how and why these cliffs move is essential for planning a safe future, yet predicting their behavior has historically been difficult because every stretch of coast has a unique mix of geology and exposure to the elements.

Researchers have developed a new computer model designed to predict how these coastal bluffs will retreat across the Salish Sea, a complex network of waterways in the Pacific Northwest. This region is home to a diverse array of sediment types, ranging from hard, compacted glacial deposits to loose, sandy soils, all of which react differently to water and waves. The team, led by scientists from the U.S. Geological Survey and Western Washington University, created a tool that accounts for this geological complexity alongside the physical forces of the ocean. Instead of treating all coastlines as the same, their model looks at the specific type of soil at each location, the height of the water during storms, and the sheer power of the waves hitting the shore. By combining these factors, the model can simulate how the cliffs will change over time, offering a much clearer picture of future risks than previous methods allowed.

The core of this new approach lies in how it measures the forces that drive erosion. The researchers focused on two main mechanisms: the saturation of the soil and the scouring action of the waves. When water levels rise due to tides, storm surges, or long-term sea level rise, the soil at the top of the cliff becomes heavy and unstable. Simultaneously, waves wash away the material at the bottom, undercutting the cliff and causing it to collapse. The model quantifies these interactions by analyzing historical data on water levels and wave energy, while also incorporating specific measurements of how tightly the soil particles stick together, a property known as cohesion. By replacing vague categories of rock types with precise numbers representing this stickiness, the model can distinguish between a cliff made of hard, resistant clay and one made of loose, easily washed-away sand. This level of detail allows the system to explain about 60 percent of the variations seen in historical erosion rates across the region, a significant improvement over earlier attempts that managed to explain only about 30 to 40 percent.

To build this model, the team mapped thousands of points along the coastline, creating a detailed digital representation of the cliffs' current shape. They then fed this map into a computer program that tested how different combinations of water levels, wave power, and soil strength influenced the rate of erosion. The model was tested against real-world measurements taken over decades at specific sites, and it proved highly accurate in predicting where and how fast the bluffs had moved in the past. Once validated, the researchers used the model to look ahead, simulating how the coastline would respond to various scenarios of sea level rise by the year 2100. They ran these simulations using data from global climate models that project future storm patterns and water levels, allowing them to see how the acceleration of sea level rise would interact with the local geology to change erosion rates.

The results of these simulations paint a clear picture of the future. The model projects that as sea levels rise and storms become more powerful, the rate at which these bluffs retreat will accelerate. This acceleration is not uniform; it depends heavily on the local geology. In areas where the soil is less cohesive, the cliffs are expected to retreat much faster than in areas with harder, more compacted ground. The model also highlights that the combination of higher water levels and increased wave energy will create a feedback loop, where the loss of sediment at the base of the cliff allows waves to reach higher up the face, causing even more rapid collapse. These findings provide land managers and community planners with a new tool to assess vulnerability. By mapping out the likely future positions of the bluff crests and the zones of highest hazard, the model helps identify which properties and ecosystems are most at risk, enabling more informed decisions about where to build, where to retreat, and how to protect the natural habitats that depend on the sediment these cliffs provide.

The study does not claim to predict the future with absolute certainty, as natural systems are inherently variable and influenced by factors that are difficult to measure, such as localized rainfall patterns or human modifications to the landscape. However, by explicitly accounting for the complex geology of the Salish Sea and the physical processes of wave scour and soil saturation, the model offers the most detailed and physically grounded assessment to date. It moves beyond simple statistical guesses to a simulation based on the actual mechanics of erosion. The outputs, which include maps showing the projected location of the bluff edge and the associated uncertainty, are now available for use by coastal planners. These tools will help communities navigate the challenges of a changing climate, ensuring that decisions about the future of the coast are based on a deep understanding of the forces that shape it.

Technical Summary: Model of Coastal Bluff Retreat Accounting for Complex Geology of the Salish Sea

Problem Statement
Sea-level rise is projected to increase the frequency and severity of wave and storm impacts, thereby accelerating shoreline and bluff erosion. This poses substantial risks to property, infrastructure, and ecosystems, particularly in the U.S. Pacific Northwest where diverse habitats rely on sediment supplied by bluff erosion. While historical bluff retreat rates have been documented, few predictive models exist to inform planning, and even fewer account for the specific geologic controls on erosion processes. Existing models often rely on statistical relationships or simplified coefficients for erodibility, failing to explicitly integrate the physical mechanisms of sediment saturation, wave scour, and the complex, heterogeneous geology of the Salish Sea. Furthermore, uncertainty regarding future bluff positions hinders effective vulnerability assessments and adaptation efforts.

Methodology
The authors developed a physically based coastal bluff retreat model for Puget Sound, derived from high-resolution topographic data, historical wave and water level reanalyses, and site-specific geologic characteristics. The workflow consists of four primary steps:

Data Compilation and Transect Generation: The model utilizes a 1-meter seamless topo-bathymetric digital elevation model (CoNED) to generate cross-shore transects every 25 meters along the shoreline. Automated tools (USGS Cliff Feature Delineation Tool and iBluff) identify bluff crest and toe positions, with manual revisions applied where automation failed due to complex morphology or land-use modifications.
Multivariate Regression Development: The study revises a previous multivariate regression model (MacLennan et al., 2018) by replacing categorical parameters with quantitative physical metrics.
- Hydrodynamics: "Tide Range" was replaced with the 95th percentile non-tidal residual (NTR) normalized by water-level range to capture storm surge effects. "Fetch" was replaced with offshore cumulative wave power (filtered for the upper 1/3 of the tide range) to represent wave energy capable of scouring bluff bases.
- Geology: Categorical geologic susceptibility was replaced with published median cohesion values (in kPa) for seven distinct glacial deposit groups (e.g., Vashon till, glaciolacustrine deposits, alluvium).
- Model Equation: The final regression model ( $EPR$ ) relates the end-point rate of bluff change to water level metrics ( $NTR95$ , $WL_{skew}$ ), wave power metrics ( $WP_{cum}$ , $WP_{skew}$ ), and geologic cohesion ($Cohesion$, $Cohesion_{range}$ ).
Historical Reconstruction: The revised regression was applied regionally across 25-meter transects using an 80-year (1940–2022) hydrodynamic reanalysis to reconstruct historical bluff retreat rates, establishing a baseline for future projections.
Future Projections: Future bluff positions were projected to the year 2100 under six sea-level rise (SLR) scenarios (0.25 m to 3.0 m). The projections utilize an ensemble mean of the multivariate regression and three 1D cross-shore evolution formulations (Bruun, Total Water Level, and Hackney 2013). These models were forced by hydrodynamic simulations derived from CMIP6 global climate models (HighResMIP) to account for changes in meteorological conditions and extreme events. Uncertainty was propagated through 1,000 bootstrap iterations.

Key Contributions

Quantitative Geologic Integration: The model explicitly incorporates quantitative cohesion values for specific glacial geologic units, moving beyond categorical erodibility proxies to better represent the complex geology of the Salish Sea.
Physical Process Representation: By utilizing non-tidal residuals and offshore cumulative wave power, the model explicitly accounts for the two primary drivers of bluff retreat: sediment saturation leading to failure and wave scour leading to undercutting and colluvium removal.
Regional Scalability: The removal of site-specific categorical parameters (such as "Measurement Feature" and "Shoretype") allows the model to be applied continuously along the coast, reconstructing historical rates even in areas lacking direct observational data.
Comprehensive Uncertainty Quantification: The output includes 95% prediction intervals for future bluff positions and hazard zones, derived from uncertainties in water levels, wave heights, wave periods, and geologic cohesion.

Results

Model Validation: The revised model demonstrates high skill in predicting historical bluff retreat rates, explaining 60% of the variability ( $R^2 = 0.60$ ) in measured historical rates across the Pacific Northwest. This represents a significant improvement over the previous model, which explained 30–40% of the variance.
Dominant Drivers: Normalized coefficients indicate that wave power and sediment cohesion are the dominant terms influencing erosion rates, exerting an order of magnitude greater influence than water level variables.
Future Projections: The model projects an acceleration of bluff retreat rates by 2100 due to increasing total water levels and cumulative wave power under CMIP6 climate scenarios. Outputs include GIS shapefiles for future bluff crest positions, uncertainty envelopes, and hazard erosion zones for various SLR scenarios.

Significance
The paper claims that this work advances the field by providing a physically based model that bridges the gap between statistical erosion models and complex process-based models. By accounting for the specific geologic controls and hydrodynamic drivers of the Salish Sea, the model outputs enable more robust vulnerability assessments for coastal erosion hazards. These outputs are intended to guide future adaptation efforts, inform land and resource management planning, and support ecosystem assessments and the protection of human life in the Pacific Northwest. The authors note that while the model improves explanatory power, unexplained variability likely remains due to processes such as rainfall-driven saturation, freeze-thaw effects, and deep-seated landslides, which are not explicitly modeled.

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