Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Johnstone, Elizabeth; Raymond, Jessica H.; Olsen, Michael J.; Driscoll, Neal W.

doi:10.2112/si76-015

Cited by 14 publications

(11 citation statements)

References 20 publications

(25 reference statements)

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“…Both events generated localized erosion rates that exceed the site-wide mean by over an order of magnitude (max. 0.50 m a −1 ); rarer large failures (≥0.1 m 3 ) at this site contributed more to the overall volume of erosion at monthly-annual timescales than frequent smaller failures (0.001-0.1 m 3 ), in line with similar observations from other rocky (e.g., [12,46,47]) and soft coast sites worldwide (e.g., [48][49][50]).…”

Section: Summary Of R = Rockfall Observationssupporting

confidence: 87%

“…Foreshore composition and characteristics are often investigated separately from cliff process studies, but, here, we show how holistic foreshore-cliff analysis aids the interpretation of the superimposition of erosion dynamics at rocky coasts. The focusing of marine energy related to incised channels in shore platform (Figure 1A), or the dissipative effect of platform material or structures, exerts key controls on cliff toe exposure and results in local divergence from classic models of undercutting and cantilever collapse [50]. Outside of periods of scaling exponent convergence (Figure 11E), we hypothesize that the erosion response of the secondary limestone is largely marine-driven, and certainly for small-(<0.1 m 3 ) and mid-range (0.1-1.0 m 3 ) failures.…”

Section: Links To Environmental Driversmentioning

confidence: 99%

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Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR

Westoby

Lim

Hogg³

et al. 2020

Remote Sensing

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A key factor limiting our understanding of rock slope behavior and associated geohazards is the interaction between internal and external system controls on the nature, rates, and timing of rockfall activity. We use high-resolution, monthly terrestrial light detection and ranging (LiDAR) surveys over a 2 year monitoring period to quantify rockfall patterns across a 0.6 km-long (15.3 × 103 m2) section of a limestone rock cliff on the northeast coast of England, where uncertainty in rates of change threaten the effective planning and operational management of a key coastal cliff top road. Internal system controls, such as cliff material characteristics and foreshore geometry, dictate rockfall characteristics and background patterns of activity and demonstrate that layer-specific analyses of rockfall inventories and sequencing patterns are essential to better understand the timing and nature of rockfall risks. The influence of external environmental controls, notably storm activity, is also evaluated, and increased storminess corresponds to detectable rises in both total and mean rockfall volume and the volumetric contribution of large (>10 m3) rockfalls at the cliff top during these periods. Transient convergence of the cumulative magnitude–frequency power law scaling exponent (ɑ) during high magnitude events signals a uniform erosion response across the wider cliff system that applies to all lithologies. The tracking of rockfall distribution metrics from repeat terrestrial LiDAR in this way demonstrably improves the ability to identify, monitor, and forecast short-term variations in rockfall hazards, and, as such, provides a powerful new approach for mitigating the threats and impacts of coastal erosion.

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Section: Summary Of R = Rockfall Observationssupporting

confidence: 87%

Section: Links To Environmental Driversmentioning

confidence: 99%

Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR

Westoby

Lim

Hogg³

et al. 2020

Remote Sensing

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“…The process of identifying the cliff base line was more challenging than that used to distinguish the shoreline. Numerous studies on cliffs assume manual delineation of the cliff baseline by relying mainly on aerial photographs, topographic maps, and in situ surveys [30][31][32][33]. Some examples of automatic delineation were reported by Palaseanu-Lovejoy and others [34], who used generalized coastal shoreline vectors, and by Terefenko and others [33], who considered a simplified methodology of rapid changes in altitude.…”

Section: Methodsmentioning

confidence: 99%

Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys

2019

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Rock cliffs are a significant component of world coastal zones. However, rocky coasts and factors contributing to their erosion have not received as much attention as soft cliffs. In this study, two rocky-cliff systems in the southern Baltic Sea were analyzed with Airborne Laser Scanners (ALS) to track changes in cliff morphology. The present contribution aimed to study the volumetric changes in cliff profiles, spatial distribution of erosion, and rate of cliff retreat corresponding to the cliff exposure and rock resistance of the Jasmund National Park chalk cliffs in Rugen, Germany. The study combined multi-temporal Light Detection and Ranging (LiDAR) data analyses, rock sampling, laboratory analyses of chemical and mechanical resistance, and along-shore wave power flux estimation. The spatial distribution of the active erosion areas appear to follow the cliff exposure variations; however, that trend is weaker for the sections of the coastline in which structural changes occurred. The rate of retreat for each cliff–beach profile, including the cliff crest, vertical cliff base, and cliff base with talus material, indicates that wave action is the dominant erosive force in areas in which the cliff was eroded quickly at equal rates along the cliff profile. However, the erosion proceeded with different rates in favor of cliff toe erosion. The effects of chemical and mechanical rock resistance are shown to be less prominent than the wave action owing to very small differences in the measured values, which proves the homogeneous structure of the cliff. The rock resistance did not follow the trends of cliff erosion revealed by volume changes during the period of analysis.

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“…Because the purpose of this study was to create reproducible solutions for analyzing the relationship between the erosion rates on coastal cliffs, a comparable procedure for extracting cliff base line was needed. While several studies realized on cliffs analyzed volumetric changes, to our knowledge, all assumed manual delineation of the cliff baseline, relying mainly on aerial photographs, topographic maps, or in situ surveys [1,18,33,34]. Some attempts of advanced automatic delineation were performed on the cliff bases of generalized coastal shoreline vectors by approximating the distance between shoreline and the cliff top [19].…”

Section: Geomorphological Indicatorsmentioning

confidence: 99%

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

et al. 2019

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Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.

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Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Cited by 14 publications

References 20 publications

Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR

Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR

Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

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