from Swirad and Young |
The majority of California's coastline is lined with cliffs with spectacular views of the Pacific Ocean. However, the erosion of the cliffs threatens highways,
railways, wastewater facilities, commercial structures, pubic structures, and residential structures. Large cliff failures occur
episodically and have historically have seemed random, making cliff-retreat forecasts
a major challenge. Cliff failures in recent decades have caused fatalities
and significant infrastructure damage. I recall watching a house I regularly passed
on Route 1 up north slide into the Ocean on the evening news.
There are some areas of the California coast that experience high
magnitude cliff failures, repeatedly. Daly
City, Portuguese Bend, and San Onofre are such examples. (Hapke et al., 2009; Young et al., 2009; Young, 2015, Young, 2018). Large-scale
quantitative studies of California cliff erosion and failure have been done
with Adam Young and Zuzanna Swirad building on their and others previous work. The goal is
to someday be able to accurately predict which areas are likely to collapse in
the future.
Despite the general
understanding of what drives coastal change (waves, rainfall), scientists are
unable to predict cliff erosion at specific locations and times. Because of the
wide range of causes and types of erosion, and highly variable geologic,
oceanographic, and climatic settings; land planners and local officials could
not know which areas were unsafe. Young found no significant correlation between
cliff erosion rates and environmental
factors such as rainfall, groundwater, waves, and relative sea level change. However, the highest
cliff face retreat rates occurred at locations with what he described as weak
rocks.
The current study was proceeded by several earlier studies. Hapke et al. (2009) measured cliff top
retreat by comparing 1920s–1930s topographic maps and 1998/2002 LiDAR datasets
for 353 km of cliffs spread throughout the state (20% of the California
coast). This study found generally higher retreat rates in northern and central
California (on average) compared to southern California.
Young
(2018) used 1998 and 2009–2011 LiDAR datasets to measure cliff erosion
and cliff face and top retreat rates for 595 km of the southern and
central California coastline (35% of the California coast), and found higher
retreat rates for cliffs fronted by sandy beaches than those without a beach.
Swirad and Young used
the 2009–2011 and 2016 LiDAR datasets to measure cliff erosion for a 595 km portion of the coast followed by a study of a different 866 km (53%) of the California coastline. They identified landslide
volume frequency relationships, and quantified state- and county-averaged cliff
face retreat rates. The new results were compared to the previous study and
yielded similar statistical results of cliff face retreat. These statistics
help account for episodic erosional events, and help improve model predictions.
For the current study using LiDAR, Swirad and Young were
able to measure both the cliff top change and changes within the cliff face. The
goal is to be able to predict collapse. The research has found it difficult to
isolate events that happen seasonally or annually and have tended in their
model building towards a statistical approach. Swirad
and Young created a one-meter digital elevation models and evaluated the cliff
erosion and retreat between 2009-2011 and 2016 in five-meter segments along 866
kilometers of California’s coast. “Because we found statistical agreement with
the previous time period, 1998 and 2009-2010, we may be more confident that the
statistical approach is the way to do it,” Swirad said. Erosion was detected
along more than half of the cliffs.
The limited time frame for the data sets is a weakness of the studies. It is too narrow a time period for developing a predictive model for geologic events. A new statewide data collection began last year. This will
provide a third time span to see if the statistical results remain consistent
with those from the earlier periods and could provide additional support for the statistical modeling
approach.
This study was funded by the California Ocean Protection
Council and California Department of Parks and Recreation and using data
collected for previous research funded by California Sea Grant. The data e is
available on the website California
Coastal Cliff Erosion Viewer. Users Though the website is designed for
coastal planning and development decision-makers, anyone can browse any cliff
in the state to see its past rate of erosion and related retreat statistics.
You can read the current study here.
Zuzanna M. Swirad, Adam P. Young, Spatial and temporal
trends in California coastal cliff retreat, Geomorphology, Volume 412, 2022, 108318,
ISSN 0169-555X, https://doi.org/10.1016/j.geomorph.2022.108318.
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