Study identifies California cliffs at risk of collapse
Cliff retreat hot spots and high cliff top hazard locations identified by the study.
Credit: Adam Young, 2017
The yellow warning signs that pepper coastal cliffs from northern California to the US-Mexico border may seem overly dramatic to the casual observer. But actively eroding cliffs make up the majority of the California coastline, and sudden landslides and collapses have caused injuries and several fatalities in recent years. In addition, eroding cliffs currently threaten highways, houses, businesses, military bases, parks, power plants, and other critical facilities—all in all billions of dollars of development.
Research suggests that erosion rates will increase as sea level rises, further exacerbating these problems. “It is critical we study current and historical cliff retreat so we can better plan for the future,” says Adam Young, a researcher at Scripps Institution of Oceanography at the University of California San Diego who recently published a unique large-scale analysis of coastal cliff erosion in California.
The study, published in the journal Geomorphology, and funded by California Sea Grant, provides accurate erosion rates for 680 miles of the California coast, from the US-Mexico border to Bodega Head in Sonoma County. It identifies areas that have eroded faster than others, and introduces a new experimental hazard scale to identify areas that may be at greater risk of impending collapse. It is the first such large-scale study in California using LiDAR data—laser elevation data recorded in aerial surveys—which were used to create detailed 3D elevation maps.
Existing cliff erosion studies are often small scale, use a variety of techniques, and often rely on lower quality data sources, providing a patchwork across the state. “What’s unique about this study is that it applies a consistent methodology across a very large area using accurate high-resolution laser data,” says Young.
While some of the basic causes of coastal cliff erosion—such as rainfall and waves—are clear, this has not translated into a simple way to predict future erosion rates or identify areas at risk. Variation in cliff geology, beach protection, exposure to weather, and other factors also complicate predicting erosion rates.
Previous research has identified clear correlations between rainfall and coastal erosion in southern California, but the impact of storms waves has been more elusive. “It’s difficult to measure,” explains Young, “We lack field observations because with powerful waves crashing against the cliff, it is not an easy place to make measurements.” Variation in cliff geology, beach protection, exposure to weather, and other factors also complicate predicting erosion rates.
Understanding erosion, preventing disaster
To create a consistent analysis of recent cliff changes, Young compared two massive LiDAR data sets, three-dimensional maps of the California coastline, recorded eleven years apart. The highest cliff erosion rates were found in San Onofre, Portuguese Bend, Palos Verdes, Big Sur, Martins Beach, Daly City, Double Point, and Point Reyes (see map). Young then compared the recent cliff erosion maps to historical records from 1932 and 1934. By comparing the different maps, he built an analysis for the majority of the state’s coastline, showing both recent and historical erosion rates.
The study shows that the historical cliff erosion rate does not always provide a good prediction of future rates. “The results show that if a cliff experienced a large of amount of erosion during one time period, it was followed by a time period with very little erosion, and the cliff could be relatively stabilized for a time,” explains Young. “It will mobilize again, but we don’t know when and more research is needed to better understand the time cycles involved.”
Young also found that cliffs with high erosion rates in recent times were often preceded by time periods with very little erosion. These are key findings, because models predicting future cliff retreat are often based on projecting the historical rates.
Young also introduced a new experimental measure to identify the riskiest precipices. Previous research had suggested that the difference between erosion rates of the cliff face compared to the cliff top could indicate instability—in short, the cliff steepness. When he applied this hazard index, Young identified worrisome spots along the California coast, including San Onofre State Beach, Big Sur, Martin’s Beach, and Daly City.