NASA study finds climate extremes affect landslides in surprising ways

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Climate change is driving more volatile precipitation patterns around the world – very dry expanses punctuated by storms that drop large amounts of rain or snow in a short period of time. Although wetter and drier periods can have some predictable effects, such as on water levels in lakes and rivers, a recent study from California reveals that they can affect slow-moving landslides in ways unexpected.

The researchers expected that slow-moving landslides — where the land descends from a few centimeters to a few feet in a year — in dry southern California would behave differently than those in rainy northern California when they are exposed to heavy rainfall and drought conditions. But that was not the case. The study authors found that landslides in wetter and drier regions of California exhibited similar sensitivity to extreme precipitation, moving on average faster and further downhill during periods of rain compared to years of drought.

Water triggers landslides, and how landslides respond to record drought or extreme precipitation can help researchers better predict their future behavior, including whether any of them might collapse or fail catastrophically. The overall goal is to develop a statewide inventory landslide behavior that would inform a surveillance network. Although slow-moving landslides don’t necessarily pose an immediate danger to people or infrastructure, they can damage things like roads and buildings over time. And in some cases they can collapse suddenly, which happened with the Mud Creek landslide near Big Sur in 2017.






This series of images shows the May 2017 Mud Creek landslide collapse along the Big Sur coast in central California, and subsequent repairs to Highway 1, which was damaged in the event . Credit: Andy Ritchie/USGS Pacific Coastal and Marine Science Center

“I thought the results would be quite different between northern and southern California,” said Alexander Handwerger, landslide scientist at NASA’s Jet Propulsion Laboratory in Southern California and lead author of the study, who was recently published in Geophysical Research Letters. Until this article, his work has focused on landslides in Northern California. So he wasn’t sure what he would see when examining the drier parts of the state.

Surprising behavior

California is home to more than 650 slow-moving landslides, and Handwerger and his colleagues focused on 247 slow-moving landslides with an average area of ​​0.2 square miles (0.5 square kilometers). They then analyzed a subset of 38 that differed in the amount of precipitation they received, the types of rocks they were made of, the environments in which they occurred (coastal or inland), and whether they were found in developed or undeveloped areas. The researchers looked at how these landslides behaved from 2015 to 2020, a period with wide variations in rainfall: while 2017 was the second wettest year on record for parts of California, 2015 and 2016 were exceptionally dry years.

They obtained information on the movement of landslides using data collected by ESA (European Space Agency) Sentinel-1 satellites. The measurements were automatically transformed into maps showing ground movements by the JPL-Caltech Advanced Rapid Imaging and Analysis (ARIA) Center for Natural Hazards project. (Caltech, in Pasadena, manages JPL for NASA.)

Researchers knew that slow-moving landslides in the wetter parts of the state remained fairly saturated throughout the year. They did not expect to find that already waterlogged landslides and their drier counterparts accelerated and moved further during wet periods compared to drier periods.

Predict the future

Better understanding why landslides react the way they do to rainfall or drought could help researchers predict future events like the Mud Creek landslide. It collapsed in a very wet year for California in which similar landslides did not collapse. “We’re trying to figure out why this is happening,” Handwerger said.

A better understanding of landslide behavior could allow a monitoring network that provides alerts to local and state officials, as well as researchers, to keep tabs on a landslide or group of landslides that have started acting differently. It could also help build a warning system for communities threatened by a landslide, as well as influence planning related to development and infrastructure.

The key to such a monitoring network is the ability to carry out large-scale detailed studies. And these are made possible by advances in satellite technology, which have allowed spacecraft such as Sentinel-1 to provide more frequent information, accurate data on changes in the Earth’s surface over larger areas. Upcoming missions like NISAR (short for NASA-Indian Space Research Organization Synthetic Aperture Radar Satellite) will monitor changes on the Earth’s surface using a different radar frequency that can “see” through vegetation better than Sentinel- 1. Like this mission, NISAR will make its data freely available to the public.

Time-consuming, data-heavy analyzes are also becoming easier to perform thanks to projects like ARIA and an upcoming NASA-sponsored project called OPERA (or Observational Products for End-Users from Remote Sensing Analysis). OPERA, managed by JPL, will use measurements from missions like Sentinel-1 and NISAR to produce data products showing changes on the Earth’s surface. These products will provide resource managers, federal agencies, and researchers, among others, with detailed measurements of much of North and Central America, eliminating the need to spend time working data in a format suitable for analysis and decision-making.


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More information:
Alexander L. Handwerger et al, Landslide Sensitivity and Response to Changes in Precipitation in Wet and Dry Climates, Geophysical Research Letters (2022). DOI: 10.1029/2022GL099499

Quote: NASA study finds climate extremes affect landslides in surprising ways (2022, October 5) Retrieved October 5, 2022 from https://phys.org/news/2022-10-nasa-climate-extreme -affect-landslides.html

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