Alaskan land eroding faster due to climate change
UTA scientist’s research shows how global warming is slowing formation of new permafrost
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Nathan D. Brown, assistant professor of earth and environmental sciences at UT Arlington
view moreCredit: Photo courtesy UT Arlington
A new study out of The University of Texas at Arlington shows that frozen land in Alaska is eroding faster than it can be replaced due to climate change.
"In the Northern Hemisphere, much of the ground is permafrost, meaning it is frozen year-round. Permafrost is a delicate natural resource. If it is lost faster than it is regenerated, we endanger infrastructure and release carbon, which can warm the atmosphere,” said Nathan D. Brown, assistant professor of earth and environmental sciences at UT Arlington. “Under a warming climate, a major question is whether arctic rivers will erode permafrost in thawing riverbanks faster than permafrost can regenerate.”
It happens slowly, but all rivers naturally change their paths over time. Floods, earthquakes, vegetation growth, and wildlife are constantly at work shifting rivers, charting new paths for water, and depositing sediment where water once flowed.
A difference seen with Alaskan rivers is that the land on riverbanks can be permanently frozen. Called permafrost, it’s a mixture of soil, gravel and sand often bound together by ice. Permafrost is important because it holds large amounts of organic carbon, which is then released when it melts. This carbon can combine with oxygen to become carbon dioxide, a greenhouse gas that warms Earth’s atmosphere.
To better understand the fate of permafrost in a warming world, Dr. Brown—along with colleagues from the California Institute of Technology; Massachusetts Institute of Technology; University of California at Santa Barbara; Los Alamos National Laboratory; the University of Chicago; and the University of Pennsylvania—mapped and dated floodplain deposits, determined permafrost extent, and characterized vegetation along the Koyukuk River in Alaska to model how permafrost formation varies with air temperature. The Koyukuk is a 425-mile feeder stream of the Yukon River and the last major tributary to flow into the Yukon before it empties into the Bering Sea, the major waterway separating America and Russia.
In the American Geophysical Union journal AGU Advances, the team reported that while new permafrost is developing along the Koyukuk River floodplain, it is not forming fast enough to replace what is disappearing due to rising temperatures.
“By dating these permafrost deposits, we found that permafrost formation in this region can take thousands of years,” said Brown. “Under a warming climate, permafrost formation is expected to take longer, while thawing permafrost riverbanks will become more susceptible to erosion. The net result will be loss of permafrost and contribution of carbon to the atmosphere.”
**Financial support for this research was provided by the National Science Foundation awards 2127442 and 2031532; Foster and Coco Stanback; the Linde Family; the Caltech Terrestrial Hazards Observation and Reporting Center; the Resnick Sustainability Institute; the National Defense Science and Engineering Graduate Fellowship; the Fannie and John Hertz Foundation Cohen/Jacobs and Stein Family Fellowship; and a Department of Energy Office of Science, Biological and Environmental Research Subsurface Biogeochemical Research Program Early Career award.**
Thursday, August 08, 2024
Alaska glacier outburst floods Juneau, damages more than 100 homes
Journal
AGU Advances
Method of Research
Observational study
Subject of Research
Not applicable
Article Title
Permafrost Formation in a Meandering River Floodplain
Sichuan Province earthquake offers lessons for landslide prediction from GNSS observations
Seismological Society of America
Using data collected from a 2022 magnitude 6.8 earthquake in Luding County in China’s Sichuan Province, researchers tested whether Global Navigation Satellite System (GNSS) observations could be used for rapid prediction of earthquake-triggered landslides.
In their report in Seismological Research Letters, Kejie Chen of the Southern University of Science and Technology and colleagues share a set of methods for near real-time GNSS landslide prediction. Some of their models accurately identified about 80% of the landslide locations that were triggered by the Luding earthquake, the researchers found.
Based on their results, Chen and colleagues show that near real-time landslide prediction for an earthquake like the Luding event could be completed in approximately 40 minutes—a time that is likely to be improved with further development of their models and higher-speed computing, they noted.
The 5 September 2022 Luding earthquake on the southeastern segment of the Xianshuihe Fault led to more than 6000 landslides, which caused severe damage over 3500 square kilometers of the region.
“The number of co-seismic landslides triggered by the Luding earthquake was significant but not entirely unexpected given the region’s topography and seismic activity. The area is known for its susceptibility to landslides, especially following large seismic events,” said Chen. “However, the scale of destruction and the specific locations affected did provide new insights into the region's risk profile and highlighted the importance of continuous monitoring and improved prediction models.”
GNSS data measure the movement of the ground during an earthquake. Chen and colleagues had already been exploring the use of GNSS data for locating earthquake sources and tsunami early warning when the Luding earthquake struck.
“For earthquakes that rupture inland, especially in mountainous areas in China, landslides become the main cascading seismic hazard,” Chen explained. “Our research has been focused on developing and refining methods for landslide prediction using GNSS. The Luding earthquake provided a critical case study that allowed us to evaluate and adapt our methods in the context of co-seismic landslides.”
The researchers developed an end-to-end GNSS prediction method, which begins by constructing slip models of the event based on the GNSS offset and displacement waveform data. They then used physics-based simulations of the earthquake with those slip models to obtain a measurement of peak ground velocity.
Finally, Chen and colleagues used the peak ground velocity with a machine learning algorithm to predict a possible spatial distribution of landslides for the event. Six Chinese earthquakes ranging in magnitude from 6.1 to 8.0 that shared geological similarities with the Luding earthquake were used to train the prediction algorithm.
One way to enhance the method would be to combine GNSS observations with data on near-fault ground motion waveforms captured by low-cost accelerometers called MEMS, Chen and colleagues noted. To improve earthquake warning and response, China has recently included more than 10,000 MEMS-based stations in a nationwide earthquake warning system.
“Using both data types in a complementary manner enhances the robustness and accuracy of landslide prediction,” Chen said. “GNSS data can validate and refine the predictions made by MEMS data, ensuring a comprehensive monitoring system.”
Journal
Seismological Research Letters
Method of Research
Computational simulation/modeling
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