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Categories: Geoscience: Landslides

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New research has revealed how an underwater avalanche grew more than 100 times in size causing a massive trail of destruction as it traveled 2000km across the Atlantic Ocean seafloor off the North West coast of Africa. Researchers provide an unprecedented insight into the scale, force and impact of one of nature's mysterious phenomena, underwater avalanches.

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The design of streets and layout of buildings have an impact on a city's resilience in the face of increasingly severe floods brought on by climate change. Researchers look at buildings and other urban structures as physicists consider elements in complex material systems. With this insight, the researchers have developed a new approach to urban flood modelling and found their results helpful in analyzing city-to-city variations in flood risk globally.

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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.

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Landslides triggered by intense rainfall can sometimes be predicted along with incoming storms, but dry-season landslides often take people by surprise. The July 2023 Rolling Hills Estates landslide that destroyed 12 homes seemed to come out of nowhere, but new research shows it began as early as December 2022. Researchers are developing a database that will enable scientists to plug in new data to monitor potential landslides in real time and possibly predict them.

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Groundbreaking research has provided new insight into the tectonic plate shifts that create some of the Earth's largest earthquakes and tsunamis.

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The names might not be familiar -- Cowee Creek, Brabazon Range, Upper Pederson Lagoon -- but they mark the sites of recent lake tsunamis, a phenomenon that is increasingly common in Alaska, British Columbia and other regions with mountain glaciers. Triggered by landslides into small bodies of water, most of these tsunamis have occurred in remote locations so far, but geologists say it may just be a matter of time before a tsunami swamps a more populated place.

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Landslides are one of the most destructive natural disasters on the planet, causing billions of dollars of damage and devastating loss of life every year. A global team of researchers has provided help for those who work to predict landslides and risk evaluations.

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If rivers overflow their banks, the consequences can be devastating -- just like the catastrophic floods in North Rhine-Westphalia and Rhineland-Palatinate of 2021 showed. In order to limit flood damage and optimize flood risk assessment, we need to better understand what factors can lead to extreme forms of flooding and to what extent. Using methods of explainable machine learning, researchers have shown that floods are more extreme when several factors are involved in their development.

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The period that liquid water was present on the surface of Mars may have been shorter than previously thought. Channel landforms called gullies, previously thought to be formed exclusively by liquid water, can also be formed by the action of evaporating CO2 ice, according to a new study.

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If storms become stronger in the future due to climate change, more nitrogen may be released from the bottom of coastal seas.

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Researchers have devised a way to remotely detect large landslides within minutes of occurrence and to quickly determine whether they are close to open water and present a tsunami hazard.

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Clearing mangroves to stop estuaries getting clogged with mud actually makes the problem worse, new research shows.

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New maps of more than 1,000 deep-seated landslides in the Puget Lowlands of Washington State provide evidence of the last major earthquake along the Seattle Fault about 1,100 years ago -- and may also hold traces of older earthquakes along the fault.

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Most coastal communities will encounter '100-year floods' annually by the end of the century, even under a moderate scenario where carbon dioxide emissions peak by 2040, according to a new study. And as early as 2050, regions worldwide could experience 100-year floods every nine to fifteen years on average.

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Terrestrial carbon sinks can mitigate the greenhouse effect. Researchers pooled various data sources and found that European carbon storage takes place mainly in surface biomass in East Europe. However, changes of land use in particular have caused this carbon sink to decline.

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As climate change progresses, rising temperatures may impact nitrogen runoff from land to lakes and streams more than projected increases in total and extreme precipitation for most of the continental United States, according to new research from a team of climate scientists.

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Researchers find large methane emissions: 'Do not flood low-lying areas with freshwater'. Their studies find that freshwater lakes emit much more methane than saltwater lagoons, bogs and wet meadows.

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Scientists once assumed that flooding and mudslides after wildfires were linked to the waxy coating that builds up on charred soil, preventing water absorption. Researchers found that water flow came from absorbed water in both burnt and unburnt areas, suggesting that water was, in fact, being absorbed into burnt ground. The discovery provides valuable insights into where and when potential flooding and mudslides may occur and how landscapes recover after a wildfire.

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A groundbreaking study reveals the critical yet severely understudied factor of salinity changes in ocean and coastlines caused by climate change.

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Many factors influence where a landslide will occur, including the shape of the terrain, its slope and drainage areas, the material properties of soil and bedrock, and environmental conditions like climate, rainfall, hydrology and ground motion resulting from earthquakes. Geologists have developed a new technique that uses artificial intelligence to better predict where and why landslides may occur could bolster efforts to protect lives and property in some of the world's most disaster-prone areas. The new method improves the accuracy and interpretability of AI-based machine-learning techniques, requires far less computing power and is more broadly applicable than traditional predictive models.