Geology topics

Bretwood Higman

Publications and source records attributed to Bretwood Higman.

5 recordsLinked to original sources

USGS2026

A 481 m-high landslide-tsunami in a cruise ship-frequented Alaska fjord

Early in the morning of 10 August 2025, a >64 × 10 6 –cubic meter landslide struck Tracy Arm fjord in Alaska. The landslide was preconditioned by glacial retreat caused by climate change. The resulting 481-meter runup megatsunami followed an initial 100-meter-high breaking wave traveling at >70 meters per second. The landslide was preceded by several days of microseismicity, which increased in rate and magnitude until ~1 hour before failure. The landslide produced globally observed long-period seismic waves equivalent in size to a moment magnitude 5.4 earthquake. A long-period (~66 second) global seismic signal, produced by a landslide-induced seiche trapped within the fjord, persisted for up to 36 hours, the second time a days-long seiche had thus been observed. With fjord regions increasingly visited by cruise ships, and climate change making similar events more likely, this unanticipated, near-miss event highlights the growing risk from landslides and tsunamis in coastal environments.

Alaska↗

USGS2025

Harnessing geospatial artificial intelligence and deep learning for landslide inventory mapping: Advances, challenges, and emerging directions

Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and model architectures, we explore innovative strategies such as feature enhancement and fusion, attention-boosted techniques, and advanced learning approaches, including active learning and transfer learning, to enhance model adaptability and predictability. We also highlight the remaining challenges and potential research directions, including the estimation of more diverse variables in landslide mapping, multimodal data alignment, modeling regional variability and replicability, as well as issues related to data misinterpretation and model explainability. This review aims to serve as a useful resource for researchers and practitioners, promoting the integration of deep learning into landslide research and disaster management.

Remote Sensing↗

USGS2020

Detection and assessment of a large and potentially tsunamigenic periglacial landslide in Barry Arm, Alaska

The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 million m 3 ) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis.

Alaska↗

USGS2019

Catastrophic landscape modification from a massive landslide tsunami in Taan Fiord, Alaska

The October 17th, 2015 Taan Fiord landslide and tsunami generated a runup of 193 m, nearly an order of magnitude greater than most previously surveyed tsunamis. To date, most post-tsunami surveys are from earthquake-generated tsunamis and the geomorphic signatures of landslide tsunamis or their potential for preservation are largely uncharacterized. Additionally, clear modifications described during previous post-tsunami surveys are often ephemeral and unlikely to be preserved. Documented geomorphic modifications of several low gradient fan deltas within Taan Fiord make it an excellent laboratory for characterizing signatures of a landslide tsunami event. Geomorphic changes to fan deltas in Taan Fiord caused by the landslide-generated tsunami included complete vegetation loss over more than 0.6 km2 of fan surfaces, formation of steep fan front scarps up to 10 m high, extensive local alterations of fan topography, and formation of new tsunami return-flow channels. Two relatively stable fan deltas in Taan Fiord were heavily vegetated prior to the Taan event and may preserve features of tsunami modification for decades to centuries. If this is the case, fan deltas may be a previously unrecognized location for preservation of tsunami signatures in the recent past. Fans in poorly monitored regions, such as Greenland, could thus hold evidence of previously unidentified recent landslide tsunami events.

Alaska↗

USGS2006

Sri Lanka field survey after the December 2004 Indian Ocean tsunami

An International Tsunami Survey Team (ITST) consisting of scientists from the United States, New Zealand, and Sri Lanka evaluated the impacts of the 26 December 2004 transoceanic tsunami in Sri Lanka two weeks after the event. Tsunami runup height, inundation distance, morphological changes, and sedimentary characteristics of deposits were recorded and analyzed along the southwest and east coasts of the country. Preliminary results show how local topography and bathymetry controlled the limits of inundation and associated damage to the infrastructure. The largest wave height of 8.71 m was recorded at Nonagama, while the greatest inundation distance of 390 m and runup height of 12.50 m was at Yala. At some sites, human alterations to the landscape increased the damage caused by the tsunami; this was particularly evident in areas of coral poaching and of sand dune removal.

Earthquake Spectra↗