Research Opportunity Description
Landslides occur in every U.S. state and most territories; they pose a significant threat to people, buildings, essential facilities and infrastructure, and natural and cultural resources. The USGS Landslide Hazards Program is dedicated to improving scientific understanding of landslides, while also developing tools that support external partners in their efforts to reduce losses. The National Landslides Preparedness Act (Public Law 116–323) has invigorated investigations into national-scale products to better understand the similarities and differences between landslide hazards across the country. In response to this mandate, the USGS has developed a national science strategy for landslide loss reduction (Godt et al., USGS, 2022) and a new project focused on national-scale maps of landslide hazards. Recent efforts include compiling an inventory of existing landslide maps totaling hundreds of thousands of points and polygons (Belair et al., USGS, 2022), as well as leveraging this geodatabase to produce a new nation-wide landslide susceptibility map (Mirus et al., in review).
These contributions represent a considerable improvement over many existing susceptibility maps developed for the continental U.S. (Radbruch-Hall et al., USGS, 1982; Godt et al., N. Am. Symposium on Landslides, 2012) and globally (Stanley and Kirschbaum, Natural Hazards, 2017; Jia et al., Geomorphology, 2021). These are opening new avenues for research, such as nationwide vulnerability and risk assessments, and are providing critical information for land-use and infrastructure planning, as well as emergency managers and government agencies. However, despite advances towards national-scale landslide assessments, the susceptibility map is static and uniform across the nation. Therefore, it does not reflect variations in the frequency of landslides or potential disparities in their size and failure mechanism, which affects mobility and destructive potential. Whereas this is a widely recognized shortcoming of any susceptibility map, hazard or risk assessments consider not just the spatial likelihood of landsliding across a landscape, but also the frequency and even the magnitude (refer to Reichenbach et al., Reviews in Earth Science, 2018). Primary obstacles to developing a national landslide hazard map are the lack of comprehensive information on landslide timing, frequency, magnitude, and mobility across the nation, as well as the limitations of methods for extrapolating model output across broad, data-sparse regions. Fortunately, these challenges present exciting research opportunities.
There are considerable obstacles to developing more generalized landslide hazard assessments that account for multiple landslide types and cover broad regions. For example, shallow landslides and debris flows are relatively small, but highly destructive and potentially deadly. And yet they are often under-sampled in landslide inventories because their signatures are quickly erased from the landscape by vegetation regrowth and sediment transport processes. In contrast, large historical and ancient landslides can be identified with lidar and other topographic datasets, but their activity can be difficult to assess. Furthermore, long-runout landslides may rapidly travel many hundreds of meters away from their initiation areas, complicating assessment of possible impacts. Nascent efforts to broadly characterize landslide hazard across the U.S. using data-driven approaches have over-parameterized their models, leading to results that mimic existing landslide occurrence data, rather than exhibiting useful predictive capabilities (i.e., Yuan and Chen, Landslides, 2023). Other modeling studies using satellite data to inform globally uniform criteria for landslide triggering conditions are impressive (i.e., Stanley et al., Frontiers in Earth Science, 2021), but results are still mixed when compared to historical events (e.g., Marc et al., Earth Interactions, 2022). There are some exciting advances in space-time modeling of landslide potential (Lombardo et al., Earth Sci. Reviews, 2020), methods for combining susceptibility maps with rainfall thresholds (Segoni et al., Front Earth Sci., 2018), and tools such as Slope Unit Maker (SUMak) to efficiently improve non-gridded model discretization, leading to better predictive performance (Woodard et al., NHESS, 2023). Thus, there are encouraging possibilities for developing new approaches for both data acquisition and model development.
The primary objective of the research opportunity is to adapt or improve existing data and models of landslide occurrence and magnitude to quantify the key elements that influence landslide hazards and losses. The successful candidate would leverage the aforementioned data and models along with high-resolution digital elevation, climate, vegetation, rainfall, hydrology, and geologic or soils information as model inputs. Use of these datasets and advanced USGS computing resources can ultimately inform the development of a national landslide hazards map or enable event-based landslide forecasting at regional or national scales. This research opportunity could explore any number of established or emerging methods including statistical or empirical approaches, physically based deterministic modeling, or machine learning algorithms and artificial neural network analysis.
Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
Proposed Duty Station(s)
Golden, Colorado
Moffett Field, California
Seattle, Washington
Areas of PhD
Geology, data science, geomorphology, geophysics, remote sensing, computational geoscience, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications
Applicants must meet one of the following qualifications: Research Geologist, Research Civil Engineer, Research Computer Scientist, Research Physical Scientist, Research Geophysicist, or Research Hydrologist.
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
