18-12. Improved regional-scale assessment of rainfall-triggered and earthquake-induced landsliding by developing geomechanical transfer functions

Landslides pose a significant geologic hazard that can be deadly and costly. Individual slope failures regularly generate losses throughout the U.S. and abroad, but an even bigger concern is widespread landsliding caused by earthquakes and extreme weather events. The USGS has an ongoing interest in developing models to rapidly characterize landslide initiation potential under both heavy precipitation and seismic activity. For example, there are models to simulate the dynamic subsurface pore-water pressure response related to rainfall-triggered landslides (e.g., Baum et al., 2010), and other models for predicting earthquake-triggered landslides that are informed by ground motions (e.g., Nowicki Jessee et al., 2018). However, a primary input for all these models involves characterization of the strength of geologic materials, which is not readily available.  

Whereas soil and root strength properties are often estimated for past landslide events using back-analyses, current methods for predicting these parameters across different regions are lacking. In particular, simple methods are needed to inform better parameterization of landslide models based on existing geodatabases and remotely sensed data. Previous work in the hydrologic sciences has successfully identified empirical relationships to establish “transfer” functions that translate readily available soils information (e.g., textural classification, organic carbon content, vegetation cover) to the hydraulic properties of interest that are more difficult to obtain (i.e., porosity, hydraulic conductivity, water retention). These transfer functions (e.g., Schaap et al., 2001) have facilitated regional and even continental scale simulations with physics-based hydrologic models. However, the transfer function approach used successfully in hydrology – relating readily available data to parameters of interest – has not been attempted for estimating geomechanical properties (i.e., angle of internal friction, cohesion, and root strength). 

The primary objective of the research opportunity is to identify and develop simple or complex transfer functions for assigning geomechanical inputs into regional slope stability models. Extensive data on soil mechanical properties and root strength parameters are dispersed throughout the literature in journal articles (e.g., Schwarz et al., 2010) and various USGS reports (e.g., Mirus et al., 2016), but these have yet to be assembled into a comprehensive database for further quantitative analysis or prediction of landslide triggering. Numerous machine learning approaches have been applied in the Earth Sciences to identify empirical relations between measurements and parameters of interest and have even been used to map landslides from lidar data (Bunn et al., 2019). However, these computational methods have not been applied rigorously to explore geomechanical properties. Relationships between strength properties and existing geospatial data on soils, geology, topography, and landcover could be explored quantitatively with a variety of techniques. The proposed post-doctoral research opportunity therefore provides many avenues for innovative research to identify suitable properties and relations that can help improve the predictive power of landslide initiation models.  

Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.

References: 

Baum, R.L., Godt, J.W., Savage, W.Z., 2010, Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration: Journal of Geophysical Research, Earth Surface. v. 115, F03013, doi:10.1029/2009JF001321.

Bunn, M.D., Leshchinsky, B.A., Olsen, M.J., Booth, A., 2019, A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives, Remote Sensing, 11(3), 303; https://doi.org/10.3390/rs11030303

Mirus, B.B., Smith, J.B., Stark, B., Lewis, Y., Michel, A.R., and Baum, R.L., 2016, Lab tests for specimens from Mukilteo, WA, 2016: U.S. Geological Survey - Data Release, doi:10.5066/F7H13033.

Nowicki Jessee, M.A., Hamburger, H.W., Allstadt, K., Wald, D.J., Robeson, S.M., Tanyas, H., Hearne, M., Thompson, E.M., 2018, A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides: Journal of Geophysical Research, 123: 1835-1859.

Schaap, M.G., Leij, F.J., van Genuchten, M.T., 2001, Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, Journal of Hydrology, 251: 163-176.

Schwarz, M., Lehmann, P., Or, D., 2010, Quantifying lateral root reinforcement in steep slopes – from a bundle of roots to tree stands, Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.1927.

Proposed Duty Station: Golden, CO

Areas of PhD: Geology, geography, geomorphology, hydrology, machine learning, civil engineering, computer science, mathematics, soil science (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 the qualifications for: Research Geologist, Research Geographer, Research Civil Engineer, Research Physical Scientist, Research Hydrologist, Research Computer Scientist, Research Mathematician, Research Soil Scientist. 

(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.)

Human Resources Office Contact: Audrey Tsujita, 916-278-9395, atsujita@usgs.gov