Jacob Woodard, PhD
Jacob Woodard is a Research Geologist at the Geological Hazards Science Center where he studies landslide hazards to better predict the location, timing, and severity of landslide events.
Jacob uses a wide range of tools to improve our ability to predict landslide occurrence including big-data analysis, machine learning, Bayesian statistical modeling, numerical modeling, remote-sensing, and geographic information systems. His current work aims to develop robust landslide hazard models over regional (>1000 km2) to national scales. Before coming to the USGS, Jacob received his M.S. and Ph.D. from the University of Wisconsin-Madison where he studied subglacial processes using geophysics (ground penetrating radar), remote sensing, and a suite of numerical, analytical, and statistical modeling techniques.
Professional Experience
2025 – Present: Research Geologist, Landslides Hazards Program, USGS, Golden, CO
2021 – 2025: Mendenhall Research Geologist, Landslides Hazards Program, USGS, Golden, CO
Education and Certifications
2021: Ph.D., Geoscience, University of Wisconsin-Madison
2017: M.S., Geoscience, University of Wisconsin-Madison
2014: B.S., Geology, Brigham Young University
Affiliations and Memberships*
American Geophysical Union
Honors and Awards
2021: Distinguished Graduate Student Award (University of Wisconsin-Madison)
2021: Outstanding Student Research Paper Award (University of Wisconsin-Madison)
2020: James J. and Dorothy T. Hanks Award in Geophysics (University of Wisconsin-Madison)
2020: Schiesser Outstanding Student Research Paper Award (University of Wisconsin-Madison)
2018: Runner up, graduate student oral presentation, Northcentral Geological Society of America (University of Wisconsin-Madison)
2014: Geological field studies student of the year (Brigham Young University)
Science and Products
Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data
Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt and others, 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective...
Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data
Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt and others, 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective...
Mapping landslide susceptibility over large regions with limited data Mapping landslide susceptibility over large regions with limited data
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Matthew Crawford, Dani Or, Ben Leshchinsky, Kate E. Allstadt, Nathan J. Wood
Constraining landslide frequency across the United States to inform county-level risk reduction Constraining landslide frequency across the United States to inform county-level risk reduction
Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal...
Authors
Lisa Victoria Luna, Jacob Bryson Woodard, Janice L. Bytheway, Gina Marie Belair, Benjamin B. Mirus
Overcoming the data limitations in landslide susceptibility modelling Overcoming the data limitations in landslide susceptibility modelling
Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus
A benchmark dataset and workflow for landslide susceptibility zonation A benchmark dataset and workflow for landslide susceptibility zonation
Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try...
Authors
Massimiliano Alvioli, Marco Loche, Liesbet Jacobs, Carlos H. Grohmann, Minu Treesa Abraham, Kunal Gupta, Neelima Satyam, Gianvito Scaringi, Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, Luigi Lombardo, Mateo Moreno, Stefan Steger, Corrado Camera, Greta Bajni, Guruh Samodra, Erwin Eko Wahyudi, Nanang Susyanto, Marko Sincic, Sanja Bernat Gazibara, Flavius Sirbu, Jewgenij Torizin, Nick Schüßler, Benjamin B. Mirus, Jacob Bryson Woodard, Hector Aguilera, Jhonatan Steven Rivera-Rivera
Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data
Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and...
Authors
Jacob Bryson Woodard, Sean Richard LaHusen, Benjamin B. Mirus, Katherine R. Barnhart
Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Ben Leshchinsky, Matthew Crawford
Mapping landslide susceptibility over large regions with limited data Mapping landslide susceptibility over large regions with limited data
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Matthew Crawford, Dani Or, Ben Leshchinsky, Kate E. Allstadt, Nathan J. Wood
Non-USGS Publications**
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2022). Inferring forms of glacier slip laws from estimates of ice-bed separation during glacier slip. Journal of Glaciology. https://doi.org/10.1017/jog.2022.63
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2021). Variations in hard-bedded topography beneath glaciers. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2021JF006326
Zoet, L.K.; Rawling, J.E.; Woodard, J.B.; Barrette, N.; Mickelson, D.M. (2021). Factors that contribute to the elongation of subglacial bedforms beneath the Green Bay Lobe, Laurentide Ice Sheet. Earth Systems Processes and Landforms. https://doi.org/10.1002/esp.5192
Helanow, C.; Iverson, N.R.; Woodard, J.B.; Zoet, L.K. (2021). A slip law for hard-bedded glaciers derived from observed bed topography. Science Advances. https://doi.org/10.1126/sciadv.abe7798
Woodard, J.B.; Zoet, L.K.; Benediktsson, Í.Ö.; Iverson, N.R.; Finlayson, A. (2020). Insights into drumlin development from ground-penetrating radar at Múlajökull, Iceland, a surge-type glacier. Journal of Glaciology. https://doi.org/10.1017/jog.2020.50
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2019). Linking bedrock discontinuities to glacial quarrying. Annals of Glaciology. https://doi.org/10.1017/aog.2019.36
Iverson, N.R.; McCracken, R.G.; Zoet, L.K.; Benediktsson, Í.Ö.; Schomacker, A.; Johnson, M.D.; Woodard, J. (2017). A theoretical model of drumlin formation based on observations at Múlajökull, Iceland. Journal of Geophysical Research: Earth Surface. https://doi.org/ 10.1002/2017JF004354
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data
Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt and others, 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective...
Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data
Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt and others, 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective...
Mapping landslide susceptibility over large regions with limited data Mapping landslide susceptibility over large regions with limited data
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Matthew Crawford, Dani Or, Ben Leshchinsky, Kate E. Allstadt, Nathan J. Wood
Constraining landslide frequency across the United States to inform county-level risk reduction Constraining landslide frequency across the United States to inform county-level risk reduction
Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal...
Authors
Lisa Victoria Luna, Jacob Bryson Woodard, Janice L. Bytheway, Gina Marie Belair, Benjamin B. Mirus
Overcoming the data limitations in landslide susceptibility modelling Overcoming the data limitations in landslide susceptibility modelling
Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus
A benchmark dataset and workflow for landslide susceptibility zonation A benchmark dataset and workflow for landslide susceptibility zonation
Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try...
Authors
Massimiliano Alvioli, Marco Loche, Liesbet Jacobs, Carlos H. Grohmann, Minu Treesa Abraham, Kunal Gupta, Neelima Satyam, Gianvito Scaringi, Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, Luigi Lombardo, Mateo Moreno, Stefan Steger, Corrado Camera, Greta Bajni, Guruh Samodra, Erwin Eko Wahyudi, Nanang Susyanto, Marko Sincic, Sanja Bernat Gazibara, Flavius Sirbu, Jewgenij Torizin, Nick Schüßler, Benjamin B. Mirus, Jacob Bryson Woodard, Hector Aguilera, Jhonatan Steven Rivera-Rivera
Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data
Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and...
Authors
Jacob Bryson Woodard, Sean Richard LaHusen, Benjamin B. Mirus, Katherine R. Barnhart
Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Ben Leshchinsky, Matthew Crawford
Mapping landslide susceptibility over large regions with limited data Mapping landslide susceptibility over large regions with limited data
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are...
Authors
Jacob Bryson Woodard, Benjamin B. Mirus, Matthew Crawford, Dani Or, Ben Leshchinsky, Kate E. Allstadt, Nathan J. Wood
Non-USGS Publications**
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2022). Inferring forms of glacier slip laws from estimates of ice-bed separation during glacier slip. Journal of Glaciology. https://doi.org/10.1017/jog.2022.63
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2021). Variations in hard-bedded topography beneath glaciers. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2021JF006326
Zoet, L.K.; Rawling, J.E.; Woodard, J.B.; Barrette, N.; Mickelson, D.M. (2021). Factors that contribute to the elongation of subglacial bedforms beneath the Green Bay Lobe, Laurentide Ice Sheet. Earth Systems Processes and Landforms. https://doi.org/10.1002/esp.5192
Helanow, C.; Iverson, N.R.; Woodard, J.B.; Zoet, L.K. (2021). A slip law for hard-bedded glaciers derived from observed bed topography. Science Advances. https://doi.org/10.1126/sciadv.abe7798
Woodard, J.B.; Zoet, L.K.; Benediktsson, Í.Ö.; Iverson, N.R.; Finlayson, A. (2020). Insights into drumlin development from ground-penetrating radar at Múlajökull, Iceland, a surge-type glacier. Journal of Glaciology. https://doi.org/10.1017/jog.2020.50
Woodard, J.B.; Zoet, L.K.; Iverson, N.R.; Helanow, C. (2019). Linking bedrock discontinuities to glacial quarrying. Annals of Glaciology. https://doi.org/10.1017/aog.2019.36
Iverson, N.R.; McCracken, R.G.; Zoet, L.K.; Benediktsson, Í.Ö.; Schomacker, A.; Johnson, M.D.; Woodard, J. (2017). A theoretical model of drumlin formation based on observations at Múlajökull, Iceland. Journal of Geophysical Research: Earth Surface. https://doi.org/ 10.1002/2017JF004354
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government