Katherine Barnhart
Katy Barnhart is a Research Civil Engineer in the Landslide Hazards program.
Katy does research on the mechanisms and impacts of landslide runout, primarily using numerical simulation. Her current work focuses on postfire debris flows and landslide tsunamis.
Professional Experience
2020-present: Research Civil Engineer, Landslide Hazards Program, Geologic Hazards Science Center
2020-2021: Mendenhall Postdoctoral Fellow
2018-2020: National Science Foundation Postdoctoral Fellow, University of Colorado at Boulder, Cooperative Institute for Research in the Environment and Department of Geological Sciences
2016-2018: Postdoctoral Fellow, University of Colorado, Cooperative Institute for Research in the Environment and Department of Geological Sciences
2015-2016: Postdoctoral Fellow, Annenberg Public Policy Center, University of Pennsylvania
Education and Certifications
University of Colorado, Ph.D., 2015, Geological Sciences
University of Colorado, M.S., 2010, Geological Sciences
Princeton University, B.S.E., 2008, Civil and Environmental Engineering
Honors and Awards
CSDMS Terrestrial Working Group Member Spotlight Award, 2020
USGS Mendenhall Fellowship, 2020
NSF-EAR Postdoctoral Fellowship, 2017
NASA Earth and Space Science Fellowship, 2012-2015
NSF Graduate Research Fellowship Honorable Mention, 2010
W. Taylor Thom Jr. Prize, Princeton Department of Civil Engineering, 2008
Arthur F. Buddington Award, Princeton Department of Geological Sciences, 2008
Science and Products
Filter Total Items: 15
Inventory of 227 postfire debris-flow volumes for 34 fires in the western United States Inventory of 227 postfire debris-flow volumes for 34 fires in the western United States
Description This data release contains a dataset of 227 postfire debris-flow volumes compiled from 34 burn areas across six states in the western United States. Specifically, it includes debris-flow volumes collected from the following fires: Arizona: 2010 Schultz, 2011 Horseshoe 2, 2011 Monument, 2011 Wallow, 2017 Frye, 2019 Museum, 2019 Woodbury, 2020 Bush, 2021 Flag, 2021 Horton, 2021...
Merged topography and bathymetry, Glacier Bay and Icy Strait, Alaska Merged topography and bathymetry, Glacier Bay and Icy Strait, Alaska
This work integrated multiple topographic and bathymetric data sources to generate a merged topobathymetric map of Glacier Bay, Alaska (AK) and vicinity, including Icy Strait and parts of Lynn Canal. Earlier topobathymetric sources covering the Glacier Bay region were originally developed by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental...
Terrestrial lidar data of debris-flow sediment in Glenwood Canyon, CO, 2021 Terrestrial lidar data of debris-flow sediment in Glenwood Canyon, CO, 2021
This release includes lidar point cloud and Wolman pebble count data for a debris-flow deposit in Glenwood Canyon, CO. The data, FullDepositRegion.las (las 1.2), were collected with a terrestrial laser scanner and includes the full deposit and portions of the slope and drainage that generated the debris flow. This .las file includes point cloud data up to 250 m upslope of the deposit...
Post-Wildfire Debris-Flow Hazard Assessment (PWFDF) Collection Post-Wildfire Debris-Flow Hazard Assessment (PWFDF) Collection
Wildfire can substantially alter the hydrologic response of watersheds to rainfall, and debris-flow activity is among the most destructive consequences of these events. To assist federal, state, and local agencies in planning for postfire hazards, the U.S. Geological Survey conducts debris-flow hazard assessments for recent wildfires. This collection contains the results of those...
Supporting Information for 'Detection of Landslide-Generated Tsunami by Shipborne GNSS Precise Point Positioning' Supporting Information for 'Detection of Landslide-Generated Tsunami by Shipborne GNSS Precise Point Positioning'
This data release contains supporting information for the manuscript: Manaster, A.E., Sheehan, A.F., Goldberg, D. E., Barnhart, K. R., & Roth, E. H. (2025). Detection of landslide‐generated tsunami by shipborne GNSS precise point positioning. Geophysical Research Letters, 52, e2024GL112472. https://doi.org/10.1029/2024GL112472 Supporting information falls into two categories: 1. Data is...
Rock mass quality and structural geology observations in Prince William Sound, Alaska (2022) Rock mass quality and structural geology observations in Prince William Sound, Alaska (2022)
Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry...
Hypothetical landslide failure extents for hazard assessment, Barry Arm, western Prince William Sound, Alaska Hypothetical landslide failure extents for hazard assessment, Barry Arm, western Prince William Sound, Alaska
This data release contains extent shapefiles for 16 hypothetical slope failure scenarios for a landslide complex at Barry Arm, western Prince William Sound, Alaska. The landslide is likely active due to debuttressing from the retreat of Barry Glacier (Dai and others, 2020) and sits above Barry Arm, posing a tsunami risk in the event of slope failure (Barnhart and others, 2021). Since...
Merged topography and bathymetry, western Prince William Sound Merged topography and bathymetry, western Prince William Sound
This work integrated multiple topographic and bathymetric data sources to generate a merged topobathymetric map of western Prince William Sound. We converted all data sources to NAD 83 UTM Zone 6 N and mean higher high water (MHHW) before compiling. In Barry Arm, north of Port Wells, we used a digital terrain model (DTM) derived from subaerial light detection and ranging (lidar) data...
digger: A python package for D-Claw model inputs digger: A python package for D-Claw model inputs
Digger is a python package that provides a number of pre- and post-processing tools for working with the D-Claw model.
Select structure observations, model results, and model input parameters for debris-flow runout model simulations of the 9 January 2018 Montecito debris-flow runout event Select structure observations, model results, and model input parameters for debris-flow runout model simulations of the 9 January 2018 Montecito debris-flow runout event
This dataset contains three comma separated value files that represent a combination of previously published structure observations and select model results at the location of each structure centroid from a separate previously published simulation study. -"buildings.csv" represents the merging of structure damage observations published in Kean et al. (2019) and structure locations from...
Tadpole Fire Debris Flow and Wood Collector Measurements May 2021 Tadpole Fire Debris Flow and Wood Collector Measurements May 2021
This is a dataset of location and photo data for the debris flow deposits measured in the Tadpole Wildfire. The data were collected using the ArcGIS Collector application by multiple individuals. The original data are stored in a geodatabase here, and the geodatabase has the following fields: Latitude (decimal degrees), Longitude (decimal degrees), Elevation (meters), GlobalID (a unique...
Tadpole Fire Field Measurements following the 8 September 2020 Debris Flow, Gila National Forest, NM Tadpole Fire Field Measurements following the 8 September 2020 Debris Flow, Gila National Forest, NM
This data release contains data summarizing observations within and adjacent to the Tadpole Fire, which burned from 6 June to 4 July 2020 in the Gila National Forest, NM. This monitoring data were focused on debris flows triggered on 8 September 2020 in four drainage basins (TAD1, TAD2, TAD3, and TAD4). Rainfall data (1a_rain_geophones.csv) are provided in a comma-separated value (CSV)...
Filter Total Items: 26
2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska
Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m3) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were...
Authors
Ezgi Karasozen, Michael West, Katherine Barnhart, John Lyons, Terry Nichols, Lauren Schaefer, Bohyun Bahng, Summer Ohlendorf, Dennis Staley, Gabriel Wolken
Cascading land surface hazards as a nexus in the Earth system Cascading land surface hazards as a nexus in the Earth system
Earth’s surface is sculpted by numerous processes that move sediment, ranging from gradual and benign to abrupt and catastrophic. Although infrequent, high-magnitude sediment mobilization events can be hazardous to people and infrastructure, leaving topographic imprints on the landscape and remarkable narratives in the historical record. Hazardous events such as fires, storms, and...
Authors
Brian Yanites, Marin Clark, Joshua J. Roering, A. West, Dimitrios Zekkos, Jane W. Baldwin, Corina Cerovski-Darriau, Sean Gallen, Daniel E. Horton, Eric Kirby, Ben Leshchinksy, H. Benjamin Mason, Seulgi Moon, Katherine Barnhart, Adam Booth, Jonathan Czuba, Scott W. McCoy, Luke McGuire, Allison Pfeiffer, Jennifer Pierce
A method to obtain remotely sensed grain size distributions from nonplanar granular deposits A method to obtain remotely sensed grain size distributions from nonplanar granular deposits
Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed...
Authors
Hayden L. Jacobson, Gabriel Walton, Katherine Barnhart, Francis Rengers
Detection of landslide-generated tsunami by shipborne GNSS precise point positioning Detection of landslide-generated tsunami by shipborne GNSS precise point positioning
Precise point positioning (PPP) of ships using Global Navigation Satellite System (GNSS) data reveals the precise movements of marine vessels. This method may quantify anomalies in sea surface height with implications for oceanographic monitoring, exploration, and tsunami warning. The GNSS PPP data from the R/V Sikuliaq, a research ship of the University of Alaska Fairbanks, were...
Authors
Adam E. Manaster, Anne Sheehan, Dara Goldberg, Katherine Barnhart, Ethan F. Roth
Uncertainty reduction for subaerial landslide-tsunami hazards Uncertainty reduction for subaerial landslide-tsunami hazards
Subaerial rock slopes may generate a tsunami by rapidly moving into the water. Large uncertainty in landslide characteristics propagates into large uncertainty in tsunami hazard, making hazard assessment more difficult for land and emergency managers. Once a potentially tsunamigenic landslide is identified, it may not be clear which landslide characteristics contribute most significantly...
Authors
Katherine Barnhart, David George, Andrew Collins, Lauren Schaefer, Dennis Staley
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 Woodard, Sean LaHusen, Benjamin B. Mirus, Katherine Barnhart
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned...
Authors
A. Prescott, L. McGuire, K.-S. Jun, Katherine Barnhart, N. Oakley
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well...
Authors
Francis Rengers, Samuel Bower, Andrew Knapp, Jason Kean, Danielle vonLembke, Matthew Thomas, Jaime Kostelnik, Katherine Barnhart, Matthew Bethel, Joseph Gartner, Madeline Hille, Dennis Staley, Justin Anderson, Elizabeth Roberts, Stephen DeLong, Belize Lane, Paxton Ridgeway, Brendan Murphy
Catchment coevolution and the geomorphic origins of variable source area hydrology Catchment coevolution and the geomorphic origins of variable source area hydrology
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic...
Authors
David Litwin, Gregory Tucker, Katherine Barnhart, Ciaran Harman
Evaluation of debris-flow building damage forecasts Evaluation of debris-flow building damage forecasts
Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building...
Authors
Katherine Barnhart, Christopher Miller, Francis Rengers, Jason Kean
Unlearning Racism in Geoscience (URGE): Summary of U.S. Geological Survey URGE pod deliverables Unlearning Racism in Geoscience (URGE): Summary of U.S. Geological Survey URGE pod deliverables
The U.S. Geological Survey (USGS) is in a unique position to be a leader in diversity, equity, inclusion, and accessibility in the Earth sciences. As one of the largest geoscience employers, the USGS wields significant community influence and has a responsibility to adopt and implement robust, unbiased policies so that the science it is charged to deliver is better connected to the...
Authors
Matthew Morriss, Eleanour Snow, Jennifer Miselis, William F. Waite, Katherine Barnhart, Andria P. Ellis, Liv Herdman, Seth C. Moran, Annie Putman, Nadine Reitman, Wendy Stovall, Meagan Eagle, Stephen Phillips
Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska
Regional mapping of actively deforming landslides, including measurements of landslide velocity, is integral for hazard assessments in paraglacial environments. These inventories are also critical for describing the potential impacts that the warming effects of climate change have on slope instability in mountainous and cryospheric terrain. The objective of this study is to identify slow...
Authors
Lauren Schaefer, Jinwook Kim, Dennis Staley, Zhong Lu, Katherine Barnhart
Non-USGS Publications**
Wickert, A.D., Barnhart, K.R., Armstrong, W.H., Romero, M., Schulz, B., Ng, G.-H.C., Sandell, C.T., La Frenierre, J., Penprase, S.B., Van Wyk de Vries, M., and MacGregor, K.R., 2023, Automated ablation stakes to constrain temperature-index melt models: Annals of Glaciology, v. 64, no. 92, p. 425–438, https://doi.org/10.1017/aog.2024.21.
Gasparini, N.M., Forte, A.M., and Barnhart, K.R., 2024, Short Communication: Numerically simulated time to steady state is not a reliable measure of landscape response time: Earth Surface Dynamics, v. 12, no. 6, p. 1227–1242, https://doi.org/10.5194/esurf-12-1227-2024.
Nudurupati, S.S., Istanbulluoglu, E., Tucker, G.E., Gasparini, N.M., Hobley, D.E.J., Hutton, E.W.H., Barnhart, K.R., and Adams, J.M., On transient semi-arid ecosystem dynamics using Landlab: Vegetation shifts, topographic refugia, and response to climate. Water Resources Research, 59, e2021WR031179. https://doi.org/10.1029/2021WR031179.
Litwin, David G, Tucker, G.E., Barnhart, K.R., and Harman, C.J., Reply to comment by Anand et al. on ‘Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes’: Journal of Geophysical Research—Earth Surface, 127, e2022JF006722. https://doi.org/10.1029/2022JF006722.
Litwin, D.G., Tucker, G.E., Barnhart, K.R., and Harman, C.J., 2022, Groundwater Affects the Geomorphic and Hydrologic Properties of Coevolved Landscapes: Journal of Geophysical Research: Earth Surface, v. 127, no. 1, p. e2021JF006239, doi: 10.1029/2021JF006239.
Tucker, G.E., Hutton, E.W.H., Piper, M.D., Campforts, B., Gan, T., Barnhart, K.R., Kettner, A.J., Overeem, I., Peckham, S.D., McCready, L., and Syvitski, J., 2022, Community Surface Dynamics Modeling System: a community platform for numerical modeling of Earth surface processes: Geoscientific Model Development, v. 15, no. 4, p. 1413–1439, doi: 10.5194/gmd-15-1413-2022.
Wiens, A., Kleiber, W., Nychka, D., and Barnhart, K. R. “Nonrigid Registration Using Gaussian Processes
and Local Likelihood Estimation”. In: Mathematical Geosciences (2021). DOI: 10.1007/s11004-
020-09917-7.
and Local Likelihood Estimation”. In: Mathematical Geosciences (2021). DOI: 10.1007/s11004-
020-09917-7.
Anderson, S.P., Kelly, P.J., Hoffman, N., Barnhart, K., Befus, K. and Ouimet, W. (2021). Is This Steady State? Weathering and Critical Zone Architecture in Gordon Gulch, Colorado Front Range. In Hydrogeology, Chemical Weathering, and Soil Formation (eds A. Hunt, M. Egli and B. Faybishenko). https://doi.org/10.1002/9781119563952.ch13
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Projections of landscape evolution on a 10,000 year timescale with assessment and partitioning of uncertainty sources”. Journal of Geophysical Research: Earth Surface (2020), 2020JF005795. https://doi.org/10.1029/2020JF005795.
A. M. Pfeiffer, K. R. Barnhart, J. A. Czuba, and E. W. H. Hutton. “NetworkSediment-Transporter: A Landlab component for bed material transport through river networks”. Journal of Open Source Software 5.53 (2020), p. 2341. https://doi.org/10.21105/joss.02341.
Barnhart, K. R., Hutton, E. W. H., Tucker, G. E., Gasparini, N. M., Istanbulluoglu, E., Hobley, D. E. J., Lyons, N. J., Mouchene, M., Nudurupati, S. S., Adams, J. M., and Bandaragoda, C.: Short communication: Landlab v2.0: a software package for Earth surface dynamics, Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, 2020.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 1, conceptualization and sensitivity analysis”. Journal of Geophysical Research: Earth Surface (2020), e2018JF004961. https://doi.org/10.1029/2018JF004961.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 2, calibration and validation”. Journal of Geophysical Research: Earth Surface (2020), e2018JF004963. https://doi.org/10.1029/2018JF004963.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M.W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 3, Determining parameter ranges for select mature geomorphic transport laws and connecting changes in fluvial erodibility to changes in climate”. Journal of Geophysical Research: Earth Surface (2020), e2019JF005287. https://doi.org/10.1029/2019JF005287.
D. Litwin, G. Tucker, K. R. Barnhart, and C. Harman. “GroundwaterDupuitPercolator: A Landlab component for groundwater flow”. Journal of Open Source Software 5.46 (2020), 1935. https://doi.org/10.21105/joss.01935.
K. R. Barnhart, R. C. Glade, C. M. Shobe, and G. E. Tucker. “Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution”. Geoscientific Model Development 12.4 (2019), pp. 1267–1297. https://doi.org/10.5194/gmd-12-1267-2019.
K. R. Barnhart, E.W. H. Hutton, and G. E. Tucker. “umami: A Python package for Earth surface dynamics objective function construction”. Journal of Open Source Software 4.42 (2019). https://doi.org/10.21105/joss.01776.
C. J. Bandaragoda, A. Castronova, E. Istanbulluoglu, R. Strauch, S. S. Nudurupati, J. Phuong, J. M. Adams, N. M. Gasparini, K. R. Barnhart, E. Hutton, D. E. J. Hobley, N. J. Lyons, G. E. Tucker, D. G. Tarboton, R. Idaszak, and S. Wang. “Enabling collaborative numerical Modeling in Earth sciences using Knowledge Infrastructure”. Environmental Modelling and Software (2019). https://doi.org/10.1016/j.envsoft.2019.03.020.
A. Wiens, W. Kleiber, K. R. Barnhart, and D. Sain. “Surface Estimation for Multiple Misaligned Point Sets”. Mathematical Geosciences 39.2 (Apr. 2019), pp. 1–16. https://doi.org/10.1007/s11004-019-09802-y.
K. R. Barnhart, E. Hutton, N. Gasparini, and G. Tucker. “Lithology: A Landlab submodule for spatially variable rock properties”. Journal of Open Source Software 3.30 (2018), pp. 979–2. https://doi.org/10.21105/joss.00979.
M. Bendixen, L. L. Iversen, A. A. Bjork, B. Elberling, A.Westergaard-Nielsen, I. Overeem, K. R. Barnhart, S. A. Khan, J. E. Box, J. Abermann, K. Langley, and A. Kroon. “Delta progradation in Greenland driven by increasing glacial mass loss”. Nature 550.7674 (2017), pp. 101–104. https://doi.org/10.1038/nature23873.
C. M. Shobe, G. E. Tucker, and K. R. Barnhart. “The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution”. Geoscientific Model Development 10.12 (2017), pp. 4577–4604. https://doi.org/10.5194/gmd-10-4577-2017.
K. R. Barnhart, C. R. Miller, I. Overeem, and J. E. Kay. “Mapping the future expansion of Arctic open water”. Nature Climate Change (2015). https://doi.org/10.1038/nclimate2848.
R. C. Mahon, J. B. Shaw, K. R. Barnhart, D. E. Hobley, and B. McElroy. “Quantifying the stratigraphic completeness of delta shoreline trajectories”. Journal of Geophysical Research-Earth Surface 120.5 (2015), pp. 799–817. https://doi.org/10.1002/2014JF003298.
K. R. Barnhart, R. S. Anderson, I. Overeem, C. Wobus, G. D. Clow, and F. E. Urban. “Modeling erosion of ice-rich permafrost bluffs along the Alaskan Beaufort Sea coast”. Journal of Geophysical Research-Earth Surface 119.5 (2014), pp. 1155–1179. https://doi.org/10.1002/2013JF002845.
K. R. Barnhart, I. Overeem, and R. S. Anderson. “The effect of changing sea ice on the physical vulnerability of Arctic coasts”. The Cryosphere 8 (2014), pp. 1777–1799. https://doi.org/10.5194/tc-8-1777-2014.
K. R. Barnhart, K. H. Mahan, T. J. Blackburn, S. A. Bowring, and F. O. Dudas. “Deep crustal xenoliths from central Montana, USA: Implications for the timing and mechanisms of high-velocity lower crust formation”. Geosphere (2012). https://doi.org/10.1130/GES00765.1.
K. R. Barnhart, P. J. Walsh, L. S. Hollister, C. G. Daniel, and C. Andronicos. “Decompression during Late Proterozoic Al2SiO5 Triple-Point Metamorphism at Cerro Colorado, New Mexico”. The Journal of Geology (2012). https://doi.org/10.1086/665793.
T. J. Blackburn, S. A. Bowring, J. T. Perron, K. H. Mahan, F. O. Dudas, and K. R. Barnhart. “An Exhumation History of Continents over Billion-Year Time Scales”. Science 335.6064 (2012), pp. 73–76. https://doi.org/10.1126/science.1213496.
**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.
Simulation code for Surprise Inlet landslides v1.0.0 Simulation code for Surprise Inlet landslides v1.0.0
This repository contains the code needed to reproduce the D-Claw simulation presented in Karasözen and others (2025). D-Claw is numerical software package for simulating granular-fluid flows (George and others, 2025; George & Iverson, 2014; Iverson & George, 2014). This code simulates a landslide generated tsunami scenario in which one of the Surprise Inlet landslides described by...
D-Claw - A library for the simulation of granular-fluid flows, v1.0.0 D-Claw - A library for the simulation of granular-fluid flows, v1.0.0
D-Claw is a software library for the simulation of dense granular flows over spatially variable topography. D-Claw builds on clawpack, is written in Fortran, and has a python wrapper. The code is hosted on code.usgs.gov and mirrored on github. D-Claw is supported on Linux/Unix only.
Barry Arm landslide complex tsunami modeling scenarios, version 1.0.0 Barry Arm landslide complex tsunami modeling scenarios, version 1.0.0
This repository contains the code needed to reproduce computations presented in Barnhart and others (under review). This readme contains instructions for reproducing results and a list of the git hashes for each source repository used. Computation for this manuscript was done on the U.S. Geological Survey Denali High Performance Computer (Falgout and others, 2024). Barnhart, K.R., George...
digger - Utility tools for landslide runout modeling digger - Utility tools for landslide runout modeling
Digger is a python package that provides a number of pre- and post-processing tools for landslide runout modeling. Many aspects of digger have been tailored for use with the D-Claw model. It may also be useful for initializing and postprocessing the results of other runout models. D-Claw requires input files and generates output files in very specific formats. Simulation output generates...
digger: A python package for D-Claw model inputs digger: A python package for D-Claw model inputs
Digger is a python package that provides a number of pre- and post-processing tools for working with the D-Claw model.
Science and Products
Filter Total Items: 15
Inventory of 227 postfire debris-flow volumes for 34 fires in the western United States Inventory of 227 postfire debris-flow volumes for 34 fires in the western United States
Description This data release contains a dataset of 227 postfire debris-flow volumes compiled from 34 burn areas across six states in the western United States. Specifically, it includes debris-flow volumes collected from the following fires: Arizona: 2010 Schultz, 2011 Horseshoe 2, 2011 Monument, 2011 Wallow, 2017 Frye, 2019 Museum, 2019 Woodbury, 2020 Bush, 2021 Flag, 2021 Horton, 2021...
Merged topography and bathymetry, Glacier Bay and Icy Strait, Alaska Merged topography and bathymetry, Glacier Bay and Icy Strait, Alaska
This work integrated multiple topographic and bathymetric data sources to generate a merged topobathymetric map of Glacier Bay, Alaska (AK) and vicinity, including Icy Strait and parts of Lynn Canal. Earlier topobathymetric sources covering the Glacier Bay region were originally developed by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental...
Terrestrial lidar data of debris-flow sediment in Glenwood Canyon, CO, 2021 Terrestrial lidar data of debris-flow sediment in Glenwood Canyon, CO, 2021
This release includes lidar point cloud and Wolman pebble count data for a debris-flow deposit in Glenwood Canyon, CO. The data, FullDepositRegion.las (las 1.2), were collected with a terrestrial laser scanner and includes the full deposit and portions of the slope and drainage that generated the debris flow. This .las file includes point cloud data up to 250 m upslope of the deposit...
Post-Wildfire Debris-Flow Hazard Assessment (PWFDF) Collection Post-Wildfire Debris-Flow Hazard Assessment (PWFDF) Collection
Wildfire can substantially alter the hydrologic response of watersheds to rainfall, and debris-flow activity is among the most destructive consequences of these events. To assist federal, state, and local agencies in planning for postfire hazards, the U.S. Geological Survey conducts debris-flow hazard assessments for recent wildfires. This collection contains the results of those...
Supporting Information for 'Detection of Landslide-Generated Tsunami by Shipborne GNSS Precise Point Positioning' Supporting Information for 'Detection of Landslide-Generated Tsunami by Shipborne GNSS Precise Point Positioning'
This data release contains supporting information for the manuscript: Manaster, A.E., Sheehan, A.F., Goldberg, D. E., Barnhart, K. R., & Roth, E. H. (2025). Detection of landslide‐generated tsunami by shipborne GNSS precise point positioning. Geophysical Research Letters, 52, e2024GL112472. https://doi.org/10.1029/2024GL112472 Supporting information falls into two categories: 1. Data is...
Rock mass quality and structural geology observations in Prince William Sound, Alaska (2022) Rock mass quality and structural geology observations in Prince William Sound, Alaska (2022)
Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry...
Hypothetical landslide failure extents for hazard assessment, Barry Arm, western Prince William Sound, Alaska Hypothetical landslide failure extents for hazard assessment, Barry Arm, western Prince William Sound, Alaska
This data release contains extent shapefiles for 16 hypothetical slope failure scenarios for a landslide complex at Barry Arm, western Prince William Sound, Alaska. The landslide is likely active due to debuttressing from the retreat of Barry Glacier (Dai and others, 2020) and sits above Barry Arm, posing a tsunami risk in the event of slope failure (Barnhart and others, 2021). Since...
Merged topography and bathymetry, western Prince William Sound Merged topography and bathymetry, western Prince William Sound
This work integrated multiple topographic and bathymetric data sources to generate a merged topobathymetric map of western Prince William Sound. We converted all data sources to NAD 83 UTM Zone 6 N and mean higher high water (MHHW) before compiling. In Barry Arm, north of Port Wells, we used a digital terrain model (DTM) derived from subaerial light detection and ranging (lidar) data...
digger: A python package for D-Claw model inputs digger: A python package for D-Claw model inputs
Digger is a python package that provides a number of pre- and post-processing tools for working with the D-Claw model.
Select structure observations, model results, and model input parameters for debris-flow runout model simulations of the 9 January 2018 Montecito debris-flow runout event Select structure observations, model results, and model input parameters for debris-flow runout model simulations of the 9 January 2018 Montecito debris-flow runout event
This dataset contains three comma separated value files that represent a combination of previously published structure observations and select model results at the location of each structure centroid from a separate previously published simulation study. -"buildings.csv" represents the merging of structure damage observations published in Kean et al. (2019) and structure locations from...
Tadpole Fire Debris Flow and Wood Collector Measurements May 2021 Tadpole Fire Debris Flow and Wood Collector Measurements May 2021
This is a dataset of location and photo data for the debris flow deposits measured in the Tadpole Wildfire. The data were collected using the ArcGIS Collector application by multiple individuals. The original data are stored in a geodatabase here, and the geodatabase has the following fields: Latitude (decimal degrees), Longitude (decimal degrees), Elevation (meters), GlobalID (a unique...
Tadpole Fire Field Measurements following the 8 September 2020 Debris Flow, Gila National Forest, NM Tadpole Fire Field Measurements following the 8 September 2020 Debris Flow, Gila National Forest, NM
This data release contains data summarizing observations within and adjacent to the Tadpole Fire, which burned from 6 June to 4 July 2020 in the Gila National Forest, NM. This monitoring data were focused on debris flows triggered on 8 September 2020 in four drainage basins (TAD1, TAD2, TAD3, and TAD4). Rainfall data (1a_rain_geophones.csv) are provided in a comma-separated value (CSV)...
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2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska
Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m3) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were...
Authors
Ezgi Karasozen, Michael West, Katherine Barnhart, John Lyons, Terry Nichols, Lauren Schaefer, Bohyun Bahng, Summer Ohlendorf, Dennis Staley, Gabriel Wolken
Cascading land surface hazards as a nexus in the Earth system Cascading land surface hazards as a nexus in the Earth system
Earth’s surface is sculpted by numerous processes that move sediment, ranging from gradual and benign to abrupt and catastrophic. Although infrequent, high-magnitude sediment mobilization events can be hazardous to people and infrastructure, leaving topographic imprints on the landscape and remarkable narratives in the historical record. Hazardous events such as fires, storms, and...
Authors
Brian Yanites, Marin Clark, Joshua J. Roering, A. West, Dimitrios Zekkos, Jane W. Baldwin, Corina Cerovski-Darriau, Sean Gallen, Daniel E. Horton, Eric Kirby, Ben Leshchinksy, H. Benjamin Mason, Seulgi Moon, Katherine Barnhart, Adam Booth, Jonathan Czuba, Scott W. McCoy, Luke McGuire, Allison Pfeiffer, Jennifer Pierce
A method to obtain remotely sensed grain size distributions from nonplanar granular deposits A method to obtain remotely sensed grain size distributions from nonplanar granular deposits
Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed...
Authors
Hayden L. Jacobson, Gabriel Walton, Katherine Barnhart, Francis Rengers
Detection of landslide-generated tsunami by shipborne GNSS precise point positioning Detection of landslide-generated tsunami by shipborne GNSS precise point positioning
Precise point positioning (PPP) of ships using Global Navigation Satellite System (GNSS) data reveals the precise movements of marine vessels. This method may quantify anomalies in sea surface height with implications for oceanographic monitoring, exploration, and tsunami warning. The GNSS PPP data from the R/V Sikuliaq, a research ship of the University of Alaska Fairbanks, were...
Authors
Adam E. Manaster, Anne Sheehan, Dara Goldberg, Katherine Barnhart, Ethan F. Roth
Uncertainty reduction for subaerial landslide-tsunami hazards Uncertainty reduction for subaerial landslide-tsunami hazards
Subaerial rock slopes may generate a tsunami by rapidly moving into the water. Large uncertainty in landslide characteristics propagates into large uncertainty in tsunami hazard, making hazard assessment more difficult for land and emergency managers. Once a potentially tsunamigenic landslide is identified, it may not be clear which landslide characteristics contribute most significantly...
Authors
Katherine Barnhart, David George, Andrew Collins, Lauren Schaefer, Dennis Staley
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 Woodard, Sean LaHusen, Benjamin B. Mirus, Katherine Barnhart
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned...
Authors
A. Prescott, L. McGuire, K.-S. Jun, Katherine Barnhart, N. Oakley
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well...
Authors
Francis Rengers, Samuel Bower, Andrew Knapp, Jason Kean, Danielle vonLembke, Matthew Thomas, Jaime Kostelnik, Katherine Barnhart, Matthew Bethel, Joseph Gartner, Madeline Hille, Dennis Staley, Justin Anderson, Elizabeth Roberts, Stephen DeLong, Belize Lane, Paxton Ridgeway, Brendan Murphy
Catchment coevolution and the geomorphic origins of variable source area hydrology Catchment coevolution and the geomorphic origins of variable source area hydrology
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic...
Authors
David Litwin, Gregory Tucker, Katherine Barnhart, Ciaran Harman
Evaluation of debris-flow building damage forecasts Evaluation of debris-flow building damage forecasts
Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building...
Authors
Katherine Barnhart, Christopher Miller, Francis Rengers, Jason Kean
Unlearning Racism in Geoscience (URGE): Summary of U.S. Geological Survey URGE pod deliverables Unlearning Racism in Geoscience (URGE): Summary of U.S. Geological Survey URGE pod deliverables
The U.S. Geological Survey (USGS) is in a unique position to be a leader in diversity, equity, inclusion, and accessibility in the Earth sciences. As one of the largest geoscience employers, the USGS wields significant community influence and has a responsibility to adopt and implement robust, unbiased policies so that the science it is charged to deliver is better connected to the...
Authors
Matthew Morriss, Eleanour Snow, Jennifer Miselis, William F. Waite, Katherine Barnhart, Andria P. Ellis, Liv Herdman, Seth C. Moran, Annie Putman, Nadine Reitman, Wendy Stovall, Meagan Eagle, Stephen Phillips
Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska
Regional mapping of actively deforming landslides, including measurements of landslide velocity, is integral for hazard assessments in paraglacial environments. These inventories are also critical for describing the potential impacts that the warming effects of climate change have on slope instability in mountainous and cryospheric terrain. The objective of this study is to identify slow...
Authors
Lauren Schaefer, Jinwook Kim, Dennis Staley, Zhong Lu, Katherine Barnhart
Non-USGS Publications**
Wickert, A.D., Barnhart, K.R., Armstrong, W.H., Romero, M., Schulz, B., Ng, G.-H.C., Sandell, C.T., La Frenierre, J., Penprase, S.B., Van Wyk de Vries, M., and MacGregor, K.R., 2023, Automated ablation stakes to constrain temperature-index melt models: Annals of Glaciology, v. 64, no. 92, p. 425–438, https://doi.org/10.1017/aog.2024.21.
Gasparini, N.M., Forte, A.M., and Barnhart, K.R., 2024, Short Communication: Numerically simulated time to steady state is not a reliable measure of landscape response time: Earth Surface Dynamics, v. 12, no. 6, p. 1227–1242, https://doi.org/10.5194/esurf-12-1227-2024.
Nudurupati, S.S., Istanbulluoglu, E., Tucker, G.E., Gasparini, N.M., Hobley, D.E.J., Hutton, E.W.H., Barnhart, K.R., and Adams, J.M., On transient semi-arid ecosystem dynamics using Landlab: Vegetation shifts, topographic refugia, and response to climate. Water Resources Research, 59, e2021WR031179. https://doi.org/10.1029/2021WR031179.
Litwin, David G, Tucker, G.E., Barnhart, K.R., and Harman, C.J., Reply to comment by Anand et al. on ‘Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes’: Journal of Geophysical Research—Earth Surface, 127, e2022JF006722. https://doi.org/10.1029/2022JF006722.
Litwin, D.G., Tucker, G.E., Barnhart, K.R., and Harman, C.J., 2022, Groundwater Affects the Geomorphic and Hydrologic Properties of Coevolved Landscapes: Journal of Geophysical Research: Earth Surface, v. 127, no. 1, p. e2021JF006239, doi: 10.1029/2021JF006239.
Tucker, G.E., Hutton, E.W.H., Piper, M.D., Campforts, B., Gan, T., Barnhart, K.R., Kettner, A.J., Overeem, I., Peckham, S.D., McCready, L., and Syvitski, J., 2022, Community Surface Dynamics Modeling System: a community platform for numerical modeling of Earth surface processes: Geoscientific Model Development, v. 15, no. 4, p. 1413–1439, doi: 10.5194/gmd-15-1413-2022.
Wiens, A., Kleiber, W., Nychka, D., and Barnhart, K. R. “Nonrigid Registration Using Gaussian Processes
and Local Likelihood Estimation”. In: Mathematical Geosciences (2021). DOI: 10.1007/s11004-
020-09917-7.
and Local Likelihood Estimation”. In: Mathematical Geosciences (2021). DOI: 10.1007/s11004-
020-09917-7.
Anderson, S.P., Kelly, P.J., Hoffman, N., Barnhart, K., Befus, K. and Ouimet, W. (2021). Is This Steady State? Weathering and Critical Zone Architecture in Gordon Gulch, Colorado Front Range. In Hydrogeology, Chemical Weathering, and Soil Formation (eds A. Hunt, M. Egli and B. Faybishenko). https://doi.org/10.1002/9781119563952.ch13
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Projections of landscape evolution on a 10,000 year timescale with assessment and partitioning of uncertainty sources”. Journal of Geophysical Research: Earth Surface (2020), 2020JF005795. https://doi.org/10.1029/2020JF005795.
A. M. Pfeiffer, K. R. Barnhart, J. A. Czuba, and E. W. H. Hutton. “NetworkSediment-Transporter: A Landlab component for bed material transport through river networks”. Journal of Open Source Software 5.53 (2020), p. 2341. https://doi.org/10.21105/joss.02341.
Barnhart, K. R., Hutton, E. W. H., Tucker, G. E., Gasparini, N. M., Istanbulluoglu, E., Hobley, D. E. J., Lyons, N. J., Mouchene, M., Nudurupati, S. S., Adams, J. M., and Bandaragoda, C.: Short communication: Landlab v2.0: a software package for Earth surface dynamics, Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, 2020.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 1, conceptualization and sensitivity analysis”. Journal of Geophysical Research: Earth Surface (2020), e2018JF004961. https://doi.org/10.1029/2018JF004961.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M. W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 2, calibration and validation”. Journal of Geophysical Research: Earth Surface (2020), e2018JF004963. https://doi.org/10.1029/2018JF004963.
K. R. Barnhart, G. E. Tucker, S. Doty, C. M. Shobe, R. C. Glade, M.W. Rossi, and M. C. Hill. “Inverting topography for landscape evolution model process representation: Part 3, Determining parameter ranges for select mature geomorphic transport laws and connecting changes in fluvial erodibility to changes in climate”. Journal of Geophysical Research: Earth Surface (2020), e2019JF005287. https://doi.org/10.1029/2019JF005287.
D. Litwin, G. Tucker, K. R. Barnhart, and C. Harman. “GroundwaterDupuitPercolator: A Landlab component for groundwater flow”. Journal of Open Source Software 5.46 (2020), 1935. https://doi.org/10.21105/joss.01935.
K. R. Barnhart, R. C. Glade, C. M. Shobe, and G. E. Tucker. “Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution”. Geoscientific Model Development 12.4 (2019), pp. 1267–1297. https://doi.org/10.5194/gmd-12-1267-2019.
K. R. Barnhart, E.W. H. Hutton, and G. E. Tucker. “umami: A Python package for Earth surface dynamics objective function construction”. Journal of Open Source Software 4.42 (2019). https://doi.org/10.21105/joss.01776.
C. J. Bandaragoda, A. Castronova, E. Istanbulluoglu, R. Strauch, S. S. Nudurupati, J. Phuong, J. M. Adams, N. M. Gasparini, K. R. Barnhart, E. Hutton, D. E. J. Hobley, N. J. Lyons, G. E. Tucker, D. G. Tarboton, R. Idaszak, and S. Wang. “Enabling collaborative numerical Modeling in Earth sciences using Knowledge Infrastructure”. Environmental Modelling and Software (2019). https://doi.org/10.1016/j.envsoft.2019.03.020.
A. Wiens, W. Kleiber, K. R. Barnhart, and D. Sain. “Surface Estimation for Multiple Misaligned Point Sets”. Mathematical Geosciences 39.2 (Apr. 2019), pp. 1–16. https://doi.org/10.1007/s11004-019-09802-y.
K. R. Barnhart, E. Hutton, N. Gasparini, and G. Tucker. “Lithology: A Landlab submodule for spatially variable rock properties”. Journal of Open Source Software 3.30 (2018), pp. 979–2. https://doi.org/10.21105/joss.00979.
M. Bendixen, L. L. Iversen, A. A. Bjork, B. Elberling, A.Westergaard-Nielsen, I. Overeem, K. R. Barnhart, S. A. Khan, J. E. Box, J. Abermann, K. Langley, and A. Kroon. “Delta progradation in Greenland driven by increasing glacial mass loss”. Nature 550.7674 (2017), pp. 101–104. https://doi.org/10.1038/nature23873.
C. M. Shobe, G. E. Tucker, and K. R. Barnhart. “The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution”. Geoscientific Model Development 10.12 (2017), pp. 4577–4604. https://doi.org/10.5194/gmd-10-4577-2017.
K. R. Barnhart, C. R. Miller, I. Overeem, and J. E. Kay. “Mapping the future expansion of Arctic open water”. Nature Climate Change (2015). https://doi.org/10.1038/nclimate2848.
R. C. Mahon, J. B. Shaw, K. R. Barnhart, D. E. Hobley, and B. McElroy. “Quantifying the stratigraphic completeness of delta shoreline trajectories”. Journal of Geophysical Research-Earth Surface 120.5 (2015), pp. 799–817. https://doi.org/10.1002/2014JF003298.
K. R. Barnhart, R. S. Anderson, I. Overeem, C. Wobus, G. D. Clow, and F. E. Urban. “Modeling erosion of ice-rich permafrost bluffs along the Alaskan Beaufort Sea coast”. Journal of Geophysical Research-Earth Surface 119.5 (2014), pp. 1155–1179. https://doi.org/10.1002/2013JF002845.
K. R. Barnhart, I. Overeem, and R. S. Anderson. “The effect of changing sea ice on the physical vulnerability of Arctic coasts”. The Cryosphere 8 (2014), pp. 1777–1799. https://doi.org/10.5194/tc-8-1777-2014.
K. R. Barnhart, K. H. Mahan, T. J. Blackburn, S. A. Bowring, and F. O. Dudas. “Deep crustal xenoliths from central Montana, USA: Implications for the timing and mechanisms of high-velocity lower crust formation”. Geosphere (2012). https://doi.org/10.1130/GES00765.1.
K. R. Barnhart, P. J. Walsh, L. S. Hollister, C. G. Daniel, and C. Andronicos. “Decompression during Late Proterozoic Al2SiO5 Triple-Point Metamorphism at Cerro Colorado, New Mexico”. The Journal of Geology (2012). https://doi.org/10.1086/665793.
T. J. Blackburn, S. A. Bowring, J. T. Perron, K. H. Mahan, F. O. Dudas, and K. R. Barnhart. “An Exhumation History of Continents over Billion-Year Time Scales”. Science 335.6064 (2012), pp. 73–76. https://doi.org/10.1126/science.1213496.
**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.
Simulation code for Surprise Inlet landslides v1.0.0 Simulation code for Surprise Inlet landslides v1.0.0
This repository contains the code needed to reproduce the D-Claw simulation presented in Karasözen and others (2025). D-Claw is numerical software package for simulating granular-fluid flows (George and others, 2025; George & Iverson, 2014; Iverson & George, 2014). This code simulates a landslide generated tsunami scenario in which one of the Surprise Inlet landslides described by...
D-Claw - A library for the simulation of granular-fluid flows, v1.0.0 D-Claw - A library for the simulation of granular-fluid flows, v1.0.0
D-Claw is a software library for the simulation of dense granular flows over spatially variable topography. D-Claw builds on clawpack, is written in Fortran, and has a python wrapper. The code is hosted on code.usgs.gov and mirrored on github. D-Claw is supported on Linux/Unix only.
Barry Arm landslide complex tsunami modeling scenarios, version 1.0.0 Barry Arm landslide complex tsunami modeling scenarios, version 1.0.0
This repository contains the code needed to reproduce computations presented in Barnhart and others (under review). This readme contains instructions for reproducing results and a list of the git hashes for each source repository used. Computation for this manuscript was done on the U.S. Geological Survey Denali High Performance Computer (Falgout and others, 2024). Barnhart, K.R., George...
digger - Utility tools for landslide runout modeling digger - Utility tools for landslide runout modeling
Digger is a python package that provides a number of pre- and post-processing tools for landslide runout modeling. Many aspects of digger have been tailored for use with the D-Claw model. It may also be useful for initializing and postprocessing the results of other runout models. D-Claw requires input files and generates output files in very specific formats. Simulation output generates...
digger: A python package for D-Claw model inputs digger: A python package for D-Claw model inputs
Digger is a python package that provides a number of pre- and post-processing tools for working with the D-Claw model.