Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3279-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3279-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2025
Research article |
| 11 Sep 2025
| 11 Sep 2025
Constraining landslide frequency across the United States to inform county-level risk reduction
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA
Jacob B. Woodard
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA
Janice L. Bytheway
NOAA Physical Sciences Laboratory, Boulder, CO 80305, USA
Gina M. Belair
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA
Benjamin B. Mirus
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA
Related authors
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Cited articles
Alaska Department of Transportation and Public Facilities: Geo Event Tracker, Alaska Department of Transportation and Public Facilities [data set], https://www.arcgis.com/home/item.html?id=7d34a6dc51194296be7a38fd965c22c2 (last access: 15 October 2024), 2022.
Allstadt, K. E., Thompson, E. M., Jibson, R. W., Wald, D. J., Hearne, M., Hunter, E. J., Fee, J., Schovanec, H., Slosky, D., and Haynie, K. L.: The US Geological Survey ground failure product: Near-real-time estimates of earthquake-triggered landslides and liquefaction, Earthq. Spectra, 38, 5–36, https://doi.org/10.1177/87552930211032685, 2022.
Arizona Geological Survey: Landslides, Arizona Geological Survey [data set], https://uagis.maps.arcgis.com/apps/webappviewer/index.html?id=98729f76e4644f1093d1c2cd6dabb584 (last access: 15 October 2024), 2017.
Ashland, F. X.: Preliminary reconnaissance inventory map data of landslides and related features, North Manitou Island, Sleeping Bear Dunes National Lakeshore, Michigan, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P92LZ8R2, 2022a.
Ashland, F. X.: Preliminary reconnaissance inventory map data of landslides and related features, South Manitou Island, Sleeping Bear Dunes National Lakeshore, Michigan, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9SQWQ37, 2022b.
Baum, R. L. and Godt, J. W.: Early warning of rainfall-induced shallow landslides and debris flows in the USA, Landslides, 7, 259–272, https://doi.org/10.1007/s10346-009-0177-0, 2010.
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration, J. Geophys. Res.-Earth, 115, https://doi.org/10.1029/2009JF001321, 2010.
Belair, G. M., Jones, J. M., Martinez, S. N., Mirus, B. B., and Wood, N. J.: Slope-relief threshold landslide susceptibility models for the United States and Puerto Rico, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P13KAGU3, 2024.
Belair, G. M., Jones, E. S., Luna, L. V., and Mirus, B. B.: Landslide inventories across the United States (ver. 3.0, February 2025), U.S. Geological Survey data release [data set], https://doi.org/10.5066/P14AJF8I, 2025.
Bessette-Kirton, E. K. and Coe, J. A.: Inventory of rock avalanches in western Glacier Bay National Park and Preserve, Alaska, 1984–2016: A baseline data set for evaluating the impact of climate change on avalanche magnitude, mobility, and frequency, U.S. Geological Survey data release [data set], https://doi.org/10.5066/F7C827F8, 2016.
Bessette-Kirton, E. K., Coe, J. A., and Kellogg, I. N.: Inventory data of rock avalanches in the Saint Elias Mountains of southeast Alaska, derived from Landsat imagery (1984–2019), U.S. Geological Survey data release [data set], https://doi.org/10.5066/P97JTGDH, 2020.
Bordoni, M., Vivaldi, V., Lucchelli, L., Ciabatta, L., Brocca, L., Galve, J. P., and Meisina, C.: Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale, Landslides, 18, 1209–1229, https://doi.org/10.1007/s10346-020-01592-3, 2021.
Bozdog, N.: North Carolina landslide points, outlines, deposits, North Carolina Department of Environmental Quality [data set], https://www.nconemap.gov/search?q=landslides (last access: 15 March 2022), 2023.
Brewer, C., Harrower, M., Sheesley, B., Woodruff, A., and Heyman, D.: ColorBrewer 2.0, [code], https://colorbrewer2.org/# (last access: 29 August 2025), 2013.
Brien, D. L., Collins, B. D., Corbett, S. C., and Perkins, J. P.: San Francisco Bay Area Reconnaissance Landslide Inventory, January 2023, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9NJ3KMG, 2023.
Bryce, E., Lombardo, L., van Westen, C., Tanyas, H., and Castro-Camilo, D.: Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica, Stoch. Environ. Res. Risk Assess., 36, 2071–2084, https://doi.org/10.1007/s00477-022-02239-6, 2022.
Bürkner, P.-C.: brms: An R package for Bayesian multilevel models using Stan, J. Stat. Softw., 80, 1–28, https://doi.org/10.18637/jss.v080.i01, 2017.
California Geological Survey: Landslide inventory, California Geological Survey [data set], https://maps.conservation.ca.gov/cgs/lsi/app/ (last access: 15 March 2022), 2019.
Coe, J. A. and Godt, J. W.: Debris flows triggered by the El Niño rainstorm of February 2–3, 1998, Walpert Ridge and vicinity, Alameda County, California, U.S. Geological Survey Miscellaneous Field Studies Map MF-2384 [data set], https://pubs.usgs.gov/mf/2002/mf-2384/ (last access: 15 March 2022), 2002.
Coe, J. A., Godt, J. W., and Tachker, P.: Map showing recent (1997-98 El Niño) and historical landslides, Crow Creek and vicinity, Alameda and Contra Costa Counties, California, U.S. Geological Survey Scientific Investigations Map 2859 [data set], https://pubs.usgs.gov/sim/2004/2859/ (last access: 15 March 2022), 2004.
Coe, J. A., Michael, J. A., and Mercado Burgos, M.: Map of debris flows caused by rainfall during 1996 in parts of the Reedsport and Deer Head Point quadrangles, Douglas County, Southern Coast Range, Oregon, U.S. Geological Survey Open-File Report 2011–1150, 9 pp. pamphlet, 1 sheet, scale 1 : 12 000, https://pubs.usgs.gov/of/2011/1150/ (last access: 15 March 2022), 2011.
Collins, B. D., Stock, J. D., Weber, L. C., Whitman, K., and Knepprath, N.: Monitoring subsurface hydrologic response for precipitation-induced shallow landsliding in the San Francisco Bay area, California, USA, in: Landslides and engineered slopes: Protecting society through improved understanding, Proceedings of the 11th International Symposium on Landslides, 2–8 June 2012, Banff, Alberta, Canada, https://pubs.usgs.gov/publication/70191838 (last access: 29 August 2025), 2012.
Collins, B. D., Oakley, N. S., Perkins, J. P., East, A. E., Corbett, S. C., and Hatchett, B. J.: Linking mesoscale meteorology with extreme landscape response: Effects of Narrow Cold Frontal Rainbands (NCFR), J. Geophys. Res.-Earth, 125, e2020JF005675, https://doi.org/10.1029/2020JF005675, 2020.
Collins, E. A., Allstadt, K. E., Groult, C., Hibert, C., Maley, J.-P., Toney, L. D., and Jensen, E. K.: Seismogenic landslides and other mass movements (ver. 2.0, December 2023), U.S. Geological Survey data release [data set], https://doi.org/10.5066/P90VGCSK, 2022.
Corbett, S. C. and Collins, B. D.: Landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California, U.S. Geological Survey data release [data set], https://doi.org/10.3133/sim3503, 2023a.
Corbett, S. C. and Collins, B. D.: Mapped polygons of landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P98MVEGI, 2023b.
Cordeira, J. M., Stock, J., Dettinger, M. D., Young, A. M., Kalansky, J. F., and Ralph, F. M.: A 142-Year climatology of Northern California landslides and atmospheric rivers, B. Am. Meteorol. Soc., 100, 1499–1509, https://doi.org/10.1175/BAMS-D-18-0158.1, 2019.
Corominas, J. and Moya, J.: A review of assessing landslide frequency for hazard zoning purposes, Eng. Geol., 102, 193–213, https://doi.org/10.1016/j.enggeo.2008.03.018, 2008.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervás, J., and Smith, J. T.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, https://doi.org/10.1007/s10064-013-0538-8, 2014.
Crameri, F.: Scientific colour maps, Zenodo [code], https://doi.org/10.5281/zenodo.8409685, 2023.
Crawford, M. M.: Kentucky Geological Survey landslide inventory [2022-01], Kentucky Geological Survey research data [data set], https://uknowledge.uky.edu/kgs_data/7/ (last access: 15 October 2024), 2022.
Crozier, M. J. and Glade, T.: Landslide hazard and risk: Issues, concepts and approach, in: Landslide Hazard and Risk, John Wiley & Sons, Ltd, 1–40, https://doi.org/10.1002/9780470012659.ch1, 2005.
Dahal, A., Huser, R., and Lombardo, L.: At the junction between deep learning and statistics of extremes: Formalizing the landslide hazard definition, J. Geophys. Res.-Machine Learning and Computation, 1, e2024JH000164, https://doi.org/10.1029/2024JH000164, 2024a.
Dahal, A., Tanyas, H., van Westen, C., van der Meijde, M., Mai, P. M., Huser, R., and Lombardo, L.: Space–time landslide hazard modeling via Ensemble Neural Networks, Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, 2024b.
DeLong, S. B., Engle, Z. T., Hammer, M. N., Jennings, C. E., Gran, K. B., Bartley, J. K., Blumentritt, D. J., Breckenridge, A. J., Day, S., Larson, P. H., McDermott, J. A., Triplett, L. D., Wickert, A. D., Richard, E. M., Swanson, M. A., Allison, M., Dahlseid, A., Dahly, D. T., Dean, B. A., Endres, M., Kurak, E., Link, S., Matti, B., Rehwinkel, R. W., Sockness, B., VanBerkel, J., Willard, J. G., and Williams, A. B.: Inventory of landslides in the northwestern, northeastern, southern, and southeastern parts of Minnesota, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P94KF6OM, 2021.
Di Napoli, M., Tanyas, H., Castro-Camilo, D., Calcaterra, D., Cevasco, A., Di Martire, D., Pepe, G., Brandolini, P., and Lombardo, L.: On the estimation of landslide intensity, hazard and density via data-driven models, Nat. Hazards, 119, 1513–1530, https://doi.org/10.1007/s11069-023-06153-0, 2023.
Dowling, C. A. and Santi, P. M.: Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011, Nat. Hazards, 71, 203–227, https://doi.org/10.1007/s11069-013-0907-4, 2014.
Du, J.: NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data, Version 1.0 (1.0), NSF NCAR Earth Observing Laboratory [data set], https://doi.org/10.5065/D6PG1QDD, 2011.
ESRI: U.S. National Atlas Water Feature Areas – Water Bodies, ESRI [data set], https://www.arcgis.com/home/item.html?id=0eb5f7b586ea4e08b5003b3554032453 (last access: 24 July 2023), 2022.
Fall, G., Kitzmiller, D., Pavlovic, S., Zhang, Z., Patrick, N., St. Laurent, M., Trypaluk, C., Wu, W., and Miller, D.: The Office of Water Prediction's Analysis of Record for Calibration, version 1.1: Dataset description and precipitation evaluation, J. Am. Water Resour. As., 59, 1246–1272, https://doi.org/10.1111/1752-1688.13143, 2023.
Federal Emergency Management Agency: National Risk Index (1.19.0), Federal Emergency Management Agency [data set], https://hazards.fema.gov/nri/data-resources (last access: 13 November 2024), 2023a.
Federal Emergency Management Agency: National Risk Index Technical Documentation, Federal Emergency Management Agency:, https://www.fema.gov/sites/default/files/documents/fema_national-risk-index_technical-documentation.pdf (last access: 13 November 2024), 2023b.
Frattini, P., Crosta, G., and Sosio, R.: Approaches for defining thresholds and return periods for rainfall-triggered shallow landslides, Hydrol. Process., 23, 1444–1460, https://doi.org/10.1002/hyp.7269, 2009.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth-Sci. Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
Glade, T. and Crozier, M. J.: A review of scale dependency in landslide hazard and risk analysis, in: Landslide Hazard and Risk, John Wiley & Sons, Ltd, 75–138, https://doi.org/10.1002/9780470012659.ch3, 2005.
Godt, J. W. and Coe, J. A.: Alpine debris flows triggered by a 28 July 1999 thunderstorm in the central Front Range, Colorado, Geomorphology, 84, 80–97, https://doi.org/10.1016/j.geomorph.2006.07.009, 2007.
Godt, J. W., Wood, N. J., Pennaz, A. B., Dacey, C. M., Mirus, B. B., Shaefer, L. N., and Slaughter, S. L.: National strategy for landslide loss reduction: U.S. Geological Survey Open-File Report 2022–1075, https://doi.org/10.3133/ofr20221075, 2022.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72, 272–299, https://doi.org/10.1016/j.geomorph.2005.06.002, 2005.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensity–duration control of shallow landslides and debris flows: an update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Halsted, C. H.: Maine landslides, Maine Geological Survey [data set], https://www.maine.gov/dacf/mgs/hazards/landslides/inland/index.shtml (last access: 15 October 2024), 2020.
Institute of Agriculture and Natural Resources: Collection of Nebraska Landslides, University of Nebraska Lincoln School of Natural Resources, https://snr.unl.edu/data/geologysoils/landslides/landslidedatabase.aspx (last access: 15 October 2024), 2025.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Jibson, R. W.: Methods for assessing the stability of slopes during earthquakes – A retrospective, Eng. Geol., 122, 43–50, https://doi.org/10.1016/j.enggeo.2010.09.017, 2011.
Juang, C. S., Stanley, T. A., and Kirschbaum, D. B.: Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR), PLOS ONE, 14, e0218657, https://doi.org/10.1371/journal.pone.0218657, 2019.
Kean, J. W., Staley, D. M., Lancaster, J. T., Rengers, F. K., Swanson, B. J., Coe, J. A., Hernandez, J. L., Sigman, A. J., Allstadt, K. E., and Lindsay, D. N.: Debris-flow inundation and damage data from the 9 January 2018 Montecito debris-flow event, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9JQJU0E, 2019.
Ko, F. W. Y. and Lo, F. L. C.: From landslide susceptibility to landslide frequency: A territory-wide study in Hong Kong, Eng. Geol., 242, 12–22, https://doi.org/10.1016/j.enggeo.2018.05.001, 2018.
Korup, O., Luna, L. V., and Ferrer, J. V.: Size scaling of large landslides from incomplete inventories, Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, 2024.
Kruschke, J.: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Academic Press, 772 pp., ISBN 978-0-12-405916-0, 2014.
Lari, S., Frattini, P., and Crosta, G. B.: A probabilistic approach for landslide hazard analysis, Eng. Geol., 182, 3–14, https://doi.org/10.1016/j.enggeo.2014.07.015, 2014.
Lifton, Z. M., Ducar, S. D., and Tate, C. A.: Landslide inventory database for Idaho, Idaho Geological Survey [data set], https://www.idahogeology.org/product/DD-10 (last access: 15 October 2024), 2021.
Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R.: Space-time landslide predictive modelling, Earth-Sci. Rev., 209, 103318, https://doi.org/10.1016/j.earscirev.2020.103318, 2020.
Luna, L. V. and Korup, O.: Seasonal landslide activity lags annual precipitation pattern in the Pacific Northwest, Geophys. Res. Lett., 49, e2022GL098506, https://doi.org/10.1029/2022GL098506, 2022.
Luna, L. V. and Woodard, J. B.: Bayesian county-level landslide frequency estimation for the 50 U.S. States, U.S. Geological Survey software release [code], https://doi.org/10.5066/P142ZLDX, 2025.
Luo, L., Lombardo, L., van Westen, C., Pei, X., and Huang, R.: From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations, Stoch. Environ. Res. Risk Assess., 36, 2243–2264, https://doi.org/10.1007/s00477-020-01959-x, 2022.
Marc, O., Meunier, P., and Hovius, N.: Prediction of the area affected by earthquake-induced landsliding based on seismological parameters, Nat. Hazards Earth Syst. Sci., 17, 1159–1175, https://doi.org/10.5194/nhess-17-1159-2017, 2017.
McElreath, R.: Statistical rethinking a Bayesian course with examples in R and STAN, 2nd edn., Chapman and Hall/CRC Press, ISBN 978-0-367-13991-9, 2020.
Meunier, P., Hovius, N., and Haines, A. J.: Regional patterns of earthquake-triggered landslides and their relation to ground motion, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031337, 2007.
Minami, M., Lennert-Cody, C. E., Gao, W., and Román-Verdesoto, M.: Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing, Fish. Res., 84, 210–221, https://doi.org/10.1016/j.fishres.2006.10.019, 2007.
Mirus, B. B., Jones, E. S., Baum, R. L., Godt, J. W., Slaughter, S., Crawford, M. M., Lancaster, J., Stanley, T., Kirschbaum, D. B., Burns, W. J., Schmitt, R. G., Lindsey, K. O., and McCoy, K. M.: Landslides across the USA: occurrence, susceptibility, and data limitations, Landslides, 17, 2271–2285, https://doi.org/10.1007/s10346-020-01424-4, 2020.
Mirus, B. B., Belair, G. M., Wood, N. J., Jones, J., and Martinez, S. N.: Parsimonious high-resolution landslide susceptibility modeling at continental scales, AGU Advances, 5, e2024AV001214, https://doi.org/10.1029/2024AV001214, 2024.
Missouri Department of Natural Resources: Landslides, Missouri Department of Natural Resources [data set], https://dnr.mo.gov/land-geology/hazards/landslides, (last access: 15 March 2022), 2025.
Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S. B., Li, W., Nguyen, H., Qiu, T., Tran, S., and Shen, C.: Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates, IEEE J. Sel. Top. Appl., 15, 4349–4370, https://doi.org/10.1109/JSTARS.2022.3177025, 2022.
National Centers for Environmental Information: Global Historical Climatology Network – Daily, National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily (last access: 15 October 2024), 2024.
National Research Council: Reducing losses from landsliding in the United States, The National Academies Press, Washington, D.C., https://doi.org/10.17226/19286, 1985.
Natural Earth: Natural Earth – Admin 0 Countries (5.5.1), Natural Earth [data set], https://www.naturalearthdata.com/downloads/50m-cultural-vectors/ (last access: 11 September 2024), 2022.
Nelson, B. R., Prat, O. P., Seo, D.-J., and Habib, E.: Assessment and implications of NCEP Stage IV quantitative precipitation estimates for product intercomparisons, Weather Forecast., 31, 371–394, https://doi.org/10.1175/WAF-D-14-00112.1, 2016.
New Jersey Geological and Water Survey: Landslides in New Jersey, Digital Geodata Series DGS06-3, New Jersey Geological and Water Survey [data set], https://www.state.nj.us/dep/njgs/geodata/dgs06-3.htm (last access: 15 March 2022), 2018.
Nowicki Jessee, M. A., Hamburger, M. W., Allstadt, K., Wald, D. J., Robeson, S. M., Tanyas, H., Hearne, M., and Thompson, E. M.: A global empirical model for near-real-time assessment of seismically induced landslides, J. Geophys. Res.-Earth, 123, 1835–1859, https://doi.org/10.1029/2017JF004494, 2018.
Oakley, N. S., Lancaster, J. T., Kaplan, M. L., and Ralph, F. M.: Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of southern California, Nat. Hazards, 88, 327–354, https://doi.org/10.1007/s11069-017-2867-6, 2017.
Omernik, J. M.: Perspectives on the nature and definition of ecological regions, Environ. Manage., 34, S27–S38, https://doi.org/10.1007/s00267-003-5197-2, 2004.
Oregon Department of Geology and Mineral Industries: Statewide landslide information database for Oregon, Oregon Department of Geology and Mineral Industries [data set], https://www.oregon.gov/dogami/slido/Pages/data.aspx (last access: 15 October 2024), 2024.
Ozturk, U., Bozzolan, E., Holcombe, E. A., Shukla, R., Pianosi, F., and Wagener, T.: How climate change and unplanned urban sprawl bring more landslides, Nature, 608, 262–265, https://doi.org/10.1038/d41586-022-02141-9, 2022.
Patton, A. I., Rathburn, S. L., and Capps, D. M.: Landslide response to climate change in permafrost regions, Geomorphology, 340, 116–128, https://doi.org/10.1016/j.geomorph.2019.04.029, 2019.
Patton, A. I., Luna, L. V., Roering, J. J., Jacobs, A., Korup, O., and Mirus, B. B.: Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA, Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, 2023.
Petersen, M. D., Shumway, A. M., Powers, P. M., Field, N., Moschetti, M. P., Jaiswal, K., Milner, K. R., Rezaeian, S., Frankel, A. D., Llenos, A. L., Michael, A. J., Altekruse, J. M., Ahdi, S. K., Withers, K., Mueller, C. S., Zeng, Y., Chase, R. E., Salditch, L. M., Luco, N., Rukstales, K. S., Herrick, J. A., Girot, D. L., Aagaard, B. T., Bender, A. M., Blanpied, M. L., Briggs, R., Boyd, O. S., Clayton, B. S., Duross, C. B., Evans, E., Haeussler, P. J., Hatem, A. E., Haynie, K. L., Hearn, E., Johnson, K., Kortum, Z. A., Kwong, N., Makdisi, A. J., Mason, H. B., McNamara, D. E., McPhillips, D. F., Okubo, P. G., Page, M. T., Pollitz, F., Rubinstein, J. L., Shaw, B., Shen, Z.-K., Shiro, B. R., Smith, J. A., Stephenson, W. J., Thompson, E. M., Jobe, J. A., Moriarty, E. W., and Witter, R. C.: Data release for the 2023 U.S. 50-State National Seismic Hazard Model – Overview, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9GNPCOD, 2023.
Petersen, M. D., Shumway, A. M., Powers, P. M., Field, E. H., Moschetti, M. P., Jaiswal, K. S., Milner, K. R., Rezaeian, S., Frankel, A. D., Llenos, A. L., Michael, A. J., Altekruse, J. M., Ahdi, S. K., Withers, K. B., Mueller, C. S., Zeng, Y., Chase, R. E., Salditch, L. M., Luco, N., Rukstales, K. S., Herrick, J. A., Girot, D. L., Aagaard, B. T., Bender, A. M., Blanpied, M. L., Briggs, R. W., Boyd, O. S., Clayton, B. S., DuRoss, C. B., Evans, E. L., Haeussler, P. J., Hatem, A. E., Haynie, K. L., Hearn, E. H., Johnson, K. M., Kortum, Z. A., Kwong, N. S., Makdisi, A. J., Mason, H. B., McNamara, D. E., McPhillips, D. F., Okubo, P. G., Page, M. T., Pollitz, F. F., Rubinstein, J. L., Shaw, B. E., Shen, Z.-K., Shiro, B. R., Smith, J. A., Stephenson, W. J., Thompson, E. M., Thompson Jobe, J. A., Wirth, E. A., and Witter, R. C.: The 2023 U.S. 50-State National Seismic Hazard Model: Overview and implications, Earthq. Spectra, 40, 5–88, https://doi.org/10.1177/87552930231215428, 2024.
Pollock, W. and Wartman, J.: Human vulnerability to landslides, GeoHealth, 4, e2020GH000287, https://doi.org/10.1029/2020GH000287, 2020.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Rengers, F. K.: Inventory of landslides triggered by rainfall on 16–17 January 2019, Los Angeles County, CA, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P97GU3UV, 2020.
Rose, C. E., Martin, S. W., Wannemuehler, K. A., and Plikaytis, B. D.: On the use of zero-Inflated and hurdle models for modeling vaccine adverse event count data, J. Biopharm. Stat., 16, 463–481, https://doi.org/10.1080/10543400600719384, 2006.
Salvatici, T., Tofani, V., Rossi, G., D'Ambrosio, M., Tacconi Stefanelli, C., Masi, E. B., Rosi, A., Pazzi, V., Vannocci, P., Petrolo, M., Catani, F., Ratto, S., Stevenin, H., and Casagli, N.: Application of a physically based model to forecast shallow landslides at a regional scale, Nat. Hazards Earth Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, 2018.
Santi, P. M., Hewitt, K., VanDine, D. F., and Barillas Cruz, E.: Debris-flow impact, vulnerability, and response, Nat. Hazards, 56, 371–402, https://doi.org/10.1007/s11069-010-9576-8, 2011.
Scheevel, C. R., Baum, R. L., Mirus, B. B., and Smith, J. B.: Precipitation thresholds for landslide occurrence near Seattle, Mukilteo, and Everett, Washington, U.S. Geological Survey Open-File Report 2017–1039, 51 pp., Reston, VA, https://doi.org/10.3133/ofr20171039, 2017.
Schmitt, R. G., Tanyas, H., Nowicki Jessee, M. A., Zhu, J., Biegel, K. M., Allstadt, K. E., Jibson, R. W., Thompson, E. M., van Westen, C. J., Sato, H. P., Wald, D. J., Godt, J. W., Gorum, T., Xu, C., Rathje, E. M., and Knudsen, K. L.: An open repository of earthquake-triggered ground-failure inventories (ver 4.0, October 2022), U.S. Geological Survey data release [data set], https://doi.org/10.5066/F7H70DB4, 2017.
Schuster, R. L.: Socioeconomic significance of landslides, in: Landslides: Investigation and mitigation, Transportation Research Board Special Report, 247, 12–35, ISBN 0-309-06151-2,1996.
Seattle Department of Construction and Inspections: ECA known slide (initiation point, scarp, affected property), Seattle Department of Construction and Inspections [data set], https://data-seattlecitygis.opendata.arcgis.com/datasets/eca-known-slide-events/explore (last access: 15 March 2022), 2023.
Segoni, S., Tofani, V., Rosi, A., Catani, F., and Casagli, N.: Combination of rainfall thresholds and susceptibility maps for dynamic landslide hazard assessment at regional scale, Front. Earth Sci., 6, https://doi.org/10.3389/feart.2018.00085, 2018.
Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. M., Tillery, A. C., and Youberg, A. M.: Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States, U.S. Geological Survey Open-File Report 2016–1106, 13, https://doi.org/10.3133/ofr20161106, 2016.
Stan Development Team: Stan modeling language users guide and reference manual, version 2.32, https://mc-stan.org/docs/2_32/stan-users-guide-2_32.pdf (last access: 29 August 2025).2023.
Steger, S., Mair, V., Kofler, C., Pittore, M., Zebisch, M., and Schneiderbauer, S.: Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects, Sci. Total Environ., 776, 145935, https://doi.org/10.1016/j.scitotenv.2021.145935, 2021.
Tanyaş, H., van Westen, C. J., Allstadt, K. E., Anna Nowicki Jessee, M., Görüm, T., Jibson, R. W., Godt, J. W., Sato, H. P., Schmitt, R. G., Marc, O., and Hovius, N.: Presentation and analysis of a worldwide database of earthquake-induced landslide inventories, J. Geophys. Res.-Earth, 122, 1991–2015, https://doi.org/10.1002/2017JF004236, 2017.
Thomas, M. A., Mirus, B. B., and Collins, B. D.: Identifying physics-based thresholds for rainfall-induced landsliding, Geophys. Res. Lett., 45, 9651–9661, https://doi.org/10.1029/2018GL079662, 2018.
Thomas, M. A., Lindsay, D. N., Cavagnaro, D. B., Kean, J. W., McCoy, S. W., and Graber, A. P.: Field-verified inventory of postfire debris flows for the 2021 Dixie Fire following a 23-25 October 2021 atmospheric river storm and 12 June 2022 thunderstorm, U.S. Geological Survey data release, https://doi.org/10.5066/P9YPX1BM, 2023.
U.S. Bureau of Labor Statistics: CPI Inflation Calculator, U.S. Bureau of Labor Statistics, https://www.bls.gov/data/inflation_calculator.htm (last access: 15 October 2024), 2024.
U.S. Census Bureau: Tiger/Line 2023 Counties (and equivalent), U.S. Census Bureau [data set], https://www.census.gov/cgi-bin/geo/shapefiles/index.php (last access: 19 September 2024), 2023a.
U.S. Census Bureau: Cartographic Boundary Files 1 : 500 000, U.S. Census Bureau [data set], https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html (last access: 11 September 2024), 2023b.
U.S. Environmental Protection Agency: Level I Ecoregions of North America Shapefile, U.S. Environmental Protection Agency [data set], https://www.epa.gov/eco-research/ecoregions-north-america (last access: 15 January 2025), 2010.
U.S. Forest Service: Tongass Landslide Initiation and Areas, U.S. Forest Service [data set], https://hub.arcgis.com/datasets/usfs::tongass-landslide-areas-feature-layer/about (last access: 15 October 2024), 2024.
van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., and Yau, C.: Bayesian statistics and modelling, Nature Reviews Methods Primers, 1, 1–26, https://doi.org/10.1038/s43586-020-00001-2, 2021.
Vehtari, A., Gelman, A., and Gabry, J.: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC, Stat. Comput., 27, 1413–1432, https://doi.org/10.1007/s11222-016-9696-4, 2017.
Vermont Agency of Natural Resources: Landslides, Vermont Agency of Natural Resources [data set], https://geodata.vermont.gov/datasets/VTANR::landslides/about (last access: 15 March 2022), 2020.
Washington Geological Survey: Washington State Landslide Inventory Database (WASLID), Washington Geological Survey [data set], https://www.dnr.wa.gov/programs-and-services/geology/publications-and-data/gis-data-and-databases (last access: 15 October 2024), 2023.
White, G. C. and Bennetts, R. E.: Analysis of frequency count data using the negative binomial distribution, Ecology, 77, 2549–2557, https://doi.org/10.2307/2265753, 1996.
Woodard, J. B., Mirus, B. B., Crawford, M. M., Or, D., Leshchinsky, B. A., Allstadt, K. E., and Wood, N. J.: Mapping landslide susceptibility over large regions with limited data, J. Geophys. Res.-Earth, 128, e2022JF006810, https://doi.org/10.1029/2022JF006810, 2023.
Wooten, R. M., Witt, A. C., Miniat, C. F., Hales, T. C., and Aldred, J. L.: Frequency and magnitude of selected historical landslide events in the Southern Appalachian Highlands of North Carolina and Virginia: Relationships to rainfall, geological and ecohydrological controls, and effects, in: Natural Disturbances and Historic Range of Variation: Type, Frequency, Severity, and Post-disturbance Structure in Central Hardwood Forests USA, edited by: Greenberg, C. H. and Collins, B. S., Springer International Publishing, Cham, 203–262, https://doi.org/10.1007/978-3-319-21527-3_9, 2016.
Yuan, R. and Chen, J.: A novel method based on deep learning model for national-scale landslide hazard assessment, Landslides, 20, 2379–2403, https://doi.org/10.1007/s10346-023-02101-y, 2023.
Zuzak, C., Mowrer, M., Goodenough, E., Burns, J., Ranalli, N., and Rozelle, J.: The national risk index: Establishing a nationwide baseline for natural hazard risk in the US, Nat. Hazards, 114, 2331–2355, https://doi.org/10.1007/s11069-022-05474-w, 2022.
Short summary
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near-continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk...
Altmetrics
Final-revised paper
Preprint