Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3261-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-3261-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Annette I. Patton
CORRESPONDING AUTHOR
Sitka Sound Science Center, Sitka, Alaska, USA
Department of Earth Sciences, University of Oregon, Eugene, Oregon, USA
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Joshua J. Roering
Department of Earth Sciences, University of Oregon, Eugene, Oregon, USA
Aaron Jacobs
NOAA National Weather Service Forecast Office Juneau, Juneau, Alaska, USA
Oliver Korup
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Benjamin B. Mirus
Geologic Hazards Science Center, US Geological Survey, Golden, Colorado, USA
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Cited
14 citations as recorded by crossref.
- Preface: Hydro-meteorological extremes and hazards: vulnerability, risk, impacts, and mitigation F. Marra et al.
- Predicting Large-Scale Landslide Initiation Based on the Local Field Factor of Safety: An Application to the Braies Catchment in Northern Italy R. Busti et al.
- Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region W. Schwanghart et al.
- Critical rainfall thresholds for landslides based on extreme rainfall–induced clustered landslides and characteristic rainfall parameter analysis: a case study in Western Qinling Mountains, China S. Liu et al.
- Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al.
- Constraining landslide frequency across the United States to inform county-level risk reduction L. Luna et al.
- Use of delayed ERA5-Land soil moisture products for improving landslide early warning N. Palazzolo et al.
- Brief communication: Threshold and probability. The conceptual difference between ID thresholds for landslide initiation and IDF curves F. Marra et al.
- A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures Y. Li et al.
- Bedrock ledges, colluvial wedges, and ridgetop wetlands: characterizing geomorphic and atmospheric controls on the 2023 Wrangell landslide to inform landslide assessment in Southeast Alaska, USA J. Roering et al.
- A Novel Framework for Spatiotemporal Susceptibility Prediction of Rainfall-Induced Landslides: A Case Study in Western Pennsylvania J. Xiong et al.
- Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems B. Mirus et al.
- An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China H. Yang et al.
- Correlation of engineering geological aspects of bi-geomaterial interface and failure patterns of rainfall-induced accumulation landslides in Huanggang area, China S. Zhang et al.
14 citations as recorded by crossref.
- Preface: Hydro-meteorological extremes and hazards: vulnerability, risk, impacts, and mitigation F. Marra et al.
- Predicting Large-Scale Landslide Initiation Based on the Local Field Factor of Safety: An Application to the Braies Catchment in Northern Italy R. Busti et al.
- Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region W. Schwanghart et al.
- Critical rainfall thresholds for landslides based on extreme rainfall–induced clustered landslides and characteristic rainfall parameter analysis: a case study in Western Qinling Mountains, China S. Liu et al.
- Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al.
- Constraining landslide frequency across the United States to inform county-level risk reduction L. Luna et al.
- Use of delayed ERA5-Land soil moisture products for improving landslide early warning N. Palazzolo et al.
- Brief communication: Threshold and probability. The conceptual difference between ID thresholds for landslide initiation and IDF curves F. Marra et al.
- A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures Y. Li et al.
- Bedrock ledges, colluvial wedges, and ridgetop wetlands: characterizing geomorphic and atmospheric controls on the 2023 Wrangell landslide to inform landslide assessment in Southeast Alaska, USA J. Roering et al.
- A Novel Framework for Spatiotemporal Susceptibility Prediction of Rainfall-Induced Landslides: A Case Study in Western Pennsylvania J. Xiong et al.
- Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems B. Mirus et al.
- An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China H. Yang et al.
- Correlation of engineering geological aspects of bi-geomaterial interface and failure patterns of rainfall-induced accumulation landslides in Huanggang area, China S. Zhang et al.
Saved (final revised paper)
Latest update: 06 May 2026
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.
Landslide warning systems often use statistical models to predict landslides based on rainfall....
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