Articles | Volume 24, issue 1
https://doi.org/10.5194/nhess-24-1-2024
© Author(s) 2024. 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-24-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jan 2024
Research article |
| 03 Jan 2024
| 03 Jan 2024
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Jacob B. Woodard
CORRESPONDING AUTHOR
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO, USA
Benjamin B. Mirus
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO, USA
Nathan J. Wood
U.S. Geological Survey, Western Geographic Science Center, Portland, OR, USA
Kate E. Allstadt
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO, USA
Benjamin A. Leshchinsky
Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR, USA
Matthew M. Crawford
Kentucky Geological Survey, University of Kentucky, Lexington, KY, USA
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Cited
23 citations as recorded by crossref.
- Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi P. Li et al. https://doi.org/10.3390/rs16163016
- Representative profile model: a new physically-based model using slope unit for hazard assessment of colluvial landslides at large scale X. Feng et al. https://doi.org/10.1007/s10064-025-04677-2
- Comprehensive analysis and assessment of extreme rainfall-induced clustered landslides: a case study of southern Qingyuan city, Guangdong province, China, in June 2020 C. Xie et al. https://doi.org/10.1080/17499518.2025.2563759
- A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy A. Riveros et al. https://doi.org/10.5194/nhess-26-2437-2026
- Slope unit-based evaluation of climate change impacts on landslide susceptibility in the Nepal Himalaya T. Bhattarai & N. Bhandary https://doi.org/10.1080/19475705.2026.2649615
- Integrating machine learning and physics-based slope stability through Bayesian geostatistics: An uncertainty-aware framework for landslide prediction A. Achu et al. https://doi.org/10.1016/j.gsf.2026.102402
- Stacking ensemble learning-driven risk assessment framework of rockfall in karst terrains: A case study in Guilin, China Y. Zhang et al. https://doi.org/10.1016/j.jrmge.2025.12.027
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. https://doi.org/10.1016/j.envsoft.2024.106231
- Towards Sustainable Heritage Conservation: A Hybrid Landslide Susceptibility Mapping Framework in Japan’s UNESCO Mountain Villages A. Bassem et al. https://doi.org/10.3390/su18010237
- Energy-driven micro-macro modeling of earthquake-induced landslides via peridynamics-discrete element method framework Y. Chen et al. https://doi.org/10.1016/j.jrmge.2026.02.010
- Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods J. Wang et al. https://doi.org/10.1016/j.ecolind.2024.112948
- Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. https://doi.org/10.5194/nhess-24-1579-2024
- Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen https://doi.org/10.1080/19475705.2024.2396908
- A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors P. Li et al. https://doi.org/10.3390/app142311279
- Slope unit-based comprehensive geohazard susceptibility assessment: SHAP interpretability and local InSAR deformation analysis P. Wang et al. https://doi.org/10.1016/j.asr.2025.03.034
- Evaluating the use of a coordinate-based tensor-product for addressing spatial autocorrelation in shallow landslide susceptibility modelling L. Pompili et al. https://doi.org/10.1007/s00477-026-03283-2
- Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
- Assessing landslide susceptibility and dynamics at cultural heritage sites by integrating machine learning techniques and persistent scatterer interferometry J. Bonini et al. https://doi.org/10.1016/j.geomorph.2024.109522
- Impacts from cascading multi-hazards using hypergraphs: a case study from the 2015 Gorkha earthquake in Nepal A. Dunant et al. https://doi.org/10.5194/nhess-25-267-2025
- Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India T. Pareek et al. https://doi.org/10.1007/s10346-024-02389-4
- Is there difference in landslide susceptibility model based on explainable artificial intelligence from the perspective of slope units with different scales? J. Huang et al. https://doi.org/10.1016/j.ress.2025.111701
- Comparative analysis of slope and grid units for co-seismic landslide susceptibility mapping using machine learning methods T. Bhattarai & N. Bhandary https://doi.org/10.1007/s44288-026-00489-3
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. https://doi.org/10.1016/j.scitotenv.2025.178545
23 citations as recorded by crossref.
- Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi P. Li et al. https://doi.org/10.3390/rs16163016
- Representative profile model: a new physically-based model using slope unit for hazard assessment of colluvial landslides at large scale X. Feng et al. https://doi.org/10.1007/s10064-025-04677-2
- Comprehensive analysis and assessment of extreme rainfall-induced clustered landslides: a case study of southern Qingyuan city, Guangdong province, China, in June 2020 C. Xie et al. https://doi.org/10.1080/17499518.2025.2563759
- A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy A. Riveros et al. https://doi.org/10.5194/nhess-26-2437-2026
- Slope unit-based evaluation of climate change impacts on landslide susceptibility in the Nepal Himalaya T. Bhattarai & N. Bhandary https://doi.org/10.1080/19475705.2026.2649615
- Integrating machine learning and physics-based slope stability through Bayesian geostatistics: An uncertainty-aware framework for landslide prediction A. Achu et al. https://doi.org/10.1016/j.gsf.2026.102402
- Stacking ensemble learning-driven risk assessment framework of rockfall in karst terrains: A case study in Guilin, China Y. Zhang et al. https://doi.org/10.1016/j.jrmge.2025.12.027
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. https://doi.org/10.1016/j.envsoft.2024.106231
- Towards Sustainable Heritage Conservation: A Hybrid Landslide Susceptibility Mapping Framework in Japan’s UNESCO Mountain Villages A. Bassem et al. https://doi.org/10.3390/su18010237
- Energy-driven micro-macro modeling of earthquake-induced landslides via peridynamics-discrete element method framework Y. Chen et al. https://doi.org/10.1016/j.jrmge.2026.02.010
- Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods J. Wang et al. https://doi.org/10.1016/j.ecolind.2024.112948
- Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. https://doi.org/10.5194/nhess-24-1579-2024
- Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen https://doi.org/10.1080/19475705.2024.2396908
- A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors P. Li et al. https://doi.org/10.3390/app142311279
- Slope unit-based comprehensive geohazard susceptibility assessment: SHAP interpretability and local InSAR deformation analysis P. Wang et al. https://doi.org/10.1016/j.asr.2025.03.034
- Evaluating the use of a coordinate-based tensor-product for addressing spatial autocorrelation in shallow landslide susceptibility modelling L. Pompili et al. https://doi.org/10.1007/s00477-026-03283-2
- Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
- Assessing landslide susceptibility and dynamics at cultural heritage sites by integrating machine learning techniques and persistent scatterer interferometry J. Bonini et al. https://doi.org/10.1016/j.geomorph.2024.109522
- Impacts from cascading multi-hazards using hypergraphs: a case study from the 2015 Gorkha earthquake in Nepal A. Dunant et al. https://doi.org/10.5194/nhess-25-267-2025
- Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India T. Pareek et al. https://doi.org/10.1007/s10346-024-02389-4
- Is there difference in landslide susceptibility model based on explainable artificial intelligence from the perspective of slope units with different scales? J. Huang et al. https://doi.org/10.1016/j.ress.2025.111701
- Comparative analysis of slope and grid units for co-seismic landslide susceptibility mapping using machine learning methods T. Bhattarai & N. Bhandary https://doi.org/10.1007/s44288-026-00489-3
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. https://doi.org/10.1016/j.scitotenv.2025.178545
Saved (final revised paper)
Latest update: 14 Jul 2026
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.
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across...
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