the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2025
Leveraging hillslope connectivity for improved large-scale assessments of landslide risk
Abstract. Landslides hinder sustainable development in mountain regions, threatening livelihoods and impacting linear and water infrastructure. Susceptibility maps are a common tool for estimating and managing landslide hazards, exposure, and risks. Yet, susceptibility maps omit hillslope connectivity, a critical shortcoming for mapping the magnitude of landslide hazards, including cascading hazards from slope failure and downslope runout. Herein we propose the COHESION (COnnected HillslopE SusceptibIlity for slOpefailure and ruNout) approach to couple susceptibility mapping with an assessment of hillslope connectivity to identify downslope-connected landslide objects (LSOs) and associated runout pathways. As we demonstrate for the Kaligandaki basin in Nepal, analyzing LSOs enables estimating the magnitude of slope failures in terms of mobilized sediment volume and to quantify additional impacts from landslide runout. After calibration using a remotely sensed landslide inventory, we find that 16 % of the basin’s slopes are susceptible to failure, while an additional 9 % of the basin area is impacted by runout. Around 33 % of buildings and 65 % of roads in the basin are on susceptible slopes, while more than 27 % of buildings and 69 % of roads are in landslide runout pathways. Omitting runout from landslide assessments would thus result in a major underestimation of risk. Our results emphasize the importance of connectivity for slope stability modeling on landscape scales, leading to improved assessments of slope hazards and management of river basin sediments.
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Status: open (until 26 Nov 2025)
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CC1: 'Comment on egusphere-2025-3733', Max Sutton, 25 Oct 2025
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AC1: 'Reply on CC1', Rafael Schmitt, 31 Oct 2025
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Dear Dr. Sutton, thank you for taking the time to comment on our manuscript. We appreciate your insights and we are glad that you found this manuscript interesting.
- Have you looked into the USGS Grfin tools as a model for connectivity? (Reid et al 2025 https://doi.org/10.3133/tm14A3). It is meant for highly mobile mass movements such as debris flows. If it applies in your case, it can automatically estimate connected areas, runout zones, etc and may be of interest to you.
Thanks for pointing us to the Grfin tool, after careful reading of the manual, this seems a very complimentary approach. Grfin can use a similar H/L ratio approach to what we use in here, in addition it implements more complex algorithms for different types of mass movements, as you say, highly mobile lahars, debris flows etc. Instead Grfin does not account for slope failure, so basically the source areas and size/volumes of those would need to be put in from a separate input. We will certainly discuss this in our manuscript.
- Another way of looking at connectivity is to use slope units (eg Woodard et al. 2024, https://doi.org/10.5194/nhess-24-1-2024) instead of a pixel/grid based approach. Using slope units has been shown to be effective at estimating susceptible area, partially because it inherently accounts for runout zones. It also provides a convenient division for aggregating other parameters such as slope over a vulnerable area. In the context of this paper, you probably would not use slope units for connectivity because it would change your volume and thus runout assessments, but I'm curious if it would be a good way to aggregate/interpolate soil or hydrology parameters
This is an interesting thought. From our reading it seems indeed as slope-units could be interesting to handle certain subcomponents, such as hydrology. However, in general, slope units seem to imply a lumping into larger units based mostly on terrain parameters / topography, which would mask more localized hillslope connectivity
- At what scales is COHESION effective? (Ie, local/basin level, regional level, global level? Similarly, especially given the coarseness of some of your inputs, have you tested if there is a minimum pixel size for which COHESION is reasonable?
We think that COHESION is most applicable on a watershed / regional scale. I think the question of scale really depends on the size of landslides that create most hazards in a given region. I would think that the herein deployed resolution (30m) is towards the upper limit of resolution, also because lower resolutions imply lower extremal values for slope.
- Your model is quite complex. While I appreciate the authors’ consideration of the many factors that impact landslide susceptibility, are all of these inputs relevant at this scale? Have you considered a factor analysis to identify the most important inputs and if you were to make the model simpler, how performance would change? I wonder if performance would actually increase, given how difficult it is to actually measure many of your input parameters? Your sensitivity analysis begins to answer these questions, but a factor analysis or similar technique could be a rigorous way to shed light on relative importance of parameters without having to do separate analyses for individual parameters
This is a good point. Indeed, we have included many parameters mostly to shed a light on potential applications. We did not sample the full parameter space, e.g., through a sobol analysis . We agree that this is an interesting area for more research, and exactly what we aimed to hint on with our parameter sampling. However, we think such an approach would me most yielding for a specific application. In this publication, we demonstrate the full complexity of the model and its modularity - including the stochastic handling of failure probability, the analyses of landuse change, and xposure. Likely, many applications would focus on more simple implementations (e.g., using a pre-derived susceptibility map), or using static rainfall thresholds. For that, it would then be quite useful to explore parameter sensitivity in more detail.
- Line 357: How sensitive are your conclusions to your choice of gamma? Larson 2010 found that small changes in gamma led to large differences in estimated landslide volumes, and that gamma changes with hillslope material type.
We did not yet include any analysis of gamma.
Many thanks again for those very pertinent comments, which we will aim to integrate in the MS.
Citation: https://doi.org/10.5194/egusphere-2025-3733-AC1
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AC1: 'Reply on CC1', Rafael Schmitt, 31 Oct 2025
reply
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CC2: 'Comment on egusphere-2025-3733', Max Sutton, 25 Oct 2025
reply
To the authors, thanks for a thought provoking paper! I have a few questions and comments after reading.
- Have you looked into the USGS Grfin tools as a model for connectivity? (Reid et al 2025 https://doi.org/10.3133/tm14A3). It is meant for highly mobile mass movements such as debris flows. If it applies in your case, it can automatically estimate connected areas, runout zones, etc and may be of interest to you.
- Another way of looking at connectivity is to use slope units (eg Woodard et al. 2024, https://doi.org/10.5194/nhess-24-1-2024) instead of a pixel/grid based approach. Using slope units has been shown to be effective at estimating susceptible area, partially because it inherently accounts for runout zones. It also provides a convenient division for aggregating other parameters such as slope over a vulnerable area. In the context of this paper, you probably would not use slope units for connectivity because it would change your volume and thus runout assessments, but I'm curious if it would be a good way to aggregate/interpolate soil or hydrology parameters?
- At what scales is COHESION effective? (Ie, local/basin level, regional level, global level? Similarly, especially given the coarseness of some of your inputs, have you tested if there is a minimum pixel size for which COHESION is reasonable?
- Your model is quite complex. While I appreciate the authors’ consideration of the many factors that impact landslide susceptibility, are all of these inputs relevant at this scale? Have you considered a factor analysis to identify the most important inputs and if you were to make the model simpler, how performance would change? I wonder if performance would actually increase, given how difficult it is to actually measure many of your input parameters? Your sensitivity analysis begins to answer these questions, but a factor analysis or similar technique could be a rigorous way to shed light on relative importance of parameters without having to do separate analyses for individual parameters.
- Line 357: How sensitive are your conclusions to your choice of gamma? Larson 2010 found that small changes in gamma led to large differences in estimated landslide volumes, and that gamma changes with hillslope material type.
Overall, I enjoyed the paper and anticipate sending it to some of my colleagues. Landslide runout is an important consideration in hazard assessment, especially since infrastructure tends to be on flatter runout zones versus steep slopes, and I believe this is an important direction for landslide hazard assessment to head in.
Citation: https://doi.org/10.5194/egusphere-2025-3733-CC2
Model code and software
COHESION slope stability model Rafael Schmitt and Shikshita Bhandari https://zenodo.org/records/16595396
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To the authors, thanks for a thought provoking paper! I have a few questions and comments after reading.
Overall, I enjoyed the paper and anticipate sending it to some of my colleagues. Landslide runout is an important consideration in hazard assessment, especially since infrastructure tends to be on flatter runout zones versus steep slopes, and I believe this is an important direction for landslide hazard assessment to head in.