Modeling the contribution of trees to shallow landslide development in a steep, forested watershed

Kim, Dong-Yeob; Im, Sangjun; Lee, Chang-Woo; Woo, Choongshik

doi:10.1016/j.ecoleng.2013.05.003

Cited by 42 publications

(35 citation statements)

References 27 publications

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“…The details of the infiltration models have been presented in previous studies (e.g. Baum et al, 2010;Iverson, 2000;Kim et al, 2013;Park et al, 2013;Salciarini et al, 2006). The result of both infiltration models is the evolution of pore pressures with depth and time as a response to the infiltration of timevarying precipitation.…”

Section: Trigrs 20 Modelmentioning

confidence: 99%

“…The three parameters are allowed to vary spatially and can be prepared as parameter maps. Table 2 shows the required parameters and their values considered in previous studies with the original TRIGRS model (versions 1.0 and 2.0) and a revised form (Kim et al, 2013). In the cited studies, the time-varying precipitation intensities are derived from meteorological stations in or near the study area.…”

Section: Trigrs 20 Modelmentioning

confidence: 99%

“…Pore pressures ψ(d, t) are passed on to the infinite slope stability model relating driving to resisting stresses (FOS): However, the original version of TRIGRS 2.0 does not account for effects of vegetation. Kim et al (2013) extended the model to include vegetation effects on hydrology and slope stability. They conclude that root reinforcement and tree surcharge can affect slope stability, while interception has only minor effects during landslide-triggering rainfall events.…”

Section: Trigrs 20 Modelmentioning

confidence: 99%

“…Kim et al, 2013), a linear decrease of c r with depth up to a given rooting depth d r (m) was assumed, accounting for the distribution of roots with depth as observed in other studies (e.g. Bischetti et al, 2005Bischetti et al, , 2009.…”

Section: Trigrs 20 Modelmentioning

confidence: 99%

“…They conclude that root reinforcement and tree surcharge can affect slope stability, while interception has only minor effects during landslide-triggering rainfall events. Following Kim et al (2013), lateral root cohesion c r (Pa) and tree surcharge s t (Pa) were added to Eq. (2):…”

Section: Trigrs 20 Modelmentioning

confidence: 99%

See 4 more Smart Citations

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Zieher

Rutzinger

Schneider‐Muntau

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 "behavioural model runs" with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

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Section: Trigrs 20 Modelmentioning

confidence: 99%

Section: Trigrs 20 Modelmentioning

confidence: 99%

Section: Trigrs 20 Modelmentioning

confidence: 99%

Section: Trigrs 20 Modelmentioning

confidence: 99%

Section: Trigrs 20 Modelmentioning

confidence: 99%

See 3 more Smart Citations

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Zieher

Rutzinger

Schneider‐Muntau

et al. 2017

Nat. Hazards Earth Syst. Sci.

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How does forest structure affect root reinforcement and susceptibility to shallow landslides?

Moos

Bebi²,

Graf³

et al. 2016

Earth Surf Processes Landf

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Forests can decrease the risk of shallow landslides by mechanically reinforcing the soil and positively influencing its water balance. However, little is known about the effect of different forest structures on slope stability. In the study area in St Antönien, Switzerland, we applied statistical prediction models and a physically‐based model for spatial distribution of root reinforcement in order to quantify the influence of forest structure on slope stability. We designed a generalized linear regression model and a random forest model including variables describing forest structure along with terrain parameters for a set of landslide and control points facing similar slope angle and tree coverage. The root distribution measured at regular distances from seven trees in the same study area was used to calibrate a root distribution model. The root reinforcement was calculated as a function of tree dimension and distance from tree with the root bundle model (RBMw). Based on the modelled values of root reinforcement, we introduced a proxy‐variable for root reinforcement of the nearest tree using a gamma distribution. The results of the statistical analysis show that variables related to forest structure significantly influence landslide susceptibility along with terrain parameters. Significant effects were found for gap length, the distance to the nearest trees and the proxy‐variable for root reinforcement of the nearest tree. Gaps longer than 20 m critically increased the susceptibility to landslides. Root reinforcement decreased with increasing distance from trees and is smaller in landslide plots compared to control plots. Furthermore, the influence of forest structure strongly depends on geomorphological and hydrological conditions. Our results enhance the quantitative knowledge about the influence of forest structure on root reinforcement and landslide susceptibility and support existing management recommendations for protection against gravitational natural hazards. Copyright © 2015 John Wiley & Sons, Ltd.

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Relationship between tree height and landslide characteristics obtained by GIS assessment

Evans¹,

Imaizumi

Ohsaka

et al. 2020

Earth Surf Processes Landf

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Landslides in forested landscapes have far-reaching implications, beyond that of just destroying the forest itself, sometimes initiating large-scale sediment disasters. Although vegetation increases slope stability through its root network, it is hard to evaluate its contribution to slope stability over a wide area. In this study, the relationship between tree height and landslide characteristics in the Ikawa catchment, central Japan, was investigated to develop a method for evaluating the effects of forest cover on slope stability over a regional extent. Catchment-wide tree height was obtained using airborne LiDAR point cloud data and used in conjunction with the root depth profile, measured for trees of various height by digging trenches. Root tensile strength per unit area of soil was calculated from individual root diameters and empirical power law equations on the relationship between root diameter and root tensile force in order to better understand the effect that tree height has on slope stability. Landslide density in the Ikawa catchment shows that landslides occur more frequently in forests with shorter trees, with occurrence decreasing as tree height increases. This is likely due to the stabilizing features of larger trees having a greater network of roots, which is supported by the general increase in total root area and the deeper penetration of root biomass into the soil as the height of trees surveyed increases. Landslide density was not solely affected by tree height, but also by slope gradient and plane curvature. Decreasing landslide occurrence and landslide area as tree height increases suggests that slope stability increases with tree height, while the random distribution of results when comparing landslide depth to tree height suggests that while tree height has an impact on relative slope stability, the landslide failure depth is independent of tree height, and thus controlled by other factors.

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Modeling the contribution of trees to shallow landslide development in a steep, forested watershed

Cited by 42 publications

References 27 publications

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Sensitivity analysis and calibration of a dynamic physically based slope stability model

How does forest structure affect root reinforcement and susceptibility to shallow landslides?

Relationship between tree height and landslide characteristics obtained by GIS assessment

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