Spatial characterization of root reinforcement at stand scale: Theory and case study

Schwarz, Massimiliano; Cohen, Denis; Or, Dani

doi:10.1016/j.geomorph.2012.05.020

Cited by 116 publications

(115 citation statements)

References 45 publications

(79 reference statements)

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“…Parameter maps for the effects of vegetation accounting for these attributes could further improve the model's predictive performance (e.g. Schwarz et al, 2010Schwarz et al, , 2012.…”

Section: Discussionmentioning

confidence: 99%

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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“…Parameter maps for the effects of vegetation accounting for these attributes could further improve the model's predictive performance (e.g. Schwarz et al, 2010Schwarz et al, , 2012.…”

Section: Discussionmentioning

confidence: 99%

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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“…The dimensions of z max and r max are determined by the root system type and define the shape of the paraboloid. Environmental factors that normally influence the distribution of the roots (Sidle and Ochiai 2006;Ghestem et al 2011;Schwarz et al 2012) are not considered. According to findings from in-field root excavations (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Based on the results of this study, we highlight that the spatial distribution of cohesion values within the approximated root system should be favoured compared to uniform cohesion values. For example, Bischetti et al (2005), Bischetti et al (2009), Thomas and Pollen-Bankhead (2010), Schwarz et al (2010) and Schwarz et al (2012) addressed the spatial distribution of roots, root tensile strength and thus dispersal of root-soil cohesion values. However, the spatial diversity of cohesion values within root systems remains to be a vague physical input which is hard to parameterise.…”

Section: Separated Vs Mixed Standsmentioning

confidence: 99%

“…The reinforcement potential of plant roots on the shallow subsurface and its importance as a model input parameter in physically based slope stability models was addressed in many previous studies (Sidle et al 1985;Greenway 1987;Sidle 1991;Sidle and Ochiai 2006;Stokes et al 2008;Fan and Chen 2010;Ghestem et al 2011;Schwarz et al 2012). Tree roots increase the soil strength during shear due to certain root characteristics, such as areal density, root distortion and root tensile strength (Wu et al 1979).…”

Section: Introductionmentioning

confidence: 99%

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Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment

Schmaltz

Mergili

2018

Landslides

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Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment Abstract Root systems of trees reinforce the underlying soil in hillslope environments and therefore potentially increase slope stability. So far, the influence of root systems is disregarded in Geographic Information System (GIS) models that calculate slope stability along distinct failure plane. In this study, we analyse the impact of different root system compositions and densities on slope stability conditions computed by a GIS-based slip surface model. We apply the 2.5D slip surface model r.slope.stability to 23 root system scenarios imposed on pyramidoid-shaped elements of a generic landscape. Shallow, taproot and mixed root systems are approximated by paraboloids and different stand and patch densities are considered. The slope failure probability (P f ) is derived for each raster cell of the generic landscape, considering the reinforcement through root cohesion. Average and standard deviation of P f are analysed for each scenario. As expected, the r.slope.stability yields the highest values of P f for the scenario without roots. In contrast, homogeneous stands with taproot or mixed root systems yield the lowest values of P f . P f generally decreases with increasing stand density, whereby stand density appears to exert a more pronounced influence on P f than patch density. For patchy stands, P f increases with a decreasing size of the tested slip surfaces. The patterns yielded by the computational experiments are largely in line with the results of previous studies. This approach provides an innovative and simple strategy to approximate the additional cohesion supplied by root systems and thereby considers various compositions of forest stands in 2.5D slip surface models. Our findings will be useful for developing strategies towards appropriately parameterising root reinforcement in real-world slope stability modelling campaigns.

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“…RAR investigated using a vertical trench profile wall method, set out in other studies (Bischetti et al, , 2009Greenwood et al, 2006;Abdi et al, 2010a, b;Ji et al, 2012;Schwarz et al, 2012). Four trees of A. mangium were selected along East-West highway, then one trench with a distance of 1 meter was dug (Abdi et al, 2010b).…”

Section: Root Area Ratiomentioning

confidence: 99%

Investigation of Root Distribution and Tensile Strength of Acacia mangium Willd (Fabaceae) in the Rainforest

Lateh¹,

Avani²,

Ghassem³

2014

GJBS

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The purpose of this study is to complement the existing body of knowledge on the root system of tropical plants and their contribution to slope stability. For this reason, four trees of Acacia mangium selected as randomly in rainforest along East-West Highway, Malaysia. Root area ratio (RAR) and tensile strength (Tr) data were collected and analyzed. RAR data were measured with the whole root system and Tr were measured by individual roots. The results showed that there is a power law equation between root diameter and Tr, with an average of 44.11 Mpa (in average root diameter of 0.9mm). There was a wide variation in the RAR in relation to soil depth. Generally, RAR reduced with the increasing soil depth. Similarly, additional cohesion by plant roots conducted by Tr and RAR. In conclusion, in order to assess soil reinforcement by vegetation roots, root distribution and tensile strength data with soil depth in different vegetation and sites is necessary.

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Spatial characterization of root reinforcement at stand scale: Theory and case study

Cited by 116 publications

References 45 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

Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment

Investigation of Root Distribution and Tensile Strength of Acacia mangium Willd (Fabaceae) in the Rainforest

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