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A distributed physically based model to predict timing and spatial distribution of
rainfall-induced shallow landslides.
Grigorios G. Anagnostopoulos and Paolo Burlando

Institute of Environmental Engineering, ETH Zurich, Switzerland
correspondence: anagnostopoulos@ifu.baug.ethz.ch

1. Introduction

3. Geotechnical Component

5. Test case: Napf catchment

A distributed physically based model, raster-based
and continuous in space and time, was developed in order to investigate the interactions between surface and subsurface hydrology and shallow landslides initiation. In this effort emphasis is
given to the modelling of the temporal evolution of
hydrological processes and their triggering effects
to soil slip occurrences.

A multidimensional limit equilibrium analysis is
utilized for the computation of the stability of every cell by taking into account the basic principles
of unsaturated soil mechanics.

Abstract No: NH 31B-1548

Napf catchment is located in Kanton Bern, Switzerland. It
spans over an area of 2, 5 km2 , 48 % of which is forested. A
3-hour precipitation event on 15-16 July 2002 caused many
soil slips.

2. Hydrological Component
Particular weight is given to the modeling of hydrological processes in order to investigate the hydrological triggering mechanisms and the importance of continuous modeling of water balance to
detect timing and location of soil slip occurrences.
• Evapotranspiration is computed using the
Priestley-Taylor method.

• The failure surface is assumed to be planar
and plastic limit equilibrium conditions are
considered.
• At the upslope and downslope faces we assume that active and passive stresses respectively are developed.

A 3x3 m DEM is available and the soil map of Switzerland is used for the identification of the soil classes present in
the catchment. As meteorological input the historical record
of the Napf station was used, which is located 5 km north of
the cathcment.

• At the lateral faces earth pressures at rest develop and the shear resistance is also taken
into account.
• Root cohesion is considered at both lateral
faces and at the base of the column.
x
y

• Root water uptake is taken into account.
• Surface run-off is routed using the kinematic
wave approach.

z

σv
Ka σv

(i-1,j)
qsurface

Hponded

(i,j-1)

(i,j-1,0)

σv

Fa

Active stresses

P

F0

Kp σv

φ
φ
W

Τlat

0

K0 σv

Τlat

φ

φ
F'0

Fp
Passive stresses

Ν

Τ

The output of our model is tested against a state-of-the-art
model (TRIGRS, Baum and Godt 2010) and against the inventory of observed landslides.

θ

• The model correctly captures most of the observed landslides (True Positive Rate: 43,2% against 23,5% of TRIGRS).

4. Soil depth modeling
Soil depth is one of the most significant parameters
controlling the factor of safety (FS) , especially for
depths of less than 1.5 m, within which small variations produce very rapid changes in the FS. The
approach of Pelletier et al. is to solve numerically
the steady-state form of the landscape evolution.

• It reduces the overestimation of landsliding cells, which
is a main artifact of most of the existing models (sliding
area: 6.5% against 13% of TRIGRS).
• The continuous modeling of soil moisture and the inclusion of many feedback mechanisms improved the predictive ability of the model. The timing is also affected compared to TRIGRS mainly due to the more detailed hydrological component.

(i,j)
qinf

(i,j-1,1)

(i,j+1)

(i,j-1,2)
(i,j+1,0)
(i,j+1,1)
(i,j-1,k)

x

σv

A

• The 3-D flow of water through soil and the
resulting water balance is considered, by taking into account both saturated and unsaturated conditions
• Soil hydraulic hysteresis is also included because it can be crucial for the continuous simulation of soil water content during storm
and inter-storm periods.

z

qinflow

(i,j+1,2)

qoutflow

(i,j+1,k)

(i,j-1,n)

(i,j+1,n)

@h
@t

p
ph
⇢b
2 e (h0 1+|rz|2 +
= ⇢s P0 1 + |rz|
⇣
⌘
hrz
D3 r 1 (|rz|/Sc )2

The parameters were determined by searching
through the parameter space the parameter set
that minimizes the root-mean-square difference between predicted and measured soil depth data.

References
[1]

G.G. Anagnostopoulos, P. Burlando, (2011). Object-oriented computational framework for the simulation of variably saturated flow, using a reduced complexity model, Submitted in Environmental Modelling
& Software

[2]

R.L. Baum, J.W. Godt, (2010). Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration. Journal of Geophysical Research, Vol 115

[3]

J. Pelletier, C. Rasmussen (2009). Geomorphically based predictive mapping of soil thickness in upland watersheds. Water Resources Research, Vol 45

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A distributed physically based model to predict timing and spatial distribution of rainfall-induced shallow landslides

  • 1. A distributed physically based model to predict timing and spatial distribution of rainfall-induced shallow landslides. Grigorios G. Anagnostopoulos and Paolo Burlando Institute of Environmental Engineering, ETH Zurich, Switzerland correspondence: anagnostopoulos@ifu.baug.ethz.ch 1. Introduction 3. Geotechnical Component 5. Test case: Napf catchment A distributed physically based model, raster-based and continuous in space and time, was developed in order to investigate the interactions between surface and subsurface hydrology and shallow landslides initiation. In this effort emphasis is given to the modelling of the temporal evolution of hydrological processes and their triggering effects to soil slip occurrences. A multidimensional limit equilibrium analysis is utilized for the computation of the stability of every cell by taking into account the basic principles of unsaturated soil mechanics. Abstract No: NH 31B-1548 Napf catchment is located in Kanton Bern, Switzerland. It spans over an area of 2, 5 km2 , 48 % of which is forested. A 3-hour precipitation event on 15-16 July 2002 caused many soil slips. 2. Hydrological Component Particular weight is given to the modeling of hydrological processes in order to investigate the hydrological triggering mechanisms and the importance of continuous modeling of water balance to detect timing and location of soil slip occurrences. • Evapotranspiration is computed using the Priestley-Taylor method. • The failure surface is assumed to be planar and plastic limit equilibrium conditions are considered. • At the upslope and downslope faces we assume that active and passive stresses respectively are developed. A 3x3 m DEM is available and the soil map of Switzerland is used for the identification of the soil classes present in the catchment. As meteorological input the historical record of the Napf station was used, which is located 5 km north of the cathcment. • At the lateral faces earth pressures at rest develop and the shear resistance is also taken into account. • Root cohesion is considered at both lateral faces and at the base of the column. x y • Root water uptake is taken into account. • Surface run-off is routed using the kinematic wave approach. z σv Ka σv (i-1,j) qsurface Hponded (i,j-1) (i,j-1,0) σv Fa Active stresses P F0 Kp σv φ φ W Τlat 0 K0 σv Τlat φ φ F'0 Fp Passive stresses Ν Τ The output of our model is tested against a state-of-the-art model (TRIGRS, Baum and Godt 2010) and against the inventory of observed landslides. θ • The model correctly captures most of the observed landslides (True Positive Rate: 43,2% against 23,5% of TRIGRS). 4. Soil depth modeling Soil depth is one of the most significant parameters controlling the factor of safety (FS) , especially for depths of less than 1.5 m, within which small variations produce very rapid changes in the FS. The approach of Pelletier et al. is to solve numerically the steady-state form of the landscape evolution. • It reduces the overestimation of landsliding cells, which is a main artifact of most of the existing models (sliding area: 6.5% against 13% of TRIGRS). • The continuous modeling of soil moisture and the inclusion of many feedback mechanisms improved the predictive ability of the model. The timing is also affected compared to TRIGRS mainly due to the more detailed hydrological component. (i,j) qinf (i,j-1,1) (i,j+1) (i,j-1,2) (i,j+1,0) (i,j+1,1) (i,j-1,k) x σv A • The 3-D flow of water through soil and the resulting water balance is considered, by taking into account both saturated and unsaturated conditions • Soil hydraulic hysteresis is also included because it can be crucial for the continuous simulation of soil water content during storm and inter-storm periods. z qinflow (i,j+1,2) qoutflow (i,j+1,k) (i,j-1,n) (i,j+1,n) @h @t p ph ⇢b 2 e (h0 1+|rz|2 + = ⇢s P0 1 + |rz| ⇣ ⌘ hrz D3 r 1 (|rz|/Sc )2 The parameters were determined by searching through the parameter space the parameter set that minimizes the root-mean-square difference between predicted and measured soil depth data. References [1] G.G. Anagnostopoulos, P. Burlando, (2011). Object-oriented computational framework for the simulation of variably saturated flow, using a reduced complexity model, Submitted in Environmental Modelling & Software [2] R.L. Baum, J.W. Godt, (2010). Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration. Journal of Geophysical Research, Vol 115 [3] J. Pelletier, C. Rasmussen (2009). Geomorphically based predictive mapping of soil thickness in upland watersheds. Water Resources Research, Vol 45