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Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza
149
DOI: 10.4408/IJEGE.2011-03.B-018
THE ROLE OF GRID CELL SIZE, FLOW ROUTING ALGOLITHM
AND SPATIAL VARIABILITY OF SOIL DEPTH ON SHALLOW
LANDSLIDE PREDICTION
T. UCHIDA
(*)
, K. TAMUR
(*)
& K. AKIYAMA
(*)
(*)
Public Works Research Institute, Ibaraki, Japan
Okimura, several physically based models predicting
shallow landslide susceptibility have been developed,
and such models are potentially a powerful way to
evaluate the spatial pattern of shallow landslide sus-
ceptibility (e.g., o
kimuRa
et alii, 1985; H
iRamatsu
et
alii, 1990; m
ontGomeRy
& d
ietRiCH
, 1994; w
u
& s
i
-
dle
, 1995; P
aCk
et alii, 1998; k
osuGi
et alii, 2002;
R
osso
et alii, 2006; t
aRolli
& t
aRboton
, 2006;
T
alebi
et alii, 2007).
These models were developed from models of
slope stability and subsurface water flow. One of the
simplest approaches combines an infinite slope stabil-
ity analysis with a steady-state shallow subsurface
flow model (e.g., o
kimuRa
et alii, 1985; m
ontGomeRy
& d
ietRiCH
, 1994; P
aCk
et alii, 1998). Recently, more
complex processes of shallow landslide occurrence
have been incorporated into physically based models
predicting the spatial patterns of shallow landslide
susceptibility (e.g., H
iRamatsu
et alii, 1990; w
u
&
s
idle
, 1995; R
osso
et alii, 2006; t
alebi
et alii, 2007).
For example, w
u
& s
idle
(1995) and R
osso
et alii
(2006) incorporated temporal change of lateral sub-
surface flow into their models, and H
iRamatsu
et alii
(1990) and k
osuGi
et alii (2002) considered the ef-
fects of water flow and storage in vadose zones, thus
improving the precision of landslide susceptibility
prediction by the models.
Although complex processes can be described
by numerical simulations, such models require that
the spatial patterns of many parameters be specified.
ABSTRACT
To assess the spatial pattern of landslide suscepti-
bility, we linked a simple hydrological model and an
infinite slope stability model to predict the spatial pat-
tern of critical steady-state rainfall required to cause
slope instability. We studied a headwater in the Aratani
River basin, western Japan. To clarify soil depth spa-
tial patterns, we measured soil depth on the hillslope
by using knocking pole tests. We compared two wide-
ly used procedures for determining local slope angle
and upslope contributing area: a single-flow-direction
procedure and an algorithm based on proportioning
flow into two downslope pixels. Further, we examined
the role of the analysis grid cell size on the precision
of landslide prediction. We showed that by choosing
an optimal grid cell size and using an optimal proce-
dure for calculation of the upslope contributing area
and by performing a detailed field survey to determine
soil depth, the precision of landslide susceptibility as-
sessment could be remarkably improved
K
ey
words
: shallow landslide, soil depth, physically based
model, grid cell size, procedure for calculation of topogra-
phic indices
INTRODUCTION
Shallow landslides are one of the key processes
that cause debris flows. Therefore, predicting where
landslides are likely to occur is key to preventing
debris flow disasters. Since the pioneering work of
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T. UCHIDA, k. TAMURA, & k. AkIYAMA
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1997). However, most of these previous studies did
not examine in detail the optimal grid cell size or ups-
lope contributing area calculation to use for assessing
landslide susceptibility.
We propose three hypotheses, i.e., that the preci-
sion of shallow landslide susceptibility assessment by
a physically based model could be improved by (1)
choosing the optimal grid cell size; (2) using the opti-
mal method for calculating topographic index parame-
ter values; and (3) inputting more detailed information
about (a) soil depth and (b) bedrock surface topogra-
phy. Here, we examine these hypotheses by comparing
model results obtained by using grid cell sizes from 5
to 25 m and between two different methods for cal-
culating topographic indexes, and by conducting a de-
tailed field survey to obtain data on soil depth and bed-
rock surface topography and then comparing results
obtained by using bedrock surface topography with
those obtained by using ground surface topography.
STUDY SITE
We conducted a detailed field investigation of a
hillslope site in the upper Aratani River basin in the
western Hiroshima Mountains, western Japan (Fig.
1). The region is humid and temperate: the mean an-
nual precipitation in this region is around 1700 mm,
and the mean temperature is around 15 °C. The site is
deeply incised and dominated by hillslopes, with no
riparian area. Slope angles range from 20 to 45°, and
However, detailed below-ground information about
the values of such parameters as bedrock topography,
soil depth, soil mechanical parameters, and soil hy-
draulic parameters, have generally been lacking. So,
it can be thought that even though the complex nu-
merical simulation models, most of processes can not
fully described, since the parameters controls these
processes cannot be measured. w
Hile
, o
kimuRa
et alii
(1985) and m
ontGomeRy
& d
ietRiCH
(1994) indicated
that the simple model combines an infinite slope sta-
bility analysis with a steady-state shallow subsurface
flow was used as a reasonable approximation of a
shallow landslide occurrence.
In most simulations for predicting landslide loca-
tions, many parameter values have been determined
by back-calculations or by calibration against past
observed events, which often were represented by
mean values of a limited number of observed data. For
example, although soil depth exerts a first-order con-
trol on shallow landslide potential on steep hillslopes
(e.g., d
ietRiCH
et alii, 1995; i
ida
, 1999), landslide pre-
diction studies have often interpolated soil depth from
a limited number of field measurements (e.g., H
iRam
-
atsu
et alii, 1990; m
ontGomeRy
& d
ietRiCH
, 1994; w
u
& s
idle
, 1995; t
aRolli
& t
aRboton
, 2006). Further,
recent hillslope hydrology studies have shown that on
steep, wet, soil-mantled hillslopes, the bedrock sur-
face and not the ground surface topography may be
the most important surface controlling the routing of
mobile water laterally downslope (e.g., f
ReeR
et alii,
2002). Moreover, it is likely that the landslide slip
surface is also strongly controlled by bedrock surface
topography. Most landslide prediction studies have
used a ground surface digital elevation model (DEM)
to calculate values of parameters used as topographic
indexes, such as local slope angle and upslope con-
tributing area, because information about the bedrock
surface topography is often lacking.
Moreover, the method and grid cell size used for
calculation of topographic index values can be expect-
ed to affect the precision of shallow landslide assess-
ment. t
aRolli
& t
aRboton
(2006) have recently dem-
onstrated the effect of grid cell size on the precision of
shallow landslide assessment and the optimal grid cell
size in Miozza catchment in north-eastern Italy was 10
m. In addition, researchers have proposed a variety of
procedures as optimal for calculation of upslope con-
tributing area by hydrological models (e.g., t
aRboton
,
Fig. 1 - Topography and soil depth of the Aratani hillslo-
pe study site
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THE ROLE OF GRID CELL SIZE, FLOW ROUTING ALGOLITHM AND SPATIAL VARIABILITY OF SOIL DEPTH
ON SHALLOW LANDSLIDE PREDICTION
Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza
151
where c is effective cohesion, Φ is the friction
angle of the soil mantle, I is the angle of the bedrock
surface, γ and γ
w
are the specific weights of soil mantle
and water, respectively, and h and hs are the soil and
saturated water depths, respectively. We assumed that γ
can be described by the equation
where γ
s
and γ
t
are the specific weights of saturated and
unsaturated soil, respectively.
According to Darcy’s law, the saturated water
depth, hs, at a given steady-state rainfall intensity, r, can
be described as
where A is the contributing area of the unit contour
length, and k
s
is the saturated hydraulic conductivity
of the soil mantle. Therefore, if h ≤ h , then the critical
steady-state rainfall required to cause shallow landslid-
ing, rc, can be determined with equations 1 through 3
by setting FS = 1, as follows:
FIELD MEASUREMENTS
We measured the surface topography using LiDAR
(light detection and ranging) data and developed a 1-m
DEM. We also mapped the edges of the shallow land-
slides that occurred in June 1999 during a field survey
(Fig. 1). Soil depth on the Aratani hillslope was meas-
ured with a cone penetrometer (knocking pole test)
with a cone diameter of 25 mm, a weight of 5 kg, and
a fall distance of 50 cm. Nd represents the number of
blows required for 10 cm of penetration. Knocking pole
tests were conducted at intervals of 10–15 m in 2005.
In all, we conducted the tests at 181 points, 16 of which
were on the shallow landslide scars (Fig. 1).
The knocking pole test results on the shallow
landslide scars where weathered bedrock was exposed
showed that the Nd value of weathered bedrock was
around 20, indicating that a layer with an Nd of less
than 20 can potentially fail, causing a shallow land-
slide. Therefore, in this study we assumed that Nd val-
ues greater than 20 indicated bedrock and we defined
the depth of the layer with Nd < 20 as the soil depth.
slope lengths from 30 to 100 m. The area is underlain
by Hiroshima Granite, and the soils are sandy. The site
is covered by secondary forest, predominately Pinus
densiflora. Specific weights of saturated (17.9 kN/m
3
)
and unsaturated (15.1 kN/m
3
) soil and the soil friction
angle (36.1°) of the Aratani hillslope were previously
measured in the laboratory in five undisturbed 100
cm
3
field cores (u
CHida
et alii, 2009).
On 29 June 1999, more than 7000 shallow land-
slides occurred in the western Hiroshima Mountains,
and these landslides triggered many debris flows. Four
of these shallow landslides occurred in the study area.
The landslide scars range from 9 to 15 m in width. The
total rainfall amount and maximum rainfall intensity
of the triggering event were 417 mm and 63 mm/h,
respectively, measured at Uokiri dam (1.4 km north
of Aratani).
Hydrometric observations of groundwater level,
soil pore water pressure, soil water content, and stream
flow rate have been conducted on the hillslope since
2003(u
CHida
et alii, 2009). These hydrometric obser-
vations showed that except for the driest period, the
surface water flow can be observed at the lower end
of the catchment. Stream water was sensitive to rain-
fall intensities during storm runoff. Groundwater level
and soil pore water pressure measurements showed
that within most of the hillslope area, the soil-bedrock
interface was not commonly saturated between events.
Several monitored large storms produced saturation at
the soil-bedrock interface and the soil pore pressures
were sensitive to the rainfall intensity.
METHOD
THEORY
We calculated the critical steady-state rainfall re-
quired to cause shallow landsliding following the meth-
ods of o
kimuRa
et alii (1985) and m
ontGomeRy
& d
i
-
etRiCH
(1994). An infinite planar slope can be used as a
good approximation of a hillslope when the soil depth
is small with respect to the length of the slope, as in the
case of our hillslope (e.g., o
kimuRa
et alii, 1985; m
ont
-
GomeRy
& d
ietRiCH
, 1994; w
u
& s
idle
, 1995; R
osso
et alii, 2006). Thus, if saturated depth is less than soil
depth, an infinite slope stability analysis can be used to
compute the factor of safety (FS) as follows:
(1)
(2)
(3)
(4)
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T. UCHIDA, k. TAMURA, & k. AkIYAMA
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5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011
calculate rc in each case.
Various procedures are available for calculating
topographic indexes. Here, we compared two widely
used procedures for determining local slope angle and
upslope contributing area, a single-flow-direction pro-
cedure (hereafter referred to as D8; o’C
allaGHan
&
m
aRk
, 1984) and an algorithm based on proportioning
flow between two downslope pixels (hereafter referred
to as D-infinity; t
aRboton
, 1997). In D8, the flow direc-
tion is computed by assigning flow from each pixel to
one of its eight neighbours, either adjacent or diagonal,
in the direction with steepest downward slope. While,
D-infinity is based on representing flow direction as a
single angle taken as the steepest downward slope on
the eight triangular facets centred at each grid point.
Upslope area is then calculated by proportioning flow
between two downslope pixels according to how close
this flow direction is to the direct angle to the downs-
lope pixel (t
aRboton
, 1997).
SOIL MANTLE PARAMETERS
The friction angle and the specific weights of
saturated and unsaturated soil were measured in un-
disturbed soil samples. Soil cohesion was estimated
by using data on both soil depth and topography. We
assumed that if the soil mantle was unsaturated (h
s
=
0), the factor of safety of most grid cells would be
larger than 1.0. Therefore, we used equations 1 and
2 to calculate the minimum cohesion, including root
strength, required for all grid cells to remain stable,
excepting one grid cell where the soil was very deep
and the local slope was very steep. We also back-cal-
culated cohesion for each of our test cases (Tab. 1, see
below) as the minimum cohesion required for all grid
cells to remain stable. The calculated cohesion data
are shown in Table 2.
Effective hillslope saturated conductivity (trans-
missivity) is often different from soil saturated con-
a
ndeRson
et alii (1997) and u
CHida
et alii (2003) de-
fined bedrock as a layer that cannot be penetrated with
a hand auger. The depth of the layer with Nd < 20 was
almost the same as the depth of the layer that could
be penetrated with a hand auger, so our definition is
consistent with that of a
ndeRson
et alii (1997) and
u
CHida
et alii(2003).
DATA PREPARATION
TOPOGRAPHY
Since we conducted our field survey after the
shallow landslides had occurred, we interpolated the
pre-landslide surface topography across the landslide
scars on the 1-m DEM. In addition, we made the 5-m
DEM of the ground surface from the 1-m DEM made
using the LiDAR data. Then, we used a Kriging inter-
polation scheme to make a 5-m DEM of the bedrock
surface from a 5-m ground surface DEM and the soil
depth data. Thus, we calculated slope angle and ups-
lope drainage area data sets for both the ground surface
topography and the bedrock surface topography. Also,
we compiled a 5-m-grid soil depth data set by subtract-
ing the bedrock surface elevation from the ground sur-
face elevation at each grid point.
To determine the most effective DEM resolution
for assessing shallow landsliding susceptibility at this
study site, we made DEMs of the bedrock surface to-
pography at three additional grid cell sizes (10, 15, and
25 m) using a Kriging interpolation scheme. We made
4, 9, and 25 datasets for the 10-, 15-, and 25-m DEMs,
respectively (see an example of 10-m DEM in Fig. 2)
so that the same number of elevation data was used to
Fig. 2 - Schematic illustration of grid cell creation. Cir-
cle represents center of grid which has elevation
data. Black and white circles in the lower panel
represent used and non-used data, respectively.
Bold and solid lines in the lower panel represent
created 10-m grid and original 5-m grid, respec-
tively
Tab 1 - Simulated case
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THE ROLE OF GRID CELL SIZE, FLOW ROUTING ALGOLITHM AND SPATIAL VARIABILITY OF SOIL DEPTH
ON SHALLOW LANDSLIDE PREDICTION
Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza
153
procedure for topographic indexes in Cases 1 and 5.
We compared differences between detailed and less
detailed soil data and between use of the ground sur-
face instead of the bedrock surface in Cases 1 and 6–8.
We used rc, the critical steady-state rainfall amount, as
an index for assessing shallow landslide susceptibil-
ity. We interpreted grid cells with lower r
c
values to
be more susceptible to shallow landsliding, and those
with higher rc to be more stable, because the higher
the rc value was, the less frequent would be the oc-
currence of a rainfall event sufficient to cause shallow
landsliding.
To test model performance, we compared simu-
lated rc values in observed landslide areas by calcu-
lating the “landslide ratio,” as follow. If the centre
of a given grid cell was inside an observed landslide
polygon, we defined the grid cell as a landslide cell.
We defined the ratio of landslide grid cells to all grid
cells as landslide ratio.
By setting h
s
= h in equation 3 and rearranging,
the fully saturated condition can be described as fol-
lows:
Thus, if a grid cell satisfies the equation
even if the soil mantle is fully saturated, FS is larger
than 1.0. Therefore, we interpreted such grid cells as
“unconditionally stable” with regard to shallow land-
sliding, and other grid cells as “potentially unstable.”
Then, we calculated landslide ratio for grid cells
which grouped into “unconditionally stable” and “po-
tentially unstable”, respectively.
Also, we examine the effectiveness of r
c
on pre-
dictions of the landslide susceptibility of potentially
unstable cells. We calculated the landslide ratio and
mean r
c
for a 20-value moving window of grid cells
sorted by r
c
as follow. According to equation 4 the
absolute value of rc is strongly controlled by k
s
. Thus,
first, we sorted potentially unstable cell according to
the simulated r
c
. Then, we picked up 20-grid cells
from the first grid cell which has the smallest rc to
the 20
th
grid cell and calculated the landslide ratio and
mean r
c
for these 20-grid cells. Next, we calculated
ductivity measured in a small undisturbed soil sam-
ple (e.g., b
azemoRe
et alii, 1994; u
CHida
et alii,
2003). Therefore, using the method of b
azemoRe
et
alii (1994) and u
CHida
et alii (2003), we estimated
the saturated conductivity of the soil (k
s
) as the ef-
fective hillslope saturated conductivity by using the
observed total amount of subsurface flow discharge
and the mean soil pore pressure in the lower part of
the hillslope. Such an estimate can be made assuming
the applicability of Darcy’s law
where Q
s
is total amount of subsurface flow discharge,
A is the flux area, and dh/dI is the hydraulic gradient.
We assumed that the Q
s
is equal to the total runoff
from the study site which measured at the lower end
of the site. The hydraulic gradient (dh/dI) of 0.72 was
assumed to be equal to the mean surface gradient in
the hillslope.. The width and the depth of the flux area
were assumed to be equal to the twice of ephemeral
stream length and the observed soil pore water pres-
sure head at the lower part of the hillslope, respective-
ly. Using the hydrometric data of the heaviest rainfall
during the observation period (total rainfall, 236 mm;
maximum rainfall intensity, 53 mm/h), we calculated
Ks of the Aratani hillslope to be 5.2 × 10
-4
m/s, which
is 17 times the averaged soil saturated conductivity
measured in the undisturbed 100 cm
3
soil samples of
u
CHida
et alii (2009)
SIMULATED CASES AND DATA ANALYSIS
We summarize the simulated cases in Table 1, and
the values of the parameters used for the calculations
in Table 2. Briefly, we compared the effects of grid
cell size in Cases 1–4 and the effect of the calculation
Tab 2 - Parameter values
(5)
(6)
(7)
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T. UCHIDA, k. TAMURA, & k. AkIYAMA
154
5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011
the landslide ratio and mean r
c
for the next 20-grid
cells which means grid cells from 2
nd
to 21
st
grids ac-
cording the order of simulated rc. We conducted this
calculation continuously. We interpreted a clear in-
verse relationship between rc and the landslide ratio
and a large difference in the landslide ratio between
potentially unstable cells and unconditionally stable
cells to demonstrate better model performance.
RESULTS
In the Case 1 the landslide ratio of uncondition-
ally stable cells was 0.11, whereas that of potentially
unstable cells was 0.28 (Fig. 3). We observed a clear
inverse relationship between rc and the landslide ratio
(Fig. 4a). These results indicate that the predicted rc
of Case 1 well described landslide locations and the
rainfall amount required to cause shallow landslid-
ing. In the Case 1 simulation result, in at least one
mesh in each landslide scar, rc was smaller than the
observed maximum 1-hour rainfall (63 mm/h) (Fig.
5). Predicted rc values were generally large outside
the landslide area.
When the grid cell size was set to 10 m (Case 2),
the landslide ratio of unconditionally stable cells re-
mained around 0.11, whereas that of potentially unsta-
ble cells was 0.20 (Fig. 3). There was a weak inverse
relationship between rc and the landslide ratio, but the
apparent slope of the relationship was smaller than it
was in Case 1 (Fig. 4b). Further, in Cases 3 (15-m grid
cell) and 4 (25-m grid cell), the landslide ratio differed
between unconditionally stable grid cells (0.10 and
0.11 in Cases 3 and 4, respectively) and potentially
unstable grid cells (0.20 and 0.17, respectively) (Fig.
3). However, r
c
and the landslide ratio did not show a
clear inverse relationship (Figs. 4c and 4d).
When we used D8 to determine the landslide ra-
tios of unconditionally stable and potentially unstable
grid cells were 0.11 and 0.30, respectively (Fig. 3),
almost the same as those of Case 1. However, there
was no clear inverse relationship between rc and the
landslide ratio (Fig. 4e).
When we used a ground surface DEM (Case 6),
instead of a bedrock surface DEM, the predicted rc
was similar to that of Case 1, so there was a large dif-
ference in the landslide ratio between unconditionally
F
ig. 3 - Landslide ratios and relative ratio (the ratio of
potentially unstable landslide ratio to the uncon-
ditionally stable landslide ratio)
Fig. 4 - Relationship between r
c
and landslide ratio
Fig. 5 - Spatial patter of simulated r
c
in Case 1
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THE ROLE OF GRID CELL SIZE, FLOW ROUTING ALGOLITHM AND SPATIAL VARIABILITY OF SOIL DEPTH
ON SHALLOW LANDSLIDE PREDICTION
Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza
155
DEM, were the same, an inverse relationship between
predicted rc and the landslide ratio was not obtained
when D8 was used, whereas when D-infinity was
used, a clear inverse relationship resulted. Both cal-
culation procedures, however, resulted in a large dif-
ference in the landslide ratio between unconditionally
stable and potentially unstable grid cells.
The equation 6 indicates that the conditions of
unconditionally stable and potentially unstable grid
cells are not affected by the upslope contributing area,
which is determined by the local slope angle, soil
depth, and soil properties, whereas equation 4 shows
that the upslope contributing area does affect the pre-
dicted r
c
. These results indicate that the effectiveness
of D-infinity in calculating the upslope contributing
area, as reported by t
aRboton
(1997) and b
oRGa
et
alii (2004), improves the precision with which shal-
low landslide susceptibility can be determined. D8, in
contrast, can reasonably distinguish between uncon-
ditionally stable and potentially unstable grid cells
because the upslope contributing area does not affect
this determination.
BEDROCk SURFACE VERSUS GROUND SUR-
FACE TOPOGRAPHY
Because the bedrock surface topography controls
the routing of subsurface flow (e.g., f
ReeR
et alii,
2002), we expected the precision of landslide predic-
tion to be improved by using the bedrock topography
instead of the ground surface topography. However,
predicted rc calculated using the ground surface DEM
(Case 6) was almost the same as that calculated using
the bedrock surface DEM (Case 1). Thus, the differ-
ence between the ground surface and bedrock surface
DEMs had little effect on the precision of landslide
susceptibility prediction. We think it likely therefore
that the difference between these two DEMs is small
on the Aratani hillslope.
SPATIAL VARBILITY OF SOIL DEPTH
Observed soil depth varied greatly spatially (Fig.
1). When we ignored the spatial variability of soil
depth by using averaged depths, grid cells with lower
rc (<20 mm/h) had higher landslide ratios than grid
cells with higher r
c
or unconditionally stable grid cells.
However, the landslide ratio did not differ between
grid cells with higher r
c
(>20 mm/h) and uncondition-
ally stable grid cells. We showed the relationship be-
stable (0.10) and potentially unstable (0.29) grid cells
(Fig. 3). Further, there was a clear inverse relationship
between rc and the landslide ratio, also similar to Case
1 (Fig. 4f).
When we used averaged soil depth (Cases 7 and
8), instead of the observed soil depth at each measure-
ment point, the differences in the landslide ratio be-
tween unconditionally stable (0.12 and 0.14 in Cases
7 and 8, respectively) and potentially unstable (0.17
and 0.18) grid cells were small (Fig. 3). Although the
landslide ratio clearly decreased as the predicted rc in-
creased (Figs. 4g and 4h), the landslide ratios of grid
cells with r
c
= 20–30 mm/h were similar to those of
unconditionally stable grid cells; thus, the landslide
ratios of grid cells with r
c
= 30–100 mm/h were small-
er than those of unconditionally stable grid cells.
DISCUSSION
ROLE OF GRID CELL SIZE
The relative landslide ratio of potentially unstable
to unconditionally stable grid cells decreased as grid
cell size increased (Fig. 3, Cases 1–4). We observed
an inverse relationship between predicted rc and the
landslide ratio, however, only in Cases 1 (5-m grid)
and 2 (10-m grid), indicating that when a grid cell size
of 15 m or greater is used, the predicted r
c
is useful
only for determining whether a grid cell is uncondi-
tionally stable. These results suggest that the most ef-
fective grid cell size at this study site is around 5 m.
t
aRolli
& t
aRboton
(2006), who also examined
the optimal grid cell size for assessing shallow land-
slide susceptibility using a physically based model,
reported that the optimal grid cell size at their site was
10 m. i
waHasHi
et alii (2009) investigated the optimal
grid cell size for assessing shallow landslide suscep-
tibility by discriminant analysis and reported that it
was strongly controlled by landslide size. Our result
is consistent with these findings, because both the
optimal grid cell size and the landslide size on our
hillslope are smaller than those reported by t
aRolli
& t
aRboton
(2006).
CALCULATION PROCEDEURE FOR FLOw
ROUTING
t
aRboton
(1997) and b
oRGa
et alii (2004) re-
ported that D-infinity performed better than D8 in
calculating the upslope contributing area. Here we
showed that even when the input data, including the
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T. UCHIDA, k. TAMURA, & k. AkIYAMA
156
5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011
mountains (e.g., o
kimuRa
, 1989; i
ida
& t
anaka
, 1997;
H
eimsatH
et alii, 1997; f
ReeR
et alii, 2002; t
esfa
et
alii, 2009). Therefore, spatial variability in soil depth is
likely to be important in determining shallow landslide
susceptibility on other steep landscapes.
CONCLUSIONS
We examined the hypothesis that a detailed field
survey of soil depth distribution and bedrock surface
topography, and the choice of an optimal grid cell size
and topographic index calculation procedure, can im-
prove the precision of shallow landslide susceptibility
assessment. We showed that the choice of an optimal
grid cell size and an optimal procedure for calculat-
ing the upslope contributing area, and a detailed field
survey of soil depth remarkably improves the precision
of landslide susceptibility assessment. Use of a ground
surface DEM instead of a bedrock surface DEM, how-
ever, had little effect on the precision of landslide sus-
ceptibility prediction. These findings indicate that the
existing simple models, if an optimal grid cell size and
procedure for topographic index calculation are cho-
sen and a detailed field survey is conducted, can yield
results useful for landslide susceptibility assessment.
We consider, of course, that improvement of numerical
simulation models is important, but this study shows
that detailed field surveys to clarify the spatial vari-
ability of soil depth and fine-resolution topographic
measurements and hydrometric observations are also
very important to improve the precision of landslide
susceptibility assessment.
tween soil depth and rc calculated using parameters
for Case 1 and 25 m
2
/m of upslope contributing area
(Fig. 6). This figure showed that effects of soil depth
on r
c
were not so large, if the grid cell categorized
as potentially unstable. While, the conditions of un-
conditionally stable and potentially unstable grid cells
are strongly affected by the soil depth. That is, if the
averaged soil depth was used instead of observed soil
depth, several potentially unstable grid cells predicted
by using observed soil depth were assessed as uncon-
ditionally stable. These results indicate that if we ig-
nore the spatial variability of soil depth, the precision
of distinguishing between unconditionally stable and
potentially unstable grid cells becomes worse.
Many previous studies have shown large spatial
variability in soil depth on individual hillslopes or
Fig. 6 - Relationship between soil depth and r
c
calculated
using parameters for Case 1 and 25 m
2
/m of ups-
lope contributing area
REFERENCES
a
ndeRson
s.P., d
ietRiCH
w.e., m
ontGomeRy
d.R., t
oRRes
R. C
onRad
m.e. & l
oaGue
k. (1997) - Subsurface flow paths in a
steep unchanneled catchment. Water Resour. Res.: 33: 2637–2653.
b
azemoRe
d.e., e
sHleman
k.n. & H
ollenbeCk
l/J. (1994) - The role of soil water in stormflow generation In a forested head-
water catchment: synthesis of natural tracer and hydrometric evidence. J. Hydrol.: 162: 47-75.
b
oRGa
m., t
onelli
f. & s
elleRoni
J. (2004) - A physically based model of the effects of forest roads on slope stability. Water
Resour. Res.: 40, doi:10.1029/2004WR003238.
d
ietRiCH
w.e., R
eiss
R., H
su
m.-l. & m
ontGomeRy
d.R. (1995) - A process-based model for colluvial soil depth and shallow
landsliding using digital elevation data. Hydrol. Processes: 9: 383–400.
f
ReeR
J., m
C
d
onnell
J.J., b
even
k.J., P
eteRs
n.e., b
uRns
d.a., H
ooPeR
R.P., a
ulenbaCH
b. & k
endall
C. (2002) - The role of
bedrock topography on subsurface storm flow. Water Resour. Res.: 38, doi: 10.1029/2001WR000872.
H
eimsatH
a.m., d
ietRiCH
w.e., n
isHiizumi
k. & f
inkel
R.C. (1997) - The soil production function and landscape equilibrium.
Nature: 388: 358– 361.
H
iRamatsu
s., m
izuyama
t. & i
sHikawa
, y. (1990) - Study of a method for predicting hillside landslides by analysis of transient
flow of water in saturated and unsaturated soils. J. Japan Soc. Erosion Control Eng.: 43(1): 5-15.
i
ida
t. (1999) - A stochastic hydro-geomorphological model for shallow landsliding due to rainstorm. CATENA: 34: 293–313.
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THE ROLE OF GRID CELL SIZE, FLOW ROUTING ALGOLITHM AND SPATIAL VARIABILITY OF SOIL DEPTH
ON SHALLOW LANDSLIDE PREDICTION
Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza
157
i
ida
t. & t
anaka
k. (1997) - The relationship between topography and soil depth measured with the portable penetration test
apparatus. Trans. Jpn Geomorph. Un.: 18: 61-78.
I
waHasHi
J., k
amiya
i. & y
amaGisHi
H. (2009) - Estimation of the Most Suitable window Size of the Slope Gradient and Convexo-
Concave Index for the Assessment of Shallow Landslides Using High-Resolution LiDAR DEM. Trans. Jpn Geomorph. Un.:
30:
15-27.
k
osuGi
k., m
izuyama
, t. & f
uJita
, m. (2002) - Accuracy of a shallow landslide prediction model to estimate groundwater table.
J. Japan Soc. Erosion Control Eng.: 55(3), 21-32
m
ontGomeRy
d.R. & d
ietRiCH
w.e. (1994) - A physically based model for the topographic control on shallow landsliding. Water
Resour. Res.; 30: 1153–1171.
o’C
allaGHan
J.f. & m
aRk
d.m. (1984) - The extraction of drainage networks from digital elevation data. Comput. Vision
Graphics Image Process.: 28: 328– 344.
o
kimuRa
T. (1989) - A method for estimating potential failure depth used for a probable failure predicting model. J. Japan Soc.
Erosion Control Eng.: 42(1): 14-21.
o
kimuRa
t., i
CHikawa
R. & f
uJii
, i. (1985) - Methods to Predict Failures on Granite Mountain Slopes by a Infiltrated water
Movement Model in a Surface Layer. J. Japan Soc. Erosion Control Eng. 37(5): 44–49
P
aCk
R.t., t
aRboton
d.G. & G
oodwin
C.n. (1998) - The SINMAP Approach to Terrain Stability Mapping. 8
th
Congress of the
International Association of Engineering Geology, Vancouver, British Columbia, Canada.
R
osso
R., R
ulli
m.C. & v
annuCCHi
G. (2006) - A physically based model for the hydrologic control on shallow landsliding.
Water Resour. Res.: 42, doi:10.1029/2005WR004369.
t
alebi
a., u
iJlenHoet
R. & t
RoCH
P.a. (2007) - Soil moisture storage and hillslope stability. Nat. Hazards Earth Syst. Sci., 7:
523–534.
t
aRboton
d.G. (1997) - A new method for the determination of flow directions and upslope areas in grid digital elevation mod-
els. Water Resour. Res.: 33: 309-319.
t
aRolli
P. & t
aRboton
d.G. (2006) - A new method for determination of most likely landslide initiation points and the evalua-
tion of digital terrain model scale in terrain stability mapping. Hydrol. Earth Syst. Sci., 10: 663-677.
t
esfa
t.k., t
aRboton
d.G., C
HandleR
d.G. & m
C
n
amaRa
J.P. (2009) - Modeling soil depth from topographic and land cover
attributes. Water Resour. Res., 45: doi: 10.1029/2008WR007474.
u
CHida
t., a
sano
y., o
Hte
n. & m
izuyama
t. (2003) - Seepage area and rate of bedrock groundwater discharge at a granitic
unchanneled hillslope. Water Resour. Res., 39: doi: 10.1029/2002 WR001298.
u
CHida
t., m
oRi
n., t
amuRa
k., t
akiGuCHi
s. & k
amee
k. (2009) - The role of data preparation on shallow landslide prediction.
J. Japan Soc. Erosion Control Eng.: 62 (1): 23-31.
w
u
w. & s
idle
R.C. (1995) - A distributed slope stability model for steep forested basins. Water Resour. Res.: 31: 2097-2110.
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