5.20 REALTIME FORECASTING OF SHALLOW LANDSLIDES

USING RADAR-DERIVED RAINFALL

Ryohei Misumi *, Masayuki Maki, Koyuru Iwanami, Ken-ichi Maruyama and Sang-Goon Park

National Research Institute for Earth Science and Disaster Prevention,

Tsukuba, JAPAN

1. INTRODUCTION

Shallow landslides induced by heavy rainfall

sometimes cause heavy damages to human lives and

properties. For example, the torrential rain in

Hiroshima Prefecture, Japan, on 29 June 1999 killed

more than 30 people mainly by shallow landslides.

Recently, Niigata-Fukushima heavy rainfall on 13 July

2004 caused devastating disasters (Fig.1). One of the

possible ways to reduce such disasters is forecast of

landslides.

A trigger of shallow landslides is the increment of

pore pressure above an impermeable layer

underground due to infiltration of rainwater. Therefore,

an accurate estimation of rainfall on slopes and

calculation of underground flow are the important

factors for landslide forecasting. Okimura and

Ichikawa (1985) developed a shallow landslide model

incorporating hydrological calculation of groundwater

flow. In their method the stability of slopes are

estimated using the calculated groundwater level,

slope angle measured with a digital elevation model

(DEM) and soil properties. A similar model is

developed by Montgomery and Dietrich (1994). Their

techniques are possible to be used as a real-time

landslide forecast system if an accurate rainfall is

given, but such systems had not been operated.

The National Research Institute for Earth Science

and Disaster Prevention (NIED), Japan, developed an

X-band polarimetric radar (MP-X radar; Fig.3) for

meteorological and hydrological use (Iwanami et al.,

2001). This radar is able to estimate rainfall over

complex terrain using specific differential phase (KDP)

without effects of beam blockage or attenuation.

In this study we developed a realtime forecast

system for shallow landslides using X-band

polarimetric radar. Quantitative precipitation forecast

(QPF) has not been incorporated into this system, but

a real-time forecast of landslides is possible by

making use of the time lag between slope failure and

rainfall peak, even if an observed rainfall is used.

2. REALTIME FORECAST SYSTEM

2.1 Framework

One of the important points to make real-time

forecast of shallow landslides is to get rainfall data as

quickly as possible. The MP-X radar, set up at Ebina,

approximately 50 km southwest of Tokyo, sends KDP

data at 2-tilt PPI scans every minute to the NIED main

office at Tsukuba through the digital leased circuit

(Fig.2). The KDP data are converted to rainfall intensity

automatically and interpolated onto 500m grids in the

observation area (80 km in radius from the radar site).

These data are then used as inputs for a rainfall-runoff

model which calculates water amount in soil every 50

m grid. The slope stability at 50 m grids is estimated

using a simple slope analysis and the results are

displayed on Web pages.

Fig.1 Photograph of a shallow landslide caused by the Niigata-Fukushima heavy rainfall on 13 July 2004. An old woman staying home was killed.

* Corresponding author address: Ryohei Misumi, National Research Institute for Earth Science and Disaster Prevention, Tsukuba 305-0006, Japan; e-mail: misumi@bosai.go.jp

2.2 X-band polarimetric radar

The MP-X radar is a linear orthogonal polarized

radar operating at 9.375 GHz (Fig.3), which is

installed at Ebina in Kanagawa Prefecture, Japan. Its

observation range is 80 km including mountainous

region to the west and Tokyo metropolitan area to the

northeast (Fig.4). The antenna scanning mode is 2-tilt

PPI at 2.1° and 4.4° in elevation angles with repeating every minute. The data at 4.4° PPI is used to fill the missing data at 2.1 ° mainly in the backside of

mountains.

Rainfall estimation is made using the R-KDP

relationship derived by Park et al. (2005). The merits

to use KDP instead of radar reflectivity are that it is not

affected by rain attenuation, almost immune to beam

blockage and the variation of drop size distribution,

and less sensitive to beam filling (Doviak and Zrnic,

1993). Moreover, X-band radar has high sensitivity to

KDP than S-band or C-band radars, so it is well suited

for rainfall estimation. Actually our previous works

(Park et al., 2005; Maki et al., 2005) showed its

capability to estimate rainfall with high resolution and

high accuracy even over mountains. The use of an X-

band polarimetric radar has great potential for real-

time landslide forecasting.

2.3 Rainfall-runoff model

The rainfall data derived by the R-KDP

relationship are used as inputs for a distributed

rainfall-runoff model which calculates soil water

amount. The formulation of the rainfall-runoff model

used here is based on that of Bell and Moore (1998),

although some simplification has been made. The

model employs a simple soil water accounting

procedure within each 50 m grid square and an

advection routing model based on isochrone.

Specifically, the following linkage function is used to

relate maximum storage capacity, Smax, to the terrain

gradient, g:

max

max

max c)g

g1(S −= (1)

The parameter gmax and cmax are upper limits of

gradient and storage capacity respectively and act as

regional parameters for catchments. A schematic

illustration of water balance of a grid square is given in

Fig.5. Here fr is a correction factor for rainfall R. Part

of the effective rainfall, frR, is assumed to run off from

the storage without getting into soil. Direct runoff is

also produced when the amount of water S exceeds

Fig.3 NIED MP-X radar (X-band polarimetric radar).

MP-X radar

Radar site NIED

R-KDP

conversion

KDP

every

minute R

Rainfall-

runoff model

conversion

Slope stability

analysis

Soil

water

Landslide

anticipated area

Web page

Fig.2 Outline of the shallow landslide forecast system

500m grids

50m grids

80km

Fig.4 Observation range of the MP-X radar.

Tsurumi River basin

the capacity Smax. Evaporation rate, Ea, is related to

the potential evaporation rate, E, as:

E)S

S(E

max

a = (2)

Drainage from the grid storage, which contributes to

the slow catchment response, occurs at the rate:

Skd d= (3)

where kd is the storage constant. The continuity

equation for S is given as:

dERffdt

dSari −−= (4)

In numerical calculation, the above equation is

replaced by a finite difference form:

t)dERff()tt(SS ari* ∆∆ −−+−= (5)

>

≤=

SSifS

SSifS)t(S

*max

**

(6)

where Δt is the time step (360 sec). The water in grid

squares is routed across the catchment using

isochrone pathways. Translation of water between

isochrones is achieved using a discrete kinematic

routing procedure as in Bell and Moore (1998). The

parameters fr, fi, gmax, cmax and kd are estimated by

calibration with the hydrographs at the outlet of the

catchment. The rainfall-runoff model is applied to Tsurumi River basin in Kanagawa Prefecture (Fig.4) with catchment area of 135.9 km2. The catchment is discretized with 50 m grids and soil water amount in grids is calculated every 6 minutes.

2.4 Slope stability analysis

The soil water amount calculated with the rainfall-

runoff model is then used for slope stability analysis.

The method used here is one of the simplest ways in

which each slope is assumed to have an infinite length.

The factor of safety, F, which represents the ratio of

shear strength available to the shear strength required

for stability for an infinite slope with inclination β is

given as (Nash, 1987):

βφ

γγ

ββγ tan

'tan)]

z

h)((1[

cossinz

'cF w−+= (7)

where c' is the cohension of soil, Φ' is the angle of

shearing resistance, γ is the density of soil, γw is the

density of water, z is the soil depth, and h is the level

of ground water from the slip surface. Here the ratio

(h/z) is approximated by the saturation ratio of water

storage :

maxS

S

z

h= (8)

Parameters of soil properties, c', Φ ' and γ are

provided from field test data. When F becomes below

1.0, the grid square is regarded as a landslide

anticipated area.

2.5 Display on Web page

The results of the slope stability analysis are sent

to a data server and automatically displayed on

internet Web pages. Examples of distribution of soil

water amount (Fig.6a) and landslide anticipated area

(Fig.6b) in Tsurumi River basin are shown. At present

this page is displayed as "an open experiment for

shallow landslide forecasting by the NIED".

Fig.5 Illustration of water balance at a grid square.

3. EVALUATION

Because the real-time landslide forecast system

shown in the previous section is just constructed in

2004 and no severe disaster occurred in the

catchment, the evaluation of the system using real-

time data has not been carried out. Instead, the model

is applied to a past event and the performance is

evaluated with simulations. The heavy rainfall in

Tochigi-Fukushima area, Japan, from 26 to 31 August

in 1998 caused more than 1000 landslides in the

upper Abukuma River basin. Several landslides

attacked a welfare facility for handicapped people and

5 lives were lost. The rainfall-runoff model is applied to

the upper Abukuma River basin (Fig.7; the drainage

area is 151.07 km2). There are mountains higher than

1800 m in the west of the catchment and the

difference of the level between the east and the west

is greater than 1500 m.

Because the data of the MP-X radar cannot be

used for this case, we used hourly 5 km-mesh rainfall

data compiled by the Japan Meteorological Agency for

this simulation. The parameters of the rainfall-runoff

model were calibrated with the hydrographs for 3-

month period before the landslide event. Based on a

cone penetration test in the catchment, the depth of

soil (m) is related to the slope gradient as:

43.2tan11.2z +−= β (9)

The values of other soil parameters are given by Kawamoto et al. (2000). The simulation period is from

00 JST (Japan standard time) on 1 August 1998 to

23JST on 2 September. Figure 8 shows the soil water

amount and factor of safety at 9 JST on 27 August

1998 when the heavy rainfall is observed. Soil water is

mainly stored in the east side of the catchment where

rainfall is concentrated. However, unstable slopes with

F < 1 are found mainly in the mountainous regions in

the west side. Variation of the factor of safety around

the welfare facility attacked by landslides (the frame in

Fig.8) is shown in Fig.9. The gray bar in the figure

indicates the building of the facility. The meshes

including actual landslides are indicated with red. At

22 JST on 26 August, 6 hours before the landslides,

the model predicts several meshes as landslide

anticipated areas (blue meshes). At 02 JST on 27

August, 2 hours before the landslide occurrence, most

of the red meshes are covered with the blue ones.

This shows that the model successfully forecast the

landslides 2 hours before the occurrence.

The statistics of the landslide forecast simulation is

shown in Table 1. Among 7388 grids, 5523 grids

(74.8 %) are given correct forecasts. However, it

contains many false alarms; the false alarm ratio

reaches 90 %.

Fig.7 Topography and river network around the upper Abukuma River basin.

Fig.6 Examples of display on the Web page. (a) Distribution of soil water amount and (b) landslide anticipated areas.

(b)

(a)

Table 1 Relationships between the predicted factor of

safety and the occurrence of landslides

Factor of Landslide

safety Occur Not occur

F < 1 205 grids 1809 grids

F > 1 56 grids 5318 grids

4. CONCLUSIONS

A real-time forecast system for shallow landslides,

using 500 m mesh rainfall estimated with an X-band

polarimetric radar every minute, has been developed.

In the system radar-derived rainfall is used as inputs

for rainfall-runoff model with imaginary storages

distributed every 50m grid. Slope stability is calculated

at each grid using amount of water in the storage,

inclination of slopes and the depth and the shear

strength of soils. When the factor of safety of slopes is

below 1, the grid is regarded as landslide anticipated

area. This calculation is repeated every 6 minute and

the results are displayed on Web pages. The lead

time of this forecast is approximately two hours which

corresponds to the time lag between rainfall peak and

slope failure. This period is too short to make warning

for people to evacuate, but useful to understand the

dangerous area at the moment. If a reliable QPF is

available, we can extend the lead time greatly. Before

applying rainfall forecast data, however, we need to

study the allowance of errors in rainfall forecast for

landslide prediction.

Soil water amount (mm)

Factor of safety

Fig.8 Distribution of soil water amount and factor of safety in upper Abukuma catchment at 09 JST on 27 August 1998.

km

Welfare facility

22 JST on 26 AUG

02 JST on 27 AUG

Fig.9 Meshes where landslides occurred in this rainfall event (red) and where landslides are forecast (blue).

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