5.20 REALTIME FORECASTING OF SHALLOW LANDSLIDES USING RADAR-DERIVED RAINFALL

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1 5.20 REALTIME FORECATING OF HALLOW LANDLIDE UING RADAR-DERIVED RAINFALL Ryohei Misumi, Masayuki Maki, Koyuru Iwanami, Ken-ichi Maruyama and ang-goon Park National Research Institute for Earth cience and Disaster Prevention, Tsukuba, JAPAN 1. INTRODUCTION hallow 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 cience 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 (K DP) without effects of beam blockage or attenuation. Corresponding author address: Ryohei Misumi, National Research Institute for Earth cience and Disaster Prevention, Tsukuba , Japan; misumi@bosai.go.jp 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. Fig.1 Photograph of a shallow landslide caused by the Niigata-Fukushima heavy rainfall on 13 July An old woman staying home was killed. 2. REALTIME FORECAT YTEM 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 K DP data at 2-tilt PPI scans every minute to the NIED main office at Tsukuba through the digital leased circuit (Fig.2). The K DP 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.

2 Radar site MP-X radar Web page Landslide anticipated area K DP every minute NIED R-K DP conversion R Rainfallrunoff model oil water 500m grids 50m grids lope stability analysis 1993). Moreover, X-band radar has high sensitivity to K DP than -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 realtime landslide forecasting. Tsurumi River basin Fig.2 Outline of the shallow landslide forecast system 80km Fig.4 Observation range of the MP-X radar. Fig.3 NIED MP-X radar (X-band polarimetric radar). 2.2 X-band polarimetric radar The MP-X radar is a linear orthogonal polarized radar operating at 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-K DP relationship derived by Park et al. (2005). The merits to use K DP 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, 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. pecifically, the following linkage function is used to relate maximum storage capacity, max, to the terrain gradient, g: max g = (1 ) cmax (1) g max The parameter g max and c max 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 f r is a correction factor for rainfall R. Part of the effective rainfall, f rr, is assumed to run off from the storage without getting into soil. Direct runoff is also produced when the amount of water exceeds

3 the capacity max. Evaporation rate, E a, is related to the potential evaporation rate, E, as: Ea = ( )E (2) max Drainage from the grid storage, which contributes to the slow catchment response, occurs at the rate: d d = k (3) where k d is the storage constant. The continuity equation for is given as: d dt = f f R E d (4) i r In numerical calculation, the above equation is replaced by a finite difference form: = ( t t) + ( f f R E d) t (5) i r a a Fig.5 Illustration of water balance at a grid square. 2.4 lope stability analysis The soil water amount calculated with the rainfallrunoff 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): (t) = if max if > (6) F c' γw h tanφ' = + [1 ( )( )] (7) γzsin βcos β γ z tan β 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 f r, f i, g max, c max and k d 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 km 2. The catchment is discretized with 50 m grids and soil water amount in grids is calculated every 6 minutes. where c' is the cohension of soil, Φ' is the angle of shearing resistance, γ is the density of soil, γ w 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 : h = (8) z max 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".

4 evaluated with simulations. The heavy rainfall in (a) Tochigi-Fukushima area, Japan, from 26 to 31 August in 1998 caused more than 1000 landslides in the upper Abukuma River basin. everal 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 2 area is km ). 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 (b) 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 3month 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: z = 2.11 tan β (9) The values of other soil parameters are given by Kawamoto et al. (2000). The simulation period is from Fig.6 Examples of display on the Web page. (a) Distribution of soil water amount and (b) landslide anticipated areas. 00 JT (Japan standard time) on 1 August 1998 to 23JT on 2 eptember. Figure 8 shows the soil water amount and factor of safety at 9 JT on 27 August 1998 when the heavy rainfall is observed. oil 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 JT on 26 August, 6 hours before the landslides, the model predicts several meshes as landslide Fig.7 Topography and river network around the upper Abukuma River basin. anticipated areas (blue meshes). At 02 JT on 27 August, 2 hours before the landslide occurrence, most of the red meshes are covered with the blue ones. 3. EVALUATION This shows that the model successfully forecast the landslides 2 hours before the occurrence. 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 realtime data has not been carried out. Instead, the model is applied to a past event and the performance is 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 %.

5 oil water amount (mm) Welfare facility 22 JT on 26 AUG Factor of safety Fig.8 Distribution of soil water amount and factor of safety in upper Abukuma catchment at 09 JT on 27 August km 02 JT on 27 AUG Fig.9 Meshes where landslides occurred in this rainfall event (red) and where landslides are forecast (blue). Table 1 Relationships between the predicted factor of safety and the occurrence of landslides Factor of Landslide safety Occur Not occur F < grids 1809 grids F > 1 56 grids 5318 grids 4. CONCLUION 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. lope 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.

6 REFERENCE Bell, V. A. and R. J. Moore, 1998: A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation. Hydrology and Earth ystem ciences, 2, Doviak, R.J. and D.. Zrnic, 1993: Doppler radar and weather observation, 2nd Edition. Academic Press Inc.,562 pp. Iwanami, K., R. Misumi, M. Maki, T. Wakayama, K. Hata and. Watanabe, 2001: Development of multiparameter radar system on mobile platform. Preprints 30th Int. Conf. Radar Meteor., Amer. Meteor. oc., Munich, Germany, Kawamoto, K., M. Oda and K. uzuki, 2000: Hydrogeological study of landslides caused by heavy rainfall on August 1998 in Fukushima, Japan. J. Natural Disaster ci., 22, Maki M., K. Iwanami, R. Misumi,.-G. Park, H. Moriwaki, K. Maruyama, I. Watabe, D.-I. Lee, M. Jang, H.-K. Kim, V. N. Bringi, H. Uyeda, 2005: emi-operational rainfall observations with X-band multi-parameter radar. Atmos. ci. Letters, 6, Montgomery, D. R. and W. E. Dietrich, 1994: A physically based model for topographic control on shallow landsliding. Water Resource Research, 30, Nash, D., 1987: A comparative review of limit equilibrium methods of stability analysis. lope tability, Editted by M. G. Anderson and K.. Richards, John Wiley & ons Ltd., Okimura, T. and R. Ichikawa, 1985: A prediction method for surface failures by movements of infiltrated water in surface soil layer. J. Natural Disaster ci., 7, Park,.-G., M. Maki, K. Iwanami, V. N. Bringi and V. Chandrasekar, 2005: Correction of radar reflectivity and differential reflectivity for rain attenuation at X-band. Part II: Evaluation and application. J. Atmos. Oceanic Technol. (in press).