Evaluating triggering and causative factors of landslides in Lawnon River Basin, Taiwan.

1

Evaluating triggering and causative factors of

landslides in Lawnon River Basin, Taiwan

Department of Civil and Environmental Engineering, National University of Kaohsiung, 4

Kaohsiung, Taiwan. 5

2

Kaohsiung Division, China Engineering Consultants, Inc., Taiwan. 6

*

23

Corresponding author, wuminhao@nuk.edu.tw; Tel/Fax: +886 7 5919 750 7

Abstract

8

This study evaluates triggering and causative factors of landslides by comparing 9

their occurrence in the Lawnon River Basin prior to and after rainfall and earthquake 10

events over a five-year period (2005-2009). The landslide ratio in the study area was 11

low (less than 4%) before 2007, and significantly increased in the wake of Typhoon 12

Morakot in 2009. The high accumulation of rainfall was the major triggering factor. 13

In addition, the major seismic activity of March 4, 2008 also contributed to landslide 14

occurrence. The combined influence of rainfall and the earthquake is evaluated based 15

on multi-variable regression analysis. Though no significant co-seismic landslides 16

were found after the March 4, 2008 earthquake, its influence on slope stability has 17

been observed from the apparent growth of landslide ratio in the four rainfall events 18

following the quake. Causative factors include the higher landslide ratios occurring in 19

sedimentary rock zones (especially sandstone formations with intercalations of shale), 20

and dip slopes, which are demonstrably prone to plane failures. 21

Keywords: Lawnon River, landslide, rainfall, earthquake. 22

2

1. Introduction

1

Taiwan is an island with abundant rainfall. The torrential rainfall accompanying 2

typhoons frequently causes slope failures such as landslides and debris flows. 3

Moreover, as the island is located in the Circum-Pacific Seismic Belt, an active 4

mountain-building region, there are frequent earthquakes. The dynamic effects of 5

earthquakes also degrade slope stability. Therefore, slope-land disasters are major 6

natural hazards and threaten both human lives and environmental ecology in Taiwan. 7

One of the most important watersheds on the island, the Lawnon River Basin in 8

southern Taiwan, was selected for study. Previous studies have rarely shown this 9

basin as susceptible to landslides and debris flows. In early August 2009, Typhoon 10

Morakot attacked Taiwan, causing significant loss of lives and property and an 11

economic loss of over US $5 billion (Ling et al., 2009).. It was the most damaging 12

typhoon to make rainfall in southern Taiwan in half a century. The administration 13

system identified 675 dead and twenty-four persons missing. Along with the typhoon, 14

the southwest monsoon brought torrential rains primarily concentrated in southern 15

Taiwan. Accumulated rainfall in the Lawnon River Basin was up to 2500 mm (Fig. 1). 16

As one of the largest river basins in southern Taiwan, it suffered from the severest 17

slope disasters during this event (Chen et al., 2009). About 1218 landslides occurred 18

and the cumulative landslide area exceeded 133.7 km2

According to previous studies (Keefer, 1984; Schuster et al., 1996; Crosta, 2004; 24

Lee et al., 2008a, 2008b), rainfall and earthquakes are two of the principal 25

, 6.6% of the entire basin’s area, 19

damaging infrastructure and transportation systems. However, before typhoon 20

Morakot, large landslides were rarely observed in this area. Consequently, the 21

potential and triggering factors of landslides in the Lawnon River Basin have seldom 22

been studied or emphasized. 23

3

mechanisms inducing landslides. Precipitation data such as accumulative precipitation 1

and rainfall intensity are usually applied to establish the thresholds of rainfall-induced 2

landslides (Caine, 1980; Vandine, 1985, Wieczorek, 1987; Keefer et al., 1987; Chen 3

et. Al., 2005). On the other hand, several studies have identified characteristics of 4

co-seismic landslides, particularly those caused by large-scale earthquakes (Keefer, 5

1984; Harp et al., 1991; Jibson et al., 1994; Harp and Jibson, 1996; Khazai and Sitar, 6

2004). Co-seismic landslides may not often accompany quakes, especially those of 7

limited scale. Nevertheless, some seismic activities influence slope stability for a long 8

period of time. In recent years, more and more researchers have paid attention to 9

landslides triggered by heavy rainfall in a region that has suffered a catastrophic 10

earthquake (Lin et al., 2004; Lin et al., 2006; Ku et al., 2006; Chiou et al., 2007, Chen, 11

2008). In addition to the triggering factors of landslides, it has been generally 12

accepted that slope failures are related to causative factors such as geomorphology, 13

lithology, geological structure and land cover (Radbruch et. al.,1976; Carrara,1983; 14

Varnes, 1978; Varnes, 1984; Hansen, 1984; Cruden,1993). Quantitative analysis of 15

the landslide record, with triggering and causative factors, is fundamental in digitally 16

investigating landslide hazards. Introduction of the Geographical Information System 17

(GIS) into landslide research has greatly enhanced the ability to collect and analyze 18

landslide data (Wang and Unwin, 1992; Hansen et. al.,1995; Mark and Ellen, 1995; 19

Dikau et. al., 1996; Dai and Lee, 2002). The relationship between landslides and the 20

various factors causing them not only provides an insight into our understanding of 21

landslide mechanisms, but can also form a basis for predicting future landslides and 22

assessing the landslide hazard. 23

This paper aims to clarify the effects of rainfall and earthquakes on landslide 24

occurrence. In addition, causative factors including lithology and topography prone to 25

4

slope failures are explored. The study area is the Lawnon River Basin, one of the 1

largest river basins in southern Taiwan. Sixteen satellite images of landslides between 2

2005–2009 differentiate the variations of landslide occurrence prior to and after the 3

typhoon and storm events. 4

2. Study Area

The Lawnon River is 137 km long with a basin area of 1373 km2. The upstream 6

of the Lawnon River is typical valley topography, formed along a major thrust fault in 7

southern Taiwan, the Lawnon Fault. The downward and lateral erosion has resulted in 8

riverbank scouring, bank collapse, valley widening and riverbank retreating. The 9

topographic characteristics of this section include river terraces, alluvial fans and 10

steep riverbank slopes. 11

The study area of this research is located upstream of the Lawnon River Basin, 12

around the Bao-Lai hot spring area with ambit of 130.4 km2(Fig. 2). The Bao-Lai hot 13

spring region, the most populous area in the neighborhood, suffered a severe flood 14

and landslides during Typhoon Morakot. Geology in the study area contains four rock 15

formations, including Tangenshan Sandstone (Tn), Changchikeng Fm. (Cc), Chaujou 16

Fm. (Co), and Mt. Bilu Fm. (Ep). Among them, Tangenshan Sandstone (Tn) and 17

Changchikeng Fm.(Cc) belong to sedimentary rock areas, and Chaujou Fm.(Co) and 18

Mt. Bilu Fm.(Ep) belong to metamorphic rock areas (Fig. 3). The Lawnon Fault is the 19

boundary line between the sedimentary rock and the metamorphic rock. In addition, 20

there are many hot spring sites within the study area, mostly located in Chaujou Fm. 21

(Co). Table 1 shows the geological ages, lithologies and rock compressive strengths 22

corresponding to the geological formations. 23

3. Methodology

5

3.1 Data sources and database

3.1.1 Rainfall 2

The Water Resource Agency (WRA) and Central Weather Bureau (CWB) in 3

Taiwan supplied digital files of hourly rainfall data for twelve rain observation 4

stations (auto-gauges) in the study area. Extremely heavy rainfall data (those with 5

twenty-four-hour accumulative rainfall over 130 mm) from 2005 to 2009 were 6

collected from rainfall observation stations within 15 kilometers of the Bao-Lai area. 7

Through pre-analysis of the rainfall data, ten significant typhoon and storm events 8

with heavy rainfall from 2005 to 2009 were selected to evaluate effects on landslides 9

for this study (Table 2), and corresponding rainfall data were collected for further 10

statistical and spatial isohyet analyses. 11

3.1.2 Satellite images 12

This study uses sixteen images from the FORMOSAT-II satellite (Table 2) from 13

before and after the ten significant typhoon and storm events of 2005 to 2009. 14

FORMOSAT-II satellite is a sun-synchronous satellite with orbital height of 891 km. 15

It has high-resolution and daily-revisit imagery and four bands with 8-m resolution 16

color mode and 2-m resolution panchromatic mode. Color imaging includes blue, 17

green, red and near-infrared bands corresponding to the wavelengths of 0.45～0.52m, 18

0.52 ～ 0.60m, 0.63 ～ 0.69m and 0.76 ～ 0.90m, respectively. Since the images 19

identified landslide areas within the study domain, they had to be cloud-free. 20

Following the selection of satellite images, preprocessing tasks including geometric 21

correction and radiometric correction were carried out before analysis of the images. 22

3.1.3 Earthquakes 23

Records of seismic activities from 2005 to 2009 were collected from Jiasian 24

seismic station (maintained by CWB), the closest seismic monitoring station to the 25

6

study area. Ten events with detected seismic intensity larger than 2 (larger than 8 gals) 1

were selected. Magnitudes and induced peak ground accelerations at the study area 2

were analyzed. 3

3.1.4 Geological and Topographical data 4

1:25,000 paper maps published by the Central Geological Survey (CGS) in 5

Taiwan provided geological and topographical data. The data were stored in vector 6

format within the Arc/info GIS software through manual digitization. A digital 7

elevation model (DEM) of 40×40 m, obtained from Aerial Survey Office, Forestry 8

Bureau, was constructed from the topographical dataset, and maps of slope-inclination 9

angle and aspect distribution were then derived from the DEM. River networks, roads, 10

and other basic geographical data were also digitized from the basic topographic 11

maps. 12

3.2 Data digitization and mapping

The accumulative rainfall of each rain observation station was calculated by 14

summarizing hourly records within the whole storm event. The accumulative rainfall 15

data of twelve observation stations within the study area was processed to create the 16

isohyets for ten typhoon and storm events. The spatial analyst function of the GIS was 17

adopted to conduct this work. 18

Satellite images identified the landslides, and landslide areas were manually 19

digitized with Arc/info GIS software. Comparing pre-event and post-event satellite 20

images, event-based landslide maps could be obtained. Reconnaissance works and 21

in-situ observations were executed in the study area to understand geological and 22

topographical conditions unseen in the satellite images. Since this study focuses on 23

landslide areas, debris flows were not analyzed. To separate transportation and 24

deposition segments of debris flows from the study area, this study only considered 25

7

land with slope gradient larger than 25 degrees. Figure 4 illustrates the post-event 1

landslide maps of all storm events for later analysis. 2

3.3 Landslide categorization

To better determine landslide characteristics, landslides recognized in each 4

satellite image were firstly classified into existing landslides and incremental 5

landslides. The difference in images before and after a storm event, showing 6

landslides caused by a specific event between images, indicates incremental 7

landslides. Existing landslides represent overlap in landslide images before and after 8

the storm event. In order to assess how many landslides are expanded from existing 9

ones, incremental landslides are further classified into new landslides and enlarged 10

landslides. New landslides can only be observed in the new image, and enlarged 11

landslides have expanded from existing landslides in the previous image. Figure 5 12

illustrates the distribution of existing landslides, enlarged landslides and new 13

landslides caused by rainfall during Typhoon Morakot. 14

3.4 Attribute assignment and combined database of landslides

Assigning various attributes to individual digitized landslides is critical to 16

quantitative analysis of the relationship between landslides and their causative factors. 17

Gathering and handling various attributes of landslides is a time-consuming task, 18

usually requiring much field reconnaissance work. GIS makes managing digital 19

databases faster and more accurate. Connecting the landslide database with other 20

internal or external databases is an efficient way of assigning causative factor values 21

to individual landslides. 22

3.5 Regression analysis of landslides and the triggering factors

In this study, the spatial overlay function of the GIS helps understand the 24

relationship between landslides and triggering factors (rainfall and earthquake) and 25

8

causative factors (lithology, geological structure and topography). Furthermore, it is 1

relevant to evaluate the associated influence of the triggering factors on the landslides. 2

Since landslides are affected by both storm events and earthquakes, a multi-variable 3

regression analysis proposed by Jeng et al. (2004) was used in this research. 4

As landslides were inter-affected by several triggering factors (e.g, x1, x2, …, xn

) ..., , , (x₁ x₂ x_n f F = ) 5

such that a function F exists, this function could be expressed as: 6

(1) 7

The function form of F was unknown; however, for the sake of simplicity, it was 8

assumed that F has the following form: 9

1( )1 2( 2) ... n( n)

F ≅ f x f x f x (2)

10

Each function on the right-hand side of Eq. 2 represents the magnitude of the 11

influence of each factor. Empirical functions of f(xi) could possibly be determined one

12

by one using a single-variable regressive analysis of actual data. The degree of 13

influence varies from one factor to another. The goal was to find the most influential 14

function, followed by the second-most-influential function, and so on. Determination 15

of the influential functions continued until the remaining factors had no meaningful 16

influence, meaning the increase of r2 < 0.05 when more factors are considered. Then 17

the function forms of f(x1), f(x2), …, f(xn

4. Analysis results

) needed to be further modified by the 18

iteration process until the coefficient of correlation stopped increasing. 19

Using the aforementioned regressive process, accumulative rainfall and peak 20

ground acceleration (PGA) are selected as the major triggering factors in this 21

research. 22

4.1 Influence of rainfall on landslides

9

Rainfall, especially from storms, is the dominant factor inducing landslides. 1

Combining the accumulative rainfall data and landslide ratio of each storm event(Fig. 2

6), landslide regions of the study area are observably growing in the past five years, 3

though some small recovery appeared between two consecutive events. Herein, 4

landslide ratio is expressed as a percentage, yielded from total landslide area divided 5

by overall area within a specific geological zone. Before 2008, the variation of rainfall 6

within the study area was not obvious, so that the variation of landslide areas was not 7

significant (Fig. 6). The extreme rainfall brought by Typhoon Morakot in 2009 8

remarkably increased the scale of landslides. Comparing rainfall data among events, 9

Typhoon Morakot has the highest accumulative precipitation (2502 mm on average), 10

followed by Typhoon Haitang (1882 mm on average). However, it is significant that 11

the average landslide ratio induced by Typhoon Haitang was only 1.29 %, much less 12

than those induced by Typhoons Jangmi and Kalmaegi in 2008. 13

4.2 Influence of seismic activities on landslides

In order to investigate the influence of other landslide-triggering factors, further 15

examination of the seismic events around the study area is warranted. 16

Seismic activity records were collected from Jiasian seismic station (Fig. 7b). 17

From 2005 to 2009, ten earthquake events with detected seismic intensity larger than 18

2 (PGA ≧ 8 gals) were selected. The peak ground acceleration of each event is 19

plotted in Fig. 7(a) and the distributions of the epicenters with magnitudes for the 20

events are shown in Fig. 7(b). As observable from Fig. 7(a), a remarkable earthquake 21

with PGA near 140 gals occurred on March 4, 2008, which is much larger than the 22

other quakes. Looking back at Fig. 6, we can see that the landslide ratio increases 23

since 2008. The seismic event might play an important role in this transition. 24

Therefore, to evaluate the combined influence of the rainfall and the earthquake, a 25

10

multi-variable regression analysis is conducted in the following section to identify the 1

relationship between landslides and their triggering factors. 2

4.3 Combined effect of triggering factors on landslides

In the procedures in section 3.5, it was assumed the landslide ratio (R), could be 4

approximately expressed as: 5 ) ( ) ( ) , (AR PGA_m f₁ AR f₂ PGA_m f R= ≅ (3) 6

where AR represents the accumulative rainfall and the unit is mm; PGAm

) (

1 AR

f

7

represents the recorded maximum PGA within one year and the unit is gal. 8

The functions and f2(PGAm) _{could be determined by iterations of the}

9

regression analysis and are found to be: 10 0.7 + (AR) 10 5.58 -(AR) 10 3.03 AR f₁( )= × -7 2 × -5 ₍₄₎ 11 ) PGA exp(0.012 1.3 PGA f₂( _m)= ∗ _m (5) 12

Substituting Eqs. 4 and 5 into Eq. 3, the landslide ratio (R) could be expressed in 13

terms of AR and PGAm

) PGA exp(0.012 0.7] + (AR) 10 5.58 -(AR) 10 3.03 1.3 PGA f R A f R= ₁( ) ₂( _m)= ∗[ × -7 2 × -5 ∗ ∗ _m as: 14 15 (6) 16

where the accumulative rainfall (AR) ranges from 150 mm to 2500 mm and the 17

PGAm

If the landslide ratio expressed by Eq. 6 represented the empirical landslide ratio, 19

it could be compared to the actual ratio (Fig. 8a). In general, the actual R and the 20

empirical R were closely related, with a correlation coefficient square (r ranges from 13 gals to 140 gals.

18

2

) of 0.955. As 21

a result, Eq. 6 provided a practical relationship for evaluating the influence of the 22

rainfall and the earthquake. Moreover, Fig. 8b illustrates the variation of landslide 23

ratio with two triggering factors, and the contour lines indicate the empirical landslide 24

11

ratio and numbers near each symbol mark the actual ratio. Before the earthquake of 1

March 4, 2008 (PGA close to 140 gals), the landslide ratios under various 2

accumulative rainfall conditions fell below 4% (Fig. 8b). After the earthquake, the 3

landslide ratios increase remarkably. Therefore, the combined effect of triggering 4

factors on landslides is significant. 5

4.4 Influence of lithology on landslides

In addition to triggering factors, Fig. 9 shows the variations of landslide ratio 7

with respect to each geological zone, and the corresponded accumulative precipitation 8

record is also plotted. The incremental landslide ratio of each geological zone 9

generally remained below 2% before 2008 (Fig. 9a). The landslides frequently 10

occurred in formation Co. However, after the 2008 earthquake the landslide ratios of 11

sedimentary formations (Cc and Tn) surpassed those of metamorphic rock areas (Co 12

and Ep) (Fig. 9b). Figure 10 illustrates the variations of enlarged and new landslides 13

with respect to different geological formations. No matter the lithology, the failure 14

ratios of enlarged landslides is larger than those of new landslides, which means most 15

landslides came from regeneration and expansion of existing landslides. In other 16

words, as a landslide occurs, it will precipitate subsequent failure nearby. 17

4.4 Influence of dip slope on landslides

A dip slope means a topographic surface where the dip is consistent with that of 19

the underlying strata. Dip slopes are commonly found in cuesta and vale topography. 20

Usually, dip slopes are quite prone to landslides, due to plane failure along 21

persistently weak planes. In the Lawnon River Basin, dip-slope topography is 22

commonly seen in the slope land. According to the definition of Central Geological 23

Survey (CGS) of Taiwan, dip slopes are the slopes with difference of slope direction 24

and dip direction of weak planes being less than twenty degrees. The distribution of 25

12

dip slopes in the study area was digitized on the GIS platform (Fig. 11). Figure 12 1

illustrates the variations of landslide ratios in the dip-slope zone, and it indicates that 2

dip-slope areas indeed possess higher landslide ratios, whether before or after the 3

earthquake in 2008. 4

5. Conclusion

This study investigates the triggering and causative factors of the storm-induced 6

landslides in the Lawnon River Basin from 2005 to 2009. The analysis led to the 7

following conclusions: 8

1. From 2005 to 2009, the landslide events and scales increased year by year and 9

reached the peak value at the storm event of Typhoon Morakot 2009, which 10

aroused attention to the landslide behavior in this area. Undoubtedly, rainfall is 11

one of the primary triggering factors causing the slides. However, in comparison 12

to rainfall data among the events except Typhoon Morakot, while Typhoon 13

Haitang in 2005 had higher cumulative rainfall (1882 mm in average) than the 14

other storm events, its landslide area is smaller. The patterns of landslide scale 15

variation and rainfall record seem to be identical after 2008, which implies the 16

existence of other factor(s) triggering the transition of landslide sensitivity. 17

2. Based on the results of multi-variable regression analysis, the combined influence 18

of the rainfall and the earthquake is evaluated. Before the earthquake of March 4, 19

2008 (PGA close to 140 gals), the landslide ratios under various accumulative 20

rainfall conditions fell below 4%. After the earthquake, the landslide ratios 21

increase remarkably. Therefore, the combined effect of triggering factors on 22

landslides is significant. 23

3. The lithological effect on landslide sensitivity is not apparent before the March 4, 24

2008 earthquake. After the quake, the sedimentary formations (Cc and Tn) are 25

13

more prone to the increment of landslide ratios than the metamorphic rock areas 1

(Co and Ep). 2

4. Existing landslide areas are more sensitive to landslide enlargement or 3

development. The failure ratios of enlarged landslides is larger than those of new 4

landslides, which indicates as a landslide occurs, it will more easily precipitate 5

subsequent failure nearby. 6

5. The results of this study proves that dip slopes have more pronounced landslide 7

ratios compared to other slope land both before and after the earthquake in 2008. 8

6. Acknowledgement

9

The research is supported by the National Science Council of Taiwan, Grant No.

10

NSC 7 97-2221-E-390 -019 and China Engineering Consultants, Inc., Plan No. 99925.

11

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Table 1 Lithological characteristics of the rock in the study area 1

2

Formation Geological age Lithology UCS

(MPa) Tangenshan (Tn) Miocene-Pliocene Thick-bedded to massive

graywacke

65.2

Changchikeng (Cc) Miocene Light gray fine-grained sandstone with intercalations of shale

41.3

Chaujou (Co) Miocene Black argillite with occasional inter-beds of sandstone

29.9

Mt. Bilu (Ep) Eocene Black slate and phyllite with occasional inter-beds of sandstone

33.1

3 4

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Table 2 Formosat- II satellite images used in the study area 1

2

Year Event Event date Accumulative rainfall (mm) Image Before After 2005 Typhoon Haitang 07/16~07/20 1633 07/10 07/30 Typhoon Talim 08/30~09/01 271 07/30 09/03 2006 06/09 Heavy rainfall 06/06~06/11 1233 04/19 06/16 Typhoon Bilis 07/12~07/15 732 06/16 07/29 2007 Typhoon Sepat 08/16~08/19 1071 01/20 09/02 TyphoonKrosa 10/04~10/07 639 09/02 10/26 2008 07/09 Heavy rainfall 07/08~07/11 155 06/22 07/14 Typhoon Kalmaegi 07/16~07/18 799 07/14 08/21 Typhoon Jangmi 09/26~09/29 507 09/21 10/08 2009 Typhoon Morakot 09/26~09/29 2502 01/08 08/24 3 4

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List of figure captions

1

Figure 1 Rainfall record of Lawnon river Basin during Typhoon Morakot 2009.

Figure 2 Domain of study area.

Figure 3 Geological mapping of study area.

Figure 4 Post-event landslide maps of all storm events.

Figure 5 Classification and distribution of landslides (during Typhoon Morakot, 2009).

Figure 6 Variation of landslide ratio in different rainfall events.

Figure 7 Measured peak ground accelerations (PGA) and distribution of seismic events from 2005 to 2009.

Figure 8 Comparison of actual landslide ratio with predicted landslide ratio. The contour curves in (b) represent the empirical function of landslide ratio defined by Eq. 6, and the actual value of landslide ratio is marked near each symbol.

Figure 9 Comparison of incremental landslide ratio with respect to different geological formations.

Figure 10 Variation of landslide ratios for enlarged and new landslides with respect to different geological formations.

Figure 11 Distribution of dip slopes in study area.

Figure 12 Variation of dip-slope landslide ratios in different geological formations. 2