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ACTUAL CONDITION AND CHARACTERISTICS OF SLOPEFAILURE IN EAST TIMOR BY MULTIVARIATESTATISTICAL ANALYSISBy:05KC051Lourenco SoaresA thesis submitted to Department of Civil and EnvironmentalEngineering, Saitama University, Japan for the Requirements ofMaster’s DegreeAugust 2007Supervisor

Text of Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

ACTUAL CONDITION AND CHARACTERISTICS OF SLOPE FAILURE IN EAST TIMOR BY MULTIVARIATE STATISTICAL ANALYSIS By: 05KC051 Lourenco Soares A thesis submitted to Department of Civil and Environmental Engineering, Saitama University, Japan for the Requirements of Masters Degree August 2007

Supervisor Professor Hidehiko KAZAMA

Department of Civil and Environmental Engineering Graduate School of Science and Engineering Saitama University, JAPAN

ACKNOWLEDGEMENTAt the outset, it is my duty to acknowledge with gratitude the generous help that I have received from my advisor, Professor Hidehiko KAZAMA. He is responsible for involving me in this masters course in the first place. He taught me how to ask questions and express my ideas. He showed me different ways to approach a research problem and the need to be persistent to accomplish any goal. I also thank Mrs. Yumiko SHIRO and Mr. Masato IWAMA for their strong support in making me acquainted with Japanese life style in the past two years. I expresses with my deepest, heart felt gratitude to Mr. Kobayashi for being helpful person. Besides my advisor, I would like to thank the rest of my thesis committee: Professor Kunio Watanabe and Assoc. Professor M. Osada, who asked me good questions, gave insightful comments and reviewed my work on a very short notice. And most of all I would like to express my heart felt thanks to all staffs in Geosphere Research Institute of Saitama University (GRIS) which have direct and indirect value for finalizing this thesis. I would like to pass my great respect and Special thanks to Japan International Cooperation Agency (JICA) and Japan International Cooperation Center (JICE) for their support in funding my tuition and living expenses through out my stay in Japan to pursue the Master program in Saitama University, Japan smoothly. Especially, I would like to express my heart felt thanks to Mr. Mizuki MATSUZAKI, Ms. Yuri OSAWA, Mrs. WATANABE and Ms. Sayaka OSHIMI for their strong support, advice, suggestions, encouragement and their kindness cooperation for helping me in every aspect of my study and my life in Japan. Last, but not least, I thank my family (Amain sayang , Maun Du, Ina Noi, Alin Eqi), the late my father Salvador Soares and my mother Andreza Soares, for giving me life in the first place, for educating me with aspects from both arts and sciences, for unconditional support and encouragement to pursue my interests, even when the interests went beyond boundaries of language, field and geography. My brothers (Maun Domingos, Enty, Rito, Abes and Aje) and friends: for sharing experience of life and dissertation to me, for listening to my complaints and frustrations, and for believing in me, most of all supporting.

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CONTENTSAcknowledgement Contents List of figures List of tables Abstract CHAPTER I Introduction 1.1 1.2 1.3 Background of study .. Propose and scope of the study .. Data collection and methodology of research ... 1 2 4

CHAPTER II Study Site Description and Literature review 2.1 Study site description 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.2 Geographical condition, location, and boundaries of study site.. Topography . Geology, landforms and soil .. Climate Vegetation .. 9 9 13 14 19 24 31

Literature review

CHAPTER III Actual Condition, Characteristics and Distribution of Slope Failure in East Timor 3.1 3.2 Introduction . Characteristics and distribution of slope failure in East Timor .. 3.2.1 3.2.2 3.2.3 Lithology Vegetation .. Inclination angle of slope . 36 40 41 44 46

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3.2.4 3.2.5 3.2.6 3.2.7 3.2.8

Direction of slope Landscape topography Elevation . Slope width. . Slope length

49 51 53 54 58

CHAPTER IV Analyzing Method 4.1 4.2 4.3 Logistic regression analysis Independent variables and sampling . GIS application for slope failure mapping .. 63 67 70

CHAPTER V Analysis Result 5.1 5.2 5.3 Introduction . All study site analysis result Specific site Analysis .. 5.3.1 5.3.2 5.3.3 Bobonaro site . Cailaco site . Zumalai site 72 72 82 82 92 100

CHAPTER Conclussion and Future Subject 6.1 Conclusions.. 6.2 Future Subject ... References . APPENDIX A: Physical Data of Slope Failure and Unfailure slope APPENDIX B: Logistic Regression Analysis 109 110 111

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LIST OF FIGURESFIGURE 1.1 1.2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 DESCRIPTION Flow chart of research methodology Slope failure location with aerial photograph Boundary of study site Study site and slope failure Study site and unfailure slope Topography of East Timor East Timor geological map physical types of East Timor Areas prone of landslide and flooding in East Timor Altitude and mean temperature correlation Monthly distribution of rainfall in East Timor (based on data from Ferreira 1965) The amount of daily rainfall from July December 2006 in Dare station The amount of daily rainfall from July December 2006 in Aileu station The amount of daily rainfall from July December 2006 in Betano station Climate Natural distribution of forest in East Timor Actual forest covers Firewood cut by community as o source of income and used for cooking Cutting and burning the forest by community Sifting agriculture (slashes and burn agriculture) Category of land cover in East Timor Older landslide topography in East Timor Older landslide topography in East Timor Recent landslide topography in East Timor Recent landslide occurred on cut slope alongside road in East Timor Surface failure on hill slopes of mountainous in East Timor Surface failure on hill slopes of mountainous in East Timor Lithology Distribution of vegetation Distribution of inclination angle of slopes failure Distribution of direction of slope Landscape topography Distribution of elevation Width of landslide Width of surface failure iv PAGE 6 7 10 11 12 14 17 18 18 20 20 22 23 23 24 26 27 28 29 29 31 37 37 38 39 39 40 44 46 48 50 52 54 56 57

3.15 3.16 3.17 3.18 4.1 4.2 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17

Width of surface failure and landslide Length of landslide Length of surface failure Length of surface failure and landslide mix Flow chart of logistic regression analysis Flow chart of Production of probabilities of slope failure maps based on GIS techniques Ranking of the top ten significant item and category based on influence ratio in all study site The top ten ranking of interaction term when combined with other variable based on influence ratio in all study site Observed groups and predicted probabilities of slope failure by logistic regression analysis Histogram of p redicting for probabilities of slope failure Map of relative slope failure susceptibility Ranking of the top ten significant item and category based on influence ratio in Bobonaro site The top ten ranking of interaction term when combined with other variable based on influence ratio in Bobonaro site Observed groups and predicted probabilities of slope failure by logistic regression analysis Histogram of predicting for probabilities of slope failure in Bobonaro site Ranking of the top ten significant item and category based on influence ratio in Cailaco site The top ten ranking of interaction term when combined with other variable based on influence ratio in Cailaco site Observed groups and predicted probabilities of slope failure by logistic regression analysis Histogram of predicting for probabilities of slope failure in Cailaco site Ranking of the top ten significant item and category based on influence ratio in Zumalai site The top ten ranking of interaction term when combined with other variable based on influence ratio in Zumalai site Observed groups and predicted probabilities of slope failure by logistic regression analysis Predicting for probabilities of slope failure in Zumalai site

58 60 51 59 69 71 76 76 80 81 81 85 86 89 90 95 96 99 100 103 104 107 108

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LIST OF TABLESTABLE 2.1 2.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 4.1 4.2 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 DESCRIPTION Land use in East Timor, Indonesia government estimation Land use, Alternative estimation, Saldanha Density of slope failure in study site Description of geological structures in each study area Lithology Distribution of vegetation Distribution of inclination angle of slope failure Distribution of direction of slope Landscape topography Distribution of elevation Width of landslide Width of surface failure Width of the mix of surface failure and landslide Length of landslide Length of surface failure Length of surface failure and landslide mix Classification of predicted the probabilities of slope failure from the logistic regression analysis Categories of the independent variables Classification table of cut value 0.50 Coefficient values and influence ratio of logistic regression of each item and category in all study site Cofficient values and influence ratio of logistic regression of interaction term with other item and category in all study site Classification of predicted the probabilities of slope failure from the logistic regression analysis Predicting for probability of slope failure Classification table of cut value 0.50 in Bobonaro site Coefficient values and influence ratio of logistic regression of each item and category in Bobonaro site Cofficient values and influence ratio of logistic regression of interaction term with other item and category in Bobonaro site Predicting for probability of slope failure in Bobonaro site Classification table of cut value 0.50 in Cailaco site Coefficient values and influence ratio of logistic regression of each item and category in Cailaco site Cofficient values and influence ratio of logistic regression of interaction term with other item and category in Cailaco site Predicting for probability of slope failure in Cailaco site Classification table of cut value 0.50 in Zumalai site vi PAGE 30 30 41 39 43 45 48 50 52 53 55 56 57 59 60 51 67 68 73 73 74 79 80 82 82

5.9 5.10 5.11 5.12 5.13 5.14

83 90 92 92 93 99 100

5.15 5.16 5.17

Coefficient values and influence ratio of logistic regression of each item and category in Zumalai site Cofficient values and influence ratio of logistic regression of interaction term with other item and category in Zumalai site Predicting for probability of slope failure in Zumalai site

101 102 107

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ABSTRACTEast Timor has risk number of natural hazards. Each year, heavy seasonal rain falling on steep slopes causes frequent flash flooding and slope failure, which are considered to be the two major natural hazards in the country. Apart from their potential to cause casualties and damage to rural communities, these events cause major disruption to the fragile road network, isolating communities and even whole districts for a long duration. Slope failures (i.e., landslide and surface failure) in mountainous terrain often occur as a result of heavy rainfall, resulting in the loss of life and damage to the natural environment. In this regard, slope failure hazard assessment as well as identify the characteristics and distribution of slope failure can provide much mitigation through proper project planning and implementation. Propose of this study are to know actual condition and characteristics of slope failure and to determine clearly the factors influencing of the slope failure occurrence in East Timor. The factors that influent to the slope failure in study area may be categories in the intrinsic variables that contribute for slope failure, such as geology, inclination angle of the slope, vegetation, elevation, direction and landscape topography of slope. Logistic regression analysis is a multivariate technique that considers several physical parameters that may affect probability. This modeling is intended to describe the likelihood of slope failure on a regional scale, and is very suitable for the assessment of slope failure actual condition and its characteristics because the observed data consist of item and category with a value of 0(absence of slope failure) or 1(presence of slope failure). The predicting and assess of slope failure occurrence for the training samples in this analysis. If we have a model that successfully distinguishes the two groups based on a classification cutoff value of 0.5. Result analysis shown that the model produced a concordance rate of 90 % with the use of 0.5 as a classification cutoff value. By examining this result to predict thats factors influencing slope failure, we can see what a different classification rule should be adopted when applying the model analysis to each factor in the study area and obtain regression model composed of significant variables. The influence of the interaction among factors contributing for slope failure occurrence was examined. When the interaction term were introduce, the proportion of the observed all items and category predicted as high influence ratio increased by 1 to 4 times of individual category, which indicated a better prediction. viii

The comparison of the results from the analysis including the interaction terms among category and the individual category, the interaction term indicate that interactions among the variables of category were found to be significant for predicting probability of slope failure. From the result, slope failure would most possibly occur in area where cover by bare land and grassland and the elevation ranges from 200m to 800m, the surface slopes is steep and thin sedimentary rocks.

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CONTENTS OF APPENDIX

Appendix A : Physical data of slope failure and un-failure A.1 Physical data of slope failure A.1.1 Bobonaro site . A.1.2 Cailaco site . A.1.3 Zumalai site A.1.4 Atsabe site . A.1.5 Maliana site A.1.6 Ainaro site . A.1.7 Hatolia site A.1.8 Hatobuilico site A.2 Physical data of unfailure slope A.2.1 Bobonaro site . A.2.2 Cailaco site . A.2.3 Zumalai site A.2.4 Atsabe site . A.2.5 Maliana site A.2.6 Ainaro site . A.2.7 Hatolia site A.2.8 Hatobuilico site Appendix B : Logistic regression analysis result B.1 B.2 All study site . Specific site B.2.1 Bobonaro site . B.2.2 Cailaco site B.2.3 Zumalai site .. 165 178 189 151 134 139 143 145 147 148 149 150 117 122 126 128 130 131 132 133

CHAPTER I INTRODUCTION1.1 Background of StudyEast Timor is a rugged island with a narrow or non existent coastal plain along its northern coast and a southern coastal plain that varies from less than a kilometers wide in some areas to as much as 20 km in others. Highest mountain with a height of 2,963 meters is the Tatamailau or Ramelau in the Ainaro district. Slopes are steep, with as much as 44% of the country having a slope of 40% or more. Slopes this steep may need a zigzag path to climb. The soils are limestone-dominated. Such soils are prone to erosion, particularly on steep slopes and where vegetation cover has been degraded by poor agricultural practices or deforestation. This is the case in many parts of East Timor where the natural vegetation has been modified by human activity over centuries leaving sparse savannah woodland or grassland in most areas. East Timor is dryer than most equatorial islands, receiving most of its rainfall during the northwestern monsoon, which occurs from December to March. Southern slopes receive additional rain during the shorter southeast trade winds period between May and July. East Timor has risk number of natural hazards. Each year, heavy seasonal rain falling on steep slopes causes frequent flash flooding and slope failure, which are considered to be the two major natural hazards in the country. Apart from their potential to cause casualties and damage to rural communities, these events cause major disruption to the fragile road network, isolating communities and even whole districts for a long duration.. East Timor has two climate seasons are wet and dry season. From November to April, the country is risk of tropical cyclones and lesser tropical storms, which can cause coastal flooding and wave damage. In the dry season, drought conditions exist in large parts of East Timor. A delay in the onset of seasonal rains can become disastrous as fires can get quickly out of control. East Timor has a very fragile environment. It is particularly dried compared with other parts of the region, and is prone to regular droughts. Deforestation combined with steep slopes, thin soils and heavy seasonal rains have resulted in erosion and soil loss.

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The situation has been exacerbated by deforestation, which has become more substantial during the last three decades. One of the countrys most valued forest resources is sandalwood which has now been reduced to just a few stands due to of over-exploitation. Another problem is that many rural communities rely on selling wood for fuel as source of family income and as a result, have contributed to deforestation. Geological hazards also threaten East Timor. Areas to the north of the island have experienced earthquakes of up to 6.9 on the Richter scale within the last 10 years. These can cause local tsunamis. A four-meter-high tsunami, caused by a major earthquake, struck the north coast of Timor in 1995. In addition, other hazards exist, including major transport accidents; urban fires and agricultural hazards. These risks are likely to increase as the nation develops unless necessary precautions are made and regulations put in place. Slope failures (i.e., landslide and surface failure) in mountainous terrain often occur as a result of heavy rainfall, resulting in the loss of life and damage to the natural environment. In this regard, slope failure hazard assessment as well as identify the characteristics and distribution of slope failure can provide much mitigation through proper project planning and implementation.

1.2 Propose and Scope of the StudyIt is difficult to examine the natural hazard as well as slope failure hazard in East Timor because of the lack of consistent data, however little data has been collected to provide this study. The primary aims of this initial study are to identify the major influence factors for slope failure in East Timor. Logistic regression analysis is a multivariate statistical analysis has been used extensively at most of previously researcher to predict the factors influence to the slope failures occurrences. The purpose of this study is to present a method that utilizes and employs statistical analysis to define the physical parameters contributing to the occurrence of landslides. This method allows a series of statistically meaningful and independent variables to be included in the assessment of the analysis model. The procedure is based on the actual slope failure cases and is therefore representative of failure conditions and relatively objective. Logistic regression analysis describing in this study is to: To know the actual condition and characteristics of slope failure in East Timor

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Identify clearly the factors that are related to slope failures, Estimate the relative contribution of factors causing slope failures, and Establish a relation between the factors and slope failures.

The scope of activities with developing and applying the logistic regression analysis in this study consist of five main steps: Pre-selection of variables based on a slope failure distribution analysis; Selection of statistically significant variables by a P-value significance test; Logistic regression modeling with those variables that passed the significance test; Logistic regression modeling with significant variables including the interaction terms; and Evaluation of the model results.

In the first step, a slope failure characteristics analysis is used to pre-select the variables that are relevant for the regression. This analysis involves overlaying the variables of category of slope failure occurrences and the variables of category of a factor (such as lithology), then calculating the percentage of coverage of the slope failure occurrence on each class for each input factor, such as slope inclination angle within elevation factor. By comparing the slope failure distributions, a preliminary ranking of the variables can be developed. Important variables will be considered in the following significance tests. In the second step, the significance p-value of 0.05 is specified as the cut-off value to choose the variable for further analyses and 0.10 is chosen as the value for elimination of insignificant variables. The variables that passed the significance test can be entered into the logistic regression modeling in the next step. After the steps of pre-selection and significance test, we can know the total of the independent variables were selected for the regression analyzing. In the third step, the model is checked for its goodness of fit by entering a variable or removing a variable. Following the SPSS procedures, 20 iterations are preferred to obtain optimal models of analysis. The final suitable logistic regression analysis is based on the variables presented in the final step of the statistical calculation in the SPSS program, and the regression coefficients are obtained.

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In the fourth step, the interaction terms representing the interactions among variables are entered into the logistic regression analysis. In particular, the interactions among variables from six category factors (i.e., lithology, vegetation cover, slope aspect, elevation inclination angle and landscape topography) are selected to form the interaction terms for the

regression. The interactions among two, three, and four variables at one time were tested. Only significant interaction terms are retained for analyzing. When interaction terms are introduced into the model, the ranking of the significance of some of the variables will change. Some of the variables showing significance in the previous step may become insignificant, and some of the interaction terms showing significance are added into the model. After many tests with the interaction terms, the model that produces the best prediction result is adopted as the final optimal model. In the fifth step, the models obtained from above and the factors influence to the slope failure occurrences generated from the models are evaluated. Slope failure probability values between 0 and 1 at each unique-condition unit are obtained from the final regression.

1.3 Data Collection and Methodology of ResearchSlope failure often occurs at specific locations under certain topographic and geologic conditions. Therefore it is important to utilize existing data (history of the problem and data review) in order to understand the topography, geology, and properties of similar slope failure. It is also important to understand their relationship with meteorologist factors, chronology of topographic change or erosion by rivers, earthquakes, and other factors which may have a relationship with the slope deformation surrounding the study site prior to the detailed investigation. In this study, data collections to provide this research are: Aerial photograph with magnitude scale 1:13,000 Topography map with magnitude scale 1:15,000 geology map with magnitude scale 1:350,000 Rainfall Data (July 2004 December 2006)

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Investigations of Aerial photographs are used to understand the chronologic and topographic changes over the country. Furthermore, in order to be able to effectively interpret the phenomena related to slope failure. By utilizing aerial photographs, it is possible to interpret landslide phenomena and warning signs, geology structure, topography and distribution of vegetation type. Topographic investigation is necessary to identify any changes in the site topography. That can be accomplished by recognizing; 1) the overall topographic feature of the site; 2) understanding the topographic characteristics of the site slopes; and 3) estimating the regional geologic structure of the site. Such methods include comparing the aerial photographs of the site and vicinity taken prior to and after the sliding, and interpreting the topographic maps and aerial photographs. Geological map is necessary to investigate geologic structure, however to identify the bedrock distribution, rocks types and rock mass engineering properties in the surrounding study site. Based on aerial photograph and topographic map in the study area, there are 506 number of slope failures from the inventory. For each slope failures inventory, it includes information such as location, slope geometry (slope inclination angle, direction, width and length), geology factor (rocks types), vegetation cover (high tree, low tree, grassland and no vegetation), landscape topography (valley, ridge and flat) and slope aspect (direction) are used for actual condition and characteristics of slope failures analysis. Considering the regional variations identified and data availability in the above, six factors were considered in this study: geology factor, vegetation cover, slope gradient (i.e., slope inclination angle), elevation, landscape topography and slope aspect (i.e., direction). Detail research methodology in this study has shown in Figure 1.1.

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Start- Topographic Map - Aerial Photograph - Geological Map - Rainfall Data

Select Study Area and Detection of Slope Failure

Map of Locations Representing of the Selecting Area of Slope Failure and Unfailure Slope by Random - Lothology - Slope Gradient - Vegetation Cover - Slope Aspect - Elevation - Landscape Topography

Extraction of Independent Variables for points representing of Slope Failure and Unfailure Multivariate Statistical Analysis by Logistic Regression Analysis

Stepwise of logistic Regression Analysis

Development of Logistic regression Analysis

Verification of the probabilities and Susceptibilities of Slope failure mapping

Result of Analysis and Discussion

Figure.1.1 Flow chart of research methodology

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Figure 1.2 Slope failure locations with aerial photograph.

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A key assumption using this approach is that the potential (occurrence possibility) of slope failure will be comparable to the actual frequency of slope failure. After the study area was selected, slope failure areas were detected in the study area by investigation of Aerial photograph. The maps of aerial photograph used were these from January 2000 (Figure 1.2), after slope failure. This air photograph, in combination with logistic regression analysis result and GIS was used to evaluate and predicted the probability of slope failure in the study area. A GIS database has been developed using ArcGIS version 3.3 software. The slope failure in the study area and the factors contributing for slope failure have been recorded and saved as separate layers in the database. All the data layers were in vector format, transformed in grids with cell size 30x30 meters.

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CHAPTER II STUDY SITE DESCRIPTION AND LITERATURE REVIEW2.1 Study Site Description 2.1.1 Geographical Condition , Location, and Boundaries, of Study SiteEast Timor is approximately the eastern half of the island of Timor, and part of the Lesser Sunda Island chain, distant from Australia by only 500 km. It is between longitudes 1270 22 and 1320 25 and latitude 80 17 and 100 22 with a general orientation of southwest to northeast. The area of East Timor as a whole is only about 15,007 km2 and the coastline is 706 km. Timors boundaries are as follows: In the north, the boundary of Wetar Strait with Ombai Strait. In the east, the boundary with the Maluku Strait. In the south, the boundary with the Timor Sea. In the west, the boundary with Nusa Tenggara Timor, the eastern region of Indonesia.

In this study, the study site at the western part of East Timor. There are lies between latitude 080 52 30 and 090 1500 to South and longitude 1250 15 30and 1260 1500to East, and has area about 1448 km2 with elevations ranging from 200m to 2100m. The study area is mountainous area, which is also landslide prone, and is quite flat in the south. The underlying bedrock is limestone, siltstone, sandstone, shale and conglomerate. Most folds are developed in the western mountainous area and a thrust fault extends from north to south of the study area. The study site are covering eight sub district in western part of East Timor, there are Bobonaro, Cailaco, Zumalai, Atsabe, Maliana, Ainaro, Hatolia and Hatobuilico (Figure 2.1, Figure 2.2 , and Figure 2.3 ).

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N

Firuge 2.1 Boundary of study site

Boundary of study site

Atsabe Cailaco Hatolia Hatobuilico

Maliana Zumalai Bobonaro

Ainaro

10

Atsabe Hatobuilico

Cailaco

Hatolia

Maliana Zumalai Bobonaro

Ainaro

Figure 2.2 Study site and slope failure 11

Atsabe

Cailaco Hatobuilico Hatolia

Maliana Bobonaro Zumalai

Ainaro

Figure 2.3 Study site and unfailure slope

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2.1.2 TopographyA mountain range runs from the east to the west of East Timor. The mountainous terrain results in many watersheds and streams, making transportation very difficult. The land is made up of limestone, coral, thick clayey soil, sand and a small amount of volcanic origin. In East Timor there are seven mountains with heights over 2000m as seen in the following table. The highest mountain with a height of 2,963 metres is the Tatamailau peak of the Ramelau Range in the Ainaro district. Name of District Height Mountain Above Sea Level 1.Tatamailau Ainaro 2,963 metres 2.Sabiria Aileu 2,495 metres 3.Usululi Baucau 2,620 metres 4.Harupai Ermera 2,293 metres 5.Cablake Manufahi 2,495 metres 6.Laklo Manatuto 2,050 metres 7.Matebian Baucau 2,373 metres As a broad outline, the watersheds of East Timor can be divided into two areas; northern and southern. Of the many rivers in East Timor, the following rivers flow all year round; the Laklo river in the district of Manatuto, the Seical river in Baucau district, the Bulobo, Marobo, Malibaka and Nunura rivers in Bobonaro district, Gleno river in Ermera district, Karau Ulun in Manufahi district, the rivers of Dilor, Uca, Uwetoko, Bebui and Irabere in Viqueque district, the Loes river in Liquica, and the Tono river in Oecussi. Overall the climate in East Timor is classified as tropical. The minimum temperature range is 18-21C while the maximum temperature range is 26-32C. In the north (as far east as Baucau) the rainy season begins in November and is usually accompanied by a westerly monsoon; the months of May and October are months of change from dry to wet season. In the east and the south the situation is different - the rainy season is at its height in April. The dry season occurs during May, and the rainy season returns at the beginning of June until August. When it is winter in Australia (August to October), sometimes the temperature in East Timor can be as low as 18c. This is also true of the opposite scenario. When it is summer in Australia, the temperature is high on the coast of East Timor, even in the rainy season.

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Figure 2.4 Topography of East Timor (Source from Internet)

2.1.3 Geology, land forms, and soilTimor is a continental fragment, not a volcanic island. The foundation is largely made up of limestone and other sedimentary deposits. This differentiates it from its neighbors to the north and west in the Sunda chain which are volcanic. It is theorized that Timor, in fact, is a piece of the Australian geological plate which, separated from the mainland, has been pushed into the Indonesian plate. (Monk et al. 1997:23) That it has been repeatedly uplifted and submerged over the millennia accounts for the presence of coral layers in the soil at heights of up to 2,000 meters above sea level. The erosion of these rocks is considerable. The topography of East Timor is dominated by a massive central backbone of up to 3,000 meters, the Ramelau mountain range, which is dissected by deep valleys prone to flash floods. Toward the northern side, the mountains extend almost to the coast without extensive

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plains. To the south, on the other hand, mountains taper off some distance from the sea leaving a wide littoral plain, more propitious for agriculture. The plain is 20 km and even 30 km wide running almost the length of East Timor and widens at the eastern end. There are more perennial streams flowing to the southern coast which allow for more agriculture and irrigation. The Fuiloro plateau, in the far East, descends in altitude southwards, from 700 meters to 1500 meters. The slope is almost unnoticeable due to the large area, which may have been the primitive lagoon of a big fossil atoll. Three other main planaltic formations surround it: Nri in the north, Lospalos to the center-west and Rare to the south. Nestled in the mountain range near the border with West Timor lies the low plateau of Maliana in what was once a gulf. This area is better suited to irrigated agriculture than the rest of East Timor. As much as 44 percent of East Timor may have a slope of land of more than 40 percent. (Monk et al. 1997:52; Dick 1991) A slope of 40 percent is difficult to descend and may need a zigzag path. Bierenbroodspot (1986 in Monk et al. 1997:107) suggested the following erodibilty classification and appropriate uses for sloping land on Timor: Land with less than 17 percent slope tends to be suitable for cultivation provided that any incipient soil erosion is controlled; Land between 17 percent and 30 percent is best used for grazing as soil erosion cannot be controlled on such steep slopes under permanent or shifting cultivation; Land over 30 percent suffering from soil erosion is unsuitable for sustainable agriculture and can require reforestation or conversion to suitable tree or perennial cover crops. Soils are ultimately the combination of base rock, topography, climate, vegetation and, to some extent, the fauna which is present in any one place. Topography influences the weathering, depth, erodibility, infiltration, and leaching of a soil. The major limitations to plant production, and therefore to agriculture, are steep slopes and shallow soils. The outerarc islands, dominated by limestone, generally have lower, rounded hills with relatively infertile, alkaline soils. Often the better soils are only on the alluvial deposits along the coasts and in depressions such as lake or lacustrine basins surrounded by steeper, eroded land. Such a lacustrine basin occurs in north central Timor (Maliana).

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Climate is perhaps the most important factor affecting the development of tropical soils (Mohr et al. 1972). The most important climatic factor affecting tropical soil fertility and structure is temperature. Up to 20C, humus forms faster than it is broken down, enriching the soils with nutrients and improving its structure (Chambers 1983). Above 20C, and particularly in hot, arid Conditions, bacteria decompose dead vegetation faster than it accumulates, with the result that humus and fertility levels diminish. Thus, many tropical soils have a low organic content and inherent low fertility. Tropical soils can maintain natural fertility where climatic conditions favor the accumulation of humus. This occurs in continuously moist soils found in wetter regions or higher altitudes; or when nutrients are resupplied from outside the system, such as when a volcanic eruption spreads mineral-rich ash deposits over the land. A second important climatic factor affecting fertility and structure is the soil moisture regime, that is, the relationship between the length of the dry season and total rainfall. Most of the area experiences a seasonal climate. Prolonged droughts are followed by total annual precipitation which falls within a few months or even days. This strongly affects the movement of salts and minerals through the soil. Soils may bake hard and crack during a prolonged dry season. These conditions are intensified in savannas, because the annual fires remove the supply of new organic matter and, at the end of the rainy season with ground cover at a minimum, heavy rainfall may result in surface runoff with potential for rill and gully erosion. The soils of the outer-arc islands tend to have less clay and, as a result, lower water holding capacity (WHC) than the inner volcanic arc islands (Carson 1989). Shallow, calcareous soils on raised coral reefs on islands such as Timor have a limited WHC; Timor's soils are 20-30 cm deep over the island (Mahadeva and Laksono 1976), except where there are lake deposits. The area with steep slopes and thin soils is naturally biased toward high rates of erosion. Some local farmers have an understanding for the fragility of the soil and have developed a sophisticated indigenous method of soil conservation. In other areas, however, soil is being lost at high rates through inappropriate land management. In particular, high losses of organic matter occur during and shortly after clearing, and before establishment of suitable cover crops. Under such conditions, intense bombardment of the soil surface by rain can quickly break down soilorgano aggregates, thus permitting high erosion losses. In addition, surface temperatures

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increase on cleared land, thus increasing oxidation and loss of organic matter. As it is difficult to restore organic matter, conservation measures such as early planting of cover crops, incorporation of plant residues and erosion control should be strictly followed (FENCO 1981).

Figure 2.5: East Timor geological map (Instituto Superior Tecnico, Portugal, 2000)

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Figure 2.6 Physical types of East TimorSource: Monk et al. 1997: Figure 2.10, originally from RePPProT 1989bThe physical types present in East Timor are 2 - tidal swamps; 4 - meander belts; 7 - Fan and lahars; 8 - terraces; 9 undulating rolling and hillocky plains; 10 - hills; and 11 - mountains. (Monk, et al. 1997:50; original RePPProT). A revised draft map is in preparation for East Timor by the Geological Research and Development Centre, Bandung -GRDC. The geology of East Timor was mapped previously by Audley-Charles (1968).

Fig.2.7 Areas prone of landslide and flooding in East Timor (Source: Monk et al. 1997: Figure 2.13originally RePPProt 1989a.

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2.1.4 ClimateKnowledge of climatic conditions is of great importance for environmental management. Climatic maps showing the amount of rainfall, including dry or drought periods indicate what crops that will grow on an island or in a particular valley, or what pests may migrate into the area if particular crops are cultivated. Much historical data exists for both temperature and rainfall from the Portuguese colonial period. East Timor continues to have more stations for measuring these and other factors than do the neighboring areas in Indonesia. Climate is a function of the latitude, wind patterns bringing rain, rainfall volume, seasonality, and intensity, soils, and the altitude above sea level. There is a clear correlation for East Timor between altitude and average temperature and seasonal variations as shown by Felgas (Figure 2.8, reproduced in Monk 1997). While the general climate in East Timor can be classified as hot (average temperature 210 C) and humid (70-80 percent), the geographic position and the topography is such that climatic conditions differ substantially between mountainous regions and lower altitudes. Even regions of the same altitude have very different climates when separated by high mountains which act like a wall. Therefore, since topography is not equal to climate, a system that separates lowlands, mountains, and plains is a useful first step to classifying climactic conditions. On the southern coast rainfall is high, with volumes of 2,000 mm or more per year spread over a longer period of months. On the northern coast, at the same altitudes, rainfall could be as little as 500-1,000 mm per year and concentrated in a shorter period of months. The Indonesian government, (RePPProT) used the Schmidt and Ferguson method of counting and comparing months with more than or less than 100 mm rainfall each and the Fontanel and Chantefort method of combining this with temperature data. The result is that the northern coast is basically seasonally dry except on the coast which is permanently dry. The southern coast is permanently moist (Monk et al. 1997:75-77). A permanently moist climate might allow for the growing of two annual harvests of crops, such as rice. However, for the purpose of land use planning, a more detailed discrimination of climate is necessary. (See sections on rainfall, vegetative cover, and agriculture, below.)

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Figure 2.8: Altitude and mean temperature correlationSource: Monk et al. 1997: Figure 2.19, originally from Felgas 1956

Figure 2.9 Monthly distribution of rainfall in Timor Leste (based on data from Ferreira 1965).

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It is difficult to examine present climate risk in East Timor because of the lack of consistent climate data. During the Portuguese period several stations measured rainfall/climate data for varying periods from 1914 to 1975, but many of these records are incomplete. It is unclear how much data was recorded during the period of Indonesian control from 1975 to 1999. Since 1999 there have been no meteorological or hydrological services available in East Timor. In November 2000, 50 rain gauges were distributed around the country by the Department of Agriculture and funded by AusAID, however little data has been collected from these gauges (Ongoing monitoring and educational activities are important to establish continuity in such programs). Automatic weather stations have been installed at the main airports (Dili, Baucau and Suai) by the Australian Bureau of Meteorology (Darwin). Weather or seasonal climate forecasts have only been used sporadically by the National Disaster Management Office, and these were based on information available from the internet. Furthermore, there are currently no means to communicate this information to the users that require it. The Australian Bureau of Meteorology will be providing weather forecasts for East Timor for as long as Australian forces are present in the territory (see http://www.bom.gov.au/reguser/by_prod/aviation/). As well as this lack of temperature and rainfall data, there is a lack of consistent data on a range of climate-related processes like river runoff, tides, floods, and groundwater levels. This lack of data makes it difficult to assess whether climate is changing in East Timor. There is also insufficient data on which to base scenarios of future climate changes and its impact on environmental and social systems. Nevertheless, some broad conclusions about climate change in East Timor can be drawn and these will be discussed in the following pages. East Timor is predominately influenced by the monsoon climate. There are two distinct rainfall patterns: the Northern Monomodal Rainfall Pattern produces a 4-6 month wet season beginning in December which affects most of the northern side of the country and tapers to the East; and the Southern Bimodal Rainfall Pattern which produces a longer (7-9 month) wet season with two rainfall peaks starting in December and again in May which affects the southern side of the country (Keefer 2000: 11). Rainfall can be broadly described as being low to very low along the northern coast of East Timor (2500mm/annum) in high altitude areas which are mostly in the west. In common with most tropical locations, extremely heavy rainfalls occasionally occur over East Timor during relatively short time intervals (Figure 2.9). The general climatic conditions define two zones: northern areas and southern areas, divided by mountains into: The northern area characterized by one rainfall peak within four to six months in the wet season. The northern coastal areas have an average yearly rainfall from 500 to 1500 mm, while higher altitudes above 500 m receive abundant rainfall from 1 500 to 3 000 mm, an average of monthly rainfall from 50mm to 150mm (Figure 2.10). The southern areas characterized by two rainfall peaks that appear within seven to nine months in the wet season. The first peak appears between December and February and the second peak appears between May and June. The southern coastal areas have an average annual rainfall from 1 500 to 2000 mm. The areas above 500 m receive more abundant rainfall from 1 700 to 3 500 mm, an average of monthly rainfall from 70 mm to 150mm (Figure 2.11 and 2.12).The amount of daily rainfall in Dare stationJuly-Dec. 2004 240 220 200 180 160 140 120 100 80 60 40 20 05.1 - 10 15.1 - 20 25.1 - 30 35.1 - 40 45.1 - 50 55.1 - 60 65.1 - 70 75.1 - 80 85.1 - 90 95.1 - 100 0

2005

2006

Days

Intensity rainfall (mm/day)

Figure 2.10 The amount of daily rainfall from July 2004 December 2006 in Dare station

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The amount of daily ranifall in Aileu StationJuly -Dec. 2004 200 180 160 140 Days 120 100 80 60 40 20 00 0.1 - 5 5.1 - 10 10.1 - 15 15.1 - 20 20.1 - 25 25.1 - 30 30.1 - 35 35.1 - 40 40.1 - 45 45.1 - 50 50.1 - 55 55.1 - 60 60.1 - 65 65.1 - 70 70.1 - 75

2005

2006

Intensity rainfall(mm/day)

Figure 2.11 The amount of daily rainfall from July 2004 December 2006 In Aileu stationThe amount of daily rainfall in Betano stationJuly-Dec. 2004 260 240 220 200 180 160 140 120 100 80 60 40 20 00 0.1 - 5 5.1 - 10 10.1 - 15 15.1 - 20 20.1 - 25 25.1 - 30 30.1 - 35 35.1 - 40 40.1 - 45 45.1 - 50 50.1 - 55 55.1 - 60 60.1 - 65 65.1 - 70 70.1 - 75 75.1 - 80

2005

2006

Days

Intensity rainfall (mm/day)

Figure 2.12 The amount of daily rainfall from July 2004 December 2006 In Betano station

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Figure 2.13: ClimateSource: Monk et al. 1997: Figure 2.17, originally from RePPProT 1989a

2.1.5 VegetationThe present vegetation cover is a combination of what could be there given the climate and the particularities of each area, and anthropic actions of settlements, clearings, agriculture, grazing, plantations, etc. This section speculates what the natural distribution of forests and grasslands in East Timor would have been. It also assesses what is known of the historical distribution of vegetation cover. It was noted previously that East Timor suffers from an exceptionally dry climate, especially in the northern half. This condition directly affects the likely historical distribution of forest. Monk suggests that classification of forests in this area is particularly difficult because of the extreme influence of altitude and rainfall patterns on forest types. These vary widely in small areas and along steep slopes. Not enough work has been done on classification specifically for East Nusa Tenggara, Maluku and East Timor. Figure 2.14 shows the types of forest which would be naturally occurring in eastern Indonesia based on the number of dry months and annual rainfall. According to the classification utilized in Monk et al. (1997), the natural vegetation for East Timor would be various kinds of forest from evergreen in the mountains, especially the southern slopes, to thorn forest along the northern coasts. Because of the influence of the mountains on rainfall in the southern part of East Timor, by the 1950s rainforest originally

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occurring on the south escarpment of the Fuiloro limestone plateau had been extensively replaced by secondary forest (Felgas 1956; van Steenis, un-publicized. in Monk et al. 1997:234). All land would be covered by different types of forest. Savanna and grassland are assumed to be secondary vegetation (Monk et al. 1997:197). This vegetation distribution would be before the indigenous people or the Portuguese began to occupy the land. Monsoon forest, one of the most sensitive and vulnerable of the tropical forest formations, is easily lost. The original monsoon forests of the dry regions have been extensively replaced by savanna and grassland. Generations have repeatedly burnt the dry forests for hunting and to accommodate shifting cultivation. (Monk et al. 1997:202) When these forest types are disturbed, principally by burning, then secondary vegetation, savanna or grasslands emerge. Figure 2.15 indicates there are very few areas of forest left. Deforestation is not a phenomenon confined to the eastern part of the island. When Crippen International carried out a detailed survey of forests in West Timor, it found that the majority of this part of the island was also covered with savannas and grasslands (Crippen International 1980 vol.14 - Forestry). It is also worth noting that when RePPProT used Landsat images from 1972 to 1986 to update aerial photos and coverage estimates, there were no aerial photos available for East Timor. Official numbers exist for the location and distribution of forest types on East Timor but these are of uncertain accuracy because of both their source and their age. Up-to date information gathered from remote sensing satellites or aerial photography, and actual in-thefield observations will be of critical importance. Monk et al. (1997: 211) concludes: "The accuracy of historical data available for East Timor is even more difficult to assess as no official survey seems to exist. Felgas (1956) quotes estimates by Ruy Cinatti, the head of the Portuguese Timor Agricultural 17 and Veterinary Technical Department indicating that there were 74 km2 of mangroves; 2149 km2 of primary forest and 2646 km2 of savanna and grassland. This suggests that closed forest cover in East Timor rose from 16 percent in the 1950s to 29 percent in the 1980s. It is, however, not likely that such extensive reforestation occurred either naturally or through human activity. This casts doubt on any forestcover figures for East Timor. Scrub forest, savannas, and grasslands areas now make up as much as three

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fourths of the land. Various grasses, xerophytic shrubs in the driest areas, and other shrubs are present including evergreens, small trees, and vines interspersed with stands of casuarina, eucalyptus, bamboo, acacia, or even palms. (Metzner 1977:104-114) Although much anecdotal information on the savannas exists, detailed quantitative descriptions are lacking. There are three ecological descriptions including two prepared by consultancy companies on West Timor (ACIL Australia Pty. 1986m; Crippen International 1980F).

Figure 2.14: Natural distribution of forest in East TimorNote: A = Evergreen rain forest; B= Semi-evergreen rain forest; C= Moist deciduous forest; D= Dry deciduous forest;E= Thorn forest Source: Monk et al 1997: Figure 4.4

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Figure 2.15 Actual forestSource: Monk et al. 1997: Figure 4.5 Based on data and maps from Collins et al. 1991 with permission from N.M.Collins of World Conservation Monitoring Centre; The National Forestry Inventory Project, from the Directorate General of Forest Inventory and Land Use Planning and Information System Development Project for the Management of Tropical Forests; RePPProT 190b; K.A. Monk pers. obs.

The main consequences of deforestation are loss of genetic resources and increased risk of erosion and flash floods resulting from bare hillsides. Even before the era of Portuguese colonization, the original forest area of East Timor was shrinking as agriculture expanded through plantations or household production. Particularly in a landscape not endowed with fertile soils and regular and bountiful rainfall, the productivity of newly cleared lands quickly falls and farmers are forced to burn and clear new lands. Particularly in a landscape not endowed with fertile soils and regular and bountiful rainfall, the productivity of newly cleared lands quickly falls and farmers are forced to burn and clear new lands. If this occurs before the soil is entirely exhausted, the area will quickly return to a secondary forest lacking the species and complexities of the primary forest. In 1994, the GOI estimated actual land use (Table 2.1). The term light forest lands is used for much of the shrub or savanna. Saldanha, (1999) describes a forest component distinct from the majority shrubs (Table 2.2). As many as 70,000 hectares of forest were burned in the last decade by official estimates but some analysts believe that the real number is higher (Gomes 1999; 65). There is not adequate information on the actual extent and conditions of the various forests and forest types given the deforestation that has occurred in recent years. From the time of the first settlers on the island there has been shifting cultivation with negative but not 27

disastrous consequences. However, in recent years with the high increase in population in certain areas, there is increased pressure on the land. Many Timorese have been displaced to more marginal lands and their former lands occupied by migrant farmers whose practices may not be adapted to Timorese conditions. The situation has been exacerbated by deforestation, which has become more substantial during the last three decades. One of the countrys most valued forest resources is sandalwood which has now been reduced to just a few stands due to of over-exploitation. Another problem is that many rural communities rely on selling wood for fuel as source of family income and as a result, have contributed to deforestation (Figures 2.14, Figure 2.15 and Figure 2.16).

Figure 2.16 Firewood cut by community as a source of income and used for cooking.

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Figure 2.17 Cutting and burning the forest

Figure 2.18 Sifting agriculture (slashes & burn agriculture)

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Tabel 2.1 Land use in East Timor, 1994, Indonesia government estimated Land useHuman settlement Irrigated rice field Non-irrigated rice field Plantation Mixed framing Light forest Bush land Lakes, ponds, swamps Critical land Others Source: Brahmana and Emmanuel, 1994

%1 3 3 3 2 76 9 0 0 1

Table 2.2 Land Use, Alternative estimation Land use %Village Rice paddies Rain fed paddies Plantation rice paddies Mix plantation Homogeneous mix Shrubs Forest Swamps, lakes Roads, rivers 1 3 4 1 1 8 81 1 0 1

Source: Saldanha, 1999

East Timor is a comparatively small but mountainous territory, extending roughly 300 km in length and 100 km at its widest point. Estimates of the extent of forest cover over East Timor are notoriously variable. One respected study using LandSat imagery established a figure of 41 per cent for the eastern half of the island, with just 29 per cent as closed forest; this figure was adopted by the Indonesian government, which recorded forest cover as 40.6 per cent.3 These totals cover a wide range of forest types, including predominantly open and mixed savanna along the drier northern coast and hinterland, extensive eucalyptus and moist upland forests in the central highlands and semi deciduous monsoon and tropical lowland forest blocks along the southern coast and hinterland (Figure 2.19)

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Figure 2.19 Category of land cover in East Timor(Modified version of map produced by GIS unit, Ministry of Agriculture and Fisheries and Japan International Cooperation Agency [JICA], Dili, East Timor,2001)

2.2 Literature ReviewA slope failure (i.e., landslide, surface failure, debris flow, rockfall and erosion) is define by Cruden (1991) for the working party on world slope failure inventory, as a movement of a mass of rock, earth or debris down a slope. Varnes (1978) indicated that slope movement would be a better comprehensive term as it does not infer process. His definition is a downward and outward movement of slope forming materials under the influence of gravity. In both the mining and civil aspects of engineering, slope failures can take lives and negate all the hard design and development processes involved in completion of a project. Slope failures can occur at any time of the year and sometimes can happen without any obvious warning signs. They can range from sinkholes to rockslides or avalanches. There are many effective ways to prevent of slope failures but uncertainties about surrounding environmental conditions that may cause a slope failure must be investigated to find the proper way to handle the potential problem.

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From the aerial photo investigation in the study area, the slope failures were mainly landslide and surface failure, and most landslide features is subdivides in recent and older landslides. Cruden and Varnes, 1996 defined that Landslide described as recent have distinct features, clearly define boundaries and have moved in the past several years. They include active, suspended, and dormant earth flows and earth slides. Older landslides have hummocky topography, muted features, and indistinct boundaries. This category includes dormant, relic, and ancient earth flows and earth slides. Data on recent and older landslides have been used to develop the landslide hazard analysis in this study. These landslides are predominantly shallow failures with basal failure planes in the soil or weathered bedrock. Although deeper earth and rock slides also occur, such deep landslides overlap areas with shallow landslides. Slope failures have caused large numbers of casualties and huge economic losses in hilly and mountainous areas of the world. In tropical country like East Timor where heavy rainfall occasionally occurred and high temperatures around the year, cause intense

weathering and formation of thick soil and weathered rock profile. With these set of climate and geological condition, combined with other causative factors, slope failure is one of the most destructive natural disasters in East Timor. Each year, a number of major slope failures were reported in East Timor, involving fill and cut of natural slopes, which results in death of people and have posed serious threats to settlements and structures that support transportation. Most of these slope failure occurred on natural slope and cut slopes or embankments alongside roads in mountainous areas. Richard Dikau at al. (1996) stated as the probability of slope failure changes, due to changing climate or increasing human activity it becomes more important to recognize the potential event as well as geomorphologies and geology and these can be catalogued, classified and mapped. A primary task, therefore, is to develop a manual of such indicators and mapping techniques, providing a basic understanding to slope failure recognition. However, potential sites that are slope failure-prone should therefore be identified in advance to reduce such damage. In this regard, actual condition, distribution and characteristics of slope failure will be known to provide much of the basic information essential for hazard mitigation through proper project planning and implementation.

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Slope failure hazard was defined by Varnes (1984) as the probability of occurrence of a potentially damaging slope failure phenomenon within a specified period of time and within a given area. The factors that determine the slope failure hazard of an area may be grouped in two categories: (1) the intrinsic variables that contribute to slope failure occurrence, such as geology, slope inclination angle, slope aspect, elevation, soil geotechnical properties, vegetation cover, and a long-term drainage patterns; and (2) the extrinsic variables that tend to trigger slope failure occurrence, such as heavy rainfall, and earthquakes (Wu and Sidle 1995); Atkinson and Massari 1998). Obviously, the probability of slope failure occurrence depends on both the intrinsic and extrinsic variables. However, the extrinsic variables may change over a very short time span, and are thus very difficult to estimate. If extrinsic variables are not taken into account, the term of actual condition, characteristics and distribution slope failure could be employed to define the likelihood of occurrence of a slope failure event. The spatial distribution of the intrinsic variables within a given area determines the spatial distribution of relative slope failure occurrences in that region (Carrara and others 1995). A variety of techniques, such as heuristic, statistical, and deterministic approaches, has been developed to predicted probabilities of slope failure occurrences. In heuristic approaches, expert opinions are used to estimate slope failure potential from data on intrinsic variables. They are based on the assumption that the relationship between probability of slope failure occurrences and the intrinsic variables are known and are specified in the model of analysis. A set of variables are then entered into the analysis model to estimate probability of slope failure occurrence (Niemann and Howes 1991; Anbalagan 1992; Pachauri and Pant 1992; Atkinson and Massari 1998). One problem with the heuristic models is that they need long-term information on the slope failure and their causal factors for a similar geo-environmental condition or for the same site, and these are, in most cases, not available. Statistical analysis models involve the statistical determination of the combinations of variables that have led to slope failure occurrence in the past. Quantitative or semi-quantitative estimates are then made for areas currently free of slope failure, but where similar conditions exist. Logistic regression analysis is one of the multivariate statistical analysis models, is useful for predicting presence or absence of a outcome based on values of a set of predictor

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variables.Klein-baum (1994), stated that the advantage of logistic regression analysis over other multivariate statistical technique, including multiple regression analysis and discriminant analysis, is that the dependent variable can have only two values an event occurring or not occurring, and that predicted values can be interpreted as probability because they are constrained to fall in the interval between 0 and 1. Mark and Ellen (1995) used logistic regression to predict the sites of rainfall induced shallow landslides that initiate debris flows in San Mateo County, California. In this study, the dependent variable is a binary variable representing of the slope failure or un-failure of slopes. Recently, there were studies on slope failures hazard evaluation using GIS, and many of these studies have applied probabilistic methods (Rowbotham and Dudycha 1998; Guzzetti et al. 1999; Jibson et al. 2000; Luzi et al. 2000; Parise and Jibson 2000; Rautelal and Lakhera 2000; Baeza and Corominas 2001; Lee and Min 2001; Temesgen et al. 2001; Clerici et al. 2002; Donati and Turrini 2002; Lee et al. 2002a,b; Rece and Capolongo 2002; Zhou et al. 2002; Chung and Fabbri 2003; Remondo et al. 2003; Lee and Choi 2003c; Lee et al. 2004b). The logistic regression method has also been applied to slope failure hazard mapping (Atkinson and Massari 1998; Dai et al. 2001; Dai and Lee 2002; Ohlmacher and Davis 2003). There are other methods for hazard mapping, such as the deterministic (or safety factor) approach used by Gokceoglu et al. (2000); Romeo (2000); Carro et al. (2003); Shou and Wang (2003), and Zhou et al. (2003). Fuzzy logic and artificial neural network methods have also been applied in various case studies (Ercanoglu and Gokceoglu 2002; Pistocchi et al. 2002; Lee et al. 2003a, b; Lee et al. 2004a). To represent the distinction quantitatively, logistic regression analysis were used. For this analysis, the calculated and extracted factors were mapped to a 30-m-resolution grid. The raster data were converted for the statistical program used. Then, using the logistic regression analysis models, the spatial relationships between the slope failure location and each slope failure-related factor, such as geology, vegetation cover, slope gradient (i.e., slope inclination angle), elevation, landscape topography and slope aspect (i.e., direction), were analyzed in the statistical program, and a formula of slope failure occurrence possibility was extracted using the relationships. The formula was used for calculating the probability of slope failure

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occurrence, which was mapped to each grid cell. Finally, the susceptibility and probabilities occurrence map was verified using known slope failure locations and success rates were calculated (Chung and Fabbri 1999) for quantitative verification. In this study, GIS software, ArcView 3.3 and statistical software, SPSS 10.0, were used as the basic analysis tools for spatial management and data manipulation.

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CHAPTER III ACTUAL CONDITION, CHARACTERISTICS AND DISTRIBUTION OF SLOPE FAILURE IN EAST TIMOR3.1 IntroductionAccording to aerial photograph investigation, the actual slope failure distribution will established in this study are landslide, surface failure and mix of landslide and surface failure. From the aerial photo investigation in the study area most landslide features is subdivides in recent and older landslides (Figure 3.1, Figure 3.2, and Figure 3.3). Cruden and Varnes, 1996 defined that Landslide described as recent have distinct features, clearly define boundaries and have moved in the past several years. They include active, suspended, and dormant earth flows and earth slides. Older landslides have hummocky topography, muted features, and indistinct boundaries. This category includes dormant, relic, and ancient earth flows and earth slides. Data on recent and older landslides have been used to develop the landslide hazard analysis in this study. These landslides are predominantly shallow failures with basal failure planes in the soil or weathered bedrock. Although deeper earth and rock slides also occur, such deep landslides overlap areas with shallow landslides. In East Timor, slope failures are common in the mountainous areas and in many regions. The high occasional rainfall, steep slopes, high weathering rates and slope material with a low shear resistance or high clay content are often considered the main preconditions for mass movement in East Timor, turning it in an inherent susceptible area of slope failure. The main causal factors for slope failure in highlands, as found in international literature, can be divided into preparatory and triggering causal factor (Glade and Crozier, 2004). Preparatory causal factors, i.e. factors making slopes susceptible to movement over time without actually initiating it, often reported for this region include the increasing population pressure with slope disturbance and deforestation as a consequence and the reduction in material strength by weathering. Triggering causal factors on the other hand can be seen as external stimuli responsible for the actual initiation of mass movements. The triggering causal factors in the region can be earthquakes, excessive rainfall events and human disturbance such as slope excavation and terracing, inconsiderate irrigation and water leakage.

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Figure 3.1 Older landslide topography in East Timor(Source:Prof. H. Kazama documentation,August 2005)

Figure 3.2 Older landslide topography in East Timor(Source:Prof. H. Kazama documentation, August 2005)

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Figure 3.3 Recent landslide topography in East Timor(Source:Prof. H. Kazama documentation,August 2005)

In many regions of the East Timor highlands, a clear insight into the local causes for mass movement is lacking. Therefore, the search for region-specific solutions is hampered. In East Timor, slope failure i.e., landslides, surface falure, erosion, and rock fall are common in the mountainous areas of all districts but so far no systematic scientific research has been conducted on this topic. Western part of East Timor, situated on the southwestern foot slopes of the mountainous of Tatamailau (Ainaro), Sabiria (Aileu), Harupai (Ermera), Atubuti (Bobonaro) is the most sensitive area for slope failure in East Timor. As a broad outline, the watersheds of East Timor can be divided into two areas; northern and southern. Of the many rivers in this study site, the following rivers flow all year round; the Bulobo, Marobo, Malibaka and Nunura rivers in Bobonaro , Gleno river in Ermera,ladibau in Hatolia, Aimera in Cailaco, Belulik in Ainaro and Mola in Zumalai. Mass movements associated with intense rainstorms are reported to have occurred sporadically in mountainous since the twentieth century but the increase in fatalities and losses as a consequence of the enormous population growth draws attention to the phenomenon nowadays. By studying the causal factors for slope failure in these mountainous areas of western part of East Timor, this study tries to contribute to the restricted knowledge

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on slope failure in East Timor. After a brief introduction of the study area and the spatial distribution and characteristics of its landslides, the preconditions, preparatory and triggering causal factors for mass movement affecting slope failure will be discussed with attention to their spatial variation.

Figure 3.4 Recent landslide occurred on cut slopes alongside road in East Timor (Source:Prof. H. Kazama documentation,August2005)

Figure 3.5 Surface failure on hill slopes of mountainous in East Timor(Source:Prof. H. Kazama documentation, August 2005)

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3.6 Surface failure on hill slopes of mountainous in East Timor

As state in above, According to aerial photograph investigation, the actual slope failure distribution will established in this study are landslide, surface failure and mix of landslide and slope failure. The distribution and characteristics of slope failure in East Timor has shown in Table 3.1 to Table 3.13 and Figure 3.7 to Figure 3.18 .

3.2 Characteristics and Distribution of Slope Failure in East TimorTwo types of slope failures were identified based on aerial photograph and topography map are surface failure and landslide. This study covers four of topographic and air photograph sheets, and 506 number of slope failure and 506 number of unfailure slope with area 1448 km2 are already mapped in the region. A significant number of these slope failures were reactivations of old slope failures. There are density of the distribution of slope failure in East Timor will describe in Table 3.1, and show that landslide and slope failure are common in East Timor with highest density in Bobonaro , Cailaco and Zumalai site, and moderately density in Hatolia and Atsabe site and the lowest density in Maliana, Ainaro and Hatobuilico study site. Types of slope failure occurred in East Timor dominantly by landslide 56% with density 0.28 Number/km2 ,surface failure are 37% with density 0.16 Number/km2 and mix of landslide and surface failure are 7% with density 0.08 Number/km2.

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Table 3.1 Density of slope failure in study site Site Area (Km2) Landslide Density (N/km2) Type and density of slope failure Surface failure Density (N/km2) Mix Density Total (N/km2) density (N/km2) Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia 259 88 150 89 75 385 255 88 93 42 21 16 5 13 5 283 0.34 1.06 0.28 0.24 0.21 0.01 0.05 0.03 0.28 61 30 33 12 15 18 7 13 189 0.24 0.34 0.22 0.13 0.20 0.05 0.03 0.09 0.16 18 10 0 6 0 0 0 0 34 0.07 0.11 0 0.07 0 0 0 0 0.08 0.64 1.51 0.50 0.44 0.41 0.06 0.08 0.12 0.35

Hatobuilico 147 Total 1448

3.2.1 LithologyLithology exerts a fundamental control on the geomorphology of a slope failure. The nature and rate of geomorphological processes, including the slope failures process, is partially on the lithology and weathering characteristics of the underlying materials. Based on the East Timor geological map with 1:350,000- scale solid and superficial geological map covering the study area were used to identify the geological groups, with each group comprising units of broadly similar lithology. For analysis, the groups were further reclassified into three categories of geological materials with similar engineering properties. They are: sedimentary rocks with a few volcanic and igneous rocks, Sedimentary rocks and littoral deposit and Sedimentary rocks and a few metamorphic rocks and volcanic rocks. Detail of lithology analysis for this study are categories in five dominant lithology based on geological map with attributes of geology in study area, namely: Sedimentary rocks (Sr), Littoral deposit rocks (Ld), Metamorphic rocks (Mr) Igneous rocks (Ir) and volcanic rock (Vr). Description of geological structures in each study area has shown in Table 3.2 and Table 3.3, and Figure 3.7. It can be seen that many number of slope failure relatively highest 41

density in sedimentary rocks and littoral deposit rocks and lowest in igneous rocks, metamorphic rocks and volcanic rocks. The Tatamailau, Sabiria , Harupai , Atubuti mountains hills located in the study area are several hundreds to two thousands meters high in elevation is the most sensitive area for slope failure. There are composed of Miocene to Pliocene sedimentary rocks such as sandstones, limestones and siltstones, in part associated with a small amount of Mesozoic volcanic rocks. These sedimentary rocks and associated volcanics make up the Bobonaro and Lolotoe formation that are arranged chronological in other. Like many of the mountain ridges in the western region, these mountains hills correspond to folded structures of anticlines and synclines, and are elongated toward the north northeast direction following the fold axes.

Table 3.2 Description of geological structures in each study areaStudy AreaBobonaro Tertiary

AgePliocene and Miocene

LithologyBobonaro Complex and Lolotoe Formation Bobonaro Complex and Viqueque Formation Bobonaro Complex, Cablaci Limestone and Cribas Formation Bobonaro Complex, Cablaci Limestone and Wailuli Formation Sedimentary Rocks and a few of Volcanic and Igneous rocks Sedimentary rocks and littoral deposit.

General Lithological DescriptionMainly composed by chaotic rock with scaly matrix and blocks of older rock ; doleritic lava, volcanic breccia, tuff, green sandstone, metagabro a,d metadiorite Mainly composed by chaotic rock with scaly matrix and blocks of older rock; alternating conglomerate, conglomerate sandstone, sandstone, a lot of foraminifera in marl and sandstone Mainly composed by chaotic rock with scaly matrix and blocks of older rock ;contains marine foraminifera, Clastic limestone, crustaline, fine coarse grained, shale, claystone, siltstone and micaceous quarts sandstone Mainly composed by chaotic rock with scaly matrix and blocks of older rock ;contains marine foraminifera, and also dominant by sandstone, shale siltstone and limestone.

Cailaco

Tertiary

Pliocene and Miocene

Zumalai

Palaozoic and Mesozoic

Miocene And Permian

Sedimentary rocks and littoral deposit

Atsabe

Tertiary and Mesozoic

Miocene and middle to Jurassic

Sedimentary rocks and littoral deposit

42

Table 3.2 ( continued) Maliana Tertiary

Late to Pleistocen e and Miocene

Ainaro

Quaternar y and Mezosoic

Early Miocene and middle to Jurassic

Viqueque Formation , Bobonaro Complex and Ainaro Formation Bobonaro Complex, Ainaro Formation and Cablaci Limestone Wailuli Formation and Aileu Formation Wailuli Formation , Lolotoe Formation anf Dartollu Limestone

Sedimentary rocks and littoral deposit

Mainly composed by chaotic rock with scaly matrix and blocks of older rock; alternating conglomerate, conglomerate sandstone, sandstone, a lot of foraminifera in marl and sandstone; mixture sand and clay Mainly composed by chaotic rock with scaly matrix and blocks of older rock ;contains marine foraminifera, mixture sand and clay

Sedimentary rocks and littoral deposit

Hatolia

Mezosoic

Early Jurassic and late to Jurassic Early Jurassic and late Eosin

Hatobuilico

Mesozoic

Sedimentary rocks and a few of Metamorphic rocks and volcanic rocks Sedimentary Rocks and a few of metamorphic rock and volcanic rocks

Dominanted by sandstone, shale silttone, limenstone; phylite, schist, amphibolite, slate, metasandstone, sandstone, shale and a few of volcanic rocks Dominanted by sandstone, shale silttone, limenstone; doleritic lava, volcanic breccia, tuff, green sandstone, metagabro a,d metadiorite

Site

1. Bobonaro 2 Cailaco 3. Zumalai 4.Atsabe 5. Maliana 6.Ainaro 7. Hatolia 8. Hatobuilico Total

Table 3.3 Lithology Lithology types and number of slopes failure Sedimentary Littoral Igneous Metamorphi Volcanic Rocks(SR) Deposit Rocks(IR) c Rocks(VR Rocks(LR Rocks(MR) ) ) 101 19 25 0 22 87 46 0 0 0 57 18 0 0 0 21 18 0 0 0 20 18 0 0 0 14 9 0 0 0 12 0 0 4 4 9 0 0 5 4 321 121 25 9 30

Total

167 133 75 39 31 23 20 18 506

43

All Site Un-failure 350 300 250 200 150 100 50 0 0SR

Lithology of Slope Failure Bononaro Cailaco Ainaro Zumalai Hatolia Atsabe Hatobuilico

Failure110 100 90 80 70 60 50 40 30 20 10 0Num ber of Slopes Failure

Maliana

Failure

N u m b e r o f s lo p e

SR

LR

IRLithology

MR

VR

1

LR

2

IR

3

MR

4

VR

5

6Bononaro Malia na90

Lithology of Unfailure Slopes

Lithology

Ca ila co Aina ro

Zuma la i Ha tolia

Atsa be Hatobuilico

SR: sedimentary rocksN m e o U -f il reS p s u br f n au lo e

80 70 60 50 40 30 20 10 0 SR LR IR Lithology MR VR

LR : Littoral deposit rocks IR : Igneous rocks MR: Metamorphic rocks VR: Volcanic rocks

Unfailure

Figure 3.7. Lithology

3.2.2 VegetationMany studies have revealed a clear relationship between vegetation cover and slope stability, especially for shallow landslides. Parameters, such as cohesion, internal friction angle, weight of the soil and pore-water pressure, all tend to be substantially modified by the presence of vegetation. Vegetation can both enhance effective soil cohesion due to root matrix reinforcement and soil suction or negative water pressure through evapotranspiration and interception. According to Selby (1993), tree-covered hillslopes are thought to increase soil shear strength by about 60% depending on the tree type. Mehrotra et al. (1996) show that landslide activity increases by up to 15% in those places where the original vegetation cover

44

has been removed or altered. In order to correlate vegetation cover with other factors affecting slope failure, a vegetation classification was carried out in this study. The intention was to discriminate between different vegetation cover types. Indeed, many studies have pointed out that the degree of soil stability provided by vegetation decreases in the following order: trees, shrub, grass and bare soil (Coppin and Richards, 1990). The presence or absence of thick vegetation may affect slope failure. Due to the characteristics of the study area, where land cover is not homogenous with the presence of natural vegetation and for the purpose of this study and based on aerial photograph interpretation these vegetation types were then simplified in to four types, namely woodland or high tree (HT), scrublands or low tree (LT), grassland (G) and bare land or no vegetation (NV). To assess the effect of vegetation cover on the slope failure, the correlation between vegetation type and number of slope failure is shown in Table 3.4 and Figure 3.8. It can be seen that the number of slope failures on bare land and grassland is highest, and is lowest on woodland and scrubland. This is in agreement with the fact that vegetation cover, especially of a woody type with strong and big root systems, help to improve the stability of slopes. Other cause of this agreement is many Timorese have been displaced to more marginal landsand their former lands occupied by migrant farmers whose practices may not be adapted to Timorese conditions. The situation has been exacerbated by deforestation, which has become more

substantial during the last three decades. Another problem is that many rural communities rely on selling wood for fuel as source of family income and as a result, have contributed to deforestation. Under such conditions, intense bombardment of the soil surface by rain can quickly break down soil-organo aggregates, thus permitting slope failure.Table 3.4 Distribution of vegetationStudy Area Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico Total Types of vegetation cover and Number of Slope failures High Tree Low Tree Grassland No Vegetation 8 21 76 62 0 10 50 73 7 33 20 15 0 8 25 6 1 8 10 12 4 5 10 4 0 6 11 3 1 3 2 12 21 94 204 187 Total 167 133 75 38 31 23 20 18 506

45

All SiteUn-failure 300 Failure80 Number of Slopes Failure

Vegetation Cover of Slopes Failure Bononaro Maliana Cailaco Ainaro Zumalai Hatolia Atsabe Hatobuilico

N um ber of typ e of slop e

250 200 150 100 50 0 0

70 60 50 40 30 20 10 0High Tree Low Tre e Grassland No Vege tation

Failure

Vegetation

HT

1

LT G Vegetation

2

3

NV

4

5Bononaro Maliana

Vegetation Cover of Unfailure Slopes

Cailaco Ainaro

Zumalai Hatolia

Atsabe Hatobuilico

HT : High treeN m e o U -F ilu slo e u b r f n a re p s

90 80 70 60 50 40 30 20 10 0

LT : Low tree G : Grassland NV: No vegetation

Unfailure

Figure 3.3 Vegetation

High Tr e e

Low Tr e e

Gr ass land

No Vege tation

Vegetation

Figure 3.8 Distribution of vegetation

3.2.3 Inclination angle of slopeSlope is the angle formed between any part of the surface of the earth and a horizontal datum. It is the means by which gravity induces stress in the slope rocks, flux of water and other materials; therefore, it is of great significance in hydrology and geomorphology. In fact, slopes affect the velocity of both surface and subsurface flow and hence soil water content, soil formation, erosion potential and a large number of important geomorphic processes. It has been widely shown that landslides tend to occur more frequently on steeper slopes (McDermid and Franklin, 1995; Cooke and Doornkamp, 1990). Slope failure tends to

46

increase with slope angle but when the slope becomes near vertical, landsliding is scarce or absent altogether. The reason is the lack of soil development and debris accumulation in such topographic conditions (Selby, 1993; Derruau, 1983). A long slope may include sections that can be affected by large movements originating further up the hills slope. The estimation of the slope angle for this study was implemented using by topographic map investigation in which slope is considered as the change in elevation over a fixed distance. Inclination angle of slope is an essential component of slope stability and an important control on slope failure. As slope inclination angle increases, the level of gravitation-induced shear stress in the residual soil increases as well. Gentle hill slopes are expected to have a flow frequency of slope failures because of generally lower shear stresses associated with low inclination angle. In this study, inclination angle of slope has categories with ranges: 60 120, 120 180, 180 240, 240 300, 300 360, 360 420 and 420 - 480. In regional slope failure (i.e., landslide and surface failure) susceptibility or hazard assessment, slope inclination angle in terms of slope failure activity in taken into consideration as an conditioning factor(Y. Duman et all, 2006). In the study site, the distribution number of slope failure occurred with inclination angle of slope has shown in Table 3.5 and Figure 3.10 . It can be seen that examination of the distribution of number of slope failure with corresponding slope inclination angle ranges shows that most of slope failures with inclination angle do have ranges increase in the 120 300 and gradually decrease in the ranges 60 120 and 300 480. This is refection that steep natural slope with outcropping bedrock and hence much higher shear strength may not susceptible to shallow landslide.

47

Table 3.5 Distribution inclination angle of slope Site60~120 1. Bobonaro 2. Cailaco 3. Zumalai 4. Atsabe 5. Maliana 6. Ainaro 7. Hatolia 8. Hatobuilico Total 17 19 5 9 3 0 2 0 55

Inclination angle and number of slope failure120~180 38 42 26 6 8 1 4 3 128 180~24 38 34 17 8 6 4 4 2 113 240~300 37 18 5 6 3 4 5 3 81 300~360 23 17 12 6 6 4 3 8 79 360~42 13 3 9 3 5 8 2 2 45 420~480 1 0 1 1 0 2 0 0 5

Total

167 133 75 39 31 23 20 18 506

Slope Inclination Angle of Slopes Failure

All Site Un-failure200

Bononaro Maliana

Cailaco Ainaro

Zumalai Hatolia

Atsabe Hatobuilico

FailureNumber of Slopes Failure

45 40 35 30 25 20 15 10 5 0 6~12 12~18 18~24 24~30 30~36 36~42 42~48

Failure

N u m b e r o f slo p es

175 150 125 100 75 50 25 0 0 2 4 6 6~12 12~18 8~24 24~30 30~36 36~42 42~48 8

Slope Inclination angle (o)Slope Inclination Angle of Unfailure Slopes Bononaro Maliana Cailaco Ainaro Zumalai Hatolia Atsabe Hatobuilico

Inclination angle ( o )Nm u ber of U n-fa re s ilu lope s

60 50 40 30 20 10 0

Unfailure

6~12

12~18

18~24

24~30

30~36

36~42

42~48

Slope Inclination angle

(o)

Figure 3.9 Distribution of inclination angle of slopes

48

3.2.4 Direction of SlopeAspect is often expressed as a compass direction .The aspect of slope failures ( i.e., the direction) has the potential to influence its physical properties and its susceptibility to slope failure. The processes that may be operating include exposure to sunlight, drying winds, rainfall, earthquake and groundwater behavior. Although, the relation between slope aspect (i.e., direction) and mass movement has long been investigated, no general agreement exists on the effect of the aspect on slope failure occurrence (Carrara et al. 1991). However, slope aspect is related to the general physiographic trend of the area and/or the main precipitation direction, and direction of the slope failure is roughly perpendicular to general physiographic trend. Several researchers have reported a relationship between slope orientation and landslide occurrence. For example, DeGraff and Romesburg (1980) point out that, to some extent, aspect gathers the structural and organic basic conditions of a slope including fault planes and climatic factors, respectively. It is reported by Lineback et al. (2001) that larger numbers of landslides occur in the wetter north-facing aspects than in drier, south facing aspects. Marston et al. (1998) report a similar finding and highlight that soil exposed on south-facing slopes are subject to several wetting and drying cycles, thus increasing landslide activity in the Himalayas. The distribution of direction among the aerial photograph and topography maps show that the general physiographic trend of the study site is East to West and an important part of slope failures in most of study area was highest number on North Northeast and Northwest facing slope, indicating that natural terrain slope failures is more common on these slopes. The frequency of slope failures was lowest on those slopes facing, south and west, while the frequency of slope failures remained moderate on the east, southeast and southwest facing slopes. The distribution of slope failures direction has shown in Table 3.5 and Figure 3.10.

49

Table 3.6 Distribution of direction of slopeDirection and number of slope failure Site1. Bobonaro 2. Cailaco 3. Zumalai 4. Atsabe 5. Maliana 6. Ainaro 7. Hatolia 8. hatobuilico Total

N42 19 4 3 0 0 1 5 74

NE49 53 28 4 0 2 0 3 139

E10 21 5 0 9 0 0 0 45

SE25 11 11 0 7 1 5 9 69

S5 0 0 0 2 0 7 0 14

SW16 0 17 6 10 0 3 0 52

W12 0 3 0 0 3 4 0 22

NW8 29 7 26 3 17 0 1 91

Total167 133 75 39 31 23 20 18 506

All SiteBononaro

Direction of Slopes Failure Cailaco Ainaro Zumalai Hatolia Atsabe Hatobuilico

Un-failure

Failure60

Maliana

Number of Slopes Failure

150 N u m b e r o f s lo p e 100 50 0 0 N NE2 E 4 SES

50 40 30 20 10 0 N NE E SE S SW W NW

Failure

Direction

6 SW W

8 NW

10Bononaro Maliana45 40 Num ber of Unfailure Slo pes 35 30 25 20 15 10 5 0 N

Direction of Unfailure Slopes

Cailaco Ainaro

Zumalai Hatolia

Atsabe Hatobuilico

Direction

Unfailure

N

: North

S

: South

NE : Northeast E : East SE : Southeast

SW : Southwest W : West

NW : Northwest

NE

E

SE

S

SW

W

NW

Direction

Figure 3.10 Distribution of direction of slope

50

3.2.5 Landscape topographyLandscape topographic represents a theoretical measure of the accumulation of flow at any point within a river basin. The landscape topography can be thought of as an abstract parameter to be used as a basis for estimating the local soil moisture status and thus slope failure areas due to surface topographic effects on hydrologic response. Soil moisture plays an important role in slope instability, particularly for shallow landslides and surface failure. Water operation may be through the accumulation of rainfall, as an agent of weathering, hydration of fine soils (i.e. clayey soils), undercutting of slopes and spontaneous liquefaction. In fact, according to Lamb (1996), hallow landslides can occur on slopes when