LANDSLIDE INVENTORY, CHARACTERIZATION AND ENGINEERING DESIGN FOR MITIGATION WORKS OF CHURE AREA IN TEN DISTRICTS
Submitted to: Submitted by: Government of Nepal Central Department of Environmental President Chure-Tarai Madhesh Science
Conservation Development Board Tribhuvan University, Kirtipur Kathmandu
September, 2016 © September 2016 President Chure-Tarai Madhesh Conservation Development Board and Central Department of Environmental Science, Tribhuvan University
Citation: TU-CDES (2016). Landslide Inventory Characterization and Engineering Design for Mitigation Works of Chure Area in Ten Districts. Central Department of Environmental Science, Tribhuvan University and Government of Nepal, President Chure-Tarai Madhesh Conservation Development Board, Kathmandu.
Project Steering Committee Chair Dr. Annapurna Das, Secretary , PCTMCDB/GoN Prof. Dr. Madan Koirala, Professor, TU-CDES Member Prof. Dr. Kedar Rijal, Head of Department, TU-CDES Member Prof. Dr. Rejina Maskey, Project Team Leader, TU-CDES Member Dr. Prem Paudel, Under Secretary, PCTMCDB/GoN Member Dr. Subodh Dhakal, Project Coordinator, TU-CDES Member Mr. Gehendra Keshari Upadhya, Joint Secretary , PCTMCDB/GoN Member Mr. Pashupati Koirala, Under- Secretary, PCTMCDB/GoN
Project Team Team Leader Prof. Dr. Rejina Maskey Project Co-ordinator Dr. Subodh Dhakal Geo-Technical Engineer Dr. Ram Chandra Tiwari GIS Expert Mr. Ajay Bhakta Mathema Geologist Mr. Suman Panday Assistant Geologist Mr. Niraj Bal Tamang Assistant GIS Analyst Mr. Padam Bahadur Budha Assistant GIS Analyst Ms. Shanta Banstola Social Surveyor Mr. Kumod Lekhak Field Assistant Mr. Nabin Nepali
Review Technical Reviewer: Dr. Ranjan Kumar Dahal English Reviewer: Dr. Dinesh Raj Bhuju
ii
ACKNOWLEDGEMENTS
Hazards like earthquake, landslide, soil erosion and sedimentation all shape the landscape and relief of the Himalaya. Land degradation of the Chure area of Nepal is primarily contributed by different types of landslides and mass wasting phenomena. Landslides and other mass wasting processes are collectively controlled by the lithospheric plate dynamics, geology, topography, intense precipitation and human interference. The upstream and downstream of the major river basins in these areas are highly interlinked and therefore such land degradation in the Chure area pose serious threat of flooding and inundation to the southern Tarai. Landslides cause a huge loss of human life and property as well as environmental degradation in Nepal requiring extra resources for relief and recovery.
With all these facts in background, conservation of Chure area has been considered as the issue of national challenge in Nepal. President Chure Tarai Madesh Conservation Development Board (PCTMCDB/GoN) has initiated some steps for the conservation of Chure area realizing the lack of adequate and appropriate research findings which is essential for the conservation of Chure itself and sustainable development in this area. To bridge this research gap, Tribhuvan University Central Department of Environmental Science (TU-CDES) and PCTMCDB/GoN signed a Letter of Agreement on August, 2015. This report is the outcome of this collaboration between PCTMCDB/GoN and TU- CDES. The ultimate goal of the agreement is to provide technical and financial plan for the mitigation of existing high risk landslides in ten districts of Chure area and to delineate the landslide susceptibility zonation for future planning. The ten working districts belong to the Chure area of central and eastern Nepal namely Bara, Dhanusha, Makawanpur, Mahottari, Rautahat, Sarlahi, Saptari, Siraha, Sindhuli, and Udayapur.
We express our heartfelt gratitude to PCTMCDB/GoN for identifying TU-CDES as the qualified collaborator to protect the Chure area through detail mapping, characterization and mitigation of landslides on priority basis. The project team completed the task performing desk study, field study and laboratory analysis at different time during the project period. We express our deep gratitude to chairman of PCTMCDB/GoN, Mr. Birendra Yadav and immediate past chairman Mr. Rameshwor Khanal for encouraging us time and again during the project period. We are highly thankful to Dr. Annapurna Nanda
iii Das, member secretary of PCTMCDB/GoN for trusting our effort and helping in all the administrative issues with the board. We are thankful to Dr. Prem Prasad Poudel, under secretary in PCTMCDB/GoN for giving suggestions time and again regarding technical issues.
We are equally thankful to the district level officials of District Soil Conservation Office (DSCO), District Forest Office (DFO) and Regional Office of PCTMCDB/GoN in the working districts and regions. TU-CDES extends its sincere thanks to all the board members of PCTMCDB/GoN and its immediate past board members. Our special thanks are to all the local residents of the working districts and communities.
Prof. Dr. Kedar Rijal Head of Department
iv EXECUTIVE SUMMARY
The Chure area is one of the distinctive physiographic belts in Nepal that occupies the area lying in the southern part and running from east to west throughout the length of the country. It touches some or major area of thirty six districts of Nepal and is situated in the form of small hills. Chure area lies between the Lesser Himalaya in the north and Tarai in the south and is located within two major geological structures namely Main Frontal Thrust (MFT) in the south and the Main Boundary Thrust (MBT) in the north. These hills are formed of very fragile, weak and young sedimentary rocks called the Siwaliks that range in age from 1 to 14 million years belonging to Middle Miocene to Upper Pleistocene times. The area is typically comprised of high percent of forest cover offering habitat for charismatic wild fauna. For millennia, the Chure hills remained almost pristine because of lack of human settlements, agriculture practices and other interferences.
As a consequence of lithospheric plate dynamics between the Indian Plate and Tibetan Plate, the young and fragile sedimentary rocks of Chure area are highly weathered and deformed. Interbedding of soft mudstone and hard sandstone beds provide differential weathering, providing plenty of options for slope instabilities and occurrence of different types of landslides in these hills. The last few decades have witnessed deforestation, over exploitation of forest products, development of road networks, forest encroachment, open grazing and unscientific use of land in the Chure area. Such activities on fragile ecosystem have exacerbated landslides in the hills and mountains and consequent flood hazards in the river valleys and lowland Tarai in the south. Due to its fragility and vulnerablity, conservation of Chure area has been considered as the issue of national challenge in Nepal. This research is intended to overcome the conservation issue of Chure area. The ultimate goal of the research is to provide technical and financial plan for the mitigation of existing high risk landslides in ten districts of Chure area and to delineate the landslide susceptibility zonation. The ten working districts belong to the chure area of central and eastern Nepal namely Bara, Dhanusha, Makawanpur, Mahottari, Rautahat, Sarlahi, Saptari, Siraha, Sindhuli, and Udayapur. Landslides inventory, landslide characterization, laboratory analysis and vulnerability and risk assessment were performed by the combination of desk study, field study and laboratory analysis to meet the objectives.
v The results of the study are presented in five thematic chapters: Landslide Inventory, Landslide Characterization, Landslide Susceptibility Mapping, Landslide Risk and Vulnerability Assessments, Landslide Mitigation Measure and Financial Plans. The following section provides highlights of different chapters.
Landslide Inventory Landslide inventory is a spatial dataset of mapped landsides usually derived from aerial photograph interpretation, satellite imagery or direct field observation. Google Pro Earth Imagery 2015/16 was used to identify and document the landslides. Similarly, GIS tool was applied to determine the location and distribution under different attributes such as Elevation, Slope, Aspects, Geo-form distribution, Soil types, Landuse/landcover etc. In total, 3456 landslides were identified and inventoried for the ten districts, which hold an area of 12.7234 square kilometer (Km2). According to the inventory, Makawanpur district consisted of highest number of landslides (792) and Mahottari district consisted of lowest number of landslides (98). Other districts with higher number of landslides were: Siraha (717), Udayapur (684) and Sindhuli (469). In terms of the density of landslides per unit area, Bara, Rautahat, Siraha and Saptari districts were found to have higher occurrence of landslides per square kilometer. Siraha was identified to be the district with highest landslide density (3.424). In terms of the size of landslides, three classes were distinguished namely small scale landslides (<1000 square meter (m2)), medium scale landslides (1000-10,000 m2) and large scale landslides (>10,000 m2). The inventory data was presented up to the ward level with distinct Landslide Identity (LID) number for each landslide. The LID was developed by using the codes of districts, Village Development Committees (VDCs), ward number and landslide numbering. The code was of 10 digits where first two digits refer to district, second three digits are for VDC, then it is followed by ward number, finally ending with three digits of landslide numbering inside a ward. The final landslide inventory maps for all working districts were printed in the existing topographical maps and the inventory maps of each district were also prepared separately given in the report itself.
Landslide Characterization Landslide characterization of more than 1000 landslides within 65 clusters was performed according to their geological and morphological characteristics. These landslides were characterized according to the types of rocks involved, major deformation structures like folds, faults and thrusts, types of landslides, orientation of rock mass discontinuities, soil
vi moisture, soil type, ground water condition etc. It was found that the landslides were highly influenced by geology in terms of the type, size and number of landslides. As for example: granular flow, debris fall and gully erosion were dominant in Upper Siwaliks.Vertical and overhanging slopes of conglomerates or the differently graded gravels were common features that control the landslide type in this area. High grade of weathering in mudstones of Lower Siwaliks indicates gully erosion, earth slides, mudflow and debris flow as the dominant processes in this geological Formation. The Middle Siwaliks mostly consist of massive sandstone alternating with incompetent mudstone layers signifying differential weathering and the processes like rock slides and rock falls. However, many landslides in the working area were complex in terms of process, type and temporal domain. At many places like in Dhanusha, swarms of landslides were identified and therefore they were analyzed in terms of clusters rather than individual landslides. Detail characterization of all the studied landslides are provided in the Landslide Atlas and in the annex of project report.
Landslide Susceptibility Identifying the areas that are most potential for landslides is important to reduce the vulnerability and risk of loss of people and property. Seven different factors maps along with the landslide distribution map were prepared in GIS environment for the susceptibility map preparation and bivariate statistical analysis method was used for landslide susceptibility mapping where a weight value for a parameter class is given as a natural logarithm of the landslide density in the class divided by landslide density of each parameter class such as a certain lithological unit or a certain slope class.
Landslide susceptibility maps were prepared for each district. It was found that the northern parts of Rautahat district like Khyaku, Thalligaun and Bhiman were highly susceptible to landslides. Similarly, central belt of Bara district along Kolgaun and Aanpchaur, northern belt of Makawanpur district along Kyampa, Lekhpani, Betini and Thumki sections and southeastern belt of the same district along Saraswatigaun, Bhamara and Sano Deujor were found to be highly susceptible to landslides. Susceptibilty map of Sindhuli district showed high susceptibility zones throughout the region as it was triggered by factors such as geological (unconsolidated bedrocks), structural (Main Boundary Thrust and Kamala Thrust) and hydrological (high rainfall occurring region) factors. Sarlahi district showed high susceptibility zones in Sano Phuljor, Aranedandagaun, Bhalukhop and Kerabari area. Mahottari district showed high
vii susceptibility zones in Nagdah, Khairmara, Betal, Pandan, Sisne and Hanumandhoka. Tulsi, Tintale, Bimire and Mainawati of Dhanusa district showed high susceptibility zones. Central and the southeastern area of Siraha district showed high susceptibility including Betaha, Ahale, Bardamar, Toribari, Dandatol and Deusegaun. Similarly, the eastern part of Saptari district showed high susceptibility zones. In Udayapur district, Palase, Shikharpur, Gohiya, Adheri, Belsot, Patalebas, Damauti, Ambote and Katle were the areas with high landslide susceptibility. The susceptibility maps of all working districts are provided in the project report and the annexes.
In each of the cases, for the area under curve calculation for Makawanpur, Sindhuli and Udayapur districts, the area under curve value of prediction rate exceeded that of the success rate which is a positive indicator for the validity of the prepared maps.
Vulnerability and Risk Assessments Spatial multi-criteria decision analysis was applied to carry out vulnerability assessment. The spatial datasets in raster format were used as input parameters. These data were analyzed and processed in GIS environment within the standard framework used worldwide. The landslide vulnerability index value for project districts ranged from 0.17 (low value) to 0.80 (high value) with mean value of 0.46 and standard deviation of 0.09. Vulnerability was classified into five classes: very low, low, moderate, high and very high. Vulnerability has been described by the elements of risk that has been considered for the study independent of spatial distribution of landslide events and is potential to be adversely affected by potential hazards. The value map showed that the majority of the areas in project districts lie in high vulnerable zone (40.20%), followed by moderate vulnerability zone (32.04%), low vulnerability zone (16%), very high vulnerability zone (11%) and very low vulnerable zone (0.22%). The vulnerability maps are provided in the report itself.
Mitigation Works of Engineering Design Landslides that are highly vulnerable and that have posed high risk to the loss of people, property and environment are given priority for the mitigation model and design. Slope stability analysis and back analysis were performed for developing mitigation measures. So far as possible, the practicability for implementation and cost effective designs were recommended. The costing for the recommended mitigation measures were based on latest respective district rates. The mitigation designs and costing are provided in the
viii project report and the annex. Based on the cause and effect analysis, and detail characterization of landslides, four major groups of mitigation measures were recommended namely modification of slope geometry, drainage management, retaining structures and internal slope reinforcement. While recommending the mitigation measures, five factors namely engineering feasibility, economic feasibility, legal/regulatory conformity, social acceptability, and environmental acceptability were also considered. Geotechnical references of the landslide sites were also obtained from laboratory analysis of samples collected from the field and the in situ test using portable machines such as Shear Vane and Pocket Penetrometer. Different test performed in the laboratory are direct shear test, grain size distribution, liquid limit and plastic limit test, water content test and specific gravity test. Engineering design for mitigation works of more than 1000 landslides within 60 clusters of the working district was developed. The designs varied from landslides to landslides but mostly bioengineering, surface and subsurface drainage, drainage management, retaining structures, cut and fill, slope hardening and suitable plantation were the general features of most of the designs.
ix TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... III
EXECUTIVE SUMMARY ...... V
TABLE OF CONTENTS ...... X
LIST OF TABLES ...... XIV
LIST OF FIGURES ...... XVI
ABBREVATIONS AND ACRONYMS ...... XXIII
SECTION I: INTRODUCTION ...... 26
1. INTRODUCTION AND APPROACH ...... 27
1.1 Background ...... 27
1.2 Problems in Chure Area ...... 27
1.3 Goal and Objectives of Project ...... 31
1.4 Scope of Project ...... 31
1.5 Methodological Approach ...... 32
1.6 Working area ...... 36
1.7 Limitations ...... 37
1.8 Expected Outcomes ...... 37
1.9 Structure of Report ...... 38
SECTION II: LANDSLIDE INVENTORY ...... 40
2. LANDSLIDE INVENTORY ...... 41
2.1 Introduction ...... 41
2.2 Significance of the Landslide Inventory ...... 42
x 3. METHODOLOGICAL APPROACHES ...... 43
3.1 Data Generation for GIS Analysis ...... 43
3.2 Available Tools and Materials ...... 44
3.3 Spatial Reference of Data ...... 44
3.4 Landslide Inventory and Attributes ...... 44
3.5 Database Preparation ...... 45
4. RESULTS ...... 47
4.1. Landslide Inventory ...... 47
4.2 District Ranking according to Landslide Occurrences ...... 50
4.3 VDC-Wise Landslide Distribution in Project Districts ...... 53
4.4 Landslide Attributes ...... 58
4.5 Landslide Identity Number ...... 60
4.6 Landslide Distribution for Each Factor Class ...... 61
4.7 Mapping and Database Management ...... 74
SECTION III: LANDSLIDE CHARACTERIZATION ...... 76
5. LANDSLIDE CHARACTERIZATION ...... 77
5.1. Introduction ...... 77
5.2. Methods...... 77
6. RESULTS ...... 83
6.1 Geological Characters ...... 83
6.2 Geological Structures ...... 90
6.3 Soil Condition ...... 92
6.4 Soil Strength Measurements ...... 93
xi 6.5 Topography/ Geomorphology ...... 93
6.6 Hydrology ...... 95
6.7 Types and Process of Landslides ...... 101
SECTION IV: LANDSLIDE SUSCEPTIBILITY ...... 104
7. LANDSLIDE SUSCEPTIBILITY ...... 105
7.1 Introduction ...... 105
7.2 Materials and Methods ...... 105
8. RESULTS ...... 110
8.1 Landslide Susceptibility ...... 110
8.2 Validation and Evaluation...... 123
SECTION V: LANDSLIDE VULNERABILITY AND RISK ASSESSMENT ...... 132
9. LANDSLIDE VULNERABILITY AND RISK ...... 133
9.1 Introduction ...... 133
10. MATERIALS AND METHODS ...... 135
10.1 Identification of Elements at Risk...... 135
10.2 Vulnerability Assessment ...... 136
10.3 Risk Assessment ...... 140
11. RESULTS AND DISCUSSION ...... 141
11.1 Landslide Vulnerability ...... 141
11.2 Landslide Risk ...... 149
SECTION VI: LANDSLIDE MITIGATION AND FINANCIAL PLAN ...... 157
12. MITIGATION MODELS OF CHURE LANDSLIDES ...... 158
12.1 Introduction ...... 158
xii 12.2 Methodological Approach ...... 158
13. MATERIALS AND METHODS ...... 161
13.1 Laboratory Work ...... 161
13.2 Numerical Tools...... 164
14. SITE SPECIFIC MITIGATION DESIGNS OF LANDSLIDES ...... 167
14.1 Setevir Landslide ...... 168
14.2 Numerical Modeling for Mitigation Design ...... 176
14.3 Mitigation Models ...... 183
14.4 Design and Cost-estimation ...... 191
SECTION VII: CONCLUSIONS AND RECOMMENDATIONS ...... 201
15. CONCLUSIONS...... 202
16. RECOMMENDATIONS ...... 204
REFERENCES ...... 205
GLOSSARY...... 210
ANNEX
xiii LIST OF TABLES
Table 1.1: Steering committee ...... 34
Table 1.2: Team composition of the project ...... 35
Table 3.1: Field visit details ...... 43
Table 3.2: Attributes of documented landslides ...... 45
Table 4.1: Distribution of landslide in the clusters ...... 48
Table 4.2: Landslide affected VDCs in ten studied districts of Chure area in Nepal...... 50
Table 4.3: Landslide density and percentage area covered by landslide (Rankings are in paranthesis)...... 52
Table 4.4: Final ranking of the selected ten districts according to the occurrence of landslide ...... 53
Table 4.5: Landslide number distribution in VDCs of Makawanpur Cluster...... 54
Table 4.6: Landslide number distribution in VDCs of Sindhuli Cluster...... 56
Table 4.7: Landslide number distribution in VDCs of Udayapur Cluster ...... 57
Table 4.8: Database management for each district ...... 75
Table 6.1: Characters of landslides with reference to stratigraphy ...... 84
Table 6.2: Characters of landslides with reference to the geological structure ...... 90
Table 6.3: Cohesion and frictional angle values by field measurements ...... 93
Table 6.4: Discharge measurement of springs of different Landslides...... 95
Table 6.5: Process and effects of surface water on landslides ...... 97
Table 8.1: Area under curve (%) for the three clusters for evaluation and validation ... 125
Table 8.2: VDC/Municipality wise landslide susceptibility condition for the Chure area of Makawanpur District in terms of percentage ...... 126
Table 12.1: Methods of Standardization ...... 140
Table 11.1: District wise distribution of social vulnerability area ...... 141
Table 11.2: District wise distribution of physical vulnerability area ...... 142
Table 11.3: District wise distribution of economic vulnerability area ...... 143
Table 11.4: Summary statistics of landslide index map ...... 150
xiv Table 12.1: Possible mitigation measures for landslides as per Popescu (2001) ...... 160
Table 13.1: Modulus of elasticity of soil based on Obrzud and Truty (2012) ...... 164
Table 13.2: Modulus of elasticity of soil based on Obrzud and Truty (2012) ...... 165
Table 13.3: Poisson's Ratio of soil by Bowles(1996) ...... 165
Table 14.1: Location of major landslides of Chure area in selected districts ...... 167
Table 14.2: Sieve analysis sheet, Setivir landslide section-1...... 169
Table 14.3: Specific gravity test sheet of soil samples...... 171
Table 14.4: Liquid limit and plastic limit test of soil sample of Setibhir landslide section-1 ...... 172
Table 14.5: Shear parameters result of different landslide ...... 174
Table 14.14.6: Friction angle without correction obtained from Roadside Geotechnical Handbook ...... 175
Table 14.7: Corrected friction angle from Roadside Geotechnical Handbook ...... 175
Table 14.8: Summary of laboratory investigation ...... 176
Table 14.9: SRF value on different landslide sections at different friction angle ...... 177
Table 14.10: Result comparison of laboratory and BA technique...... 179
Table 14.11: SRF and deformation values at different GWT position ...... 179
Table 14.12: Result comparison of phase2 and slide software ...... 181
Table 14.13: Result from s ...... 183
Table 14.14: GWT depth to obtain SRF value 1 in different landslides ...... 183
Table 14.15: SRF Vs deformation at normal soil condition and with vegetation ...... 184
Table 14.16: SRF Value and Deformation at Normal Condition and Geosynthetic Use 186
Table 14.17: SRF vs deformation with slope modification ...... 188
Table 14.18: Mitigation design of case and site-specific analysis of 16 major landslides ...... 191
Table 14.19: Cost estimation of landslide mitigation measures ...... 199
xv LIST OF FIGURE
Figure 1.1: Generalized Geological Map of Nepal Himalaya (Source: Dhakal, 2014) ... 28
Figure 1.2: Schematic Diagram Showing Major Problems of Chure (After Dhakal, 2015) ...... 29
Figure 1.3: Methodological Framework used in Landslide Study ...... 33
Figure 1.4: Working Area of project ...... 36
Figure 1.5: Outline of Report ...... 39
Figure 4.1: Distribution of Landslides Recorded in the Project Districts ...... 47
Figure 4.2: Distribution of Landslides in the Makawanpur Cluster ...... 48
Figure 4.3: Landslide inventoried and their distribution in Sindhuli Cluster ...... 49
Figure 4.4: Landslide Inventoried and their Distribution in Udayapur Cluster ...... 50
Figure 4.5: Snap shot 1 of an attribute table ...... 58
Figure 4.6: Snap shot 2 of an attribute table ...... 60
Figure 4.7: Landslide Distribution Based on Geological Formation, Area (%), in Makawanpur District ...... 61
Figure 4.8: Landslide distribution based on different topographic factors, area (%) in Makawanpur District ...... 62
Figure 4.9: Landslide distribution based on geological formation, area (%), in Bara District ...... 63
Figure 4.10: Landslide distribution based on topographic factors, area (%), in Bara District ...... 63
Figure 4.11: Landslide distribution based on geological formation, area (%), in Rautahat District ...... 64
Figure 4.12: Landslide Distribution Based on Topographic Factors, Area (%), in Rautahat District ...... 65
Figure 4.13: Distribution of Landslides Based On Geological Formations, Area (%), In Sindhuli District ...... 65
Figure 4.14: Landslide distribution based on topographic factors, area (%), in Sindhuli District ...... 66
Figure 4.15: Landslide distribution based on geological formations, area (%), in Sarlahi District ...... 67
xvi Figure 4.16: Landslide distribution based on topographic features, area (%), in Sarlahi District ...... 67
Figure 4.17: Landslide distribution based on geological formation, area (%), in Mahottari District ...... 68
Figure 4.18: Landslide Distribution Based on Topographical Features, Area (%), Mahottari District ...... 68
Figure 4.19: Landslide Distribution Based on Geological Formations, Area (%), in Dhanusha District ...... 69
Figure 4.20: Landslide Distribution Based on Topographical Features, Area (%), Dhanusha District ...... 70
Figure 4.21: Landslide Distribution Based on Geological Formation, Area (%), in Udayapur District ...... 71
Figure 4.22: Landslide Distribution Based on Topographic Features, Area (%), in Udayapur District ...... 71
Figure 4.23: Landslide Distribution Based On Geological Formation, Area (%), In Udayapur District ...... 72
Figure 4.24: Landslide Distribution Based On Topographical Features, Area (%), Siraha District ...... 72
Figure 4.25: Landslide Distribution Based On Geological Formation, Area (%), In Saptari District...... 73
Figure 4.26: Landslide distribution based on topographical features, area (%), Saptari District ...... 74
Figure 5.1: Pocket Penetrometer ...... 80
Figure 5.2: Shear Vane Measurements ...... 81
Figure 6.1: Generalized geological map of Nepal (Modified from Amatya and Jnawali 1994) ...... 83
Figure 6.2: Landslides in Upper Siwalik (Conglomerate), Thulitar, Sindhuli...... 87
Figure 6.3: Landslide (Rock Fall Phenomena) in Middle Siwalik (Sandstone) Observed at River Section of Amlekhgunj VDC Near Bridge No. 3...... 87
Figure 6.4: Landslide Observed at Pattarakot VDC Sarlahi, Lower Siwalik (Mudstone, The Rocks are in the Form of Residual Soil and the Slides are Shallow)...... 88
Figure 6.5: Photograph Showing Comparison between Hill Slope and Orientation of Bedding Plane ...... 89
xvii Figure 6.6: Photograph Showing Rock Cliff at Raigaon VDC ...... 89
Figure 6.7: Effect of Fault on Landslide ...... 91
Figure 6.8: Landslide Observed at Contact between Lower Siwalik and Pre Siwalik at Beteni VDC of Makawanpur...... 92
Figure 6.9: Landslide Observed at Colluvial Soil at Shripur Chhattiwan VDC, Makawanpur ...... 93
Figure 6.10: Spring observed at Simalchaur Landslides ...... 96
Figure 6.11: Toe cutting on middle siwalik sandstone by Bakaiya River at Amdamar, Makawanpur ...... 98
Figure 6.12: River Bed Erosion by Pattharkot khola results valley deepening ...... 99
Figure 6.13: Photograph showing valley widening process by eroding both bank of river at Sirutar Sindhuli ...... 99
Figure 6.14: photograph showing Head erosin process by eroding both bank of river at Gaighat Udayapur...... 100
Figure 6.15: Surface Trigger by Rainfall on Weather and Fragile Soil of Barren Land at Bishnupurkatti Siraha ...... 100
Figure 6.16: Debris Fall with Granular Flow Type of Landslide Seen At Gaighat Udayapur District ...... 101
Figure 6.17: Gulley Erosion Seen At Shripur Chhattiwan, Makawanpur District ...... 102
Figure 6.18: Surficial Landslide Seen at Madhupati, Saptari District ...... 103
Figure 7.1: Flowchart Showing the Processes Involved Throughout the Preparation of the Susceptibility Map ...... 107
Figure 8.1: Landslide Susceptibility Map of the Chure Region of the Makawanpur District ...... 110
Figure 8.2: VDC/Municipality wise landslide susceptibility condition for the Chure Region of the Makawanpur District ...... 111
Figure 8.3: Landslide Susceptibility Map of the Chure Region of the Bara District ...... 112
Figure 8.4: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Bara District ...... 112
Figure 8.5: Landslide Susceptibility Map of the Chure Region of the Rautahat District 113
Figure 8.6: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Rautahat District ...... 113
xviii Figure 8.7: Landslide Susceptibility Map of the Chure Region of the Sindhuli District . 114
Figure 8.8: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Sindhuli District ...... 115
Figure 8.9: Landslide Susceptibility Map of the Chure Region of the Sarlahi District .. 115
Figure 8.10: VDC/Municipality wise landslide susceptibility condition for the Chure Region of the Sarlahi District ...... 116
Figure 8.11: Landslide Susceptibility Map of the Chure Region of the Mahottari District ...... 117
Figure 8.12: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Mahottari District ...... 117
Figure 8.13: Landslide Susceptibility Map of the Chure Region of the Dhanusa District ...... 118
Figure 8.14: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Dhanusa District...... 119
Figure 8.15: Landslide Susceptibility Map of the Chure Region of the Udayapur District ...... 120
Figure 8.16: VDC/ Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Udayapur District ...... 120
Figure 8.17: Landslide Susceptibility Map of the Chure Region of the Siraha District . 121
Figure 8.18: VDC/Municipality wise landslide susceptibility condition for the Chure Region of the Siraha District ...... 121
Figure 8.19: Landslide Susceptibility Map of the Chure Region of the Saptari District 122
Figure 8.20: VDC/Municipality Wise Landslide Susceptibility Condition for the Chure Region of the Saptari District ...... 122
Figure 8.21: Success rate curves and prediction rate curves for validation of the susceptibility maps of a) Makawanpur, b) Sindhuli and c) Udayapur Districts...... 125
Figure 10.1: Conceptual Framework for Vulnerability Analysis ...... 136
Figure 11.1: Vulnerability Index Map of Bara ...... 144
Figure 11.2: Vulnerability Index Map of Dhanusa ...... 144
Figure 11.3: Vulnerability Index Map of Mahottari ...... 145
Figure 11.4: Vulnerability Index Map of Makawanpur...... 145
xix Figure 11.5: Vulnerability Index Map of Rautahat ...... 146
Figure 11.6: Vulnerability Index Map of Saptari ...... 146
Figure 11.7: Vulnerability Index Map of Sarlahi ...... 147
Figure 11.8: Vulnerability Index Map of Sindhuli...... 147
Figure 11.9: Vulnerability Index Map of Siraha ...... 148
Figure 11.10: Vulnerability Index Map of Udayapur ...... 148
Figure 11.11: Vulnerability Distribution in ten districts ...... 149
Figure 11.12: Percentage of Area Covering Different Risk Classes ...... 149
Figure 11.13: Risk Map of Project District ...... 150
Figure 11.14: Risk Distribution in Bara District...... 151
Figure 11.15: Risk Distribution in Dhanusa District ...... 152
Figure 11.16: Risk Distribution in Mahottari District ...... 152
Figure 11.17: Risk Distribution in Makawanpur District ...... 153
Figure 11.18: Risk Distribution in Rautahat District ...... 153
Figure 11.19: Risk Distribution in Saptari District ...... 154
Figure 11.20: Risk Distribution in Sarlahi District ...... 154
Figure 11.21: Risk Distribution in Sindhuli District ...... 155
Figure 11.22: Risk Distribution in Siraha District ...... 155
Figure 11.23: Risk Distribution in Udayapur District ...... 156
Figure 14.1: Photograph of Setibhir Landslide ...... 168
Figure 14.2: Grain Size Distribution Curve of Soil Sample of Setevir Landslide Section-1 ...... 170
Figure 14.3: Liquid limit plot of soil sample from Setevir landslide section-1 ...... 173
Figure 14.4: Normal Stress and Shear Stress Plot from Data of Setevir Section-1 ...... 174
Figure 14.5: Graph of SRF vs Maximum displacement of Setibhir landslide section-1 . 178
Figure 14.6: Deformation vectors at SRF one on Setibhir landslide section-1...... 178
Figure 14.7: SRF vs Displacement plot of Setevir landslide section-1 with GWT variation ...... 180
xx Figure 14.8: Setevir Section-1 Model at Fully Saturated Condition and Deformation Vectors...... 180
Figure 14.9: FOS Value on Simalchaur Section-1 at Normal Condition ...... 181
Figure 14.10: Correlation between Phase2 and Slide result ...... 182
Figure 14.11: Landslide model with Bioengineering in Setibhir section-1 ...... 184
Figure 14.12: Increase in SRF with bioengineering in Setibhir section-1 ...... 185
Figure 14.13: Comparison of vegetative effects in different landslide slope sections .... 185
Figure 14.14: Application of geosynthetic on landslide model ...... 186
Figure 14.15: Landslide model with Geosynthetic on Setibhir section-1 ...... 187
Figure 14.16: Geosynthetic effect on Setevir section-1 ...... 187
Figure 14.17: Comparison chart of Geosynthetic use and normal soil condition ...... 188
Figure 14.18: Effect of slope modification in Setibhir section-1 ...... 189
Figure 14.19: Setevir landslide section-1 with slope modification ...... 189
Figure 14.20: Comparison of slope modification effect ...... 190
Figure 14.21: Setevir Landslide Section: 1-1 ...... 191
Figure 14.22: Cut and Fill Provision ...... 192
Figure 14.23: Bio-engineering Measures ...... 192
Figure 14.24: GWT Reduction Up To 5 M with Surface and Subsurface Drain ...... 193
Figure 14.25: Providing Check Dam (Top Width = 0.5m, Height = 4 M and Bottom Width= 1 M) ...... 193
Figure 14.26: Providing Retaining Wall (A1= 0.6m, A3= 5m, A4= 0.35m, A5= 0.15m And A6= 1.6m) ...... 193
Figure 14.27: Setevir Landslide Section 2-2 ...... 194
Figure 14.28: Setevir Section 1-1 ...... 194
Figure 14.29: Setevir Section 1-1 ...... 195
Figure 14.30: Setevir Section 1-1 ...... 195
Figure 14.31: Setevir section 1-1...... 196
Figure 14.32: Setevir Section 2-2 ...... 196
xxi Figure 14.33: Setevir section 1-1...... 197
Figure 14.34: Setevir section 2-2...... 197
Figure 14.35: Sketch of Mitigation measures in Setevir Landslides ...... 198
xxii ABBREVATIONS AND ACRONYMS °C Degree Celsius CAP Common Agricultural policy AASHTO American Association of State Highway and Transportation Officials CBS Centre Bureau of Statistics CDES Central Department of Environmental Science CH Clay of High Plasticity CL Clay of Low Plasticity cm Centimeter CSRC Community Self-Reliance Centre DEM Digital Elevation Model DHM Department of Hydrology and Meteorology DMG Department of Mines and Geology DoS Department of Survey EcVI Economic Vulnerability Index EnVI Environmental Vulnerability Index FEM Finite Element Analysis FGD Focus Group Discussion FoS Factor of Safety G Specific Gravity GDP Gross Domestic Product GIS Geographic Information System gm Gram GoN Government of Nepal GPS Global Positioning System GW Ground Water GWT Ground WaterTable HFT Himalayan Frontal Thrust IADB Inter-American Development Bank IEC Information, Education and Communication ISDR International Strategy for Disaster Risk Reduction km Kilometer KN Kilo Newton kp Kilopond
xxiii Kpa Kilopascal KTT Kamala Tawa Thrust LDRMP Local Disaster Risk Management Planning LEM Limit Equillibirium Method LID Landslide Identity LULC Landuse/Landcover m Meter MBT Main Boundary Thrust MFT Main Frontal Thrust MKT Marin Khola Thrust ML Silt mm Millimeter MoE Ministry of Environment MoFSC Ministry of Forests and Soil Conservation MoHA Ministry of Home Affairs MUTM Modified Universal Transverse Mercator NAPA National Action Plan for Adaption Nos. Numbers NPR Nepali Rupees OH Organic Clay OL Organic Silt PCTMCDB President Chure Tarai Madesh Conservation Developmental Board PhVI Physical Vulnerability Index PL Plastic Limits rm Running Meter SAA Shape Accel Array SOTER Soil and Terrain Database SP Poorly Graded Sand Sq. Square SRF Strength Reduction Factor SRTM Shuttle Radar Topography Meter SVI Social Vulnerability Index SW Surface Water TLA Total Landslide Area
xxiv ToR Terms of References TU Tribhuvan University UNDP United Nations Development Programme UNDRC United Nations Disaster Relief Coordinator UNDRO United Nations Disaster Relief Organization UNISDR United Nations International Strategy for Disaster Reduction USCS United Soil Classification System VDC Village Development Committee WT Water Table
xxv
SECTION I: INTRODUCTION
26 1. INTRODUCTION AND APPROACH
1.1 BACKGROUND
Landslide is defined as the outward and downward movement of slope forming materials along the definite plane of failure under the influence of gravity (Dhakal, 2012). The materials involved in the landslides can vary from loose soil to competent rock masses or their combination. Their movements can be very fast like avalanche, imperceptible like creeping or in between these extremities. The nature of damage, destruction or disaster by landslides therefore varies according to their types, materials involved and speed of movement. Landslides are the product of a complex interplay of various triggering and conditioning in-situ factors. When combined with human interferences, they become complex and hazardous. Most of the damage and major proportion of human and infrastructure losses in mountain and hilly areas are often associated with landslides. Landslide not only acts as major disaster event but it is also recognized as associated disaster along with major events such as storm and earthquake (Castellanos Abella and Van Westen 2005). That is why in global scale landslide are considered as third type in terms of worldwide importance (Castellanos Abella and Van Westen 2005; Zillman 1999).
Genetically formed by the very weak, fragile and youngest rocks of the Himalaya, and lying in the most dynamic area within the Himalaya, Chure area is exposed to various types of slope instabilities and landslide hazard. Geologically, Chure hills are popularly known as Siwaliks belonging to age of the Middle Miocene to Upper Pleistocene (1 to 14 million years old). These hills occupy the southern portion of the Himalaya and are connected with southernmost Tarai of Nepal. The Churia hills lie between the Lesser Himalaya in the north and Tarai in the south and are lying within two major geological structures namely Main Frontal Thrust (MFT) in the south and the Main Boundary Thrust (MBT) in the north.
1.2 PROBLEMS IN CHURE
Chure hills are formed of very fragile, weak and young sedimentary rocks called the Siwaliks (Figure 1.1) belonging to Middle Miocene to Upper Pleistocene times. Generally, the geology of the Siwaliks is further sub-divided into the Upper Siwaliks
27 (weakly cemented conglomerate beds), the Middle Siwaliks (predominantly thick beds of sandstone interbedded with thin beds of mudstone), and the Lower Siwaliks (predominantly mudstone beds interbedded with thin sandstone beds). Therefore, these young sedimentary rocks are highly weathered and deformed, and interbedding of soft mudstone and hard sandstone beds provide differential weathering providing plenty of options for slope instabilities and occurrence of different types of landslides. The last five and half decades have witnessed the deforestation, over exploitation of forest products, development of road networks, forest encroachment, open grazing and unscientific use of land in the Chure area. The biodiversity and productivity of the land is decreasing gradually which has brought negative impacts in the ecosystem. Such activities on fragile ecosystem have exacerbated landslide in the hills and mountains and consequent flood hazards in the river valleys and Tarai lowland.
Figure 1.1: Generalized geological map of Nepal Himalaya (Source: Dhakal, 2014)
28 With its fragile geology and exploitation of natural resources, distribution of landslides and other mass wasting phenomena are high in Chure area of Nepal. People are living with disaster by adapting their socio-economic activities and accepting hazards as part of life. The threat to the Chure area has been identified as problems evolved by combination of two major factors: natural and human interference (Figure 1.2). Both of these major factors have exacerbated the occurrence and threat of landslides in Chure area. The root cause of landslide problem in Chure is its fragile and dynamic geology evolved by the lithospheric plate collisions between Indian Plate and Tibetan Plate (Dhakal, 2015). These collisions have developed plastic and brittle deformation making land unstable and triggering various landslides. The occurrence of landslides is further triggered by the intense monsoon precipitation and climate change impact (Dhakal, 2015). Human activities such as deforestation, haphazard road construction and over exploitation of construction materials have triggered landslide in Chure area. These problems have been creating risk not only to the local population but also to the environmental and economic assets.
Figure 1.2: Schematic diagram showing major problems of Chure (After Dhakal, 2015)
29 Since Chure conservation is related with multi-faces, multi-sectoral, and multi- stakeholders paradigm, it is imperative to address the issues of conservation of environment and sustainable development of Chure area in coordinative and effective way. However, there is lack of detail study of landslides and related phenomena in Chure area that creates dilemma about the landslide hazard, its distribution, and possibility of future occurrences, landslide risks and mitigation measures. Hence, a detail inventory of landslides with detail characterization including attributes of rock types, soil types, slope conditions, major causes and more importantly their mitigation measures has been an urgent task to reduce the loss from the landslides and to preserve the Chure area. This information can be archived in computer based geospatial database that can be monitored and updated periodically, which is severely lacking in Nepal. In addition, a geographically informed Atlas of Chure area describing its geology, topography, land use and land cover, and human activities will complement in understanding the problems in this area. This fully attributed inventory and Landslide Atlas can be used as information for understanding and explaining the hill slope processes, identifying hazard, assessment of watershed/sub watershed condition and finding their mitigation measures. Such geo- information will also be very instrumental for making conservation and sustainable development strategies and plan based on ground truthing.
To address the issues of conservation of environment and sustainable development in coordinative and effective way, the Government of Nepal has enacted President Chure- Tarai Madhesh Conservation Development Board (PCTMCDB). The Board has also identified the slope instability and landslides as key environmental problems in Chure region. Hence, the Board has emphasized on preparing a detail landslide inventory along with landslide characterization, which will inform location, activity, and type, process and the attributes like topography, geology and structure, soil type, land use and land cover, vegetation condition and elements of risk. In addition, finding the major causes and mitigation measures of most vulnerable landslides is needed to reduce loss from landslides. This information will be crucial for making conservation strategy and plans based on scientific geospatial data.
30 1.3 GOAL AND OBJECTIVES OF THE PROJECT
Considering the necessity of landslide information of the entire Chure area in the context of limited scientific available data on landslide distribution and their properties, the major objective of the proposed study was characterization of landslides distributed throughout Chure area of Nepal and recommending structural mitigation measures for those landslides which are at high risk.
The specific objectives were:
1. To prepare landslide inventory of the ten selected Chure districts (Chure area of central and eastern Nepal namely Bara, Dhanusha, Makawanpur, Mahottari, Rautahat, Sarlahi, Saptari, Siraha, Sindhuli, Udayapur), 2. To characterize the landslides in terms of location, size, volume, geological, engineering geological, geotechnical, hydrological and topographical attributes and their causes, 3. To produce landslide susceptibility maps of the studied Chure area, 4. To develop technical and financial plan for the mitigation of landslides that are at high risk, 5. Support planners, policy makers and other stakeholders to conserve the Chure area by providing database as well as mitigation approach and associated cost, 6. To disseminate information and knowledge through scientific journals, mass media and consultations, 7. To build nation research capacity on Chure research and conservation.
1.4 SCOPE OF PROJECT
Detail inventory of landslides, with detail characterization including attributes of rock types, soil types, slope conditions, major causes and more importantly their mitigation measures have been an urgent task to reduce the loss from the landslides. This information can be archived in computer based geospatial database that can be monitored and updated periodically, which is severely lacking in Nepal. In addition, a geographically informed Atlas of Landslide related to geology, topography, land use and land cover, and human activities will enhance the landslide information and knowledge. This fully attributed inventory and Landslide Atlas can be used as information for understanding and explaining the hill slope processes, identifying hazard, assessment of
31 watershed/sub watershed condition and finding their mitigation measures. Such geo- information will also be very instrumental for making conservation and sustainable development strategies and plan based on ground truthing.
1.5 METHODOLOGICAL APPROACH
As mentioned in the ToR (Annex 1.1), this study has adhered to "environmental sustainability" as the main guiding principle. It pursued the following guiding principles:
1. Landslide characterization with detail investigation of landslides, 2. Linkages between socio-economic situation with resource management dimension, 3. Gaps in past and future management potentialities for Chure environment, 4. Cause –effects relationships
This project has focused on landslide characterization, risk management and mitigation to aid the PCTMCDB for landslide hazard management in order to preserve Chure area. The outline of the methodology of study is given in Figure 1.3. The methodological approaches that were applied in various part of research have been described in respective sections from Section II to Section VI.
32 Landslide inventory and characterization was made by integrating two approaches: Desk study and field work of the landslide sites in the study area. Desk study was conducted for landslide inventory mapping using satellite imagery, landslide susceptibility mapping, vulnerability and risk mapping. While, field study was conducted for landslide inventory validation and its finalization, as well as for geological, kinematic, geometrical, geotechnical, risk assessment and volume calculation. For design purpose, laboratory testing and modeling in Phase 2 software was performed.
Figure 1.3: Methodological framework used in landslide study
33 1.5.1 CONSULTATION
For the better implementation of the project, a series of project management committee meetings were conducted (Annex 1.2). Review and reflection meetings were organized on quarterly basis between PCTMCDB and TU-CDES during the project period. The feedbacks and suggestions from the meeting were incorporated for future activities of the program. Similarly, close co-ordination with PCTMCDB and other stakeholder was maintained from the initial stage of the program implementation. The field research sites were visited by a team consisting from PCTMCDB, TU-CDES and stakeholders for better understanding of the program activities.
1.5.2 STEERING COMMITTEE
In order to facilitate and coordinate at official level, a Steering Committee of the project was formed under the joint chairing of Professor from Central Department of Environmental Science and Member Secretary of President Chure Tarai Madhesh Conservation Development Board. Different personnel involved in the formation of steering committee are mentioned in Table 1.1.
Table 1.1: Steering committee
Chair Professor from TU-CDES and Dr. Annapurna Das PCTMCDB/GoN and Member Secretary from and Prof. Dr. Madan TU-CDES PCTMCDB Koirala Member Head of Department Prof. Dr. Kedar Rijal TU-CDES Member Team Leader of Project Prof. Dr. Rejina TU-CDES Maskey Member Under Secretary Dr. Prem Paudel PCTMCDB/GoN Member Project Coordinator Dr. Subodh Dhakal TU-CDES Member Joint Secretary Mr. Gehendra Keshari PCTMCDB/GoN Upadhya Member Under- Secretary Mr. Pashupati Koirala PCTMCDB/GoN
34 1.5.3 TEAM COMPOSITION
The study team comprised of Team Leader, Project Co-ordinator (Engineering Geologist), Geologists, Geo-technical Engineer, GIS Expert, Environmental Scientists and Socioeconomic Analyst (Table 1.2). The team was assisted by the assistants in extensive field work while technical assistants helped in GIS and remote sensing works.
Table 1.2: Team composition of the project
S.N. Position Name Responsibility
1 Team Leader Prof. Dr. Rejina Proposal writing, leading project Maskey implementation activities, overall supervision of the study project, finalize reports 2 Project Co-ordinator Dr. Subodh Dhakal Co-ordinate between upper tier and lower tier, Supervise the project activities and finalize the reports 3 Geo-Technical Dr. Ram Chandra Develop Engineering Design for Engineer Tiwari mitigation works and also determine the costing of those measures 4 GIS Expert Mr. Ajay Bhakta Develop methodology for landslide Mathema inventory and database preparation, monitor and supervise Assistant GIS Analyst 5 Geologist Mr. Suman Panday Geological mapping, landslide characterization, landslide susceptibility, impacted area determination. 6 Assistant Geologist Mr. Niraj Bal Geological mapping, landslide Tamang characterization, landslide susceptibility, impacted area determination. 7 Assistant GIS Analyst Mr. Padam Bahadur Landslide inventory, characterization, Budha database preparation for further analysis of landslides and report preparation. 8 Assistant GIS Analyst Ms Shanta Banstola Landslide inventory, characterization, database preparation for further analysis of landslides and report preparation and perform other administrative works 9 Social Surveyor Mr. Kumod Lekhak Identify socio-economic characteristics, landslide risk and vulnerability assessment and mapping 10 Field Assistant Mr. Nabin Nepali Assist field work and other technical aspect of the project regularly.
1.5.4 KNOWLEDGE GENERATION PROGRAM
All together three master thesis were produced in 2015/2016. Likewise, scientific articles on national and international journals about working area's issues will be published in the
35 same period. Series of Information Education and Communication (IEC) materials and publication was published in different media platform based on the research findings.
1.5.5 REPORTING
The project outcomes, challenges and opportunities were shared formally and informally with PCTMCDB. However, progress reports, in the prescribed format of PCTMCDB, were submitted on quarterly basis. The financial report was submitted as per the rule of PCTMCDB/GoN. The final report was submitted at the end of the project period.
1.6 WORKING AREA
This study covered the Chure area of 10 districts in between Rapti River and Koshi River namely Makawanpur, Bara, Rautahat, Sindhuli, Sarlahi, Mahottari, Dhanusa, Udayapur, Siraha and Saptai districts of central and eastern Nepal (Figure 1.4). These districts were selected on the basis of the distribution of landslides, access criteria and coverage of some particular river basins and as suggested by the PCTMCDB.
Figure 1.4: Working area of the project
36 1.7 LIMITATIONS
Some of the limitations of the study are:
1. Present study used the recent images provided by Google Earth, however, imageries of previous year were also used for better clarity. 2. It was difficult to characterize the landslides in vertical slopes and in poor accessible areas. 3. There were too many landslides in the working districts, only major landslides have been included in the present study. 4. In many areas, landslides are found in group rather than individual, and therefore it was difficult to describe and show the mitigation plans in the photograph itself.
1.8 EXPECTED OUTCOMES
The detail logical framework regarding the major activities, objectives and expected outcomes are provided in Annex 1.3. Some of the major outcomes that were expected at the end of the project duration are listed below: 1 GIS database of the location, extent and type of landslides and their attributes 2 Maps of landslides (inventory map) at scale (1:50,000) 3 Document about landslides versus natural processes and human activities in Chure area and identification of elements at risk and exposure 4 Map atlas of landslides photos with essential scientific description 5 Mitigation measures and financial plan for selected landslides that have posed high risk 6 Landslide susceptibility maps of Chure area of working districts based on expert knowledge and attributes of studied landslides 7 Provide Baseline data for planners, policy makers, development partners and research institutions for Chure conservation 8 Three master theses on the issues related to landslides in Chure 9 Two PhD research works on Chure issues especially in landslide 10 Publication of the outputs in National and International Journals 11 Information and output sharing through a regional workshop and a national workshop
37 1.9 STRUCTURE OF REPORT
This project report deals with landslide, their characterization, and engineering design for mitigation measures in ten selected districts of Chure area in Nepal. This study focused on landslides in various dimensions such as detail characters of the landslides which include geological and geo-morphological characters, their causes, and impact on the socioeconomic aspects. The final target was to develop mitigation model for landslides to reduce the risk of loss and/or damage in population, development, infrastructure and natural environment.
This report has been sub-divided into 6 parts to address the scope of the research work (Figure 1.5). All of the chapters are related to the scale at which the work was performed. The first section of the report introduces the study and discusses the context under which the study was formulated. The objectives and scope of the research including the methods and approaches set out for this research are discussed in this section.
The second section of the report describes the “landslide inventory” carried out for this project. This section describes the remote sensing and GIS processes carried out to generate spatial database of the landslides of the study area. The database generated by this section formed the bases of the all other analyses carried out in this study. The third section of the report discusses “landslide characterization”. This section deals with the characterization of landslides on several criteria, namely - geological characteristics, geological structure, soil condition and soil strength measurements, topography and geomorphology, and hydrology.
The fourth section of the report discusses “landslide susceptibility”. This section discusses the modeling work carried out to map the area susceptible to the landslides. This modeling work was based on the analysis of several factors namely - geomorphology, topography, engineering geology factors, landuse and hydrology. The fifth section of the report discusses “landslide vulnerability and risk”. This section carried out spatial multi-criteria decision analysis approach to rank VDCs based on the vulnerability and risk of these VDCs to landslides. This work was carried out to prioritize implementation of the landslides mitigation measures discussed in the sixth section.
The sixth section of this study discusses “mitigation model of the Chure landsides’. This section discusses on improving safety factors of the existing landslides by introducing
38 civil and bioengineering mitigating measures. This section discusses the laboratory analysis and computer models deployed in designing of the mitigation models.
Section 1 Introduction
Section 2: Section 3
Inventory Characterization Section 4 Susceptibility
Section 5 Vulnerability and Risk
Section 6 Mitigation Design with Cost Estimation
Figure 1.5: Structure of the report
.
39
SECTION II: LANDSLIDE INVENTORY
40 2. LANDSLIDE INVENTORY
2.1 INTRODUCTION
Landslide inventory is a spatial dataset of mapped landsides usually derived from aerial photograph and satellite image interpretation (Hasegawa et al. 2009, Dahal et al. 2012; Van Den Eeckhaut et al. 2009), and/or direct field mapping. Sometimes database of historical movements within an area are assessed for the landslide inventory. It is the most basic requirement and an essential part of any landslide zoning. The landslide inventory is the first step in the landslide hazard assessment process where individual landslides are identified and digitized (Dangol 2009).This step involves locating the landslide, date of occurrence, and state of landslides, their classification, and estimation of volume and extent of travel distance in an area (Fell et al. 2008).The ultimate output of the landslide inventory is the spatial distribution of landslides as points or to the scale translated in the landslide inventory map. These maps, however, only provide information for a short period of time, and they provide no insight into temporal changes in landslide distribution as the landslide processes are dynamic.
Nowadays Geographic Information Systems (GIS) is extensively used in the world, and it is widely used in Nepal as well mostly for landslide hazard mapping as it is user friendly, efficient, effective and powerful tool for data acquisition, management and analysis (Dangol 2009). GIS is a computer-based information system for input, management, analysis, and output of geographic data and information. It deals with collection, storage, retrieval, manipulation, analysis, and display of spatially related information. In GIS various qualitative and quantitative techniques are applied in order to analyze the relationship between landslides and their influencing parameters and thus producing hazard maps (Ayalew and Yamagishi 2005).
There are three spatial levels of zonings in hazard analysis, which are regional, local and site-specific zoning. Among these, the first two are recommended for landslide inventory zoning as the inventory would be used for the generation of information and advisory purpose (Fell et al. 2008). In regional scale inventory for shallow landslides it is considered incomplete as shallow failures are rapidly removed by humans, covered by grasses and herbs and are inconspicuous in the imagery used for inventory (Van Den
41 Eeckhaut et al. 2009). The inventory at this scale can be considered as preliminary research and form the basis for detail analysis or study of areas with higher impacts.
2.2 SIGNIFICANCE OF THE LANDSLIDE INVENTORY
The landslide inventory is the preliminary step in any kind of landslide studies. Basic information that the inventory work might provide include locations, sizes and types of the landslides. The detail landslide inventory can put forth the facts of landslides relating to the geology, geomorphology and hydrology. The susceptibility or hazard assessments of landslides for an area use the landslide inventory as basic inputs and gives ideas about the location and spatial dimensions of occurrences of landslides in relation to factors affecting them. Beside such studies, the inventory can also be used in other social science researches like vulnerability assessments. The data generated can be used to identify the elements of an area at risk and to plan accordingly for safety of people and infrastructures.
In this study, high resolution images of 2015 available in the Google Earth were used to develop the landslide inventory of all the ten working districts, which were validated and verified from the field study. The tool is time efficient, easy to use and economic as well as the data gathered are generally of high accuracy. The information derived by the inventory developed the database of landslides for each district. The distribution of landslides is correlated with different factors like geology and morphology. The results were presented according to respective watershed so that the upstream downstream linkages of the same watershed can be described in terms of landslide hazard and associated disasters. The landslide number, size and landslide density are also analyzed for all the districts. Each landslide is given unique ten digits ID which comprises district, VDC and ward number followed by landslide number. The database can be widely used in landslide susceptibility mapping, land use planning as well as formulating mitigation plans of the landslide hazards. The number and statistics of the inventoried landslides can also be used in facts and figures during awareness campaigns and in educational activities of schools of the project area.
42 3. METHODOLOGICAL APPROACHES
3.1 DATA GENERATION FOR GIS ANALYSIS
The study was carried out with an application of GIS tool to analyze the landslides for their geological and morphological characteristics. The first and foremost step of this study was preparation of the dataset that were compatible for the envisioned GIS analysis. The data preparation can be categorized in two parts: i) Desk study and ii) Field study
3.1.1 DESK STUDY
Documentation of landslide in the study area was carried out as the first step of landslide inventory. Open source high resolution Google Earth Pro data for the duration of 2015- 2016 were extensively used to digitize the landslides of the study area as a part of desk study. This data was supplemented by the GIS data obtained from various agencies such as: Topographic digital data from Department of Survey, GoN (1990), Geological maps of Nepal from Department of Mines and Geology, SOTER soil data, SRTM 90m DEM CARTOSAT 30m DEM
3.2.1 FIELD STUDY
Extensive field works were carried out to verify and collect Geographical Positioning System (GPS) data during the field visits on the landslides and its attributes such as boundary of landslides, size of landslides, types of landslides, severity of landslides, direct and indirect impact of landslides etc. The field visits details are presented in Table 3.1. Table 3.1: Field visit details S.N Visited District Date 1 Makawanpur, Bara, Rautahat 3rd Dec, 2015 to 2nd Jan, 2015 2 Sindhuli, Sarlahi, Mahottari 15th Jan, 2016 to 14th Feb, 2016 3 Udayapur, Siraha and Saptari 7th Apr, 2016 to 27th Apr, 2016
43 3.2 AVAILABLE TOOLS AND MATERIALS
GIS Layers, Digital Topographic Map from Department of Survey, GoN 1990 AD Geological Map from Department of Mines and Geology, 2000, 2001, 2004 AD ArcGIS 10.1 Google Earth Pro Imagery, 2015/016
3.3 SPATIAL REFERENCE OF DATA
Spatial reference of the data is necessary because it enables the geographic datasets to represent its actual common location for integration. The type of spatial reference given to project the datasets in our study was Modified Universal Transverse Mercator 84 (MUTM -84) since the projection system for all the available data available from Department of Survey are with the same projection system.
3.4 LANDSLIDE INVENTORY AND ATTRIBUTES
Landslide inventory and characterization was made by integrating two methods:
i) Delineation from Google Earth Imagery, and ii) Validation through the field visit of the landslide sites by collecting GPS points.
The landslides were identified by visually interpreting the shape, size, texture and location etc. The landslides generally appear as gray to light gray tone or light color, spoon like, tree like or triangular shape. These were mainly located near ridges, cutoff slopes of riverbanks, gully heads or were frequent of topographic activities having its axis along with the direction of gravity. These landslides were delineated as individual solid polygons using visual interpretation from Google Earth Pro Imagery 2015/16 AD. These documented landslides were analyzed further in ArcGIS 10.1 to characterize them through different geological as well as morphological attributes. The attributes of landslides are listed in the Table 3.2.
44 3.5 DATABASE PREPARATION
All the non-redundant GIS based data were prepared and combined district wise. Different sets of data such as District Boundary, VDC Boundary, Wards Boundary, Hydrological Data, Watershed Boundary, Road Network, Soil Types, Geo-Form, Digital Elevation Model (DEM) and its Derivatives including Slope, Aspect and Elevation, Settlements, Spot Height and Villages Name were developed for each district in separate folder and compiled in a common disk.
Table 3.2: Attributes of documented landslides S.N. Attributes Description 1 Landslides Identity of each landslide was developed. The ID consists of 10 digits whole Identity number. These 10 digits are composed of District code (2 digits), VDC code (3 digits), Ward code (2 digits) and Landslide number (3 digits). . 2 Elevation Elevation of individual district was classified into four classes. <500m Low 500m-1000m Moderate 1000m-1500m High >1500m Very High 3 Slope Slope of all the study was classified into five classes. 0-25 Gentle to Steep 25-45 Steep 45-60 Very Steep >60 Cliff 4 Aspect Aspect was classified into six classes. (-1) Flat 45 ° North 45-135° East 135-225° South 225-315° West 315-360° North 5 Watershed Watersheds within the study districts were identified. The source of water layer was from Department of Survey, Government of Nepal, 1990 A. D.
45
6 Land use Land use was classified into seven different classes Forest(including Scattered trees, Forest and Bamboos), Cultivation (including Cultivation, Orchard, Nursery), River Body (including Sand and River), Ponds and Lake (including Pond or Lake, Swamp), Bush & Grass ( including Bush and Grass), Barren Land ( including Barren area, Barren Land and Cliff) and Built -up Area 7 Geo-Forms Geo-Forms were based upon the geological map of the study area. The map was extracted from the Department of Mining and Geology, 2001 A.D. The three different class of the Geo-Forms are Lower Siwalik Middle Siwalik Upper Siwalik 8 Soil Types Soil Types was classified based on soil map prepared through the visual interpretation from Google Earth Imagery 2015/016. The Soil Types were classified into four types namely: Alluvial Soil Colluvial Soil Rock Exposure Residual Soil
46 4. RESULTS
4.1. LANDSLIDE INVENTORY
Landslide inventory was conducted in ten districts of Chure area of eastern Nepal. The extent of the study area, however, did not cover the entire VDC. The project area consisted of 143 Village Development Committees (VDCs) including a portion of Parsa Wildlife Reserve. Most of the VDCs do not lie completely inside the Chure boundary derived from physiographic map of Nepal, as there is variation in boundary delineation of districts and Chure. Out of 143 VDCs, landslides were identified in 116 VDCs (Table 4.1). Within the ten districts, 3456 numbers of landslides were identified totaling an area of 12.7234 Km2.The distribution of landslides in each of the working districts in terms of numbers of landslides is shown in Figure 4.1. Higher number of landslides was found in Makawanpur (792), Siraha (717), Udayapur (684) and Sindhuli (469) districts which comprised almost 77% of total inventoried landslides. These districts represent about 77% area of all the project districts. Details of landslides distribution in ten districts are given in Annex 2.1.
Figure 4.1: Distribution of landslides recorded in the project districts
Further these districts were divided into three units as clusters (Table 4.1) namely Makawanpur, Sindhuli and Udayapur clusters consisting three, four and three districts respectively. Clusters are continuous from north to south and possess valleys in north
47 surrounded by Chure hills. Whereas southern portion of those clusters rises from the Tarai of Nepal. The northern part of the valley is surrounded by the Chure hills, whereas the southern portion rises from Tarai. The distribution of landslides was higher in hills and lower in the valleys. Some landslides were also observed in the valley, which were mainly due to toe cutting by rivers. Majority of the landslides were seen in the hilly areas. The landslides in the hills occurred in the river headwaters and areas with weak geology.
Table 4.1: Distribution of landslide in the clusters
Makawanpur Cluster Sindhuli Cluster Udayapur Cluster District Number District Number District Number Makawanpur 792 Sindhuli 469 Udayapur 684 Bara 110 Sarlahi 103 Saptari 216 Rautahat 163 Mahottari 98 Siraha 717 Dhanusha 104 Total 1065 774 1617
Makawanpur Cluster (Figure 4.2) occupies nearly 30.6% of the total study areas and includes Makawanpur, Bara and Rautahat districts. Landslide distribution showed 1065 landslides in this cluster and occupied 31% of total landslide inventoried. In Figure 4.2 the red marks denote landslides. It is evident that eastern half of the cluster lying in between the borders of Makawanpur and other districts comprised most of the landslides.
Figure 4.2: Distribution of landslides in the Makawanpur Cluster
48 Whereas the Sindhuli Cluster (Figure 4.3) made up of four districts namely Sindhuli, Sarlahi, Mahotatari and Dhanusha showed 774 landslides in the inventory. This cluster occupied 38.8% of total project area and 22% of total landslides inventoried. The concentration of landslides was seen at the middle part of Sindhuli district.
Figure 4.3: Distribution of landslides in the Sindhuli Cluster
In case of Udayapur Cluster (Figure 4.4) comprising three districts, Udayapur, Siraha and Saptari, the number of landslides was the highest. It consisted of 1617 landslides comprising 47% of all landslides in the study site. Most of the landslides in this cluster were scattered in Siraha district. Some of the major landslides were located in eastern portion of Udayapur district.
49
Figure 4.4: Distribution of landslides in the Udayapur Cluster
4.2 RANKING OF THE DISTRICTS ACCORDING TO LANDSLIDE OCCURRENCES
Ranking of ten districts according to the landslides occurrences was carried on the basis of four major parameters, (a) number of affected VDCs, (b) number of landslides, (c) density of landslides, and (d) percentage of area covered by landslides. The most simplified ranking of the districts was on the basis of the total number of VDCs with landslide. Similarly, percentile of the VDCs with landslides to total number of VDCs within the Chure area for each district was calculated. Rank of each district based on numbers of affected VDCs is presented in Table 4.2.
Table 4.2: Landslide affected VDCs in ten studied districts of Chure area in Nepal
District No. of VDCs No. of VDCs with Landslides Percentage of Rank in Chure affected VDCs region Makawanpur 23 20 86.96 1 Bara 5 4 80.00 4 Rautahat 5 4 80.00 4 Sindhuli 32 27 84.38 2 Sarlahi 9 7 77.78 6 Mahottari 4 3 75.00 9 Dhanusha 9 7 77.78 6
50 Udayapur 23 19 82.61 3 Siraha 12 9 75.00 9 Saptari 21 16 76.19 8
The number of landslide affected VDCs in Sindhuli district was 27 and by percentage calculation, about 84.38% of VDCs were affected in the district. Sindhuli lies in second rank in terms of landslide affected VDCs. On the other hand, Makawanpur district had 20 landslide affected VDCs out of 23 VDCs. The district was ranked as top with 86.96% of landslide affected VDCs by percentage.
The rank of ten districts according to the number of landslide occurrence showed a slightly different picture. Higher the number of landslides in a district higher will be the rank of that district. Table 4.3 shows that highest number of the landslides was in Makawanpur followed by Siraha and Udayapur with 792, 717 and 684 landslides respectively. Hence, these districts were ranked as first, second and third accordingly. On the other side, the number of landslides in Mahottari, Sarlahi and Dhanusa was 98, 103 and 104 respectively putting them in last rank according to the landslide occurrences.
The more scientific way of ranking districts for landslide occurrence will be comparison of landslide density. Landslide density in this case is ratio of the total numbers of landslide in a district to the area of Chure area of that district. It results into higher values when occurrences of landslides are greater in smaller areas. Here, Siraha district had landslide density 3.424, which means around 3 landslides occur within1km2area, and was ranked as the first.
Similarly, Rautahat and Saptari districts were ranked as second and third respectively. Besides landslide density, the percentage of area covered by landslides area can also be used for ranking. Observing this category, Siraha district was ranked as the first and Rautahat and Bara were ranked as second and third respectively. In both criteria for ranking, Dhanusha district occupied last position i.e., rank tenth.
51 Table 4.3: Landslide density and percentage area covered by landslide (Rankings are in parenthesis) District No. of Total Landslide VDC area in Landslide Percentage Landslides Area (Km2) Chure (Km2) Density of area covered Makawanpur 792 2.9155 1406.58 0.5631 (5) 0.207 (6)
Bara 110 0.9112 204.39 0.5382 (7) 0.446 (3)
Rautahat 163 0.4444 99.25 1.6424 (2) 0.448 (2)
Sindhuli 469 2.5239 1434.47 0.3269 (9) 0.176 (7)
Sarlahi 103 0.5796 234.33 0.4396 (8) 0.247 (5)
Mahottari 98 0.4484 161.43 0.6071 (4) 0.278 (4)
Dhanusha 104 0.3482 336.20 0.3093 (10) 0.104 (10)
Udayapur 684 2.1586 1264.40 0.5410 (6) 0.171 (8)
Siraha 717 2.0023 209.40 3.4241 (1) 0.956 (1)
Saptari 216 0.3959 239.15 0.9032 (3) 0.166 (9)
Total 3456 12.7281
The ranking of districts explained here is only for the occurrences of landslides and its spatial coverage in each districts. But incorporating the impacts done by these landslides and the vulnerability of people due to this particular hazard the ranking scenarios may results in different values.
Finally the ranking of the individual districts based on different basis were summed up to obtain total scores as shown in Table 4.4. Here, low score signifies that district is highly affected by landslide occurrences whereas higher score denotes that district is slightly affected by landslide occurrences. Makawanpur and Siraha possessed 13 as the final score which denotes that they lie in the first rank among all, whereas Rautahat and Udayapur have final ranks third and fourth respectively. Dhanusha and Sarlahi were ranked as tenth and ninth respectively showing lower impact of landslides.
52 Table 4.4: Final ranking of the selected ten districts according to the occurrence of landslide
District Ranking Basis Total Final VDCs Total Landslide Percentage Scores Ranking Affected Number of Density of area Landslides covered by Landslides
Makawanpur 1 1 5 6 13 1 Bara 4 7 7 3 21 5 Rauthat 4 6 2 2 14 3 Sindhuli 2 4 9 7 22 6 Sarlahi 6 9 8 5 28 9 Mahottari 9 10 4 4 27 8 Dhanusha 6 8 10 10 34 10 Udayapur 3 3 6 8 20 4 Siraha 9 2 1 1 13 1 Saptari 8 5 3 9 25 7
4.3 VDC-WISE LANDSLIDE DISTRIBUTION IN PROJECT DISTRICTS
4.3.1 LANDSLIDE DISTRIBUTION IN VDCS OF MAKAWANPUR CLUSTER
Makawanpur District Table 4.5 presents the number of landslide no. distribution in 22 VDCs of Makawanpur Cluster. In Makawanpur district, 792 landslides were observed in 20 VDCs. The VDCs with landslides are listed in Table 4.5. Within the district, Hetauda Muncipality, Padampokhari and Sukaura VDCs were found to be free from landslides. Here 8 VDCs had landslide number less than 10, 8 VDCs have landslide number from 10 to 50 and 4 VDCs had more than 100 landslides. Phaparbari, Raigaun, Shripur-Chhatiwan and Dhiyal were the VDCs with hundred plus landslides i.e. 192, 171, 151 and 114 landslides respectively. These landslides were further classified according to their size as <1000 (small), 1000-10,000 (medium) and >10,000 (large). Among 792 landslides of Makawanpur district, 52 landslides were large, 519 medium and 221 small in size. Dhiyal, Phaparbari and Shripur-Chhatiwan are possessed 9, 22 and 9 respectively out of 52 large landslides.
53 Bara District Chure portion of Bara district consists of 4 VDCs and small portion of Parsa Wildlife Reserve. A total of 110 landslides were observed in Bara district, out of which Ratanpuri VDC had 62 landslides, Amlekhganj, Bharatganj-Sinaul and Nijgadh had landslide numbers between 10 and 50. In this district, out of total landslides 13 were small, 75 were medium and 22 were large sized. Ratanpuri VDC holded 13 large landslides while Amlekhganj VDC holded 5 large landslides.
Rautahat District Rautahat district holded a total of 163 landslides which were observed in 4 VDCs. Out of these, Chandranigahapur had 72 numbers of landslides. Judibela and Paurai possessed 50 and 34 numbers of landslides respectively and Rangapur includeed 7 landslides. Kanakpur VDC of this district did not have any landslides. The size distribution of this district showed 52 were small slides, 100 were medium and 11 were large landslides. Paurai VDC had 5 large landslides out of 11.
Table 4.5: Landslide number distribution in VDCs of Makawanpur Cluster
District Landslide numbers in VDCs >100 50-100 10-50 <10 Makawanpur Dhiyal - Betini Ambhangyang Pharbari Churiyamai Basamadi Raigaun Handikhola Bhainse Shripur Harnamadi Makawanpur Chhatiwan Hatiya Gadhi Kankada Manthali Manahari Raksirang Thingan Sarikhet Palase Shikharpur Bara - Ratanpuri Amlekhganj Bharatganj Sinaul Nijgadh Rautahat - Chandranigahpur Judibela Rangapur Paurai
54 4.3.2 LANDSLIDE DISTRIBUTION IN VDCS OF SINDHULI CLUSTER Sindhuli District Sindhuli district accounted for 469 landslides where 27 VDCs out of 32 were found to be affected by landslide occurrence. Here the numbers of landslides were less than 100 in all affected VDCs. Thirteen VDCs possessed landslide number less than 10 and 12 VDCs possessed landslide number between 10 and 50. Dadigurase VDC and Kamalamai Municipality occupied higher number of landslides between 50 and 100 in this district. They possessed 75 and 100 landslides respectively. Similarly, VDCs without landslides in Sindhuli were Bastipur, Jinakhu, Lampantar, Netrakali and Sateshowori. The size distribution showed 133 small, 284 medium and 52 large landslides. Thirty large landslides of 52 were located in four VDCs namely Pipalmadhi, Mahendrajhyadi, Mahadevsthan and Kyaneshor. Ten large landslides were situated in Kamalamai Municipality and Kapilakot VDC possessing 5 in each VDC.
Sarlahi District Out of 9 VDCs in Sarlahi, 7 VDCs were influenced by landslide occurrence. There were a total 103 landslides in Sarlahi district. Out of these, all VDCs have landslide number less than 50. Dhungre Khola, Hariyon and Patharkot had landslide number less than 10, whereas Atrouli, Kalinjor, Narayan Khola and Parwanipur VDCs had landslide number between 10 and 50. Parwanipur VDC had highest (35) number of landslides. There were 5 large, 86 medium and 12 large landslides in Sarlahi.
Mahottari District Landslides in Mahottari district were located in three VDCs only. Landslides were not spotted in Bardibas VDC. There were altogether 98 landslides where 5 were small, 83 were medium and 10 were large sized. All 3 VDCs have landslides numbers between 10 and 50.
Dhanusha District Similarly in Dhanusha district, 7 out of 9 VDCs were affected by landslides. There were altogether 104 landslides in this district. Tallogodar VDC possessed 54 landslides which was highest among all affected VDCs. On the other side, Bengadwar, Puspabalpur and Yagyabhumi had landslide number less than 10 and Bharatpur, Nakatajhijij and Tulsi had landslide number between 10 and 50. Out of total 104 landslides, 26 were small, 71 were
55 medium and 7 were large sized. Here, 4 out of 7 large landslides were present in Bharatpur VDC.
Table 4.6: Landslide number distribution in VDCs of Sindhuli Cluster
District Landslide numbers in VDCs >100 50-100 10-50 <10 Sindhuli - Dadiguranse Belghari Amalae Kamalamai Municipality Bhimsthan Arunthakur Hariharpur Gadhi Bhadrakali Hatpate Dudhouli Kalpabrikshya Harsahi Kapilakot Jarayotar Kyaneshor Kakur Thakur Mahadevsthan Ladhabhir Mahendra Jhyadi Nipane Pipalmadhi Sirthauli Ranibas Tamajor Ranichuri Tandi Tribhuvan Ambote Sarlahi - - Atrouli Dhungre Khola Kalinjor Hariyon Narayan Khola Patharkot Parwanipur Mahottari - - Gauribas - Khayarmara Maisthan Dhanusha - Tallo Godar Bharatpur Bengadwar Nakatajhij Puspabalpur Tulsi Yagyabhumi
4.3.2 LANDSLIDE DISTRIBUTION IN VDCS OF UDAYAPUR CLUSTER
Udayapur District
In Udayapur district, the total number of landslides was 684. In this district, 19 out of 23 VDCs were found to be affected by landslide occurrence. Nine VDCs had landslide number less than 10 and five VDCs had landslide number between 10 and 50. BhalayaDanda and Mainamaini had landslide number between 50 and 100. Katunjebawala, Tribeni and Trijuga Municipality had landslide number greater than 100. They possessed 121, 119 and 136 numbers of landslides respectively. Size distribution of landslides in this district showed 201 small, 443 medium and 40 large sized landslides.
56 Mainamaini VDC alone had 14 large landslides. Table 4.6 shows the landslide number distribution in VDCs of Udayapur Cluster
Table 4.7: Landslide number distribution in VDCs of Udayapur Cluster
District Landslide numbers in VDCs >100 50-100 10-50 <10 Udayapur Katunjebawala Bhalaya Katari Basaha Tribeni Danda Panchawati Beltar Trijuga Municipality Mainamaini Risku Chaudandi Sundarpur Hadiya Tapeshwari Jalpa Chilaune Jogidaha Saune Siddhipur Thoksila Siraha Bishnupur Katti Phulbariya Chandra Udayapur Bhadramal Dhodana Jamdaha Karjanha Taregana Muksar Ramnagar Mirchayia Gobindapur
Saptari - Bhangaha Bakdhuwa Jandaul Dharampur Khojpur Ghoganpur Khoksar Parbaha Hardiya Pansera Kalyanpur Phattepur Prasbani Sitapur Rupnagar Theliya Terhauta
Siraha District
Siraha district had 717 landslides altogether and almost half i.e. 308 were found in Bishnupur-Katti VDC. In this district, 10 VDCs were found to be affected by landslide whereas 2 VDCs did not possess landslides. Taregana-Gobindapur and Dodhana VDCs in same district had 128 and 109 number of landslides. Phulbariya VDC had 52 landslides. Remaining 6 VDCs had landslide number less than 50. Here, the sizes of landslides reveal that 184 were small, 506 were medium and 27 were large landslides. Fifteen out of 27 large landslides were situated in Bishnupur-Katti VDC.
57 Saptari District In Saptari district, there were 216 landslides. Here, 16 VDCs were affected by landslides out of 21. All VDCs possessed landslides less than 50 in numbers. Higher number of landslides was found in Kalyanpur, Bhangaha and Pansera VDCs which possessed 36, 35 and 32 landslides respectively. In this district, only small and medium sized landslides were found where 91 were small and 125 were medium in size.
4.4 LANDSLIDE ATTRIBUTES
The GIS dataset of the landslides were prepared with eight different attributes in the ten selected districts of Chure area. The attributes are described as follows:
(a) Geographic location: Here, a geographic location means the point of the landslide on Earth's surface. They represent the X and Y coordinates of the centroid of the landslide. Geographic locations in the attribute table are expressed as longitude (LONG) and latitude (LAT) as shown in Figure 4.5. Latitude is the measure, in degrees, of the distance of a location from the equator, which divides the Earth into Northern and Southern Hemispheres. Longitude is the measure, in degrees, of a location from the prime meridian, the starting point from which the time zones are calculated.
Figure 4.5: Snap shot 1 of an attribute table
58 (b) Administrative location: Administrative location in attribute table is represented by the name of VDC and ward number of a district for the entire landslide inventoried. This implies the location of a landslide in a particular ward number of a particular VDC. They are expressed as VDC and WNO in attribute table as shown in Figure 4.5.
(c) Size of the landslide: Size of the landslide represents the spatial extent of the landslide on the Earth's surface in square meter. In other words, it is the area occupied by landslides. In attribute table, it is represented by AREA.
(d) Land use: Land-use, depicted as LULC in the attribute table as shown Figure 4.6, stands how human utilize the land usually related to land cover. Here, the major land uses considered are forest, cultivation, river body, pond or lake, bush and grass, barren land and built-up area. The land use types may be different based on the purpose of the research. The land use here in attributes for each landslide represents the past conditions of land before occurrence of landslides.
(e) Name of the watershed: The landslides inventory was overlaid over the boundaries of the watershed to have an idea of the location of landslide in particular watershed. In attribute table, this is shown by WSHED. Each landslide's watersheds were recorded.
(f) Geological formation: Geological formation represented as GEOFORM in the attribute table represents the types of geology of the area. The sediment sequence of Siwalik Groups is classified into three major groups as lower, middle and upper Siwaliks. Beside these three major sequences, few other geological Formations were also found.
(g) Soil types: Soil types are classified based on their locations. All the soils are categorized into alluvial soil, colluvial soil, residual soil and rock exposures. In attribute table, it is represented as SOIL_TYP.
(h) Topographic features- elevation, slope, aspect: Topographic features are derived from the DEM developed from the contour data of 20m interval. Elevation is categorized into low (<500m), moderate (500-1000m), high (1000-1500m) and very high (>1500m). It is represented as ELEVATION in attribute table and expresses the altitude of landslide. Slope gradient represents the angle of inclination of the slope and is classified into gentle (<25o), steep (25o-45o), very steep (45o-60O) and cliff
59 (>60o). It is denoted as SLOPE_GRAD in attribute table and shows the inclination of slope where the landslide is located. Aspect gives an idea about direction the slope is facing. It is expressed as south, north, east and west with reference to major ridge or stream. In attribute table, it is represented as ASPECT.
Figure 4.6: Snap shot 2 of an attribute table
4.5 LANDSLIDE IDENTITY NUMBER
Landslide Identity (LID) in attribute table represents special number assigned to each landslide. LID is developed using codes of districts, VDCs, ward number and landslide numbering. The code is of 10 digits where the first two digits refer to district, second three digits refer to VDC, then it is followed by ward number finally ending with three digits of landslide numbering inside a ward. Thus, LID gives an idea about the location of the landslide from district up to ward level (Annex 2.2).
60 4.6 LANDSLIDE DISTRIBUTION FOR EACH FACTOR CLASS
The description of distribution of landslide can be best described when the percentage of area occupied in certain factor is considered. Here, the distribution of landslides in different geological formations, and topographic factors are shown in charts and figures. Whereas the information on distribution data for other factors such as watersheds, soil types and land use can be obtained from Annex 2.3. Also, it provides numerical and areal values of each factor class used in development attribute table for landslides occurrences.
4.6.1 LANDSLIDE DISTRIBUTION FOR FACTOR CLASSES IN MAKAWANPUR CLUSTER
Makawanpur District The total landslide area (TLA) of the Makawanpur district is 2.9155 Km2 for 792 occurrences. Most of the landslide areas were found to be occurring in Middle Siwaliks in the district which was 48.74% of TLA. Similarly, 35.2% of TLA was observed in Upper Siwaliks, whereas 13.31% of landslides was found in Lower Siwaliks. The landslide area in Quaternary deposits was very little i.e. 0.03% (Annex 2.3). In valley deposits, the landslides observed were mainly due to the toe cutting of the hill slope by rivers. Figure 4.7 shows the detail about the percentage of landslide area on different geological formation.
2.73
13.31
Lower Siwaliks Middle Siwaliks 35.20 Upper Siwaliks Pre Siwaliks Quaternery Deposit
48.74
Figure 4.7: Total Landslide Area (TLA) in percentage by geological Formations in
Makawanpur district
61 Similarly, considering topographic factors, landslide area was found to be higher for southern aspect of hills, steep slopes and low elevations. Here, south aspect consisted 34.98% of TLA than other aspects which was followed by east, west and north aspects. Landslides area was abundant for steep slopes which occupieed 55.59% of TLA and was followed by very steep slopes. In case of elevation, 60.87% of TLA were found in low altitude which is shown in Figure 4.8.
100
80 55.59 60.87 60 34.98 38.37 40 27.04 19.71 17.63 18.97 19.02 20
Percentage(%) 6.41 0.65 0.76
0
Flat
East
Cliff
High
West
Low Low
Steep
South
North
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factors
Figure 4.8: Total Landslide Area (TLA) in percent by topographic factors in Makawanpur district
Watersheds of Makawanpur with higher number of landslides were Chaudaha-Khola- South, Lal-Bakaiya-Nadi, Chiruwa and Lower-Bagmati. Most landslides were recorded in forest area but many were found occurring in origin of rivers, barren lands and in bordering area of cultivated lands and forests. Colluvial soil was observed for higher coverage of landslides than other soil types.
Bara District The 110 landslides occurring in Bara District made a total area of 0.9112 Km2. Considering the geological formations in this district, the area occupied by landslide was higher in Middle Siwaliks which was followed by Lower Siwaliks. Middle Siwaliks comprised 64.19% of TLA whereas the Lower Siwaliks comprised 28.46%. Remaining 7.35% of landslide area was found to be occurring in Upper Siwaliks. Figure 4.9 shows the detail status of landslide distribution on geological formation in Bara district.
62 7.35
28.46
Lower Siwaliks Middle Siwaliks Upper Siwaliks
64.19
Figure 4.9: Total Landslide Area (TLA) in percent by geological formation in Bara district
In topographic factors of district, landslides were found mostly in south slopes with steep gradient and moderate altitude as shown in Figure 4.10. Watersheds with higher landslide area in this district were Lalbakaiya-Nadi, Dhansar-Nadi and Pasaha-Nadi. Higher number of landslides was observed in area with prior forests and colluvial soil in Bara district.
100
percentage of landslide area 80 64.20 58.91 60 41.09 29.71 40 26.69 28.64 23.29 14.96 20 Percentage(%) 6.66 5.85
0
East
Cliff
West
Low Low
Steep
South
North
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factors
Figure 4.10: Total Landslide Area (TLA) in percent by topographic factors in Bara district
63 Rautahat District In Rautahat district, 163 landslides were marked with boundaries that covered an area of 0.4444 Km2. Geological formation in the Chure portion of the district consists of Lower and Middle Siwaliks only where the area of landslide occupied was 24.31% and 75.69% respectively as show in Figure 4.11.
24.31
Lower Siwaliks Middle Siwaliks
75.69
Figure 4.11: Total Landslide Area (TLA) in percent by geological formation in Rautahat district
Also, most landslide area was observed in south slopes, very steep slope gradients and moderate elevations. The areas of landslides in those factors were 54.28%, 41.97% and 58.23% of TLA respectively as shown in Figure 4.12. Landslide locations were also observed in east and west aspects. Steep slopes have almost equal area with that of very steep slopes. In this district, higher area of landslide was observed in Lower-Bagmati and Chandi-Khola watersheds. The pattern of landslide occurrence was similar to that of preceding districts in case of land use. Most landslides were observed in forests. Regarding the soil types, the areal distribution of the landslide was higher in colluvial soil and rock exposures (cliffs).
64
100
80 58.23 60 54.28 41.83 41.97 41.77 40 23.93 20.16 14.67 20 Percentage(%) 1.63 1.53
0
East
Cliff
West
Low Low
Steep
South
North
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factors
Figure 4.12: Total Landslide Area (TLA) in percent by topographic factors in Rautahat district
4.6.2 LANDSLIDE DISTRIBUTION IN FACTOR CLASSES FOR SINDHULI CLUSTER
Sindhuli District Total landslide number marked in Sindhuli district was 469 that occupied an area of 2.5239 Km2. Almost 90% of landside area was occupied by three major geological formations. Middle, Lower and Upper Siwaliks hold area in decreasing order of percentages. They occupied 36.78%,14% and 20.56% of TLA respectively which is shown in Figure 4.13.
2.53 1.02 5.96 Geological Formation 20.56 Quarternery Deposits Pre Siwalik Lakharpata Group 33.14 Lower Siwalik Middle Siwaliks Upper Siwaliks 36.78
Figure 4.13: Total Landslide Area (TLA) in percent by geological formation in Sindhuli district
65 The area coverage of south and west facing slopes was 21.44% and 36.32% of TLA respectively. Landslides gentle and steep slope gradients occupied 47.78% and 49.41% of total landslide area respectively. Low and moderate elevation had higher area covered by landslides. Together they hold an area of 98.25% of TLA as shown in Figure 4.14. Most of the landslide areas were observed higher for Marin-North, Kamala-North and Kyan- Khola watersheds. Some watersheds occupied larger landslide area but the number of landslides was lower. In land use category, forest sub-class occupied higher area of landslides whereas among soil types, colluvial soil accounted for higher area of landslides.
100 percentage of landslide area
80
60 47.78 49.41 47.10 51.15 36.32 40 19.97 21.44 Percentage(%) 20 9.50 12.77 2.54 0.27 1.75
0
Flat
East
Low
Cliff
High
West
Steep
North
South
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factors
Figure 4.14: Total Landslide Area (TLA) in percent by topographic factors in Sindhuli district
Sarlahi District In Sarlahi district, the summed area of all landslides was 0.5796 Km2. Total landslides marked were 103. Here, higher occurrence of landslide was observed in Middle Siwaliks similar to that of previous districts as shown in Figure 4.15. It occupied 71.45% of TLA. North aspect, gentle slopes, and lower elevation were morphological features having higher area of landslides in this district as shown in Figure 4.16.
66 2.18
26.37
Lower Siwalik Middle Siwaliks Quarternery Deposits
71.45
Figure 4.15: Total Landslide Area (TLA) in percent by geological formation in Sarlahi district
100 92.27
80 64.44 60 38.74 40 31.96 25.19
21.02 Percentage(%) 20 8.07 7.73 6.97 3.59 0 Flat East South West North Gentle Steep Very Low Moderate Steep Aspect Slope Elevation Topographic factors
Figure 4.16: Total Landslide Area (TLA) in percent by topographic factors in Sarlahi district
Watersheds with higher area of landslide were Lakhandehi-Nadi and Jhim-Nadi. Forest and cultivation were the land use type with higher landslide occurrence; whereas in soil types, colluvial soil accounted for higher area of landslides.
67 Mahottari District Mahottari district had 98 landslides making an area of 0.4484 Km2. Here also, the landslides were higher in Middle Siwaliks as depicted in Figure 4.17. This geological formation possessed 78.94% of TLA in the district. Lower Siwaliks had landslides area of 16.98% of TLA. All landslides of district appeared in low elevations. Larger landslides were seen in east and south aspects. Gentle slopes had higher area coverage than steep slopes as shown in Table 4.18
3.04 1.04
16.98
Lower Siwalik Middle Siwaliks Upper Siwaliks Quarternery Deposits
78.94
Figure 4.17: Total Landslide Area (TLA) in percent by geological formation in Mahottari district
120 100.00 100 80 53.98 60 46.02 35.16 40 16.77 21.27 15.76 Percentage(%) 20 11.04 0 Flat East South West North Gentle Steep Low Aspect Slope Elevation Topographic factors
Figure 4.18: Total Landslide Area (TLA) in percent by topographic factors in Mahottari district
68 Out of three watersheds lying in Chure area of this district, Ratu-Nadi and Mahara-Nadi had larger area of landslides. Most of the landslides were observed in forests land use category and colluvial soil in soil types.
Dhanusha District Total landslides found in Dhanusha district were 104 and they had total area of 0.3482 Km2. In this district, maximum area of landslide was observed in Middle Siwaliks i.e. 83.18% of TLA. Then, 12.58% of landslide area was observed from Upper Siwaliks. Forests, river body in land use/cover and colluvial soil in soil types were factors with higher number of landslides. High impacted watersheds based on the presence of landslide were Charnath, Kamala-Belsot-Jogiya and Ratu Nadi. Figure 4.19 shows that landslide distribution based on geological formation in area (%) in Dhanusha district.
4.24
12.58
Middle Siwaliks Upper Siwaliks Quarternery Deposits
83.18
Figure 4.19: Total Landslide Area (TLA) in percentage by geological formation in Dhanusha district
Figure 4.20 shows that almost all the landslides were under lower elevations. The landslides observed were mostly in gentle and south facing slopes. About 65.03% of TLA in the district was on gentle slopes whereas southern portion accounted for 33.11% of TLA.
69 120
100.00 100
80 65.03 60
Percentage(%) 40 33.11 34.97
19.94 17.62 20 14.17 15.16
0 Flat East South West North Gentle Steep Low Aspect Slope Elevation Topographic factors
Figure 4.20: Total Landslide Area (TLA) in percent by topographic factors in Dhanusha district
4.6.3 LANDSLIDE DISTRIBUTION IN FACTOR CLASSES OF UDAYAPUR CLUSTER
Udayapur District In Udayapur district, the landslide numbers observed were 684 and area made by those landslides were 2.1586 Km2. Here, 59.15% of TLA was found in Middle Siwaliks. Then, Lower Siwaliks had 24.16% of TLA of the district as shown in Figure 4.21. Watersheds with higher area of landslides in this district were Sun-Koshi, Balan, Tawa-South and Gideri Khola. The landslides were observed in forest areas and barren lands. Similarly, colluvial soil plays vital role for landslide occurrence because of their loosely joined varying size of particles.
70 0.24 0.31
7.52 Lower Siwakiks 8.62 24.16 Middle Siwaliks Upper Siwaliks Quaternery Deposit Gawar Formation Charchare Formation 59.15
Figure 4.21: Total Landslide Area (TLA) in percent by geological formation in Udayapur district
While considering topographic features, higher landslide area was observed in south, west and east aspect. They occupied 28.96%, 27.48 and 24.88% of TLA of the district. Steep slope gradient and lower elevations were other considerable factors for higher landslide occurrence. About 55.16% of TLA was observed in steep slopes and 64.22% of TLA in lower elevations as shown in Figure 4.22.
100.00
80.00 64.22 60.00 55.16
40.00 29.86 33.41 24.88 28.96 27.48 18.68 Percentage(%) 20.00 14.97 2.37
0.00
East
High
West
Low Low
Steep
South North
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factors
Figure 4.22: Total Landslide Area (TLA) in percent by topographic factors in Udayapur district
71 Siraha District Siraha is the district with second highest number of landslides among the ten selected districts of Chure area. It had 717 numbers of landslides and all of them made an area of 2.0023 Km2. About 72.23% of TLA in this district was observed in Middle Siwaliks whereas 13.08% of TLA in Upper Siwaliks as shown in Figure 4.23.
4.77
9.92 13.08 Lower Siwakiks Middle Siwaliks Upper Siwaliks Quaternery Deposit 72.23
Figure 4.23: Total Landslide Area (TLA) in percent by geological formation in Siraha district
Balan-Khola was the watershed that had the highest TLA in the district. Prior land cover conditions of the landslide occurred areas were mostly forests and then bush and grasses. Here, higher area of landslide was in colluvial soil types.
100 90.33
80 58.44 60 30.60 36.13 33.72 40 22.98
20 10.29 7.84 9.67 Percentage(%)
0
East
West
Low Low
Steep
South
North
Gentle
Moderate Very Steep Very Aspect Slope Elevation Topographic factore
Figure 4.24: Total Landslide Area (TLA) in percent by topographic factors in Siraha
district
72 West and south facing slopes had higher frequency of landslide occurrence. They had 36.13% and 30.6% of TLA of the district respectively. Similarly, in this district the gentle slopes and lower elevation possessed higher occurrence of landslides. Gentle slopes occupied 58.44% of TLA whereas low elevations had 90.33% of TLA of the landslides.
Saptari District
A total of 216 landslides were reported from Saptari district which made the total area of 0.3959 Km2. In this district, most of the landslide area was found to be in Middle Siwaliks and little of landslide area was shared by Upper Siwaliks and Quaternary Deposits. Middle Siwaliks occupied 88.81% of TLA as shown in Figure 4.25
5.51 5.68
Middle Siwaliks Upper Siwaliks Quaternery Deposit
88.81
Figure 4.25: Total Landslide Area (TLA) in percent by geological formation in Saptari district
As shown in Figure 4.26, all of the landslide areas fall in lower elevation. Higher landslides were observed in gentle and steep slope gradients. They occupied 59.58% and 36.76% of TLA respectively. In case of aspect, west face of the slopes had higher area of landslides. It had 41.42% of TLA of the district. Then, east and south aspect of the slopes each had almost 24% of TLA of the district. The detail of landslide distribution in ten districts according to the geological formation as well as other different factors of attributes is shown in Annex 2.3.
73 120 100.00
100
80 59.58 60 41.42 36.76 40
Percentage(%) 24.01 24.79 20 9.78 3.66 0 East South West North Gentle Steep Very Steep Low Aspect Slope Elevation Topographic factors
Figure 4.26: Total Landslide Area (TLA) in percent by topographic factors in Saptari district
The watersheds having larger area of landslides were Khado-Khola, Bihul-Nadi and Khadak-Khola. Most landslides were found on soil types having colluviums and in case of land use, forests, bush/grasses and river body were classes with higher occurrences of landslides. Details of landslide distribution according to the watershed in ten districts are shown in Annex 2.4.
4.7 MAPPING AND DATABASE MANAGEMENT
Different maps of the landslide inventory were developed. The detail maps of scale 1:50,000 for clusters were printed separately. The inventory maps for each district are included in Annex 2.5. The detail maps for the landslides having higher impacts were assessed separately for mitigation purpose and their maps are included in report of mitigation/atlas. Similarly, topographic maps including all the essential topographic features with landslides are shown in Annex 2.6.
Database for each districts were generated. The soft copy includes different shape files of various factors used to develop attributes of the landslide inventoried (Table 4.7). The data were compiled in disk and available in soft copies.
74 Table 4.8: Database management for each district
Sub Folders Data type (GIS file) Data Sources Boundary Vector (Polygon) Department of Survey (DoS) Contours Vector (Polyline) DoS DEM and Derivatives Raster DoS Geology Vector (Polygon) Department of Mines and Geology Landslides Vector (Polygon) Google Earth Land use Vector (Polygon) Remote Sensing Rivers Vector (Polyline) Remote Sensing Roads Vector (Polyline) Google Earth Settlements Vector (Polygon) Google Earth Soil Types Vector (Polygon) SOTER and Google Earth Spot Heights Vector (Point) DoS Tributaries Vector (Polyline) DoS VDCs Vector (Polygon) DoS Village Names Vector (Point) DoS Wards Vector (Polygon) DoS Watersheds Vector (Polygon) Chure Board Mxd File for a district including all the data layers.
75
SECTION III: LANDSLIDE CHARACTERIZATION
76 5. LANDSLIDE CHARACTERIZATION
5.1. INTRODUCTION
Landslide characterization of more than 1003 landslides within 52 clusters was performed according to their geological and morphological characteristics. These landslides were characterized according to the types of rocks, major deformation structures like folds, faults and thrusts, types of landslides, orientation of rock mass discontinuities, soil moisture, soil type, ground water condition etc based on the data obtained from the field study and satellite images. It was found that the landslides were highly influenced by geology in terms of the type, size and number of landslides. As for example, granular flow, debris fall and gully erosion were dominant in Upper Siwaliks. Vertical and overhanging slopes of conglomerates or the differently graded gravels were common features that control the landslide type in this area. The high grade of weathering in mudstones of Lower Siwaliks indicate gully erosion, earth slides, mudflow and debris flow as the dominant processes in this geological Formation. The Middle Siwaliks mostly consist of massive sandstone alternating with incompetent mudstone layers signifying differential weathering and the processes like rock slides and rock falls. However, many landslides in the working area were complex in terms of process, type and temporal domain. At many places like in Dhanusha, swarms of landslides were identified and therefore they were analyzed in terms of clusters rather than individual landslides. Detail characterization of all the studied landslides were provided in the Landslide Atlas and in Annex 3.1. Some important overviews of the characteristics of landslides are presented in the following sections.
5.2. METHODS
5.2.1. LANDSLIDE MAPPING
In the working area, a quite big part of the area is accessible by roads and trails. Most of the settlements were located at the river banks and ridges. The villages were connected by trails. Due to the caldron shaped topography of the working area, one can have a good overview from the good accessible slopes onto the opposite sides. By driving and walking along all roads and trails, landslides can be detected and the slides at remote areas can be scanned by eyes. Though most of the observed landslides appear beside roads, there were
77 also some located in remote areas which were more difficult to reach by foot. The coordinates of landslides were marked by using Global Positioning System (GPS).
For the investigation of landslides, data sheets (Annex 3.1) were used to note all relevant observations. As input, 11 groups of data were divided: extension, soil parameters, vegetation and human influences, water influence, geology, causative factors, landslide type, rock failure mechanism, morphology, impacts factors and other observations. Values for length, width and depth had been estimated because most of the landslides were too big to measure them by measuring tape or other method. By climbing to the crown of the slides, the extension of the failure zone, length and depth could be determined with acceptable values. The type of landslide had been noted based on detail study.
Soil parameters had been measured with pocket penetrometer and shear vane. If there was a certain bulk of water coming out of the sliding mass, water temperature was measured by thermometer. Flow rate had been determined by taking the time of accumulating a bulk of 1 Liter water.
For landslide classification, the one purposed by Varnes 1978 was followed. Two major criteria were used for the classifications: type of movement and type of material. Six types of movements were distinguished: falls, topples, slides, spreads, flows and complex. Generally, there were a lot of terms describing types of landslides and mixed terms in different combinations which should explain all varieties of landslides.
5.2.2 GEOLOGICAL INVESTIGATION
Most of the landslides are characterized by different types of rocks underneath, although the exposed part can be soil of certain thickness. The rock type, thickness, weathering conditions and the orientation of discontinuities all play important role on landslide process as they control the shear strength as well as factor of safety of the slope. Therefore, detail geological investigation is necessary. The geological map prepared by Petroleum Exploration, Nepal in a scale of 1: 250,000 was used as base map and was validated and modified according to the findings of this study.
The best sites to study the rock types, weathering and discontinuities are the exposures and outcrops along the river section, road section, trails and the slided areas. The
78 information about all necessary properties of rocks including the strike and dip of discontinuities were documented in the data sheet. For collecting all these information, Brunton compass and geological hammer were used. Chemicals namely diluted hydrochloric acid was also used whenever needed. The geological information in landslide area was especially given priority as detail characterization of landslide is one of the most important objectives of the study. The attitude of major discontinuities in the rock masses of different landslide sites are given in Annex 3.1.
5.2.3 SOIL INVESTIGATION BY FIELD MEASUREMENTS
The bedrock in the working area is strongly affected by weathering processes. Residual and colluvial soil of varying thickness cover the underlying bed rock with merged transition. One of the tasks of soil investigation is to determine the grain size distribution. It was roughly estimated in the field by simple field techniques like test by visual observation. To get an idea about the geotechnical parameters of the loose material, which is mostly involved in sliding processes, pressure strength and shear strength had been measured with Pocket Penetrometer and Shear Vane. Measurement is only possible in cohesive fine grained material. For each measuring method, fresh native soil at the exposed slip surface of landslides was chosen. If the surface was very dry and hard, it was chipped with hammer to get a representative soil surface. For each parameter, ten values were measured with even spatial distribution. The average distance of measuring points ranged from 0.1 to 1m depending on the possibility of measurement according to the field condition.
Pocket penetrometer The pocket penetrometer, produced by Eijelkamp Agri Search Equipment in the Netherlands, was used to determine compression strength (σ) of cohesive soils. The in- situ test represents the strength of undrained soil. The tool consists of a handle with integrated spring, a scale with indicator ring, and a foot at the top (Figure 5.1). The cylindrical foot has to be pushed in normal direction into the soil to a depth of 6.35 mm, which is represented by a ring mark. The indicator ring is pushed along the scale and its top gives the result for pressure strength. The scale ranges from 0 to 4.5 kp/cm² with a scaling of 0.25 kp/cm²; 4.6 kp/cm² would require a load of 7.713 kg. This large value of compression force results from the shear surface of the immersed foot. Its diameter is 6.35 mm and the cylindrical shear surface has 1.265 cm². A possible error of 0.125 on the
79 scale is noted by the manufacturer. While choosing the measuring point one has to be careful to avoid gravel and areas of disturbed material (www.eijkelkamp.com)
Figure 5.1: Pocket penetrometer
Shear vane The same approach had been chosen for the measurement of shear strength with a pocket shear vane. Produced by the same manufacturer, there are three different adapter of vane diameter. For all measurements in this study, the middle vane CL 100 was used. The smaller and the bigger one would be only necessary in very soft and very hard material not appearing in the measured landslide areas. The shear vane consists of a handle with integrated spring and scale and a foot with circular vane adapter (Figure 5.2). By pushing the vane into the soil prior to turn the handle until the material experience shear failure, the index shows its shear strength. The scale ranges from 0 to 1kp/cm² with 1 Cadran representing 0.1 kp/cm²; 1 kp (kilopond) is equal 9.80665 N. The scaling is 0.05kp/cm² or 0.5 Cadran. Before using the values for calculating the model parameters, one has to convert the values using the conversion chart. It gives shear strength in kg/cm² depending on the adapter used; 10 Cadran correlate with1.1 kp/cm². The measuring points are chosen with the same approach like pocket penetrometer tests. However, a flat surface of at least 25 mm in diameter is required for this test. It has to be noted that there are no pairs of values for shear strength and pressure strength for each measurement because all measuring points are chosen randomly distributed, so the number of vane measurement does not correlate with the number of penetrometer measurement (www.eijkelkamp.com).
80
Figure 5.2: Shear vane measurements
5.2.4 LANDSLIDE MAPPING WITH ARCGIS
The Geographic Information System (GIS) of the international supplier Esri is a geodatabase management software which is used to generate geographic maps based on geo referenced data. The field observations and GPS points and track around landslide, Google earth images were used to define the margin of landslides respectively to create landslide polygons in ArcGIS. Areas of forest, cultivated land, bare land, river deposits, and settlements were distinguished using Google earth images.
Landslide polygons display the margin of all observed landslides, each with its characterizing features. They are further classified into erosional area, depositional area and gully erosional area, effect of river and drainage pattern in landslide with distinct crown line listed in the attribute table (Annex 3.3). The landslides were named according to the name of nearer village.
5.2.5 DATA ANALYSIS AND INTERPRETATION
After the compilation of all available, required and newly generated data, it was processed and analyzed for interpretations. Compiled data includes geological information (lithology, orientation of beds, geological structures), landslide inventory with soil condition, hydrological data, precipitation data, land coverage, slope condition, human and influences. These data were documented, processed, analyzed and interpreted for detailed characterization and causative factor analysis. In detailed characterization,
81 landslide area was calculated with ArcGIS, volume was determined by field estimation of landslide depth at different section. Single landslide having variations in depth were divided in different polygons and finally their area was multiplied with the depth. Estimated volumes of 28 watersheds are given in Annex 3.4 and the same data for all studied landsides is given in Annex 3.1.
The method of using Mohr´s circle to calculate cohesion and friction angle in geometric kind is supposed to be a very rough approximation. Compressive strength and shear strength are needed to transfer into cohesion and friction angle, then only soil strength parameters can be described. The law of Mohr is used to design the criterion of failure with two stress circles of σ and τ, resulting in a straight line of failure. It is characterized by its adjustment on the X-axis described by the cohesion c, and its inclination described by the angle of internal friction. The geometrical solution is described in Annex 3.5.
82 6. RESULTS
6.1 GEOLOGICAL CHARACTERS
6.1.1 LITHOSTRATIGHRAPHY
The Chure area is separated by the Main Frontal Thrust (MFT) in the south from the Tarai Plain and by the Main Boundary Thrust (MBT) in the north from the Lesser Himalaya (Figure 6.1). The rocks of the Siwaliks are grouped into three geological Formations. From top to bottom, they are Upper Siwaliks, Middle Siwaliks and Lower Siwaliks. The Upper Siwaliks consist of the boulder-cobble conglomerate. The Middle Siwaliks consist of coarse-grained, “Pepper and Salt” sandstone, mudstone, shale and pebbly sandstone. The Lower Siwaliks consist of the grey-green mudstone with red purple and greenish gray shale.
Figure 6.1: Generalized geological map of Nepal (Modified from Amatya and Jnawali, 1994)
As a whole, the stratigraphic succession shows the coarsening-upward (e.g., mudstone, sandstone and conglomerate) which reflects the rising Himalaya, while fluvial succession shows the coarsening-upward succession. Complete sequence of the Siwaliks was well
83 observed along rivers, streams, roads and many foot trails in the study area. Landslides of the Siwalik or Chure area are mostly controlled by geology. The nature of landslides, mechanism, types as well as process depends upon the rock type and geological structures. The general rock types of Chure area are relatively soft, weak and fragile in nature which makes the area most vulnerable for landslides and related processes. The geological formations and their typical rock types are listed in Table 6.1 and the properties of major rock types are described in the following section. The geological maps of all the ten working districts produced by Department of Mines and Geology (DMG), Government of Nepal were modified and validated from the field study and provided in Annex 4.4.
Table 6.1: Characters of landslides with reference to stratigraphy
Lithology Characteristics affecting on Landslides Examples
Stratigraphy Boulder-pebble Cementing materials of conglomerates are going Most of landslides conglomerate to weaken, converting the rock mass into gravels. of Phaparbari
with subordinate Exclusively dry throughout the year but saturated VDC of sandstone and during raining period. Makawanpur. mudstone Almost landslides occur in dry stream head Thulitar landslide
portions. Sindhuli etc. Upper Siwalik Upper Fine to coarse Less compact and weak inter-granular bonding. Landslides along grained sandstone Presence of mud layers in between massive and the Bakaiya river, with interbeds of thickly bedded and blocky sandstones easily Makawanpur, siltstone and deformed due to toe cutting and valley deepening Ratmata mudstone in by river/stream. landslides of lesser proportion Most of middle Siwalik landslides are fresh and Sindhuli, rock failure, clear and total debris or sediments Amlekhgunj are washed, fresh white and shiny exposures are landslides, Bara.
seen,which are passive. Middle Siwalik Middle
Fine grained Rock exposures are very soft (almost clay or fine Setebhir sandstone with soil.), highly fractured and medium to thickly landslide, interbeds of red bedded. Makawanpur,
colored Nature of clay minerals, like swelling properties Ahale landslide Lower siwalik Lower
84 mudstone, shale, of clay and spheroidal weathering of mudstone. Sindhuli and most siltstone and Most of landslides are debris and mud flow type of the landslides occasional marl due to action of monsoon in weathered mudstone concentrated beds. and surface erosion along the lower southernmost portin of Chure region. Undifferentiated. Very few landslides are observed in pre- siwalik Grey, white gray rocks which are concentrated along the contacts stromatolitic with siwalik rocks and peak region of the dolomite, dolomitic limestone terrain. dolomitic limestone, pink gray limestone, pink or red and gray white sandstone,
quartzitic
sandstone and
Siwalik purple, green, -
gray shale. Pre
Conglomerates Conglomerates were mainly found in Upper Siwaliks and were typically present in the form of huge masses of cemented gravels (Figure 6.2). At many places, the cementing materials had been already washed out and therefore they were present in the form of very loose or less compacted gravels, cobbles or boulders. The cementing materials in these rocks were mostly highly weathered red clay materials. Well sorted to poorly sorted clast sizes of the conglomerate beds ranged from pebble to boulders. These were highly permeable due to weak cementing materials. Mostly, these rocks were cohensionless and looked like the loose alluvial deposits.
Sandstones Sandstones were fine to coarse grained, massive to medium bedded but moderately to weakly compacted (Figure 6.3). These rocks are the characterstic features of Middle Siwaliks but they are present in Lower Siwaliks in small amount and also in some places
85 in Upper Siwaliks. The rocks were less jointed and fractured and the minerals present in the sandstone beds were mostly feldspars followed by quartz and micas. These were permeable due to coarse grained and water can penetrate through bedding plane. These were also cohesionless and found in fresh to weathered condition. Weathered feldspar are converted into clay as Kaolinite (white clay) which is highly susceptible to activate the landslides. For example: Ratmata landslide of Ranichuri VDC of Sindhuli district is active due to weathered sandstones with Kaolinite minerals.
Mudstones Mudstones are mixture of silt and clay minerals. These rocks were present mostly in Lower Siwaliks and thin layers were present in Middle Siwaliks alternating with thick sandstone beds. The Lower Siwalik mudstones were medium bedded to massive in structure with less or very few joints. Mudstones are very weak and most vulnerable for physical weathering, and therefore the mudstones in Lower Siwaliks are highly to completely weathered, which were in the form of residual soil in the surface at many places (Figure 6.4). These are less permeable but can be easily eroded by rain action due to soft nature and eroded mass are soluble with water and moved through erosional gullies.
86
Figure 6.2: Landslides in Upper Siwaliks (Conglomerate), Thulitar, Sindhuli
Figure 6.3: Landslide (Rock fall phenomena) in Middle Siwaliks (Sandstone) observed at river Section of Amlekhgunj VDC near bridge no. 3
87
Figure 6.4: Landslide observed at Pattarakot VDC Sarlahi, Lower Siwaliks (Mudstones are in the form of residual soil and the slides are Shallow)
6.1.2 ORIENTATION OF DISCONTINUITIES
A major connection between landslides and geology is the three dimensional position of geological dividing planes. Their relative position to the slope is an essential factor of landslide susceptibility. The major and most characteristic discontinuity in the rock is the bedding plane. If it is parallel to the slope, it can easily act as sliding surface because it represents, due to its spatial position, no resistance for gravitational forces. In Darbardanda landslides (Figure 6.5, Annex 3.1), the bedding was dipping nearly parallel to the slope towards North-West. All of them were situated in little slope cuts, where the slope face turns from its main direction towards North, to North-West and becomes parallel to the bedding of the basement rock. Darbardada-2 landslide, which should rather be described as a plane failure, was based on slightly weathered sandstones. Its bedding was dipping 300/55, whereas the slope dipped 285/45, so the dipping direction was nearly the same. But there was also action of toe cutting by river. All the landslide and orientation of strata is shown in Annex 3.1.
88 Slope Direction
Dipping of Bedding Plane
Figure 6.5: Photograph showing comparison between hill slope and orientation of bedding plane In the massive sandstones of the Middle Siwaliks, retreat was by granular disintegration of the sandstones forming the cliff face, and by slab failure as controlled by jointing. This was due to expansion of the massive rocks, when erosion exposed rocks that were once buried. When river incise deeply into the massive rocks, joints develop parallel to the canyon walls, and retreats of wall and scarps depends on these fractures. Most of the landslides with cliff at river bank of middle Siwalik sandstones are results of such mechanism of joints. For example, big landslides of Raigaon VDC (Figure 6.6) are seen white rock cliff of sandstone in passive condition.
Figure 6.6: Photograph showing rock cliff at Raigaon VDC
89 6.2 GEOLOGICAL STRUCTURES
Major geological structures in the study area were anticline and syncline folds, local scale faults and major thrusts including MFT and MBT. These geological structures were the reasons of landslide occurrence and reactivation at many places. The details of the landslides activated by geological structures are described in Table 6.2.
Table 6.2: Characters of landslides with reference to the geological structure
Structure Description Characters effects on Examples landslides Main Boundary Separates Lesser meta Movements create Two big scale Thrust (MBT) sedimentary rocks to weakening and landslides were found Siwalik sedimentary deform the rock in Sindhuli district, succession. Breccia and materials. Chiyabari and fault gauze can be seen Chakmake landslides. along the fault section. Himalyan Separates Siwaliks Movements and Maximum landslides Frontal Thrust succession from Tarai compression by this were concentrated (HFT) region. fault change the along the lower deformed rock mass, boundary Chure area southern portion of including Bara, Chure area results the Dhanusha, and Sarlahi very fragile geological district. condition easily prone to erosion of rockmass. Marin Khola This thrust repeats the River flows through Bank erosion along Thrust (MKT) Siwalik succession again these fault line, active Marin khola section and Kamala above the upper Siwaliks. valley widening and erosional area of Tawa Thrust process is occurring in Nepane- Sirutar area (KTT) these section indicated of Dadigurase VDC of by the bank erosion of Sindhuli district. both side of river, river valleys are filled with huge amount of the fresh gravel
90 materials. Anticlines Small- medium scale Core of anticline is several anticlines are found weak zone due to in the middle siwalik steep or vertical sandstone and lower bedding of the rocks. siwaliks.
Siwalik Landslide
Landslides
M B T Lesser Himalaya
Figure 6.7: Effect of fault on landslide
91
Figure 6.8: Landslide observed at contact between Lower Siwaliks and Pre Siwaliks at Beteni VDC of Makawanpur
6.3 SOIL CONDITION
Soil is categorized into four different classes namely residual soil, colluvial soil, alluvial soil, and rock exposed and distribution of landslides are shown in Annex 3.1.Most of the landslides occurred on the colluvial soil. Also some considerable amount of landslides occurred in the residual soil. It is apparent as colluvial material lack strength and easily slide down a slope with even an insignificant triggering. In the past, there were many big landslide events in the study area, so that there must be remains of old deposits, which had been reworked and became colluvial soils. Distinct characteristics of colluvial soils are unsorted grain composition with angular grains and stones, swimming in a fine grained matrix. This can be observed at landslides Betini and Shripur Chhatiwan VDC of Makawanpur (Figure 6.8) and Rampur and Ranuchuri VDC of Sindhuli district. Areas of colluvial soils are very prone to fail due to the superimposed load, leading to increase in shear stress. Furthermore, susceptibility increases in case of slope cutting. Darbardada landslides had been reactivated due to toe cutting by river in landslide deposits. Moreover, small water outlets could have been detected in the landslide masses. Ground openings of older landslides are very susceptible to infiltrate water into the underground, which can detain precipitation from soil. Landslide of Betini was situated in older mass
92 movement deposits. There were already young trees growing above the crack zone. It was characterized by three landslide branches converging in a very steep basin, accompanied by laterally deep gully erosion.
Figure 6.9: Landslide observed at colluvial soil at Shripur Chhattiwan VDC, Makawanpur 6.4 SOIL STRENGTH MEASUREMENTS Detail soil strength parameters were determined by geotechnical studies in the laboratory. Filled methods using some portable machines had been tested in this study as it was not always possible to measure the parameters in the field. Results of pocket penetrometer and shear vane measurements are given in Table 6.3. It was not possible to measure the shear strength parameters of the soil in most of the slided area due to the presence of cemented and gravelly soil or hard rock material. The measurements for four major landslides are tabulated in Table 6.3. Table 6.3: Cohesion and frictional angle values by field measurements Landslides Cohesion(C) kg/cm2 Friction Angle (Ø) (0) Setevir landslide, Makawanpur 2.2 50 Amlekhgunj landslide , Bara 1.8 45 Darbardada-2 landslide, Makawanpur 3.0 48 Apdamar landslide Makawanpur 0.8 44 Simalchaur Landslide, Bara 1.3 50
6.5 TOPOGRAPHY/ GEOMORPHOLOGY
93 In detailed study of landslide, it was found that the landslide crack zones had slope angles between 30 and 42 degrees. Steep slopes were situated in different locations of the erosional valley system of the Chure area. In higher levels of the watershed, where surface runoff is converging into streams, deep valleys start to cut into the landscape due to increasing erosion force of increasing amount of water. At this point, a slope bend develops. For example, almost landslides including Bhuwanchuli landslides of Upper Siwaliks have such characters.
Darbardada landslide is situated directly at this slope bend at a slope angle of 45 degree in Shripur Chattiwan VDC, Makawanpur. It is rather a rockfall triggered by bedding and soil condition and steepened slope due to toe cutting by Darbar Khola. Next landslides of Darbardada were situated quite in the middle elevation between the slope bend and the deepest valley floor. It was characterized by a slope angle of 50 degree, with highly weathered colluvial soil. Bedding and the artificial steepening by road construction were the reason for the steep slope angle. Secondary processes in this case were gully erosion in the cultivated fields above the landslide and a water pipe which is crossing the crack zone. Although the recultivation of the sliding area is acting as a stabilizing method, future sliding processes can be expected in case of heavy rain falls. The fine to coarse grained loosened soil formed to step agricultural fields can be stable due its aquicluding nature and stabilization by roots. During fieldwork in dry season, the recultivated fields had been dry which leads to mud cracks and deep water infiltration in rainy season. This suggests further sliding processes at this location. Landslides in Middle and Lower Siwaliks along Bagmati and Bakaiya rivers were located near the valley floor where these rivers and their tributaries are forming deep canyons. They were situated at the steep river banks of 40° and 90° exclusively facing south. Here, the foliation, respective bedding planes were dipping north into the slope, so that steep slopes develop preferably. In the same manner, all landslides associated with steep slopes reached a maximum soil depth of 2-3 m. The occurrence of landslide in steep slopes in densely vegetated area was relatively low. Here, very steep slopes were prevented from land sliding due to the protective effect of the vegetation. Forest reduces the effective precipitation by interception and transpiration. It reduces soil water saturation by pore water removal. Roots additionally support stability.
94 6.6 HYDROLOGY
6.6.1 GROUNDWATER CONDITION
One big problem is the changing soil water content due to the alternating dry and rainy seasons. Field work was conducted in dry season due to the increasing hazards and inaccessibility in rainy season. However, most of the landslides were triggered by rainstorms and saturated soils. Porous materials like gravels sand change its behavior increasingly if it is affected by internal water. Pore water pressure and internal erosion. Drainage measurements between 0.1 and 1.2 L/s are in fact small, but still in dry season affect slope stability seriously. Landslides present in Upper Siwaliks conglomerates are exclusively dry throughout the year except in monsoon period due to presence of gravel materials of low water holding capacity. Gravels can penetrate the rain or surface water with high velocity. Therefore, during rainy season, these areas become rich in groundwater. Half of all investigated landslides of Middle and Lower Siwaliks showed discharge of little amounts of water, even in dry season. In five of them, the amount of water was high enough to make a discharge measurement (Table 6.4, Figure 6.9).
Table 6.4: Discharge measurement of springs of different Landslides.
SN Landslides No. of springs Discharge (l/s) Month 1 Setevir landslide 5 1.2 December 2 Apdamar landslide 3 0.7 December 3 Simalchaur landslide 2 0.4 December 4 Ahale landslides 6 1.2 February 5 Pathharkot Landslides 3 0.1 February
Soil water discharge indicates a subsurface water channel which leads to internal erosion and boosting hollows. Furthermore, soil water in case of sliding process acts as lubricating film. So, every kind of water amount in the underground has destabilizing effect on the slope and contributes to sliding processes. But also surficial water caused by human intervening, for example irrigation of rice or corn fields, drainage channels or leaking water pipes, influence landslides. Reason for this is mostly the cultivation of fields in the direct surrounding, in the form of dammed terraces, crossing drainage channels or leaking water pipes acting as supply system for villages.
95 Spring
Figure 6.10: Spring observed at Simalchaur Landslides
6.6.2 SURFACE WATER CONDITION
A frequent factor affecting landslides in the study area is the influence of surface water. Surface water has generally a destabilizing effect on the slope. Pore water pressure and hydrostatic uplift decrease the shear strength of soil. Bedding surfaces, bedding planes or joints let surficial water penetrate into the rock and accelerate weathering processes. The water which is influencing landslides can be both soil water or surficial water. In the study area, more than half of the rivers were ephemeral, which flows only when there is rain and rest of the year there is just a dry river bed with no water. Remaining rivers are seasonal or perennial.
Major source of surface water in Chure area is precipitation and rainfall of monsoon of Himalaya. About 2451 mm mean annual rainfall (Annex 4.5) was recorded from 1989 to 2015 in this region and 403 mm precipitation was recorded in July 22, 1993 in Sindhuligadi station of Sindhuli district (DHM, 1989-2015).It is understood that maximum amount of rainwater flows through surface and very little amount is infiltrated or recharged into the ground which is illustrated by the presence of dry landslides and
96 ephemeral rivers. Rainfall triggers on the surface of very fragile and loose geology which results in surface erosion and landslides including debris and earth flow. Among all, the effect of water was observed in the form of bank scouring, riverside erosion and toe cutting of the slope and old landslides by river (Figures 6.10 to 6.12). The surface water also plays dominant role on head erosion and the initiation of landslides from the top of the slope (Figures 6.13 to 6.14). Mainly the three major processes act on landslides of Chure area which is strongly controlled by the geological condition, precipitation and river types of the area (Table 6.5).
Table 6.5: Process and effects of surface water on landslides
Features Characteristics Examples Examples based on Geology Toe cutting Diverting the river meanders of river Landslides of Apdamar by deposition of debris materials in middle and Landslides, other bank. Lower siwaliks Makawanpur, Presence of soft thin layer in between massive rocks. Beldada Dipping of rock is parallel hillslope. landslides of Hillslope is covered by thick Mahottari weathered soil. Head Erosion Increase the area of small gully Landslides of Rajbas erosional area by surface runoff of Upper Siwaliks dakkas- rainwater. and some Lower Lamkana Penetrating of groundwater with high Siwalik of landslides of velocity. Siraha and Makawanpur, Presence of loose or soft soil and Saptari districts BP higway, rock. Gauribas Head erosion is found maximum at Mahottari head region of ephemeral rivers. Valley Widening/ These processes are mixed process of Almost Hattigauda Deepening/ Rising both toe cutting and head erosional landslides landslides of River Bed process. Dhanusha, Transportation of huge debris Sirutar- materials with high velocity after rain Nipane
97 caused both bank erosion of both Landslides. bank. Fluctuation in rain fall effects deposition as well as down erosion of debris materials on river bed. In less compacted massive sandstone and mudstone, downward erosion by the attrition process of running water.
Depositional Side
Figure 6.11: Toe cutting on Middle Siwaliks sandstone by Bakaiya River at Amdamar,
Makawanpur
98 Mudstone River Bed
Figure 6.12: River bed erosion by Pattharkot khola results valley deepening
Erosionalleft bank
Erosional right bank Ephemeralriver with huge Debris
Figure 6.13: Valley widening process by eroding both bank of river at Sirutar, Sindhuli
99 Catchment area
Stream
Figure 6.14: Head erosion process by eroding both bank of river at Gaighat, Udayapur
Small Surficial gullies developed by rainfall
Figure 6.15: Surface trigger by rainfall on weather and fragile soil of barren land at
Bishnupurkatti, Siraha
100 6.7 TYPES AND PROCESS OF LANDSLIDES
The distribution of landslide types is dependent on the kind of material involved in the sliding processes, which is the main determining factor for landslide type. It is difficult to categorize the landslides as special types because most of them are mixed types. Hard rock dominated locations with very steep slopes are prone to rock falls or rock slides, if preexisting discontinuties, like bedding plane, joint, act as sliding surface (Upreti and Dhital, 1996). These types can be found in areas of Middle Siwaliks sandstones with reduced weathering degree. And thin mudstone bed presence in between hard sandstone increases the rock fall.
More difficult to distinguish are the landslides of the Upper Siwaliks, where the landslide types varying from gulley erosion, fall, and granular flow can be found. Most of the loose gravel materials can easily develop such type of landslides. Vertical and overhanging portion of landslide justified the landslides types (Figure 3.15).
Figure 6.16: Debris fall with granular flow type of landslide seen at Gaighat, Udayapur
101 There are deposits of old landslides or sliding processes covering the original ground, new landslides can be triggered by the discontinuity from in-situ to disturbed material, and infiltrated water, which acts as lubricant. Deeper landslides (deep seated) are mostly kinds of curvilinear slips. Perfect rotational slips mostly occur in isotropic material like high grade weathered sandstones and mudstones with less developed bedding structure and small grain sizes. Apdamar and Betini landslides are the good example of such landslides.
An example of landslides of Lower Siwaliks, limited rockfall and debris flow, mud flow and gully erosion of small to big deformed rockmass, which was characterized by highly fractured mudstone rock (Figure 3.16). Fracture on mudstone was developed by the change in water content during monsoon and dry season. Simalchaur landslides, Setebhir landslides are good examples of such type of landslides.
Figure 6.17: Gulley erosion seen at Shripur Chhattiwan, Makawanpur
Surfacial landslides with very shallow depth of about 1m mostly are defined as translational. They are supposed to be in the beginning stadium of development, when weathering is not preceded as far and rock structures still are remain in the shallow underground. These landslides area develop due to surface triggering of rainwater on barren soil land of gentle slope surfaces. Most of the landslides of Siraha, Saptari and Dhanusha districts are good examples of these landslides (Figure 6.17).
102
Figure 6.18: Surficial landslide seen at Madhupati, Saptari
Another type of landslide in the Chure area was elongated and truncated debirs flow. If there are major structural features present, the erosional and flow path follows the discontinuity, and truncated landslides develop. Landslides present along the fault line form such processes, as seen in Chiyabari landslide, along the MBT. It is supposed that most landslides in the working area were developed by constantly occurring failure processes like retrograde erosion and slab failure. The Chure area is frequently affected by initial failure in topographic hollows and valleys, developing scars and debris masses, which are prone to erosion and water infiltration. Slab failure, debris flows, gully erosion and other fluvial processes take place in highly weathered and weak rock masses. During the fieldwork for this study, lots of small landslides with an area less than 1000 square meter, had been observed, most of them were supposed to be of erosional origin. However, they can also develop very big landslides, like the landslides found in Bhuwanchuli area if erosional processes continue in multiple steps. Scars are moving upwards and sidewards, and debris is transported further downwards by fluvial processes.
103
SECTION IV: LANDSLIDE SUSCEPTIBILITY
104 7. LANDSLIDE SUSCEPTIBILITY
7.1 BACKGROUND
As a result of active plate tectonics and its dynamics, Chure area themselves are very dynamic. The area without landslides at present can easily convert to the area of landslide at any time in future. Different factors that control the landslides in Chure area of Nepal are already discussed in Section 3 and their distribution in present day is discussed in Section 2. However, to be safe from the landslide disaster and to preserve the Chure area itself, information about the potential landslide zone in the given area is essential. Reducing the vulnerability and risk of landslide to the people and the property can be started by identifying the area most potential for landslides in future. This process is popularly described by the term landslide susceptibility mapping. Landslide susceptibility gives the quantified representation of the state of a particular area with respect to landslide. This section describes the methods and results of landslide susceptibility mapping in the ten working districts.
7.2 MATERIALS AND METHODS
Landslide susceptibility usually involves preparing a landslide inventory together with an assessment of the areas with a potential to experience landslide in the future, but with no assessment of the frequency (annual probability) of the occurrence of landslides (Fell et al. 2008) Landslide susceptibility and hazard maps are prepared on the basis of the intrinsic factors such as bedrock geology, geomorphology, soil depth, soil type, slope gradient, slope aspect, slope curvature, elevation, land use pattern, drainage pattern and so on along with extrinsic factors such as rainfall, earthquakes and volcanoes (Cevik and Topal 2003; Dahal et al. 2008; Dai et al. 2001; Siddle et al. 1991). Varnes (1984) emphasized on the consideration of different factors while preparing landslide susceptibility maps. Similarly, Soeters and Westen (1996) divided those factors into five groups as follows:
Geomorphological factors such as data of digital terrain unit, geomorphological sub-units and types of landslides. Topographic factors such as data of digital terrain model, slope direction and length and concavities.
105 Engineering geological factors such as data of lithology, material sequences, structure of geology and seismic acceleration. Land use factors such as data of infrastructure development (recent and older) and land use map (recent and older). Hydrological factors such as data of drainage, catchment area, rainfall, temperature, evaporation and water table map.
Dhital and Upreti (1996) stated that the landslide susceptibility maps show the area likely to experience landslide hazard in the future by correlating some of the principal factors. Statistical analyses are popular because they provide a more quantitative analysis of slope instability, have the ability to examine the various effects of each factor on an individual basis and decide on the final input maps in an interactive manner (Aleotti and Chowdhury 1999). Statistical methods can broadly be classified into two types: bi-variate and multivariate (Kayastha et al. 2013).The bi-variate method is a modified form of the qualitative map combination in which each individual thematic data layer is compared to the existing landslide distribution and the weight value for each category of causative factors is assigned based on statistical relationships between past landslides and each category of causative factors (Van Westen 1994). The weights for the factors and their categories are integrated to produce the landslide susceptibility index map, which may be further reclassified into different zones of susceptibility.
7.2.1 INFOVAL METHOD
Yin and Yan (1988) first introduced a method of determining the weights of the assigned classes in the factor maps. Van Westen (1997) proposed this bi-variate statistical analysis method for landslide susceptibility mapping. In this method (Figure 4.1), a weight value for a parameter class is defined as a natural logarithm of the landslide density in the class divided by landslide density of each parameter class such as a certain lithological unit or a certain lithological unit or a certain slope class (Pradhan et. al, 2012).
In this method, weight for each factor maps is calculated using the following formula:
106 Where, Wij = weight given to a certain class I of parameter j, fij = landslide density within * the class I of parameter j, f = landslide density within the entire map, A ij= area of * landslide in certain class I of parameter j, Aij= area of certain class I of parameter j, A = total area of landslide in the entire map, A = total area of entire map.
In the case of no landslide occurrence in certain class of the parameter, the weight is assigned as 0 (Van Western, 1997; Yalcin, 2008). All the factor maps are intersected with the landslide map and the landslide density is calculated for each class.
Figure 7.1: Flowchart showing the processes involved in the preparation of the
susceptibility map
107 7.2.2 FACTOR MAPS FOR THE ANALYSIS
Seven different factors maps along with the landslide distribution map (Annex 4.1) were prepared in GIS environments for the susceptibility map preparation. Digital Elevation Model (DEM) of 20m×20m was used for the analysis. Slope of a feature is defined as its inclination relative to a horizontal plane or its steepness. Slope plays a key role in the stability of the mass at certain height because it will be more prone to falling or sliding as the slope angle increases. It is an important factor because slopes become less stable as the slope angle increases generally. The slope value ranges from 0˚ to 90˚. The slope classes were divided into seven classes for this analysis and are 0˚ to 5˚, 5˚ to 10˚, 10˚ to 15˚, 15˚ to 20˚, 20˚ to 25˚, 25˚ to 45˚ and greater than 45˚.
Soil type indicates the type of soil material present in a given area and its ground condition. Alluvial soil are deposits of the river, residual soil are remnants of the bedrock due to weathering while the colluvial soil are the transported material due to gravity. Rock exposed areas have no or very thin layer of soil. Colluvial soil indicates the movement of materials; alluvial soil indicates the action of the river in present or past while residual soil indicates calm ground condition in general. Soil type is classified as the residual, colluvial, alluvial soil and rock exposed. Probability of occurrence of landslide is generally very low in residual and alluvial soil.
Geology defines the type of the exposure present within the given area. The physical, chemical and engineering properties of the rock types also play a major role in landslide initiation and triggering. Each rock type of the Chure area (Siwaliks) has its own characteristics. General classification of the Siwaliks rocks into the Upper, Middle and Lower Siwaliks have been followed in this analysis with appearance of Pre Siwaliks rocks at some locations. The sedimentary strata of the Siwaliks comprising alternate layers of sandstone and mudstone have different resistance to erosion and weathering processes. The sandstone beds are relatively stronger than the mudstone. This provides the differential conditions within the same area. Similarly, the conglomerate of the Upper Siwaliks are matrix supported and loosely packed which makes it prone to debris flow, debris, slide, granular flow and gully erosion. The geological map was prepared on the basis of field work and the geological maps of petroleum exploration produced by DMG were taken as reference maps. The geological contacts were modified to higher scale on the basis of field work and morphological analysis.
108 Land use gives the surficial condition of the land and its type. It gives the general idea whether the surface is in its natural condition or been influenced by human and other natural processes. Also, it gives the location of the settlements which are near the landslides. It has been classified as barren land, bushes/grasses, cultivated land, forest, riverbed, settlement and water bodies.
Elevation is the vertical height taken from a constant reference level (mean sea level in this case). It is a very important factor because it is directly related to the volume as well as the distance through which a mass moves down slope. Generally, higher the elevation more is the velocity and the volume of mass moving downwards. The class interval of 100 m was taken for this study.
Streams are one of the major hydrological agents of erosion, transportation and deposition of the sediments. They are one of the important triggering factors for the landslides especially through toe cutting. Streams slowly erode the banks in normal condition which increases abruptly during flood events. In such cases, they cause huge loss of landmass and properties. So, distance of the exposure from the river is taken as one of the factors. The distances have been classified as 25 m, 50 m, 100 m and greater than 100 m.
Aspect is the direction at which the slope is facing. It is also an important factor because it controls the physical and chemical weathering condition of the exposure. The slopes facing the sun are generally dry while that not facing the sun are moist. The aspect is divided into nine classes which are flat, north, northeast, east, southeast, south, southwest, west and northwest.
109 8. RESULTS
8.1 LANDSLIDE SUSCEPTIBILITY
The landslide susceptibility map (Figure 8.1) for the Chure area of the Makawanpur district showed very high susceptibile zones in northern belt along the Kyampa, Lekhpani, Betini and Thumki sections and southeastern belt along the Saraswatigaun, Bhamara and Sano Deujor. The VDC/Municipality wise distribution (Figure 8.2) of the susceptibility class for the Makawanpur District showed that the Bhainse, Dhiyal, Manthali, Thingan had relatively higher proportion of very high susceptibility class.
Figure 8.1: Landslide susceptibility map of the Chure area of Makawanpur district
110
Figure 8.2: VDC/Municipality wise landslide susceptibility condition for the Chure area of Makawanpur District
The landslide susceptibility map (Figure 8.3) for the Chure area of the Bara district showed very high susceptibility zones majorly in central belt along the Kolgaun and Anpchaur. Similarly, the VDC/Municipality wise distribution (Figure 8.4) of the susceptibility class for the Bara district showed that the Ratanpuri had relatively higher proportion of very high susceptibility class while Parsa had only susceptible zone up to the moderate class.
The landslide susceptibility map (Figure 8.5) for the Chure area of the Rautahat district showed very high susceptibility zones majorly in northern belt along the Khyaku, Thalligaun and Bhiman. Similarly, the VDC/Municipality wise distribution (Figure 8.6) of the susceptibility class for the Rautahat district showed that the Judibela had relatively higher proportion of very high susceptibility class while other VDC/ Municipality also had similar distribution of the classes.
111
Figure 8.3: Landslide susceptibility map of the Chure area of Bara district
Figure 8.4: VDC/Municipality wise landslide susceptibility condition for the Chure area of Bara District
112
Figure 8.5: Landslide susceptibility map of Chure area of Rautahat district
Figure 8.6: VDC/Municipality wise landslide susceptibility condition for the Chure area of Rautahat district
113 The landslide susceptibility map (Figure 8.7) for the Chure area of the Sindhuli district showed high susceptibility zones throughout the region. This region had been affected by most of the major factors such as geological (unconsolidated bedrocks), structural (Main Boundary Thrust and Kamala Thrust), hydrological (high rainfall occurring region), human influence and others.
Figure 8.7: Landslide susceptibility map of the Chure area of the Sindhuli district
Similarly, the VDC/Municipality wise distribution (Fig. 8.8) of the susceptibility class for the Sindhuli district showed that most of the VDC/Municipality had relatively higher proportion of the high susceptibility class which included Amale, Arun Thakur, Bastipur, Hariharpur, Kapilakot, Santeshwar and Tribhuwan.
114
Figure 8.8: VDC/Municipality wise landslide susceptibility condition for the Chure area of Sindhuli district The landslide susceptibility map (Figure 8.9) for the Chure area of the Sarlahi district showed high susceptibility zones throughout the region including Sano Phuljor, Aranedandagaun, Bhalukhop and Kerabari. Similarly, the VDC/Municipality wise distribution (Figure 8.10) of the susceptibility class for the Sarlahi district showed that most of the VDC/Municipality had relatively higher proportion of the high susceptibility class which included Atrauli, Kalinjor, Kalpabrikshya and others. Sasapur had very low susceptibility class.
Figure 8.9: Landslide susceptibility map of the Chure area of Sarlahi district
115
Figure 8.10: VDC/Municipality wise landslide susceptibility condition for the Chure area of Sarlahi district
The landslide susceptibility map (Fig. 8.11) for the Chure area of Mahottari district showed high susceptibility zones throughout the region including Nagdah, Khairmara, Betal, Pandan along with some traces of very high susceptible class in some parts including Sisne and Hanumandhoka.
Similarly, the VDC/Municipality wise distribution (Figure 8.12) of the susceptibility class for Mahottari district showed that most of the VDC/Municipality had relatively higher proportion of the high susceptibility class which included Gauribas, Khairmara and Maisthan.
116
Figure 8.11: Landslide susceptibility map of the Chure area of Mahottari district
Figure 8.12: VDC/Municipality wise landslide susceptibility condition for the Chure area of Mahottari district
117 The landslide susceptibility map (Figure 8.13) for the Chure area of Dhanusa district showed high susceptibility zones throughout the region including Tulsi and Tintale along with some traces of very high susceptible class in the southern parts including Bimire and Mainawati.
Similarly, the VDC/Municipality wise distribution (Figure 8.14) of the susceptibility class for the Dhanusa district showed that most of the VDC/Municipality had relatively higher proportion of high susceptibility class which included Yagyabhumui, Tulsi, TalloGodar, Nakatajhij and Bharatpur while Bharatpur and Yagyabhumi had higher proportion of very high susceptible class.
Figure 8.13: Landslide susceptibility map of the Chure area of Dhanusa district
118
Figure 8.14: VDC/Municipality wise landslide susceptibility condition for the Chure area
of Dhanusa district
The landslide susceptibility map (Figure 8.15) for the Chure area of Udayapur district showed high susceptibility zones throughout the region including Palase, Shikharpur, Gohiya and Adheri along with some traces of very high susceptible class in the southwestern and northeastern parts including Belsot, Patalebas, Damauti, Ambote and Katle.
Similarly, the VDC/Municipality wise distribution (Figure 8.16) of the susceptibility class for the Dhanusa district showed that most of the VDC/Municipality had relatively higher proportion of the high susceptibility class. These included Tribeni, Bhalayadanda, Siddhipur, Saune, Tawashri while Bhalayadanda, JalpaChilaune, Katunjebawla, Mainamaini.
119
Figure 8.15: Landslide susceptibility map of the Chure area of Udayapur district
Figure 8.16: VDC/ Municipality wise landslide susceptibility condition for the Chure area of Udayapur district
The landslide susceptibility map (Figure 8.17) for the Chure area of Siraha district showed high susceptibility zones majorly in the central and southeastern region including Betaha, Ahale, Bardamar along with some traces of very high susceptible class in the southeastern region including Toribari, Dandatol, Deusegaun and others.
120 Similarly, the VDC/Municipality wise distribution (Figure 8.18) of the susceptibility class for the Siraha district showed that most of the VDC/Municipality had relatively higher proportion of high susceptibility class in Taregana, Ramnagar, Karjanha, Dhodana and Bishnupur while there were higher proportions of very high class in Dhodana, Taregana and Bishnupur.
Figure 8.17: Landslide susceptibility map of the Chure area of Siraha district
Figure 8.18: VDC/Municipality wise landslide susceptibility condition for the Chure area
of Siraha district
121 The landslide susceptibility map (Figure 8.19) for the Chure area of Saptari district showed high susceptibility zones majorly in the eastern region while traces of this class were found in scattered form in the southern and central belt. Similarly, the VDC/Municipality wise distribution (Figure 8.20) of the susceptibility class for the Saptari district showed that most of the VDC/Municipality had relatively higher proportion of very high susceptibility class in Khojpur, Kushaha and Pansera while there were higher proportion of high class in most of the places including Bakdhuwa, Theliya, Terhauta, Jandaul, Bhangaha and Ghoghanpur.
Figure 8.19: Landslide susceptibility map of the Chure area of Saptari district
Figure 8.20: VDC/Municipality wise landslide susceptibility condition for the Chure area of Saptari district
122 For the detail information of the landslide susceptible areas, the landslide susceptibility maps along with tabulated data of the individual watersheds lying within the Chure area of the project districts were prepared and are attached in Annex 4.2 and 4.3. Altogether, landslide susceptibility maps for the 41 individual watersheds which may be complete watershed or partial lying within the Chure Region were prepared.
8.2 VALIDATION AND EVALUATION
The landslide susceptibility maps were evaluated and validated by preparing the success rate curves and prediction rate curves for each district. Verification is a fundamental step in the development of susceptibility and determination of its quality. The quality of a landslide susceptibility model is usually estimated by using independent information that is not available for building the model (Pradhan and Kim, 2013). The total landslides were divided into two classes as working landslides and validating landslides before starting the analysis. Out of the total landslides, 75% were taken as working and 25% were taken as the validating landslides. The sampling was done using the Systematic Random Sampling Method. Then, two curves were obtained by plotting the cumulative % of the susceptibility class versus the cumulative % of the landslide occurrence for both working and validating landslides after the preparation of the susceptibility map. The curve for working landslides is the success rate curve while the curve for the validating landslides is the predication rate curve.
123 a
b
124 c
Figure 8.21: Success rate curves and prediction rate curves for validation of the susceptibility maps of a) Makawanpur, b) Sindhuli and c) Udayapur Districts.
The success and prediction rate curves (Figure 8.21) prepared for the three major and bigger districts among the project area showed area under curve of more than 70% which indicate that the landslide susceptibility maps prepared in this study is good. Same method was followed for all the project districts and therefore the maps for the other project districts should have predicted the landslides reasonably.
In each of the cases for the area under curve calculation for Makawanpur, Sindhuli and Udayapur districts (Table 8.1), the area under curve value of prediction rate exceeds that of the success rate which is a very good indicator for the validity of the prepared maps.
Table 8.1: Area under curve (%) for the three clusters for evaluation and validation
Cluster Area Under Curve (%)
Success Rate Prediction Rate Makawanpur 76.49 73.83 Sindhuli 73.54 72.7 Udayapur 85.25 82.68
125 Table 8.2: VDC/Municipality wise landslide susceptibility condition for the Chure area of Makawanpur District in terms of percentage
District VDC/ Municipality Susceptibility Class (Area Percentage) Total Very Low Moderate High Very (%) Low High Ambhanjyan 0 2 29 42 27 100 Basamadi 0 11 29 40 20 100 Beteni 7 27 19 26 21 100 Bhainse 0 8 21 32 39 100 Churiyamai 0 38 43 19 1 100 Dhiyal 0 1 17 49 33 100 Handikhola 0 8 45 43 4 100 Harnamadi 0 17 39 38 7 100
Hatiya 0 29 34 30 7 100
Hetauda Municipality 0 48 26 18 8 100 Kankada 0 1 24 57 19 100 Makawanpur 0 5 36 43 16 100 Manahari 0 17 36 36 11 100 MAKAWANPUR Manthali 0 2 20 46 32 100 Padampokhari 0 40 42 17 1 100 Phaparbari 4 15 30 41 10 100 Raigaun 3 9 30 44 15 100 Raksirang 0 5 24 44 27 100 Sarikhet 0 5 29 43 24 100 Shikharpur 19 26 20 22 13 100 Shripur Chattiwan 1 8 26 45 20 100 Thingan 7 19 15 31 27 100 Amlekhganj 0.2 13.1 36.4 41.6 8.7 100 Bharatganj 2.3 11.5 31.4 46.5 8.3 100
Nijgadh 7.4 11.7 30.0 46.2 4.7 100
Parsa 51.1 43.6 5.3 0.0 0.0 100 BARA Ratanpuri 2.3 11.9 30.7 43.0 12.0 100
Chandra 2.70 9.83 32.53 47.15 7.79 100
UT AT
RA AH
126 Nigahapur
Judibela 1.69 8.87 26.38 51.71 11.34 100 Paurai 2.13 15.84 33.67 38.36 10.00 100 Rangapur 6.34 12.33 33.32 43.03 4.98 100 Amale 0.00 0.00 0.74 92.47 6.79 100 Arun Thakur 0.00 0.00 1.97 94.96 3.07 100 Bastipur 0.00 0.00 2.67 97.33 0.00 100 Belghari 1.01 5.22 7.65 85.32 0.79 100 Bhadrakali 1.14 13.05 6.59 73.25 5.98 100 Bhimsthan 8.36 59.58 3.04 28.34 0.67 100 Dandiguran 5.57 6.74 10.37 74.88 2.43 100
Dudhauli 44.10 11.99 8.18 34.08 1.64 100 SINDHULI Hariharpur 0.37 0.33 3.74 93.11 2.46 100 Harsahi 17.71 43.84 11.21 27.24 0.00 100 Hatpate 14.12 8.78 1.13 74.37 1.60 100 Jarayotar 0.55 4.86 2.94 90.81 0.83 100 Jinakhu 0.00 0.00 3.67 96.33 0.00 100
127 Table 8.2 contd….
Kakur Thakur 0.32 0.05 5.05 90.08 4.49 100 Kalpabriks 8.84 3.43 30.24 57.33 0.16 100 Kamalamai 7.88 18.71 7.83 63.83 1.75 100 Kapilakot 5.69 2.37 2.15 83.54 6.26 100 Kyaneshwar 1.48 7.76 13.51 75.85 1.41 100 Lampantar 0.16 1.75 2.27 95.21 0.61 100 Mahadevsth 10.96 5.77 15.11 64.31 3.85 100 Mahendra Ladabhir 7.69 3.62 29.03 55.72 3.94 100 Mahendrajhyadi 6.49 6.27 8.12 76.84 2.28 100 Netrakali 0.00 0.00 1.00 97.63 1.37 100 Nipane 20.89 41.60 9.98 27.53 0.00 100 Pipalmadi 7.91 15.77 12.54 63.27 0.52 100 Ranibas 6.29 23.13 15.28 54.76 0.55 100 Ranichauri 3.73 18.68 3.83 72.63 1.13 100 Santeshwar 0.00 0.00 1.64 90.71 7.66 100 Sirthauli 15.76 11.82 3.15 64.05 5.21 100 Tamajor 0.00 0.00 1.24 98.54 0.22 100 Tandi 29.84 8.80 21.36 39.14 0.86 100 Tribhuwan Ambote 0.00 0.00 0.54 95.59 3.87 100 Atrauli 23.8 1.6 15.9 57.7 1.0 100 Dhungrekho 0.0 0.0 15.4 83.8 0.8 100 Hariaun 44.8 2.2 46.3 6.7 0.0 100
Kalinjor 4.9 0.6 13.9 77.1 3.5 100
Kalpabriks 0.0 0.0 0.0 100.0 0.0 100 Karmaiya 6.4 3.6 15.8 73.9 0.3 100
SARLAHI Narayan Khola 5.2 16.8 18.9 58.4 0.7 100 Parwanipur 3.9 0.3 16.3 77.7 1.8 100 Pattharkot 19.3 1.3 17.6 60.1 1.7 100 Sasapur 100.0 0.0 0.0 0.0 0.0 100
128 Table 8.2 contd….
Bardibas 83.59 16.41 0.00 0.00 0.00 100 Gauribas 17.18 8.27 19.34 54.11 1.10 100 Khairmara 4.14 0.82 18.16 74.45 2.43 100
Maisthan 6.30 1.05 14.26 76.58 1.80 100 MAHOTTARI Bengadawar 19.87 0.53 32.66 46.15 0.79 100 Bharatpur 2.12 0.57 27.54 61.27 8.50 100 Dhalkebar 9.61 1.01 28.11 59.51 1.76 100 Hariharpur 0.00 0.00 45.99 53.88 0.13 100
Nakatajhij 0.00 0.23 33.88 63.94 1.95 100 DHANUSA Puspabalpu 2.06 0.30 26.52 66.33 4.78 100 Tallo Godar 4.01 0.17 25.70 65.19 4.92 100 Tulsi 28.80 1.73 16.26 51.03 2.18 100 Umaprempur 44.26 1.93 27.47 26.20 0.14 100 Yagyabhumi 7.74 0.77 24.41 59.47 7.61 100 Basaha 0.317 68.19 5.316 26.107 0.065 100 Beltar 0.013 79.72 8.835 11.106 0.323 100 Bhalaya Danda 0.000 0.309 2.827 78.289 18.575 100 Chaudandi 0.000 11.28 6.646 74.295 7.777 100 Hadiya 4.540 39.33 6.765 49.289 0.074 100 Jalpa Chilaune 0.000 0.833 4.390 74.681 20.097 100
Jogidaha 1.945 43.42 3.498 50.763 0.368 100
Katari 0.542 34.74 8.286 54.767 1.660 100 Katunjebaw 0.000 1.925 4.480 72.273 21.322 100
Khanbu 0.000 0.000 79.821 14.350 5.830 100 UDAYAPUR Mainamaini 0.000 1.348 7.040 60.098 31.514 100 Panchawati 0.000 0.807 5.886 88.007 5.300 100 Risku 0.294 14.22 10.110 74.375 0.998 100 Saune 0.000 0.127 1.389 96.613 1.871 100 Siddhipur 0.000 2.681 2.079 93.609 1.631 100 Sundarpur 0.045 45.60 5.323 48.923 0.102 100 Tapeshwari 0.000 48.54 5.809 45.042 0.604 100
129 Table 8.2 contd….
Tawashri 0.000 0.000 0.000 100.00 0.000 100 Thoksila 0.005 65.87 15.215 18.606 0.295 100 Tribeni 0.001 1.765 1.390 86.453 10.392 100 Trijuga municipality 0.490 24.23 5.008 55.893 14.371 100
Badharamai 3.1 0.6 56.3 38.6 1.5 100
Bishnupur pra. 0.7 1.4 39.5 46.7 11.6 100
Chandra 8.4 1.2 55.2 32.3 2.8 100 SIRAHA Chandralal 100.0 0.0 0.0 0.0 0.0 100 Dhodana 5.7 0.9 14.1 62.6 16.7 100 Jamdaha 12.6 2.1 56.6 25.3 3.4 100 Karjanha 0.0 0.2 39.5 58.3 1.9 100 Lalpur 96.7 3.3 0.0 0.0 0.0 100 Muksar 9.6 1.5 54.3 29.2 5.3 100 Phulbariya 20.9 1.7 39.7 35.7 2.0 100 Ramnagar 0.0 0.7 37.8 59.1 2.4 100 Taregana govindapur 2.9 2.1 21.7 60.9 12.4 100
Bakdhuwa 3.30 2.03 19.86 73.46 1.35 100 Bhangaha 8.04 0.87 17.73 66.85 6.50 100 Dharampur 0.00 0.00 73.56 22.89 3.56 100 Ghoghanpur 0.15 0.08 18.24 79.51 2.01 100 Hardiya 76.53 16.69 3.61 2.03 1.14 100 Jandaul 0.00 0.27 15.85 81.10 2.78 100
Kalyanpur 26.10 13.62 12.74 40.88 6.66 100
Kamalpur 19.34 7.57 21.75 51.28 0.07 100 Khojpur 5.86 2.86 3.61 58.14 29.53 100
SAPTARI Khoksar parbaha 5.30 0.30 15.71 75.84 2.85 100 Kushaha 0.00 0.00 0.00 61.87 38.13 100 Pansera 0.00 0.00 0.44 63.64 35.92 100 Phattepur 36.57 6.08 43.16 14.19 0.01 100 Pipra pashim 74.55 18.89 4.94 1.48 0.14 100 Prasbani 0.00 0.00 36.42 59.26 4.32 100 Rupnagar 0.00 0.00 18.71 73.34 7.95 100
130 Sitapur 0.00 0.00 19.39 64.46 16.15 100 Terhauta 0.00 0.00 16.32 76.40 7.27 100 Theliya 0.00 0.00 13.76 81.34 4.90 100
131
SECTION V: LANDSLIDE VULNERABILITY AND RISK ASSESSMENT
132 9. LANDSLIDE VULNERABILITY AND RISK
9.1 INTRODUCTION
United Nations/International Strategy for Disaster Reduction (ISDR 2009) defines vulnerability as the "characteristic and circumstance of a community, system or asset that make it susceptible to the damaging effects of a hazard" (ISDR 2009). In line with UNISDR terminology, the underlying understanding for this research is that in order to manage risk, decision makers and local communities need to understand the threat posed by a hazard, the magnitude of lives and values exposed to the threat, the specific susceptibility towards hazards through present vulnerabilities, and the range of capacities and measures to protect against risk. According to LDRMP Guideline (2011), six components viz. human loss, affected families; household damage; financial loss; damage of arable land and forest; and social impact, are often taken into consideration while developing vulnerability index.
In the field of landslide research, vulnerability has been taken in context of qualitative analysis (Flanagan et al. 2011) but some of the research had taken quantitative analysis (Kaynia et al. 2008) but there is no simple and unique method available for landslide vulnerability assessment within the framework of landslide risk mapping (Ghimire 2010). Modeling and quantification of landslide is still considered as difficult task (Ghimire 2010; Mezughi et al. 2011; Van Westen et al. 2006). Spatial and non-spatial data are required for doing landslide vulnerability and risk assessment (Castellanos Abella and Van Westen 2005; Cosic et al. 2011; Flanagan et al. 2011; Haugen and Kaynia 2008). Vulnerability to any event is a function of several indicators (social, physical, economic and environmental) over given area, which is exposed to certain disaster. In recent years, an increasing number of global and local initiatives have been launched to measure risk and vulnerability with a set of indicators and indices (Birkmann 2007). Identifying and measuring risks and vulnerability before a disaster occurs and also after disasters have happened are essential tasks (Birkmann 2007; Crozier and Glade 2006; Van Westen et al. 2006) for effective and long-term disaster risk reduction. In this regard, measuring vulnerability encompasses both quantitative and qualitative methods to describe and operationalize vulnerability (Birkmann 2007; Glade et al. 2000; Kaynia et al. 2008). The vulnerability assessment is considered as the analysis of the potential impact of loss from a successful attack as well as the vulnerability of the facility/location to an attack.
133 Assessing vulnerability of a community beforehand can be considered as the safety measure from probable damage due to disaster. After the vulnerability analysis, it becomes easy to develop strategies and prioritize disaster mitigation measures in order to reduce the vulnerability of the community (Cosic et al. 2011; Crozier and Glade 2006). In this study, element of risk of landslides was quantified and vulnerability of the study area for landslides were identified based on vulnerability index. This was determined by integrating social, economical, environmental and physical vulnerabilities.
134 10. MATERIALS AND METHODS
10.1 IDENTIFICATION OF ELEMENTS AT RISK
Using data from household survey, focus group discussion, key informant interviews and literature review along with consultation experts, various elements at risk were identified. Structured and semi-structure questionnaires were prepared for the focus group discussion, schedule survey and key informant interview (Annex 5.1) to understand the effect of landslide in community and environment in past years. Schedule survey was conducted in community located nearby the zone of landslide. Survey on project district was conducted in various time frames from 3rd Dec 2015 to 2nd Jan, 2015 in Makawanpur Section (Makawanpur, Rautahat,and Bara) while from 15th Jan, 2016 to 14th Feb, 2016, field survey in Sindhuli Section (Sindhuli, Sarlahi, Mahottari) was conducted. In last phase of study from 7th April, 2016 to 27th April, 2016 study of Udayapur Section (Udayapur, Dhanusa, Siraha and Saptari) were accomplished. For the study, a total of 341 household survey comprising 120 communities were performed along with 60 FGD, and 31 key informants interview were conducted.
To evaluate landslide risk and vulnerability, it is very necessary to analyze the elements at risk (Castellanos Abella and Van Westen 2005; Flanagan et al. 2011) which may or had been affected by landslide events. There is no any worldwide understanding of listing elements at risk for any disaster events (UNDRO, 1991) thus, for this study nearby areas to landslide (Birkmann 2007; Dominey-Howes et al. 2010; Haugen and Kaynia 2008; Kaynia et al. 2008) (UNDP,2004 and IADB,2008) was used, which cites elements at risk are subdivided into infrastructure, economic activities, population (Castellanos Abella and Van Westen 2005; Mezughi et al. 2011; Nyaupane and Chhetri 2009; Papathoma- Köhle et al. 2011; Samir 2013) etc. This approach was modified further in this study and listing was done in four sub groups such as: social (population), physical infrastructure, economic and environmental.
Literature review was performed for the secondary data collection where published and unpublished documents about impact of landslide and relevant secondary information were reviewed. The official documents for example CAP Strategy 2008, Chure research report of CSRC 2007 and various research report published by Ministry of Forests and Soil Conservation, Central Bureau of Statistics 2011, Department of Survey etc. were
135 reviewed to identify the elements at risk. Data related to population, physical infrastructure, production and other socio-environmental setting were collected from the field survey. From these data, sensitivity, adaptive capacities of various entity of environment were assessed. Along with these, loss and damage data were also acquired and their mitigation and adaption measures were also documented. Among the elements at risk, those elements which were impacted by landslide were listed as risk.
10.2 VULNERABILITY ASSESSMENT
Spatial multi-criteria decision analysis was applied to carry out vulnerability assessment. The spatial datasets in raster format were used as the input parameters. These data were analyzed and processed in the GIS environment within the framework of Analytical Hierarchical Process (Mezughi et al. 2011) developed by Satty (1980). Figure 10.1 shows the conceptual framework for vulnerability assessment.
Figure 10.1: Conceptual framework for vulnerability analysis
This study was intended to determine the significance of landslide risk of the VDCs within the Chure area of ten districts viz. Makawanpur, Bara, Rautahat, Sindhuli, Dhanusha, Mahottari, Sarlahi, Udayapur, Siraha and Saptari.
136 10.2.1 CRITERIA AN INDICATOR
The significance of the landslide vulnerability was determined based on the following four criteria, namely:
1. Social vulnerability 2. Economic vulnerability 3. Environmental vulnerability 4. Physical vulnerability
These four groups of criteria are further elaborated in indicators which are described below:
Social Vulnerability The social vulnerability was carried out based on the following parameters - population density, sex ratio and dependency ratio as an indicator. The population size determines the overall vulnerability of the VDC towards the landslide. Higher the population size, higher will be the vulnerability towards landslide (Oven 2009). Furthermore, the gender composition as well dependency ratio shows the coping capacity of that population (Samir 2013) in the VDC. Higher the gender imbalance and higher the dependency ratio, the vulnerability to the landslide will be high.
The data required for the parameters to be used for social vulnerability were mainly derived from recent census data for 2011 published by Central Bureau of Statistics. Furthermore, the GIS data required for the analysis was obtained from Department of Survey.
The population size data was fed into the GIS data at the VDC level. The population density was estimated using size of population/area of VDC and municipalities. The population density indicates the social assets of the area. Size of male and female population was also acquired from the census 2011 (CBS), which was used to determine the sex ratio. Population demography was also prepared using the same census to estimate dependency ratio at the VDC level.
Economic Vulnerability Because of insufficient data, GDP was dropped as the economic indicator. Nepal is agrarian society with majority of population engaged with agriculture (mostly subsistence
137 and some commercial) for their livelihood (MoE 2010; Samir 2013; Sudmeier-Rieux et al. 2012). Therefore, area of cultivated land available in the VDC was selected as the indicator for economic vulnerability. Higher the size of cultivated land in the landslide prone area, higher will be the economic vulnerability of the VDC.
Physical Vulnerability Physical infrastructure such as road, house type, transmission line, pipeline etc. shows the infrastructure development of any area. Hence, stronger the infrastructure, lesser the population will vulnerable to any disaster (Birkmann 2007; Haugen and Kaynia 2008; Kaynia et al. 2008; Papathoma-Köhle et al. 2011). So to assess the physical vulnerability, house condition and road density were used as indicator (Castellanos Abella and Van Westen 2005). To assess the house condition, housing index were developed from the available data from field and CBS, 2014. Also housing condition was considered as highly correlated with population (Van Westen et al. 2006; Wisner and Luce 1993), but it was more relevant to take housing index as indicator. The lifeline of development and a major physical infrastructure, especially in post disaster scenario, the roads were also included in the assessment.
Environmental Vulnerability Natural resource such as forest acts as the source for various resources to the local people in rural Nepal as well as in some urban area of country (Ghimire 2010; Samir 2013; Sudmeier-Rieux et al. 2012). From the field survey, it was found that majority of population were dependent upon the forest for their daily activities such as fodder and firewood collection as well as use of natural resource.
10.2.2 PREPARATION OF DATABASE AND BASE MAPS
Database for each indicator, sub indicators, was prepared at VDC level. These databases were used to prepare a base map for each district up to VDC level in Arc GIS 10.1 environment. These base maps were prepared in vector format which were then converted into raster format for each indicators.
10.2.3 STANDARDIZATION AND ASSIGNMENT OF WEIGHTAGE
All the factors and indicators were in different format (nominal, ordinal, interval or ratio) and their value were different. In order to facilitate multi-criteria analysis various layers
138 of indicators need to be standardized from their original value to 0-1 scale (Castellanos Abella and Van Westen 2005; Glade et al. 2000; Mezughi et al. 2011). After selecting and preparation of maps for indicators and sub indicators, hierarchical structural weights were assigned to all by defining their standardization and weightage value (Table 10.1).
The normalized value to the sub-indicators was assigned using the following formulae:
Rank -sum method by using formula (Janseen and Van Herwijneen, 1984).
n k k n i (n i)
Where, WK = normalized value
n = maximum rank
Maximum Method (Khanal 2008)
Zi,j= (Xi,j – Xi min)/(Ximax-Ximin)
Where,
Zi,jis the standardized indicator index of type i of community j,
Xi,j is the unstandardized indicator index of type i of community j,
Ximax is the maximum value of the indicator index over community j, and
Ximin is the minimum value of the indicator index over community j.
Inverse Maximum Method (Castellanos Abella and Van Westen 2005)
Zi,j= (Ximax –Xi,j)/(Ximax-Ximin)
Zi,jis the standardized indicator index of type i of community j,
Xi,j is the unstandardized indicator index of type i of community j,
Ximax is the maximum value of the indicator index over community j, and
Ximin is the minimum value of the indicator index over community j.
139 Table 12.1: Methods of standardization Component Vulnerability Sub indicators Weight Standardization Indicator Social Population Population 0.475 Maximum Methods Density Dependency Maximum Method Ratio Sex Ratio Maximum Method Physical House Type Housing Index 0.257 Inverse Maximum Method Transportation Highway 0.090 Maximum Method Gravel Road Foot trails Economic Economic Agricultural 0.157 Maximum Method Production Land Environmental Ecological areas Forest 0.040 Ranking Method Grass Land Shrubs land/Bushes Barren Land
10.2.4 PREPARATION OF VULNERABILITY INDEX MAP To prepare final vulnerability map, standardized base map were overlaid on ArcGIS environment by multiplying it with its weight. The weightage value for different indicators was adopted from Castellanous Abella (2008). 10.3 RISK ASSESSMENT
A qualitative landslide risk assessment method was adopted as it is suitable for statistical approach based on landslide hazard mapping (Van western Birkmann, 2006; Thywissen 2006) which have incorporated coping capacity, exposure and susceptibility in calculating risk. Coping Capacity refers to the means by which people and/or institutions use the available capacities and resources to face adverse consequences related to a disaster. Landslide risk index for project area was calculated using following equation:
Risk = Vulnerability×Hazard/ Adaptive Capacity
140 11. RESULTS AND DISCUSSION
11.1 LANDSLIDE VULNERABILITY
11.1.1 SOCIAL VULNERABILITY INDEX
Population density was found between 27 individuals/km2 (Triyuga Municipality) and 1773 individuals/km2 with an average density 241 individuals/km2 (Annex 5.2). This showed that the population density were higher in urban area whereas lowest in rural area of Chure districts. Largest group of VDCs had a population density ranging from 28 individuals/km2 to 328 individuals/km2 (Annex 5.2). Similarly, sex ratio showed the gender composition of demography. Using sex ratio as indicator helped to understand gender vulnerability. Thus, from gender perspective, VDCs of Rautahat and Mahottari were highly sensitive. Also, dependency ratio was found in between 26 to 112 per 100 economically active populations with an average of 83 per 100 economically active populations. From the human perspective of vulnerability, among project districts, majority of VDCs were in low (39.19%) to moderate zone (25.87%) of social vulnerability (Annex 5.2). Table 11.1 shows the social vulnerability of various districts in project area whereas VDC/Municipality and watershed wise social vulnerability has been described in Annex 5.2.
Table 11.1: District wise distribution of social vulnerability area
Very Low Low Moderate High Very High Bara 0 50.75102 37.70197 0 11.54701 Dhanusa 52.80271 19.53556 0 7.893217 19.76851 Mahottari 59.99906 1.767458 37.27956 0.39015 0.563771 Makawanpur 31.40029 39.9877 24.43742 0.646531 3.528056 Rautahat 17.83762 1.695749 15.30327 0.10039 65.06298 Saptari 10.73991 47.56592 36.28798 4.911841 0.494347 Sarlahi 30.39247 40.21716 11.45707 14.13777 3.795521 Sindhuli 9.595532 5.803877 45.63965 37.3719 1.589036 Siraha 12.78364 54.27908 17.01465 12.57204 3.350594 Udayapur 0.294188 3.426505 71.1422 17.88764 7.249462
141 11.1.2 PHYSICAL VULNERABILITY INDEX
Since houses are highly correlated with population (Castellanos Abella and Van Westen 2005), it is more relevant to use housing index as one of the indicator of physical vulnerability. Results from housing characteristic shows that (Annex 5.4) majority of VDCs in rural area had low housing index than municipalities in urban areas. Similarly, roads have been the lifeline of rural Nepal from decades. Those areas connected to road have more infrastructures which provide services such as health post, hospital, community centers etc. in pre and post disaster scenario. These road play vital role in coping with disaster. From the study in project area, majority of the area had very low road density (Annex 5.4).
Thus, from the analysis of physical vulnerability index (Annex 5.5), VDCs in remote area had less developed infrastructure and thus were more vulnerable. Majority of the area in project district were in moderate (22.37 %) to very high (18.19%) vulnerable zone (Table 11.2).
Table 11.2: District wise distribution of physical vulnerability area
Very low Low Moderate High Very High Bara 37.08531 21.97345 0.048152 40.2905 0.602596 Dhanusa 8.288067 65.07604 18.6708 3.38444 4.580654 Mahottari 37.27956 0.953921 61.60753 0.158987 0 Makawanpur 14.91899 0.118475 42.10886 24.2505 18.60318 Rautahat 1.387724 62.06215 36.44973 0 0.10039 Saptari 14.77703 36.37416 37.07084 10.70377 1.074209 Sarlahi 0.044635 46.50737 42.59479 3.840676 7.012526 Sindhuli 2.331646 18.50733 8.721478 47.95281 22.48674 Siraha 9.991762 20.12554 29.5345 39.99151 0.356678 Udayapur 5.302057 26.86391 25.71565 12.34396 29.77443
11.1.3 ECONOMIC VULNERABILITY
From the analysis of economic vulnerability index (Table 11.3), majority of area was comprised by very low vulnerability zone (32.16%) followed by moderate vulnerability zone (27.97%), low vulnerability zone (16.08%), very high vulnerability zone (12.59%) and least area was of high vulnerability zone (11.19%). Annex 5.7 shows the VDCs in different vulnerability zones in terms of economic resources.
142 Table 11.3: District wise distribution of economic vulnerability area
Very Low Low Moderate High Very High Bara 36.80851 21.99704 0.840606 0.031904 40.32194 Dhanusa 22.34919 20.2834 4.714829 0 52.65258 Mahottari 0.569236 1.899934 28.79689 0 68.73394 Makawanpur 20.65964 4.175808 25.99968 23.9375 25.22737 Rautahat 0.297147 18.84608 36.97087 0.636226 43.24967 Saptari 16.19802 31.54774 21.43297 4.806859 26.01441 Sarlahi 2.939603 9.611609 21.70473 0.789393 64.95467 Sindhuli 6.98602 25.65787 37.80406 15.63138 13.92068 Siraha 8.894887 16.77741 12.44523 12.26555 49.61692 Udayapur 12.11942 38.13093 20.47307 5.719957 23.55663
11.1.4 ENVIRONMENTAL VULNERABILITY
After analyzing the forest and other natural assets from the natural resource map (Annex no.), majority of VDC/Municipalities consisted high land area with forest. From the analysis of environmental vulnerability index map (Annex no.), majority of the areas were in moderate vulnerability zone (60%) while very less area (10%) were in high vulnerable zone. Areas near settlements and river were among the high vulnerable zone due to high human influence on natural resources.
11.1.5 OVERALL VULNERABILITY INDEX
The landslide vulnerability index value for project district ranges from 0.17 (low value) to 0.80 (high value) with mean value of 0.46 and standard deviation of 0.09 as shown in Figure 18. The map is classified into five classes very low, low, moderate, high and very high vulnerability area (Figure 11.5). The value map (Figure 11.6) showed that the majority of the area in project district were in high vulnerable zone (40.20%), followed by moderate vulnerability zone (32.04%), low vulnerability zone (16%), very high vulnerability zone (11%) and least area was of very low vulnerable zone with 0.22% of area.
143
Figure 11.1: Vulnerability index map of Bara
Figure 11.2: Vulnerability index map of Dhanusa
144
Figure 11.3: Vulnerability index map of Mahottari
Figure 11.4: Vulnerability index map of Makawanpur
145
Figure 11.5: Vulnerability index map of Rautahat
Figure 11.6: Vulnerability index map of Saptari
146
Figure 11.7: Vulnerability index map of Sarlahi
Figure 11.8: Vulnerability index map of Sindhuli
147
Figure 11.9: Vulnerability index map of Siraha
Figure 11.10: Vulnerability index map of Udayapur
148 100% 90%
80%
70% Very High 60% High 50% Moderate 40% Low
30% Vulnerablility (%) 20% Very Low 10% 0%
Districts
Figure 11.11: Vulnerability distribution in ten districts
LANDSLIDE RISK
11.2.1 RISK ANALYSIS
From the analysis of risk map, landslide risk index was found high in the moderate zone with area comprising about 39% followed by low, very low, high and very high (Figure 11.7).
45
40 Total Area (%)
35
30
25
20
15 Area coveraheArea (%) 10
5
0 Very Low Low Moderate High Very High Landslide risk
Figure 11.12: Area coverage (%) in different risk classes
149 The summary table of risk value showed that the majority of areas in ten districts of Chure area were in moderate risk zone with value 0.29.
Table 11.4: Summary statistics of landslide index map
Summary Statistics Minimum Maximum Mean Predominant Std. Dev. 0.02 0.7 0.19 0.29 0.06
The landslide risk map (Figure 11.13) shows the spatial distribution of the relative risk index in entire ten district of Chure area. From the map, it can be interpreted that area near the boundary of Chure area was found to be in high risk.
Figure 11.13: Risk map of project district
As various indicator map was used to prepare risk index map and because of different characteristics of available datasets, it was not possible to avoid administrative boundary. In order to conduct more detail study, the risk index values were analyzed at district and VDC/Municipal level.
150 11.2.2 DISTRICT WISE RISK ANALYSIS
As can be observed (Figure 11.14 to 11.23), the landslide risk index was moderate to high in the ten districts. From risk analysis of ten districts, Siraha was considered at high landslide risk with value ranging from 0.01 to 0.7 with dominant area with 0.21 followed by Udayapur, Rautahat, Sindhuli, while Dhanusa and Saptari had low landslide risk. Table 11.2 shows landslide statistics for different districts. This is mainly caused by very high value of hazard indicators and low value of adaptive capacity of respected districts. Chure districts have been considered as region having high precipitation among others physiographic zones in Nepal. Along, with precipitation, fragile geological condition and poor land use practices were the reasons for the high risk values in these districts. Adaptive capacity also plays significance role in the increase in the landslide risk in these districts. The lesser development in infrastructure, number of educated people, health facilities and larger population with dependent population made these districts high risk while district with good infrastructure development, health facilities and less dependent population had been in low landslide risk.
Figure 11.14: Risk distribution in Bara
151
Figure 11.15: Risk distribution in Dhanusa
Figure 11.16: Risk distribution in Mahottari
152
Figure 11.17: Risk distribution in Makawanpur
Figure 11.18: Risk distribution in Rautahat
153
Figure 11.19: Risk distribution in Saptari
Figure 11.20: Risk distribution in Sarlahi
154
Figure 11.21: Risk distribution in Sindhuli
Figure 11.22: Risk distribution in Siraha
155
Figure 11.23: Risk distribution in Udayapur
156
SECTION VI: LANDSLIDE MITIGATION AND FINANCIAL PLAN
157 12. MITIGATION MODELS OF CHURE LANDSLIDES
12.1 INTRODUCTION
Landslide is a very complex process with respect to both space and time domain. With a clear understanding of the site and case specific problem, mitigation measures have to be employed in order to stop or reduce the landslide movement so that the resulting damages can be minimized. However, it was not easy to prepare mitigation models of each landslide especially in the Chure area, where thousands of landslides exist. In addition to this, there is no proper methodology to describe mitigating models of all such landslides. Thus, the main challenge of this work was to address huge numbers of landslides as well as address hundreds of landslide in clusters. Therefore, the landslides were prioritized based on the social, economic, environmental and physical vulnerabilities as described in section 5, and the mitigation designs were recommended only for those prioritized landslides. In this chapter, the methods and results of mitigation designs are described along with the estimated cost for the implementation of those mitigation measures.
12.2 METHODOLOGICAL APPROACH
12.2.1 SLOPE STABILITY ANALYSIS
Slope stability analyses are performed to assess the safety factor of a particular slope for given geologic and physical conditions. To perform a slope stability analysis, the geometry of the slope, external and internal loading, soil stratigraphy and strength parameters and variation of the ground water table all along the slope must be defined. In this study, Finite Element Method (FEM) was used for the slope stability analysis. FEM offers a number of advantages over traditional limit equilibrium method (Griffiths et al. 2011; Griffiths and Lane 1999) including: 1. Elimination of assumption on the shape and location of failure surface. 2. Elimination of assumptions regarding the inclinations and locations of inter slice forces. 3. Progressive failure. 4. Robustness. Fredlund and Scoular (1999) reviewed the development of the finite element method and proposed this method for solving the slope stability problem. The overall factor of safety
158 computed using finite element method showed good agreement with several limit equilibrium methods. It computed the stresses and strains and the shear strength very accurately. It was able to monitor progressive failure including overall shear failure (Griffiths and Lane 1999).
12.2.2 DESIGN OF MITIGATION MEASURES
According to Popescu (2001), landslide remedial measures are arranged in four practical groups, namely: modification of slope geometry, drainage, retaining structures and internal slope reinforcement. Selection of an appropriate remedial measure depends on: a) engineering feasibility, b) economic feasibility, c) legal/regulatory conformity, d) social acceptability, and e) environmental acceptability. There are a number of levels of effectiveness and levels of acceptability that may be applied in the use of these measures, because one slide may require an immediate and absolute long-term correction, another may only require minimal control for a short period. As many of the geological features, such as sheared discontinuities are not known in advance, it is more advantageous to put remedial measures in hand on a “design as you go basis”. That is the design has to be flexible enough to accommodate changes during or subsequent to the construction of remedial works. The possible mitigation measures for the landslides could be as described in Table 12.1.The geotechnical data and information of the landslide area, which is required for designing the appropriate mitigation measures were collected by using different methods like desk study, detail field study, sampling and laboratory analysis.
The methods for finding the spatial distribution of landslides and their attribute data like geological, morphological, topographical, hydrological, social, physical and environmental data are already described from sections 2 to 5. In this section, methods of laboratory analysis of the samples collected from the field to get the primary geotechnical data of respective landslide sites are described in Table 12.1.
159 Table 12.1: Possible mitigation measures for landslides as per Popescu (2001)
S.N. Types of Mitigation Mitigation details measures 1 Modification of Geometry (i) Removing material and substituting with light weight material (ii) Adding material to maintain stability 2 Drainage (i) Surface drain (ii) Sub- surface drain (iii) Vegetation (iv) Drainage galleries 3 Retaining Structures (i) Gravity retaining walls (ii) Crib block walls (iii) Gabion walls (iv) Cantilever R.C.C walls 4 Internal Slope (i) Rock bolts Reinforcement (ii) Soil nailing (iii) Geo-synthetics (iv) Anchors (v) Grouting (vi) Stone/gravel piles/ columns (vii) Vegetation
160 13. MATERIALS AND METHODS
13.1 LABORATORY WORK
The soil samples collected from the field were tested in the Central Testing Laboratory of Institute of Engineering, Pulchok, Lalitpur, Nepal. Standard tests were performed to find the shear strength parameters and classify the soils. Equipment used were: Direct shear test machine, Cassagrande’s device for measuring liquid limit, Sieves, Hydrometer, Oven, Weighing machine (electronic and mechanical), Measuring cylinder, Mixer, Thermometer, Pans, Tray, Spatula, Flask etc. Different tests performed are: 1. Direct shear test to determine of the soil strength parameters (cohesion and internal friction angle) 2. Grain size distribution (sieve analysis and hydrometer analysis) 3. Liquid and plastic limit 4. Water content 5. Specific gravity
13.1.1 GRAIN SIZE DISTRIBUTION ANALYSIS
Samples collected from the site contained both finer (d<75μm) and coarser grain (d> 0.75μm) soil grains. Both sieve analysis and hydrometer analysis were carried out because the content of finer grains exceeded 5 % of the total soil sample.
13.1.2 SIEVE ANALYSIS
About 500 gm of oven dried soil sample were sieved through the standard sieves of size (25.5 mm, 9. mm, 2.7 mm, 9.52 mm, 4.76 mm, 2 mm, 0.84 mm, 420 μm, 250 μm, 50 μm, 75 μm). Both dry and wet sieve methods were followed. Samples with more fine particles were sieved using wet analysis method. Washing for wet method was carried out using distilled water.
13.1.3 HYDROMETER ANALYSIS
For finer particles (d< 75 μm), hydrometer was used to find the grain distribution. About 50 gm of samples smaller than75 μm were taken and soaked in distilled water for 24 hours. Before soaking, it was well mixed with the dispersing agent sodium hexa-
161 metaphosphate. Then the solution was poured in a 1 liter cylinder and mixed. Then hydrometer tube was inserted into the solution and the readings were taken in (30 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 8 minutes 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours and 24 hours). Temperature of the solution was noted for each hydrometer reading using thermometer.
It works on the principle that the settling time of the grains is proportional to the diameter of the particles. The heavier and bigger particles settle earlier. As time elapses, the hydrometer goes deeper and gives smaller reading. The reading we take consists of some error such as meniscus error and it needs to be corrected. The correction was made. The calibration curves for the hydrometer were used. Using those calibration curves the grain distribution was determined.
13.1.4 SOIL DENSITY TEST
The density of the soil grain was determined using a calibrated pycnometer. The density is the ratio of mass and volume contained by the soil. This test was performed by measuring the mass of the pycometrer and soil as given below.
M1 = dry mass of the soil sample used in pycnometer
M2 = mass of pycnometer with test sample and distilled water
M3 = mass of filled up pycnometer with water
Ρw = density of water
Density of soil =
13.1.5 ATTERBERG LIMITS
Atterberg limits are a measure of water contents of a fine grained soil. There are three types of Atterberg limits: i) Liquid limit, ii) Plastic limit and iii) Shrinkage limit.
13.1.6 LIQUID LIMIT
The liquid limit is the water content at which the clayey soil changes from plastic to liquid. Cassagrande device, grooving tools are the tools used in this test. At first, water
162 was added to the soil and made a paste. Then it was put in the cup of Cassangrande device and the surface was leveled to 1 cm thick at the middle of the sample. Grove was made using the Cassagrande grooving tool with thickness of 2 mm at the bottom, 11 mm at the top and depth of 8 mm. The cup was then repeatedly dropped from about 10 mm onto the hard rubber base at a rate of 120 blows per minute. Due to the impact the grove closed up gradually. The number of blows for the groove to close was recorded. This was done four times varying the water content. The water content at 25 blows was the liquid limit of the given soil sample.
13.1.7 PLASTIC LIMIT
Plastic limit is the water content at which the soil changes from semi solid to plastic or vice-versa. It was determined by rolling out a thread of the fine portion of the soil on a flat and non-porous surface. A plastic soil retains its shape to a very narrow diameter. The soil sample was mixed with water and a paste was made. The sample was remolded and the test was repeated. The sample was made to fail at diameter of 3 mm. The water content at that point was the plastic limit of the given soil sample.
13.1.8 DIRECT SHEAR TEST
Direct shear test is the laboratory method of determining the shear strength parameter of soil. It consists of a mould to cut the soil sample to a size used in the shear box, shear box is used to apply loads on the soil, load arrangements for both normal and shear force and graduate rings to measure the shear force and displacements.
At first, the sample was prepared in a mould and then put in the shear box. Initial readings in the graduated rings was made zero. The vertical load was applied (5 kg, 10 kg and 15 kg) and horizontal displacements and corresponding horizontal forces were noted in regular interval for each load until the soil failed. These measurements were used to plot the stress strain curve of the sample during the loading for the given normal stress. Results of different tests were presented with normal stress as X- axis and shear stress as Y- axis. A linear curve fitting was used. The slope of the line is the internal angle of friction of the soil and the Y-ordinate of the line at zero abscissa gives the cohesion of the soil.
163 13.2 NUMERICAL TOOLS
AUTOCAD and Arc GIS software’s were used to prepare contour map of landslide area. RocScience software was used to prepare mitigation models. It works on strength reduction method. This option was fully automated and used with either Mohr-Coulomb or Hoek-Brown strength parameters.
Material models for rock and soil include Mohr-Coulomb, Generalized Hoek-Brown and Cam-Clay. Powerful new analysis features for modeling jointed rock automatically allow generating discrete joint or fracturing networks according to a variety of statistical models. With new 64-bit and multi-core parallel processing options, program can solve larger and more complex models in shorter times.
13.2.1 MATERIALS MODEL
The soil material model was used as per Unified Soil Classification System (USCS). The failure criterion of the soil model used in the study was mostly assumed as Mohr- Coulomb criterion and the model consisted of following parameters: Ф′ Friction angle, c′ Cohesion, Ψ Dilation angle, E′ Young’s modulus, ν′ Poisson’s ratio and γ Unit weight. Modulus of elasticity, E can be used based on Obrzud and Truty (2012) (Table 13.1 and 13.2).
Table 13.1: Modulus of elasticity of soil based on Obrzud and Truty (2012)
E (Mpa) Granular material USCS Loose Medium Dense GW,SW 38-80 80-160 160-320 SP 010-30 30-50 50-80 GM,SM 007-12 20-Dec 20-30
164 Table 13.2: Modulus of elasticity of soil based on Obrzud and Truty (2012)
Cohesive USCS Very Soft to Soft Medium Stiff to Very Stiff Hard ML 2.5-8 10.0-15 15-40 40-80 ML,CL 1.5-6 6.0-10 10.0-30 30-60 CL 0.5-5 5.0-8 8.0-30 30-70 CH 0.35-4 4.0-7 7.0-20 20-32 OL 0.5-5 OH 0.5-4
The Poisson’s ratio was used according to Foundation Analysis and Design (5th Edition), by Bowles (1996) (Table 13.3). A model generation is one of the important steps in the computational work. The profile of the landslide was prepared using the Google Earth and GIS. The domain selection was crucial part of the work. Both numerical accuracy and stability were based on numerical and computation accuracy impact factors. The boundary condition as per the site was used in 2D domain. The profile from all the sides except the above free surface was restrained in both directions in 2D analysis.
Table 13.3: Poisson's ratio of soil by Bowles(1996)
Clay Saturated 0.4 to 0.5 Clay Unsaturated 0.1 to 0.3 Sandy Clay 0.2 to 0.3 Silt 0.3 to 0.35 Sand, Gravelly Sand Rock 0.1 to 0.3 Loess 0.1 to 0.3 Most Clay soils 0.4 to 0.5 Saturated clay soils 0.45 to 0.5 Chesionless, medium and dense 0.3 to 0.4 Cohesionless, loose to medium to 0.35
165 13.2.2 BACK ANALYSIS
Sometimes, the soil samples collected in the site are not the representative samples due to the limitation of resources and the equipment of the laboratory. The shear strength parameters obtained from the laboratory may not be as per the site condition. In order to check and make a correction in the laboratory work, back analysis is necessary. Back analysis is an approach used in geotechnical engineering to estimate material parameters. A conservative design assumption will be more innovative when used in back analysis because the calculated strength in back analysis would be overestimated. Although this is one of the common approaches to estimate the shear strength but sometimes it may lead to misinterpretation of strength. Factors influencing the interpreted shear strength during back analysis are:
1. Soil is heterogeneous and to accurately back-calculate the strength, the strength of all other materials must be known. 2. The slip surface analyzed and the actual rupture surface must be the same. The back analysis was done making an assumption that the soil is cohesionless and the friction angle is varied. It was performed with hit and trial method in RocScience programs to find the respective Strength Reduction Factor (SRF). The results of the back analysis was correlated with the laboratory results and also compared with the studies made by Roadside geotechnical problems: a practical guide to their solution GoN, 2007.
166 14. SITE SPECIFIC MITIGATION DESIGNS OF LANDSLIDES
The study was carried out on 16 major landslides of Chure region of ten districts. The selection of landslides from Chure area was done based on the different geographical region, vulnerability of landslide and its quick mitigation action needed. This section presents the detail laboratory analysis and mitigation designs of the Setebhir Landslide as a sample case. The location of the landslides is given in Table 14.1. Details of other landslides are given in Annex 6.1 to Annex 6.4.
Table 14.1: Location of major landslides of Chure area in selected districts
S. N. Name of Landslide District Remarks
1 Setibhir landslide (Profile 1 and 2) Makawanpur
2 Betini landslide (Profile 1) 3 Simalchour landslide (Profile 1) Bara 4 Ahale landslide Sindhuli
5 Athuwa Khola Landslide (Profile 1 & Profile 2)
6 Chakmake landslide 7 Chiyabari Landslide (Profile-1)
Chiyabari Landslide (Profile-2
8 Thulitar, Rampur landslide
10 Kamalamai River bank landslide (L52) 11 Dumsidanda landslide Mahottari
12 Patharkot landslide Sarlahi
13 Solighopte landslide Siraha
14 Gad Khola, Dumsidada Dhanusha 15 U6 landslide Udayapur
16 U13 landslide
167 14.1 SETEBHIR LANDSLIDE
The Setevir landslide, at the origin of Setevir Khola, is one of the most active landslides in the study area. It is located in Makawanpur District at Shripur Chhattiwan VDC. The landslide was initiated more than 50 years ago. The landslide was observed in the mudstone and siltstone of Lower Siwaliks. The detail geological characteristics of this landslide are given in Annex 3.2. The photograph is given in Figure 14.1. Some geotechnical properties of the site as obtained from laboratory analysis are presented in the following section.
Figure 14.1: Photograph of Setebhir landslide
14.1.1 PARTICLE SIZE DISTRIBUTION
Results obtained from the five samples collected from the different landslide sites are tabulated in Table 14.2 and the particle size distribution curve is given in Figure 14.2. Other details are listed in Annex 6.1.
168 Table 14.2: Sieve analysis sheet, Setebhir landslide section-1
SIEVE ANALYSIS
Project: Landslide: Setevir Location: Makawanpur Section: (1-1) Name of Lab test date : researcher: Weight of sample taken 486 gm S.N Observation Calculation Sieve size, mm Soil retained Percentage Cumulative Percentage (gm) retained percentage finer retained 1 4.750 9.2 1.893 1.893 98.107 2 2.000 33.9 6.975 8.868 91.132 3 0.840 49.4 10.165 19.033 80.967 4 0.420 19.9 4.095 23.128 76.872 5 0.250 23.9 4.918 28.045 71.955 6 0.149 9.7 1.996 30.041 69.959 7 0.075 83.6 17.202 47.243 52.757 8 pan 256.4 52.757 100.000 0.000 486 100.000
169
Figure 14.2: Grain size distribution curve of soil sample of Setevir landslide section-1
14.1.2 SPECIFIC GRAVITY OF SOIL
The specific gravity of soil was determined by using a pycnometer. Specific gravity is the ratio of the mass of unit volume of soil at a stated temperature to the mass of the same volume of gas-free distilled water at a stated temperature. The specific gravity of a soil was used in the phase relationship of air, water, and solids in a given volume of the soil. Specific Gravity G is defined as the unit weight of dry soil with respect to unit weight of water at 4oC and unit atmospheric pressure at normal temperature. This test was performed to determine the water (moisture) content of soils.
=( – )(( – )– ( – ))
Where, G= Specific gravity of solids
M1=mass of empty Pycnometer
M2= mass of the Pycnometer with wet soil
M3= mass of the Pycnometer and soil filled with water
M4 = mass of Pycnometer filled with water only
170 Commercially available fly ash named ‘Pozzoplus’ was used as additives in addition with black soil. Before using, the fly ash was pulverized and sieved through sieve no. 200. The specific gravity test results for three landslides which are studied in detail are presented in Table 14.3. Table 14.3: Specific gravity test sheet of soil samples
SPECIFIC GRAVITY
Project: Landslide: Location: Makawanpur and Bara Section: Name of student: Lab test date : S.N. Observations Landslide
Setibhir Section-1 Section-2 1 Pycnometer No. 13 14 2 Mass of empty 43.9 47.7 pycnometer (M1), gm 3 Mass of pycnometer 59.2 67.3 +wet soil (M2), gm 4 Mass of pycnometer soil 161.25 167.4 filled with water (M3),gm 5 Mass of pycnometer 153.1 156.5 filled with water only (M4),gm Calculations 8 G=(M2-M1) / ((M2-M1)- 2.140 2.253 (M3-M4))
171 14.1.3 ATTERBERG LIMIT
This test is performed to determine the plastic and liquid limits of a fine grained soil. The plastic limit (PL) is the water content, in percent, at which a soil can no longer be deformed by rolling into 3.2 mm diameter threads without crumbling. The plastic limit is the moisture content that defines where the soil changes from a semi-solid to a plastic (flexible) state. The liquid limit is the moisture content that defines where the soil changes from a plastic to a viscous fluid state and these Atterberg limits are also used to classify a fine-grained soil according to the Unified Soil Classification system or AASHTO system. Sample calculation table and graph of the one landslide section is shown in Table 14.4 and Figure 14.3, and the remaining results are listed on Annex 6.1.
Table 14.4: Liquid limit and plastic limit of soil sample of Setebhir landslide section-1
LIQUID LIMIT & PLASTIC LIMIT Tasted By Location : Makawanpur Landslide Setibhir Section (1-1)
S. N. Description Liquid Limit Plastic Limit 1 No of Blows 42 35 22 15 2 Wt of Container+Wet Soil gms 40.02 36.82 42.19 39.01 23.90 24.53 3 Wt of Container+Dry Soil gms 34.63 31.99 35.74 33.01 22.84 23.47 4 Wt of Water Present gms 5.39 4.83 6.45 6.00 1.06 1.06 5 Wt of Empty Container gms 17.34 17.27 17.72 17.72 16.77 17.97 6 Wt of Dry Soil gms 17.29 14.72 18.02 15.29 6.07 5.50 7 Moisture Content % 31.17 32.81 35.79 39.24 17.46 19.27 8 Liquid Limit/Plastic Limit (%) 35.50 18.37
172 Liquid Limit Graph
45.00
40.00 15, 39.24
35.00 22 35 30.00 42 Setibhir section-1 25.00 20.00 Linear 15.00 (Setibhir
% Moisture Content Moisture % 10.00 section-1) 5.00 0.00 0 10 20 30 40 50 No. of Blow
Figure 14.3: Liquid limit plot of soil sample from Setebhir landslide section-1
14.1.4 SHEAR STRENGTH PARAMETERS(C &ɸ)
The shear strength is one of the important engineering properties of a soil, because it is required whenever a structure is dependent on the soil’s shearing resistance. Direct Shear Test was used in this study to find these parameters.
From the plot of the shear stress versus the horizontal displacement, the maximum shear stress is obtained for a specific vertical confining stress. After the experiment is run several times for various vertical-confining stresses, a plot of the maximum shear stresses versus the vertical (normal) confining stresses for each of the tests was produced. From the plot, a straight line approximation of the Mohr -Coulomb failure envelope curve can be drawn, and finally cohesion c and friction angle ɸ can be determined.
Sample of calculation table and graph for three landslides of different locations are listed in Table 14.5 and Figure 14.4, the details of other landslides are given in Annex 6.1.
173 Table 14.5: Shear parameters result of different landslide
S.N Parameters Landslide Setibhir Betini Simalchaur Setction-1 Section-2 Section-1 Setction-1 Section-2 1 C (KN/m2) 8.5 8 7 8 7.15 2 φ degrees 31 30 32 27 25
After calculating the shear parameters, the calculation of friction angle of different section was determined from the method described in Roadside geo-technical problems, a handbook published by Department of Road, Nepal Government. The process of calculating the friction angle is described below and the results are given in Table 14.6 and the corrected friction angles are given in Table 14.7.
Figure 14.4: Normal stress and shear stress plot from data of Setebhir section-1
174 Table 14.14.6: Friction angle without correction obtained from Roadside Geotechnical Handbook Friction Angle without correction (φ0 = A + B + C + D) Landslide Setebhir Section 2 Grain size range Fraction weight (%) Dividers Parameters Quotient (deg.) <0.002 mm 11 7 A 1.57 0.002-0.01 mm 16 5 B 3.20 0.01-0.2 mm 32 3 C 10.67 0.2-60 mm 41 2.5 D1 16.40 >60 mm 0 2.5 D2 0.00 Total 100 sum 31.84 Landslide Setibhir Section 1 Grain size range Fraction weight (%) Dividers Parameters Quotient(deg.) <0.002 mm 18 7 A 2.57 0.002-0.01 mm 14 5 B 2.80 0.01-0.2 mm 26 3 C 8.67 0.2-60 mm 42 2.5 D1 16.80 >60 mm 0 2.5 D2 0.00 Total 100 sum 30.84
Table 14.7: Corrected friction angle from Roadside Geotechnical Handbook Property Criteria Correction (φ effective = φ0 + φ1 +φ2 +φ3) Setibhir Sec-1 Sec-2 Grain shape + 1° for sharp angular grains 1 1 (φ1) ± 0° for medium angular grains - 3° for rounded grains Distribution - 3° for poor gradation (or uniform 0 0 curve (φ2) size) ± 0° for medium gradation + 6° for well distributed grain sizes Compactness - 6° for loose layer of soil 0 0 of soil (φ3) ± 0° for medium loose layer of soil + 6° for compact layer of soil Corrected effective friction angle (φ effective) 31.84 32.84
175 14.2 NUMERICAL MODELING FOR MITIGATION DESIGN
14.2.1 MATERIAL MODEL
The soil material model was prepared as per the Unified Soil Classification System (USCS). The failure criterion of the soil model used in the study is assumed as Mohr- Coulomb criterion and the model consists of following parameters:
Ф′: Friction angle c′:Cohesion
Ψ: Dilation angle (= 0)
E′: Young’s modulus (= 105kN/m2)
ν′: Poisson’s ratio ( 0.3)
γ: Unit Weight
14.2.2 NUMERICAL MODEL Slope geometry, profile and sections best representative for the landslide zone were extracted from the field observation and contours available. Shear strength parameters were obtained from laboratory analysis (Table 14.8). AUTOCAD and Arc GIS software’s was used for the preparation of contour map of landslide area, from which the profiles of different longitudinal sections were prepared. PHASE 2software is a powerful 2D finite element program for soil and rock applications (RS2= Rock and Soil 2-dimensional analysis program). Results in the form of SRF and displacement were plotted. Table 14.8: Summary of laboratory investigation S.N Parameters Landslide Setibhir Sec-1 Sec-2 1 Cohesion, C (KN/m2) 8.5 8 2 Friction angle, φ degrees 31 30 3 Specific Gravity, G 2.140 2.253 4 Liquid Limit, LL (%) 35.50 32.50 5 Plastic Limit, PL (%) 18.37 21.14
176 The detail of the laboratory investigation tables and graphs of different tests are shown on Annex 6.1.
14.2.3 BACK ANALYSIS RESULT
After the long iteration and trails, the friction angle which gives the factor of safety, were determined out and their results are tabulated in Table 14.9.
Table 14.9: SRF value on different landslide sections at different friction angle
SRF Value Obtained at Different Section of Landslide φ value Landslide Setibhir sec-1 sec-2 29 0.89 30 0.91 31 0.85 0.93 32 0.88 0.98 33 0.91 1 34 0.94 35 0.98 36 1 Final value of φ sec-1 sec-2
36 33
Result of back calculation in graph format is shown in graph using Phase2 tools, results of one section is listed below (Figures 14.5to14.8) and other results are shown on Annex 6.2.
177 1.2 Frictional angle 31 1 Frictional angle 34 0.8 Frictional angle 32 0.6 Frictional angle 33 0.4 Frictional
Strength Reduction Factor Reduction Strength angle 35 0.2 Frictional angle 36 0 0 0.5 1 1.5 2 2.5 3 3.5 Maximum Total Displacement [m]
Figure 14.5: Graph of SRF vs maximum displacement of Setibhir landslide section-1
The displacement vectors and maximum shear strain value at different nodes on phase2 software were visualized during back calculation of one section as shown below and other results are shown on Annex 6.2.
Figure 14.6: Deformation vectors at SRF one on Setebhir landslide section-1
178 After getting the friction angle from back analysis, the result of back analysis and laboratory data were compared. The data obtained from both ways were tabulated in Table 14.10.While comparing the result, it was found that the result of BA technique was more satisfactory and reliable which is because the soil sample collected from the site was from the slip mass not from the exact failure surface. Table 14.10: Result comparison of laboratory and BA technique
S.N Methods Parameters Landslide Setibhir Sec-1 Sec-2 1 Laboratory Cohesion, C (KN/m2) 8.5 8 Friction angle, φ degrees 31 30 2 Back Cohesion, C (KN/m2) 8.5 8 analysis Friction angle, φ degrees 36 33
14.2.4 STABILITY ANALYSIS WITH GWT VARIATION This work was focused to determine the FOS of the different landslide section of Chure region considering GWT. By performing the stability analysis in the different sections of three landslides with reduction of GWT, different SRF value of the landslide slope are shown in Table 14.11. Table 14.11: SRF and deformation values at different GWT position S.N GWT position Landslide Setibhir Sec-1 Sec-2 SRF Values 1 Dry condition 1 1 2 Saturated condition 0.27 0.36 3 WT 5m down 0.51 0.54 4 WT 10m down 0.69 0.64 5 WT 12m down 0.7 0.69 6 WT 15m down 0.81 0.74 Deformation values 7 Dry condition 5.94 5.23 8 Saturated condition 7.32 5.67 9 WT 5m down 7.09 5.63 10 WT 10m down 6.95 5.46 11 WT 12m down 6.72 5.39 12 WT 15m down 6.67 5.32
179 The graph plot of SRF versus respective deformations of Setebhir landslide section-1 is shown below. Other plots of graph are shown on Annex 6.2.
Setibhir Section-1
1.2
1 Fully saturated 0.8 WT 15m down 0.6 WT 10m down 0.4 WT 5m down 0.2 Dry
Strength Reduction Factor Reduction Strength condition 0 0 5 10 15 20 Maximum Total Displacement [m]
Figure 14.7: SRF vs displacement of Setebhir landslide section-1 with GWT variation
The deformation vectors and maximum shear strain obtained on saturated condition of Setebhir landslide section 1 with SRF value visualized on phase2 software is shown below. Other respective models are shown on Annex 6.2.
Figure 14.8: Setebhir section-1 model at fully saturated condition and deformation
vectors
180 14.2.4 VERIFICATION OF MODEL
The verification of the work was performed by two ways i.e., verification with different tool and verification with literature. Both ways of verification are described below.
Verification with different tools For the verification with different tool, limit equilibrium method based commercial software ‘Slide’ was selected. For the comparison of result of phase2 and Slide software, the Simalchaur landslide section was selected and analyzed (Table 14.12).
Table 14.12: Result comparison of phase2 and slide software
S.N Tools PHASE2 SLIDE GWT position 1 Dry condition 1 1.102 2 Saturated condition 0.44 0.51 3 WT 5m down 0.74 0.74 4 WT 10m down 0.92 0.85 5 WT 12m down 0.94 0.93 6 WT 15m down 0.96 0.95
Figure 14.9: FOS value on Simalchaur section-1 at normal condition
181 The respective SRF values at different water table position and their models are shown in Annex 6.2.
Correlations between the results obtained from software (PHASE2 and SLIDE) were largely same and were in best correlation (Figure 14.11). The correlation coefficient between the SRF obtained from two methods was 95.64.
1.2
1
0.8
0.6 Phase 2 0.4 Slide
0.2
0 0 0.2 0.4 0.6 0.8 1 1.2
1.2
1
0.8
0.6 PHASE 2
0.4 SLIDE
0.2
0 Dry Saturated WT 5m WT 10m WT 12m WT 15m condition condition down down down down
Figure 14.10: Correlation between phase2 and slide result
182 Verification with literatures The model output with back calculation was verified with the literature “Road Side Geo- Technical Problems: A Practical Guide to their Solutions” which was published by the Department of Roads which is shown on Table 14.13.
Table 14.13: Result from back analysis and Roadside Geo-technical Problems S.N Frictional angle Landslide Setibhir Sec-1 Sec-2 1 Back Analysis 36 33 2 Roadside Geo-technical Problems 31.84 32.84
14.3 MITIGATION MODELS There are various mitigation measures available to control the landslide failure. Among them, the following mitigating measures were selected:
Drainage Management Internal Slope Reinforcement Modification of Slope Geometry
14.3.1 DRAINAGE MANAGEMENT
This section enlightens how GWT reduction helps to increase safety factor. With employing drainage galleries, safety factor can be achieved to that extent. Surface and sub-surface drain can reduce GWT up to 3-4 meters (Table 14.14). Table 14.14: GWT depth to obtain SRF value 1 in different landslides S.N GWT position Landslide Setebhir Sec-1 Sec-2 1 WT 15m down 0.81 0.74 2 WT 16m down 3 WT 17m down 4 WT 18m down 5 WT 20m down 0.92 6 WT 23m down 0.98 7 WT 24m down 1 0.9 8 WT 26m down 0.94 9 WT 28m down 1
183
14.3.2 INTERNAL SLOPE REINFORCEMENT
The effect of vegetation and geosynthetic material can be implemented to enhance internal slope reinforcement. Use of Bio-engineering Root reinforcement effects were considered. The grasses, shrubs and trees were respectively employed in the top, middle and bottom of the landslide slopes. The depth of root zone effect of grass, shrubs and trees were taken as 0.5m, 2m 4m respectively. The vegetative effect was applied on two sections of Setebhir landslide at normal condition and respective increase on factor of safety was noted which is tabulated in Table 14.15.
Table 14.15: SRF Vs deformation at normal soil condition and with vegetation
S.N Conditions Value Landslide Setibhir Sec-1 Sec-2 1 Normal condition SRF 1 1 Deformation 5.94 5.23 2 Vegetation SRF 1.16 1.07 Deformation 2.76 4.54 The model and plot of SRF vs deformation at normal condition and with bioengineering of Setebhir section-1 is shown in Figure 6.12-6.13. Other graphs are shown on Annex 6.2.
Figure 14.11: Landslide model with bioengineering in Setebhir section-1
184 Bioengineering Effect on Setibhir Section-1
1.4
1.2 1 0.8 Normal condition 0.6 0.4 Bioengineering
0.2 Strength Reduction Factor Reduction Strength 0 0 5 10 15 Maximum Total Displacement [m]
Figure 14.12: Increase in SRF with bioengineering in Setebhir section-1
The comparison chart with bioengineering effect is shown in Figure 14.13.
Figure 14.13: Comparison of vegetative effects in different landslide slope sections
From Figure 14.13, the application of bioengineering seems to be more effective on Betini section-1 and Simalchaur section-1 as compared to the other landslide sections.
185 Use of Geosynthetic material The Geosynthetic material can be assigned on Phase2 software by defining geogrid as a liner material. The liner was assigned on landslide slope profile horizontally at equal interval of 2-4 meter. After installment of liner, the sand fill above the geosynthetic was done. After installment of geosynthetic material to all the model of Setebhir landslides, the respective value of SRF was noted after iteration and compared with the normal condition (Figures 14.14, Table 14.16).
Figure 14.14: Application of geosynthetic on landslide model
Table 14.16: SRF value and deformation at normal condition and geosynthetic use
S.N Conditions Value Landslide Setebhir Sec-1 Sec-2 1 Normal condition SRF 1 1 Deformation 5.94 5.23 2 Geosynthetic use SRF 1.06 1.08 Deformation 3.82 5.12
The model and plot of SRF vs deformation at normal condition and with geosynthetic of Setebhir section-1 is shown below (Figures 14.15 – 14.16). Other graphs are shown in Annex 6.2.
186
Figure 14.15: Landslide model with geosynthetic on Setibhir section-1
Geosynthetic use on Setibhir section-1 1.4
1.2 1 0.8 0.6 Normal condition 0.4 Geosynthetic use 0.2 0
Strength Reduction Factor Reduction Strength 0 2 4 6 8 10 12 14 Maximum Total Displacement [m]
Figure 14.16: Geosynthetic effect on Setebhir section-1
From the comparisons given in Figure 14.17, the application of geosynthetic seems to be more effective on Simalchaur section-1 and 2 as compared to the other landslide sections.
187
Figure 14.17: Comparison chart of geosynthetic use and normal soil condition
Modification of slope geometry The slope profiles of Setebhir landslides were modified with simple cut and fill approach. The benching was provided at certain interval one after slope. Then the slope was imported in the phase2 software and respective factor of safety or SRF were computed (Table 14.17, Figures 14.18 – 14.19).
Table 14.17: SRF vs deformation with slope modification
S.N Conditions Value Landslide Setibhir Betini Simalchaur Sec-1 Sec-2 Sec-1 Sec-1 Sec-2 1 Normal SRF 1 1 1 1 1 condition Deformation 5.94 5.23 1.03 1.23 0.7 2 Slope SRF 1.81 1.73 1.28 1.55 1.24 modification Deformation 4.83 5.05 1.4 1.68 0.67
188
Figure 14.18: Effect of slope modification in Setebhir section-1
Figure 14.19: Setevir landslide section-1 with slope modification
Other models and plot of SRF vs. displacement are shown on Annex 6.2.
The effect of slope modification on the Setebhir landslide sections seems to be more effective as compared to the other landslide sections. The comparison is shown in Figure 14.20.
189 Normal profile Slope modification
1.81 1.73 1.55
1.28 1.24 1 1 1 1 1
Setibhir-1 setibhir-2 Betini-1 Simalchaur-1 Simalchaur-2
Figure 14.20: Comparison of slope modification effect
14.3.3 IMPLEMENTATION OF LANDSLIDE MITIGATION MEASURES
In view of previous workouts, landslide slope should be mitigated with following measures. They are: modification of slope geometry, bio-engineering applications, surface and sub-surface drainage and check dams and retaining structures. In case of rock slopes, rock barrier and catchment trench can also used.
For the use of bioengineering, plant selection was done based on the drought factor (relative hotness and dryness). The method of plant selection was done by following the guidelines given on book “Roadside Bioengineering Handbook” published by Department of Roads.
Possible small legumes-grasses: Amliso, Babiyo, Dhonde, Kans, Khar, Kush, Narkat, Nigalo etc.
Possible shrubs/small trees: Areri, Bayer, Bhujetro, Dhanyero, KandaPhul, Keraukosh, Simali, Tilka, etc.
Possible large climbing bamboo: Mai Bans
Possible large trees: Bakaino, Chilaune, Kalosiris, Khanyu, Khayer, Lankuri, Painyu, etc
190 14.4 DESIGN AND COST-ESTIMATION
The design of mitigation measures as listed in Table 14.18 were based on landslide slope stability analysis of some major landslides. The mitigation models as presented in this section were based on both case-site specific and general solutions. Present section also presents the cost estimation of mitigation measures to be implemented in Chure landslides in 54 clusters as per rate analysis of individual items in the present district rates.
Table 14.18: Mitigation design of case and site-specific analysis of 16 major landslides
S.N Name of Normal soil GWT at With mitigating Remarks Landslide condition surface measures 1 Setevir 0.85 1.11(1.32) landslide
The designs of retaining wall, surface drain and check dams are described in Figure 14.17, 14.18 and 14.19 respectively.
Figure 14.21: Setebhir landslide section: 1-1
191
Figure 14.22: Cut and fill provision
Figure 14.23: Bio-engineering measures
192
Figure 14.24: GWT reduction up to 5 m with surface and subsurface drain
Figure 14.25: Providing check dam (top width = 0.5m, height = 4m and bottom width= 1 m)
Figure 14.26: Providing retaining wall (a1= 0.6m, a3= 5m, a4= 0.35m, a5= 0.15m and a6= 1.6m)
193
Figure 14.27: Setebhir landslide section 2-2
Rock fall analysis:
Figure 14.28: Setebhir section 1-1
194
Figure 14.29: Setebhir section 1-1
Figure 14.30: Setebhir section 1-1
195
Figure 14.31: Setebhir section 1-1
Figure 14.32: Setebhir section 2-2
196
Figure 14.33: Setebhir section 1-1
Figure 14.34: Setebhir section 2-2
197
Figure 14.35: Sketch of mitigation measures in Setebhir landslides, Chhatiwan VDC, Makwanpur
The detail cost-estimation format of each landslide cluster is shown in Table 14.19. Based on this table, the cost of whole 53 clusters was calculated as per present district rates.
198 Table 14.19: Cost estimation of landslide mitigation measures
District: Makawanpur Setebhir Landslide
S. N. Mitigation Landslide ID Measures 1 2
Quantity Rate Amount Quanti Rate Amount (NPR) ty (NPR) 1 Slope geometry 0 304 0 0 304 0 modification (sq m) 2 Grass (sq m) 523.25 142.46 74542.2 172.91 142.46 24633.7 7 3 Shrub (sq m) 523.25 161.45 84478.71 172.91 161.45 27917.4 7 4 Tree (sq m) 523.25 161.45 84478.71 172.91 161.45 0 7 5 Wattle fence (rm) 0 1644.45 0 0 1644.45 0
6 Bolster (rm) 733.54 0 733.54 0
7 Surface drain only 0 0 0 0 (rm) Main 0 1104.875 0 0 1104.875 0
Branch 0 941.3625 0 0 941.3625 0
8 Surface and sub- 0 0 surface drain (rm)
Main 97.95 8445.86 827271.6 28.75 8445.86 242818
Branch 216 7870.75 1700082 75 7870.75 590306
9 Check dam (nos) 0 0
Type-I 8 54469.14 435753.1 4 54469.14 326815
Type-II 0 90579.90 0 0 90579.90 0
10 Material trap wall 0 0
Type-I 40224.65 0 40224.65 0
Type-II 66631.91 0 66631.91 0
10 Retaining wall (rm) 0 0
Type-I 40224.65 1407863 40224.65 1005616
199 Type-II 66631.91 0 66631.91 0
11 Barrier and retaining 0 0 wall (rm)
Type-I 35 40224.65 1407863 25 40224.65 1005616
Type-II 66631.91 0 66631.91 0
12 River training 0 0 structure, spur (rm)
Type-I 157490 0 0 157490 0
Type-II 372336.5 0 372336.5 0
Sub-Total 6022332 3223723
Overhead cost @ 15% 903349.8 483558
Total 6925682 3707282
VAT @ 13% of 70% 900338.6 481947 Amount Grand Total 7826020 4189228
Total Amount excluding 15% overhead and 13% VAT 11682306
Total Amount including 15% overhead and 13% VAT 15181156
200
SECTION VII: CONCLUSIONS AND RECOMMENDATIONS
201 15. CONCLUSIONS
The landslides present in the ten districts of Chure area until the study period have been identified and mapped. The working districts belong to the Chure area of central and eastern Nepal namely Bara, Dhanusha, Makawanpur, Mahottari, Rautahat, Sarlahi, Saptari, Siraha, Udayapur and Sindhuli. The distribution of landslides and their activity vary from district to district; the landslide activity and type both are largely controlled by the geological formations. On the basis of numbers of landslides, Makawanpur district is ranked first whereas the lowest number of landslides is seen in Mahottari district. Other districts with high number of landslides are Siraha, Udayapur and Sindhuli. The total area of landslide within the working districts reaches 12.7234 Km2. The GIS based database of each landslide with unique identity named as Landslide Identity (LID) is developed by using the codes of districts, VDCs, ward number and landslide numbers. The landslide inventory maps are produced for each working districts and respective watersheds.
Types and processes of landslides in the Chure area of working district have also been identified. The detail characterization of the landslides within the study area is performed and the database is created. The characterization comprises of geological, geotechnical, hydrological and topographical data. The major cause of the landslides is identified and their impact entities are also evaluated. Based on the distribution of landslides, Middle Siwaliks and Upper Siwaliks are identified as the most potential zones for landslides. Lower Siwaliks and the area of Quaternary deposits are other landslide occurrence zones. However, the type of landslides in these different geological zones varies. Rock slides and rock falls are typical of Middle Siwaliks, whereas debris fall and granular flow are typical of Upper Siwaliks. Earth slide, debris slide and shallow surface failures are typical of Lower Siwaliks. River bank failure typically occurs in quaternary deposits. Most of the landslides are dry during the study time from January to April. In many areas, swarms of landslides have been identified instead of a single big landslide.
Based on the analysis of causative factors, different factor maps were generated in GIS environment and landslide susceptibility maps of the working district are produced. The susceptibility maps are also produced for all the watersheds in the study area. The vulnerability index is calculated based on the analysis of social, economical, physical and environmental vulnerability indicators. Vulnerability analysis depict that majority of the
202 study area are in moderate to high vulnerable zones. The landslide risk assessment is also conducted based on the elements of risk analysis and evaluation of their consequences. The vulnerability and risk maps are produced for all the working districts and the watersheds within these districts.
Landslides that are highly vulnerable and have posed high risk to the people, property and environment are identified and their mitigation model and designs are recommended. Slope stability analysis and back analysis were performed for developing mitigation models. So far as possible, the practicability for implementation and cost effective designs are recommended. The costing for the recommended mitigation measures are calculated based on latest respective district rates. Mitigation models can be grouped into four major categories: modification of slope geometry, drainage management, retaining structures and internal slope reinforcement. Geotechnical references of the landslide sites for the mitigation models are obtained from laboratory analysis of samples collected from the field and the in-situ test using portable machines such as Shear Vane and Pocket Penetrometer. Engineering designs for mitigation works of more than 1000 landslides within 60 clusters of the working districts have been developed.
203 16. RECOMMENDATIONS
As the Chure area of Nepal including that within the working districts are dynamic in nature, the landslide process and activity changes quickly due course of time. As a consequence, the results and outputs obtained from this study may vary if compared after few years. It is therefore strongly recommended that the mitigation models and the designs produced in this study be implemented as soon as possible. Furthermore, the cost of mitigation can vary if the district rate of engineering construction changes. It will be more practical and justifiable if some pilot landslide sites are selected and the responsibility of implementing the mitigation model is given to TU-CDES so that the applicability of the designs can be judged by the people responsible for designing the models, and those sites can become the laboratory sites to enhance the capacity of concerned line agencies. Moreover, it is recommended that similar works are replicated to all the thirty six Chure districts of Nepal.
204
REFERENCES
205 Aleotti, P., and Chowdhury, R. (1999). "Landslide hazard assessment: summary review and new perspectives." Bulletin of Engineering Geology and the environment, 58(1), 21-44. Ayalew, L., and Yamagishi, H. (2005). "The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan." Geomorphology, 65(1), 15-31. Birkmann, J. (2007). "Risk and vulnerability indicators at different scales: applicability, usefulness and policy implications." Environmental Hazards, 7(1), 20-31. Castellanos Abella, E., and Van Westen, C. (2005). "Development of a system for landslide risk assessment for Cuba." hdl: 1874/44080# AGGREGATION CHAPTER 4, 1-10. Cevik, E., and Topal, T. (2003). "GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey)." Environmental geology, 44(8), 949-962. Cosic, D., Popov, S., Sakulski, D., and Pavlovic, A. (2011). "Geo-information technology for disaster risk assessment." Acta Geotech Slov, 1, 65-74. Crozier, M. J., and Glade, T. (2006). "Landslide hazard and risk: issues, concepts and approach." Landslide hazard and risk. Wiley, West Sussex, 1-40. Dahal, R. K., Hasegawa, S., Bhandary, N. P., Poudel, P. P., Nonomura, A., and Yatabe, R. (2012). "A replication of landslide hazard mapping at catchment scale." Geomatics, Natural Hazards and Risk, 3(2), 161-192. Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S., and Paudyal, P. (2008). "Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence." Geomorphology, 102(3), 496- 510. Dai, F., Lee, C., Li, J., and Xu, Z. (2001). "Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong." Environmental geology, 40(3), 381- 391. Dangol, V. (2009). "Status of Landslide Hazard Mapping in Nepal." Dhakal, S. (2015). Evolution of Geomorphologic Hazards in Hindu Kush Himalaya, In: Shaw, R. & Nibanupudi, H.K (Eds.), Mountain Hazards and Disaster Risk Reduction, Springer Japan, DOI 10.1007/978-4-431-55242-0_4. Dhakal, S. (2014). Geological Divisions and Associated Hazards in Nepal, In: Khadka, U.R. (Eds.), Contemporary Environmental Issues and Methods in Nepal, Central Department of Environmental Science, Tribhuvan University Nepal, pp. 100-109. Dhakal, S. (2012). Fundamentals of Geology, A Reference book for students of Geology, Civil Engineering, Environmental Science and Forestry, Kalyani Subedi Publishers, 167p.
206 Dominey-Howes, D., Dunbar, P., Varner, J., and Papathoma-Köhle, M. (2010). "Estimating probable maximum loss from a Cascadia tsunami." Natural Hazards, 53(1), 43-61. Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z. (2008). "Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning." Engineering Geology, 102(3), 99-111. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., and Lewis, B. (2011). "A Social Vulnerability Index for Disaster Management." Journal of Homeland Security and Emergency Management, 8(1). Ghimire, M. (2010). "Landslide occurrence and its relation with terrain factors in the Siwalik Hills, Nepal: Case study of susceptibility assessment in three basins." Natural Hazards, 56, 21. Glade, T., Crozier, M., and Smith, P. (2000). "Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model”." Pure and Applied Geophysics, 157(6-8), 1059-1079. Griffiths, D., Huang, J., and Dewolfe, G. F. (2011). "Numerical and analytical observations on long and infinite slopes." International Journal for Numerical and Analytical Methods in Geomechanics, 35(5), 569-585. Griffiths, D., and Lane, P. (1999). "Slope stability analysis by finite elements." Geotechnique, 49(3), 387-403. Hasegawa, S., Dahal, R. K., Yamanaka, M., Bhandary, N. P., Yatabe, R., and Inagaki, H. (2009). "Causes of large-scale landslides in the Lesser Himalaya of central Nepal." Environmental geology, 57(6), 1423-1434. Haugen, E., and Kaynia, A. (2008). "Vulnerability of structures impacted by debris flow." Landslides and engineered slopes. Taylor & Francis, London, 381-387. ISDR, U. (2009). "Making Disaster Risk Reduction Gender-Sensitive Policy and Practical Guidelines". City. Kayastha, P., Dhital, M. R., and De Smedt, F. (2013). "Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal." Journal of the Geological Society of India, 81(2), 219-231. Kaynia, A., Papathoma-Köhle, M., Neuhäuser, B., Ratzinger, K., Wenzel, H., and Medina-Cetina, Z. (2008). "Probabilistic assessment of vulnerability to landslide: application to the village of Lichtenstein, Baden-Württemberg, Germany." Engineering Geology, 101(1), 33-48. Khanal, N. R. (2008). GLOF Risk Assessment in Bhotekosi/Sunkosi Basin. Integrated Water and Hazard Management International Centre for Integrated Mountain Development, Kathmandu. Mezughi, T., Akhir, J. M., Rafek, A. G., and Abdullah, I. (2011). "A Multi-class Weight of Evidence Approach for Landslide Susceptibility Mapping Applied to an Area Along the E-W Highway (Gerik – Jeli), Malaysia." EJGE, 16, 14.
207 MoE. (2010). "Climate Change Vulnerability Mapping for Nepal". City: Kathmandu, Nepal. Nyaupane, G. P., and Chhetri, N. (2009). "Vulnerability to climate change of nature- based tourism in the Nepalese Himalayas." Tourism Geographies, 11(1), 95-119. Oven, K. (2009). Landscape, livelihoods and risk: community vulnerability to landslides in Nepal, Durham University. Papathoma-Köhle, M., Kappes, M., Keiler, M., and Glade, T. (2011). "Physical vulnerability assessment for alpine hazards: state of the art and future needs." Natural Hazards, 58(2), 645-680. Samir, K. (2013). "Community vulnerability to floods and landslides in Nepal." Ecology and Society, 18(1), 8. Siddle, H., Jones, D., and Payne, H. "22. Development of a methodology for landslip potential mapping in the Rhondda Valley." Presented at Slope stability engineering developments and applications: Proceedings of the international conference on slope stability organized by the Institution of Civil Engineers and held on the Isle of Wight on 15–18 April 1991. Sudmeier-Rieux, K., Jaquet, S., Derron, M.-H., Jaboyedoff, M., and Devkota, S. (2012). "A case study of coping strategies and landslides in two villages of Central- Eastern Nepal." Applied geography, 32(2), 680-690. Van Den Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., and Poesen, J. (2009). "Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium." Nat Hazards Earth Syst Sci, 9(2), 507-521. Van Westen, C. (1994). "GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia." Mountain Environments and Geographic Information Systems. Taylor & Francis, Basingstoke, UK, 135-165. Van Westen, C. J., Van Asch, T. W. J., and Soeters, R. (2006). "Landslide hazard and risk zonation—why is it still so difficult?" Bullettin of Engeneering Geology Environment, 65, 17. Wisner, B., and Luce, H. R. (1993). "Disaster vulnerability: scale, power and daily life." GeoJournal, 30(2), 127-140. Zillman, J. W. (1999). "The national meteorological service." Bulletin of the World Meteorological Organization, 48(2), 129-158.
208
209
GLOSSARY
210 Adaptation The adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities Alluvial Soil The soil materials deposited by the action of fluvial (river/streams) action. Anchors An engineering device designed to support structures especially driven into ground. Angle of Dip It is the angle between the rock bed/strata and horizontal plane. Anticlines These are the inverted cup shaped geological structures with older strata towards the central portion. Aspect The positioning of a structure in a particular direction. Atterberg Limits The Atterberg limits are a basic measure of the critical water contents of a fine-grained soil, such as its shrinkage limit, plastic limit, and liquid limit. Attributes These are the characteristics and information of a field or thing. AUTOCAD AutoCAD is a commercial software application for 2D and 3D computer- aided design (CAD) and drafting. Base maps The primary maps which are taken as references or considerations for certain tasks or works. Bedding Plane The line of contact which separates one rock type from another. Bio-Engineering Application of biological components for aiding the engineering structures or acting as the measures themselves. Bivariate Involvement or dependency upon two variables. Check Dams A check dam is a small device constructed of rock, gravel bags, sandbags, fiber rolls, or other proprietary product placed across a natural or man-made channel or drainage ditch. Cluster Group of similar things situated close to each other. Cohesion The property that causes materials to get attached to each other. Colluvial Soil The soil materials deposited by the action of the gravity i.e. downslope movement of the materials. Compression The capacity of a material or structure to withstand loads tending to Strength reduce size, as opposed to tensile strength, which withstands loads
tending to elongate. Conglomerate The sedimentary rock formed by the consolidation and lithification of gravel.
211 Crib wall A crib wall, concrete or timber, is a gravity retaining structure that consists of interlocking concrete or timber elements. Database A collection of information that is organized so that it can easily be accessed, managed, and updated. Differential Weathering that occurs at different rates, as a result of variations in Weathering composition and resistance of a rock or differences in intensity of weathering, and usually resulting in an uneven surface where more resistant material protrudes above softer or less resistant parts. Digital Elevation A digital model or 3D representation of a terrain's surface. Model Disaster A serious disruption of the functioning of a community or society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources. Economic Economic vulnerability is defined as the exposure of an economy to vulnerability exogenous shocks, arising out of economic openness, while economic resilience is defined as the policy-induced ability of an economy to withstand or recover from the effects of such shocks. Elements at risk The population, buildings and engineering works, economic activities, public services utilities, infrastructure and environmental features in the area potentially affected by landslides Environmental Degree to which environmental factors such as biodiversity, trees, rivers vulnerability and their deposits etc are susceptible to harm, degradation or destruction on being exposed to a hostile agent or factor. Erosion A type of weathering in which surface soil and rock are worn away through the action of glaciers, water, and wind. Exposure The state of having no protection from something harmful. Factor Maps Primary maps or base maps used for preparing secondary maps or analysis. Factor of Safety Factor of safety (FoS), also known as (and used interchangeably with) safety factor (SF), is a term describing the load carrying capacity of a system beyond the expected or actual loads. Fault In geology, a fault is a planar fracture or discontinuity in a volume of rock, across which there has been significant displacement as a result of rock mass movement.
212 Friction Angle The angle of a plane to the horizontal when a body placed on the plane will just start to slide. Gabion wall A cage, cylinder, or box filled with rocks, concrete, or sometimes sand and soil for use in civil engineering, road building, military applications and landscaping. Geomorphology The scientific study of the origin and evolution of topographic and bathymetric features created by physical, chemical or biological processes operating at or near the Earth's surface. Geospatial Relating to or denoting data that is associated with a particular location. Geosynthetics Synthetic products used to stabilize terrain including eight main product categories: geotextiles, geogrids, geonets, geomembranes, geosynthetic clay liners, geofoam, geocells and geocomposites. GIS A geographic information system or geographical information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. GPS The Global Positioning System (GPS) is a space-based navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites. Grouting Process of filling gaps by a particularly fluid form of concrete. Hazards A condition with the potential for causing an undesirable consequence. The description of landslide hazard should include the location, volume (or area), classification and velocity of the potential landslides and any resultant detached material, and the probability of their occurrence within a given period of time. Head Erosion Headward erosion is a fluvial process of erosion that lengthens a stream, a valley or a gully at its head and also enlarges its drainage basin.
Himalayan Frontal The thrust separating the Chure Region from the Tarai Region. Thrust Hydrological factors Factors that are related to the movement, distribution and quality of water. Index map Index maps are types of finding aid that allow users find a set of maps covering their regions of interest along with the name or number of the relevant map sheet. Inventory List or managed catalog of a particular thing.
213 Landslides The downward falling or sliding of a mass of soil, detritus, or rock on or from a steep slope due to various reasons. Latitude The angular distance of a place north or south of the earth's equator, or of the equator of a celestial object, usually expressed in degrees and minutes. Liquid Limits The moisture content at which soil begins to behave as a liquid material and begins to flow. Longitude The angular distance of a place east or west of the Greenwich meridian, or west of the standard meridian of a celestial object, usually expressed in degrees and minutes. Mitigation Model Plans, policies and structures prepared for reducing risk of loss from the occurrence of any undesirable event. Multi-criteria Multi-criteria analysis is generally defined as a decision-aid and a Analysis mathematical tool allowing the comparison of different alternatives or scenarios according to many criteria, often conflicting, in order to guide the decision maker towards a judicious choice. Physical Degree to which physical factors such as roads, house, transmission line, Vulnerability pipeline etc are susceptible to harm, degradation or destruction on being exposed to a hostile agent or factor. Plastic Limits The moisture content at which soil begins to behave as a plastic material.
Pocket The Pocket penetrometer is a device that is specifically used to determine Penetrometer the penetration resistance of top layers (measuring depth 5 mm) and of samples in the field or in the laboratory. Poisson Ratio Poisson's ratio, named after Siméon Poisson, also known as the coefficient of expansion on the transverse axial, is the negative ratio of transverse to axial strain. When a material is compressed in one direction, it usually tends to expand in the other two directions perpendicular to the direction of compression. This phenomenon is called the Poisson effect. Poisson's ratio is a measure of this effect. Projection A projection is one of many methods used to represent the 3-dimensional surface of the earth or other round body on a 2-dimensional plane in cartography. Quaternary Deposit The deposits from the geological time ranging from 2.588 ± 0.005 million years ago to the present.
214 Residual Soil Residual soils are soils that develop from their underlying parent rocks and have the same general chemistry as those rocks. The soils found on mesas, and plains are residual soils Retaining Wall Retaining walls are structures that support a vertical face of earth, usually a cut or fill face. Risk A measure of the probability and severity of an adverse effect to health, property or the environment. Risk is often estimated by the product of probability and consequences. River Training ‘River training’ refers to the structural measures which are taken to improve a river and its banks. River training is an important component in the prevention and mitigation of flash floods and general flood control, as well as in other activities such as ensuring safe passage of a flood under a bridge Rock Bolts A rock bolt is a long anchor bolt, for stabilizing rock excavations, which may be used in tunnels or rock cuts. It transfers load from the unstable
exterior, to the confined (and much stronger) interior of the rock mass.
Shear Strength In engineering, shear strength is the strength of a material or component against the type of yield or structural failure where the material or component fails in shear. Siwaliks Siwalik is a highland region between the Mahabharat and Chure mountain ranges in Nepal. Slope stability Slope stability analysis is performed to assess the safe design of human- analysis made or natural slopes (e.g. embankments, road cuts, open-pit mining, excavations, landfills etc.) and the equilibrium conditions. Slope stability is the resistance of inclined surface to failure by sliding or collapsing.
Social vulnerability Social vulnerability refers to the inability of people, organizations, and societies to withstand adverse impacts from multiple stressors to which they are exposed. Soil nailing Soil nailing is a construction technique that can be used as a remedial measure to treat unstable natural soil slopes or as a construction technique that allows the safe over-steepening of new or existing soil
slopes. Spatial multicriteria Spatial multicriteria decision making refers to the application of decision multicriteria analysis in spatial context where alternatives, criteria and other elements of the decision problem have explicit spatial dimensions.
215 Specific Gravity Specific gravity is the ratio of the density of a substance to the density of a reference substance, equivalently; it is the ratio of the mass of a substance to the mass of a reference substance for the same given volume. Spot Heights The altitude of a point shown on a map for convenience. Strength The capacity of an object or substance to withstand great force or pressure. Stress Force applied per unit area. Subsurface drainage A subsurface drainage system consists of a surface or subsurface outlet and subsurface main drains and laterals. Subsurface drainage is used where the soil is permeable enough to allow economical spacing of the drains and productive enough to justify the investment. Surface drainage Surface drainage is the removal of water that collects on the land surface. A surface drainage system consists of shallow ditches and should include land smoothing or land grading. Susceptibility The property of likely or liable to be influenced or harmed by a particular thing Susceptible Likely or liable to be influenced or harmed by a particular thing Terrace Step-like landform consisting of a flat or gently sloping geomorphic surface, called a tread that is typically bounded one side by a steeper ascending slope. Toe Cutting The process of eroding or removal of the lower portion of the slope. Topography The arrangement of the natural and artificial physical features of an area.
Unconsolidated The rocks which didn't get enough pressure and temperature to get hard Bedrocks enough or properly lithified. Upper Siwaliks A geological Formation in the Chure Region mainly consisting of the gravelly conglomerates in the lower section and boulder conglomerates in the upper section with interbedding of mudstones and sandstones in some places. Vulnerability The degree of loss to a given element or set of elements within the area affected by the landslide hazard. It is expressed on a scale of 0 (no loss) to 1 (total loss). Watershed An area or ridge of land that separates waters flowing to different rivers, basins, or seas.
216 Wattle Fence Fence prepared by lightweight construction material made by weaving thin branches (either whole, or more usually split) or slats between upright stakes to form a woven lattice Youngs Modulus A mechanical property of linear elastic solid materials which defines the relationship between stress (force per unit area) and strain (proportional deformation).
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