Thesis Pena Rincon3

Transcription

Thesis Pena Rincon3

Modelling Shallow Landslides
A case study from the Moulin catchment, Draix,
South French Alps
Gina Ibeth Peña Rincón
March, 2008
Course Title:
Geo-Information Science and Earth Observation
for Environmental Modelling and Management
Level:
Master of Science (Msc)
Course Duration:
September 2006 - March 2008
Consortium partners:
University of Southampton (UK)
Lund University (Sweden)
University of Warsaw (Poland)
International Institute for Geo-Information Science
and Earth Observation (ITC) (The Netherlands)
2006-08
GEM thesis number:
ii
Modelling Shallow Landslides
A case study from the Moulin catchment, Draix, South French Alps
by
Gina Ibeth Peña Rincón
Thesis submitted to the International Institute for Geo-information Science and Earth
Observation in partial fulfilment of the requirements for the degree of Master of
Science in Geo-information Science and Earth Observation for Environmental
Modelling and Management
Supported by the Programme Alβan, the European Union Programme of High Level
Scholarships for Latin America, scholarship No.: E06M103305CO
Thesis Assessment Board
Dr.Andrew Skidmore (Chair)
Prof. Dr. Dinand Alkema
Dr. Kasia Dabrowska-Zielinska
Dr. Cees van Westen
Prof. Dr.Victor Jetten
International Institute for Geo-Information Science and Earth Observation
Enschede, The Netherlands
iii
Disclaimer
This document describes work undertaken as part of a programme of study at
the International Institute for Geo-information Science and Earth Observation.
All views and opinions expressed therein remain the sole responsibility of the
author, and do not necessarily represent those of the institute.
iv
Abstract
Due to the high frequency and low impact of shallow landslides, their occurrence
and characteristics are difficult to study. In general they are not recorded in landslide
inventories and the assessment of their influence in other physical processes such as
erosion or change in the relief are usually taken for granted. This work attempts to
provide with an assessment of contributing factors and their incidence in the
occurrence of shallow landslides in black marls, or "terres noires" through the study
of a small experimental test; the Moulin catchment located at Draix, Southern
France.
To achieve this goal, the influence of different physical factors was analysed,
existing data and new measurements were optimized for producing basis data maps;
and a comprehensive methodology was applied to assess landslide susceptibility. A
statistic approach was applied to estimate the relative contributions of the factors
responsible for instability and assess the landslide susceptibility. The study was
divided in six main stages: landslide inventory update, geological characterization,
relief characterization, soil depth modelling, indirect susceptibility modelling and
finally, the model is validated by comparing the results with the landslide inventory.
1
Acknowledgements
I would like to start my acknowledgements by thanking the people who have made it
possible for me to come this far and made all of this possible.
To my supervisors Dr. Cees van Westen and Dr. Jean Phillipe Malet, I am deeply
grateful for your enormous support and making this research a reality. Your
patience, guidance and constructive criticism have helped me bring my work to
paper. My many thanks and gratitude towards Dr. Didier Hantz for having the time
to go through the measurement and interpretation of the geological structure of my
study area. I am very thankful to you, for helping me learn so much.
I thank Sebastian Koltz for the excellent the support he gave me during my stay in
Draix, France for the fieldwork. I thank you for your wonderful and warm
hospitality and inviting me to your home. I had the greatest pleasure in meeting your
family and spending time with you.
I would also like to thank Samuele Segoni for taking out the time to help me run the
soil depth model in Florence, Italy. I also appreciate the efforts put in by Giacomo
Falorni for making all the necessary arrangements in Florence, for the length of my
duration there. Thank you so much, for making it possible!
To Sekhar Lukose Kuriakose and Enrique Castellanos, I thank them for their
fantastic support and guidance through the last phases of my work. I am grateful for
the time and effort they have put in towards guiding me in the modelling phase and
their immense support during the finishing of the thesis.
A mamá y a Liz: gracias por estar siempre ahí para mi aunque haya todo un océano
de por medio. A Francisco, gracias por tu apoyo y ánimo siempre presentes. Saili:
thank you for always being there for me, and for your amazing daal. A Pilar y
Fernando gracias por los tips técnicos y ayuda tan oportunamente brindados.
To all my GEM classmates, thank you for the opportunity to get to know you all in
this course and for all the fun moments.
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Table of contents
1.
Introduction ............................................................................................ 8
1.1.
Research Problem ......................................................................... 8
1.2.
Background And Significance ...................................................... 9
1.3.
Research Objectives And Research Questions ........................... 11
1.3.1. AIM ......................................................................................... 11
1.3.2. SPECIFIC OBJECTIVES, RESEARCH QUESTIONS AND
ASSOCIATED METHODS AND MATERIALS ........................................... 11
1.4.
Research Hypothesis................................................................... 13
1.5.
Research Approach ..................................................................... 13
1.5.1. CONCEPTUAL RESEARCH APPROACH ..................................... 13
1.5.2. METHODOLOGICAL RESEARCH APPROACH ............................ 15
2. Study Area............................................................................................ 16
3. Relief Characterisation ......................................................................... 22
3.1.
Slope ........................................................................................... 22
3.2.
Aspect ......................................................................................... 22
3.3.
Curvature .................................................................................... 22
3.4.
Equal aspect and slope units ....................................................... 23
4. Landslide Inventory.............................................................................. 27
4.1.
Superficial instability processes in the Moulin basin.................. 27
4.1.1. EROSION AND SOLID TRANSPORT PROCESSES ....................... 27
4.1.2. TRANSLATIONAL SLIDES......................................................... 29
4.1.3. ROCK SLIDES AND DEBRIS SLIDES .......................................... 30
4.1.4. SUPERFICIAL SEDIMENT FLOWS .............................................. 32
4.2.
Field work ................................................................................... 32
4.3.
The landslide database ................................................................ 33
4.4.
Characteristics of the landslides in the Moulin catchment.......... 40
5. Geological characterisation .................................................................. 42
5.1.
“Terres Noires” ........................................................................... 42
5.2.
Fieldwork .................................................................................... 43
5.3.
Methodology and resluts............................................................. 44
6. Soil depth measurement and modelling ............................................... 52
6.1.
Composition of the superficial weathered layer.......................... 52
6.2.
Determination of superficial soil thickness................................. 53
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6.3.
Field test campaign ..................................................................... 55
6.4.
Test results .................................................................................. 56
6.5.
Soil depth modelling ................................................................... 60
7. Landslide indirect susceptibility Analisys............................................ 64
7.1.
The landslide suceptibility analysis model ................................. 64
7.2.
Analysis procedure...................................................................... 65
7.2.1. CALCULATION OF WEIGHT MAPS ............................................ 66
7.2.2. CALCULATION OF SUCCESS RATE AND PREDICTION RATE ...... 73
7.3.
Analysis of results and limitations:............................................. 78
8. Conclusions .......................................................................................... 79
9. References ............................................................................................ 83
10.
Apendices ........................................................................................ 89
APPENDIX 1 Landslides evolution maps ............................................... 89
APPENDIX 3. Landslides stereonets....................................................... 97
APPENDIX 4 – ...................................................................................... 106
APPENDIX 5 PROFILES OF PENETROMETER TEST CLASSIFIED
................................................................................................................ 123
APPENDIX 6 ......................................................................................... 131
Weight of evidence model formulation.................................................. 131
10.1.1. PRIOR PROBABILITY .............................................................. 131
10.1.2. CONDITIONAL PROBABILITY ................................................. 131
10.1.3. POSITIVE AND NEGATIVE WEIGHTS....................................... 132
10.1.4. CALCULATION OF FINAL WEIGHTS AND CONTRAST FACTORS
134
4
List of figures
Figure 1-1. Conceptual Research Approach................................................. 14
Figure 1-2. Methodological Research Approach.......................................... 15
Figure 2-1. Location of the Draix experimental basins. ................................
Figure 2-2. 3D view of the Moulin Catchment. ........................................... 17
Figure 2-3. Superficial soil in the Moulin catchment................................... 20
Figure 2-4. Land use and vegetation cover in the Moulin catchment .......... 21
Figure 2-5 General view of the Moulin catchment ...................................... 21
Figure 3-1 Slope Map................................................................................... 24
Figure 3-2 Aspect map and Figure 3-3 Curvature map................................ 25
Figure 3-4 Equal slope-aspect units (30) and Figure 3-5 Equal slope-aspect
units (9) ........................................................................................................ 26
Figure 1-1. Debris landslide in the Moulin Catchment…………………………29
Figure 1-2 Debris slide in the Moulin catchment………………………………..31
Figure 1-3. Rock slides in the Moulin Catchment……………………………….31
Figure 1-4. Landslide database form for each landslide……………………35
Figure 1-5. Legend used in the database form to describe landslide evolution.
Figure 1-6. Landslides map………………………………………………...39
Figure 1-7. Landslide type distribution……………………………………….….40
Figure 1-8. Landslide Volumes. ……………..……………………………….….40
Figure 1-9. log Landslide Volumes ……………………...……………………….40
Figure 1-10. Number of landslides of different depths………………………….41
Figure 1-11. Number of landslides of different slope configuration……………41
Figure 5-1. Geological Measurements. ........................................................ 43
Figure 5-2. Geological Structure. ................................................................. 47
Figure 5-3. Bedding Strike. .......................................................................... 47
Figure 5-4. Bedding Dip............................................................................... 48
Figure 5-5. Bedding stereonet. The ranges are given in density percentage.48
Figure 5-6. Geological Structure. ................................................................. 49
Figure 5-7 Stereonet for landslide 70. .......................................................... 50
Figure 5-8 Main types of slope failure and stereoplots of structural
conditions likely to give raise to thse failures.. ............................................ 51
Figure 6-1. Description of the portable dynamic penetrometer with variable
energy PANDA ............................................................................................ 54
5
Figure 6-2. Interpretation of the penetrograms: organisation of the weathered
profile and associated strength profile.......................................................... 55
Figure 6-3. Penetrometer tests in the Moulin Catchment............................. 56
Figure 6-8. Penetrometer Tests and Equal Slope and Aspect Areas. ........... 61
Figure 6-9. Soil Depth map resulting from multivariate regression model.. 63
Figure 7-1. Training and test landslides datasets derived for the totality of
landslides ever observed in the Moulin catchment....................................... 66
Figure 7-2. Success rate of individual factors. ............................................. 76
Figure 7-3. Success rate of different scenarios............................................. 76
Figure 7-4. Classification of scenario H6 with natural breaks method. ....... 77
Figure 7-5. Prediction rate of scenario H6. .................................................. 78
Figure 7-6. Landslide susceptibility derived from the classification of H6 . 79
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List of tables
Table 3-1. Aspect (or dip direction) categories ........................................................ 22
Table 3-2. Categories of equal slope and aspect units: Figure 3-4 ........................... 23
Table 3-3. Categories of aspect units for Figure 3.5................................................. 24
Table 4-1. Landslide classification ........................................................................... 28
Table 4-2. Landslide type codes in the database....................................................... 34
Table 4-3. Slope and stream configuration in the database ...................................... 36
Table 6-1. Results from Dynamic Penetrometer Tests ............................................. 59
Table 7-1. Total weights of landcover factors .......................................................... 67
Table 7-2. Total weights of slope factors ................................................................. 67
Table 7-3. Total weights of aspect factors................................................................ 67
Table 7-4. Total weights of equal aspect units (30) factors ...................................... 68
Table 7-5. Total weights of equal aspect units (9) factors ........................................ 68
Table 7-6. Total weights of bedding dip factors ....................................................... 68
Table 7-7. Total weights of bedding dip direction factors........................................ 69
Table 7-8. Total weights of slope minus bedding dip (natural break classification)
factors ....................................................................................................................... 69
Table 7-9. Total weights of slope minus bedding dip (natural breaks classification)
factors ....................................................................................................................... 70
Table 7-10. Total weights of slope minus bedding dip (quantile classification)
factors ....................................................................................................................... 70
Table 7.12 Table 7-11. Total weights of aspect minus bedding dip direction (natural
break classification) factors ...................................................................................... 70
Table 7-12. Total weights of aspect minus bedding dip direction (equal intervals
classification) factors................................................................................................ 71
Table 7-13. Total weights of aspect minus bedding dip direction (quantiles
classification) factors................................................................................................ 71
Table 7-14. Different factors used in the composition of 12 scenarios..................... 75
Table 7-15. Landslides percentages of both training and test datasets compared to
hazard classes. .......................................................................................................... 77
Table 7-16. Landslides percentages of test dataset compared to hazard classes....... 78
7
1.
Introduction
Landslides and associated erosive processes represent a major natural hazard, having
significant destructive impacts in the economy and society of many countries. For
that reason, the studies of landslides have become more and more important and a
lot of methods have been developed to analyse their occurrence and associated risks.
In general, such methods can be applied to assess landslide susceptibility of different
areas.
Landslides such as debris flows are often generated as a combination of many
different environmental factors which interact and fluctuate spatially and temporally.
Deterministic models have been developed to analyse the interaction of those
various different factors by attempting to quantify the failure and the uncertainties
associated, considering the spatial dimension of the events, hence, evaluating the
landslide susceptibility. However, it is recognised that such deterministic models are
less accurate when there is a need to develop a more general appreciation of stability
of slopes over a wider area, such as in hazard assessment (Moon and Blackstock,
2004). In these circumstances, stochastic models are an alternative to assess the
spatio-temporal probabilities of landslides and the relative influence of those factors
in the generation of the instability.
This research focuses on investigating the shallow landslides susceptibility of an 8.9
ha experimental test catchment located at Draix, Southern France. A statistical
approach was applied to assess landslide susceptibility in a Geographic Information
Systems (GIS) environment using spatial data such as geology, topography, location
of current landslides and land use. A model validation was attempted comparing the
results of the model against actual landslide occurrence in the experimental
catchment.
1.1.
Research Problem
The erosion processes of the black marls that constitute the “Terres Noires”
formation are a continuous problem in many areas in SE France. The strong
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weathering permits the disintegration of the soil when erosive processes such as
alternating freeze-thaw and wet-dry cycles occur. The marls are subject to a
continuous cycle of destabilization, retrogression, denudation by the mobilization of
the weathered material through erosion and finally removal of the debris produced
by the erosion processes. The regolith reconstitutes through weathering and the
process begins again. The lithological conditions also impose a strong susceptibility
to landslides. These conditions ensure the constant degradation of the landscape that
ultimately results in badlands.
According to Antoine (1995), during dry periods the fine materials generated by the
weathering processes are supplying an enormous amount of superficial sediment that
is transported by the first heavy rain, not only by the action of the run-off but also in
the form of shallow landslides. Descroix and Olivry (2002) recognized that the
erosion rate of the black marls can reach values close to 100 m3 ha-1 year-1. They
found that the mass movements in the area depend mainly on the lack of vegetation
cover on hill slopes, the dip-slope angle, the soil thickness, and the effects of
previous weathering.
The behaviour of the black marls of the "Terres Noires" geological formation has
been widely studied in terms of erosion and run-off processes (Antoine et al., 1995;
Descroix and Olivry, 2002; Esteves et al., 2005; Malet et al., 2003; Mathys et al.,
2003; Mathys et al., 2005; Wijdenes and Ergenzinger, 1998). However, there exists
a lacuna in quantifying the susceptibility of the area to landslides, which a first step
towards hazard quantification. Landslide susceptibility can be seen as a relative
indication of the spatial probability of landslide occurrence (Singh et al., 2005).
1.2.
Background And Significance
The incidence of landslides and associated magnitudes of erosion and sediment yield
often leads to changes in the drainage pattern, deforestation, agradation, etc. As
human settlements extend more and more towards green field zones, usually in the
peripheral and more landslide susceptible areas it becomes increasingly necessary to
quantify the hazard probabilities for framing effective risk reduction strategies.
The concepts of landslide hazard and landslide hazard assessment have also been
introduced in the past decades and various methodologies have been developed for
evaluating the potential threatening effects of landslides and associated processes.
9
Varnes (1978) proposed the most widely adopted definition for landslide hazard as
“the probability of occurrence of a potentially damaging phenomenon (landslide)
within a given area and in a given period of time”. As Guzzetti et al., (1999) stated,
this definition incorporates the concepts of spatial location, time recurrence and
magnitude as crucial elements in the prediction of landslides and subsequent
sediment yield processes. Landslide susceptibility is a relative indication of spatial
probability of landslide occurrence (Singh et al., 2005). For the landslide
susceptibility assessment it is assumed that areas susceptible to future landslides can
be predicted by statistical relationships between past landslides and the spatial data
set of a set of predisposing factors such as slope angle, slope aspect, lithology,
geomorphology and land use (Zêzere, et al., 2004).
GIS tools have been applied extensively to landslide hazard research, in particular
within the last decade, taking advantage of its analytical, data storage and
cartographic capacities that allow a relatively quick and easy landslide hazard
assessment for a given region. Many studies have been made in the past addressing
the occurrence of landslides and the various factors involved in their generation,
development and consequences. As summarized by (Sekhar, 2006), there are several
approaches to quantify landslide hazard probabilities using GIS. Those approaches
can be grouped into four categories, as follows: (i) landslide inventory based (e.g.,
Chau et al., 2004), (ii) heuristic (e.g.,(Vijith and Madhu, 2007)), (iii) statistical (e.g.,
(Guzzetti et al., 2005)) and, (iv) deterministic (e.g., (Bathurst et al., 2007)).
Landslide susceptibility maps can be prepared by any of the above methods, but both
spatial and temporal probability estimations are necessary if a quantitative risk
assessment is to be done. Spatial and temporal probability can be assessed using
dynamic models (Chen and Lee, 2003; Liu and Huangm, 2006; van Beek, 2002) or
by using statistical methods (Bacchini and Zannoni, 2003; Calcaterra et al., 2003;
Guzzetti et al., 2006).
Although it has been proven that these approaches work well, there are some
limitations to consider. It is often difficult to validate the results of landslide hazard
studies (Chung and Fabbri 1999; Chung et al. 1996; Jade and Sarkar, 1993) and
consequently such studies are difficult to interpret. Validation of the results is
complex because of the various limitations implicit in the landslide analysis process,
including: (i) intrinsic complexity of the natural phenomena, with a wide range of
influencing factors (ii) difficulties in obtaining all the necessary information with the
quality required to analyse the phenomena, (iii) the difficulty of identifying causes,
triggering factors and cause-effect relationships, (iv) the lack of complete historical
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data concerning the frequency and evolution of landslides and, (v) the discontinuous
nature of slope failures in both space and time.
As Guzzetti et al., (1999) pointed out, strictly speaking, validation of the prediction
of future landslides is not possible because of the many limitations such as those
described above. However, many attempts have been undertaken to assess the
validity of landslide susceptibility predictions (Zêzere et al., 2004). The attempts
only provide a validation of the ‘success-rate’ of the model given a landslide
inventory data set which is used to construct the stochastic function and to further
validate the model results. The success of the prediction depends on the temporal
comprehensiveness of the landslide inventory. The importance of including
historical landslide data has been pointed out by Brunsden and Ibsen (1994),
Guzzetti et al. (1994), Guzzetti et al. (1999) and Ibsen and Brunsden (1996).
1.3.
1.3.1.
Research Objectives And Research Questions
Aim
Aim of the study is to assess the landslide susceptibility of Moulin basin using a
statistical bivariate method. Such an assessment has scientific, logical and sequential
steps involved.
1.3.2.
Specific Objectives, Research Questions And Associated
Methods And Materials
Specific Objectives
To update the existing
landslide inventory
database with dates of
occurrence, volumes
and types of movement
from 2004 to present.
Research Question
• How
representative is
the LiDAR DTM
of the true
landscape
considering the
existing landslides.
• How many new
landslides have
occurred since
2004 in the
catchment and
what are their
characteristics?
Methods
- Photointerpretation
- Mapping in
situ with GPS
Materials
- Existing
database
- Aerial
photographs
- Orthophotographs
- Fieldwork
To characterize the
geology and lithology
• What are the main
directions of
- Fieldwork
sampling
- Results from
fieldwork
11
of the area.
bedding and
discontinuities and
how does this
compare with the
occurrence of
landslides?
• What are the soil
characteristics of
the catchment?
• What are the relief
characteristics of
the catchment?
- Statistical
analysis
To collect
spatial/temporal
information on known
landslide causal factors
such as material,
topography, geological
structure and rainfall.
• Can the landslide
inventory be
related to factors
as the geology
structure, the
topography or
rainfall?
- Overlay
operations
- Comparison
of date of rain
events and
date of
landslides
To map the landslide
susceptibility using a
statistical approach
• What are the
parameters that
determine the
occurrence of
landslides in this
catchment?
• How susceptible is
the catchment to
landslides?
- Statistical
analysis
To define and
characterize areas of
similar geotechnical
parameters based on
results from in situ
measurements such as
penetrometer and
presurometer tests
12
- Statistical
modelling of
soil depth
- Fieldwork:
Penetrometer
tests
- LiDAR DTM
- Fieldwork
- Secondary
data of
hydrological
and
geotechnical
properties
- Geological
and lithological
map
- Soil units map
- Geological
map
- Lithological
and tectonic
discontinuity
map
- Soil units map
- Hydrological
data
- LiDAR DTM
- Geological
map
- Lithological
and tectonic
discontinuity
map
- Soil units map
- Hydrological
data
- Relief
characterization
- Landslide
inventory
1.4.
Research Hypothesis
1- The presence of shallow translational landslides in the catchment is related to
the lithology and structure of the geological formations present in the area.
Directions of bedding and discontinuities can be associated to angles of planar
failure for shallow translational slides, wedge displacements and debris flows in
the catchment.
2- The catchment area can be classified in terms of similar geotechnical
characteristics obtained from in situ measurements such as penetrometer and
presurometer tests. The resulting geotechnical zonification map can be used for
the landslide susceptibility assessment.
3- LiDAR data derived DTM is in good agreement with the actual land surface and
is a potential contributor to landslide susceptibility mapping.
1.5.
Research Approach
A bivariate weighted evidence model is applied to estimate the relative contributions
of the factors responsible for instability and assess the landslide susceptibility. The
model is based on the Bayesian probability model, which applies the weight of
evidence method (Bonham-Carter, 1994) to estimate the relative contributions of the
factors responsible for instability and assess the landslide susceptibility.
Geographical information systems (GIS) were used to manage spatial information
such as geology, topography, location of current landslides and land use. Finally,
the model was validated by comparing the resulting landslide volumes against actual
measurements taken in the catchment.
This study was divided into seven main stages: landslide inventory update,
geological characterization, relief characterization, soil depth modelling, statistical
susceptibility modelling and validation. The relationships between these stages and
the procedures followed to fulfil the objectives are shown in Figures 1-1 and 1-2:
conceptual and methodological research approaches.
1.5.1.
Conceptual Research Approach
13
Landslide
inventory update
Relief
characterization
Geological
characterization
Soil depth
modelling
Statistical
susceptibility
modelling
Figure 1-1. Conceptual Research Approach
14
Geomorphologic,
geological, land
use, DTMs,
ortophotographs
1.5.2.
Methodological Research Approach
Figure 1-2. Methodological Research Approach
15
2.
Study Area
The experimental study area is located in the French Alps, southeast of France, in
Draix, at 15 kilometres of Digne-les-baines (Figure 2-1) in terrains dominated by the
“Terres Noirs” geological formation. There are four catchments within the study
area, which have been monitored and studied focusing on torrential erosive
processes and climate.
One of the four experimental catchments is “Moulin”, in which this study focuses.
On account of being an experimental site of Cemagref (Agricultural and
Environmental Engineering Research), the Draix catchments have an extensive and
continuous amount of data available on terrain characteristics, geotechnical
characteristics, rainfall, water discharge and sediment load. According to (Descroix
and Olivry 2002), this basin includes all the different stages in the rainfall-runoff–
erosion process, and for this reason it was chosen to conduct more detailed research
on this topic.
The Moulin catchment covers an area of 8.9 ha, with elevations ranging from 880 to
980 m. The main stream of the Moulin catchment is 300 m long descending with an
average 4% gradient. There are four different types of land cover, comprising
pastures (26%), cultivated land (3%), Forest (18%) and bare rock (53%). The
precipitation is 900 mm/year in average. There are two main rainfall periods in April
– May and September – November, although the highest intensity rains take place in
July and August.
In general, the slope gradients vary from <10 to 75° across the basin. The steeper
zones are located to the northeast zone of the catchment, in the main contributory
area, and all along the main stream valley with most of the slopes being more than
40° steep. A smaller satellite contributory area is located to the west end of the
catchment with steeper slopes up to 50°, whereas the middle western area is
predominantly flat (see Figure 3.1).
16
Figure 2-1. Location of the Draix experimental basins. The Moulin catchment is
indicated with red.
Modified from (Descroix and Olivry 2002)
Figure 2-2. 3D view of the Moulin Catchment. (CEMAGREF)
This geology in the area is dominated by the “Terres Noires” formation, with
regional sedimentary structure bedding towards east direction. (CEMAGREF). The
“Terres Noirs” formation is mainly composed by black marls. It covers an extensive
area of more than 2000 km2 in the southern France (Antoine et al., 1995) and as a
17
remark, it is very sensitive to erosion, especially in the form of debris flows, gully
erosion and shallow landslides. These mass movements are generated by the
Mediterranean and mountainous climate with frost in winter and high intensity
rainfall in summer (Mathys et al., 2003) and the great susceptibility of the marls to
weathering. As a result of this combination a terrain similar to badlands is generated,
shaped by dense drainage networks that erode the landscape through denudation
associated to water.
According to Antoine, Giraud et al. (1995), The "Terres Noires" formation displays
the same morphology as badlands, being characterized by steep, rounded ridges with
vertical sides generated by a dense drainage network because of their almost total
impermeability. Antoine, Giraud et al. (1995) also pointed out that specific erosion
processes occur in these areas, promoted by the rapid disintegration of the material
through weathering. This causes voluminous solid transport and causes generalized
superficial instability. Further descriptions on the geology of the catchment are
presented in Chapter 5, Geological characterisation.
The Draix catchments are affected by three types of dynamic processes: superficial
erosion, shallow landslides and mud flows. In the Moulin catchment specifically,
translational slides involving soil and debris are the most common, but also rocks
slides and superficial sediment flows can be found. A more detailed description of
the dynamic instability processes affecting the Moulin basin is presented in Chapter
4: Landslide Inventory.
Results of extensive experimental measures carried out in the area have shown that
the sediment yield of the basin is constantly changing due to the high rate of
erodable material produced thanks to the weathering inflicted in the marls by the
action of the weather by the freezing–thawing and wetting–drying processes. The
loose superficial material is washed by the run-off and deposited and taken away
again by the next rain event. At the same time the soil is being weakened and new
loose material is generated, but not only by run-off generated erosion but also by
debris flows and other shallow landslides types.
Because of the lack of vegetation cover and the easily weathered nature of the black
marls, the area is covered by very shallow soils. As (Maquaire, Ritzenthaler et al.
2002) pointed out, the superficial soils increase in density and compactness with
depth. The typical arrangement of superficial soils in the area comprises a most
superficial layer of loose debris cover, which is composed by locally produced
18
colluvial material sensitive to erosion. The next layer is the regolith of marl, which
can be subdivided into the loosened upper regolith, where the marl plates and
structure are deteriorated, and the compact lower regolith, which conserves the marl
structure but not its cohesion. In the bottom of the horizon is the bedrock, with a lot
of compactness, cohesion and a good structure.
According to (Mathys, 2006), the structure of the slopes is composed of a layer of
weathered materials, with a maximum depth in the ridges, minimal in the stream
and rather homogeneous thickness in the slope walls. The debris cover is subject to
erosion mainly due to run-off, while the regolith layer is subject of other erosive
processes such as shallow landslides which constitute the focus of the proposed
study. A further discussion on the characteristics of the superficial layer of soils in
the area is presented in Chapter 6: Soil Depth.
Superficial soil distribution in the catchment is presented in Figure 2.3. It can be
seen that marly soils are exposed across most of the catchment area, especially at the
steeper contributory zones and the main stream valley, where erosion processes are
more likely to prevent the depositation of superficial soils, and hence, the marl rock
is subject to constant exposure and weathering. More gentle areas are covered by
colluvial superficial deposits, mainly towards the toe of the stream valleys. Flatter
areas on the middle west are subject to low energy processes, therefore allowing the
deposition of superficial silty and clayley sediment layers. . In figure 2-5 a general
view of the catchment is presented.
19
Legend:
In-Situ formations:
Fau2: Incipiently developed soils from marl
alteration
Fau 1: Marls and shales
Transported formations:
Fal 1:Conglomerate of blocks and pebbles in a
silt- clayley matrix marron and beige
Rem: Deposited soils
Fal 5: Stratified succession of calcareous gravels
from marls embedded in a silty-clayley matrix
Fal2: Colluvial deposits
Fal 4: Silty-clayley soils ochre color with
siliceous gravels
Fal6: Alluvial terrace deposits
Figure 2-3. Superficial soil in the Moulin catchment. (After Mathys, 2006)
Vegetation and land use across the area is also intimately related to the topography.
Figure 2-4 shows the distribution of land use and vegetation on the basin. It can be
seen that no vegetation is present in the steeper areas where the marl is exposed,
whereas the flatter areas to the middle west are predominantly covered by grass.
20
Forest areas are also found in isolated spots at the centre and edges of the catchment,
at areas where the intense erosive processes have not yet arrived.
921700
921800
921900
922000
922100
212500
MOULIN CATCHMENT
Draix, France
212500
212600
212700
±
212600
212700
921600
LANDUSE MAP
Figure 2.4
landuse_map_moulin
212400
212400
LAND_COVER
Arable land
Exposed marls
Forest
Grass
921600
921700
921800
921900
25
50
100
150
Meters
922000
212300
0
922100
Figure 2-4. Land use and vegetation cover in the Moulin catchment
Figure 2-5 General view of the Moulin catchment
21
3.
Relief Characterisation
The relief characterisation was performed using as a base a Digital Terrain Model
(DTM) generated from Light Detection and Ranging (LiDAR) data acquired on the
06th April 2007 by CEMAGREF. The data after filtering (vegetation removal) had a
density of 2.5 points/m2. This process was performed using the SCOP++ software.
The DTM has a resolution of 1 m horizontally, a vertical accuracy of 0.10 m and a
statistical vertical accuracy of 0.19 (for 95% confidence interval) (Cavalli, 2007)
For this characterisation slope, aspect and curvature maps were derived from the
DTM. Additionally, two more maps were constructed conforming units of equal area
and slope. The first map was classified in 30 categories, as explained in table 3.2,
and the second one was generalized and contains 9 categories.
3.1.
Slope
The slope was classified in 7 categories composed by intervals of 10 degrees. The
slope map is presented as Figure 3.1. The percentages of total area of each category
are also found in the map. All categories of slopes are present in the same
proportions (around 20%) except for the highest slopes: 3.8% for 50°-60° and 0.19
for 60°-75°.
3.2.
Aspect
Aspect refers to the dip direction of the slopes. The aspect was classified into 8
categories, as indicated in table 3.1. The slope map can be found as Figure 3.2 with
the percentages for each category. The aspect predominant direction is south (S =
19.54%, SE = 21.24%, SW = 17.65%), the E and W direction characterise 11.65%
and 15.7% of the area respectively, while together N an NE directions come up only
4.85% of the area.
Category
Range
N
NE
E
SE
S
SW
W
NW
337.5° –
22.5°
22.5° 67.5°
67.5° 112.5°
112.5° 157.5°
157.5° 202.5°
202.5° 247.5°
247.5° 292.5°
292.5° 337.5°
Table 3-1. Aspect (or dip direction) categories
3.3.
Curvature
The slope curvature was produced with a filter of 3x3 on the 1m resolution DTM.
The negative vales are considered as concave and the positive as convex. The range
of values obtained varies between – 161 and 225. These values are considered to be
22
high because they explain very local variation. Other trials were effectuated with
higher resolutions DTMs (0.5m) and with the base DTM (1m) without filtering.
Ultimately the values for the filter 3x3 were chosen. Figure 3.3 shows the results.
The classes with most pixels are in the range of -4 to 4 (flat) with 39.87% (21.26%
for -4 to 0 and 18.61% for 0 to 4). The percentage of concave area is 28.96% and the
convex area is 31.17% of the total.
3.4.
Equal aspect and slope units
Two maps of equal aspect and slope units were produced to be used in different
analyses in further chapters. The equal aspect and slope units are those areas that
present values that vary within a definite range of slope (i.e 10°-20°) and dip
directions (i.e North).
The first map was created with 8 aspect and 7 slope
categories defined in the sections 3.1 and 3.2. For the creation of this map it was
necessary to delete units smaller than 50 m2, which were included in surrounding
units with bigger areas. Regardless of their aspect, all the units with slopes between
0° and 10° were merged into the same category with the purpose of facilitating the
analysis producing fewer units. This same criterion was applied to the areas with
slopes between 10° and 20°. In total 30 categories were obtained for 595 units. The
results are presented in Figure 3.4, and the list of categories in table 3.2.
Aspect
Slope
-
0°-10°
10°20°
N
20°30°
40°50°
NE
E
SE
20°30°
30°40°
40°50°
20°30°
30°40°
40°50°
20°30°
30°40°
40°50°
S
20°30°
30°40°
40°50°
50°60°
SW
20°30°
30°40°
40°50°
50°60°
W
20°30°
30°40°
40°50°
50°60°
NW
20°30°
30°40°
40°50°
50°60°
Table 3-2. Categories of equal slope and aspect units: Figure 3-4
The second map of equal aspect and slope generalized units is divided into 494 units
that correspond to 9 categories. It is presented in Figure 3.5. The slope was divided
in three groups: 0°-20°, 20°-39° (39° is the lowest angle of friction for the regolith of
the black marls) and 39°-75°. These categories were named as low slope, medium
slope and high slope. The aspect or dip direction was divided according to ranges
defined by the mean and the standard deviation of the bedding dip direction, analysis
that is explained in Chapter 5. Three categories were then defined: cataclinal (same
dip direction as bedding), anaclinal (for slopes progressing in a direction opposite to
the dip of the bedding) and orthoclinal (a different dip direction from cataclinal or
23
anaclinal). The ranges of the aspect categories are presented in Table 3.3. The
highest percentages of all slope ranges correspond to an orthoclinal dip direction
because the orthoclinal range is the widest. The cataclinal dip direction presents a
4.06% of the area in low slopes, 5.51% in medium slopes and 1.07% for high slopes.
The percentages are similarly low for anaclinal aspects.
Category
Range
Cataclinal
Anaclinal
Orthoclinal
72.65° –
252.65° 0° 117.05°
297.05°
72.65°
Table 3-3. Categories of aspect units for Figure 3.5
921700
921800
921900
922000
297.05° 360°
922100
MOULIN CATCHMENT
Draix, France
±
212700
212700
921600
117.05° 252.65°
SLOPE MAP
212600
212600
Figure 3.1
SLOPE
<VALUE>
0 - 10
10 - 20
20 - 30
212500
212500
30 - 40
SLOPE
24.24 %
17.19 %
212400
212400
20.22 %
3.8 %
14.77 %
921600
921700
921800
50
921900
100
922000
Figure 3-1 Slope Map
24
150
Meters
212300
25
Data derived from DTM
originated from Lidar acquisition
in 04/2007 Resolution = 1 m
Projected Coordinate System:
Projection: Lambert Conformal
Conic 3
False Easting: 600000.0
False Northing: 200000.0
Central Meridian: 0.0
Standard Parallel : 49.0
19.6 %
0
50 - 60
60 - 75
0-10
10 - 20
20 - 30
30 - 40
40 - 50
50 - 60
60 - 75
0.19 %
40 - 50
922100
921700
921800
921900
922000
922100
MOULIN CATCHMENT
Draix, France
±
212700
212700
921600
ASPECT MAP
Figure 3.2
ASPECT
212600
212600
<VALUE>
N
NE
E
SE
S
212500
212500
SW
W
NW
DIP DIRECTION
N
E 11.65 %
W 15.7 %
212400
NE 3.29 %
N 1.56 %
SW 17.65 %
212400
NW 9.38 %
SE 21.24 %
50
100
150
Meters
212300
25
921600
921700
921800
921900
922000
922100
921600
921700
921800
921900
922000
922100
MOULIN CATCHMENT
Draix, France
±
212700
212700
Conic 3
S 19.54 %
0
CURVATURE MAP
Figure 3.3
CURVATURE
212600
212600
<VALUE>
-161 - -44
-44 - -26
-26 - -13
-13 - -4
-4 - 4
212500
212500
4 - 13
CURVATURE
13 - 26
26 - 44
44 - 85
85 - 226
-26 - -13 8.73 %
-161 - -44 0.62 %
-13 - -4 16.32 %
212400
85 - 226 0.06 %
44 - 85 0.49 %
212400
-4 - 4 39.87 %
in 04/2007
Resolution = 3 m
After filter mean 3x3 applied to
DTM resolution = 1m
4 - 13 18.84 %
0
921600
921700
921800
25
50
921900
100
922000
26 - 44 2.63 %
150
Meters
212300
-44 - -26 3.3 %
13 - 26 9.14 %
Conic 3
922100
Figure 3-2 Aspect map and Figure 3-3 Curvature map
25
25
921700
921800
921900
922000
922100
±
212700
212700
921600
MOULIN CATCHMENT
Draix, France
212600
212600
EQUAL SLOPE
AND ASPECT
UNITS
Figure 3.4
10-20 24.73 %
212500
212500
E 20-30 3.12 %
0-10 13.61 %
E 30-40 3.89 %
40-500.65
1.06%
%
NE40-50
N 20-30 0.37 %
NE 40-50 0.07 %
NE 20-30 0.57 %
NE30-40
0-10 0.45
0.24 %
%
NE
NW 20-30 0.65 %
W 50-60 0.12 %
NW 30-40 2.5 %
W 40-50 3.9 %
NW 40-50 4.66 %
W 30-40 4.01 %
NW 50-60 0.25 %
S 20-30 2.61 %
W 20-30 4.97 %
S 30-40 2.39 %
EQUAL SLOPE - ASPECT
NW 20-30
SE 40-50
GROUP_TXT
NE 30-40
S 40-50
0 -10
E 30-40
SW 40-50
10 - 20
SE 30-40
W 40-50
N 20-30
S 30-40
NW 40-50
NE 20-30
SW 30-40
S 50-60
E 20-30
W 30-40
SW 50-60
S 20-30
NW 30-40
W 50-60
SE 20-30
N 40-50
NW 50-60
SW 20-30
NE 40-50
SW 50-60 0.38 %
S 40-50 3.8 %
SW 40-50 3.13 %
W 20-30
212300
0
921700
921800
921800
921900
100
150
Meters
922000
921900
212400
SW 20-30 2.22 %
SW
SE 40-50 2.31
%30-40 3.59 %
E 40-50
50
Conic 3
922100
922000
922100
212700
±
MOULIN CATCHMENT
Draix, France
EQUAL SLOPE AND
ASPECT UNITS
GENERALIZED
212600
212700
921600
921700
SE 30-40 4.48 %
212600
921600
25
SE 20-30 4.96 %
212300
212400
S 50-60 0.3 %
Figure 3.5
GROUP
LOW - CATACLINAL
LOW - ANACLINAL
LOW - ORTHOCLINAL
MEDIUM - ANACLINAL
MEDIUM - ORTHOCLINAL
HIGH - CATACLINAL
HIGH - ANACLINAL
HIGH - ORTHOCLINAL
212400
212400
4.06 %
2.67 %
33.18 %
5.51 %
6.64 %
27.16 %
1.07 %
3.66 %
16.05 %
212500
212500
MEDIUM - CATACLINAL
Equal slope and aspect areas
921600
921700
921800
25
50
100
921900
922000
150
Meters
212300
212300
0
Conic 3
922100
Figure 3-4 Equal slope-aspect units (30) and Figure 3-5 Equal slope-aspect units (9)
26
26
4.
4.1.
Landslide Inventory
Superficial instability processes in the Moulin basin
Three main types of superficial instability processes have been found in the Moulin
area: translational soil slides, rock slides and debris slides. These processes are
generally present all over the Draix area and involve the instability of both the marly
superficial weathered layers and the exposed fresh rock. In some cases, translational
landslides in soil involved larger volumes than the majority of other movements.
Superficial sediment flows are also of great importance, involving water and
sediments usually leading to flash floods with a high level of solid transport.
All superficial instabilities are constantly influenced by sheet and channelized
erosion processes, although a differentiation has to be made in that regard: erosion is
described as the displacement of solid particles caused by transporting agents such
as wind, ice or water, whereas superficial instability events involve the movement of
soil or rock masses that are principally displaced under the influence of gravity, and
where water is not directly the transporting agent.
Table 4.1 presents the general landslide classification system proposed by Varnes,
(1978). Based on that classification, it is possible to state that all translational slides
are present in the area of study. A more general description of the various different
types of events in the Moulin area is presented below.
4.1.1.
Erosion And Solid Transport Processes
In general, several types of water erosion are present in the area of study, from
generalized sheet erosion to more localized gully patterns along trenches or canals.
Rill erosion is also present in the form of small, transient flow paths, which function
as both sediment source and sediment delivery systems.
27
Type of material
Type of movement
Bedrock
Engineering soils
Predominantly
fine
Predominantly
coarse
Falls
Rockfall
Earth fall
Debris fall
Toppling
Rock topple
Earth topple
Debris topple
Rock slump
Earth slump
Debris slump
Few
units
Rock block
slide
Earth block slide
Debris block
slide
Many
units
Rock slide
Earth slide
Debris slide
Rock spread
Earth spread
Debris spread
Rock flow
Earth flow
Debris flow
Rotational
Slides
Translational
Lateral spreads
Rock
avalanche
Flows
(Deep
creep)
Complex and compound
Debris avalanche
(Soil creep)
Combination in time and/or space of two or more
principal types of movement
Table 4-1. Landslide classification (After Varnes, 1978)
In general, rills are active and very well distributed over the steep slopes and depths
of rills are typically on the order of a few centimetres or less, evolving into deeper
canals in gully type of erosion at the toe of the slopes. The whole erosion network
gathers in the form of valley erosion at the main stream, with continued water flow
along the linear features of the ground. The erosion is both downstream, deepening
the valley towards the outlet of the catchment, and upstream, extending the valley
into the hillside. The valleys in the area develop a typical V cross-section and the
stream gradients are relatively steep. When some base level is reached, the erosive
activity switches to lateral erosion, which widens the valley floor and creates a
narrow floodplain.
(Antoine et al., 1995)
28
As discussed by Antoine et al, (1995) during periods of no runoff (especially in dry
winters), the fine materials generated by weathering provide a considerable amount
of superficial sediment available for transport by the first heavy shower; the muddy
runoff collects in reaches of the local watercourses, and is then transported
downstream. Solifluction caused by saturation of the weathered layer, is a
phenomena that mainly occurs on steep valley slopes at the end of the winter season
when abundant snow has fallen. The solifluction bulges are generally small and
localized, but they are considered as non localized processes due to the fact that they
occur in many places in the catchment area. The supply of a considerable amount of
loose material and the saturation of this weathered material during winter explain
why the first major spring rainstorm always cause a flood with a high level of solid
transport, and sometimes even debris flows
Localized, non water erosion processes also take place in the area of study. Such
processes consist of localized collapse of debris from more calcified layers which
protrude as the result of faster degradation of the more marly underlying layers. This
process is not necessarily linked to a specific rainfall event.
4.1.2.
Translational slides
As defined by Varnes, (1996) translational
slides are those where “the mass displaces
along a planar or undulating surface of
rupture, sliding out over the original
ground surface”. A shallow translational
landslide consists of a slip along an
interface dividing a shallow upper soil
layer from an underlying stronger and
often less permeable lower soil layer or
bedrock. The soil is subject to two major
opposing influences: the down slope
component of soil weight, which acts to
shear the soil along a potential failure
plane parallel to the hillslope; and the
resistance of the soil to shearing. The
relationship between the two influences Figure 4-1. Debris landslide in the Moulin
is expressed as a factor of safety. One Catchment
example is presented in Figure 4-1.
29
If the down slope weight is such that its component parallel to the slip surface
exceeds the resistance to shearing, the value FS becomes less than 1 and the
hillslope is expected to fail. Factor of safety analysis therefore forms the basis for
simulating landslide occurrence in most models.
According to Antoine et al, (1999) the majority of the slides in the Draix area are
shallow and developed in the weathered superficial top soils. The thickness of this
soil in the Moulin basin cover ranges between 0.0 and 0.80 m, therefore, slide
thicknesses are mainly within that range, although processes of more than 1 m may
occur and only three major events of 2 m were identified. Some slides may be
developed in slopes with non favourable orientation, along foliation planes within
the marl and triggered by long periods of rainfall. The length of the slides varies
from 1 to 25 m with only few events of more than 15m. According to Antoine et
al,1999, the landslides can be reactivated in a particular year, separated by inactive
periods ranging from 5 to 12 years. This could be detected by dendrochronological
research on bent tree stems over a maximum period of 100 years BP (Van Asch and
Van Steijn, 1991; Van Asch, 1995).
Antoine et al,(1995) also reported results from ground water modelling on a monthly
basis over 25 years, using a two-layer model for the superficial weathered materials,
which showed a yearly fluctuation in the phreatic water table in the colluvial
material of the lower layer, with peaks at the end of the winter period and at the
beginning of spring, and minimum values during the dry summer period.
They reported that the hydrological monitoring in the Draix area already suggested
that long periods of rainfall are needed to create a permanent perched water table in
the top weathered layer in order to induce significant flows to the sub-layer. The
hydrological investigations and modelling of failure conditions in layer 1 and layer 2
of colluvial material indicated that the installation of interception drains in the 1.5m-thick permeable upper layer of the landslides should probably constitute an
effective measure for stabilisation of these superficial slides in the colluvial material
(Antoine et al., 1995).
4.1.3.
Rock slides and Debris slides
Debris slides in the Moulin catchment are characterized by unconsolidated marly
platelets and rock fragments that have moved downslope along a relatively shallow
failure plane, together with finer material. Debris slides are characterized by the
steep, uncovered scars in the slope and irregular, hummocky deposits in the toe of
30
the slopes. The scars usually remain uncovered for many years due to the difficulty
of vegetation development and the constant instability. Those debris slides are most
likely to occur on slopes greater than 65 % where unconsolidated and weathered
material overlies the shallow marl. The shallow sliding surface is usually less than 1
m deep. The probability of sliding is low where bedrock is exposed, except, where
weak unfavourable planes and extensive bedrock joints and fractures are parallel to
the slope.
These debris masses are very
heterogeneous in size, but
their
composition
is
homogeneous,
with
the
majority of the fragments
coming from the parental
marl and showing a typical
plated shape.
Rock falls and rock slides are
also present in the area and
are more associated to the
existence of joint sets and
Figure 4-2 Debris slide in the Moulin catchment
fractures. Once a joint is
open along a certain distance, water can flow into the space and degrade the contact,
leading to the development of
rock slabs that can slide as an
entire body, or break into
multiple wedges that move
independently and are formed
when two joint sets intersect.
Rock falls and rock slides are
less common than other
instability process in the
Moulin catchment which can
be explained due to the
relatively low amount of
joints and fractures of the
Figure 4-3. Rock slides in the Moulin Catchment
parental rock.
31
4.1.4.
Superficial sediment flows
Superficial sediment flows are also of great importance, involving water and
sediments usually leading to flash floods with a high level of solid transport.
The supply of a considerable amount of loose material and the saturation of this
weathered material during winter usually leads to flash floods with high levels of
solid transport, and sometimes even debris flows, during the spring season.
As pointed out by Antoine et al. (1995), the erosion processes described in the
previous sections are complicated by the hydraulic phenomena which occur in the
drainage area (temporary storage of sediments, deposition and retransportation along
the reaches). If solid transport processes are to be modelled, it will be necessary to
combine a hydrological flood simulation model, an erosion model, and, finally, a
solid transport model (Borges et al., 1994). The system would, thus, be highly
dependent on the nature of the flow, i.e., the type of mixture and its behaviour.
Because the marl platelets disintegrate easily, the particle size distribution of the
transported solids changes considerably during the transport process, leading rapidly
to a predominance of fine materials. The ratio of the volume of materials deposited
in the sediment traps to the total volume varies from one flood to another.
According to Mathys et al. (2008), the concentration of transported solids varies
considerably from one flood to the next. During high floods experienced in spring,
or during summer storms after a long dry period, the solid flow to liquid flow ratio
may be as high as 30% (concentration per unit volume, Cv=0.23).
The floods generated in small mountainous basins such as the Moulin basin are often
devastating flash floods. The damages due to sediment transport are often more
important than those due to the water itself. The presence of mainly black marls
which are very sensitive to runoff erosion results in particularly high sediment
production and transport, reaching annual erosion rates over 107 kg/km2 per year
(Mathys et al, 2008). Most of the sediment delivery is due to the flood events: with
the largest floods representing 50 to 60 % of the total sediment yield. The rest is due
to laminar erosion and landslides.
4.2.
Field work
A walk over survey campaign was carried out in October 2007 in the Moulin basin
area. The main purpose of the survey was to establish the current state of the
previously identified landslides across the area, determine their evolution and verify
32
the presence of new or reactivated processes. Visual inspections were carried out
collecting geomorphological data such as landslide type and dimensions, material
types, and mechanisms of failure. Apart from the dynamic penetrometer measures
used to estimate the soil depth in various points (Chapter 5), no other intrusive
inspections were undertaken, due to limitations in time and resources.
A total number of 65 instability processes have been identified during the field
survey, most of them of the translational type, involving the superficial marly
regolith. Only 21 processes were found to be active by October 2007 because of the
degradation of the landslide scars and debris due to the rains of the previous summer
season. Many landslides generate or reactivate during the spring season. In general,
because of the size and the speed in which these shallow landslides degrade, it is
difficult to have a precise register of their occurrence. Instead, they are recorded
during field campaigns that take place depending on the availability of researchers.
Table 4.2 presents a summary of the active landslides found in the various walk over
inspections. This table can be found in Appendix 2.
4.3.
The landslide database
The existing database contains information about the landslides that have been
acquired in several reconnaissance campaigns including the one performed in the
fieldwork of the present project that took place in October 2007. The dates of the
campaigns are:
1. November 2002
2. March 2003
3. May 2003
4. June 2003
5. July 2003
6. April 2004
7. May 2004
8. October 2004
9. May 2005
10. August 2007
11. October 2007
Unfortunately the dates of the landslides do not correspond to the event that
triggered the landslide, but to the day in which the landslides were detected during a
campaign. This means that the date is only indicative of the period in which the
landslides took place. The lengths of these periods vary depending of the delay from
one campaign to the other, and in the case of 2005, 2006 and 2007 this period was
very long because there is no data from May 2005 to August 2007.
The landslide information is collected in a series of forms (Figure 4.4) where the
characteristics of each landslide are reported and then incorporated into an existent
33
Microsoft Access® database created in 2004 under the scope of CEMAGREF. The
database was modified in this study to allow the further introduction of future data.
Each landslide has a unique descriptive form in which historical information from
different visits is filled in systematically with the following fields:
1.
2.
Identifier: unique number that identifies the landslide in the database
Initial information: This information is collected when the landslide is
identified for the first time and comprises the following information:
a.
Date: First date in which the landslide was recorded
b.
Localization: zone, sector and average coordinates
c.
Geomorphologic description
- Type: Here the type of landslide that can appear in the black marls is noted. There
can be many types of landslides depending on the material in which the landslide
takes place (rock, debris or soil), the type of movement (fall, slide, toppling, flow or
lateral spread) and on the configuration of the movement compared to the bedding
dip (against or in the same direction of the bedding dip) as described in the Table 4-3
below:
Landslide
type code
DS AvP
DS AmP
DS
Meaning
Debris slide in slope with the same dip as the
bedding (dipslope)
Debris slide in slope with opposite dip to the
one of the bedding (faceslope)
Debris slide where the direction of the slope is
neither the same nor opposite to bedding
Number of
landslides
23
23
5
RBS AvP
Rock block slide along dipslope
9
RBS AmP
Rock block slide along faceslope
5
Table 4-2. Landslide type codes in the database
-
Material (Intact marls, regolith, detrital cover and soil)
Direction (strike of the slope in degrees)
Orientation (Dip of the slope: N, NE, E SE, S, SW, W, NW)
Dip angle
Evolution trend (progressive, retrogressive, bidirectional, regressive,
stable or lateral expansion)
34
Figure 4-4. Landslide database form for each landslide
35
d.
e.
Geological description:
Configuration of the slope face: cataclinal, anticlinal or orthoclinal
(according to Table 4.3 and 4.4)
Discontinuity density (low, medium, strong)
Canvas: position of the structural discontinuity planes
Initial dimensions of the landslide: length, width and thickness
Type of
configuration
Description
Cataclinal
Number of landslides
with this slope
configuration
28
Slope with the same dip as the
bedding (dipslope)
Anaclinal:
Slope with a dip opposite to
22
the bedding (faceslope)
Orthoclinal:
The direction of the landslide
15
or the slope is not either
cataclinal or anaclinal
Table 4-3. Slope and stream configuration in the database
f.
Similar configurations: Describes if the mechanism is similar to another landslide
of the same area
3.
Graphic or photographic representation
Association of photographs useful to recognize the movement in a later
study
4.
Evolution: It describes the status of the different parts of the landslide:
scarp, debris form and debris progression at different dates of inspection
(one line per date). Figure 4-3 presents the legends used to describe the
evolution of the processes.
-
Current status of the features:
Scarp: fresh, attenuated, very diminished, disappeared
Debris form: block chaos or clasts and fine debris
36
-
Debris progression: Accumulation at mid-slope, accumulation at the toe of
the slope, filling of the talweg, debris undermining, debris meteorization or
debris disappearance.
A scheme containing all the landslides ever recorded for the catchment is presented
in Figure 4-6.
37
Classification of the landslide features:
Shape of the scarp:
Debris progression:
Active, fresh:
Accumulated at mid slope:
Diminished, attenuated:
Accumulated at the toe:
Very diminished:
Filling up of the toe:
Vanished:
Erosion of debris:
Debris weathering:
Debris shape:
Block chaos:
Debris removal:
Clasts and fine debris:
Figure 4-5. Legend used in the database form to describe landslide evolution.
After Database Manual (Notice explicative, Base de Donnes de Mouvements de
Terrain, Bassin versant expérimental du Moulin, Draix)
Figure 4-6. Landslides map (next page)
38
212700
212600
212500
212400
921600
212300
921700
±
921700
1
2
921800
11
107
9
8 102
3
24
109
115
4
62
61101
5
100
23
42
26
2527
22 108
19
7
6
70
21
57 20
921800
56
921900
10 64
71
13106
72
15
14
17
16
65
113114
18 112
59
110
43
58
1255
66
104 105
60
111
921900
0
67
68
28
25
54
50
922000
69
922000
100
922100
150
Meters
922100
212700
212600
212500
212400
212300
921600
39
Projection: Lamber Conformal
Conic 3
Orthophoto taken 09/2004
Data acquired in several
fielwork campaings from
2002 to 2007.
Landslides
Moulin Catchment boundary
Hydrography
Figure 4.6
LANDSLIDES MAP
MOULIN CATCHMENT
Draix, France
4.4.
Characteristics of the landslides in the Moulin catchment
Maps of the landslides identified in each campaign are presented in the Appendix in
Figures 4.6.1 to 4.6.11. In terms of type of landslide, the majority of the events were
identified as translational DS AvP and DS AmP, or debris slide dipslope and debris
slide downslope. In the first ones, foliation is dipping in the same direction of the
slope; therefore, the instability is associated with the structure of the rock. There
were 14 rock slides, all involving volumes of less than 4 m3, except the landslide 55
which involved approximately 375 m3 of marl rock blocks, being one of the mayor
landslides in the catchment. Landslide type distribution is as shown in Figure 4-7
Figure 4-7. Landslide type distribution
Frequencies of landslide volumes
Frequencies of landslide volumes
40
10
8
Frequency
Frequency
30
20
6
4
10
2
0
0
630.00
610.00
590.00
570.00
550.00
530.00
510.00
490.00
470.00
450.00
430.00
410.00
390.00
370.00
350.00
330.00
310.00
290.00
270.00
250.00
230.00
210.00
190.00
170.00
150.00
130.00
110.00
90.00
70.00
50.00
30.00
10.00
-10.00
-30.00
0.10
1.00
10.00
100.00
log LANDSLIDE VOLUMES (M3)
LANDSLIDE VOLUMES (M3)
Figure 4-9. log Landslide Volumes
Figure 4-8. Landslide Volumes.
40
1,000.00
In terms of magnitudes and mobilised volumes, most of the landslides are in the
order of 1 to 60 m3 with only 5 events (7.7%) being larger than 150 m3 whereas 16
events (24.6%) were smaller than 1 m3. Sizes vary from < 1 m to 25 m long and
from < 1 m to 23 m wide. From the observation of Figure 4-6 it becomes evident
that landslides 115, 24 and 28 are the largest events, with volumes of 690, 450 and
400 m3 respectively. A histogram showing the landslide volume frequencies is
presented in Figure 4-8 above. Figure 4-9 displays the same frequencies but
presented in a logarithmic scale, along with the normal distribution of the volumes
characterised by mean = 32.37 and standard deviation = 89.2 for a 65 data N.
Major landslides are translational and involve the superficial 2 m of weathered soil
and soft rock, with slip surfaces developing some centimetres within the rock rather
than in the soil-rock interface. The vast majority of the events have thicknesses of
less than one meter, which evidences that they are sliding along the soil-rock
interface. Figure 4-10 presents a number of landslides of different depths.
There were more landslides identified as taking place in cataclinal configuration,
where the slope is parallel to the foliation. However, this tendency was not very
persistent, as shown in Figure 4-11, mainly because landslides mostly generate along
the soil-rock interface rather than foliation or other structural planes. The occurrence
of landslides in other configurations can be explained by the degradation of the rock,
which causes landslides practically everywhere.
Frequencies of depths of landslides
20
Frequencies of landslides in different
slope configurations
30
Count
Count
15
10
20
10
5
0
Anaclinale
Cataclinale
Orthoclinal
Slope configuration
0
0.1
0.2
0.25
0.3
0.4
0.5
DEPTH
0.6
0.69999
1
1.5
2
(m)
Figure 4-10. Number of landslides of different
depths
41
Figure 4-11. Number of landslides of
different slope configuration
5.
5.1.
Geological characterisation
“Terres Noires”
According to Antoine et al. (1999) the "Terres Noires" formation is found in
south-eastern France with a thickness exceeding 2 km in some places. It
consists predominantly of dark marly materials deposited during the Jurassic
in a large basin, which currently extends approximately to the Rhône Valley
to the west, the latitude of Grenoble to the north, the front of the internal
Alps to the east and the ridges of the Provence to the south. This formation is
known for its high susceptibility to weathering and erosion, its instability and
its tendency to supply solid material to watercourses.
Stratigraphic studies carried out by Artru (1972) have shown that the "Terres
Noires" can be subdivided into two units, both essentially marly, separated
by a harder median marker layer:
(1) The lower unit (upper Bajocian to lower Bathonian) consists of black
marls, cut into fine platelets;
(2) The marker layer (upper Bathonian and lower Callovian) is harder, acts
as a good reference layer, and consists of clayey, occasionally dolomitic
limestone, with a brownish patina; and
(3) The upper unit (lower Callovian to the middle Oxfordian) also consisting
of marls cut into platelets, with carbonated nodules. The outcrops of marls
that can be observed in he study area correspond to this unit.
The formation is composed by of highly stratified rocks that include a
detrital and a carbonated phase and often exhibiting fine bedding inherited
from their original deposition by turbidity currents. The "Terres Noires" can
be considered as a homogeneous lithological mass with average
characteristics as defined by Phan Thi San Ha (1992). The carbonate content
(composed essentially of calcite) varies from 20 to 80% of the total volume,
with corresponding facies ranging from marls to clayey limestones. Phan Thi
San Ha reported that the detrital phase comprises mainly silt (predominantly
quartz) with a small proportion of clayey materials (mainly illite), and that
42
average plasticity indexes of the fine fraction are 11%, thus confirming the
silty nature of the detrital fraction.
5.2.
Fieldwork
The sampling of the geology consisted in two campaigns in which the strike,
dip and dip direction of bedding and joints and were measured. This
measurements were taken with a Brunton Compass Additionally,
measurements of aperture, extension and a roughness coefficient (JRC) were
recorded for all the data.
The first campaign was performed by Didier Hantz in May 2007. From this
work 187 measurements along the main stream were obtained.
The second campaign took place in October 2007 under the scope of this
research. From this campaign 242 measurements were obtained along
secondary streams and some ridges. It total 429 measurements exist for this
area. The location of these measurements is shown in Figure 5-1.
BLACK MARLS
212600
»
»
»
»»
»»»
»
»
»¹
¹
¹»
¹
»¹ ¹»¹ ¹¹ ¹
¹
¹
»
212500
»
LANDCOVER
Forest
Grass
Bare marls
»
GEOLOGY_POINTS
Data acquired in
05/2007 and
10/2007
Stratigraphy: existent layer
derived from photo-interpretation
Nature
Â¹
»»
»»
»
STRATIGRAPHY
LANDCOVER
Arable land
»»
»
»
»
»
»
15
30
60
Joints
Bedding
90
Meters
»
¹
¹»
»
0
Fault
»
»
»
»
921800
Figure 5.1
Hydrography
»
»
212500
»»
»»»»»» »
» » » »»
»
»
»
»
Â
»
»
»
»
»
»
¹»
»
»
MOULIN CATCHMENT
Draix, France
GEOLOGICAL
MEASUREMENTS
»
»
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»
»»
»
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»»
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¹
»
»
212600
»
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»» »
212700
»
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»»
» »
» »
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»»»
»»
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»»
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»» »» »
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»»
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922000
¹
»
¹
»
» ¹¹
¹
¹
¹
»
¹
¹
¹
»
»
¹
»
»
¹
¹
»
¹
»
¹
»
¹
¹
¹
»
»
¹
»
¹
¹
»»
¹
¹
¹
»
¹
¹ ¹
¹ ¹
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¹
¹
»
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¹
»¹
¹
¹¹ ¹¹
»
¹ ¹
»
¹
»
»¹ »
»¹
¹
»¹
»
¹
¹
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¹
»
¹
»
»
¹ ¹¹
»
¹ ¹¹
¹
¹
¹
¹ ¹¹¹¹
¹
¹¹
¹
¹
±
921900
¹¹¹
¹ ¹¹¹¹»»¹¹» »
»¹
¹
»
»
¹
¹
¹
¹
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¹
¹¹¹¹
¹
¹»¹
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¹
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212700
921800
921900
922000
Figure 5-1. Geological Measurements.
43
Conic 3
5.3.
Methodology and resluts
The second part of the work consisted in georreferencing all the data
obtained by the first campaign. The location of those measurements was
indicated through direction lines defined along the streams, their strike and
the respective abscise of each measurement.
Most of the measurements of the second campaign were referenced with
GPS, but all data were recorded in the same way as described above, for
which it was necessary to locate many points.
In a third stage it was necessary to separate the bedding and joints data
because even when this information was part of the attributes of each point
recorded, sometimes calcite layers were mistaken as joints when they were
bedding, or the other way around. For this separation maps of the bedding
and joints were plotted separately and assuming a more or less constant
direction and dip of bedding some non correspondent points were identified.
Subsequently all data was evaluated together to obtain the main tendencies
of the bedding structure. For this density contours were traced in stereonets
under a Schmidt (equal area) lower hemisphere projection using 150 points
corresponding to bedding measurements. For the calculation of these
contours, the spherical Gaussian gridding method was used. With this
procedure numerical densities are calculated by means of a Gaussian
weighting function. A grid of 67 x 67 grids is used for determining density
values. Each node or counting station is incremented by an amount
proportional to the angular distance between each point and the grid node.
The analysis was performed in Rockworks 2002. The results can be
observed in Figure 5-6 and 5-7. According with these results, the mean dip
direction is 94.847, the standard deviation is 22.197. The mean lineation of
the plunge is 54.2, the great circle azimuth, 218, and the great circle plunge
is 58.9.
In a latter stage the bedding structure was considered variable. The area with
measurements was divided into 14 areas that presented similar bedding dip
and dip direction. The same procedure than the stated in the above paragraph
44
was followed for each of these areas and then the results can be found in
Figure 5-6. Geological structure. The size of the stereonets shown in the
figure correspond to the proportion of stream length that each of these
stereonets represent.
Using the results for a non variable bedding structure, 9 units of equal slopeaspect were defined comparing the slope strike to the mean of the bedding
strike. The ranges were defined using one standard deviation. They can be
found in Table 3-3.
The units of equal slope-aspect (30) that were defined in Chapter 3 were
used to distribute the values of bedding among the basin. In places where
there were no measurements the method of inverse distance weighting was
applied to assign the bedding structure values to those units. If there were
various values within the same unit, the mean was taken. The values of
bedding strike and dip and dip direction are represented in Figures 5-2, 5-3
and 5-4.
In general it was found that the bedding has a more or less constant
direction. The bedding has a strike of about 5 degrees dipping to the east
around 35 degrees. If a more detailed analysis is required, it is also possible
to consider also the local values, although there are no important
geomorphological changes such as folding that can change much the
direction. In the stereonets presented in Figure 5-4 it is possible to discover
some variability within the area and some families of joints, especially in the
lines 1, 3, 4, 5, 6,8 and 12.
45
922000
»
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»» » »
»
»
»»» » »»»
»
» »»»»»»» »»
»
»» »» » »
» »» »»»»» » » »» »
» » »
»
»
» »»»» »»» »» » »
» » »»
»
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»
»»» »»
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»» » » »»
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»»»»»»»» »» » » » » »»» »
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33
27
27
33 27
Fal6
30 30
212600
Fal2Fal4
67
35
GEOLOGICAL
STRUCTURE
BLACK MARLS
Fal1
35
10-20 24.73 %
37
27 30 Fal5 35
30
Fal2 30 40 30
35 31
E 20-30 3.12 %
Fal5
Rem
32Fal6Fal6
30
Fal2Fal6
45 38 38
40
Fal6
37 35
35
36 36
Bedding structure
36
42
W 40-50 3.9 %
NW 40-50 4.66 %
NE 20-30
N 40-50
Soil type
E 20-30
NE 40-50
FS_TYPE
S 20-30
E 40-50
40
Fal1
SE 20-30
SE 40-50
Fal2
SW 20-30
S 40-50
Fal4
W 20-30
SW 40-50
Fal5
NW 20-30
W 40-50
Fal6
NE 30-40
NW 40-50
Rem
40 40 40 40
35
37
37 37
37
40 40
40
50
Fal4
38
38
212300
38
921600
38
E 30-40
S 50-60
BLACK MARLS
SE 30-40
SW 50-60
GROUP_TXT
S 30-40
W 50-60
0 -10
SW 30-40
NW 50-60
10 - 20
W 30-40
N 20-30
NW 30-40
Fal2
38
38 38
38
38
»»»
»»
»» »
»
»
»
38
Fal6
Fal6
» »
38
38
38 Fal5 Fal5
Fal5
0
921700
921800
25
50
921900
100
150
Meters
922000
212300
212400
Fal4
38
212400
43
Fal2
W 50-60 0.12 %
NW 30-40 2.5 %
36
40
40
40
40
40
40
40
Fal2
36 36
30
45 35 28
30
40-500.65
1.06%
%
NE40-50
N 20-30 0.37 %
NE 40-50 0.07 %
NE 20-30 0.57 %
NE30-40
0-10 0.45
0.24 %
%
NE
NW 20-30 0.65 %
Fal2
36
31
31
0-10 13.61 %
E 30-40 3.89 %
38
30 30
32
35
Fal5
212500
»
25
Figure 5.2
44
27
Fal4
MOULIN CATCHMENT
Draix, France
45
Fal2Fal2
Fal2
35
50
33
35
Fal4
67
55
65
»
28
Rem
55
45
212500
»
30
45
45
27
40 45 45
45
23 32
70
45 45
40
40
45
45
Fal2
50
40 45 45
35 37 23 40
30
38
42
50 40
45
40 Fal2
56
22 22
40 Fal2
30
30
45
31 45 37 45
45
30
45
56
40
45
43
60
40
30 30
30 30
30
27
40
55
30
35
45
35
30
37 25
37 37
33
50
35
50
30
21 47
45 40
45
30
30 33
53
47
40
45
27 21
43
47
47
44
24
Fal6
40
40
35
27 25 25
40
45
20
45
Fal4
20 20
47
45
24 24 19
45 45
45
42
27 27
4545
45
40 37
45
Fal4
33
33
33
45
45
45
30
922100
62 62
62 67
53
33
±
212700
921900
212700
921800
212600
921700
»
921600
W 30-40 4.01 %
NW 50-60 0.25 %
S 20-30 2.61 %
W 20-30 4.97 %
S 30-40 2.39 %
SW 50-60 0.38 %
S 40-50 3.8 %
SW 40-50 3.13 %
S 50-60 0.3 %
SE 20-30 4.96 %
SE 30-40 4.48 %
SW 20-30 2.22 %
SW
SE 40-50 2.31
% 30-40 3.59 %
and from field measurements
from 05/2007 and 10/2007
Conic 3
922100
Figure 5-2. Geological Structure.
921700
921800
45
45
45
Fal2 Fal4
Fal5
27 30Fal5 35
30
30
30
40
Fal2
35 31
32 Fal6Fal6
212500
»
30
Fal4
37 35
35
Fal2
37
37 37
37
40
36 36
30
45 3838
40
Fal6
40
40
40
40
Fal2
36
31
31
45 35 28
30
40
36 36
36
36
40
40 40
40
40 40 40 40
35
42
40
50
43
38
Bedding structure
BLACK MARLS
STRIKE
Fal6
Fal6
38
0-2
Fal2
212300
921600
38
921700
38 Fal5 Fal5
Fal5
0
921800
341 - 352
23 - 30
353 - 358
51 - 320
38
38 38
38
38
»»»
»»
»»
»
»
»
»
38
38
38
321 - 340
16 - 22
31 - 50
3-8
38
9 - 15
212400
38
25
50
921900
100
150
Meters
922000
212300
Fal4
Fal4
» »
212400
Fal2
Figure 5.3
38
30 30
32
35
Fal5
35
44
37
27
Fal4
BLACK MARLS
Fal1
45
Fal2Fal2
Rem
35
50
33
Fal2
MOULIN CATCHMENT
Draix, France
BEDDING STRIKE
212600
»
»
35
Fal2Fal6
45 45
65
35
25
67
67
55
43
28
Rem
55
212500
»
33 27
30 30
212600
45
45
45
27
40
45
23 32
70
45 45
40
40
45
Fal2
45
50
40 45 45
30
35 37 23 40
38
42
50 40
45
40 Fal2
56
2222
40 Fal2
30
30
45
31 45 37 45
45
30
45 60
45
40 56 40
30 30
30 30
30
27 30
40
55
35
45
35
30
37 25
37 37
33
50
35
47
50
40
30
21
45
45
30
30 33
53
47
40
21
45
27
43 44
47
47
Fal6
24
40
40
35
27 25 25
40
45
20
45
Fal4
20 20
47
45
24 24 19
45 45
45
42
27 27
4545
45
40 37
45
Fal4
33
33
30
212700
27
27
Fal6
922100
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»
»
»
»
»» »
»
»»» »»
»»
» »»
»» » » »»
»»» »»»»» »»
»
»»
»
»
»»»»»
» » » »»»»»»»»
»
»»»
»» » » »
»
»»»»
»»»»»»»»» »» » » » » »»» »
»
» »»»
»»»»»
»»»
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»
»
»
»
»
»
» »»»» » »
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» »
»
»»
»
33
33
30
922000
62 62
62 67
53
33
±
212700
921900
»
921600
Data derived from Lidar DTM
acquired in 04/2007
Resolution = 1 m
from 05/2007 and 10/2007
Conic 3
922100
Figure 5-3. Bedding Strike.
47
47
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»
»
»
»
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»
»
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»
» »»»»»»» » »»
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» » » » »» »» » » »
»
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» »» » »»»»»»»»
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»»»»»»»»» »» » » » » »»» »
»
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» »»»» » »
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»
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»
»
33
27
27
45
45
45
»
Fal2Fal4
35
27
Fal4
212500
»
30
35
37 35
Fal2
37
37 37
37
40
40
40
40
40
40
36
40 40
36
40
40 40 40 40
35
42
40
50
Bedding structure
BLACK MARLS
DIP
0
1 - 23
212400
38
38
212300
38
921600
38
Fal6
Fal6
38
38
921700
38
24 - 27
28 - 32
33 - 36
37 - 40
41 - 45
46 - 50
51 - 60
61 - 70
Fal2
38
38 38
38
38
»»»
»»
»»
»
»
»
»
38
Fal4
» »
Fal4
36 36
40
43
Fal2
Figure 5.4
Fal2
36 36
30
45 38 38
35
35
36
31
45 35 28
40
BLACK MARLS
Fal1
38
30 30
32
31
30
Fal6
BEDDING DIP
37
Fal2
32 Fal6Fal6
Fal5
35
MOULIN CATCHMENT
Draix, France
44
27 30 Fal5 35
30
30
30
40
35 31
Fal5
Fal2Fal6
67
45
Fal2Fal2
Fal2
Rem
Fal4
67
55
50
33
35
25
55
38 Fal5 Fal5
Fal5
0
921800
25
50
921900
100
922000
150
Meters
212300
»
30 30
28
Rem
45
212600
Fal6
27
212500
33
33
212600
45
45
30
922100
45
40 45
45
23 32
70
45 45
40
40
45
45
Fal2
50
45
40
35 37 23 40
30
38
45
42
50 40
45
40 Fal2
56
2222
40 Fal2
33 27
30
30
45
65
45
37
31 45
45
30
45 60
45
43
40 56 40
30 30
30 30
30
27 30
40
55
35
45
35
30
37 25
37 37
33
50
35
47
50
40
30
21
45
45
30
30 33
53
47
40
45
27 21
43 44
47
47
24
Fal6
40
40
35
27 25 25
40
45 45
20
Fal4
20 20
47
45
19
24
45 45
24
45
42
27 27
45 45
45
37
40
45
Fal4
33
30
922000
62 62
62 67
53
33
±
212700
921900
212700
921800
212400
921700
»
921600
acquired in 04/2007
Resolution = 1 m
from 05/2007 and 10/2007
Conic 3
922100
Figure 5-4. Bedding Dip.
10.0
38.0
36.0
34.0
32.0
30.0
28.0
26.0
24.0
22.0
20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
Figure 5-5. Bedding stereonet. The ranges are given in density percentage.
48
Figure 5-6. Geological Structure.
49
921700
17.0
16.0
15.0
14.0
13.0
12.0
11.0
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0.0
2.0
4.0
6.0
8.0
10.0
14.0
12.0
16.0
18.0
20.0
22.0
24.0
26.0
28.0
30.0
1
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
22.0
0.0
2.0
4.0
1
1
7
921800
1
32.0
30.0
28.0
26.0
24.0
22.0
20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
6
921600
5.0
±
22.0
24.0
26.0
8
10
11
1
11
28.0
10
8
4
9
0.0
6.0
4.0
2.0
14.0
12.0
10.0
8.0
18.0
16.0
22.0
20.0
28.0
26.0
24.0
30.0
3 3
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
22.0
24.0
26.0
28.0
5.0
14
921900
5.0
24.0
2
2
15
921900
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
22.0
2
921800
5.0
212700
212600
212500
212400
1
1
11 .0
10 .0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
20 .0
19 .0
18 .0
17 .0
16 .0
15 .0
14 .0
13 .0
12 .0
5.0
16 6
1
12
5. 0
921700
0
20.0
19.0
18.0
17.0
16.0
15.0
14.0
13.0
12.0
11.0
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
13
25
3 13
131
13
50
0.0
2.0
4.0
6.0
8.0
12.0
10.0
14.0
18.0
16.0
22.0
20.0
24.0
26.0
28.0
30.0
922000
13
19. 0
18. 0
17. 0
16. 0
15. 0
14. 0
13. 0
12. 0
11. 0
10. 0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
922000
100
17.0
16.0
15.0
14.0
13.0
12.0
11.0
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
20.0
19.0
18.0
17.0
16.0
15.0
14.0
13.0
12.0
11.0
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
150
Meters
922100
0.0
2.0
4.0
6.0
8.0
10.0
14.0
12.0
18.0
16.0
22.0
20.0
26.0
24.0
30.0
28.0
922100
5.0
921600
5.0
212300
2
5.
0
212700
212600
212500
7
5
212400
14
212300
14
Figure 5.5
GEOLOGICAL
STRUCTURE
BLACK MARLS
(Bedding and
Joints)
Conic 3
Data derived from
field measurements
of 05/2007 and 10/2007
Bedding density (%)
Schmidt projection
38.0
36.0
34.0
32.0
30.0
28.0
26.0
24.0
22.0
20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
MOULIN CATCHMENT
Draix, France
10.0
5.0
1
10
.0
In appendix 3 the stereonets of the landslides are presented. They contain slope, face
and joints direction and dip. With the aid of this stereonets it is possible to deduce
what kind of failure the landslide had. For instance, landslides 70, in figure 5-7, it is
evident that the failure mechanism was wedge. The green line represents the
bedding and the red ones, discontinuities.
Figure 5-7 Stereonet for landslide 70.
Figure 5.8 shows the different kinds of possible failure and their relationship with
the stereonet representation.
According to this, landslides 4, 7, 8, 16, 17, 22, 23, 28 and 42 (among others) had
planar failure. Landslides 6, 11, 12, 44, 58 and 70 presented wedge failure, and 1, 11
and 19 had probably circular failure. Unfortunately it was not possible to map these
types of failures because of time constraints, but the means are given in this research
for further studies to be able to achieve that.
50
Figure 5-8 Main types of slope failure and stereoplots of structural conditions
likely to give raise to thse failures. After Hoek et al., 1981.
51
6.
Soil depth measurement and modelling
Determination of the thickness of the superficial soil layer is of fundamental
importance for the estimation of landslide susceptibility, as it is precisely in this
layer where erosion processes and shallow landslides take place. Superficial deposits
are the most susceptible to erosion processes and therefore contribute directly to the
sediment load in the catchment. An accurate estimation of superficial soil
thicknesses and soil depth distribution is therefore necessary to achieve realistic
results from the simulations. This chapter summarizes the procedures and results of
a field test programme carried out to measure soil depths in the Moulin basin.
Dynamic penetrometer tests have been used to determinate the superficial soil
thickness and the results have been summarized in the form of a soil depth map of
the basin, which was later incorporated in the landslide susceptibility analysis.
6.1.
Composition of the superficial weathered layer
Antoine et al., (1995) reported the following composition of the superficial
weathered zone in the "Terres Noires" formation: (1) A soft, disorganised, surface
zone (approximately 10 cm thick) consisting of loose marl platelets; (2) An
intermediate unit (up to 50 cm thick), in which the laminated structure of the "Terres
Noires" is preserved, marking the transition between sound parent rock and the
completely weathered surface zone; and (3) Intact rock, 30 to 50 cm below the
surface (determined by portable penetrometer tests, Dumollard, 1984). They also
reported that in areas with a dense vegetation cover, the weathered zone is much
thicker, and in some places exceeds several metres. In such cases, horizontal
landslides may occur at the contact with the underlying intact bedrock, affecting on
the forest cover.
According to their research (Antoine et al., 1995), superficial loose soil layers are
constantly regenerated by environmental agents and transported by runoff. Erosion
processes in the area are promoted by the abrupt topography and the dense drainage
network of the terrain leading to a rapid disintegration of the material through
weathering. This causes voluminous solid transport and, promotes superficial
instability. Erosion and weathering of the "Terres Noires" is a continuous cyclic
process. Antoine et al (1995) also identified storms with high rainfall intensities as
other important events, which destroy the structure of the weathered layers, partly
through splash effects, promoting marl platelet degradation into silt products and
facilitating material transport by surface runoff. More localized erosion processes
can also be identified in the area in the form of debris flows which occur as the
52
result of faster erosion of the underlying marl layers. This process is not necessarily
linked to a specific rainfall event.
Maquire et al (Maquaire et al., 2002) presented more descriptive characteristics of
the superficial soil layers in the area as follows:
(1) Loose detrital cover, made of locally produced clasts or colluvial material
sensitive to erosion;
(2) Regolith of marl, more or less fragmented by decompression, which can be
subdivided into (2a) the loosened upper regolith, where the marl plates and structure
are deteriorated, and (2b) the compact lower regolith, conserving the marl structure
but not its cohesion (schistosity opening);
(3) Bedrock at the bottom, which is very compact, structured and cohesive.
The test procedure and the results from penetrometer tests are presented in the
following sections, along with an explanation on how those results were
incorporated into the simulations.
6.2.
Determination of superficial soil thickness
Intrusive and non intrusive site investigation techniques can be applied to determine
thicknesses of superficial soil layers. Direct intrusive methods such as trenches and
trial pits are usually the most reliable as they allow a direct inspection of superficial
soil thickness and the nature of the soil-rock interface. However, there is a limitation
to the amount of pits and trenches that can be done in a particular area. Therefore,
other intrusive methods such as the use of penetrometers have been applied thanks to
their flexibility and the relative quick set up and test procedures.
Precise definition of the soil-rock interface is often difficult due to the various
existing criteria to distinguish hard soils from soft rocks. Criteria in terms of
penetration or point load resistance and compressive strength are commonly
adopted. During this study, dynamic penetrometer tests, with variable energy were
used to assess superficial soil thicknesses. As described by Maquire et al (2002), a
dynamic penetrometer with variable energy records a mechanical resistance profile
when a rod is manually driven into the soil using a standardised hammer (Figure
6.1). It allows identifying thin, low resistance layers, by modulating the blows of the
hammer. Each penetrometer blow produces a vertical resistance profile called
penetrogram that can be interpreted to identify different layers and estimate their
thickness using two main criteria: well-defined resistance thresholds and the shape
of the penetrogram.
53
As for most geophysical methods, this method requires calibration for the specific
environment. Maquaire et al (2002) systematically carried out tests and then
calibrated and validated the results by comparison against observed pit profiles at
two sites, as shown in Figure 6.2. Their study arrived to the following criteria to
estimate soil depth:
–
Penetrometer readings below 5 MPa are representative of the loose detrital
cover (layer 1) and accumulated material transiting on slopes or
sedimentary load in talwegs;
–
Penetrometer readings between 5 and 35 MPa mostly characterize the marl
regolith (layer 2), with some possible distinction between (2a) the loosened
upper regolith (regular increase in resistance with depth) and (2b) the
compact lower regolith (high but largely variable resistance due to marl
plates breaking);
–
Penetrometer readings above 35 MPa are considered to be representative
for the resistant compact marl bedrock (layer 3).
Hammer (M)
Head (cells) (T)
Penetration
measurement
Data register
and calculation
Penetrometer
rod (Tt)
Measure transducer
Point (p)
Determination of point resistance Qd (MPa). Hollanadais equation:
A= Point area (cm2)
M= Hammer mass (kg)
V= Impact velocity (l/t)
l= distance between cells
t= time interval between cells
P= System mass Pt+T+Tt+p (kg)
e= penetrometer rod (m)
Figure 6-1. Description of the portable dynamic penetrometer with variable
energy PANDA (After Maquaire et al, 2002)
54
Calibration example: Test No 95, site 5 (Laval basin, Draix)
Penetrometer data interpretation:
(geotechnical criteria)
Observed profile:
(geomorphologic descriptions)
Depth
(cm)
Qd range
Depth
(cm)
Multi-sized debris in a fine
matrix. Loose, homogeneous
layer (reduced cohesion)
Debris cover
Less preserved structure.
Upper
regolith
Preserved structure. Foliation
plates, joints and other
features
compact
regolith
Intact marl
Compact appearance and
closed schistocity
Validation example: Test No 89, site 5 (Laval basin, Draix)
Penetrometer data interpretation:
(geotechnical criteria)
Observed profile:
(geomorphologic descriptions)
Depth
(cm)
Depth
(cm)
Debris cover
Little marl plates in a fine
matrix
Less preserved structure.
Little plates and schistocity
plans are preserved.
Upper regolith
Strong diagonal
approx. 5cm
plate
of
Preserved structure. Foliation
plates, joints and other
features
compact
regolith
Compact appearance and
closed schistocity
Intact marl
Figure 6-2. Interpretation of the penetrograms: organisation of the weathered
profile and associated strength profile. a: Calibration; b: validation (After
Maquaire et al, 2002)
This same strength criteria were applied in this research to interpret penetrometer
test results in the Moulin basin. The results are presented in the following sections.
6.3.
Field test campaign
The campaign consisted of 21 tests that were performed in the northern
portion of the catchment mainly aligned along sections. The localization of
the tests is represented in Figure 6.3.
55
921800.000000
921900.000000
922000.000000
922100.000000
212700.000000
921700.000000
212700.000000
!!!!!
!!!!!
212600.000000
!! !
!
!
!
!
!! !
!
! ! ! !!
! !
!
!!
!!!
!
!
!
!! !!
!
!!
!! !
!!
!
!!
!!
!!
!
!
!
!
!!!
!
!
!
!!!!!
!!
!
!
!!!!
!
!
!!!!!
!!
!
!! !
! !! !!!
!
!!
!
!
212400.000000
212400.000000
212500.000000
!
! !!
!
!
212500.000000
!
!
212600.000000
921600.000000
Penetrometer tests in
the Moulin
Catchment
!
!
!
!
!
Penetrometer
tests 1999-2003
!
!
Penetrometer tests October 2007
212300.000000
!
921600.000000
921700.000000
921800.000000
921900.000000
922000.000000
922100.000000
Figure 6-3. Penetrometer tests in the Moulin Catchment.
6.4.
Test results
Results from the interpretation of dynamic penetrometer tests are shown in
Appendix 6 in the form of strength vs. depth charts, and are also summarized in
Table 6.1. A total of 134 tests were considered in the analysis, from information
collected during this study and previous test campaigns. The analysis was performed
not only for the latest data, but for all data available. This can be consulted in
Appendices 4 and 5.
Soil-rock interface was assumed to be located were a maximum 35 MPa resistance
was achieved. In some cases, such as the test shown in Figure 6.4, this limit was
temporarily reached and then decreased again, evidencing the presence of stiffer
layers or blocks within the superficial deposits overlying the parental rock.
56
Penetrometer resistance vs depth
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
Resistance (MPa)
20
30
40
50
0.00
TEST 8
DATE
11/2002
0.05
0.10
0.15
0.15
0.20
0.20
0.25
0.25
0.30
0.30
Depth (m)
Depth (m)
0.10
10
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
Figure 6-4. Sudden increase in
resistance within layer 2b (lower
regolith)
TEST 15
DATE
11/2002
Figure 6-5. Sudden increase in
resistance within layer 2b (lower
regolith)
This was more frequent in the layer 2b, where a scatter in the penetrometer
measurements is associated with the presence of stiffer plates from the parental marl.
Figure 6.5 shows more clearly the difference between the upper and the lower
regolith. In the upper layer there is less scatter due to the relatively homogeneous
response of the weathered material to the concentrated load caused by the
penetrometer. All tests showed a general increase of resistance with depth, although
in some cases the resistance appeared steadier with depth, fluctuating between 10
and 25 MPa across the layer 2. In these cases, there was less differentiation between
the upper and the lower regolith, as shown in Figure 6.6.
In general, there was a sudden increase of resistance near the end of the test, clearly
evidencing the presence of the rock. In some tests, such as shown in Figure 6.7, this
sudden increase was not enough to reach the nominal resistance of 35 MPa. Here, it
is reasonable to assume that the soil-rock interface is located where the sudden
increase in resistance took place, because this is there where the relative movement
can occur and slip surfaces can develop. On the other hand, some tests showed a
57
60
gradual transition between the soil deposit and the rock and then the nominal value
of resistance (35 MPa) was used to determinate the location of the interface. At least
27 tests of the whole dataset (20%) did not reach the 35 MPa criteria, therefore in
those cases, the soil depth was considered to be the maximum depth reached during
the test.
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
Resistance (MPa)
20
30
40
50
60
0.00
TEST
MOUL
514
0.05
0.10
0.15
0.15
0.20
0.20
0.25
0.25
0.30
0.30
Depth (m)
Depth (m)
0.10
10
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
Figure 6-7. Resistance varying steadily
with depth within layer 2.
TEST 20
DATE
11/2002
Figure 6-6. Sudden increase in resistance
at the soil rock interface.
The dynamic penetrometer test has been proven to be a rapid and reliable method for
identifying the soil depth in the study area. However, the existing tests are clustered
in the northern area of the catchment and they were performed over many campaigns
and in an extensive period of time (1999 – 2007).
The results of the tests showed that the depth to the bedrock fluctuates between 0.06
and 0.87 m in the bare black marls of the basin.
Several profiles of various penetrometer tests were analysed grouping them by date
and by their localization in different slope faces with similar
58
DATE
dd/mm/yyyy
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
08/11/1999
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
02/05/2000
04/04/2001
04/04/2001
04/04/2001
04/04/2001
05/04/2001
05/04/2001
05/04/2001
05/04/2001
05/04/2001
59
59
Max test
Layer 1
Layer 2
Soil depth
DATE
Max test
Layer 1
Layer 2
Soil depth
depth (m) bottom (m) bottom (m)
(m)
dd/mm/yyyy depth (m) bottom (m) bottom (m)
(m)
0.22
0.09
0.16
0.22
07/11/2002
0.24
0.07
0.12
0.24
0.15
0.06
0.11
0.16
07/11/2002
0.26
0.08
0.15
0.26
0.39
0.09
0.34
0.39
07/11/2002
0.16
0.10
0.11
0.11
0.25
0.04
0.14
0.25
07/11/2002
0.21
0.05
0.07
0.21
0.19
0.06
0.12
0.18
07/11/2002
0.44
0.10
0.44
0.44
0.51
0.08
0.26
0.51
07/11/2002
0.22
0.07
0.16
0.21
0.38
0.06
0.12
0.26
07/11/2002
0.14
0.06
0.08
0.14
0.37
0.06
0.09
0.37
07/11/2002
0.24
0.09
0.17
0.17
0.72
0.09
0.56
0.72
07/11/2002
0.37
0.11
0.30
0.36
0.46
0.11
0.39
0.46
07/11/2002
0.15
0.05
0.10
0.15
0.17
0.06
0.11
0.17
07/11/2002
0.15
0.05
0.10
0.14
0.06
0.06
0.06
0.06
07/11/2002
0.32
0.25
0.30
0.30
0.14
0.06
0.10
0.15
07/11/2002
0.24
0.14
0.17
0.17
0.35
0.06
0.19
0.35
07/11/2002
0.56
0.34
0.56
0.56
2.38
1.10
1.75
2.30
23/07/2003
1.10
0.25
0.52
0.81
2.82
0.39
0.90
2.79
23/07/2003
0.32
0.09
0.17
0.17
2.82
0.30
2.82
2.82
23/07/2003
0.92
0.11
0.51
0.84
0.27
0.09
0.14
0.26
23/07/2003
0.36
0.07
0.24
0.31
0.17
0.07
0.14
0.17
23/07/2003
0.61
0.17
0.44
0.53
0.27
0.12
0.15
0.22
23/07/2003
1.52
0.15
0.75
1.50
0.44
0.11
0.16
0.23
08/10/2007
0.41
0.14
0.23
0.41
0.37
0.08
0.12
0.28
08/10/2007
0.10
0.04
0.09
0.10
0.15
0.06
0.11
0.13
08/10/2007
0.11
0.03
0.08
0.11
0.92
0.12
0.30
0.87
08/10/2007
0.31
0.13
0.26
0.31
0.33
0.04
0.11
0.32
08/10/2007
0.54
0.09
0.39
0.54
0.27
0.05
0.26
0.26
08/10/2007
0.30
0.07
0.07
0.30
0.27
0.05
0.16
0.22
08/10/2007
0.18
0.08
0.08
0.18
0.55
0.07
0.14
0.46
08/10/2007
1.15
0.16
0.23
1.15
0.84
0.15
0.29
0.84
08/10/2007
0.58
0.09
0.09
0.58
0.35
0.12
0.19
0.32
08/10/2007
0.40
0.09
0.16
0.40
0.47
0.32
0.35
0.42
08/10/2007
0.46
0.08
0.08
0.46
0.45
0.10
0.26
0.44
08/10/2007
0.41
0.09
0.09
0.41
0.68
0.21
0.42
0.63
08/10/2007
0.26
0.07
0.19
0.26
0.55
0.07
0.36
0.54
08/10/2007
0.21
0.06
0.12
0.21
0.49
0.10
0.31
0.49
08/10/2007
0.27
0.07
0.07
0.24
0.19
0.06
0.10
0.19
08/10/2007
0.35
0.14
0.14
0.34
0.37
0.17
0.36
0.36
08/10/2007
0.27
0.10
0.20
0.27
0.20
0.07
0.19
0.19
08/10/2007
0.28
0.18
0.23
0.23
0.15
0.04
0.10
0.14
08/10/2007
0.50
0.10
0.10
0.50
0.19
0.07
0.14
0.19
08/10/2007
0.45
0.10
0.24
0.38
0.44
0.21
0.44
0.44
08/10/2007
0.45
0.14
0.42
0.45
0.39
0.11
0.36
0.39
08/10/2007
0.85
0.10
0.57
0.85
0.18
0.04
0.11
0.18
08/10/2007
0.31
0.20
0.20
0.31
0.21
0.05
0.10
0.17
08/10/2007
0.60
0.14
0.26
0.59
Table 6-1. Results from Dynamic Penetrometer Tests (see Appendix 4 and 5)
Max test
Layer 1
Layer 2
Soil depth
DATE
depth (m) bottom (m) bottom (m)
(m)
dd/mm/yyyy
0.37
0.04
0.19
0.30
05/04/2001
0.20
0.04
0.14
0.20
05/04/2001
0.31
0.08
0.25
0.31
05/04/2001
0.48
0.12
0.37
0.48
05/04/2001
0.32
0.17
0.27
0.30
05/04/2001
0.30
0.13
0.22
0.29
05/04/2001
0.40
0.14
0.27
0.37
05/04/2001
0.53
0.23
0.42
0.51
05/04/2001
0.41
0.16
0.26
0.37
05/04/2001
0.15
0.07
0.12
0.15
05/04/2001
0.37
0.14
0.27
0.36
05/04/2001
0.51
0.12
0.34
0.48
05/04/2001
0.28
0.08
0.14
0.27
05/04/2001
0.18
0.04
0.12
0.17
05/04/2001
0.22
0.07
0.12
0.21
08/11/2001
0.67
0.17
0.27
0.66
08/11/2001
0.48
0.14
0.23
0.47
08/11/2001
0.72
0.11
0.33
0.72
04/11/2002
0.30
0.07
0.21
0.29
04/11/2002
0.56
0.12
0.41
0.52
04/11/2002
0.28
0.07
0.16
0.25
04/11/2002
0.43
0.12
0.35
0.42
04/11/2002
0.25
0.09
0.16
0.24
04/11/2002
0.30
0.12
0.19
0.31
04/11/2002
0.45
0.12
0.22
0.45
04/11/2002
0.61
0.19
0.32
0.61
04/11/2002
0.36
0.17
0.32
0.36
04/11/2002
0.20
0.07
0.16
0.19
04/11/2002
0.34
0.07
0.19
0.33
04/11/2002
0.48
0.11
0.33
0.47
04/11/2002
0.23
0.09
0.19
0.23
04/11/2002
0.35
0.07
0.23
0.23
06/11/2002
0.31
0.09
0.25
0.26
06/11/2002
0.47
0.09
0.42
0.46
06/11/2002
0.71
0.09
0.24
0.70
06/11/2002
0.43
0.07
0.24
0.26
06/11/2002
0.39
0.06
0.19
0.34
06/11/2002
0.15
0.06
0.12
0.15
06/11/2002
0.20
0.03
0.13
0.20
06/11/2002
0.33
0.16
0.25
0.34
06/11/2002
0.48
0.09
0.14
0.47
06/11/2002
0.21
0.07
0.11
0.21
06/11/2002
0.28
0.10
0.16
0.25
06/11/2002
0.30
0.07
0.17
0.30
06/11/2002
Characteristics of general slope gradient and aspect, as defined in Chapter 3. This
analysis was performed with the objective of finding a typical slope profile for each
unit with the same slope and aspect, but it was very difficult to find a pattern of soil
depth behaviour because the profiles are not comparable. The difference between
profile orientation, dates and slope-aspect unit did not allow reaching a conclusion
regarding this matter. The location of the profiles is presented as Figure 6.8, along
with the dates of the different tests. The profiles can be consulted in Appendix 6.
6.5.
Soil depth modelling
Two methods for the soil depth modelling were compared: the one presented by
Catani et al (2007) and a multivariate regression model. The first methodology links
soil thickness to gradient, horizontal and vertical slope curvature and relative
position within the hillslope profile. In this methodology, unitary factors (Slope S,
Curvature C and relative position IP) are calculated for each variable and the final
product of each of them is adjusted with the real depths obtained from the
measurements. Through a script the relative position of a point in a slope was
calculated, where IP = 0 for the ridge and 1 for the stream. The slope S factor was
calculated assigning S = 1 where the slope (in radians) is minor than the internal
angle of friction of the material for each area, and S = 1/(1+tg θ) where the slope is
more than this value. A linear regression was produced to relate the minimum and
maximum values of soil depth (0 – 1) to the equivalent of curvature (-161.54 to
225.35). The second assumption made in this methodology was that in the streams
the soil depth is zero, and in the ridges the soil depth is maximum and the variation
within these two maximums is linear. These two assumptions differ from the ones
applied by Catani et al, (2007). They applied the methodology for a different study
area in which the behaviour of the soil depth is opposite in the sense that the
minimum soil depth occurs in the maximum curvature, or in other words, the
maximum soil depth is seen in the streams, where there is deposition, which is not
the case of the Moulin catchment, where soil depth instead of deposition is necessary
to be measured.
The results obtained from this method were highly influenced by the assumptions
regarding the distribution of the depth along the slope, by the curvature and more
importantly, by the measured values of soil depth.
60
61
033029
12
107
048
047
35 046
042 044
045
36 043
031
035 030
4
106
019
102 101
14109
15
108 110 103
105 104
13
053 020
054
052
049
050
051
009 112
111
114
022
040
1
508
024
20
504
502
503
505 513
506
23
24
520
510
509
511
501
512
524
521
039
023 19025
1021
7
16
113
018
026
007
017
006
016 008
001
028
011
060 014
058 059
013
056
012
5
057 055
010
003
11
Figure 6-8. Penetrometer Tests and Equal Slope and Aspect Areas.
921800
034
004
7
002
027
37
±
8
032
005
038
25
037 036
0
2
515
26
514
516
517
519
10
30
116
29
117
118
120
119
32
135 136
142
139 141
140
137 138
921900
0
DATE
5
10
09-10/10/2007
23/07/2003
04-06/11/2002
04-05/04/2001
02/05/2000
08/11/1999
20
143 144
30
Meters
PENETROMETER TESTS
121 122
125 126
123 124
127
31
131
134
132
133
115
921900
34
304 305
302
303
212700
212600
921800
6
9
212700
212600
21
3
18
22
27 28
33
Conic 3
acquired in 04/2007
Resolution = 1 m
Penetrometer tests taken with
PANDA from 1999 to 2007.
HIGH - ORTHOCLINAL
HIGH - ANACLINAL
HIGH - CATACLINAL
MEDIUM - ORTHOCLINAL
MEDIUM - ANACLINAL
MEDIUM - CATACLINAL
LOW - ORTHOCLINAL
LOW - ANACLINAL
LOW - CATACLINAL
GROUP
SUB_BASIN
PROFILES
PENETROMETER TESTS
AND EQUAL SLOPE AND
ASPECT AREAS
Figure 6.8
MOULIN CATCHMENT
Draix, France
The results of the method were compared with 20% of the soil depth tests data. A
mean error of 70% was obtained.
The second method used for soil depth mapping considered variables such as slope,
curvature, altitude, the curvature index obtained by the first method (C), wetness
index and relative position in the slope (IP) for a multivariate regression analysis for
predicting soil depth. For this analysis the values for all the variables were extracted
in the points in which the soil depth was known and various linear regressions were
performed using 80% of the data, and leaving 20% for validation. This was done for
the whole set of 124 penetrometer tests that are located within the bare marls, and 12
corresponding proxy points that were located in the streams, where the soil depth is
considered to be zero. The process was repeated for a subset in which 81 of the tests
were used, along with 8 proxies. These tests are all allocated within the sub-basin
indicated in Figure 6.8. The use of only the tests in the sub-basin did not improve the
results. The set described below provided with the results with the highest
correlation. The relationship obtained was:
SD = 0.156 + 0.004 * [slope] + 0.147 * [C] - 0.224 * [IP]
(1)
Where SD = soil depth
Slope = slope in degrees
C = 0.0026 * curvature + 0.4171 (relationship obtained in the first method)
Ip = relative position in the slope from 0 in the ridge to 1 in the streams
The relationship (1), stated above presented with a mean error 22% and r2 = 0.200.
The map product of this analysis is presented in Figure 6.9. The results from the
testing of multivariate regression analysis with more variables were not considered
satisfactory. Nevertheless, their relationship (1) presents the lowest mean error and
highest correlation of all, including the first method discussed here. The values
obtained vary within the range of 0 to 0.50 m, although the maximum value found in
the tests was 0.87 m.
62
921700
921800
921900
922000
922100
±
212700
212700
921600
MOULIN CATCHMENT
Draix, France
SOIL DEPTH
212600
212600
Figure 6.9
SOIL DEPTH
Value
212500
212400
212400
212500
High : 0.500670
921600
921700
921800
25
50
921900
100
922000
150
Meters
212300
212300
0
Low : 0
acquired in 04/2007
Resolution = 1 m
and from PANDA penetrometer
tests taken from 1999 to 2007
Conic 3
922100
Figure 6-9. Soil Depth map resulting from multivariate regression model.
Additionally, the low values of soil depths are true for low slopes, such as the ones
found in the streams, but not for low slopes in which there are colluviums or other
types of deposits. In the north-east portion of the basin this kind of materials forms
deposits that have low slopes and larger depths to the bedrock (in average 0.6 m).
The results for this area were depths between 0 and 0.20 m. The difference in range
is due to the unavailability of measurements for the deposits that rest in the low
slope areas of the black marls.
The low quality of the soil depth map results can be explained mainly as a
consequence of the following facts: a. the soil depth measurements on which all the
analysis are based are clustered in a small portion of the catchment, b. these
measurements were taken in various dates over a long period of time, c. the soil
depth for this area can not be considered as static in time.
Although there are enough measurements for characterising each uniform slopeaspect unit in the sub-basin, the measurements are not comparable because of their
difference in time. According to (Mathys et al., 2003)), the annual sediment yield in
63
the Moulin Catchment is very high: 115 tons/ha, which represents a mean equivalent
ablation of 12 mm of the weathered layer or 6 mm of bedrock per year in the bare
marls. They also state that the variability of this rate is high between different years
depending on the climate conditions for the period. In the catchment the sediment
yield of 1988 was 36 ton/ha, while in 1992 it was 100 ton/ha. The variability of the
erosion takes place both as a temporal factor and as a spatial factor. The existence of
harder layers such as calcite prevents the erosion. The slope, the amount of rain and
the fracturation intensity for each particular slope are factors that influence the
weathering and erosion rates.
Because of this variability it is not possible to obtain a static soil depth result. It
would be necessary to study both the weathering front and erosion front advance in
order to establish soil growth rates making use of an erosion model such as the ones
used by (Oostwoud Wijdenes and Ergenzinger, 1998) or (Mathys et al., 2003) and
the absolute values of bedrock depth and bedrock height, in order to determine the
advance of the erosion front with other years.
7.
7.1.
Landslide indirect susceptibility Analisys
The landslide suceptibility analysis model
Landslide susceptibility in the Moulin catchment was evaluated by indirect mapping,
using a bivariate statistical approach based on a formulation of the Bayesian
probability model, known as weight-of-evidence method. Thematic spatial
information was managed with GIS applications as a basic tool. The various
conditioning factors such as topographical characteristics and geology, among
others, were combined and categorized to determine their interrelation and their
relative influence on the landslide susceptibility. The methodology applied here is
based on the weight-of-evidence method as described by Bonham-Carter (1994).
As stated by van Westen et. al. (2003), indirect landslide susceptibility mapping is
characterized by predicting the degree of landslide susceptibility on the basis of the
factors that condition the occurrence of landslides, such as lithological units, slope
classes or land use types. Those factors are handled with ILWIS and then contrasted
against the actual landslide distribution of the area under study, determining their
relative incidence on the landslide occurrence.
64
As was also pointed out by van Westen et. al. (2003), the following limitations are
implicit in the use of indirect landslide susceptibility methods:
-
Simplification of the factors that condition landslides, by taking only those that
can be relatively easily mapped in an area, such as slope angle or lithology.
Generalization, to assume that the occurrence of landslides takes place under the
same combination of factors throughout the study area.
Independency, each landslide type has its own set of causal factors, and
therefore should be analyzed individually (Kojima et al., 2000).
A lack of expert opinion on describing and categorizing different landslide types
and processes, which is common if these methods are applied by GIS-experts,
and not by earth scientists. For most earth scientists it is also very difficult to
formulate their expert knowledge that they apply in the direct susceptibility
mapping, into clear decision rules.
The statistical approach has been applied to the Moulin catchment area as described
in Appendix 7, based on the information collected during the various phases of the
study and subjected to the limitations described above. A discussion on the influence
of those limitations on the final results is presented in section 7.4.
7.2.
Analysis procedure
The following maps were used as input for the analysis:
-
-
-
Landuse (Figure 2.4). It has been observed that the majority of landslides
occur on the exposed marls. Nevertheless, there are a couple of landslides
with higher size than the mean and have vegetal coberture.
Slope (Figure 3.1) The slope is believed to have a direct relationship with
the factor of safety.
Aspect (Figure 3.2). The aspect determines the position of the slope in
comparison to the bedding, which could be an important factor in the
favourability for landslides.
Equal slope and aspect units – 30 categories (Figure 3.4)
Equal slope and aspect units – 9 categories (Figure 3.5)
Dip direction of the bedding (modification of Figure 5.3, strike of the
bedding)
Dip of the bedding (Figure 5.4)
Calculation of slope minus bedding dip
Calculation of aspect minus dip direction of the bedding.
65
Map of all landslides divided in two subsets of 80% and 20% of the
landslide events. (Figure 7.1)
921700
921800
921900
922000
922100
212600
212700
±
212600
212700
921600
212500
MOULIN CATCHMENT
Draix, France
212500
-
LANDSLIDE DATASETS
Figure 7.1
Landslides training dataset
Hydrography
212400
212400
Landslides test dataset
921600
921700
921800
921900
25
50
100
922000
150
Meters
212300
0
922100
Figure 7-1. Training and test landslides datasets derived for the totality of
landslides ever observed in the Moulin catchment.
The calculations of slope minus bedding dip and aspect minus dip direction of the
bedding were classified by three methods (quantile intervals, natural breaks or
Jenks’ method and equal intervals). These three classifications were performed in
order to optimise the data because no other criteria for the classification of angle
difference were used. The slope and the aspect were classified in 7 and 8 groups
respectively, as indicated in Chapter 3. The dip and dip direction of the bedding
were classified in ranges of one degree.
7.2.1.
Calculation of weight maps
The total number of landslides present in the region and the total area occupied by
those landslides was calculated from the landslide training set map without
discriminating type of landslides. The total area of landslides represents 1.85% of
the total area, for which the prior probability calculated based on equation 7.1 is then
1.85% for a single landslide class.
66
Following the procedure described from Sections 7.2.1 to 7.2.4, prior and
conditional probabilities, positive and negative weights, final weight and contrast
factors were calculated for all factors mentioned in 7.3.1 individually. The total
weights are listed in Tables 7.2 to 7.14
Factor
map
Landcover
Factor
(class)
Forest
Grass
Exposed
Marls
Arable land
Total
Weight
-9.229
-6.738
1.258
Factor
map
Equal
aspectslope
units
(30)
-7.094
Table 7-1. Total weights of
landcover factors
Factor
map
Slope
Factor (class)
0-10
10-20
20-30
30-40
40-50
50-60
60-75
Total
Weight
-2.466
-2.051
-0.425
0.817
0.931
0.813
1.326
Table 7-2. Total weights of slope
factors
Factor
map
Aspect
Factor (class)
N
NE
E
SE
S
SW
W
NW
Total
Weight
0.200
-0.137
0.844
0.129
-0.506
-1.022
-0.539
0.746
Table 7-3. Total weights of aspect
factors
67
Factor (class)
0-10
10-20
E 20-30
E 30-40
E 40-50
N 20-30
N 40-50
NE 0-10
NE 20-30
NE 30-40
NE 40-50
NW 20-30
NW 30-40
NW 40-50
NW 50-60
S 20-30
S 30-40
S 40-50
S 50-60
SE 20-30
SE 30-40
SE 40-50
SW 20-30
SW 30-40
SW 40-50
SW 50-60
W 20-30
W 30-40
W 40-50
W 50-60
Total
Weight
-5.561
-3.928
-2.148
1.994
1.331
-1.901
-2.474
-0.862
-2.202
-2.104
-0.277
-2.472
-0.370
1.587
-1.523
-3.820
0.955
0.881
-1.680
-1.983
1.566
0.614
-0.996
-0.339
-0.126
-1.927
-1.054
-4.324
0.318
-0.767
Table 7-4. Total weights of equal
aspect units (30) factors
Factor
map
Equal
aspectslope
units (9)
Factor (class)
High slope
Orthoclinal
High slope
Anaclinal
Low slope
Anaclinal
Low slope
Orthoclinal
Moderate
slope
Anaclinal
Moderate
slope
Orthoclinal
Low slope
Cataclinal
Moderate
slope
Cataclinal
High slope
Cataclinal
Factor
map
Bedding
dip
Total
Weight
1.122
0.461
-3.899
-2.531
-3.496
0.214
-2.723
1.232
0.482
Table 7-5. Total weights of equal
aspect units (9) factors
Factor (class)
0
19
20
21
22
23
24
25
27
28
30
31
32
33
35
36
37
38
40
42
43
44
45
47
50
53
55
56
60
62
65
67
70
Total
Weight
-5.381
0.120
-2.179
-1.340
-1.052
-1.244
-2.246
-5.237
-0.930
-3.900
0.065
3.116
2.144
-3.860
0.536
-3.641
-0.431
0.404
-0.765
0.984
-1.723
-0.222
0.846
-3.283
2.258
-2.007
0.438
1.833
-1.774
-2.038
-0.921
-2.229
-1.168
Table 7-6. Total weights of bedding
dip factors
68
Factor
map
Factor (class)
Total
Weight
Bedding
dip
direction
40
50
60
64
65
70
73
75
77
80
82
85
86
87
88
90
92
93
94
95
96
97
98
99
100
102
103
105
106
107
108
110
111
112
113
115
116
117
119
120
125
128
130
140
0.140
1.961
-2.022
-5.886
-0.789
-1.914
-0.399
2.064
2.277
-0.766
-1.714
1.083
2.789
-3.726
-1.067
-0.575
-0.372
2.274
-3.656
0.201
-0.461
1.697
1.715
-0.813
-1.511
-1.608
-2.199
0.682
1.381
0.264
-0.067
0.959
-1.022
-0.085
-3.885
-1.661
-3.170
2.669
1.783
-0.846
-1.803
2.133
0.829
0.067
Factor
map
Slope
minus
bedding
dip
(Jenks)
Factor (class)
-62.23 - 37.93
-37.93 - 29.29
-29.29 - 22.70
-22.70 - 16.52
-16.52 - 10.34
-10.34 - -4.17
-4.17 - 2.41
2.41 - 9.41
9.41 - 17.24
17.24 - 43.18
Total
Weight
-1.220
-2.168
-1.208
-1.283
-1.070
0.0425
0.6067
1.031
0.482
0.164
minus bedding dip (natural break
classification) factors
Table 7-7. Total weights of bedding
dip direction factors
69
Factor
map
Slope
minus
bedding
dip
(equal
interval)
Factor (class)
-62.23 - 51.69
-51.69 - 41.15
-41.15 - 30.60
-30.60 - 20.06
-20.06 - -9.52
-9.52 - 1.01
1.01 - 11.55
11.55 - 22.10
22.10 - 32.64
32.64 - 43.18
-62.23 - 51.69
4.88 - 11.06
11.06 43.18
-62.23 - 27.23
Total
Weight
0.034522
-0.47699
Factor
(class)
-62.23 - 27.23
-27.23 - 20.64
-20.64 - 16.52
-16.52 - 12.81
-12.81 - 8.70
-8.70- -4.58
-4.58 - -0.05
-0.05 - 4.88
-2.16739
minus bedding dip (quantile
-2.31263
-1.28532
Factor
map
Aspect
minus
bedding
dip
direction
(Jenks)
-1.26031
0.411295
1.002881
0.220257
0.553984
0.409383
0.034522
minus bedding dip (natural breaks
Factor
map
Slope
minus
bedding
dip
(quantiles)
0.95705
0.313473
Total
Weight
-2.16739
-1.03882
-1.2493
Factor (class)
-127.151 - 46.17
-46.17 - -6.51
-6.51 - 26.54
26.54 - 57.94
57.94 - 89.34
89.34 120.74
120.74 152.14
152.14 183.54
183.54 218.25
218.25 295.93
-127.151 - 46.17
Total
Weight
0.423939
1.248561
0.040372
0.091199
-0.35482
-0.9595
-0.48666
-0.8053
0.154447
1.09493
0.423939
Table 7.12 Table 7-11. Total
weights of aspect minus bedding
dip direction (natural break
-1.17848
-0.59875
0.190395
0.529635
0.779637
70
Factor
map
Aspect
minus
bedding
dip
Direction
(equal
interval)
Factor
(class)
-127.15 - 84.84
-84.84 - 42.54
-42.54 - -0.23
-0.231 42.07
42.074 84.38
84.38 126.69
126.69 169.00
169.00 211.31
211.31 253.62
253.62 295.93
120.74
120.74 147.18
147.18 170.32
170.32 198.42
198.42 295.93
Total
Weight
-0.81947
0.6575
1.162808
0.045053
Factor
(class)
-127.151 1.75
1.75 - 29.84
29.84 52.98
52.98 76.12
76.12 99.26
99.26 -
-0.72263
-0.74342
0.917319
minus bedding dip direction
(quantiles classification) factors
-0.17871
-0.86028
-0.58034
-0.33712
1.116143
0.338855
minus bedding dip direction (equal
intervals classification) factors
Factor
map
Aspect
minus
bedding
dip
Direction
(quantiles)
-0.47963
Total
Weight
0.995771
-0.01281
0.10585
-0.32011
-0.60515
-1.01274
71
Based on the weights on the factors it is clear that some factors have a high
incidence on the occurrence of landslides and others do not. The factors with
negative values of weight close to 0 have no influence at all, while others that are
close to 1 have a high influence. For instance, because most of the landslide areas
are located in the exposed marls, the weight of them is 1.26. Other factors with high
weight values are the slopes higher than 30 degrees. The values increase with the
slopes, reaching a weight of 1.33 for slopes higher than 60 degrees. Regarding the
aspect, the classes with most influence are NW and E, which in general can be
interpreted as directions opposite and parallel to the bedding dip.
The values of weight for the bedding dip and bedding dip directions have higher and
lower weights because of the narrowness of the classes. It is possible that a single
landslide falls into a class and because of this the values are high, of around 1.5. To
discuss the values of each of these classes does not give a great insight into their
influence. In the other hand, these factors acquire more significance when
conjugated with the slope and the aspect. These differences were calculated and
classified by three methods as mentioned above, and with a success rate analysis
(Section 7.3.2) it was found that the equal intervals method gave better results.
According with the ranges in this classification, there were more landslides where
the difference of slope and bedding dip was between 1 and 11.5 degrees (or had the
same direction of the bedding), and in a proportion of half, when this difference was
around this same range, but with negative values (opposite direction to the bedding).
For the difference between aspect and bedding dip direction 1.16 was the weight for
angles of -42.54 to 0.23. This means that the highest weight was obtained when the
direction of the slope was the same as the one of the bedding, or 2 standard
deviations less than the mean direction according with what was found in Chapter 5.
7.2.2.
Calculation of success rate and prediction rate
The success rate indicates how much percentage of all landslides occurs in the pixels
with the highest values in the different combination maps. Success rate is calculated
by sorting the pixels of a susceptibility map in a number of classes, from high to low
values, based on the frequency information from the histogram. After that, an
overlay is made with the landslide inventory map, and the joint frequency is
calculated. The success rate was calculated for each value individually as shown in
Figure 7.2 and according to the success rate for the first 10% of the map,
combinations of factors were used to produce 12 maps, as stated in table 7.15. In
addition to the success rate, other criteria were used to combine the factors. For
example, in H1 the factors with highest success rates within the 10% of the map
were combined. In H2 in addition to the factors of H1, the geological structure
properties were included. H3 considered the same factors as H2, but including also
landcover. In H4 both topographical properties slope and aspect were considered,
73
and also their difference with the lithological structure. H5 considered the same
difference but this time instead of the topography, the absolute values of dip
direction and dip of the bedding were used. H6 used the same factors as H5, in
addition to the land use and the equal aspect and slope units. H7 considered
everything. In H8 both classification of units were used and so was the slope. In H9
the difference between topography and lithological structure were used conjugated
with the equal aspect and slope units (9). Hj, He and Hq considered only these
differences, but using different classifications for both.
Map
H1
H2
H3
H4
H5
H6
Factors
-
Slope
Aspect
Aspect minus bedding dip direction (quantile)
Slope minus bedding dip (natural breaks or Jenks)
Equal aspect and slope units (9)
Slope
Aspect
Bedding dip direction
Bedding dip
Aspect minus bedding dip direction (equal interval)
Slope minus bedding dip (quantile)
Slope
Aspect
Bedding dip
Land cover
Slope
Aspect
Slope minus bedding dip (equal interval)
Bedding dip
74
H7
H8
H9
Hj
He
Hq
-
Bedding dip
Land cover
Slope
Aspect
Bedding dip
Land cover
Slope
Aspect minus bedding dip direction (natural breaks or Jenks)
Slope minus bedding dip (natural breaks or Jenks)
Table 7-14. Different factors used in the composition of 12 scenarios.
In Figure 7.3, the behaviour of each of these scenarios can be evaluated according
with the success rate. There are three groups of graphics which can be clearly
defined:
1.
A group corresponding to He, Hj, Hq, H1 and H4 for which 35 percent of
all landslides are explained by 10 percent of the pixels with the highest
value in the map. These groups have in common the consideration of only
75
Success Rate
100
90
80
Percentage of all landslides
70
60
50
Landuse
Bedding Dip
Slope
Slope - bedding dip quantile
Slope - bedding dip Jenks
Slope - bedding dip equal interval
Equal slope aspect units (9)
Equal slope-aspect units (30)
Aspect
Aspect - dip dir quantile
Aspect - dip dir equal interval
Aspect - dip dir Jenks
40
30
20
10
0
0
10
20
30
40
50
60
70
80
Percentage of weight map ordered from high to low
90
100
Figure 7-2. Success rate of individual factors.
Success Rate
100
90
Percentage of all landslides
80
70
H6
H3
H7
H5
H2
H8
H9
H1
H4
He
Hj
Hq
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
Figure 7-3. Success rate of different scenarios.
lithological structure properties, and H1 and H4, also slope and aspect. The
graphics have the same tendency until they reach 20% of the map, or 60% of
the landslides, where the maps that considered slope and aspect, reach
76
higher values of landslide prediction than the others. Because H1 also
considered the equal slope and aspect units, it separates from H4 and has
even better values.
2. A group conformed by H8 and H9, which only have in common the use of
the equal aspect and slope units (30), but have better performance than the
previous group: 45% of landslides are explained by 10% of the pixels in the
map.
3. Group conformed by H6, H3, H7, H5 and H8. All explain about 74% of
landslides with 10% of the pixels, but H6, H3 and H7 reach the highest
values in the batch because they use the land cover factor.
No susceptibility
Low
Moderate
High susceptibility
Considering that H6 has the best performance predicting landslides in this area,
further analysis is done only for this map. A classification was performed with the
weights map using the natural breaks method. All the negative values were classified
as “no hazard’ because they don’t have any influence in the occurrence of landslides.
The rest of the values were categorized as low, moderate and high hazard as
indicated in Figure 7.4.
Figure 7-4. Classification of scenario H6 with
natural breaks method.
Landslide % in
Susceptibility
classes
61.21 high
28.34 moderate
4.76 low
5.68 no hazard
Table 7-15. Landslides
percentages of both training
and test datasets compared to
hazard classes.
A comparison between the susceptibility map (Figure 7.7) was made with all the
landslides. According to Table 7.16, 62.21% of the landslides are located in high
hazard zones. 5.68% are located in no hazard, although this value was expected to be
zero.
A prediction rate is carried out with the two landslide datasets: training and test. The
training set is used to generate the model and the weight map is then combined with
the test map in order to test how well it predicts. This prediction rate is shown in
Figure 7.5. Although the performance of the map is satisfactory when compared to
77
the training dataset (Table 7.16), the prediction rate is very low for high hazard when
compared to the test landslides dataset. This can also be seen in Table 7.17. This low
rate is due to the difference in mean landslide area that both datasets present. There
are not any major area landslides in the test dataset because this set was generated
randomly.
Prediction rate
100
Percentage of landslides test set
90
Landslide % in
Susceptibility
classes
61.21 high
28.34 moderate
4.76 low
5.68 no
susceptibility
80
H6 - landslide test set
70
60
50
40
30
20
10
0
0
5
10
15
20
25
Figure 7-5. Prediction rate of scenario H6.
7.3.
Table 7-16. Landslides
percentages of test dataset
compared to hazard classes.
Analysis of results and limitations:
Since this information exists in the database and as an attribute of the landslides, the
landslide types and dates could have been considered in the analysis separately. This
was not possible because of time restrictions.
The training and test landslides datasets could have been chosen more
representatively in terms of areas, but for this analysis the datasets were randomly
generated from the initial data, that had vector format. For this reason they were
chosen as units disregarding the areas.
Another way in which the analysis could have performed would have been to take
maps of different dates (Figures 4.1 to 4.11) and compare the prediction rate
temporally.
Many of the factors have a very high spatial correlation, but sometimes factors
derived from two dependent maps (such as equal slope-aspect units and slope) give
better results because their combination gives more weight to some factors that have
more influence in the occurrence of landslides. In any case, this is rather relative
because of the rates of erosion and weathering, the landslides occur virtually
everywhere in the black marls.
78
921600
921700
921800
921900
922000
922100
212700
212600
212500
MOULIN CATCHMENT
Draix, France
212500
212600
212700
±
212400
212400
LANDSLIDE SUSCEPTIBILITY
Weight of evidence method
No susceptibility
Low susceptibility
Moderate susceptibility
High susceptibility
921600
921700
921800
25
921900
50
100
922000
150
Meters
212300
212300
0
922100
Figure 7-6. Landslide susceptibility derived from the classification of H6(Aspect
minus bedding dip direction (equal interval), Slope minus bedding dip (equal
interval), Bedding dip direction, Bedding dip, Equal aspect and slope units (9),
Land cover)
(Lee and Choi, 2004) (van Westen et al., 2003)
8.
Conclusions
One of the preliminary objectives of the research was the comparison of
the LiDAR data with other DTMs of different dates in terms of landslide
dates for landslide identification and mapping. It was not possible to find
any relationship due to the fact that the size of the landslides sometimes
is too small to be detected with other data with resolution less than 1m.
The high variability of the relief also prevents the possible comparison
of datasets of different dates and characteristics. The resolution of the
79
DEM is a sensitive issue because of the dimension and scale of the
processes involved in landslides. Such fine scale data when used to
derive terrain attributes, some derivatives could give values that are too
local and do not represent the characteristics of the terrain attributes as
intrinsic factors leading to landsliding. An example is the curvature, that
had to be applied over a resampled DTM to 3x3 m in order to be used
for the analysis. The sampling of the same dataset was also tried
generating a 0.5m resolution DTM but the local characteristics also
made difficult the analysis, especially if a flow analysis is desired. The
sinks filling generates more fake values that if using a 1 m resolution
DTM.
Through the relief characterisation it was possible to observe that the
majority of slopes have a moderate gradient (20-40 degrees) and that
they face mostly the SW and NW directions. The generation of equal
slope and area units was a long process but it contributed to the mapping
of the structure of the geology properties and to the modelling.
Comparing the properties of the geological structure it was also possible
to generate wider units that follow the trend of the bedding.
During the period of 2004 -2007 only 10 new landslides were recorded.
All of them had areas equal or less to 20 m2 expect for the landslide 115
that was also registered in the database, but dated from another period. It
is possible that many other landslides took place in this period but
disappeared because of erosive processes.
The observational interpretation of landslide features and correct
classification into different landslide types requires significant
experience and deep understanding of the different possible mechanisms
of failure. This represented an important limitation during the landslide
recognition campaign because most of the instability processes were not
visible thanks to the time of the year and to the phases of weathering and
soil growth many the remaining of many landslides had been washed
over the summer. A unique mechanism of failure was difficult to define
at first sight because of the erosive processes that erode both the
landslides scarps and bodies, but with the aid of the geological data it
80
was possible to determine the mechanisms of failure for each particular
landslide, which in the area can be wedge, circular or planar failure.
Accuracy on the estimation of soil depth across the area is determined by
the correct interpretation of the dynamic penetrometer test results. In
many cases, the soil-rock interface was evident due to a sudden increase
in resistance occurring when the penetrometer reached the fresh rock.
However, also in many cases the interface was not as clear and therefore
it had to be defined on the basis of a limit strength criterion (35MPa).
High differences in strength across a thin layer of material are indicative
of the presence of interfaces, where slip surfaces can develop and
relative movement can take place. Therefore, during the definition of the
soil-rock interface, priority was given to sudden changes in strength
rather than limit strength values.
More importantly, the comparison of multitemporal data of soil depth for
the area is only useful if performed for the same slope and in
combination with an erosion model that allows to predict the both the
soil erosion and the soil growth for a period of time. Through the
comparison of slopes of the same characteristics for the same dates
(Appendix 5) it was not possible to relate in any way data that varies not
only in time but also in space.
The analysis indicated that the factors that were found to have the
highest influence in the susceptibility to landslides were a combination
of the relationships between the structural characteristics of the lithology
and the topographical properties of the slopes, conjugated with the equal
aspect and slope units (9) which ultimately boost the weight of these last
factor, conjugated with the land cover. The analysis of results and
limitations of the modelling can be consulted in Chapter 7, under the
section 7.3.Analysis of results and limitations.
It would have been interesting to study the shallow landslide hazard with
a deterministic model, but because of the low quality of the soil depth it
was no possible to obtain sensible results in the preliminary tests that
were performed in a simple deterministic model.
81
82
9.
References
Antoine, P., Giraud, A., Meunier, M. and Van Asch, T., 1995. Geological
and geotechnical properties of the "Terres Noires" in southeastern
France: Weathering, erosion, solid transport and instability.
Engineering Geology, 40(3-4): 223-234.
Artru, P., 1972. Les "Terres Noires" du bassin rhodanien: stratigraphie,
sédimentologie, géochimie. Thèse docteur essciences. Univ. Claude
Bernard, Lyon.: 178 pp.
Bacchini, M. and Zannoni, A., 2003. Relations between rainfall and
triggering of debrisflow: case study of Cancia (Dolomites,
Northeastern Italy). Natural Hazards and Earth System Sciences, 3:
71-79.
Bathurst, J.C., Moretti, G., El-Hames, A., Begueria, S. and Garcia-Ruiz,
J.M., 2007. Modelling the impact of forest loss on shallow landslide
sediment yield, Ijuez river catchment, Spanish Pyrenees. Hydrology
and Earth Systems Sciences, 11(1): 569-583.
Bonham-Carter, G.F., 1994. Geographic Information Systems for
Geoscientists; modelling with GIS. Comp. Meth. Geos. Pergamon
Press, Vol. 13: pp. 398.
Borges, A. L., Brochot, S. and Meunier, M., 1994. Modele
hydrosedimentologique des 6coulements hyperconcentres d'un petit
torrent des Alpes du Sud. La Houille Blanche. 3/94:71 80.
Brunsden, D. and Ibsen, M.L., 1994. The nature of the european archive of
historicallandslide data, with specific reference to the United
Kingdom, in R. Cascale, R. Fantechi and J.C. Flagelo11et (eds.),
Temporal occurrenece and/orecasting oflandslides in the European
Communily, European Community, pp. 21-70.
Calcaterra, D., Parise, M. and Palma, B., 2003. Combining historical and
geological data for the assessment of the landslide hazard: a case
study from Campania, Italy. Natural Hazards and Earth System
Sciences, 3: 3-16.
Cavalli, M., 2007. Assessment of quality and vertical accuracy on Le Moulin
airborne LiDAR dataset. Progress meeting of GIS Draix (Cemagref),
5 December 2007 (Powerpoint presentation) Paris, France.
Chau, K.T., Sze, Y.L., Fung, M.K., Wong, W.Y., Fong, E.L. and Chan,
L.C.P., 2004. Landslide hazard analysis for Hong Kong using
landslide inventory and GIS. Computers & Geosciences, 30(4): 429443.
83
Chen, H. and C. F. Lee, 2003. A dynamic model for rainfall-induced
landslides on natural slopes. Geomorphology 51(4): 269-288.
Chung, C.-J. F., Fabbri, AG. and van Westen, C.J., 1996. Multivariate
regression analysis for landslide hazard zonation. Kluwer Academic
Publishers, Dordrecht, pp. 107-134.
Descroix, L. and Olivry, J.C., 2002. Spatial and temporal factors of erosion
by water of black marls in the badlands of the French southern Alps.
Hydrological Sciences Journal-Journal Des Sciences Hydrologiques,
47(2): 227-242.
Esteves, M., Descroix, L., Mathys, N. and Marc Lapetite, J., 2005. Soil
hydraulic properties in a marly gully catchment (Draix, France).
CATENA, 63(2-3): 282-298.
Filippo Catani, Samuele Segoni, Giacomo Falorni, 2007. A process-based
approach to the spatial prediction of soil thickness at catchment scale
(non published).
Guzzetti, F., Cardinali, M. and Reichenbach, P., 1994. The AVI Project: A
bibliographical and archive inventory oflandslides and floods in
Italy, EnvironmentaJ Management 18 (4),623-633.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.,, 1999. Lanslide
hazard evaluation: a review of current techniques and their
application in a multi-scale study, Central Italy. Elsevier, 31: 181216.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M. and Ardizzone, F.,
2005. Probabilistic landslide hazard assessment at the basin scale.
Geomorphology, 72(1-4): 272-299.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F. and Cardinali, M.,
2006. Landslide hazard assessment in the Collazzone area, Umbria,
Central Italy. Natural Hazards and Earth System Sciences, 6: 115131.
H.Vijith and G.Madhu., 2007. Estimating potential landslide sites of an
upland sub-watershed in Western Ghat’s of Kerala (India) through
frequency ratio and GIS. Environmental Geology 0943-0105, 14320495 (Online). Springer Berlin / Heidelberg.
Hoek, E. and Bray, J., 1981. Rock slope engineering. The Institution of
Mining and Metallurgy, London, 358 pp.
Ibsen, M.-L. and Brunsden, D., 1996. The nature, use and problems of
historical archives fOT the temporal occurrence of landslides, with
specific reference to the south coast of Britain, Ventnor, Isle of
Wight, Geomorphology IS (3-4), 241-258.
Jade, S. and Sarkar, S., 1993. Statistical-Models For Siope Instability
Classification. Engineering Geology 36 (1-2): 91-98.
Lee, S. and J. Choi, 2004. Landslide susceptibility mapping using GIS and
the weight-of-evidence model. International Journal of Geographical
Information Science 18(8): 789-814.
84
Liu, K.F. and Huangm, M.C., 2006. Numerical simulation of debris flow
with application on hazard area mapping. Computational
Geosciences, 10: 221-240.
Malet, J.P. et al., 2003. Soil surface characteristics influence on infiltration
in black marls: Application to the Super-Sauze earthflow (southern
Alps, France). Earth Surface Processes and Landforms, 28(5): 547564.
Mathys, N., S. Brochot, et al., 2003. Erosion quantification in the small
marly experimental catchments of Draix (Alpes de Haute Provence,
France). Calibration of the ETC rainfall-runoff-erosion model."
CATENA 50(2-4): 527-548.
Mathys, N., Brochot, S., Meunier, M. and Richard, D., 2003. Erosion
quantification in the small marly experimental catchments of Draix
(Alpes de Haute Provence, France). Calibration of the ETC rainfallrunoff-erosion model. CATENA, 50(2-4): 527-548.
Mathys, N., Klotz, S., Esteves, M., Descroix, L. and Lapetite, J.M., 2005.
Runoff and erosion in the Black Marls of the French Alps:
Observations and measurements at the plot scale. Catena, 63(2-3):
261-281.
Mathys, N., 2006. Analyse et modélisation à différentes échelles des
mécanismes d'érosion et de transport de matériaux solides. Cas des
petits bassins versants de montagne sur marne (Draix, Alpes-deHaute-Provence), Institut National Polytechnique de Grenoble,
Grenoble, 346 pp.
Mathys, M. E. a. S. K., 2008. Water and sediment yield during extreme
events in mountainous marly catchments (Draix, Alpes-de-HauteProvence, France). Geophysical Research Abstracts, EGU Vol. 10,
EGU2008-A-06867, 2008.
Maquaire, O., A. Ritzenthaler, et al., 2002. Characterisation of alteration
profiles using dynamic penetrometry with variable energy.
Application to weathered black marls, Draix (Alpes-de-HauteProvence, France)." Comptes Rendus Geoscience 334(11): 835-841.
Moon, V. and Blackstock, H., 2004. A Methodology for Assessing Landslide
Hazard Using Deterministic Stability Models. Natural Hazards,
32(1): 111-134.
Oostwoud Wijdenes, D. J. and P. Ergenzinger, 1998. Erosion and sediment
transport on steep marly hillslopes, Draix, Haute-Provence, France:
an experimental field study." CATENA 33(3-4): 179-200.
Phan Thi San Ha, 1992. Propriétés physiques et caractéristiques
géotechniques des "Terres Noires" du sud-est de La France. These
Univ. Joseph Fourier, Grenoble: 246 pp.
85
Sekhar, L.K., 2006. Effect of Vegetation on Debris Flow Initiation:
Conceptualisation and Parameterisation of a Dynamic Model for
Debris Flow Initiation in Tikovil River Basin, Kerala, India, using
PCRaster
(http://www.itc.nl/library/Academic_output/AcademicOutput.aspx?p
=1&y=6&l=20). M.Sc Thesis, International Institute for Geoinformation Science and Earth Observation, Enschede, The
Netherlands and Indian Institute of Remote Sensing, Dehradun,
India, 133 pp.
Singh, L.P., van Westen, C.J., Ray, P.K.C. and Pasquali, P., 2005. Accuracy
assessment of InSAR derived input maps for landslide susceptibility
analysis: a case study from the Swiss Alps. Landslides, 2(3): 221228.
Van Asch, Th.W.J. and Van Steijn, H.. 1991. Temporal patternsof mass
movements in the French Alps, Catena. 18: 515-527.
Van Asch, Th.W.J., 1995. Modelling ground water fluctuations and the
frequency of movement of a landslide in the Terres Noires region of
Barcelonnette (France). Earth Surface Process. Landforms.
van Beek, L.P.H., 2002. Assessment of the influence of changes in Landuse
and Climate on Landslide Activity in a Mediterranean Environment.
PhD Thesis, Department of Physical Geography, University of
Utrecht, Utrecht, The Netherlands, 363 pp.
Varnes D. J., 1978. Slope movement types and processes. In: Schuster R. L.
& Krizek R. J. Ed., Landslides, analysis and control. Transportation
Research Board Sp. Rep. No. 176, Nat. Acad. oi Sciences, pp. 11-33,
1978.
Vijith, H. and Madhu, G., 2007. Estimating potential landslide sites of an
frequency ratio and GIS. Environmental Geology, In press.
Antoine, P., Giraud, A., Meunier, M. and Van Asch, T., 1995. Geological
and geotechnical properties of the "Terres Noires" in southeastern
France: Weathering, erosion, solid transport and instability.
Engineering Geology, 40(3-4): 223-234.
Bathurst, J.C., Moretti, G., El-Hames, A., Begueria, S. and Garcia-Ruiz,
J.M., 2007. Modelling the impact of forest loss on shallow landslide
sediment yield, Ijuez river catchment, Spanish Pyrenees. Hydrology
and Earth Systems Sciences, 11(1): 569-583.
Bonham-Carter, G.F., 1994. Geographic Information Systems for
Geoscientists; modelling with GIS. Comp. Meth. Geos. Pergamon
Press, Vol. 13: pp. 398.
Descroix, L. and Olivry, J.C., 2002. Spatial and temporal factors of erosion
by water of black marls in the badlands of the French southern Alps.
Hydrological Sciences Journal-Journal Des Sciences Hydrologiques,
47(2): 227-242.
86
Esteves, M., Descroix, L., Mathys, N. and Marc Lapetite, J., 2005. Soil
hydraulic properties in a marly gully catchment (Draix, France).
CATENA, 63(2-3): 282-298.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.,, 1999. Lanslide
hazard evaluation: a review of current techniques and their
application in a multi-scale study, Central Italy. Elsevier, 31: 181216.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M. and Ardizzone, F.,
2005. Probabilistic landslide hazard assessment at the basin scale.
Geomorphology, 72(1-4): 272-299.
Lee, S. and Choi, J., 2004. Landslide susceptibility mapping using GIS and
the weight-of-evidence model. International Journal of Geographical
Information Science, 18(8): 789-814.
Malet, J.P. et al., 2003. Soil surface characteristics influence on infiltration
in black marls: Application to the Super-Sauze earthflow (southern
Alps, France). Earth Surface Processes and Landforms, 28(5): 547564.
Maquaire, O. et al., 2002. Characterisation of alteration profiles using
dynamic penetrometry with variable energy. Application to
weathered black marls, Draix (Alpes-de-Haute-Provence, France).
Comptes Rendus Geoscience, 334(11): 835-841.
Mathys, N., 2006. Analyse et modélisation à différentes échelles des
mécanismes d'érosion et de transport de matériaux solides. Cas des
petits bassins versants de montagne sur marne (Draix, Alpes-deHaute-Provence), Institut National Polytechnique de Grenoble,
Grenoble, 346 pp.
Mathys, N., Brochot, S., Meunier, M. and Richard, D., 2003. Erosion
quantification in the small marly experimental catchments of Draix
(Alpes de Haute Provence, France). Calibration of the ETC rainfallrunoff-erosion model. CATENA, 50(2-4): 527-548.
Mathys, N., Klotz, S., Esteves, M., Descroix, L. and Lapetite, J.M., 2005.
Runoff and erosion in the Black Marls of the French Alps:
Observations and measurements at the plot scale. Catena, 63(2-3):
261-281.
Moon, V. and Blackstock, H., 2004. A Methodology for Assessing Landslide
Hazard Using Deterministic Stability Models. Natural Hazards,
32(1): 111-134.
Oostwoud Wijdenes, D.J. and Ergenzinger, P., 1998. Erosion and sediment
transport on steep marly hillslopes, Draix, Haute-Provence, France:
an experimental field study. CATENA, 33(3-4): 179-200.
Sekhar, L.K., 2006. Effect of Vegetation on Debris Flow Initiation:
Conceptualisation and Parameterisation of a Dynamic Model for
Debris Flow Initiation in Tikovil River Basin, Kerala, India, using
PCRaster
87
(http://www.itc.nl/library/Academic_output/AcademicOutput.aspx?p
=1&y=6&l=20). M.Sc Thesis, International Institute for Geoinformation Science and Earth Observation, Enschede, The
Netherlands and Indian Institute of Remote Sensing, Dehradun,
India, 133 pp.
Singh, L.P., van Westen, C.J., Ray, P.K.C. and Pasquali, P., 2005. Accuracy
assessment of InSAR derived input maps for landslide susceptibility
analysis: a case study from the Swiss Alps. Landslides, 2(3): 221228.
van Westen, C.J., Rengers, N. and Soeters, R., 2003. Use of
Geomorphological Information in Indirect Landslide Susceptibility
Assessment. Natural Hazards, 30(3): 399-419.
Varnes, D.J., 1978. Slope Movement Types and Processes. In: R.L. Schuster
and R.J. Krizek (Editors), Landslides: Analysis and Control.
National Academy of Sciences, Washington, USA.
Vijith, H. and Madhu, G., 2007. Estimating potential landslide sites of an
frequency ratio and GIS. Environmental Geology, In press.
Wijdenes, D.J.O. and Ergenzinger, P., 1998. Erosion and sediment transport
on steep marly hillslopes, Draix, Haute-Provence, France: an
experimental field study. Catena, 33(3-4): 179-200.
Zêzere, J.L. et al., 2004. Integration of spatial and temporal data for the
definition of different landslide hazard scenarios in the area north of
Lisbon (Portugal). Nat. Hazards Earth Syst. Sci., 4(1): 133-146.
88
10.
Apendices
APPENDIX 1 Landslides evolution maps
921900
±
922000
212700
212700
921800
22
MOULIN CATCHMENT
Draix, France
43
28
42
21
LANDSLIDES
EVOLUTION MAP
16
212600
212600
20
19
7
6
Figure 4.6.1
Campaign
November 2002
13
LEGEND
Hydrography
Landslides 11/2002
4
212500
212500
24
44
0
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
22
Conic 3
3
1
2
fieldwork campaings from
2002 to 2007.
MOULIN CATCHMENT
Draix, France
43
LANDSLIDES
EVOLUTION MAP
28
16
212600
212600
Figure 4.6.2
Campaign
March 2003
19
7
6
13
LEGEND
Hydrography
Landslides 03/2003
212500
4
212500
24
3
0
1
2
921800
25
50
921900
100
Meters
922000
89
2002 to 2007.
Conic 3
921900
±
922000
212700
212700
921800
22
MOULIN CATCHMENT
Draix, France
43
28
LANDSLIDES
EVOLUTION MAP
212600
212600
16
19
7
6
Figure 4.6.3
Campaign
May 2003
13
LEGEND
Hydrography
Landslides 05/2003
4
212500
212500
24
0
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
22
Conic 3
3
1
2
2002 to 2007.
MOULIN CATCHMENT
Draix, France
43
26
27 25
28
18
17
16
LANDSLIDES
EVOLUTION MAP
212600
212600
15
14
19
7
6
Figure 4.6.4
Campaign
Jun 2003
13
LEGEND
Hydrography
Landslides 06/2003
12
10
8
4
212500
212500
24
44
9
3
0
1
2
921800
25
921900
50
100
Meters
922000
90
2002 to 2007.
Conic 3
921900
±
922000
212700
212700
921800
22
26
27 25
54
18
17
16
28
LANDSLIDES
EVOLUTION MAP
212600
15
14
212600
MOULIN CATCHMENT
Draix, France
43
19
7
6
Figure 4.6.5
Campaign
July 2003
13
LEGEND
Hydrography
Landslides 07/2003
12
10
4
8
212500
212500
24
44
9
0
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
22
Conic 3
3
1
2
2002 to 2007.
MOULIN CATCHMENT
Draix, France
43
26
27 25
54
42
18
17
16
21
28
LANDSLIDES
EVOLUTION MAP
20
Figure 4.6.5a
212600
212600
15
14
19
7
6
February 2004
13
LEGEND
Hydrography
Landslides 02/2004
12
10
8
4
212500
212500
24
44
9
3
0
1
2
921800
25
921900
50
100
Meters
922000
91
2002 to 2007.
Conic 3
921900
±
922000
212700
212700
921800
22
43
MOULIN CATCHMENT
Draix, France
54
67
26
27 25
68
60
42
28
18
17
16
21
LANDSLIDES
EVOLUTION MAP
20
212600
212600
15
14
19
7
6
Figure 4.6.6
Campaign
April 2004
13
LEGEND
Hydrography
Landslides 04/2004
58
12 55
66
61
62
64
10
23
4
212500
24
212500
63
44
9
8
11
0
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
59
22
43
65
MOULIN CATCHMENT
Draix, France
54
67
26
2527
68
60
42
28
18
69
LANDSLIDES
EVOLUTION MAP
17
16
21
57 20
15
212600
14
212600
Conic 3
3
1
2
2002 to 2007.
19
7
6
Figure 4.6.7
Campaign
May 2004
13
LEGEND
Hydrography
Landslides 05/2004
58
12 55
66
61
62
23
63
10
5
64
4
24
212500
212500
56
44
9
8
3
11
0
1
2
921800
25
921900
50
100
Meters
922000
92
2002 to 2007.
Conic 3
921900
±
922000
212700
212700
921800
59
22
43
65
67
26
2527
68
60
42
28
18
69
LANDSLIDES
EVOLUTION MAP
17
16
21
57 20
15
212600
14
212600
MOULIN CATCHMENT
Draix, France
54
19
7
6
Figure 4.6.8
Campaign
October 2004
13
LEGEND
Hydrography
Landslides 10/2004
58
12 55
66
61
62
23
63
10
5
64
4
24
212500
212500
56
44
9
8
11
0
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
Conic 3
3
1
2
2002 to 2007.
22
MOULIN CATCHMENT
Draix, France
67
LANDSLIDES
EVOLUTION MAP
17
212600
212600
57
19
7
6
Figure 4.6.9
Campaign
May 2005
13
LEGEND
Hydrography
Landslides 05/2005
58
5
212500
101
212500
23
9
8 102
100
11
0
1
2
921800
25
921900
50
100
Meters
922000
93
2002 to 2007.
Conic 3
921900
±
922000
212700
212700
921800
22
108
MOULIN CATCHMENT
Draix, France
67
113114
110
LANDSLIDES
EVOLUTION MAP
17
212600
212600
57 20
19
7
13
106
Figure 4.6.10
Campaign
August 2007
LEGEND
Hydrography
Landslides 08/2007
104
105
212500
212500
23
107
8
50
100
Meters
921800
921900
922000
921800
921900
922000
±
212700
212700
25
22
Conic 3
11
0
2002 to 2007.
108
MOULIN CATCHMENT
Draix, France
67
111
113114
110
112
LANDSLIDES
EVOLUTION MAP
17
212600
212600
20
19
7
13
106
Figure 4.6.11
Campaign
October 2007
LEGEND
Hydrography
Landslides 10/2007
115
105
212500
212500
23
109
107
11
0
921800
25
921900
50
100
Meters
922000
94
2002 to 2007.
Conic 3
APPENDIX 2 Landslide occurrences and activity state.
Landsl
ide ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
42
43
44
54
55
56
57
58
59
60
61
62
63
64
65
66
67
Nov02
A
A
A
A
A
A
A
Mar
-03
A
A
A
A
May
-03
A
A
A
A
Jun
-03
A
A
A
A
Jul03
A
A
A
A
Apr
-04
A
A
May
-04
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
May
-05
A
A
A
A
A
A
A
A
Oct
-04
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Aug
-07
A
A
Oct
-07
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
95
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Landsli
de ID
68
69
70
71
72
100
101
102
104
105
106
107
108
109
110
111
112
113
114
Nov
-02
Mar
-03
May
-03
Jun
-03
Jul03
Apr
-04
A
A
May
-04
Oct
-04
A
May
-05
A
Aug
-07
Oct
-07
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Table 10-1. Landslide occurrence and activity state. Moulin catchment
(A=Active, shaded cells =landslide identified for the first time)
96
A
A
A
A
A
A
A
A
A
A
APPENDIX 3. Landslides stereonets.
STRUCTURE AT LANDSLIDE AREAS – STEREONETS
SCHMIDT PROJECTION, INFERIOR HEMISPHERE
SLOPE
FOLIATION
JOINT SETS
SLOPE AND FOLIATION
Landslide ID 1
Landslide ID 2
Landslide ID 3
Landslide ID 4
97
Landslide ID 5
Landslide ID 6
Landslide ID 7
Landslide ID 8
Landslide ID 9
Landslide ID 10
98
Landslide ID 11
Landslide ID 12
Landslide ID 13
Landslide ID 14
Landslide ID 156
Landslide ID 16
99
Landslide ID 17
Landslide ID 18
Landslide ID 19
Landslide ID 22
Landslide ID 23
Landslide ID 28
100
Landslide ID 42
Landslide ID 43
Landslide ID 44
Landslide ID 54
Landslide ID 55
Landslide ID 56
101
Landslide ID 57
Landslide ID 58
Landslide ID 59
Landslide ID 60
Landslide ID 61
Landslide ID 62
102
Landslide ID 63
Landslide ID 64
Landslide ID 65
Landslide ID 66
Landslide ID 67
Landslide ID 68
103
Landslide ID 69
Landslide ID 70
Landslide ID 72
Landslide ID 73
Landslide ID 74
Landslide ID 75
104
Landslide ID 102
Landslide ID 104
Landslide ID 110
105
APPENDIX 4 – Interpretation of soil depth from penetrometer tests
acquired with portable dynamic penetrometer with variable energy PANDA.
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 2
DATE
08/11/99
0.10
0.15
50
60
0
TEST 3
DATE
08/11/99
0.10
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.15
0.20
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-2. PANDA Test 2.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
0
60
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
TEST 5
DATE
08/11/99
TEST 6
DATE
08/11/99
0.05
0.10
0.15
0.10
0.15
0.25
0.30
0.30
0.30
Depth (m)
0.25
Depth (m)
0.20
0.25
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Resistance (MPa)
20
30
40
0.05
0.20
0.40
10
50
0.00
0.20
0.35
60
0.35
0.45
50
TEST 4
DATE
08/11/99
0.10
0.15
0.40
Resistance (MPa)
20
30
40
0.05
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.05
Depth (m)
10
106
TEST 66
DATE
08/11/99
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 7
DATE
08/11/99
0.10
50
60
0
TEST 8
DATE
08/11/99
0.10
0.15
0.10
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.40
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 10
DATE
08/11/99
0.05
0.10
0.15
TEST
11
DATE
0.10
0.15
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 13
DATE
08/11/99
TEST 14
DATE
08/11/99
0.05
0.10
0.15
0.15
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
50
0.10
0.25
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Resistance (MPa)
20
30
40
0.05
0.20
0.40
10
0.00
0.20
0.35
60
0.40
0.45
50
0.35
0.45
Resistance (MPa)
20
30
40
TEST 12
DATE
08/11/99
0.05
0.20
0.40
10
0.00
0.05
0.35
60
0.40
0.45
50
0.35
0.45
Resistance (MPa)
20
30
40
TEST 9
DATE
08/11/99
0.05
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.15
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.05
Depth (m)
10
107
TEST 15
DATE
08/11/99
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 16
DATE
08/11/99
0.05
0.10
0.15
TEST 17
DATE
08/11/99
0.10
0.15
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
0
10
Resistance (MPa)
20
30
40
50
60
0.00
0.05
TEST 19
DATE
08/11/99
0.10
0.15
0
10
50
0.15
0.40
0.10
0.15
0.25
0.25
0.30
0.30
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
0.50
0.55
0.60
60
0.35
0.40
0.45
50
0.20
0.20
Depth (m)
Depth (m)
0.35
Resistance (MPa)
20
30
40
TEST 2
DATE
01/05/00
0.05
0.10
0.30
10
0.00
60
TEST 1
DATE
01/05/00
0.05
0.25
Fig A4-21.
Fig A4-20.
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
0.10
0.15
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 3
DATE
01/05/00
TEST 4
DATE
01/05/00
0.05
0.10
0.15
0.05
0.10
0.15
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.40
Depth (m)
0.20
0.35
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-22.
10
Resistance (MPa)
20
30
40
50
0.00
Depth (m)
Depth (m)
0
Resistance (MPa)
20
30
40
0.00
0.20
Depth (m)
60
0.35
0.45
50
TEST 18
DATE
08/11/99
0.10
0.20
0.40
Resistance (MPa)
20
30
40
0.05
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
10
Fig A4-23.
Fig A4-24.
108
TEST 5
DATE
01/05/00
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 6
DATE
01/05/00
0.10
50
60
0
TEST 7
DATE
01/05/00
0.10
0.15
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-26.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-27.
0
60
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
TEST 9
DATE
01/05/00
0.05
0.10
0.15
0.15
0.10
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
Depth (m)
0.20
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-29.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-30.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 13
DATE
01/05/00
0.10
0.15
0.05
0.10
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.40
Depth (m)
0.20
Depth (m)
0.20
0.35
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-31.
10
Resistance (MPa)
20
30
40
50
0.00
0.05
TEST 12
DATE
01/05/00
60
0.40
0.45
50
0.35
0.45
Fig A4-28.
Resistance (MPa)
20
30
40
TEST
11
DATE
0.05
TEST 10
DATE
01/05/00
0.10
0.40
10
0.00
0.05
0.35
60
0.35
0.45
Fig A4-25.
50
TEST 8
DATE
01/05/00
0.10
0.20
0.40
Resistance (MPa)
20
30
40
0.05
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.15
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.05
Depth (m)
10
Fig A4-32.
Fig A4-33.
109
TEST 14
DATE
01/05/00
60
10
Resistance (MPa)
20
30
40
50
0
60
50
60
0
0.05
0.05
0.10
0.10
0.15
0.15
0.20
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.40
TEST 16
DATE
01/05/00
0.15
0.35
0.40
0.40
0.45
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
Fig A4-35.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-36.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 1
DATE
04/04/01
0.05
0.10
0.15
TEST
1bis
DATE
04/04/01
0.10
0.15
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-38.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-39.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 3
DATE
04/04/01
TEST 1
(42)
DATE
05/04/01
0.05
0.10
0.15
0.05
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-40.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.40
0.45
50
0.35
0.45
Fig A4-37.
Resistance (MPa)
20
30
40
TEST 2
DATE
04/04/01
0.05
0.20
0.40
10
0.00
0.05
0.35
60
0.35
0.50
Fig A4-34.
50
TEST 17
DATE
01/05/00
0.10
0.45
0.70
Resistance (MPa)
20
30
40
0.05
0.45
TEST 15
DATE
01/05/00
10
0.00
Depth (m)
0.35
0.65
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.60
Depth (m)
10
0.00
Depth (m)
Depth (m)
0
Fig A4-41.
Fig A4-42.
110
TEST 2
(43)
DATE
05/04/01
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 3
(44)
DATE
05/04/01
0.10
50
60
0
TEST 4
(45)
DATE
05/04/01
0.10
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.15
0.20
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-44.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-45.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
TEST 7
(48)
DATE
05/04/01
0.10
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-47.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 1
(49)
DATE
05/04/01
TEST 2
(50)
DATE
05/04/01
0.10
0.05
0.10
0.15
0.20
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.40
Depth (m)
0.15
0.35
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-49.
10
Resistance (MPa)
20
30
40
50
0.00
0.05
Depth (m)
0.15
Fig A4-48.
0.00
60
0.15
Depth (m)
0.20
Depth (m)
0.20
Fig A4-46.
50
0.10
0.15
0.40
Resistance (MPa)
20
30
40
0.05
0.15
0.35
10
0.00
0.05
TEST 6 (47)
DATE
05/04/01
0.10
60
0.35
0.45
Fig A4-43.
50
TEST 5
(46)
DATE
05/04/01
0.10
0.20
0.40
Resistance (MPa)
20
30
40
0.05
0.15
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.15
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.05
Depth (m)
10
Fig A4-50.
Fig A4-51.
111
TEST 3
(51)
DATE
05/04/01
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 4
(52)
DATE
05/04/01
0.05
0.10
TEST 5
(53)
DATE
05/04/01
0.10
0.05
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-53.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-54.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 1
(55)
DATE
05/04/01
0.05
0.10
0.15
TEST 2
(56)
DATE
05/04/01
0.10
0.25
10
Resistance (MPa)
20
30
40
50
60
0.00
0.05
0.20
60
0.35
0.45
Fig A4-52.
50
0.15
Depth (m)
0.15
0.20
0.40
Resistance (MPa)
20
30
40
TEST 6 (54)
DATE
05/04/01
0.10
0.15
0.35
10
0.00
0.05
Depth (m)
Depth (m)
10
TEST 3
(57)
DATE
05/04/01
0.05
0.10
0.15
0.15
0.20
0.20
0.25
0.25
0.30
0.30
0.35
0.40
Depth (m)
Depth (m)
Depth (m)
0.30
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.60
0.55
0.55
0.65
0.60
0.60
0.70
0.65
0.65
0.75
0.70
0.70
0.50
0.55
Fig A4-55.
Fig A4-56.
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
0.05
0.05
0.10
0.10
0.20
Depth (m)
0.25
0.30
0.35
0.40
Resistance (MPa)
20
30
40
50
60
0
TEST 5
(59)
DATE
05/04/01
0.05
0.10
0.15
0.20
0.20
0.25
0.25
0.30
0.30
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-58.
10
Resistance (MPa)
20
30
40
50
0.00
0.15
TEST 4
(58)
DATE
05/04/01
Depth (m)
0.15
10
Depth (m)
0
Fig A4-57.
Fig A4-59.
Fig A4-60.
112
TEST 6 (60)
DATE
05/04/01
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
60
0
0.60
0.80
0.80
1.00
1.00
1.00
1.20
1.20
1.20
Depth (m)
0.60
1.60
1.40
1.60
1.80
2.00
2.00
2.00
2.20
2.20
2.20
2.40
2.40
2.40
2.60
2.60
2.60
2.80
2.80
2.80
3.00
3.00
3.00
Fig A4-62.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-63.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 1
DATE
11/2002
0.05
0.10
0.15
TEST 2
DATE
11/2002
0.10
0.15
0.05
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-65.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-66.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 4
DATE
11/2002
TEST 5
DATE
11/2002
0.05
0.10
0.15
0.05
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-67.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.40
0.45
50
0.35
0.45
Fig A4-64.
Resistance (MPa)
20
30
40
TEST 3
DATE
11/2002
0.10
0.20
0.40
10
0.00
0.05
0.35
60
1.60
1.80
50
1.40
1.80
Fig A4-61.
Resistance (MPa)
20
30
40
TEST 3
DATE
08/11/03
0.40
0.80
1.40
10
0.20
TEST 2
DATE
08/11/03
0.40
Depth (m)
0.60
Depth (m)
50
0.00
0.20
TEST 1
DATE
08/11/03
0.40
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.20
Depth (m)
10
Fig A4-68.
Fig A4-69.
113
TEST 6
DATE
11/2002
60
0
10
0.00
0.05
Resistance (MPa)
20
30
40
50
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 7
DATE
11/2002
0.10
0.15
0.20
10
Resistance (MPa)
20
30
40
50
60
0.00
0.05
0.15
TEST 9
DATE
11/2002
0.05
TEST 8
DATE
11/2002
0.10
0.10
0.15
0.20
0.20
0.35
0.25
0.25
0.40
0.45
0.30
0.30
0.50
0.55
0.60
Depth (m)
0.30
Depth (m)
Depth (m)
0.25
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.80
0.55
0.55
0.85
0.60
0.60
0.65
0.65
0.70
0.70
0.65
0.70
0.75
0.90
0.95
1.00
Fig A4-70.
Fig A4-71.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-72.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
0.15
0.10
0.15
0.20
0.20
0.25
0.25
0.30
0.30
0.05
TEST 11
DATE
11/2002
0.05
TEST 10
DATE
11/2002
0.10
10
Resistance (MPa)
20
30
40
50
60
0.00
TEST 12
DATE
11/2002
0.10
0.15
0.20
0.25
0.30
0.40
Depth (m)
Depth (m)
Depth (m)
0.35
0.35
0.35
0.40
0.40
0.45
0.50
0.55
0.45
0.45
0.50
0.50
0.65
0.55
0.55
0.70
0.60
0.60
0.65
0.65
0.85
0.70
0.70
0.90
0.60
0.75
0.80
Fig A4-73.
Fig A4-74.
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
0.10
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 13
DATE
11/2002
0.05
TEST 14
DATE
11/2002
0.10
0.15
0.10
0.15
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
0.40
Depth (m)
0.20
0.35
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-76.
10
Resistance (MPa)
20
30
40
50
0.00
0.05
Depth (m)
Depth (m)
0.15
Fig A4-75.
Fig A4-77.
Fig A4-78.
114
TEST 15
DATE
11/2002
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 16
DATE
11/2002
0.10
50
60
0
0.15
0.10
0.15
0.20
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.40
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-80.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-81.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 19
DATE
11/2002
0.05
0.10
0.15
TEST 20
DATE
11/2002
0.10
0.15
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-83.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-84.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 22
DATE
11/2002
TEST 23
DATE
11/2002
0.05
0.10
0.15
0.05
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
Fig A4-85.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.40
0.45
50
0.35
0.45
Fig A4-82.
Resistance (MPa)
20
30
40
TEST 21
DATE
11/2002
0.05
0.20
0.40
10
0.00
0.05
0.35
60
0.40
0.45
50
0.35
0.45
Fig A4-79.
Resistance (MPa)
20
30
40
TEST 18
DATE
11/2002
0.05
TEST 17
DATE
11/2002
0.10
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.15
Depth (m)
Resistance (MPa)
20
30
40
0.00
0.05
Depth (m)
10
0.70
Fig A4-86.
Fig A4-87.
115
TEST 24
DATE
11/2002
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 25
DATE
11/2002
0.10
0.05
0.15
Depth (m)
0.20
0.25
0.30
0.35
Resistance (MPa)
20
30
40
50
60
TEST 27
DATE
11/2002
0.05
TEST 26
DATE
11/2002
0.10
0.15
10
0.00
0.10
0.15
0.20
0.20
0.25
0.25
0.30
0.30
Depth (m)
0.05
Depth (m)
10
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
0.40
0.45
Fig A4-88.
Fig A4-89.
0
10
Resistance (MPa)
20
30
40
0
50
Fig A4-90.
60
10
Resistance (MPa)
20
30
40
TEST
28.2
DATE
11/2002
0.15
0.20
0.20
0.25
0.30
0.35
Fig A4-91.
10
0.05
0.10
0.15
Resistance (MPa)
20
30
40
50
0.25
0.30
0.30
0.35
0.40
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
0
Resistance (MPa)
20
30
40
50
60
0
0.20
0.30
0.05
0.10
0.15
0.20
0.25
0.35
0.40
0.30
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-94.
10
Fig A4-95.
Fig A4-96.
116
Resistance (MPa)
20
30
40
50
0.00
TEST
29.3
DATE
11/2002
0.15
0.30
0.40
10
0.10
0.25
0.35
Fig A4-93.
0.05
0.25
60
0.40
0.00
TEST
29.2
DATE
11/2002
50
0.35
0.45
60
Depth (m)
Depth (m)
0.20
0.20
0.25
Depth (m)
0
0.15
0.00
Resistance (MPa)
20
30
40
TEST
29.1
DATE
11/2002
0.10
Fig A4-92.
10
0.05
Depth (m)
Depth (m)
Depth (m)
0.10
0.15
0
0.00
0.05
TEST
28.1
DATE
11/2002
0.10
60
0.00
0.00
0.05
50
TEST
30.1
DATE
11/2002
60
0
10
Resistance (MPa)
20
30
40
0
50
60
10
Resistance (MPa)
20
30
40
50
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0.00
0.00
0.05
TEST
30.2
DATE
11/2002
0.10
0.15
0.20
0.15
Depth (m)
0.30
0.35
0.40
0.20
0.25
0.25
0.30
0.30
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-98.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-99.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 33
DATE
11/2002
0.05
0.10
0.15
TEST 34
DATE
11/2002
0.10
0.15
0.05
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.40
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-101.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-102.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 36
DATE
11/2002
TEST 37
DATE
11/2002
0.05
0.10
0.15
0.05
0.10
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-103.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.40
0.45
50
0.35
0.45
Fig A4-100.
Resistance (MPa)
20
30
40
TEST 35
DATE
11/2002
0.10
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.15
0.20
0.45
Fig A4-97.
Depth (m)
0.10
Depth (m)
Depth (m)
0.25
TEST 32
DATE
11/2002
0.05
TEST 31
DATE
11/2002
0.10
Depth (m)
0.05
Fig A4-104.
Fig A4-105.
117
TEST 38
DATE
11/2002
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST 39
DATE
11/2002
0.05
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
0.05
0.05
TEST 40
DATE
11/2002
0.10
0.15
10
Resistance (MPa)
20
30
40
50
60
TEST 41
DATE
11/2002
0.10
0.15
0.10
0.20
0.25
0.20
0.25
0.25
0.30
0.30
Depth (m)
Depth (m)
Depth (m)
0.15
0.20
0.35
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
0.30
0.35
0.40
Fig A4-106.
Fig A4-107.
0
10
Resistance (MPa)
20
30
40
50
Fig A4-108.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
0
60
0.00
0.05
0.15
TEST 43
DATE
11/2002
0.05
TEST 42
DATE
11/2002
0.10
10
Resistance (MPa)
20
30
40
50
60
0.00
0.10
0.15
TEST 44
DATE
11/2002
0.05
0.10
0.25
0.30
0.30
0.35
0.40
0.20
Depth (m)
0.20
0.25
Depth (m)
Depth (m)
0.15
0.20
0.35
0.40
0.25
0.30
0.35
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.55
0.70
0.70
0.60
0.40
0.45
Fig A4-109.
Fig A4-110.
0
10
Resistance (MPa)
20
30
40
50
0.20
Fig A4-111.
60
0
0.00
0.10
0.50
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.10
0.10
0.15
0.20
0.15
0.30
0.25
0.60
0.70
0.80
0.20
0.30
0.25
0.35
0.40
0.30
Depth (m)
0.50
Depth (m)
Depth (m)
0.40
0.35
0.40
0.50
0.55
0.60
0.65
0.50
0.70
0.75
0.55
0.80
0.60
0.85
0.90
1.10
0.65
1.20
0.70
Fig A4-112.
0.45
0.45
0.90
1.00
Resistance (MPa)
20
30
40
50
0.05
TEST 2
DATE
11/2003
0.05
TEST 1
DATE
11/2003
10
0.00
0.95
1.00
Fig A4-113.
Fig A4-114.
118
TEST 3
DATE
11/2003
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
0.05
0.05
0.10
0.10
0.15
0.15
10
Resistance (MPa)
20
30
40
50
60
0
10
Resistance (MPa)
20
30
40
50
60
0.00
0.10
TEST 5
DATE
11/2003
TEST 6
DATE
11/2003
0.20
0.30
0.40
0.25
0.30
0.50
0.25
Depth (m)
Depth (m)
0.20
TEST 4
DATE
11/2003
0.35
0.40
0.60
0.30
Depth (m)
0.20
0.35
0.40
0.70
0.80
0.90
1.00
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
1.50
0.70
0.70
1.60
1.10
1.20
Fig A4-115.
10
Resistance (MPa)
20
30
40
50
Fig A4-117.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST
MOUL501
DATE
08/10/2007
0.05
0.10
0.15
TEST
MOUL502
DATE
08/10/2007
0.10
0.15
0.10
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.40
0.35
0.40
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-119.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-120.
60
0
0.00
TEST
MOUL504
DATE
08/10/2007
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.00
0.05
0.05
TEST
MOUL505
DATE
08/10/2007
0.10
0.15
0.20
0.20
0.25
0.25
0.30
0.30
10
0.10
0.15
0.20
0.25
0.30
Depth (m)
0.40
Depth (m)
Depth (m)
0.35
0.35
0.35
0.40
0.40
0.45
0.50
0.55
0.45
0.45
0.50
0.50
0.65
0.55
0.55
0.70
0.60
0.60
0.65
0.65
0.85
0.70
0.70
0.90
0.60
0.75
0.80
Fig A4-121.
60
0.40
0.45
50
0.35
0.45
Fig A4-118.
Resistance (MPa)
20
30
40
TEST
MOUL503
DATE
08/10/2007
0.05
0.20
0.35
10
0.00
0.05
Depth (m)
Depth (m)
1.40
Fig A4-116.
0
1.30
Fig A4-122.
Fig A4-123.
119
Resistance (MPa)
20
30
40
50
TEST
MOUL506
DATE
08/10/2007
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
10
Resistance (MPa)
20
30
40
0
50
0.00
TEST
MOUL507
DATE
08/10/2007
0.05
Resistance (MPa)
20
30
40
0.10
0.15
0.20
50
60
TEST
MOUL
509
0.05
TEST
MOUL508
DATE
08/10/2007
0.05
0.10
10
0.00
60
0.10
0.15
0.20
0.25
0.20
Depth (m)
0.15
Depth (m)
Depth (m)
0.25
0.30
0.35
0.40
0.30
0.35
Fig A4-124.
0
10
Resistance (MPa)
20
30
40
50
0.60
0.65
0.65
0.70
0.70
60
0
TEST
MOUL
510
0.10
Fig A4-126.
10
Resistance (MPa)
20
30
40
50
60
0
TEST
MOUL
511
0.10
0.20
0.15
0.20
0.25
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-128.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-129.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST
MOUL
513
TEST
MOUL
514
0.05
0.10
0.15
0.05
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-130.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.35
0.45
Fig A4-127.
50
TEST
MOUL
512
0.10
0.15
0.40
Resistance (MPa)
20
30
40
0.05
0.15
0.35
10
0.00
0.05
Depth (m)
Depth (m)
0.55
0.60
0.00
0.05
Depth (m)
0.50
0.55
0.00
0.40
0.50
Fig A4-125.
0.35
0.45
0.45
0.25
0.30
Fig A4-131.
Fig A4-132.
120
TEST
MOUL
515
60
10
Resistance (MPa)
20
30
40
50
0
60
TEST
MOUL
516
50
60
0
0
10
TEST
MOUL
517
0.15
Resistance (MPa)
20
30
40
50
0.15
0.25
0.30
0.30
Depth (m)
0.20
0.25
0.35
0.40
0.10
0.15
0.40
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
Fig A4-135.
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
TEST
MOUL
520
0.10
0.15
0.15
0.25
0.30
0.30
0.30
Depth (m)
0.25
Depth (m)
0.20
0.25
0.35
0.40
0.40
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-137.
0
10
Resistance (MPa)
20
30
40
50
0.05
0.10
0.15
Fig A4-138.
60
0
0.00
10
Resistance (MPa)
20
30
40
50
60
0
0.00
TEST
MOUL
22
TEST
MOUL
23
0.05
0.10
0.15
0.05
0.10
0.15
0.25
0.25
0.30
0.30
0.30
Depth (m)
0.20
0.25
Depth (m)
0.20
0.40
0.35
0.40
0.35
0.40
0.45
0.45
0.45
0.50
0.50
0.50
0.55
0.55
0.55
0.60
0.60
0.60
0.65
0.65
0.65
0.70
0.70
0.70
Fig A4-139.
10
Resistance (MPa)
20
30
40
50
0.00
0.20
0.35
60
0.35
0.45
50
TEST
MOUL
21
0.10
0.20
Fig A4-136.
Resistance (MPa)
20
30
40
0.05
0.20
0.40
10
0.00
0.05
0.35
60
0.35
0.45
TEST
MOUL
519
50
TEST
MOUL
518
0.10
0.20
60
Resistance (MPa)
20
30
40
0.05
Fig A4-134.
0.00
10
0.00
0.10
Depth (m)
Resistance (MPa)
20
30
40
0.05
Fig A4-133.
Depth (m)
10
0.00
Depth (m)
Depth (m)
0
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
Fig A4-140.
Fig A4-141.
121
TEST
MOUL
24
60
0
10
Resistance (MPa)
20
30
40
50
60
0
0.00
0.05
0.15
0.20
0.20
0.25
0.25
0.30
0.30
0.40
0.35
0.40
0.45
0.45
0.50
0.50
0.55
0.55
0.60
0.60
0.65
0.65
0.70
0.70
Fig A4-142.
50
TEST
PRESSIO2
0.10
0.15
0.35
Resistance (MPa)
20
30
40
0.05
TEST
PRESSIO1
Depth (m)
Depth (m)
0.10
10
0.00
Fig A4-143.
122
60
APPENDIX 5 PROFILES OF PENETROMETER TEST CLASSIFIED
WITHIN SAME SLOPE AND ASPECT AREAS AND SAME TEST DATES
LOW SLOPE – CATACLINAL
LOW SLOPE – ANACLINAL
LOW SLOPE – ORTHOCLINAL
no data
no data
2002
2001
894
0.40
879
0.30
894
0.35
879
0.25
894
0.30
893
0.25
H eig h t (m )
879
893
0.20
893
0.15
893
0.10
879
0.05
879
0.00
1.4
879
RELIEF
BEDROCK
DEPTH
893
893
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.15
879
0.10
RELIEF
BEDROCK
DEPTH
0.0
Relative distance (m )
2002
0.20
879
0.5Relative distance
1.0
(m)1.5
0.05
0.00
2.0
2002
PROFILE 29- DATA 06/11/2002 (116,120,119)
Low slope - Orthoclinal direction
892
0.70
0.60
892
0.60
0.50
891
0.40
0.30
0.20
RELIEF
BEDROCK
DEPTH
0.50
891
890
0.40
890
0.30
889
RELIEF
BEDROCK
DEPTH
889
0.10
888
0.00
0
1
2
3
0.20
0.10
888
4
0.00
0
Relative distance (m)
MEDIUM SLOPE – CATACLINAL
123
1
1
2
2
3
3
4
D e p th (m )
0.70
H eig h t (m )
890
890
890
890
889
889
889
889
889
888
888
888
PROFILE 30- DATA 06/11/2002 (115-117)
Depth (m )
Height (m )
D ep th (m )
PROFILE 15 - DATA 04/11/2002 (102,101)
Depth (m)
Height (m)
PROFILE 25- DATA 04/04/2001 (37-38)
1999
2000
PROFILE 8 - DATA 02/05/2000 (31-32)
Me dium s lope - Anticlinal direction
PROFILE 37- DATA 08/11/1999 (10-18)
Medium slope - Orthoclinal direction
887
0.50
0.45
0.45
896
886
0.40
0.40
0.20
894
0.30
Height (m)
0.25
Depth (m)
0.30
895
0.35
886
0.35
895
Height (m)
0.50
885
0.25
0.20
885
RELIEF
BEDROCK
DEPTH
894
884
0.10
0.15
RELIEF
BEDROCK
DEPTH
0.15
0.10
0.05
0.05
893
0.0
1.0
2.0
884
0.00
4.0
3.0
Depth (m)
896
0.00
0
1
2
3
Re lative distance (m )
4
2002
2007
1.00
902
1.20
0.80
901
1.00
900
0.80
899
0.60
888
0.60
887
887
0.40
RELIEF
BEDROCK
DEPTH
886
886
Height (m)
H eig h t (m )
888
1.40
D ep th (m )
889
903
898
0.20
897
0.00
885
0
1
2
3
4
5
6
0.40
RELIEF
BEDROCK
DEPTH
0.20
0.00
896
0
2
4
6
8
MEDIUM SLOPE – ANACLINAL
2000
2001
PROFILE 19- DATA 04/04/2001 (39-40)
Medium slope - Anaclinal direction
PROFILE 20- DATA 02/05/2000 (24-25)
892
0.35
891
0.30
0.25
891
0.20
890
0.15
890
0.10
889
RELIEF
BEDROCK
DEPTH
0.10
889
889
RELIEF
BEDROCK
DEPTH
889
0.0
0.5
1.0
1.5
2.0
2.5
0.05
0.05
888
3.0
0.00
3.5
0.0
1.0
2.0
3.0
Relative dis tance (m )
124
4.0
0.00
5.0
Depth (m)
0.15
890
Height (m)
0.20
890
Depth (m)
0.25
891
Height (m)
891
Depth (m)
PROFILE 26- DATA 10/10/2007 (514-517)
Medium slope - Cataclinal direction
PROFILE 12 - DATA 04/11/2002 (106-107)
Medium slope - Cataclinal direction
2003
2003
PROFILE 34- DATA 23/07/2003 (301-303)
High slope - Anaclinal direction
903
0.28
903
0.28
902
Height (m)
895
0.28
895
0.27
894
0.27
901
0.27
901
894
0.50
0.40
0.30
902
895
RELIEF
BEDROCK
DEPTH
902
902
0.20
0.10
0.00
0.0
894
1.0
0.27
0.0
0.5
Relative
distance1.0
(m)
0.70
0.60
902
Depth (m)
0.28
Height (m)
895
0.90
0.80
RELIEF
BEDROCK
DEPTH
2.0
3.0
4.0
5.0
6.0
1.5
2007
PROFILE 24 - DATA10/10/2007 (524,521,520)
Medium slope -Anaclinal direction
909
0.45
908
0.40
0.35
908
0.30
907
0.25
907
0.20
906
0.15
906
0.10
905
0.05
905
0
1
2
3
4
5
R el at i ve d i st ance ( m)
RELIEF
6
7
BEDROCK
DEPTH
0.00
MEDIUM SLOPE – ANACLINAL
2001
2000
PROFILE 19- DATA 04/04/2001 (39-40)
Medium slope - Anaclinal direction
PROFILE 20- DATA 02/05/2000 (24-25)
0.35
891
0.30
0.25
891
0.20
0.20
890
0.15
890
889
RELIEF
BEDROCK
DEPTH
889
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Height (m)
0.25
891
Depth (m)
Height (m)
891
890
0.15
890
0.10
0.10
889
0.05
889
0.00
3.5
888
RELIEF
BEDROCK
DEPTH
0.0
1.0
2.0
3.0
125
4.0
0.05
0.00
5.0
Depth (m)
892
Depth (m)
PROFILE 23- DATA 23/07/2003 (501,502)
High slope - Orthoclinal direction
895
2003
2003
PROFILE 34- DATA 23/07/2003 (301-303)
High slope - Anaclinal direction
903
0.28
903
0.28
0.28
895
0.27
894
Depth (m)
895
902
902
894
901
901
0.27
894
0.30
0.20
0.10
902
0.27
RELIEF
BEDROCK
DEPTH
0.00
0.0
1.0
2.0
0.27
0.0
0.5
Relative
distance1.0
(m)
0.70
0.60
0.50
0.40
902
0.28
Height (m)
895
RELIEF
BEDROCK
DEPTH
902
Height (m)
895
0.90
0.80
3.0
4.0
5.0
6.0
1.5
2007
PROFILE 24 - DATA10/10/2007 (524,521,520)
Medium slope -Anaclinal direction
909
0.45
908
0.40
0.35
908
0.30
907
0.25
907
0.20
906
0.15
906
0.10
905
0.05
905
0
1
2
3
4
5
R el at ive d i st ance ( m)
RELIEF
6
7
BEDROCK
DEPTH
0.00
MEDIUM SLOPE – ORTHOCLINAL
1999
1999
PROFILE 4 - DATA 08/11/2001 (2,17,1)
PROFILE 3 - DATA 08/11/1999 (7,8,16)
0.60
0.30
897
0.20
RELIEF
BEDROCK
DEPTH
897
Depth (m)
Height (m)
0.40
898
Height (m)
0.50
898
0.10
896
0.00
0
1
2
3
903
902
902
901
901
900
900
899
899
898
898
897
0.70
0.60
0.50
0.40
0.30
126
0.10
0.00
0
4
0.20
RELIEF
BEDROCK
DEPTH
2
4
6
8
Depth (m)
899
Depth (m)
PROFILE 23- DATA 23/07/2003 (501,502)
895
2000
2000
PROFILE 21 - DATA 02/05/2000 (31,32)
PROFILE 17 - DATA 02/05/2000 (26,21,22,23)
891
0.60
891
885
0.35
884
0.30
884
0.25
884
0.20
884
0.15
0.50
0.30
889
Height (m)
890
Depth (m)
0.20
889
884
RELIEF
BEDROCK
DEPTH
888
888
0
1
2
3
4
0.10
884
0.00
884
0.10
RELIEF
BEDROCK
DEPTH
0.00
0
5
1
1
2001
PROFILE 35 - DATA 05/04/2001 (45-46)
890
890
890
890
890
RELIEF
BEDROCK
DEPTH
889
889
0
1
2
3
4
5
Height (m)
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
891
Depth (m)
891
Height (m)
2
2001
PROFILE 5 - DATA 05/04/2001 (56-58)
6
882
882
882
882
882
882
882
882
882
881
881
0.35
0.30
0.25
0.20
0.15
0.10
RELIEF
BEDROCK
DEPTH
0
2002
1
1
0.05
0.00
2
2
2002
PROFILE 13 - DATA 04/11/2002 (103-105)
PROFILE 14- DATA 04/11/2002 (108,110)
0.30
884
0.25
884
0.35
883
0.20
884
0.30
882
0.15
881
0.10
880
0.05
879
Height (m)
885
D ep th (m )
H eig h t (m )
0.05
Depth (m)
Height (m)
0.40
Depth (m)
890
0.00
0
2
0.25
884
0.20
883
0.15
883
0.10
883
6 RELIEF 8
BEDROCK
DEPTH
4
0.05
883
0.0
127
RELIEF
BEDROCK
0.5
DEPTH
1.0
1.5
2.0
0.00
2.5
2002
2002
892
0.50
0.80
895
0.45
891
894
0.50
894
0.40
0.30
RELIEF
BEDROCK
DEPTH
893
893
0.20
1.0
0.00
3.0
2.0
0.40
0.35
891
0.30
890
0.25
0.20
890
0.15
RELIEF
BEDROCK
DEPTH
889
0.10
892
0.0
Height (m)
0.60
Depth (m)
0.70
895
Height (m)
PROFILE 27 - DATA 06/11/2002 (121-124)
0.90
0.10
0.05
889
0.00
0
1
2
3
4
5
2002
2002
PROFILE 28 - DATA 0811/2002 (127,133,132,134)
PROFILE 32 - DATA 07/11/2002 (135 - 144)
889
0.60
915
0.30
914
889
0.25
0.15
888
0.10
912
Height (m)
889
D ep th (m )
0.20
889
888
0.05
1
1
2
2
3
3
4
899
0.45
0.40
899
0.35
898
0.30
0.25
898
0.20
897
0.15
0.10
897
0.05
896
0.00
2
RELIEF
BEDROCK
DEPTH
0
PROFILE 22 - DATA10/10/2007 (509-5100)
Medium slope -Orthoclinal direction
1
0.20
905
2007
1
909
906
0
0.30
910
907
0.00
888
0
0.40
911
908
RELIEF
BEDROCK
DEPTH
888
0.50
913
889
H e ig h t (m )
Depth (m)
PROFILE 18 - DATA 04/11/2002 (111,112)
896
2
R el at i ve d i st ance ( m)
3
RELIEF
3
BEDROCK
DEPTH
128
5
10 distance
15 (m )
Relative
20
25
0.10
0.00
HIGH SLOPE – CATACLINAL
2001
888
0.50
888
0.45
887
0.40
887
0.35
886
0.30
886
0.25
885
885
0.20
RELIEF
BEDROCK
DEPTH
884
Depth (m)
0.15
884
0.10
883
0.05
0.00
883
1
2
3
4
5
HIGH SLOPE – ANACLINAL
no data
HIGH SLOPE – ORTHOCLINAL
1999
1999
PROFILE 2 - DATA 08/11/1999 (11,14,12,13)
PROFILE 7 - DATA 08/11/1999 (3-5)
0.60
895
894
0.50
0.50
898
0.45
897
0.40
896
894
893
0.30
893
Height (m)
0.40
Depth (m)
Height (m)
899
0.20
892
892
891
0.0
1.0
2.0
0.30
894
0.25
893
0.20
892
891
0.10
0.05
889
888
0.00
3.0
0.00
0
4.0
5
2001
10
15
20
2001
PROFILE9 - DATA 05/04/2001 (47,48,51)
PROFILE 6 - DATA 05/04/2001 (59-60)
893
0.40
893
0.35
887
0.25
886
0.20
892
0.15
892
0.10
Height (m)
892
0.10
883
0.05
882
0.00
881
1
0.15
884
892
RELIEF
0
0
1
1
BEDROCK
DEPTH Relative distance (m )
0.20
885
892
0
0.25
888
0.30
Depth (m)
893
Height (m)
0.15
RELIEF
BEDROCK
DEPTH
890
0.10
RELIEF
BEDROCK
DEPTH
0.35
895
Depth (m)
0
1
RELIEF
BEDROCK
DEPTH
0.0
1.0
2.0
3.0
Depth (m)
Height (m)
PROFILE 36 - DATA 05/04/2001 (42-44)
High slope - Cataclinal direction
0.05
0.00
4.0
129
129
2001
2002
PROFILE 16 - DATA 04/11/2002 (113,114)
887
0.45
887
0.40
0.50
0.35
0.40
0.30
0.20
1.0
2.0
3.0
0.30
886
0.25
0.20
885
0.10
884
0.0
0.00
5.0
4.0
0.15
RELIEF
BEDROCK
DEPTH
885
884
RELIEF
BEDROCK
DEPTH
0.0
Height (m)
886
1.0
2.0
Depth (m)
0.60
891
890
889
888
887
886
885
884
883
882
881
Depth (m)
Height (m)
PROFILE 11 - DATA 05/04/2001 (49-53)
0.10
0.05
0.00
4.0
3.0
2002
2003
PROFILE 31- DATA 06/11/2002 (125-126)
884
0.35
0.35
884
0.30
0.30
0.25
894
0.20
893
0.15
RELIEF
BEDROCK
DEPTH
893
2
3
0.10
0.05
0.0
0.5
1.0
1.5
2.0
0.00
1
0.15
883
883
0.05
0
0.20
883
883
0.10
892
0.25
884
4
Depth (m)
894
0.40
Height (m)
895
Height (m)
PROFILE 33- DATA 23/07/2003 (304,305)
0.45
Depth (m)
895
0.00
2.5
RELIEF
BEDROCK
DEPTH
2007
903
0.90
902
0.80
901
0.70
900
0.60
899
898
0.50
0.40
RELIEF
BEDROCK
DEPTH
897
896
0.30
Depth (m)
Height (m)
PROFILE 1 - DATA 9/10/2007 (504,505,506,508,513)
High slope -Orthoclinal direction
0.20
895
0.10
894
0.00
0
2
Relative
4
distance
6
8(m )
10
12
130
130
APPENDIX 6
Weight of evidence model formulation
This section describes the basic formulation implicit in the weight-of-evidence
method, to assess the probabilities of occurrence of landslides within the study area.
The following explanation of the formulation has been synthesized from BonhamCarter (1994) and Van Westen et. al. (2003)
10.1.1.
Prior probability
Given a known landslide distribution map for an area (Area(slide)) the prior
probability can be defined as the probability that a certain pixel within the total
domain (Area(total)) will have a landslide. In other words, it is the number of pixels
with landslides, divided by the total number of pixels in the map (i.e. landslide
density) and can be expressed as follows:
Pprior = P{S } =
10.1.2.
A{S } Area ( Slide)
=
A{T } Area (Total )
(Eq. 7.1)
Conditional probability
Suppose that a given information pattern (called binary variable map, B), such as a
slope class map or a type of soil class map, occupying “Area{B}” pixels, occurs in
the total domain such that a number of landslides take place preferentially within
that pattern (e.g. landslides occurring preferentially in “steep slope” class areas),
hence A{S∩B}; then the favourability of locating a landslide given the presence of
the pattern can be expressed as a conditional probability as follows:
P{S B} =
P{S ∩ B} Area{S ∩ B}
=
P{B}
Area{B}
(Eq. 7.2)
In other words, this is the conditional probability of having landslides within a
certain information pattern (e.g. “steep slope” class areas), calculated as the number
of pixels with landslides within that pattern, divided by the total number of pixels
occupied by the pattern (i.e. density of landslides within the pattern). The
conditional probability of having landslides while not in unit B can be expressed as:
131
{S ∩ B}
{ } P{PS{∩B}B} = Area
Area{B}
PS B =
(Eq. 7.3)
The conditional probability of not having landslides while in unit B is:
{S ∩ B}
Area{B}
PS B =
(Eq. 7.4)
And the conditional probability of not having landslides while not in unit B is:
{S ∩ B}
Area{B}
PS B =
10.1.3.
(Eq. 7.5)
Positive and negative weights
In the weight of evidence method, positive and negative weights are allocated to
each pixel of the factor maps. Those weights are defined as:
Wi + =
log e P{Bi S }
log e P Bi S
{
}
(Eq. 7.6)
Wi − =
log e P B i S
log e P B i S
{
{
}
}
(Eq. 7.7)
and,
Where:
Bi = Presence of a potential landslide conditioning factor
Bi = Absence of a potential landslide conditioning factor
Si = Presence of a landslide
Si = Absence of a landslide
Positive weight W+ is used for those pixels of a specific factor or class (e.g. steep
slope) to indicate the incidence of the presence of those factors in the landslide
occurrence. If W+ is positive, the presence of the factor contributes to the landslide
occurrence, if negative, it does not contribute.
On the other hand, W- is used for those pixels of a specific factor or class (e.g. slope)
to indicate the incidence of the absence of those factors in the landslide occurrence.
132
If W- is positive, the absence of the factor contributes to the landslide occurrence, if
negative, it does not contribute.
There are four possible combinations for each factor, of which the frequency,
expressed as number of pixels, can be calculated with a GIS (see Table 7.1)
Table 10-2. Four possible combinations of a potential landslide conditioning
factor and a landslide inventory map (after van Westen et. al.2003)
Npix=number of pixels
Weights of evidence can then be written in terms of number of pixels, as follows:
(Eq. 7.8)
and,
(Eq. 7.9)
In order to be able to calculate the four combinations presented in Table 7.1 for
multiclass maps, it is necessary first to calculate the following columns in the jointfrequency tables of each factor:
nmap = total number of pixels in the map
nslide = number of pixels with landslides in the map
nclass = number of pixels in the class
nslclass = number of pixels with landslides in the class
The values needed for the weight formulas are:
npix1 = nslclass
npix2 = nslide-nslclass
npix3 = nclass-nslclass
npix4 = nmap-nslide-nclass+nslclass
The weights are then calculated based on equations 7.8 and 7.9 as:
133
W+ = ln((npix1/(npix1+npix2)) / (npix3/npix3+npix4))
W+ = ln((npix1*(npix3+npix4) / ((npix1+npix2)*npix3)
W- = ln((npix2/(npix1+npix2)) / (npix4/npix3+npix4))
W- = ln((npix2*(npix3+npix4) / ((npix1+npix2)*npix4)
10.1.4.
Calculation of final weights and contrast factors
The total weight of a particular analysed factor is obtained by adding the weight of
the factor itself to the negative weights of the other factors in the map. This can be
written as:
W final = W plus + Wmin total + Wmin
(Eq. 7.10)
Where Wmintotal is the total of all negative weights in a multiclass map. Then, to
quantify the spatial association between a map class and the occurrence of
landslides, the contrast factor as mentioned in Bonham-Carter (1994) is defined:
CW = W + − W −
(Eq. 7.11)
134

Thesis Pena Rincon3

Transcription

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