4 GIS-BASED ASSESSMENT
by M Harrison, A Gibson, A Forster, D Entwisle and G
This section describes the development of the GIS-based
assessment tool for debris flow hazard assessment in respect of the
entire Scottish road network and illustrates the data and results
from this process.
In order to ensure that a comprehensive knowledge of debris
flows, the road network and the interaction between these two
entities was fully captured within the work being undertaken a
working group was formed. This group comprised the authors of this
section, who were tasked with producing the actual assessment, the
editors of this report and other specific individuals with relevant
experience (see acknowledgements).
A series of four meetings were held to consider the following
Meeting 1: The specification for the work.
Meeting 2: The available data sets and their relevance to the
task in hand.
Meeting 3: The scorings and weightings to be assigned to each
Meeting 4: Fine-tuning of the results from scorings and
weightings in the light of the group’s knowledge and
The process of knowledge capture and input used in this work was
akin to the process that is used to capture information and develop
rules for knowledge-based systems (e.g. Winter and Matheson, 1992).
This approach forms a vital part of any knowledge and rule-based
interpretation of data.
The methodology developed is based on assessing the propensity
for debris flow formation. In order to establish the hazard to
sites on the road network, further interpretation of the outputs is
required in order to establish the likelihood of any given area
that exhibits a propensity to debris flow formation producing a
flow that might intersect the road (see Section 5).
GIS-based imagery relevant to the local road network was
distributed to the individual Local Authorities for further action
in the context of their particular needs.
4.2.1 Causal Factors for Debris Flow in Scotland
Winter et al. (2005a, p30, 31, 58),
describing the findings of the initial study, identified 86
different factors that contribute to the debris flow hazard to the
Scottish Roads network. Each of these factors is valid, and could
be individually considered for inclusion in a system that seeks to
model debris flow potential. However, for the purposes of this
study it was important to use datasets that possessed reasonably
consistent coverage across the whole country and for which some
form of quality could be assured and for which availability could
In effect this meant that many ‘point’ datasets,
such as borehole records and site investigations could not be
included within the analysis. However, many of the properties
recorded as ‘point’ data are to some extent, described
by spatially continuous datasets; for instance, data on
permeability, grain size, and cohesion are intrinsically linked to
polygons of different lithologies described by a geological
4.2.2 Data Sources
The working group identified three relevant data sources that
were available for the entire study area:
1. BGS DiGMap: GIS layers of geology at 1:50 000 scale showing
bedrock and superficial deposits (supplied by BGS). Each polygon of
the geological map is attributed with a code that describes the
litho-stratigraphic unit to which the rock type belongs. That is,
each polygon is labelled with a code that describes the polygon in
terms of the type and age of the rock.
2. NEXTMap Britain: a digital terrain model derived from the
INTERMAP Digital Terrain Model product. NEXTMap is a
high-resolution elevation model of Great Britain. It was generated
from a 2005 airborne survey in Scotland where the time it takes for
a signal to be sent down to the ground and bounce back was
measured. This was calibrated with a GPS on board the plane to give
the height of the ground surface, accurate to 0.5 m.
The initial NEXTMap product was a digital surface model that
represented the height of the surface of the ground. This dataset
contains all ‘cultural’ features such as buildings and
wooded areas. The second product from INTERMAP is a digital terrain
model, which is the same product but with the cultural features
removed. The algorithms that have been employed to remove these
features are in the most part very effective. However, some areas
of woodland are still shown by areas of raised elevation. Although
this can cause localised error in the data, the NEXTMap digital
terrain model is a very accurate and high-resolution dataset, and
it provides continuous coverage for all of Scotland.
3. CEH (Centre of Ecology and Hydrology) land use data: CEH
Landcover 2000 is a digital map that gives a comprehensive picture
of the UK Broad Habitats (LCM 2000). Sixteen Target classes
(Level-1) and 27 subclasses (Level-2) allowed construction of the
Broad Habitats. The subclasses are described in greater detail in
Level-3. It was mapped by analysing satellite spectral reflectance
data on a grid of approximately 25 m square pixels. Ground survey
assessed the spectral characteristics of the Broad Habitats and an
automated system selected the most likely class for each pixel in a
remotely sensed image. Accuracy was checked against ground survey
and other information. The minimum mappable unit is about half a
hectare. Data is available as digital outlines of the level 2
subclasses, which are treated as ‘objects’ in
At an early stage of the research, it was proposed that rainfall
data was also included. However, the working group concluded that,
as intense rainfall could occur anywhere within the geographical
study area, it was not necessary to include this as a separate
factor. Therefore, Meteorological Office rainfall data have not
been utilised at this stage.
The research described by this report considered how best to
integrate aspects from the three data sources described above to
provide a reasonable model for debris flow hazards affecting the
Scottish road network. This has mainly been carried out through an
iterative process of attributing or manipulating each dataset to
represent as many of the factors described by Winter et. Al.
(2005a). Thus, expertise in the geology of Scotland has been
applied to DiGMap to change the standard attribution of polygons
(age and type of rock) to numerical codes that estimate bedrock
permeability and the degree to which source material for debris
flows can be formed.
4.3 DEBRIS FLOW POTENTIAL ASSESSMENT
The working party concluded in the initial study that five main
components should be considered when determining the hazard
potential of debris flows affecting the road network:
1. Availability of debris material.
2. Hydrogeological conditions.
3. Land Use.
4. Proximity of Stream Channels.
5. Slope Angle.
It was considered that information regarding each of these could
be usefully retrieved from the datasets described in Section 4.2.
The interpreted data could then be combined to produce a working
model of debris flow hazard that could be validated by comparison
with scenarios taken from accounts of investigated debris
The possibility of seismic acceleration as a causal factor was
raised at a working group meeting. After discussion it was thought
unlikely to be a significant factor in the generation of debris
flows that could impact significantly of the trunk road network as
the ground accelerations developed by anticipated earthquakes were
an order of magnitude less than those typically generated by heavy
construction plant. However, further research into this subject may
be useful at a later stage to properly consider the implications of
a major seismic event.
4.3.1 Availability of Debris Material
For a debris flow to occur, there must be an available source of
material, usually granular, often with a very wide particle size
range in such a state that it would easily be mobilised by the
action of a fluid (usually water). Thus the material that has the
highest potential for debris flow activity is likely to be
non-cohesive, with significant particle granularity. Material that
is cohesive due to high clay content or inter-granular cement would
be difficult to mobilise.
188.8.131.52 Analytical Method
On this basis, the lithologies represented by polygons in DiGMap
were interpreted against a scale that indicated the degree to which
the bedrock or superficial unit at surface would provide
non-cohesive granular material as a source of debris. The ROCK_D
(BGS Rock Description code) attribute of each polygon was
re-interpreted by asking the following two questions:
‘Is this material capable of being mobilised by water into
a debris flow in its fresh, unweathered state?’
‘Is this material likely to have a weathered regolith or
covering of head that could be mobilised by water to form a debris
Each ROCK_D description has been assigned a number on a scale of
1 to 10 to give an indication of its potential to supply the
material, from within its outcrop, that would be capable of
generating a debris flow. The judgement is based on the indicated
grain size distribution or its assumed probable grain size
distribution (for superficial material) or the likely ‘block
size’ distribution of near-surface material/regolith (bedrock
materials) as inferred by expert judgment. Table A.1 (Appendix A) shows the criteria used to assign each of the 4200 BGS codes
identified. These are based upon the general principles outlined by
Terzaghi (1955a, 1955b) and updated in BS8002:1994, BGS
geologists’ knowledge of each lithology and guidance from the
In consultation with the working group, these were adjusted to
account for the significance given to chemical weathering of
certain rock types and the lower likelihood of the generation of
clays by chemical weathering in some lithologies in Scotland. The
interpreted codes were stored within the BGS Scottish Debris Flow
Attribution Database (BGS SDABD).
184.108.40.206 Analytical Method – ‘Accumulation
Zone’ Supplementary Dataset
It was recognised by the working group that in many locations,
BGS data did not record the presence of peat or other deposits that
may form sources of debris flow material. This was a function of
the age of the BGS data used and the mapping methods historically
employed by BGS mapping teams. In previous decades, priority was
given to recording the presence of bedrock materials –
superficial materials were considered ubiquitous and not mapped. To
counter this, the working group recommended that a method be sought
that could identify areas where deposits of non-cohesive material
could collect and form source areas for debris flows.
The NEXTMap digital elevation model was analysed, using GIS to
identify those areas where material was likely to accumulate. The
analytical method used, highlighted those areas where changes in
the shape (morphology) of the ground meant, that a flow of water
would be slowed, and any material held in the water flow might be
deposited. The two types of slope are identified by the method
1. Convergent slopes – where horizontal bends in the
ground mean that flows come together to form ‘sinks’,
(Figure 4.1). This is termed a change in horizontal or ‘plan
2. Slope bases – where the relief of the ground changes
quickly from a steep gradient to a shallow gradient, (Figure 4.2).
This is termed a change in vertical or ‘profile
Figure 4.1 – Diagram showing how material flows away
from (a) higher, convex (divergent) ground (b) and towards lower,
concave (convergent) ground (after Shary, 2002).
Figure 4.2 – Diagram to show where accumulation might
be expected in an area of relief change, where slope gradient
changes from steep to shallow (after Shary, 2002).
Characterisation of plan curvature and profile curvature were
analysed together, to identify zones where the ground surface is
‘convergent’, where a flow of water would decelerate
and deposition can occur (Figure 4.3). Likewise, the analysis can
identify ‘divergent’ ground, where a flow of water
would accelerate and erosion would occur.
Figure 4.3 – Landform classification based upon a
combination of horizontal curvature and vertical curvature (Troeh,
1964, Shary, 2002).
220.127.116.11 Method to Calculate the ‘Accumulation Zone’
Although a number of methods are available to perform this
analysis, experience in other BGS projects has shown the
K-Accumulation model by Shary (2002) works well and is flexible to
different types of terrain. The method can be given by the
Kaccum is a value indicating the shape of the
ground, positive values indicate areas of accumulation.
Meancurve, Plancurve and Profilecurve are all numbers
that mathematically describe the shape of the ground, calculated
from the 25 m pixel NEXTMap DEM using ArcGIS.
- Meancurve is the average normal section. Positive values
highlight a broadly convex slope, negative values describe broadly
- Plancurve is the rate of change of horizontal curvature.
Positive values highlight a divergent slope, negative values a
convergent slope (Figure 4.4).
- Profilecurve is the rate of change of vertical curvature
or slope. Positive values indicate convex slope, negative values
indicate concave profiles (Figure 4.4).
Figure 4.4 – Visual representation of plan curvature
and profile curvature.
The output from the algorithm is a number rating
(Kaccum) that indicates the likelihood that
material will be deposited, as a result deceleration of a flow.
Although previous use of the method by BGS was to estimate areas
where head may accumulate in the south of England, it has been
possible to adapt the method to indicate areas where material may
accumulate in the Scottish Highlands. This was carried out by an
iterative process whereby the original formula was applied to known
areas and a visual assessment made, comparing the estimated area of
material deposition with local knowledge of areas of deposition.
Where the estimated area of deposition was incorrect (for instance
on cliffs or in stream channels), the score given by the formula
was discounted. After a number of iterations, it was decided that
those areas likely to be depositional zones had a values in the
range 0 – 1. Table A.2 (Appendix A) shows the result of this
analysis, with suggested values to be used in the overall debris
Figure 4.5 shows the availability of debris material scoring for
two areas – Glen Ogle and Inverness – as examples.
These same examples will be followed for each of the stages in the
report. The diagrams show that the generally more granular,
alluvial or glacial materials in the base of the valley in Glen
Ogle and more widely distributed in Inverness, have higher scores
and therefore greater influence on the result in this part of the
methodology. In this analysis these materials are regarded a
potential sources areas for landslide debris.
Figure 4.5 – Availability of debris material. Excerpts
from the GIS showing the variation in availability of debris
material scoring for two areas; (left) Glen Ogle and (right)
Inverness. The legend in the diagram refers to this as lithology
index, reflecting the scoring of the source material. (OS Data
© Crown Copyright. All rights reserved Scottish Government
4.3.2 Water Conditions
Two aspects of water conditions are relevant to the generation
of debris flows.
1. The ability of water, as rainfall or overland flow to
infiltrate a potentially mobile deposit (permeability of the
- This has been taken into account in the scoring for
Availability of Debris Material (Section 4.3.1), which
combines judgements on grain-size and permeability.
2. The ability of water to remain within the deposit to an
extent where pore water pressures can build to a level where the
shear strength is sufficiently reduced to initiate failure
(permeability of the underlying material).
- A factor was required that would take account of the
permeability of the underlying bedrock, this is considered in this
The substrate beneath potentially mobile deposits may exert
either a positive (destabilising) or a negative (stabilising) input
to debris flow generation. A positive input will be generated where
the substrate is impermeable. In such a case, infiltration through
the surface material is impeded, leading to a build up of
pore-water pressures, a lowering of effective shear strength and
increasing the likelihood of a ground failure. Most bedrock
materials may be locally expected to be relatively impermeable with
regard to the timescale of a high intensity rainfall event.
A negative (stabilising) input to debris flow potential is
generated where the substrate is permeable. If a debris flow moves
over permeable ground it may be slowed by under-drainage –
(water draining from the moving mass into the substrate) with a
consequent increase in shear strength. It is unlikely that this
mechanism will have a significant effect except where the debris
flow has flowed onto shallow, very permeable slopes and has spread
out to allow under drainage over a large area (as seen in the
lowest part of some debris flows).
The permeability of a rock type will be a function of grain size
distribution for superficial materials and discontinuity spacing
and dilation for bedrock materials. For superficial materials,
coarse, clean gravels will be the most permeable and clay the least
permeable. Consideration of the permeability of bedrock in Scotland
needs to consider the possibility that in most places, relatively
impermeable bedrock lies beneath a potentially permeable and mobile
regolith. However, depending upon specific rock type,
discontinuities in the bedrock may have been developed and dilated
by thermal, physical and chemical weathering. At depth, most
bedrock lithologies in the study area are likely to be interlocked
and unlikely to be incorporated in a debris flow.
It should be borne in mind that, in many locations, there will
often be a pre-existing drainage system that will have a
significant impact upon the nature and distribution of pore-water
pressures. Although such systems are likely to be a significant
control on debris flow potential, there is no proven method
available at this time that can be used to digitally analyse this
using existing data. It was considered by the working group that
this may be an avenue for further investigation at another stage of
18.104.22.168 Analytical Method
Lithologies represented in DiGMap were interpreted on a scale
that indicated the relative permeability of substrate materials.
The ROCK_D (BGS Rock Description code) attribute of each polygon
was interpreted by asking the following question:
‘What is the permeability of this rock type?’
Each ROCK_D description has been assigned a number on a scale of
1 to 10 to give an indication of its permeability within its
outcrop. The judgement is based on the indicated grain size
distribution or its assumed grain size distribution (superficial
material), consolidation/cementation and discontinuities. Table A.3
(Appendix A) shows the criteria used to assign each code.
Figure 4.6 shows the result of these assessments in the two
example areas. High values represent areas of low permeability
hence a higher likelihood of contributing to debris flow
4.3.3 Vegetation and Land Cover
Vegetation may have three beneficial effects in maintaining
1. Intercepting rainfall to reduce infiltration into the
2. Removing soil moisture
3. Reinforcement of the ground by a root network.
The amount by which particular vegetation improves the stability
of a slope will vary with the type of vegetation. Trees are likely
to be more beneficial than shrubs, which would be better than
Other land uses are likely to have adverse influence on slope
instability, for instance, bare soil or cultivated (bare) ground
would be prone to debris flow, as it is often unbound and in a
loose condition. Urban or rural development may also be detrimental
to stability due to the possibility of the inappropriate disposal
of surface water, or leaking services that may feed water into a
susceptible slope leading to high antecedent water level prior to a
high magnitude event or a focusing of a high magnitude event such
as to initiate debris flow activity.
Figure 4.6 – Excerpts from the GIS showing the
variation in water conditions (permeability) for two areas; (left)
Glen Ogle and (right) Inverness. (OS Data © Crown Copyright.
All rights reserved Scottish Government 100020540, 2008.)
22.214.171.124 Analytical Method
Expert judgement has been used to assign appropriate scores for
the land use categories in the CEH land use dataset (Table A.4 of Appendix A) by asking the
‘What is the likely effect of this landcover upon debris
flow potential at a site?’
The judgement was based upon the assumptions described above and
on guidance from the working group. Each cover type was given a
rating between 0.7 and 1.2 to indicate by how much the vegetation
may improve stability. The lowest value is for woodland and the
highest value for annual crops where the ground is regularly
disturbed producing an open structure with little root
strengthening. Other land use and vegetation cover have
Figure 4.7 shows the result of these assessments in the two
example areas. As can be seen in the Inverness area, the built
environment gives a high stabilising factor, whereas in the Glen
Ogle example, the vegetation would have a more limited affect on
Figure 4.7 – Excerpts from the GIS showing the
variation in Vegetation conditions from satellite data for two
areas; (left) Glen Ogle and (right) Inverness. (OS Data ©
Crown Copyright. All rights reserved Scottish Government 100020540,
Peat was initially considered separately, as it may fail in
slides and bog bursts in certain environments. When it fails it may
form a source of water and material to debris flows and may be part
of the initial slide or, if part way down slope, may add impetus to
the flow. A method of combining peat and slope that identified
areas of peat above roads with slope of greater than 5° was
discussed. However, the stability of peat involves many complex
factors, some listed below, and a proper understanding of peat
behaviour would require field assessment. As peat could not be
suitably assessed using available national datasets, it was decided
that this was a specialist issue and would not be pursued in detail
during this project.
Some factors affecting the stability of peat:
- Peat layer overlies a relatively impermeable material.
- A convex slope or break of slope at its head.
- Proximity to local drainage including seeps, flushes and
- Connection between surface drainage and the base of the
As a primer to the peat assessment, BGS have used the NEXTMap
data to calculate flat areas (that could contain peat) that lie
above the trunk road network, with a connecting slope.
The first step was to locate areas of steep and flat ground
(Figure 4.8). For the purpose of this exercise, steep ground over
which peat could move was assessed as anywhere that had a slope
greater than 5 degrees. Flat ground, where peat materials may form
was identified as anywhere with a slope less than 2 degrees.
This was performed on a slope model for Scotland, derived from
NEXTMap Digital Terrain Models (DTM), resampled to a 50m cell size.
ESRI’s Spatial Analysis extension was used to create two
grids: one for steep ground and one for flat ground.
The steep and flat grids were converted to shapefiles, resulting
in polygons representing instances of steep and flat ground. In
some cases these polygons were quite large in size, so they were
broken up into smaller polygons for analysis. This was achieved by
intersecting the polygons by the trunk road and a catchment dataset
calculated from the underlying DTM.
It was decided that only flat areas within a 3km buffer from the
roads should be included for analysis, to reduce the data volumes.
The intersected layers for steep and flat areas were both clipped
to a 3km buffer of the trunk road network.
The polygons representing steep ground for the area around Glen
Ogle are shown below.
Figure 4.8 – Ground sloping greater than 5 degrees in
the Glen Ogle area. (OS Data © Crown Copyright. All rights
reserved Scottish Government 100020540, 2008.)
Zonal statistics, in ArcMap were applied to the intersected
shapefiles to find the average height of each polygon. The height
was abstracted from the NEXTMap DTM.
Every flat polygon was interrogated. If the flat polygon was
within 150m of a steep polygon, and the average height of the flat
polygon was larger than the average height of the steep polygon,
then the steep polygon was interrogated. If the steep polygon was
within 150m of a trunk road, and the average height of the steep
polygon was higher than the average height of the road, then the
flat polygon that originally intersected the steep polygon was
exported to another layer. (The statistics for each road segment
have already been supplied as part of the project deliverables.
These statistics include average height of road segments, which
have been used here.) This exercise was repeated for each flat
The algorithm used is detailed in Table A.5 (Appendix A).
126.96.36.199 Peat Limitations
This is a fully automated methodology, and is only as accurate
as the data used.
The methodology identifies areas of flat ground above both steep
ground and roads. It does not necessarily identify the ideal
profile for peat flow.
4.3.4 Stream Channels
Stream channels are often associated with debris flows. This is
primarily because they may focus the flow of water during extreme
events and supply large volumes of water that can mobilise
available material. They may also act as collectors for loose
material during moderate flows forming debris dams and at times of
extreme flow there is the possibility of their actively promoting
landsliding of additional material from the walls of the channel
and from these debris dams. Thus the working group concluded that
identifiable streams should be buffered for an appropriate distance
from their centre line to take into account the erosion catchment
area and nature of the adjacent material. Discussions within the
working group suggested that a buffer, at least, as wide as an
assumed 15° side slope should be employed. For a 3m deep
channel this would give a buffer width of ±15m and
for a 10m deep channel a ±50m buffer.
Using this method, it was found that the buffer covered very
large areas of ground. Therefore, for the first iteration of the
dataset, it is proposed to use a 50 m buffer centred on stream
channels and to score this 10.
188.8.131.52 Analytical Method
The location of streams were automatically generated from
NEXTMap digital terrain models using hydrological modelling
techniques. The NEXTMap dataset is detailed in Section 4.2.2.
ESRI’s hydrologic modelling toolset from the Spatial
Analyst extension in ArcGIS 9.1 was used to generate the stream
network. A filter of 1500m was used to ensure that the correct
density of streams was identified. Full details of the method are
given in by Tarboten et al (1991). As described above, the
automatically generated stream network was buffered to a width of
50 m. Any ground within this buffer zone has been given a
score of 10.
Figure 4.9 shows the stream locations calculated from the DTM in
the example areas. The low-lying Inverness area has many more
streams present than the steeper and more deeply-incised Glen Ogle
4.3.5 Slope Angle
Slope angle influences the balance of stabilising and
destabilising forces on all slopes. When the destabilising forces
exceed the shear strength of the materials forming the slope the
failure occurs. Therefore, the steeper the slope the greater is the
susceptibility of the material to initiate a debris flow.
Figure 4.9 – Excerpts from the GIS showing the
processed stream data as extracted from the Digital Terrain Model
for two areas; (left) Glen Ogle and (right) Inverness. (OS Data
© Crown Copyright. All rights reserved Scottish Government
184.108.40.206 Analytical Method
The slope categories significant in the generation of debris
flows that were indicated by Winter et al. (2005a) were modified
following further discussions within the working group, based on
the experience of those present. These were used as the criteria to
allocate scores to be included in the overall debris flow hazard
assessment Table A.6 (Appendix A)
Figure 4.10 shows the effect of these classifications in the two
example areas. Slope is one of the most significant factors in the
initiation of debris flows and as can be seen in the diagrams, in
the low-lying Inverness area the slope index is very low. In Glen
Ogle the pale colours indicate high slope values above the A85.
4.3.6 Weighting of Causal Factors
It was recognised by the working group that it would be
important to include some form of weighting factor into the
algorithm to allow the relative importance of each factor to be
expressed. Although it is impossible to understand, in detail, the
precise interaction between each of the factors described, the
working group generated a series of weighting factors. These are
based upon the knowledge and experience of members of working group
involved in the investigation and management of debris flows in
Scotland. This allowed different scenarios to be modelled in
working group meetings to use real-world examples to validate the
model results. The factors are given in Table A.7 (Appendix A).
Figure 4.10 – Excerpts from the GIS showing the
processed slope index values as extracted from the Digital Terrain
Model for two areas; (left) Glen Ogle and (right) Inverness. (OS
Data © Crown Copyright. All rights reserved Scottish
Government 100020540, 2008.)
4.4 GIS METHODOLOGY
The five variables listed in Section 4.2 (availability of debris
material, water conditions, vegetation and land use, stream
channels, and slope angle) have been combined in a geographical
information system (GIS) in order to analyse their distribution and
be able to spatially combine their contributing hazard scores.
The system used to prepare data was ESRI’s ArcGIS 9.1 and
ArcWorkstation 9.1. For example, the geology was selected and
clipped (cut-out) to the area of interest, and the stream network
was ‘cleaned’ to remove anomalous areas such as lochs
from the dataset. Once the datasets were clean and ready for
analysis they were converted into grids and processed using
ESRI’s ArcWorkstation 9.1. ArcWorkstation is a command line
driven GIS. Although it doesn’t display the data graphically,
so certain processor-intensive functions can be performed more
All data were converted into grids with a cell size of 25 m.
This was necessary in order to process the data efficiently, though
the conversion process was not always straightforward. Grids are a
very efficient way of processing large volumes of data and they are
ideal when applying weighting factors. Using a simple arithmetic
grid calculator, it is possible to multiple every cell in a grid by
a certain amount. This enables any final weighting factor to be
easily incorporated into the methodology. The weighting factor for
each variable is then applied to the grid and resultant grids are
added together to produce a final model representing the landslide
Figure A.1 (Appendix A) shows a series of flow charts that
summarise each of the methodologies.
The results of the study are presented as GIS layers available
separately to this report. They are presented as ArcView format
shapefiles for inclusion in further GIS analysis and as a table
summarised against Transport Scotland’s road network
sections. Please refer to the limitations statement and contract
For those who do not have access to full GIS, ArcReader is
available as a free download from http://www.esri.com/software/arcgis/arcreader/indexl
using this tool the GIS format outputs can be viewed, simply use
the <Scottish landslide.pmf> (ArcReader published map
document) to view the data. The landslide data are provided with
the Scottish trunk road network and local road network that BGS
were provided with, that has been built into a network that can be
viewed in the ArcReader software. For convenience, also located on
the data DVD is a coastline as downloaded from the National Oceanic
and Atmospheric Administration (NOAA) World Vector Shoreline
website and Shuttle Radar Topography Mission (SRTM) 90 m Digital
Elevation Model. These two products can be freely distributed. When
analysing the Scottish Landslide Data against the SRTM data care
should be taken as the Landslide data were produced using a more
accurate and validated Digital Terrain Model.
Geotiff (Georegistered tiff images) have been created to load
into any GIS or CAD software, or Adobe Acrobat format PDF files
have been created for data inspection.
In order to present the data in this form, a legend was
developed that summarised the data into classes Table A.7 (Appendix
A) show the class values used. The classes are identified on an A
to E scale, where A has the least potential to initiate debris flow
landslides and E has the highest potential. Figure 4.10 shows the
landslide hazard layers for the two example areas. As one would
expect from the topography and general lack of contributory
factors, the Inverness area in the diagram has very low potential
for debris flow initiation. In contrast, the Glen Ogle area shows
several areas of high potential mainly focused along stream
channels. Certain of these channels were the initiators and focus
of the landslide events of August 2004 that began the present
That the models identify these areas, indicates that the working
group have been able identify the principal factors that led to the
2004 events (Figure 4.11). However the power of the GIS technique
is that these same groups of factors have been identified for the
whole of the Scottish road network as can be seen in the diagrams
Figure 4.12 and 4.13. This is a 1:2,000,000 printout from the final
landslide hazard layer. At this scale it is impossible to see the
detail included in the 1:50000 modelling, however an overall
indication of the level of hazard from debris flows across the
Scottish Road network is possible.
The data is also presented in a tabulated form found on the data
DVD as ScottishRoadLandslideStatistics.mdb. In this form the data
were summarised using the database primary key of the trunk road
network that BGS originally received. This allows users with no
access to GIS to open the data in a spreadsheet or Microsoft Access
database. Because of restrictions in field name lengths, these
statistics are produced against shortened names. For a full
explanation of these see Table A.9 (Appendix A).
Figure 4.11 – Excerpts from the GIS showing the
landslide hazard assessment for two areas: (left) Glen Ogle and
(right) Inverness. (OS Data © Crown Copyright. All rights
reserved Scottish Government 100020540, 2008.)
Figure 4.12 – Indicative 1:2,000,000 debris flow hazard
across Scotland. For more detail please refer to the digital data
available separately. (Note that as reproduced herein the map is
not to scale.)
Figure 4.13 – Indicative 1:2,000,000 debris flow hazard
across Scotland showing the Trunk Road Network. For more detail
please refer to the digital data available separately. (Note that
as reproduced herein the map is not to scale.)
This has been a desk-based study, undertaken by BGS and the
working group. Resources were not available for field survey. The
results presented here from the first part of a landslides study.
Because of the techniques employed in the processing of these data,
there are some notable areas where misleading results could be
inferred. These errors relate to factors that are not simple to
encode into a system generated in the way described in the
foregoing chapters. Figure 4.14 indicates an example of the errors
that should be expected. In the Montrose basin, Raised Marine
deposits of sand silt and clay, classified in our assessment as
having moderate potential as source material for landslides have
steeply sided gullies incised into them, which are highlighted by
the slope model. This leads to a high score for landslide
potential, especially when considering the land classification of
bare ground and the presence of streams. In this instance, with
extra knowledge that these deposits do not sit above a road, we can
assume that they will not be involved in debris flow activity,
however, the computer system does not know this fact, nor would it
be straightforward on a national-scale to calculate this. It is at
this stage that human intervention is required.
Figure 4.14 – Data anomalies in the Montrose Basin.
(OS Data © Crown Copyright. All rights reserved Scottish
Government 100020540, 2008.)
A methodology has been agreed by the Scottish Road Network
Debris Flow Hazard Project Group to provide an outline assessment
of debris hazard to selected sections of the Scottish Road
The selection of factors to be included in the study was based
upon relevance, usability and availability over the whole of
Factors represented in the GIS methodology are the availability
of debris material, water conditions, land cover, proximity of
stream channels and slope angle. These have been combined using a
GIS to estimate the hazard to the road network from debris
The results have been tested against a number of areas where the
degree and spatial extent of debris flow hazard are reasonably well
known by members of the working party. The results of these tests
have been used to ‘tune’ the methodology to better
represent real world conditions.
The datasets provide a reasonable estimation of the hazard to
the Scottish Road Network, as carried out at a national scale and
are fit for purpose for use in helping to determine priority areas
for the next phase of work in the Scottish Road Network Debris Flow