Methodology for Risk Assessment
Rashmin Gunasekera
_____________________________________________________________________
Framework for a Methodology to Integrate
Vulnerability to Develop Natural Hazard Risk Profiles
for Sri Lanka
Rashmin C. Gunasekera
(rashmin.gunasekera@gmail.com)
1.0 Executive Summary
Sri Lanka is vulnerable to a range of natural hazards, and as such, its population,
infrastructure, buildings and (urban and agricultural) land are at risk of damage and/or
destruction. Natural hazard risk assessment is crucial in enabling effective disaster
risk reduction and increasing disaster resilience. As such, it is vital that there is a
coherent method of evaluating these risks in order that policies can be evolved for risk
mitigation and for the benefit of other stakeholders. Heretofore, the problem with
creating such a methodology has been manifold; amongst its biggest stumbling blocks
were the differences in spatial scales involved, the variable parameters, the many
influencing factors, the gaps in data required, and the conceptual problem of
designing a model which could adequately address all these issues.
This report outlines a methodology which could assess natural hazard risk at national
and sub-national level, identifying areas of high exposure and vulnerability. It also
evaluates the exposure and vulnerability of the population, agriculture, property and
transportation (+ utility networks). This methodology has the advantage of being
designed to take into account a wide range of mutually influencing factors in the form
of an interactive map, which can identify the location, relative severity, and extent of
risks, for individual and for multiple natural hazards. It is also a significant advantage
that this methodology is able to utilise high resolution local data and translate it into a
risk analysis at sub national level.
2.0 Introduction
The term risk is a highly complex one, and been defined in number of ways in the
academic literature of natural hazards, incorporating several sophisticated concepts
within this single term. Kron’s (2002) succinct definition identifies three variables
which determine “risk”: hazard, vulnerability and exposure. At its most basic, these
are the ways in which each of these terms (which together comprise risk) are
understood:
– Hazard: the threatening natural event, its extent and severity of occurrence.
– Exposure: the values and/or humans that are present at the location of interest.
– Vulnerability: represents the damageability. It is the lack of (or low) resistance to
damaging/destructive forces.
As mentioned above, risk is a complex combination of the components of hazard,
exposure and vulnerability. This particular concept of risk, widely employed in US
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Methodology for Risk Assessment
Rashmin Gunasekera
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and Europe, aims to quantify loss and could be regarded from a scenario-based or
probabilistic view point. This is a mathematical multiplication of exposure and
vulnerability components.
However, utilising mathematical operators requires strictly quantitative indicators. As
a consequence, most risk assessments conducted using this derivation are calculated
in economic terms. This makes it difficult to take less immediately tangible, less
easily quantifiable factors such as environmental, social and indirect damage, and the
inability to avoid or recover from a natural disaster, or lack of resilience into
consideration.
The physical sciences attempt to quantify this definition of risk, measurable in
absolute values, while the social sciences consider relative and perceived risk, not
easily quantifiable and space and time dependent. For a comprehensive approach to
risk profiling, these different disciplinary approaches have to be integrated for the
purposes of disaster preparedness, mitigation and policy planning, and the
methodology has to reflect this interdisciplinarity. The new approach to risk profiling
proposed in this report, which integrates socio-economic aspects as defined by
UNISDR (2003), involves evaluating the full range of traditional and modern
approaches to quantify natural hazards. This approach involves viewing the problems
of natural hazards and disasters and long-term safety survivability within the context
of sustainable development (Parker, 2000).
3.0 Proposed Methodology
This section presents a stepwise approach to deriving a risk index for Sri Lanka at
national level from single and/or multiple hazards. The flow chart below details the
key steps considered. It consists of three components of Risk: Hazard, Exposure and
Vulnerability. The integral of the three provides a relative risk for each or combined
hazard.
The proscribed methodology advocates the inclusion of Landuse data in grid cells in
addition to the Exposure and Vulnerability data already collected in Grama Niladhari
(GN) divisions. The Integrated Risk Map being proposed has to incorporate all these
diverse elements and provide a method of bringing them together to mutually inform
the national picture of risk, while remaining sensitive to the local variation of risk.
The methodology suggested has to be one which is flexible and accommodates gaps
in data sets, the changing nature of parameters that shape risk, and allows for a certain
margin of error.
The proposed methodology has incorporated original and other modelling techniques
from various vulnerability assessment approaches. These include:
 Application of Remote Sensing and GIS for Flood Risk Analysis: A case
study at Kalu‐Ganga River, Sri Lanka (GeoInfromatics Center, 2008) –
ranking methodology
 Fine Scale Natural Hazard Risk and Vulnerability Identification Informed by
Climate in Sri Lanka (Zubair et al., 2005) - Grid based approach
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Methodology for Risk Assessment
Rashmin Gunasekera
_____________________________________________________________________
 An assessment of weather-related risks in Europe: maps of flood and drought
risks (Genovese et al., 2007) - Scaling methodology
 Dissolution based on landuse data (this study)
 Relative Vulnerability of GN Divisions to Food Insecurity, (UNWFP, 2006) Use of Principle Component Analysis
 Geographically weighted Regression analysis (Fotheringham et al., 2002)
 Multi-risk assessment of Europe’s regions (Greiving, 2006) - Calculation of
Integrated Risk Index
Figure 1: Flowchart of proposed methodology for a particular peril deducing risk.
3.1 Hazard Component
The significance of the risk posed by any given hazard would be in part informed by
its relative weight to other hazards. This concept it at the core of the Hazard index
qualifying relative risk.
Hazard data in the form of maps of spatial extent and relative severity produced at
different national institutes and universities would be collated by the Disaster
Management Centre (DMC) Sri Lanka. These data sets will include Landslide data
from the National Building Research Organisation (NBRO), Tsunami (and sea level
rise, erosion and storm surge) data from the Coastal Conservation Department and
windstorm (cyclone) data from the Department of Meteorology. There will also be
flood data and drought data from the University of Peradeniya and the Department of
Irrigation respectively. However, this data and its parameters currently differs from
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Methodology for Risk Assessment
Rashmin Gunasekera
_____________________________________________________________________
hazard to hazard. (For example, cyclone data may have a 10 km spatial resolution,
while Tsunami inundation data may have a 50 m spatial resolution.) It would be
necessary to generate a consistent multi-hazard dataset based on an agreed
standardised spatial resolution for national level risk analysis. A possible common
spatial resolution would be 1 km grid cells.
Once a consistent hazard layer has been established, clarification would be required
for what could be deemed high, medium and low risk levels across hazards. The
classification and the commonalities for the different hazard types would also need
clearer definition and consensus. This would be carried out in consultation with the
partner departments and institutes mentioned above.
In order to determine the relative weight assigned to each hazard, two approaches are
suggested. First, a hazard or loss database could be used to evaluate frequency of
occurrence and severity. Examples of such a database would be DesInventar or Centre
for Research on the Epidemiology of Disasters CRED. However, these databases are
limited by the paucity and quality of historical records. The second option would be to
conduct an expert elicitation exercise using techniques such as the Delphi method.
3.2 Exposure Component
We will consider four key types of exposure: population, property, agriculture and
transportation (+ utility) network. These exposures were selected based on the
following criteria:
a) Wide spectrum of parameters that covers socio-economic impacts which
would directly affects policy planning
b) Ability to complements other studies conducted without as well as within Sri
Lanka at national, regional and local scales.
c) Availability of high resolution census data (at GN level).
However we do recognise that these 4 exposures alone will not adequately incorporate
all influencing issues such as effect of poverty. Therefore, there is flexibility in the
approach to extend to more than the four parameters proscribed.
For each hazard, the most suitable exposure parameters for population, property,
agriculture and transportation (+ utility) network would be considered. For each of
these exposure types 3 parameters are considered. The parameters are limited to 3 in
number to preserve computational efficiency in calculating the vulnerability matrices
for each type of exposure. It should be noted that for the parameters of demography,
residential property and agriculture, each parameter has a binary option.
Table 1 outlines the parameters considered and the proscribed ranking for flood
hazard. For demography, the selected parameters include gender (male or female), age
(20-59, or other), and economic status (active or non-active). Parameters of residential
property (all properties were considered as single 1 storey buildings) are type of
construction (brick-cabook-cement or other), type of toilet (water sealed or other) and
access to water supply (yes or no). For agriculture, the parameters considered were
crop type 1 (home gardens or commercial), crop type 2 (paddy or non-paddy) and
spatial extent (< 20 ha or other). For transport, infrastructure parameters considered
are A-roads, B-roads and rail tracks.
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Methodology for Risk Assessment
Rashmin Gunasekera
_____________________________________________________________________
As the spatial extent of the GN divisions are not uniform, both absolute numbers and
percentage contribution of each factor in each parameter have to be retained. The
significance of using percentage contributions or proportions in addition to absolute
numbers is to ensure that the size of the GN divisions do not unduly skew the
representation of data. This would provide a better platform from which to conduct
statistical analysis and avoid spatial biases within the datasets.
3.3 Vulnerability Component
The vulnerability of demography, agriculture, transport and residential property was
also assessed for each hazard. To assess the vulnerability of the chosen parameters of
the four exposure classes, first a matrix of each exposure type was created, with 8
permutations for the 3 parameters and binary options provided (2x2x2). These 8
permutations were then ranked dependant on the hazard concerned with the ranking of
1 being the least vulnerable and the ranking of 8 being the most vulnerable. The
ranking is dependant on subjective interpretation which requires expert elicitation.
Therefore, equation in Figure 1 represents:
Vul stp1GN(i) = ∑ CExp% GN(i) RHzE GN(i)
Where Vul stp1GN(i) is the step I of the vulnerability assessment for a particular GN
division i, CExp% GN(i) is the calculated exposure percentage for a particular GN
division i and RHzE GN(i) is the ranking of the exposed permutation for the
particular hazard for a particular GN division, i. Constriction to 8 permutations
decreases the variability of the model but still maintains sufficient flexibility and
computational efficiency (an example of which is shown in Table 1).
Age Group
Employment
Gender
Ranking
20-59
Econ.
Male
1
20-59
Econ.
Female
2
20-59
Non. Econ.
Male
3
20-59
Non. Econ.
Female
4
Other
Econ.
Male
5
Other
Econ.
Female
6
Other
Non. Econ.
Male
7
Other
Non. Econ.
Female
8
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Methodology for Risk Assessment
Rashmin Gunasekera
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Construction Type
Access to Safe Water
Toilet
Ranking
Brick, Cabook, Cement
Yes
Water seal
1
Brick, Cabook, Cement
Yes
No Water seal 2
Brick, Cabook, Cement
No
Water seal
Brick, Cabook, Cement
No
No Water seal 4
Other
Yes
Water seal
Other
Yes
No Water seal 6
Other
No
Water seal
Other
No
No Water seal 8
3
5
7
Crop Type1
Crop Type2
Extent
Ranking
Non Home garden
Other
>20 ha
1
Non Home garden
Paddy
< 20 ha
2
Non Home garden
Other
>20 ha
3
Non Home garden
Paddy
< 20 ha
4
Home garden
Other
>20 ha
5
Home garden
Paddy
< 20 ha
6
Home garden
Other
>20 ha
7
Home garden
Paddy
< 20 ha
8
Table 1: Example of Population (top), residential property (middle) and agriculture (bottom)
exposure type and matrix permutations for flood hazard.
By multiplying the percentage exposure of each GN by the ranking used, we derive a
weights matrix for each GN. By employing the landuse classification at 50 m cells (to
comply with tsunami inundation hazard data at 50 m resolution), we distribute the
weighted matrix over GN division based upon landuse type. This calculation would
be on a GIS based multi-band raster. This step would also act to normalise the spatial
variability of the different sizes of GN divisions.
However, each of the exposure datasets also show significant inherent spatial and
attribute inter-dependence. To negate this bias and reduce spatial variance, a well
known spatial statistical approach, Principal Components Analysis (PCA), is used (eg.
Shaw, 2003). This technique is used to transform the data weighting key principal
components whereby compression of data would eliminate redundancies. PCA does
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Methodology for Risk Assessment
Rashmin Gunasekera
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this by transforming from an input multivariate attribute space into a new multivariate
attribute space whose axes are rotated with respect to the original space. Therefore,
the resultant axes (attributes) in the new space are uncorrelated. The first principal
component will have the greatest variance, the second will show the second most
variance not described by the first, and so forth. For example, data redundancy is
evident in a dataset comprising elevation, slope, and aspect (on a continuous scale).
Since slope and aspect are usually derived from elevation, most of the variance within
the study area can be explained by elevation. PCA conducts computations faster,
while heightening the accuracy of the computation.
This process will create spatially uncorrelated eigen values that could be then used as
the contributing weights for the parameters considered for vulnerability analysis. The
methodology could be extended to include spatial regression, making it applicable on
a national scale.
Geographically Weighted Regression (GWR) is a fairly recent contribution to
modelling spatially heterogeneous processes (Brunsdon et al., 1996; Fotheringham et
al., 2002). The underlying premise of GWR is that parameters may be estimated
anywhere in the study area given a dependent variable and a set of one or more
independent variables which have been measured at places whose location is known.
Taking Tobler’s (1970) observation about nearness and similarity into account, we
might expect that if we wish to estimate parameters for a model at some location, then
using observations which are nearer that location should have a greater weight in the
estimation than using observations which are further away.
The resultant matrix would be an integrated vulnerability index that could be used at
national or sub-national level. However, a significant component of this methodology
and key to its success would be the careful selection of vulnerability classifications
described above. This should be done at least at GN level and we need to address the
problem of applicability of the data for a particular hazard taking into account that the
boundary marks for rivers could also be the boundary of the GN division.
Transport and utility networks should be considered under the binary approach of
areas intersecting with hazard area as totally vulnerable. There are well established
methodologies to determine common cause failures and system overloading which are
particularly applicable for vulnerability of utility and lifeline networks (Javanbarg et
al., 2009; Berdica, 2002). However, these considerations need to further evaluated for
their appropriateness within the context of Sri Lanka. Therefore, to assess
vulnerability of transportation network, we propose a road length density weights
matrix for each grid cell to be adopted. This same methodology could be extended to
utility networks such as electricity and water.
3.4 Risk Ranking Component
Risk would be regarded as a combination of the degree vulnerability and hazard
intensity. To progress from this single hazard risk matrix to multiple hazard, several
of the matrices could be stacked together to provide a composite risk profile.
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Methodology for Risk Assessment
Rashmin Gunasekera
_____________________________________________________________________
We could also then modify the resilience matrix to consider aspects such as
seasonality, mitigation (evacuation of people from cyclone paths due to Disaster Risk
Reduction measures being implemented). The resilience index that would conduct this
mitigation calculation would also be subject to expert elicitation to determine
common consensus among risk practitioners.
Hazard Intensity
Degree of Vulnerability
1
2
3
4
5
1
2
3
4
5
6
2
3
4
5
6
7
3
4
5
6
7
8
4
5
6
7
8
9
5
6
7
8
9
10
Table 2: Risk Ranking matrix for each hazard (Modified from Greiving, 2006)
4.0 Other Considerations
The risk ranking matrix merges local data (at GN level) with sub-national level data.
The methodology for this composite risk matrix is concise, enabling policy makers to
identify areas of concern quickly and easily. It also allows the user to identify which
component has a greater impact on an area of interest (for example, hazard or
vulnerability).
4.1 Limitations
This methodology is not without its limitations. Since the methodology proscribes an
index, the risk is quantifiable only as a relative risk. Moreover, the methodology does
not include any probability of the likelihood element of risk. It does not also include a
measurement of uncertainty, either epistemic or alletory.
It should also be noted that there are problems concerning data quality. Some of the
key data quality problems include:
i) the availability of data
ii) limits to measurability – some aspects being difficult to quantify
iii) the weighting problem – the Delphi method provides weighting results which
would need regular and expert updating
4.2 Future Work
The next stage would be to develop an inactive risk map within a GIS-based macro.
This would allow the user to have some control over the hazard and vulnerability
options being used, which would strengthen this methodology.
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Methodology for Risk Assessment
Rashmin Gunasekera
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To address the issue of data quality, the next Sri Lankan census of 2011 could
incorporate information elicitation which could go towards mitigating some key
limitations. Further, better and higher resolution landuse mapping (currently being at
a scale of 1:50,000 from 1992) should be used to differentiate between urban and nonurban; residential and non-residential areas.
5.0 Case Study: Ayagama and Elapatha
We applied the proposed methodology to two District Secretariat (DS) in the
Ratnapura district: Ayagama and Elapatha of Sri Lanka. The selection of these two
DS divisions was based on knowledge of frequency and historical floods and
landslides in the area. This study area has 42 GN divisions (Figure 2). However, we
did not evaluate the landslide risk and concentrated on the pluvial flood risk of the
area from the Kalu Ganga basin.
Figure 2: GN divisions of the study area
We evaluated the risk from exposure type of residential property to the flood hazard.
Using Table 1, we weighted and ranked the exposure. Consequently, a PCA analysis
was conducted to determine the variance of the results using SPSS statistical program
and ArcGIS ® Spatial Analyst PCA tool. The results show that there some degree of
correlation of the eight variables evaluated. Three significant variables that account
for 91% of the total variance observed was determined by the first three PCA
components (Figure 3).
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Methodology for Risk Assessment
Rashmin Gunasekera
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Figure 3: PCA results of the study area from both SPSS and ArcGIS software.
By integrating the hazard level (considered as a synthetic hazard level 3), we
produced a 50 m cell based risk ranking (Figure 4). This ranking in turn can be
evaluated either at GN or national level. We did not consider the effect of GWR in
our analysis.
Figure 4: Flood risk index for the Study area. It shows the arithmetic combination of the hazard
and vulnerability indices.
By applying the proposed methodology for the selected study area, we successfully
created a flood risk profile that considers vulnerability of residential property. This
risk profile identified the severity of hazard as well as the severity of vulnerability.
We also evaluated the scope of and procedures to integrate other vulnerability
parameters such as population and agriculture within the study area.
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Methodology for Risk Assessment
Rashmin Gunasekera
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6.0 References
Berdica, K., (2002), An introduction to road vulnerability: what has been done, is done and
should be done, Transport Policy, 9, 117-127.
Brunsdon, C., A. S. Fotheringham and M.E. Charlton, 1996, Geographically Weighted
Regression: A Method for Exploring Spatial Nonstationarity, Geographical Analysis, 28(4),
281-298
Fotheringham, A. S., C. Brunsdon, C., and M. E. Charlton, 2002, Geographically Weighted
Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
Genovese, E., N. Lugeri, C. Lavelle, J. I. Barredo, M. Bindi, and M. Moriondo, (2007), An
assessment of weather-related risks in Europe: maps of flood and drought risks,
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/8471/1/eur_23208_2007_
adam.pdf.
GeoInformatics Center, (2008), Application of Remote Sensing and GIS for Flood Risk
Analysis: A case study at Kalu‐Ganga River, Sri Lanka, Asian Institute of Technology.
Greiving, S. (2006), Integrated risk assessment of multi-hazards: a new methodology. Natural
and technological hazards and risks affecting the spatial development of European regions.
Geological Survey of Finland, Special Paper, 42, 75–82
Javanbarg, M. B., C. Scawthorn , J. Kiyono, Y. Ono, (2009), Minimal path sets seismic
reliability evaluation of lifeline networks with link and node failures,
http://www.willisresearchnetwork.com/Lists/Publications/Attachments/50/WRN_Mohammad
_TCLEE09.pdf
Kron, W., (2002), Flood risk = hazard x exposure x vulnerability, In: Wu m. et al., (ed), Flood
Defence, Science Press, New York, 82-97
Parker, D. J., (2000), Floods, Vol I and II, Routledge, London and New York.
Shaw, P,J,A, (2003), Multivariate statistics for the Environmental Sciences, Hodder-Arnold,
New York.
Tobler W., (1970) A computer movie simulating urban growth in the Detroit region,
Economic Geography, 46(2), 234-240
UNISDR, (2003), United Nations documents related to disaster reduction 2000-2002, 2,
pp425, http://unisdr.org/publications/v.php?id=3620
UNWFP, (2006), Relative Vulnerability of GN Divisions to Food Insecurity, Colombo, pp57
Zubair, L., R. Perera, B. Lyon, V. Ralapanawe, U. Tennakoon, Z. Yahiya, J. Chandimala, S.
Razick., (2005), Fine scale natural hazard risk and vulnerability identification informed by
climate in Sri Lanka,
http://portal.iri.columbia.edu/portal/server.pt/gateway/PTARGS_0_2_1032_0_0_18/Sri%20L
anka%20Hazard%20Risk%20and%20Vulnerability%20Report%20Highlights.pdf
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