The use of Risk and Vulnerability Analysis in Climate Change
Adaptation
Jens Laugesen
Det Norske Veritas, Høvik, Norway
Bodil Aamnes Mostue
SINTEF Technology and Society, Trondheim, Norway
Ingrid Bouwer Utne
Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Trondheim,
Norway
Jørn Vatn
Department of Production and Quality Engineering, NTNU, Trondheim, Norway
ABSTRACT
A wide range of risk and vulnerability analysis (RVA) methods exist in the literature and are also used in
relation to climate changes. A traditional approach to RVA might be sufficient to assess risks and
vulnerabilities on a superior level, but for climate change adaption more detailed analyses are necessary to
improve the results. For flooding this involves detailed mathematical and probabilistic modeling of
hydrology, surface absorption capacities, snow melting, and failure or breakdown of critical components or
system. The challenge is to link the identified threats and vulnerabilities to the risk picture taking physical
models and climate projections explicitly into account. The objective of the paper is therefore to extend
current RVA methods to combine physical models with results from climate projections. In the paper, a
typical approach to RVA is applied to a flooding event and challenges associated with the method and the
results of the analysis are discussed as basis for evaluating the need for extensions of the traditional RVA. We
propose a seven steps method that integrate the dose-response analysis related to flooding into RVA.
1 INTRODUCTION
Climate change is on the agenda worldwide.
Municipalities have important responsibilities in local
climate change adaptation, for example, with respect
to assessment of risks related to land use planning and
construction in urban areas. Today, risk and
vulnerability analysis (RVA) is often used by
municipalities and infrastructure owners in Norway as
a basis for their emergency preparedness planning and
to ensure that public safety are sufficiently integrated
in land use planning (DSB 1994).
RVA is a method used to identify and document risks
and vulnerabilities in a specific system, and resembles
a preliminary hazard analysis (PHA) (Ericsson 2005).
The main objective of the analysis is to improve the
system’s robustness towards hazards and threats. By
combining knowledge from experts, managers and/or
users in a structured analysis and decision process,
undesired events are identified and ranked, depending
on their risks. Then the risk picture for the system can
be described and used as a basis for determining risk
and vulnerability reducing measures (DSB 1994).
RVA was developed in the early 1990’s as a response
to the need for risk assessments based on laws and
regulations. At that time, it was recognized that small
and medium enterprises and the public sector did not
have sufficient resources to carry out extensive
analyses, such as probabilistic safety analysis (PSA)
and quantitative risk analysis (QRA). Thereafter, RVA
has been applied for many years successfully.
However, the recent need for climate change adaption
to mitigate societal risks, such as flooding and
landslides has made it necessary to reconsider RVA as
a feasible risk assessment tool for municipalities in
their land use planning.
Currently, there are many tools and frameworks
suggested for different challenges related to climate
change adaptation, as suggested by UNFCCC (2008),
Jones (2001) and Lindley et al. (2006).
For climate change adaption a traditional approach to
RVA might be sufficient to assess risks and
vulnerabilities on a superior level. However, more
detailed analyses involving detailed mathematical and
probabilistic modeling are necessary to improve the
results. A challenge is to link the identified threats and
vulnerabilities to the risk picture taking physical
models and climate projections explicitly into account.
The objective of the paper is to extend the current
RVA method so that it is possible to combine physical
models, e.g., drainage models, with the results from
climate projections. The paper starts with describing
the basic process of RVA. Then a typical approach to
RVA is applied to a flooding event and challenges
associated with the method and the results of the
analysis are discussed as basis for evaluating the need
for extensions of the traditional RVA. Last, a seven
step method that integrates the dose-response analysis
related to flooding into RVA is proposed.
The work described in this paper has been undertaken
as part of two research projects; AdaptCRVA (20072009) and FloodProBe (2009-2013), as well as a case
study with two municipalities, Stavanger and Sandnes,
in Norway. The paper presents results based on
experiences from these projects.
2 THE RVA METHOD
RVA can be used as an initial risk study in an early
stage of a project, as an initial step of a detailed risk
analysis of a system concept or an existing system, or
as a complete risk analysis of a rather simple system.
All hazards and possible accidental events must be
identified. It is important to consider all parts of the
system, operational modes, maintenance operations,
safety systems, and so on. In general, the RVA is a
semi-quantitative method used to:
1. Define
objective
and
determine
RVA
prerequisites, and establish a RVA team consisting
of personnel with system knowledge and expertise
on risk assessment methodology.
2. Identify hazards and hazardous events.
3. Estimate
frequencies/probabilities
and
consequences.
4. Rank risks, determine risk reducing measures and
follow-up actions.
A hazardous event may lead to a wide range of
consequences, ranging from negligible to catastrophic.
Various types of consequences can be considered,
e.g.: life/health, material assets and environment. By
using RVA, municipalities can identify relevant
hazards or undesired events, such as:
 Natural events (e.g. flooding, landslides, extreme
weather conditions).
 Damage to/loss of critical infrastructure, e.g.,
energy supply, water supply, information and
communication technologies (ICT).
 Large traffic accidents
 Fires and explosions
 Large industrial accidents
 Breaking of dikes
 Acute pollution
 Sabotage and terror
 Radioactive fall-out
The risk related to an undesired event is a function of
the frequency/probability of the event and the severity
of its potential consequences. The assessment of
probabilities and consequences of the events may be
based on categories, such as the examples shown in
Table 1 and 2. Depending on the system and the scope
of analysis, other categories may be defined.
Often it is practical to put the different hazardous
events into a risk matrix to compare the events, as a
basis for prioritizing of further actions, for example by
using the As Low As Reasonable Practicable
(ALARP) principle (Norsok 2001). Decisions on
acceptance criteria and acceptable risks are subject to
company or municipality decisions and regulations,
and should be established before the analysis is
initiated.
Table 1: Probability/frequency categories (example)
Category Probability
Frequency
1
2
3
4
5
Very unlikely
Remote
Occasional
Probable
Frequent
<1/1000 year
1/100-1/1000 year
1/10-1/100 year
1/1-1/10 year
>1/year
Table 2: Consequence categories (example)
Category Consequence Life/health Loss of
material
assets
€
1
Insignificant Small
Less
personal
than
injuries
25,000
2
Minor
Serious
personal
injuries
25,000250,000
3
Major
1-2
fatalities
0.25 –
2.5 mill
4
Critical
3-10
fatalities
2.5 -25
mill
5
Catastrophic
>10
fatalities
>25
mill
Environment
Small area
affected,
short
restitution
time.
Large area
affected,
short
restitution
time.
Small area
affected,
long
restitution
time.
Large area
affected,
long
restitution
time.
Permanent
damage.
Even though the risk matrix usually gives an
indication of how to prioritize risk reducing measures,
it may not be easy to determine which measures
should be implemented. As a starting point a
cost/benefit approach may be used, but in many cases
it may be difficult to determine costs, effectiveness,
and benefits. Decisions may depend on stakeholders’
preferences and risk perception. Often, the decisions
are results of political processes in which other aspects
than safety are taken into considerations.
3 CLIMATE PROJECTIONS
Climate projections, also called climate change
scenarios, provide a picture of how future climate is
expected to be. Expected climate changes in Norway
with respect to precipitation, wind and temperature
towards 2100 are as follows (Hanssen-Bauer et al.
2009):
Precipitation:
 Increase in precipitation in all regions and for all
times of the year.
 Average increase in annual precipitation between
5 to 30 % towards the end of the century.
 The winter precipitation can increase with over 40
% in eastern, southern and western parts of
Norway towards the end of the century.
 Increase in days with heavy precipitation. The
average
precipitation
for
these
days is higher in all regions and for all seasons.
Wind:
 Climate models provide little or no change in the
mean wind conditions during this century.
 The frequency of higher wind forces can increase,
but due to systematic weaknesses in climate
models, it is too early to conclude about this.
Temperature:
 It will be warmer in all regions and for all seasons.
 Annual mean temperature will increase by 2.3 to
4.6 °C within 2100.
 Highest temperature increase during winter, and
least during summer.
 Highest temperature increase north, where annual
mean temperature is estimated to increase 3.0 to
5.4 °C.
The climate projections are uncertain due to lack of
knowledge of future emissions of greenhouse gases
and particles, combined with deficiencies and
simplifications in the climate models. The
uncertainties are larger in local scale than global.
Nevertheless, the climate projections give an
indication of what kind of changes to expect in the 21
century.
4 USE OF RVA FOR CLIMATE CHANGE
ADAPTATION IN MUNICIPALITIES
In the following, the typical approach to RVA is
applied to a flooding event, as basis for evaluating the
need for climate change adaptations of the current
method. Thereafter, a method for an extended RVA is
proposed.
4.1 RVA of flooding
Undesired event: Due to rising sea levels a coastal
municipality risks flooding at the housing area “Fjord
dream” built on piles in the harbour area of the
municipality.
The municipality assumes that the probability for such
an event to occur is every 50-100 years, and it is
assumed that the probability will increase in the future
due to climate change. Using the probability/
frequency categories in Table 1 gives a probability
category of 3 (occasional).
The consequences are evaluated by the municipality
and they conclude that (Table 2):
 Life/health (category 1 – insignificant): It is
assessed that the consequence for life/health is
insignificant because the area can be evacuated in
due time before the houses get flooded.
 Material assets (category 4 - critical): The area
contains 120 houses which have been constructed
for € 0.4 million each. A flood will destroy all the
houses and these have to be reconstructed at 120 x
0.4 = € 48 million. It is assumed that it will cost an
additional € 10 million to clean up the area before
the area can be reconstructed.
 Environment (category 2 - minor): It is assumed
that the whole housing area in the harbour can be
affected, after the area has been cleaned up the
area, it will have a short restitution time.
Probability/
Consequence
1
Very
unlikely
2
3
4
Remote Occasional Probable
5
Frequent
5
Catastrophic
4
Critical
Material
assets
3
Major
2
Minor
Environment
1
Insignificant
Life/
Health
Figure 1: Risk assessment matrix for flooding.
The result is that for the material assets, further
evaluations should be considered. Based on the
ALARP principle, risk reducing measures should be
carried out provided that they are not
disproportionately large compared to the risk
reduction that is achieved.
Two different risk reducing measures are presented to
the municipality:
1. Raising all the piling foundations for the houses by
1 m and thereby reducing the probability for a
flooding to less than 1 in every 1000 years (very
unlikely). The operation is relatively expensive
because every house has to be jacked up to be able
to install the extension of the piles. The cost is
estimated to be € 60 000 pr. house, totaling € 7.2
million.
2. Build a dike in the sea outside the housing area to
protect the houses. The dike will go 1.5 m over the
average sea level today and have a marina inside
for the boats of the residents. The access to the sea
for the boats will be ensured by establishing a
sluice gate in the dike. The probability for a
flooding with this solution is less than 1 in every
1000 years (very unlikely). The cost for
establishing the dike and the sluice is estimated to
be € 4.5 million.
As both risk reducing measures reduce the probability
to the same value the municipality could decide to go
for the dike alternative which has the lowest cost.
When assessing the risk associated with each risk
reducing measure we need to consider various
climatic scenarios. A first approach would be to use a
most likely scenario as a basis, and conduct the
analysis based on this scenario. In our situation this
means to fix a given sea level increase. The
assessment in our example is made based on the most
likely increase in sea level. However, if a worst
scenario assumption is made, the result will change. In
our example it is expected that the return period for
flooding will increase for both scenarios, but not
necessarily in the same manner. For example if the
flooding frequency is considerably higher with a dike
outside the housing area compared to raising the piling
foundations for the houses in a worst case scenario
this gives stronger arguments to choose the piling
alternative despite the higher cost. Sensitivity
analyses of scenarios and associated cost-benefit
analyses are therefore recommended. Sensitivity
analyses may be carried out without any consideration
of the likelihood for the various scenarios, whereas a
full cost-benefit analysis also requires assessing the
probabilities for the various scenarios. In the present
example we do not present explicit sensitivity
analyses nor cost-benefit analyses.
4.2 Challenges with the traditional approach to RVA
for flooding
The traditional approach to RVA gives in many cases
limited decision support. More structuring in
collecting and adapting knowledge are often necessary
to estimate probabilities and consequences of 1)
events when several simultaneous weather conditions
occur, e.g. great wave height, storm surge and rising
of sea level in the example above and 2) events
reflecting different extent of climate changes, e.g.
uncertainty whether the sea level rising is 0.5 meters
or 1.5 meter in 100 years. More detailed analyses are
necessary to improve the decision support. For
flooding this involves detailed mathematical and
probabilistic modeling of hydrology, surface
absorption capacities, snow melting, and failure or
breakdown of critical components or system. Such
modeling is similar to well known dose-response
models. The “dose” here is the various weather
conditions in combination with system failure, and the
“response” is how the surroundings absorb
precipitation, and how water accumulates and builds
up in the waterways.
A dose-response model is in principle a deterministic
model implementing a range of physical laws.
Uncertainty, and hence risk, comes to play in two
different manners: One dimension is the uncertainty
regarding parameters in the physical (response)
model. For example, it is hard to assess the
permeability of the surroundings to the necessary
precision. To some extent such uncertainty may be
reduced by model calibration. This, however, requires
access to weather data, and measurement of the flow
situation in the actual waterway. Today, in planning of
new residential and industrial areas it is common to
establish such models, and to some extent calibrate the
models with real observations.
The second type of uncertainty relates to the relevant
“dose” scenarios since there is almost an indefinite
number of undesired events or scenarios to consider.
Hence, there has to be an initial screening when it
comes to which types of scenario to include in the
model. For example, precipitation is often described
by combination of intensity-, duration- and frequency.
The combination of intensity and duration is a
challenge, as well as to include factors like
temperature, wind condition, and snow depth in the
analysis. Thus, there is an uncertainty regarding
whether the most relevant scenarios have been
selected for inclusion in the model.
Next comes the uncertainty regarding the frequencies.
To some extent there are historical data available to
describe the situation as it is today, but it is necessary
to link the projection models for climate change to the
dose-response models in order to improve the input to
RVA. “New” nature phenomena might occur with
significantly changed frequency, strength, in new
times of the year or at new sites. Examples of natural
disasters that may come to act in unanticipated ways
are water saturated landslides, rock falls triggered by
the freeze/thaw processes, slush avalanches,
precipitation floods in small rivers, urban flooding,
and sea level rise (Aaheim et.al. 2009). Groven et al
(2008) argue that the use of traditional approaches
may not pick up such "new" natural phenomena. Palm
(1995) points out that assessment of probabilities of
natural events/hazards are strongly dependent on
whether one has experienced an event or not.
Similar to most types of risk analyses RVA faces the
problem of “scope”, and a detailed analysis of every
aspect is impossible. Therefore, risk screening is
necessary which may follow various axes, e.g., the
various project phases, the physical locations of a
plant, the mission phases for a flight, and so on. When
it comes to flooding, the screening may be difficult.
One axis to trace is to consider how the various land
areas are linked to waterways. Another axis is to look
for various weather conditions, often related to a
combination of external influencing factors, such as
heavy rain, melting snow from the surroundings,
upstream tide, and failure of some of the protective
measures. From a methodological point of view it is a
challenge to develop a consistent method for
screening. Work is ongoing in this field, for example
in the project KlimaGIS (http://www.klimagis.no),
where semi-automatic tools are developed to visualize
impact of extreme weather conditions on land areas.
The basis for such a model is 3D maps of the
surroundings, and coarse descriptions of the
waterways and drainage systems.
4.3 A new method for RVA related to climate change
Based on the implications discussed in the flooding
example, it is obvious that extensions of the traditional
RVA are needed. These extensions may be integrated
into the RVA method for climate change adaptations
as follows:
1. Scope and limitations. The very first step of a
RVA is usually to define the scope of the study
and limitations. It is of uttermost importance to
clarify which decisions the risk analyses shall
support. Sometimes the establishment of risk
acceptance criteria, trade-offs between risk and
economic values (investment cost) are also part of
this initial phase.
2. Screening. The main objective of the screening is
to identify relevant land areas for which, e.g.,
flooding is regarded as an important risk element.
The identified spots are often visualized in various
geographical information systems in order to get
an impression of how issues like how populated
the areas are and if industry is threatened.
3. Physical response model/drainage analysis. The
objective of this step is to establish the necessary
physical models describing the flooding situations
under various strains (doses). An important part of
the modeling is model calibration where
calculated values are compared to observed values
(in heavy rainfall situations). Another important
part of the physical response model is to identify
model uncertainty which has not been accounted
for in the calibration process. As a first step the
impact should be described in terms of which
areas are flooded for a given strain. Damage
analysis might be treated in a later step for the
most critical scenarios.
4. Dose scenarios identification. The purpose of this
step is to identify the various combination of
duration/intensity of heavy rain situations, further
combined with temperature variation, variation in
the snow depth, etc. These scenarios are also
deterministic in the sense that for a given
identified scenario the impact of the (dose)
scenario on the physical response model should be
analyzed to reveal the final consequences, e.g., in
terms of which areas will be flooded. A huge
number of scenarios may be relevant. A challenge
is then to select the most representative scenarios.
This might require various search algorithms
where dose scenarios are propagated through the
response models in order to search for similar
scenarios but with unanticipated stronger impacts.
The probability (or frequency) of each scenario is
not a part of the scenario identification, but rough
consideration of frequencies is necessary.
5. Frequency analysis. The purpose of this step is to
establish frequencies or probabilities for the
various dose scenarios. Frequencies are in the first
place established on historical data.
6. Impact of climatic changes. So-called climatic
projections now exist where future scenarios in
terms of, e.g. CO2 emissions are taken into
account to present some kind of “long term
weather forecast”. Such projections exist in
Norway on a basis of one by one square kilometer
with respect to precipitation and temperature
(Hanssen-Bauer et.al. 2008). Various models can
also predict increase in sea level and other critical
factors when it comes to the “dose” modeling. At
the time being, the climatic projections are not
provided on the format required for the “dose
scenario” models, hence post-processing of the
climatic projections are required. One of the
challenges is that existing climatic projections
presents e.g., frequencies of “heavy rain” periods
of durations days and weeks, but the most critical
part in some of the response models is periods of
duration in the order of minutes and hours.
7. Compiling and presenting risk profiles. The final
step in the extended RVA is to combine the
deterministic response models with the
probabilistic dose scenarios and other uncertainty
elements in order to establish a risk profile. When
presenting the risk profile, it is important to also
present the sources of uncertainty, especially
focusing on the “today’s variability”, and the
future changes.
Compared to the traditional RVA, step 3-4 and 6-7 are
relatively different and require more resources than
typically used today. The above-mentioned steps do
only include the risk assessment part. Identification of
risk reducing measures and emergency preparedness
analysis also need to be considered for a complete risk
management method, and should be included in, e.g.,
the preparation of the development plan for a new
residential area. Today, these plans often take into
account historical data on precipitation, but it is a
challenge to take future climate scenarios into
account. A challenging question is how much
redundancy to put into the drainage system in order to
cope with future climatic changes concerned with
large uncertainties.
There are at least two types of uncertainties needed to
cope with in the modeling of risks related to climate
change, which is especially relevant to consider in
step 6: The first is of aleatory (random) nature
describing variability, i.e., some years there will be
intense precipitation periods, whereas in others not.
The other type of uncertainty is of epistemic (lack of
knowledge) nature and reflects the lack of knowledge
related to, for example future CO2 emission and how
accurate the models are. These uncertainty elements
need different coping strategies.
Aleatory uncertainty is something that may strike
tomorrow and has to be accounted for in the land use
planning today. Epistemic uncertainty basically
reflects future development. New insight may be
gained with respect to future development, i.e., the
success in reducing CO2 emissions may be observed
or results from projections can be compared with
observations over, e.g., a new ten year period to
improve the modeling capabilities. Therefore, in some
of the long term planning there are some degrees of
freedom when it comes to epistemic uncertainty. In
some situations it is not necessary to take into
consideration all “worst case” scenarios today, if there
are decision points tomorrow that can handle these as
well as today. In other situations, possible worst case
scenarios have to be dealt with already today, because
the decision may have a long term impact.
5 CONCLUSIONS
This paper presents RVA in general, and discusses
challenges and uncertainties related to risk assessment
of climate change adaptation. In Norway, RVA is used
as a tool for assessing societal risks, based on
requirements in laws and regulations. By preparation
of land use plans for example, the municipal planning
and building authorities shall ensure that RVA is
carried out (The Planning and Building Act, 2010).
RVA for future climate change is an important part of
this work. RVA is also a basis for municipalities and
infrastructure
owners’
in
their
emergency
preparedness planning.
RVA is usually carried out by a working group
involving professionals, often with a multidisciplinary
and cross-sector background. The knowledge of these
individuals constitutes the basis for the analysis. The
traditional approach to RVA might be sufficient to
assess risks and vulnerabilities on a superior level, but
for climate change adaption more detailed analyses
are necessary to improve the results. For flooding this
involves detailed mathematical and probabilistic
modeling of hydrology, surface absorption capacities,
snow melting, failure or breakdown of critical
components or systems. Such modeling is similar to
well known dose-response models. The “dose” here is
the various weather conditions in combination with
system failure, and the “response” is how the
surroundings absorb precipitation, and how water
accumulates and builds up in the waterways.
We propose a seven step approach to integrating the
dose-response analysis related to flooding into RVA,
which contributes to improved accuracy of results.
The disadvantage is that RVA used for climate change
adaptation will require more resources available than
with the current approach.
Note that in a traditional RVA the probabilities are
assessed by the expert group in a non formal manner
where a historical perspective will play an important
role (Palm 1995). If we do not have historical
“evidence” assessment is very difficult. When turning
the RVA into a “modeling” method, the probabilities
are assessed based on a combination of “all
variability” we are able to express; hence it is to some
extent “easier” to assess unlikely events.
6 FURTHER WORK
To increase the usefulness of RVA for climate
changes, supporting tools should be developed to
support the analysts in all the steps of the analysis.
One example is development of climate information,
geology, and historical data feasible for evaluating the
risk of flooding or landslide. Increased effort should
also be put into including steps for risk mitigation and
determining acceptable risks regarding climate
change.
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