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SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE
VULNERABILITY: THE NORTH-RHINE WESTPHALIA STUDY
J. P. KROPP1 , A. BLOCK1 , F. REUSSWIG1 , K. ZICKFELD1 ,
and H. J. SCHELLNHUBER1,2
1
Potsdam Institute for Climate Impact Research
P.O. Box 60 12 03, 14412 Potsdam, Germany
E-mail: [email protected]
2
Tyndall Centre for Climate Change Research
University of East Anglia, Norwich, NR4 TTJ, UK
Abstract. Climate change will bring about a sea change in environmental conditions worldwide during the 21th century. In particular, most of the extreme events and natural disaster regimes prevailing
today will be transformed, thus exposing innumerable natural and socio-economic systems to novel
risks that will be difficult to cope with. This crucial component of vulnerability to anthropogenic
interference with the climate system is analyzed using powerful pattern recognition methods from
statistical physics. The analysis is of intermediate character, with respect to spatial scale and complexity level respectively, and therefore allows a rapid regional assessment for any area of interest.
The approach is based on a comprehensive inventory of all those ecological and socioeconomic assets in a region that are significantly sensitive to extreme weather (and climate) events. Advanced
cluster analysis techniques are then employed to derive from the inventory a set of thematic maps
that succinctly summarize – and visualize – the differential vulnerabilities characteristic of the area in
question. This information can prepare decision makers and the general public for the climate change
hazards to be faced and facilitates a precautionary climate change risk management. The semiquantitative methodology described and applied here can be easily extended to other aspects of climate
change assessment.
1. Introduction
In spite of the many new observations provided by the Third Assessment Report
(TAR) of the Intergovernmental Panel on Climate Change (IPCC), it has become
clear that climate change science has still a very long way to go. In particular, the
TAR was not really able to present any formal analysis of differential geographical
vulnerability to anthropogenic global warming and the concomitant transformation
of environmental conditions at large. Vulnerability defined by the IPCC as the
degree to which a system is susceptible to, or unable to cope with, adverse effects
of climate change. It is a function of the climate-related stimuli to which a system
is exposed, its sensitivity and its adaptive capacity (Cutter, 1996; McCarthy et al.,
2001). Although the precise definition of vulnerability is still a matter of debate
between distinct schools (for a detailed discussion, see Füssel and Klein (2005) and
the references therein) climate policy makers would actually be highly interested
in learning from the scientific community which regions (or even sub-regions)
Climatic Change (2006) 76: 265–290
DOI: 10.1007/s10584-005-9037-7
c Springer 2006
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of our planet are most likely to be endangered by climate change – in order to
activate coping capabilities and to negotiate for compensation measures as soon as
possible. As a matter of fact, the entire issue of sustainable development outside
the industrialized countries will crucially depend on such information.
Why is this body of evidence not yet available? There are three major obstacles
to be overcome: first, adaptation research has not yet reached a stage that allows the
numerical derivation of coping capacities from basic socio-economic indicators like
GDP or certain infrastructural data (Klein et al., 1999; Smit et al., 2000). Second,
the available collection of climate impact studies is – geographically and topically –
highly fragmented. Moreover, there exists no harmonized calibration with respect to
common scenarios for social driving forces (Nakicénovic and Swart, 2000) and for
the resulting transformations of atmospheric concentrations (Covey et al., 2003).
Third, it is quite difficult to identify – and to establish – the “right” degree of formal
complexity for a vulnerability analysis based on expert judgment (see the classic
study by Morgan and Keith, 1995).
Several attempts to generate differential vulnerability maps were made during
the recent TAR process, but not released for publication due to their premature
character. There are, on the other hand, elaborate studies that try to account for all
possible costs and benefits of climate change including all conceivable mitigation
and adaptation measures. Most results of this approach are published in the socalled “integrated assessment” literature (cf. Parson, 1995; Schneider, 1997; IPCC,
1997; Rotmans and Dowlatabadi, 1998; Schellnhuber, 1998; Tóth and Hizsnyik,
1998; Lorenzoni et al., 2000; Aggrawal and Mall, 2002). But even if these attempts
succeeded in reflecting reality appropriately (by accounting also for non-market
values, extreme weather events or large-scale geophysical discontinuities (Smith
et al., 2001)) it would be unfeasible to carry out this type of analysis in comparative
depth for all regions, or even sub-regions, of the world.
Therefore, it seems reasonable to settle for an intermediate level of complexity
(see, e.g., Tóth and Hizsnyik, 1998). A semi-quantitative approach to vulnerability
assessment (i) should be apt to consider the crucial vulnerability inventory of a
given geographical region, (ii) should be applicable to any region of the world, in
principle, and (iii) should allow for quick (and moderately “dirty”) integration of
rough numerical indicators of adaptive capacity. In this paper, we demonstrate this
approach by presenting a model case study for the German state of North-Rhine
Westphalia (NRW) (see Figure 1), which illustrates the application of modern dataprocessing methods for deriving a geographically explicit vulnerability classification on the community level. This classification is well-defined and rigorous,
yet transforms away most of the complex details contained in the empirical information input. As a consequence, the resulting vulnerability ranking of subregions
is quite robust with respect to imprecisions and uncertainties associated with the
data-base.
Our approach also differs from the majority of previous vulnerability studies
by focusing on extreme weather events rather than on smooth modifications of
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Figure 1. North-Rhine Westphalia (NRW) and its location in Europe (red inset). NRW is the study
region and the most populous state of Germany (≈18 Mio. residents in 2004, 34,070 km2 ). The red
squares denote the district capitals. NRW comprises 396 communities with rural, rangy, peri-urban,
or urban characteristics. The latter type of community mainly located in the Ruhr basin (DuisburgEssen-Dortmund axis).
mean values of crucial climate parameters. Researchers have emphasized time
and again the importance of “singular” phenomena for evaluating the real risks
associated with anthropogenic climate change (Nordhaus, 1994; Schneider, 1996;
Smith et al., 2001), but little progress has been made so far to incorporate these
singularities in cost-benefit or proper vulnerability analysis (cf., for example, Subak
et al., 2000; Azar and Schneider, 2002). This is deplorable because global warming
is likely to modify the probability distributions for extreme events (storms, heavy
precipitation, droughts, etc.) considerably (Trenberth, 1999), and may even bring
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about abrupt regional or global changes in the present mode of operation of the
ecosphere machinery (Schellnhuber, 1999).
The pace of climate change and the increased intensity of extreme weather
events push us to the limits of adaptive capacity for the future. Although it is
rather difficult to determine local vulnerability, there exists sufficient evidence
that a variety of assets located in densely populated areas could be affected most
seriously (MunichRe, 2003). It is, of course, a formidable scientific task to estimate
the possible damages that might arise for ecological and socio-economic systems
by irregular events accompanying climate change in these areas (for a review see,
for instance, Kunkel et al., 1999). The associated impacts are generally unevenly
distributed among social structures and economic sectors (Kasperson et al., 1996).
The values at stake do not only refer to markets assets, but also to ecosystem
functions, human well-being, and sociopolitical stability. The standard items to be
considered sensitive to climate functions are agricultural yields, traffic capacities,
human settlements, energy production systems, etc. (Renn, 1992).
Evidently, a long-term forecast of individual meteorological hazards in unfeasible. A systematic investigation of the sensitivity of a given exposure unit with
respect to a statistical ensemble of singular events can be performed, however. In
a similarly averaging way, the adaptive capacities of the exposure units can be
assessed quite satisfactorily.
In the following, we will use a neural networks approach (Kohonen, 2001) for
establishing a climate vulnerability typology for North-Rhine Westphalia (NRW)
(Figure 1), which provides an integrated ranking of the communities of this state.
2. Analyzing Climate Vulnerability by Indicators of Varying Complexity
As a basis for this vulnerability assessment we carried out a systematic stocktaking
of all conceivable types of damage caused by extreme weather events (see Table I)
such as heat waves, cold spells, intense precipitation events, wind- and hailstorms,
floods, foggy days, etc. (Etkin, 1999). A set of basic vulnerability indicators for each
category of damage was then identified. In general, these indicators can be used as
measures for the degree of susceptibility of a given sector or region in the face of
certain climate stimuli, and are therefore suitable for comparative risk assessments.
For instance, in mountain regions in winter a hill slope of >30◦ is commonly related
to a high risk of avalanches. Thus, the hill slope can be used as one indicator to
assess potential vulnerability in the case of heavy snowfall. Further, the indicators
allow the analyst to systematize and condense a large number of observations and
a great deal of information into key variables in order to evaluate the current state
of the system under consideration. If the indicators are chosen correctly, even a
fraction of the available data is sufficient to characterize a complex situation. For
this reason, several types of indicators are used, ranging in hierarchy from “simple”
over “composite” to “systemic”. Nevertheless it is difficult to develop a universally
Grady and Kapsalis,
2002
Weather
sensitivity
of sectors
Heat-stress
Klinenberg, 2002
Galea and Vlahov, 2005
Changnon et al., 2000
Andrey et al., 2003
Just-in-time
production
Number of
elderly people
population density
Drouineau et al., 2000
Agriculture
Forestry
Hill slope soil type
Orographic
index tree type
Number of
commuters
Frequency of
traffic accidents
Seasonal
un-employment
References
Proxy for
Indicator
Human health
Industry services
Industry
Cultured ecosystems
Storm damage
Affected sectors
Cases of illness
Production &
revenue loss
Production loss
Soil erosion
Categories of damage
More hot and sultry days
More cold days
More intense precipitation, more
intense and frequent storms,
more intense and frequent storms
More intense precipitation, flooding
more intense and frequent hail
and/or fog
Pertinent extreme weather events
TABLE I
Examples for possible indicators (proxy variables) to estimate climate vulnerability defined on the municipal community level
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acceptable “metric” defining vulnerabilities. This incommensurability is due to
the inherently normative nature of any vulnerability concept, e.g., valuing impacts
under largely unknown probabilities of occurrence. An integral part of any vulnerability concept is the exposure which is defined as the degree to which a system
is exposed to climate variations. Since the latter is only measurable with great
difficulty – due to the highly uncertain nature of regional climate change – we use
the inventory characteristics of communities as a risk measure. Thus, we understand vulnerability as a measure indicating the potential susceptibility of a system
to adverse effects of climate change. Examples of proxy variables and hypothezised
key relationships that were used in constructing the vulnerability index are shown
in Table I.
Simple indicators are measurable quantities which can be used, for instance, to
identify vulnerable settings within a region or sector, when synergistic or antagonistic properties are not taken into consideration. From the observation, for example,
that heat-stress related illnesses occur predominantly in urban areas where environmental stresses are multiple, and affect mainly elderly people (Semenza et al.,
1996), population density and the age class can be deduced as a simple indicator
for the vulnerability of people to heat waves (Klinenberg, 2002; Galea and Vlahov,
2005).
Composite indicators are specific combinations of system variables which are
independent of each other and are able to indicate more complex properties of
the particular system. It is well known, for instance, that the forest sector can be
severely damaged by intense storms. From the analysis of this damage, tree type,
hill slope, location and age class are identified as the decisive factors in terms of
damage potential (cf. Drouineau et al., 2000). These simple indicators can then
be aggregated into a more complex composite measure of forest vulnerability (see
Figure 2b).
Another instructive example is given by the construction of a systemic indicator.
By analyzing the intra-annual change of the unemployment rate, one can qualitatively evaluate the dependency of the unemployment level on weather variations.
As basic indicators for the vulnerability of the employment situation in different
sectors, the effects of seasonal weather variations on unemployment (differences
summer/winter) and the total employment rate in the municipal community under
investigation can be used (cf. Grady and Kapsalis, 2002). Jobs which are affected
by seasonal and intra-seasonal weather conditions are mainly found in agriculture,
tourism and the construction sector. The aggregated indicator then allows for the
direct intercomparison of vulnerabilities on the community level (see Figure 2c).
A higher unemployment rate translates into damage through a loss of purchasing
power, decreased revenues from taxation and more transfer payments. The same
complexity holds if one regards a certain category of economic loss caused by
weather-induced perturbations of traffic (Changnon and Hewings, 2001; Andrey
et al., 2003). The vulnerability of the production process can, e.g., be evaluated
by the number of commuters (see Figure 2d). No general-purpose technique exists
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Figure 2. Some examples of calculated vulnerability maps for North-Rhine Westphalia: (a) susceptibility to heat waves estimated by population density and number of elderly people, (b) vulnerability
of the forest sector assessed by combining the landscape orography and certain types of trees, (c) susceptibility of local labour market calculated on the basis of the seasonal rate of non-employment, and
(d) production loss by traffic collapse through extreme weather conditions on the basis of the number
of commuting employees for each community (The borders correspond to the 396 communities).
for the identification and assessment of the potential impacts on climate-sensitive
inventories of nature and civilization. The choice and construction of indicators as
measures and assessment instruments requires a fundamental understanding of the
interrelations between systems variables, supported by expert-guesses and simple
experience. Nevertheless, this step is guided by empirical evidence and theoretical
considerations. In general, one has to construct a hierarchy of indicators and appropriate rule-based aggregation schemes to assemble simple or basic indicators into
more complex indicators up to systemic levels. The result is ultimatively a spatially
resolved figure of the susceptibilities of different sectors and regions in the face of
an increased occurrence of extreme weather events.
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The decision to choose the indicators listed in Table II was made from a pragmatic (data availability) as well as from a systematic point of view (importance of
sectors). NRW is densely populated and the federal state with the highest economic
power in Germany. This is taken into account by the over-representation of variables which relate to society and the economic sector. This choice is, of course,
associated with some degree of subjectivity which depends on the evaluator’s experience, degree of knowledge, and opinions (cf. King, 2001; Adger et al., 2004).
One way to deal with these circumstances is to make value judgements transparent.
Our choice to over-represent human dominated and built-up systems reflects (i)
an expression of a more anthropocentric position in the spectrum of ecocentrismanthropocentrism, but it also considers (ii) the fact that North-Rhine Westphalia
is the most populous and the most economically powerful of the German states,
provided with a modern supporting infrastructure. It should be noted that the overrepresentation of some variables with respect to others is equivalent to an implicit weighting process. The latter becomes crucial when regions with very different characteristics are compared (e.g., NRW vs. Bangladesh). As we do not
attempt such a comparison and the physical assets can be assumed to be homogeneous across NRW, uniform weights are used in our analysis. This argument holds
also for the adaptive capacity, which varies greatly between different regions of
the world, therefore influencing the vulnerability substantially from a world wide
view. Regarding NRW we have not provided specific indicators for adaptive capacity, since weather extremes are a common feature of actual climate, and humans
have adapted to them to some extent. Coping strategies are already in place in
NRW to a greater extent than in other regions. In addition, revenue equalization
on the state and community level presupposes similar adaptive capacity. If we account for the minor spatial scale and assume that the high degree of “business
as usual” preparedness for weather-related extreme events is evenly distributed
across the country, no internal differentiation of adaptive capacity occurs, and the
final total vulnerability measure is structurally equivalent to the initial measure of
the region’s susceptibility. Given the novelty of intensified weather extremes, or
their combined occurrence, this assumption may however turn out to be too naive
and should be replaced by an explicit stock-taking of adaptive capacity indicators
for the 396 communities, a step we have not taken here, but which is possible
indeed.
3. Integrated Consideration of Potential Damage
In the prior section a sample of different categories of indicators measuring ecological or socio-economic impacts has been identified. These “pointers” facilitate a geographically resolved appraisal of vulnerability which compares different communities with respect to a combination of climatic events and corresponding adverse effects. An increase of foggy days, rapid frozen rain showers,
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more hot and sultry days, heavy rainfall or hail storms affect different ecological and socio-economic settings within the communities. However, individual
measured indicators are not sufficient to adequately describe or assess the state
of the complex environmental situation like the overall vulnerability to climate
change.
Integrative assessment is therefore facing the almost unsolvable problem of rating different climate-related stimuli and the affected damage categories as part of a
total structure. An instructive example from medicine may elucidate this: the susceptibility of a person facing different diseases imposes a structurally related risk.
Different bacteriological diseases, virological illnesses and many further causes
can affect the organism. Whether a person falls ill or not depends largely on the
individual state or specific conditions of their organs or the state of physiological
and metabolic processes. Nevertheless, these specific elements are summarized to
produce an integrative general view of the person’s overall constitution. In medicine
this has always been done by regarding typical patterns or combinations of affected
organs or parts of the organism. From individual measurements or appraisals of
specific blood, metabolic or physiologic variables, an experienced medical diagnostician is able to evaluate the state of the total organism or its susceptibility to
possible diseases.
In the case of climate impact research this kind of integrative assessment of
complex environmental systems is missing. There is still a lack of knowledge
about the detailed metabolic interactions and linkages between nature and humankind, although new approaches are likely to achieve results here (Schellnhuber
and Wenzel, 1998). As a consequence, steps towards an assessment of the overall
vulnerability of regions to extreme weather may only be achieved by using phenomenological data-synthesis approaches. Common multivariate techniques such
as principal component analysis (PCA), hierarchical, or non-hierarchical cluster
analysis have limitations in this context. They are either linear, have a bad performance, or need several a-priori assumptions. For this reason, neural-networkbased nonlinear PCA technologies have recently been introduced (Hsieh, 2001;
Hsieh and Wu, 2002). For our approach we use a self-organizing map (SOM)
combined with an algorithm quantifying distortions of topology (neighborhood)
relations (Bauer and Pawelzik, 1992; Kohonen, 2001). The benefit of this method
is twofold. First, a nonlinear PCA and cluster analysis with a maximum of information compression and a minimum of information loss is provided. Second,
neigborhood relations are preserved which allows one to discuss the clustering in
the context of adjacent clusters. In order to utilize this method for the integration
of individual features of susceptibility which are pertinent to increased occurrence
of extreme weather events, an appropriate data-set (see Table II) was put together.
It consists of an inventory of identified indicators sensitive to these impacts and
was collected for every community of NRW, generating a 24-dimensional dataset (24 indicators) with 396 entries (communities). Before the data feed into the
neural network they are normalized to the unit cube in order to make data from
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SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
TABLE II
List of indicators for certain sectors used in the entire analysis. The numbers in brackets correspond
to those in Figure 4.
Society
Managed Ecosystems
Tourism
Population density (1)
Number of motor vehicles (9)
Frequency of traffic
Accidents (10)
Total population (11)
Population > 50 yr (12)
Total agricultural area (2)
Area of monocultures (3)
Heterogeneous agricultural
area (4)
Coniferous forest area (5)
Area of fruit cultures (6)
Touristic infrastructure (13)
Number of overnight
stays (14)
Length of stay (15)
Economy
Others
Local employment (16)
Number of commuters (17)
Non-employment rate (18)
Max. seasonal non-employment
rate (19)
Employees (forest industry) (20)
Employees (building industry) (21)
Employees (trade and services) (22)
Purchasing power and
prosperity (23)
Area of lakes/rivers (7)
Pending erosion (8)
Length of electric lines (24)
distinct domains comparable. The minima/maxima of each indicator are used as
1
lower/upper bound .
3.1.
DATA SYNTHESIS BY SELF-ORGANIZED FEATURE MAPS
The self-organized feature map (SOM) is a special purpose technique from the
field of artificial-intelligence research (Kohonen, 2001). In combination with certain optimization criteria (Bauer and Pawelzik, 1992) it can solve a problem which
occurs frequently both in living organisms and in technical applications: the reduction of a superabundant flood of data to the essential information. For more details
regarding the algorithm, see appendix. Unlike other widely used neural network
types the SOM is inspired by its biological counterpart: the brains of mammals.
It has been found that in areas of the brain neocortex the neurons are organized
in ways that reflect some physical characteristics of the signals stimulating them
1
It should be emphasized that is also possible to normalize the indicators with respect to, e.g.,
globally available minimum/maximum values. This is equivalent to a rescaling inducing a compression
of the data structure. The results are quite similar, since the SOM aims to resolve the most densely
occupied part of the input data space. However the applied normalization methods allows a maximum
spreading for each indicator set easing the adaption process.
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(Bauer et al., 1996). Signals received from adjacent peripheric receptor fields are
processed also in neighboring neuronal domains (e.g visual cortex) which can be
considered as a topology preserving mapping (see Nauta and Feirtag, 1992). A
SOM works in a similar way dealing with both (i) relationships among data rather
than their algebraic attributes (values, magnitudes, signs, etc.) and (ii) extracts structural information from numerical data as opposed to memorizing all of it. Thus, a
SOM seeks – in a self-supervised way – samples with simular attributes and forms
topological ordered groups (frequently realized states of the system) of archetypal
patterns.
Thus, in contrast to traditional cluster-analysis methods (Marriot, 1974) the
application of self-organized feature maps provides compressed information and –
as a most important feature – generates a clustering of data which does not simply
rely on linear relations within the data set. For this purpose no a priori knowledge,
(e.g., number of classes, optimal embedding dimension etc.) about the underlying
system is necessary. As an outcome a phenomenological data model of archetypal
patterns is gained which represents the input data in a topology-preserving form,
i.e., all similarity relations between the obtained classes are preserved. This opens an
excellent way for comparison of one data category in relation to the others. A broad
variety of successful applications (see, e.g., Kropp, 1999; Reibnegger and Wachter,
1996; Ambroise et al., 2000; Kropp and Schellnhuber, 2006) have demonstrated
the potential of this technique.
3.2.
RESULTS OF THE
SOM ANALYSIS
A couple of simulations using neural networks with different configurations (with
16, 20 and 24 nodes (classes) and 1 to 4-dimensional geometries, respectively) were
carried out to check the optimal projection space and number of categories. For each
possible network configuration 5 runs were performed in order to guarantee the robustness of the achieved results. The analyses revealed an optimal compression of
information if the data are projected onto a 2-dimensional network consisting of
6 × 4 nodes which is equivalent to the examination of 24 clusters. However, this
network is not an equidistantly gridded map (see Figure 3). The differences in bond
length measure the differences between different classes. This (nonlinear!) projection is free of topological disturbances like cusps (cf. Figure 7b). Thus the input
data can be mapped consistently onto a topologically equivalent 2-dimensional subspace. In summary, it is found that the 24-dimensional input data can be represented
by a 2-dimensional manifold. Let us emphasize here that this result does not imply
the importance of only 2 dimensions and, consequently, the others could be omitted.
Each node of the network represents a cluster of communities, which are characterized by a distinct mixture of input features, i.e., a characteristic combination of
the 24-dimensional input vectors.
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SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
Figure 3. Projection of the trained neural network (6×4 nodes) onto a 2-D plane via multidimensional
scaling (cf. Sammon, 1969) in order to visualize the relative similarity between obtained classes. The
length of the bonds refers to the similarity between neighbored classes. The nodes are associated with
the community names representing the categories (the typical representative is indicated in capital
letters).
4. Discussion of Selected Vulnerability Classes
The spectra shown in Figure 4 present the relative shares of the input data, as
they are associated to the different nodes (clusters). At the first glance they look
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Figure 4. Spectral data of the obtained data categories (cf. Figure 3) representing the archetypal
pattern characteristics of identified classes. It is shown – with the exception of #1 – that directly
adjacent classes are more similar than neighbors of the second or third order (The number of variables
on the x-axis corresponds to those in Table II and refers to the type of indicator).
like rather similar, especially for neighbored classes (with the exception of #1).
However, it is one specific feature of self-organizing maps to arrange classes in a
topology-preserving manner, i.e. archetypal categories are neighbored by similar
classes on the neural grid (see appendix). Consequently, each node of the neural
network (which is equivalent to one class of vulnerability) is represented through
a certain composition of the inventory of natural and socio-economic settings and
relates this information to other neighbored clusters. One has to mention, however,
that these measures are not absolute. They have to be discussed and interpreted in
relation to the other vulnerability classes. In addition, a low value of one variable
does not indicate irrelevance for the investigated community, it signifies only its low
importance in the context of the set of explored cities. Thus, the phenomenological
classification of the 396 communities of NRW allows one to describe them from
a functional point of view, i.e. to specifically discuss vulnerability issues under
consideration of some prominent variables and with respect to the communities’
local setting. Transferring the classification back to the NRW map it is conspicuous
that in certain cases – although this information is not used in the classification
process – the categorization of communities is related to regional features (Figure 5).
In the following this will be discussed in more detail for four exemplary vulnerability
typologies, which are typical for North-Rhine Westphalia’s physical setting.
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Figure 5. Regional distribution of the four discussed vulnerability classes. They are characterized by
a pronounced regionality; an information which is not used in the training of the SOM. For a better
overview the other 20 categories are neglected on the right panel.
4.1.
THE INDUSTRIAL CENTRES
This vulnerability class (#1, see Figures 3 and 5 right panel, yellow) represents the
industrial centres of NRW (mostly located in the Rhine-Ruhr basin). It comprehends
communities which are characterized by a dense building and infrastructure, by an
aggregation of industrial and public assets, and whose associated communities have
been heavily influenced by structural change during recent decades. The traditional
heavy industries (coal mining, steel production) have become less important and
new industrial branches are evolving, e.g., in the media, e-commerce, energy, research and development, or the IT sector. The representative city of Duisburg, for
instance, is the owner of Europe’s largest domestic port; Dusseldorf and Cologne
host large international airports, universities and international trade shows, while
Essen is a center of the power industry. The elements of this group may be affected
by an increase of extreme-weather events in a similar way: aspects like individual
well-being or traffic issues can increase the integral vulnerability of urban centers
significantly and emphasize their susceptibility in the face of weather extremes.
For instance, in these cities the net car density (cars/km street) is the highest. This
implies a high risk for an individual to get involved in a traffic accident. Regarding extreme weather events, the traffic sector is vulnerable from various points of
view. Detailed studies have shown that abrupt changes, e.g., heavy rainfall, double
the traffic accidents in city areas (Changnon, 1996). In addition, the occurrence of
more hot and sultry days has similar effects due to indisposition and inattention
(Arminger et al., 1996). Another point coming into play is the weather dependency
of commuting. It may be possible that extreme weather events, such as freezing
rain, disrupt the traffic in these centers and induce – due to late or non-arrival of
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employees and the temporal disruption of the transportation chain of goods – production losses (Changnon and Hewings, 2001; Andrey et al., 2003). Focusing in
more detail on the economy the spectra of cluster #1 show that the seasonality of
the non-employment is low, signifying a low dependency of the labor market on
seasonal weather variations. The sectoral distribution exhibits a clear dominance
of trade and services. This indicates the importance of these cities as centers of
commerce and services, but also as centers for cultural and business trips. This is
signified by the indicator “touristic infrastructure” (indicator #13) which is high
and “length of stay” (indicator #15) which is small (indicating short stays) (Figure
4). Both measures indicate the relevance of business journeys and short-term city
trips for these communities. This brief discussion makes clear that any disastrous
weather event could weaken the economic prosperity of these communities, inducing a variety of secondary effects. Other impacts are due to persistent weather
situations, e.g., heatwaves. In the associated communities the number of elderly
people above 50 years is rather high and in combination with a high population
density this indicates the susceptibility of human well-being (cf. Semenza et al.,
1996; Klinenberg, 2002).
4.2.
THE RECREATION REGIONS
Vulnerability class #6 show a regionally constrained distribution comprising areas
in the mountain ranges of Eifel, Sauerland and Rothaargebirge (Figure 5 right
panel, orange). The associated communities are characterized by largely wooded
(>35%) and – compared to the NRW average – sparsely populated areas. The
economy of the associated communities shows, in comparison to other classes, a
significant dependence on tourism. And even though the touristic infrastructure
is not so relevant as, e.g., for class #1, these communities are characterized by
above-average length of stays indicating the importance of holiday-makers for
these communities. Their economic infrastructure is characterized by trade and
services and by small and medium-sized enterprises in the manufacturing industry.
Discussing these communities under the aspects of climate-related vulnerability
one has to consider their natural resources, since tourists in these regions focus on
outdoor activities. Communities like Winterberg are strongly engaged in winter
sports as well as in summer tourism. Increasing warming can shorten the winter
season and more rainy days in summer can decrease the number of longer overnight
stays. Both could have tremendous effects on the communities’ economy. In addition, the employment structure displays a pronounced dependence on seasonality
(cmp. with #1). Comparing the relative share of working places for different sectors, the importance of the trade and services is significant, but also a non-negligible
number of positions are associated to the building and forest industries. This also
refers to the recreation communities’ vulnerability against weather extremes, e.g.,
storms.
280
4.3.
SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
SUBURBS AND LOW DIVERSIFIED CITIES
Class #13 (Figure 5 right panel, blue color) comprises communities mainly from
two different regions of NRW: those lying in the Niederrhein lowlands and those
which are located in the north-eastern part of North-Rhine Westphalia (Weserbergland). Communities located in the Niederrhein lowlands are mostly suburbs, e.g.,
Mettmann, Meerbusch, Kaarst, Korschenbroich, Viersen, of the greater urbanized
areas (cf. class #1, #7). Some of them, e.g., Kerpen, Jülich, Pulheim, or Erftstadt, are
direct neighbors of large lignite surface mining areas. Those situated in the northern
part are characterized by agriculture and partly by the health industry (e.g., Bad
Salzuflen, Bad Oeynhausen, Porta Westfalica) with an exceptional infrastructure of
spas and clinics. For instance, for Bad Oeynhausen approx. 60% of its economic
power is related to medical facilities.
The economic structure of these communities offers only low diversity. Unna and
Viersen are characterized by an exceptional tertiary sector and Viersen, Lübekke
and Herford by the textile industry. A point which catches the eye (cf. Figure 4)
is the high purchasing power and prosperity of these communities. This can be
explained by the proximity to larger cities (e.g., Kaarst, Mettmann: Dusseldorf),
i.e., there are a lot of commuters working in the industrial centers but living
in these dormitory townships. Thus these communities share in the economic
prosperity and are also influenced by extreme weather events impacting, e.g.,
urban transportation (see above). Other prominent variables which can be derived from the feature spectra (Figure 4) are soil erosion which is clearly related to the agricultural area, and the proportion of employees in trade and services which can be related to the communities’ infrastructure (bathing, local
authorities).
4.4.
THE RURAL COMMUNITIES
The last group of communities discussed belongs to class #24 (Figure 5
right panel, pink color). The main characteristic of this class is that they
are exclusively located in the northern part of NRW. The large urban centers are relatively far away and the economy is – to a wide extent –
agricultural or dependent mainly on a few specific sectors, e.g., agro-tourism. It
is interesting that although intensive agriculture dominates, soil erosion is rather
unimportant. This might be due to the fact that most of these communities are
situated in the northern lowlands of Germany, i.e., they have no very steep terrain.
Another point which has to be discussed here is the high proportion of residents
above 50 years old. It is clear that hot and sultry days influence human well-being,
but in these communities population density is quite low. Thus, the vulnerability of
these communities to weather extremes is rather low.
J. KROPP ET AL.
4.5.
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RANKING OF INTEGRATED VULNERABILITY CLASSES
However, the classification of the communities according to the characteristic types
of vulnerability provided by the neural-net algorithm is only the first step towards
an integrated assessment of regional vulnerability. As shown in the previous sections a variety of facets can be discussed in this context. We emphasize that the
present analysis has a diagnostic character. Moreover, we focus only on weather
extremes. Regarding the different physical setting of NRW’s communities this implies that different communities may be particularly affected by different types of
extremes. For instance, heat waves may have an impact in densely populated areas,
while storm damage may have a larger impact in forested regions. But the question
remains as to how these communities can be compared or ranked with respect to
their vulnerability. In general, different strategies exist to define ranking systems.
For instance, the simplest one can be constructed by asking experts to compare
randomly chosen pairs of clusters with respect to their different vulnerability by
considering exemplary members. Such an approach could be called “expert bubble
sorting” similar to the algorithm used for sorting lists in ascending or descending
manner.
Here, it seems possible to use the spectra in order to rank the computed 24 classes
of communities in North-Rhine Westphalia in a manner pertinent to their integrated
value of vulnerability by an objective function. Considering the spectra (see Figure
4) and the discussions in the preceding sections, one can assume that each component of the socio-economic and natural settings contributing to the different clusters
can be summed up by an integral over the area of each cluster spectrum, providing
a measure for ranking the different vulnerability classes. This measure considers
the circumstance that, e.g., a heatwave related death toll can have a similar weight
as windthrow in a forest. But the chance that both factors together would influence
industrial centers and rural regions simultaneously and additively is rather low.
Nevertheless, each singular event may have a similar relevance for the affected
community. Thus our approach measures the sum of all vulnerability aspects. The
working hypothesis is the following: the larger the integral over the spectrum (area),
the larger the integrated vulnerability of this class of communities. Of course, this
implies that we have defined the correct variables indicating climate vulnerability in
sufficient way. But by investigating the spectra in detail it becomes clear that such a
ranking is well-founded and meaningful, because it provides a comparative assessment, e.g., with respect to the natural setting of the communities (cf. Figure 5b). For
instance, the recreational regions are less vulnerable that the urban areas, since the
“assets” threatened by extreme events are lesser (cf. also MunichRe, 2003). Table
III shows the ranking of the integrated vulnerability classes on the basis of such
a natural measure. However, at first glance, it seems to be rather counter-intuitive
that highly industrialized and today even post-industrializing urban centers such as
Dusseldorf, Cologne or Essen should be especially vulnerable to climate change.
The current public discussion of climate change impacts is dominated by the most
282
SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
TABLE III
“Ranking” of vulnerability classes pertinent to an integrated vulnerability measure. By calculating
similarity between the node and the spectra of the associated communities the representative is
estimated.
High
↓
Vulnerability
↑
Low
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Ranking
(node number)
Cluster Index
measure
Integrated vulnerability
community
class 1
class 2
class 7
class 13
class 3
class 21
class 19
class 8
class 14
class 9
class 20
class 22
class 17
class 4
class 15
class 16
class 10
class 6
class 18
class 5
class 12
class 23
class 24
class 11
7.29338
4.93218
4.90505
4.71128
4.50519
4.47783
4.38626
4.19684
4.00666
3.99625
3.90307
3.88165
3.85079
3.79067
3.77679
3.62207
3.60526
3.59267
3.54422
3.40840
3.39099
3.35535
3.30069
3.27797
Duisburg
Solingen
Dueren
Erftstadt
Schwelm
Lienen
Erkelenz
Huerth
Grevenbroich
Rheinbach
Geilenkirchen
Billerbeck
Kreuzau
Gummersbach
Hueckelhoven
Marsberg
Kleve
Lennestadt
Stadtlohn
Meschede
Oer-Erkenschwick
Metelen
Warendorf
Eslohe
Representative
suceptible sectors, agriculture and forestry, in developing countries. Still, one has
to keep in mind that extreme weather events can hit civilizational systems at least
as much as natural ones: buildings, traffic systems, and infrastructure are more
developed and much more concentrated in urban areas, rendering them susceptible
to impacts from extreme weather events. Here our findings are fully in line with
global assessments of vulnerability to weather extremes (Changnon et al., 2000).
Vulnerability clearly depends upon the values at stake, upon their empirically
detected susceptibility, and upon the specific pattern of vulnerable assets in a community. It should be emphasized here that the chosen heuristic climate risk assessment strategy cannot deliver exact, quantitative estimations. Nevertheless, it
provides a well-founded reproducible evaluation of climate vulnerability which
is – in contradiction to previously stated caveats – quite useful for comparative
assessments. In particular, most susceptible communities can be identified. However, the exact numerical values presented in the third column of Table III are not
J. KROPP ET AL.
283
Figure 6. Geographically explicit distribution of integrated vulnerabilities determined for climate
risks on community level.
of special interest. They indicate only a qualitative estimation of which class is
currently vulnerable and which one appeared to be more robust. If we want to
achieve prognostic statements, additional information, e.g., on how the inventory
of the communities will change in the future is needed. This increases the complexity of the analysis, but in general, keeps it feasible with the help of the method
introduced here.
We subdivided the range of values (Table III, column 3) into five intervals
representing different categories of climate vulnerability marked from “very low
vulnerability” up to “very high vulnerability”. Figure 6 shows the geographical distribution of the climate-related risks of NRW at the community level. As discussed
previously, the urbanized regions are the most vulnerable (compare Figures 1 and
6). It is shown that some rural communities are also highly vulnerable, so that no
linear bias between urbanization and vulnerability can be stated.
The results of our approach provide a well-founded basis for a deeper analysis:
information clustering and compression by self-organizing maps also generate a
low-dimensional data model which offers the occasion to test different management
strategies in a clear-cut way. By reducing the number of different natural or socioeconomic settings adversely affected by extreme weather events, the corresponding
284
SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
input data is changed and communities could pass to other clusters representing
higher or lower integrated vulnerability. With this tool complex environmental management strategies can be explored theoretically within the framework of climate
risk management and mitigation strategies. As our results are at least twofold, so
might be the practical use one could make of them: (i) the overall assessment of
vulnerability is of interest for all who need to get a first overview in order to detect
hotspots and to improve adaptation strategies in an aggregated manner, such as
government ministers or treasury secretaries. But this overall ranking is no substitute for a more detailed view, focusing on the real causes for concern with regard
to sectoral or structural vulnerabilities (e.g., of the forest sector or the population
structure). Our result provides sector-oriented decision makers with the specific
profiles of community types, suggesting well-tailored solutions for the community
type defined by the archetypal properties (cf. Figure 4) instead of the total ranking. It is an additional strength of the approach that it is independent of (highly
uncertain) regional climate predictions. Nevertheless the approach can be refined
fundamentally, e.g., by introducing more sophisticated weighting factors which
are able to value certain indicators with respect to climate impacts in order to
make the index comparable with respect to other countries. In addition, if statistical data are accessible for distinct time points it is possible to assess vulnerability
over time.
5. Conclusion
There is much evidence that regional impacts of climate change will be substantial.
But up to now fine-grained regional climate-change forecasts are highly insecure –
if not impossible to deduce – from general circulation models. Still, the need for
some kind of integrated assessment of regional vulnerability is evident, as mitigation and/or adaptation measures have to be taken if myopic attitudes and policies
should not prevail. This article undertakes such a regional integrated assessment
of climate change impacts in the German state of NRW. The assessment focuses
on the susceptibility of natural and social systems to extreme weather events, as
these perturbations of the climate system are more decisive in the face of future
climate change than averaged quantities. It is our intention to give a complete and
systematic overview of indicator-based regional (resolution level: communities)
vulnerability to climate change. The integration of the climate vulnerability indicator set is done not by systems analysis, but by a new tool developed in artificial
intelligence research. Our knowledge of systems interactions is still too limited
to go the other way exclusively. It is shown that self-organizing networks are a
well-suited application which seeks to understand patterns complex pattern of data
representing the physical, ecological and socio-economic inventory of communities. The selection of the data and indicators used might well be debated, as well
as the synthesizing weighting process. Other researchers or decision makers might
J. KROPP ET AL.
285
have come up with different indicator selections, due to either a different assessment of the systemic processes involved, or a different valuation of the adversity
of potential impacts. Due to progress in climate impact research and to different
value systems in impact assessment and decision making, the overall assessment of
what is dangerous climate change will continue to be a field of fervent discussion
(cf. ECF/PIK, 2004). Nevertheless, even if substantial progress in climate impact
research provides us with more sound knowledge on what the impacts are, and even
if stakeholders and/or scientists come to an agreement on how to weight different
kinds and degrees of impacts with regard to their severity, we would still be in need
of a synthesizing methodology, able to assemble and process the body of information relating to different sectors or processes. It was the main idea of this paper to
utilize a non-reductionist data condensation technique that provides a reproducible
way of synthesizing various sets of indicators.
The overall result shows – contrary to intuition and to some climate impact
studies – that, in particular, the more highly industrialized and urbanized regions
of NRW are vulnerable to an increase in extreme weather events, due to the fact
that the civilizational inventory at risk is much more developed and concentrated.
The regional evaluation of these patterns make it possible to inform policymakers
and the general public about climate-change hazards on the basis of an integrated,
but regionally resolved climate risk assessment with a focus on vulnerable inventories and regions. The qualitatively oriented integrative methodology can be used
as a tool that can easily be transferred to other regions and tasks in climate impact
assessment – even if the predictive capacities of the regional climate models can
be improved in future.
Acknowledgements
This work was supported by the Ministry of the Environment, Regional Planning
and Agriculture of North-Rhine Westphalia. The results presented here are part of
a comprehensive climate risk assessment for the state of NRW (Federal Republic
of Germany). We would like to thank Dr. Lienenkamp, R. Wesselhöft, and many
other unnamed collaborators for their support, helpful discussions and constructive
remarks.
Appendix
The self-organizing feature map is a unsupervised and self-learning algorithm capable of deriving the essential information from complex data (Kohonen, 2001).
The input data manifold V comprises the elements νi = (v1 , v2 , . . . , vd )T , i ∈
{1, . . . , 396}, where d = 24 denotes the dimensionality (# of indicators) of the
data vectors (input vectors, each of them representing a community). A SOM
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SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
employs a set of neurons (nodes) A which are arranged in a regular network of
dimensionality d ≤ d, d ∈ N, and which can be either rectangular or hexagonal.
The “synaptic strength” of a neuron i ∈ A is given by its associated reference
vecto wi = (w1 , w2 , . . . wd )T . Furthermore, a metric · is needed to calculate the similarity between the reference vector of the neurons and a chosen input
stimulus.
The formation of a SOM follows an iterative process during which the continuous
input space V is mapped onto the discrete network A (output space)
V →A : ν ∈ V −→ i(ν) ∈ A.
(1)
The stochastically chosen input vectors ν ∈ V are mapped onto that neuron whose
synaptic strength is most similar to v, which is calculated by the vector quantization
method (Gersho and Gray, 1992)
ν − ωi ≤ min ν − w j .
∀ j ∈A
(2)
Subsequently, the synaptic strengths of the winner neuron i and a few neighbors
are updated according to the rule
ωi (t + 1) = ωi (t) + ε(t)h i, j (t)(ν(t) − ωi (t)).
(3)
Here ε denotes
the learning step width which can defined as a decay function, e.g.,
− εt
0
ε(t) = e · h i, j (t) indicates the neighborhood kernel which determines the vicinity
around the winner neuron in which the adjacent neurons learn from the same input
stimulus. The neighborhood function is usually formulated as a Gaussian
h i, j (t) = e
2
− (i− j)2
2·σ (t)
,
(4)
where σ is defined by a monotonously decreasing function. h i, j (t) has its maximum
at i = j and approaches zero as |i − j| increases. The value σ denotes the stiffness
parameter, whose time dependence can be approximated by any function of the
type σ (t) ∝ t (−α) . Due to this formulation h i, j (t) on a chosen neuron i decays to
zero if |i = j| and is at maximum when i equals j. The learning process can be
stopped if the network has generalized the essential features of an input data set,
e.g., if the average change rate of the map remains under a predefined threshold
value.
In order to assess the quality of the obtained learning result the self-organizing
map is combined with an algorithm which provides a quantitative measure (topographic product) of topology distortions in maps between spaces of possibly
different dimensionality (Bauer and Pawelzik, 1992). For this purpose two distance
ratios have firstly to be defined
D V w j , wn kA ( j)
Q 1 ( j, k) = V (5)
D w j , wn kV ( j)
J. KROPP ET AL.
287
Figure 7. (a) Measurement of the distances to the next neighbor of order one, two, respectively, if the
points lying in IR2 are mapped onto IR1 . (b) Topology distortion of a two-dimensional map. Due to
the twisted nature of the map two nodes (black bullets, cluster centers) become neighbors which are
separated the in case of a roughly planar network.
and
D A j, n kA ( j)
,
Q 2 ( j, k) = A D j, n kV ( j)
(6)
where n kV ( j) and n kA ( j) denote the k-th order (next) neighbour of the point j in
the input and output space, respectively (cf. Figure 7b). The distance between the
points is measured in the input space (D V ) and output space (D A ) by using the
node coordinates j and the reference vectors ω j . In the IR2 it is given by n 1V ( j) = i
and in the IR1 by n 1A ( j) = i , respectively. For the distance ratio measured in V one
obtains Q 1 ( j, 1) > 1, because D V ( j, i ) > D V ( j, i) (see Figure 7a). For the output
space A, it follows analogously that Q 2 ( j, 1) < 1, indicating the neighborhood
distortion in consequence of the mapping from IR2 to IR1 . The example in Figure
7b clearly shows results of potential network distortions. The nodes act as cluster
representatives and, e.g., twisted networks provide similarity relations which do
not exist in reality. Therefore, it is the aim of our analysis to obtain an approximately optimal data representation. However, only in the case that Q 1 = Q 2 = 1
do the points within the output and input space coincide and the topology is preserved. Therefore, potential errors have to be minimized by evaluating an adequate
criterion. This can be performed by the calculation of the so-called topographical
product which measures the neighborhood violation for all neurons and orders of
neighborhood by the average of the logarithm of the product of the two combined
ratios (see above), i.e.:
⎛
2k1 ⎞
N −1
N k
1
P=
·⎝
log
Q 1 ( j, l)Q 2 ( j, l) ⎠ .
(7)
N (N − 1)
j=1 k=1
l=1
Regarding the network representation P measures the preservation of the neighborhood between the neural units j in A and the weight vectors pointing into V .
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SEMIQUANTITATIVE ASSESSMENT OF REGIONAL CLIMATE VULNERABILITY
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(Received 18 March 2004; in revised form 2 November 2005)