Site Loader

Implementing a Flood Vulnerability Index in urban coastal areas with industrial activity
Ch. Giannakidou a (), D. Diakoulaki a, C.D. Memos b
Lab. of Industrial and Energy Economics, Dept II, School of Chemical Engineering, National Technical University of Athens, Athens, Greece ([email protected] ; [email protected])
Lab. of Harbour Works, Dept of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece ([email protected])
24765037465Corresponding author
As the world’s population increases, and development induces vulnerabilities, climate change impacts are aggravating the effect of natural disasters. Risk, as the expected loss of life or damage of assets, is present throughout the globe. On one hand, natural hazard is a constant risk that humanity must deal with. The varying levels of vulnerability and hazards define the level of risk. The use of indices facilitates the comparison between areas facing disasters, which is a very useful tool for risk management, the cultivation of approaches that strengthen the ability to cope with disaster risks, and the preparation against future events. On the other hand, the post industrialization cities conceal additional vulnerabilities for their residents, as the remaining within their network industries can generate accidents, triggered by natural hazards, with severe impacts. Calculating the urban vulnerabilities could be very complicated in partially de-industrialized coastal cities. Many factors, which are often hard to quantify, can influence the result in risk management process. This article, based on the Coastal City Flood Vulnerability Index (CCFVI), calculates the vulnerability of six coastal urban areas in Greece, which within their network include industrial activities that could potentially be affected by coastal flooding risk due to climate change. It focuses on the difficulties of the “administrative and institutional “subsystem’s applicability of the CCFVI as all areas belong to the same country and proposes alternatives instead. Calculating CCFVI for the selected areas, indicates the need for addition, a sperate indicators’ subsystem in particular, which will be able to measure the special attribute of these areas. The remaining industrial activity in the urban network could impact severely the vulnerability and it should be considered in the designing of the risk management plans of these areas. Moreover, the article highlights the importance of weighing the indicators used for the index calculation.

Keywords: Coastal City Flood Vulnerability Index (CCFVI), risk management tool, indicators, coastal flooding, industries, urban areas
Climate change is a dynamic risk worldwide, carrying changes in natural phenomena, such as coastal flooding. Flood frequency is uncertain and changing over time, requiring further consideration of a range of response options and ‘what if’ scenarios (IPCC, 2007, Yohe, 2009). The way in which the built environment has expanded over the past 20 years, without paying much attention to the evolving climatic conditions, or how humans alter their environment and are thereby positively and negatively affected, has placed several areas in a precarious position. It seems clear that the constant trend to build and develop taking into account only the economic aspects has contributed significantly towards many disasters and/or has worsened their effects (Lewis 1999, Wisner et al. 2004). Several analyses show the relationship that exists between development and natural disasters, and how this can determine the vulnerability of activities, areas, and people (Wamsler 2008; Wisner et al. 2004). For example, the people living at risk of floods worldwide is expected to double from one billion in 2004 to two billion by 2050 (United Nations University, 2004). Thus, as Mileti (1999) points out, emergencies are expected, as a result of interactions between the physical environment, the built environment and the communities that experience them (Makgill, and Rennie, 2012).

Natural hazards are a part of human history and coping with them is a serious element of how communities has been evolved and how they have used their resources. Progressively coastal zones will need more adaptive responses to cope with a plethora of natural hazards arising because of climate change. Many hazards in coastal areas may develop to disasters, driven by environmental change and by human action. Coastal areas are complex systems, regarding their geo-morphological characteristics, which respond in various ways to extreme weather events. Historically, coastal floods are among the most dangerous and harmful of natural disasters, regarding mortality (Douben, 2006). The shoreline features attract human activities and they usually develop fast to urban areas. These areas are associated with large and growing concentrations of human population, settlements, and socio-economic activities. There is, therefore, a need for an easily understood method to calculate risk in such areas and to contribute in this way to their resilience. The resilience of coastal societies is firmly linked to more complex and larger-scale processes today than in the past. In coastal regions, this is often easier to observe in the vulnerabilities created by global tourism, where the growing demands of visitors’ impact previously undeveloped coastal area (Escuder-Bueno et al., 2012). The necessity of an effective risk management in coastal areas increases while the climate change creates unpredictable natural hazards.

In modern industrial societies, the control of natural hazards has become a major concern of the public and a growing responsibility of government. Many cities around the world have expanded in certain ways their land uses due to the rapid development of industrial areas. Many industrial activities remain in their initial establishment surrounded by housing and other economic activities. This integrated urban structure brings several concerns to decision makers, because the spatial structure of industrial distribution in cities shapes urban spatial morphology linking with land use, transportation, economic activities, and housing. When the natural hazard management comes into discussion the industrial activities position has a significant role as it can strongly influence the resilience of an urban area. Research into coastal urban industrial activities and their vulnerability against coastal flooding may contribute urban planners and decision makers to making safer choices. Despite massive efforts to manage natural hazards, such as coastal flooding many people feel increasingly vulnerable to these risks and believe that the worst is yet to come. Climate change is expected to cause accelerated sea-level rise with elevated tidal inundation, increased flood frequency, accelerated erosion, rising water tables, increased saltwater intrusion, increasing storm surges and increasing frequency of cyclones (Fenster, and Dolan, 1996). Apart from this, population growth and increasing urbanization cause further marine and coastal degradation (UNEP, 2002).
The vulnerability indices used for vulnerability calculations are usually based on approaches that examine mostly the underlying socio-economic and institutional factors, and, less, the political and cultural factors, in order to determine how people, respond to and cope with climate hazards. Studies on vulnerability or resilience do not require a detailed knowledge of how climate will vary over time to be carried out. The vulnerability of a region, system or population group can be assessed for a range of existing or hypothetical hazards based on an analysis of factors that estimate how it is likely to be affected the hazards in question. Using vulnerability indices is therefore a useful tool for assessing people’s needs in terms of adaptation or improvements in their ability to cope with hazards. Therefore, in many cases the vulnerability assessments do not require detailed climate information generated by models and they should not wait for more developed predictions in order to be used. Adaptation policies may be developed despite the uncertainties inherent in the science of climate change. All the measures -related to the types of threat that societies are expected to face in the future- required to strengthen the resilience of the societies, should be adopted, despite the lack of detailed knowledge of likely or potential future climate (Adger et al., 2004).

The indicators used in vulnerability assessment have been proved very important in representing with accuracy the relevant variables and processes involved and in helping to understand the distribution of and variation in vulnerability (Adger, 2006). The purpose of establishing an indicator set is to produce understandable information, which can be analyzed quickly for decision-making. However, flooding, and any associated adaptation and mitigation measures related to it, are the result of a complex synthesis and variety of factors that include environmental, social, and economic factors (Kryvasheyeu et al., 2016, Wu et al., 2017). Therefore, creating or selecting an appropriate index to analyze vulnerability quantitatively is a very challenging process. IPCC (2007) defined vulnerability as “the degree to which three components, namely, exposure, sensitivity, and adaptive capacity, can influence its character, rate, and scope.” Hence, to assess the vulnerability to coastal floods in a selected area, a synthetic index should be constructed for exposure, sensitivity, and adaptive capacity (Chang and Huang, 2015). Thus, a system is considered vulnerable, when the exposure and sensitivity are high, while the adaptive capacity is low.

Climate change vulnerability assessment is one of the popular research subjects in recent years. Therefore, various methods, such as the use of the synthetic indices, are constantly developed and improved. The general process of the synthetic index method deals with the selection of indicators that reflect various factors, as it is constructed to enable measurement of complex attributes and to assist communication between groups in concise form. However, many subjective factors influence indicator selection and weighting (Dong et al., 2015). In literature, there are quite a few well documented reviews for the efficiency of indices that contribute in calculating the risk in coastal flooding management. Indicator sets for vulnerability assessment have been developed as a rapid and consistent method for characterizing the relative vulnerability of different areas. One of the simplest methods is to assess the physical vulnerability of an area, while the more complex is to examine the economic and social vulnerability. This paper aims at applying the Coastal City Flood Vulnerability Index (CCFCI) (Balica et al. 2012) in order to calculate the vulnerability of six (6) coastal urban areas in Greece, which within their network include remaining industrial activities that could potentially be affected by coastal flooding risk due to climate change.

Indicators as risk management tools
In economic, social, and environmental analysis indicators and indices are broadly used (Adger, 2005, Birkmann, 2006, Cutter et al. 2003, Cutter et al., 2008). In recent years, especially the use of sustainability and vulnerability indicators and idices has been significantly increased (Bebbington, 2007). A comprehensive definition for indicators is given by Gallopin (1997), defining indicators as variables that represent adequately an attribute or feature, such as quality or/and characteristics of a system. In general, indicators are management tools which describe and have the ability to present complex system characteristics in a quantitative and transparent way. Therefore, indicator and indices tend to assist expressively the communication between theoretical concepts and decision making. This ability provides to decision makers a comparative analysis tool, which can support them in complex decision situations, e.g. in crisis management and emergency planning.
Various approaches using indicators for vulnerability assessment are very common in the field of disaster risk analysis (Cardona, 2004, Cutter et al 2003, Dilley et al., 2005, Peduzzi, 2006, Balica et al, 2009). All these approaches aim at assessing risk and vulnerability quantitatively by means of indicators to compare different regions or communities (Birkmann, 2007, Schmidtlein et al., 2008). Trying to calculate vulnerability assessment, indicators represent an operational representation of a characteristic or a quality of a system able to provide useful information regarding the exposure, the susceptibility, and the resilience of a the chosen to be studied system to an impact of a disaster (Birkmann, 2006). Different factors reflecting the special characteristics of a system determine in general its vulnerability. Therefore, the vulnerability couldn’t be calculated or assessed by using just one single indicator. Instead multi-dimensional concepts, such as composite indices, are required to assess the vulnerability of a system. When individual indicators are compiled to a single index, they form composite indices based on an underlying theoretical vulnerability model (Nardo et al., 2005).
As described above, in risk perceptions the understanding of vulnerability is very broad and current literature encompasses many different definitions, concepts, and methods to systemize vulnerability (Birkmann, 2007, Cutter et al., 2003). Vulnerability could be defined as the susceptibility of a system to be affected or susceptible to damage as proposed by Villagran de Leon (2006). Due to the complexity of vulnerability, its calculation and measurement (and especially for the social component) can be challenging (Brooks et al., 2005). The use of indicator approaches is one common methodology to evaluate the vulnerability in the field of natural disaster risk assessment (Adger, 2005, Cardona, 2006, Dilley et al., 2005, Pelling, 2004, Perduzzi, 2006). In general, the data of most interest from the point of view of vulnerability assessment are those relating to mortality and the numbers of people adversely affected by climate-related events. While economic damage is also an important indicator of the severity of the impacts of climate-related disasters, data relating to the cost of disasters are relatively sparse and are also difficult to estimate.
One of the main challenges in the analysis of vulnerability is the identification of appropriate indicators for the operationalization of the concept. Only with valid, reliable and objective indicators can vulnerability be adequately examined. A review of the literature in the field reveals that very different indicators are being employed to capture phenomena of, and trends in, vulnerability. The choice of indicators is influenced by the specific purpose of the studies, the availability of data, but also by the theoretical perspective taken on vulnerability (Prior et al., 2017). To compose the appropriate for the study vulnerability framework indicators with the following features should be chosen: i) simple to apply, ii) quantitative, iii) sensitive to changes, and iv) representative.

The Coastal City Flood Vulnerability Index (CCFVI)
The Coastal City Flood Vulnerability Index (CCFVI), as presented in the article “A flood vulnerability index for coastal cities and its use in assessing climate change impacts” of Balica et. al, 2012 and in PhD Thesis “Applying the Flood Vulnerability Index as a Knowledge base for flood risk assessment” of Balica,S-F., 2012, is one of the few indicators based tools that measure the coastal flooding vulnerability. It is based on the systems approach, which aims to identify the interactions of different actors or components within certain defined boundaries. To be more specific, it identifies three (3) interdependent subsystems in the coastal vulnerability system:
The natural subsystem (NS), in which hydro-geological processes take place;
The socio-economic subsystem (SES), which includes the societal (human) activities related to the use of the natural system; socio-economic systems are made up of rules and institutions that mediate human use of resources as well as systems of knowledge and ethics that interpret natural systems from a human; and
The administrative and institutional subsystem that includes administration, legislation and regulation, where the decision, planning and management processes take place.

The CCFVI has been developed for measuring the vulnerability of coastal cities. Each coastal city can be seen as a set of interconnecting systems; the system is composed of interacting elements where different processes are carried out using various types of resources. In this context, one must define the system through its components and interactions. It should be pointed out how each single element of the system, as well as the individual interactions in every element, is vulnerable. Coastal floods distress three components of the coastal vulnerability system, each of them is used to represent one of the subsystems described above, and their interactions affect the possible short-term and long-term damages. The components can be assessed by different indicators that contributing in understanding and calculating the vulnerability of the system to coastal floods. Every indicator, in order to reflect better the vulnerability aspect, is sorted in one of the following categories; Exposure (E), which refers to the inventory of elements in an area in which hazard events may occur, Susceptibility (S), which refers to the state or the fact of being likely or liable to be influenced or harmed by a hazardous event, and Resilience (R), which refers to the capacity and resources for recovering from a hazardous event. The system components are hydro-geological, socio-economic and politico-administrative and the indicators used for each component’s assessment are presented in the following table 1.

Table 1: The CCFVI subsystems and indicators
Indicator Abbreviation Definition Units Exposure (E), Susceptibility (S), and
Resilience (R)
Subsystem: Natural (Hydro-geological)
Sea Level Rise SLR how much the level of the sea is increasing in 1 year mm/yrE
Storm Surge SS rapid rise in water level cm E
# of Cyclones C number of cyclones in the last years E
River Discharge RD Max discharge of the last 10 years m3/s E
Foreshore Slope FS Average slope of foreshore beach % E
Soil Subsidence SL How much is the area decreasing m2 E
Coastline CL km of coastline along the study area km E
Subsystem: Socio-economic
Cultural Heritage CH Number of historical buildings in danger when coastal flood occurs E
Shelters S Number of shelters per km^2 R
Population close to coastline PCL Number of people exposed to coastal hazard people E
Growing Coastal Population GCP % of growth of the population in area in the last 10yrs % E
% Vulnerable Population VP % of population with disabilities, also people aged ;12 and 65; % S
Awareness and Preparedness AP Is the CP aware and prepared for floods? Any experience the past 10yrs – R
Recovery Time RT Time needed by the city to recover after flood events days R
Km of Drainage D Km of Canalization km R
Subsystem: Administrative – Institutional
Flood Hazard Maps FHM Existence is vital for land use planning – S
Institutional Organisations IO Number of Institutional Organisations R
Uncontrolled Planning Zone UPZ % of the surrounding coastal area (10km of shoreline) is uncontrolled % E
Flood Protection FP Existence of structural measures to percent floods – R
The calculation of each subsystem – component of CCFVI (natural (hydro-geological), social, and administrative – institutional) is based on the general formula of Flood Vulnerability Index (FVI) (eq. 1).

Therefore, the Coastal City Flood Vulnerability Index for natural (hydro-geological) subsystem is expressed as follows in eq. 2:
CCFVIHydro – Geological=f {SLR, SS, C, FS, RD, SL, CL} (2)
The Coastal City Flood Vulnerability Index for social subsystem is expressed as follows in eq. 3:
CCFVISocio-economic=f {CH, PCL, GCP, VPAP, S, RT, D}(3), and
The Coastal City Flood Vulnerability Index for Administrative – Institutional subsystem is expressed as follows in eq. 4:
CCFVIAdministrative-Institutional= f {FHM, UPZIO, FP}(4)
Total Coastal City Flood Vulnerability Index is calculated as follows:
TCCFVI= CCFVIHydro-Geological + CCFVISocial-Economic + CCFVIAdministrative-Institutional3(5)
And the relevant formula:
TCCFVI= f{SLR, SS, C, FS, RD, SL,CL} + f{CH, PCL, GCP, VPAP, S, RT, D}+ f{FHM, UPZIO, FP}3(6)
The integrated Coastal City Flood Vulnerability Index is a method to combine multiple aspects of a system into one number. On a global perspective, the results will be presented in values between 0 and 1, where 1 represents the highest vulnerability calculated in the urban areas studied and 0 the lowest vulnerability.

Application of the CCFVI
In this paper, the vulnerability system under consideration is urban coastal areas with established industrial activity. Specifically, six (6) urban coastal areas in Greece have been chosen that within their network have industries implanted in order to apply the CCFVI. This special feature plays a very important role in the calculation of their vulnerability as the closeness to an industry raises the risk in case of a coastal flooding event. During the years of the industrialization and urbanization of these areas the fact of their coastal character was very crucial for their development process in which the structural transformation from agriculture into manufacturing and services involved a shift of labor out of rural areas and into urban ones. These areas have been expanded rapidly. The additional housing demand had been covered by building houses around the industries. Eventually, several functions concentrated also in these areas to fulfill various needs of the population established around the industries, such as economic, social or cultural, at an individual and public level. Therefore, this development model had as a result to create industrial lodgements within cities’ network.
It should be noted that in the selected coastal cities the industrial growth was not of the size or expansion of other European industrial cities, even the use of the term “industrial revolution” would be disputed. However, industrialization did happen, changing radically the conditions in all sectors and influencing to a large extent the growth of cities. In late 80’s these cities started changing, due to deindustrialization, and they were trying to adjust to different circumstances. This transformation had and still has, in combination with the economic crisis in Greece, great impact not only on their social and economic life but also on their structured environment (Mitoula et al., 2013), affecting highly the vulnerability of these cities.

In particular, the chosen areas have industrial activities within their network, established in zones that are not determined for industries and they lay under the 50m contour Line. In some cases, when big technical works exist, such as roads, railways, etc., the area’s limit has been taken in lower elevation, as these constructions work as boundary concerning the coastal flooding.
These selected areas are the following as presented in Fig. 1:

Fig.1: The selected urban coastal areas where industrial activities are established within their network.

Elefsina: Elefsina, with 24,900 inhabitants (2011), is situated about 18 kilometers northwest from the center of Athens, and located in the Thriasian Plain, at the northernmost end of the Saronic Gulf. North of Elefsina are Mandra and Magoula, while Aspropyrgos is to the northeast. Most of the industrial activities in Attiki are established in the before mentioned areas. Elefsina is a major industrial center, with the largest oil refinery in Greece. Moreover, steel industry, shipyards, chemical industry are some of the industrial activities with important economic activity in the area.

Fig. 2: The selected area in ElefsinaThessaloniki: It is the second-largest city in Greece, with over 1 million inhabitants in its metropolitan area. Thessaloniki is located on the Thermaic Gulf, at the northwest corner of the Aegean Sea. It is bounded on the west by the delta of the Axios. Thessaloniki is Greece’s second major economic, industrial, commercial and political center; it is a major transportation hub for Greece and southeastern Europe, notably through the Port of Thessaloniki. The heavy industrialization of the city’s suburbs began in the late 1950s. A large industrial zone was created, containing refineries, oil refinery and steel production. The zone attracted also a series of different factories during the next decades. Thessaloniki remains a major business hub in the Balkans, with a number of important Greek companies headquartered in the city.

Fig. 3: The selected area in Thessaloniki
Rio, Patras: Patras is the regional capital of Western Greece and the country’s third largest city. It is located 215 kilometers west of Athens, within Achaia Prefecture, while the port of the city serves as the main gateway to/from Western Europe. Patras has met a period of prosperity due to industrial activity, which has gone into a decline by the end of the 80’s. As a result, non-functional spaces appeared among the city net. Remaining industrial activities are mostly located close to Rio, which is a town in the suburbs of Patras and a former municipality in Achaea, West Greece. The municipal unit has an area of 98.983 km2, and it is considered as a part of the Metropolitan city of Patras.

Fig. 4: The selected area in Rio, Patras
Heraklion: Heraklion is the largest city and the administrative capital of the island of Crete. It is the fourth largest city in Greece with 140,730 inhabitants (2011). It extends over an area of 684.3 km2. The city’s port is one of the ten Greek ports with national importance and has an exceptionally strategic position, since it is located in the center of the south-eastern Mediterranean Basin and interconnects three continents. 57% of the industrial activities in Crete are located in the urban area of Heraklion. Industrial activity mainly involves the processing of the products of the primary sector (foods and drinks), the constructions sector and the sector of plastics.

Fig. 5: The selected area in Heraklion.

Volos: Volos is a coastal city in Thessaly situated midway on the Greek mainland, about 330 kilometers north of Athens and 220 kilometers south of Thessaloniki. It is the capital of the Magnesia regional unit. Volos is the only outlet to the sea from Thessaly, the country’s largest agricultural region. With a population of 144,449 (2011), it is an important industrial center, while its port provides a bridge between Europe, the Middle East and Asia. Volos is one of the most industrialized provincial cities of Greece. Industry is intensely specialized in steel production and manufacturing. The port that lies upon ancient Iolkos was founded in 1893 and was the most significant element for the industrial development of the area. Today, Volos has the 3rd largest cargo port in Greece (after Piraeus and Thessaloniki), carrying agricultural and industrial products.

Fig. 6: The selected area in Volos.

Chryssoupolis, Kavala: Chrysoupolis is a town and a former municipality in the Kavala regional unit. It has an area of 245.181 km2, with 16.000 inhabitants. Nowadays, it is considered as a part of the Metropolitan area of Kavala. At the region there are mainly agricultural activities. Kavala exhibits mixed economy where almost all sectors of economic activity are represented. After the country’s industrialization, Kavala became a center of the tobacco industry in northern Greece. Oil deposits were found outside the city in the mid of 20th century and are currently exploited by an oil rig.

Fig.7: The selected area in Chryssopolis, Kavala.

For the calculation of the TCCFVI, as presented in eq. 5 and 6, values for all the indicators presented in table 1, have been collected from available online data sources, which are presented in table 2. It should be mentioned that from the available data sources only those that could guarantee a homogenous methodology for the calculation of the values have been preferred, therefore studies focusing only in one of the chosen areas have not been used, despite of the potential better accuracy of their values.
Table 2: Data sources used for the quantification of the TCCV
Indicator Abb. Units Data Sources
Subsystem: Natural (Hydro-geological)
Sea Level Rise SLR mm/yrwww.eea.euStorm Surge SS cm Krestenitis et al., 2015
# of Cyclones C Non-applicable for GreeceRiver Discharge RD m3/s Data for the rainfall intensity (i) have been taken from and the river discharge has been calculated according to the lecture notes “Hydraulic design and flood preventing technical works” Mamasis, 2014
Foreshore Slope FS %
Soil Subsidence SL m2 www.oikoskopio.grthe data are for soil erosion
Coastline CL km Subsystem: Socio-economic
Cultural Heritage CH S!/
Population close to coastline PCL people www.statistics.grGrowing Coastal Population GCP % Vulnerable PopulationVP % www.statistics.grAwareness and Preparedness AP –
Recovery Time RT days Not available data
Km of Drainage D km Data from the sewage company in each area
Subsystem: Administrative – Institutional
Flood Hazard Maps FHM –
Institutional Organisations IO Planning Zone UPZ % www.ypeka.grFlood Protection FP – 3 presents the values deriving from the data sources, mentioned in table 2, of the indicators that the subsystems of CCFVI consist. In cases where the indicator used in CCFVI was not applicable, it has been noted as N/A.

Table 3: Indicators’ values for each subsystem
AbbUnits ElefsinaThessalonikiRio-PatrasHeraklion VolosChryssoupolis-KavalaSubsystem: Natural (Hydro-geological)
SLR mm/yr2 4 3 4 1 4
SS cm 0,35 0,4 0,35 0,3 0,375 0,425
RD m3/s 388,78 481,27 329,04 933,08 437,55 56,17
FS % 7% 2% 8% 3% 8% 4%
SL m2 0 0,25 0,5 1 0 0
CL km 21 25,76 19 22,81 24,11 27,92
Subsystem: Socio-economicCH 3 14 1 18 7 0
S 3 10 4 2 1 0
PCL people 60.153 377.309 213.984 173.993 144.449 22.331
GCP % 3% -7% -1% 7% 0% -10%
VP % 58% 40% 75% 58% 50% 67%
AP – 6 19 7 5 4 6
RT days N/A N/A N/A N/A N/A N/A
D km N/A N/A N/A N/A N/A N/A
Subsystem: Administrative – InstitutionalFHM – N/A N/A N/A N/A N/A N/A
FP – N/A N/A N/A N/A N/A N/A
*where N/A refers to “Not applicable”
The real values presented in table 3 are normalised following the formula NVi=RViMaxi=1,nRVi (7), where RVi represents the real value of the indicator i and Maxi=1,nRVi represents the maximum real value from a set of n computed real values of the indicator i, where n is the number of spatial elements under consideration, six (6) for the needs of this article. Normalised indicators are subsequently used for TCCFVI calculations. Only for the normalisation of indicator GCP the formula NVi=X-XminXmax-Xmin(8) has been used due to the negative values. X represents the real value of indicator i, Xmin and Xmax represents the minimum and the maximum accordingly real value from a set of n computed real values of the indicator i, where n is the number of spatial elements under consideration, six (6) for the needs of this article.
Table 4: Indicators’ normalized values for each subsystem
Abb. Units ElefsinaThessalonikiRio-PatrasHeraklionVolosChryssoupolis-KavalaSubsystem: Natural (Hydro-geological)
SLR mm/yr0,50 1,00 0,75 1,00 0,25 1,00
SS cm 0,82 0,94 0,82 0,71 0,88 1,00
C 0,00 0,00 0,00 0,00 0,00  0,00
RD m3/s 0,42 0,52 0,35 1,00 0,47 0,09
FS % 0,80 0,25 1,00 0,33 1,00 0,50
SL m2 0,00 0,25 0,50 1,00 0,00 0,00
CL km 0,75 0,92 0,68 0,82 0,86 1,00
Subsystem: Socio-economicCH 0,17 0,78 0,06 1,00 0,39 0,00
S 0,30 1,00 0,40 0,20 0,10 0,00
PCL people 0,16 1,00 0,57 0,46 0,38 0,06
GCP % 0,73 0,00 0,64 1,00 0,68 0,62
VP % 0,78 0,54 1,00 0,78 0,67 0,89
AP – 0,32 1,00 0,37 0,26 0,21 0,32
RT days N/A N/A N/A N/A N/A N/A
D km N/A N/A N/A N/A N/A N/A
Subsystem: Administrative – InstitutionalFHM – N/A N/A N/A N/A N/A N/A
FP – N/A N/A N/A N/A N/A N/A
*where N/A refers to “Not applicable”
Applying the formulas described in equations (2), (3), (4) and (6) CCFVIHydro – Geological, CCFVISocio-economic, CCFVIAdministrative-Institutional and TCCFVI are calculated and the results are presented in table 5.

Table 5: CCFVI calculated results for each subsystem and TCCFVI
CCFVI ElefsinaThessalonikiRio-PatrasHeraklionVolosChryssoupolis-KavalaCCFVI Hydro geological0,68 0,80 0,85 1,00 0,71 0,74
CCFVI Socio-Economic 0,53 0,26 0,30 0,85 1,00 0,92
CCFVI Administrative – Institutional0,00 0,00 0,00 0,00 0,00 0,00
TCCFVI 0,40 0,35 0,38 0,62 0,57 0,55
The results are visualized in figures 8 and 9.

Fig. 8: CCFVI performance for each subsystem and TCCFVI performance

Fig. 9: Classification of the selected urban coastal areas according to their vulnerability, calculated based on the TCCFVI
The application of the CCFVI in the six (6) urban coastal areas in Greece shows that in comparison Thessaloniki is the most resilient area against coastal flooding, when Heraklion is the most vulnerable, as presented in fig. 9. By assessing each subsystem separately, Elefsina is the most resilient in the hydro-geological subsystem, when Heraklion is again the most vulnerable, followed by Rio, Patras. Unfortunately, Elefsina hasn’t been proved resilient enough, when in November 2017 a flash flood caused fatal impacts. Accordingly, in the socio-economic subsystem, Thessaloniki is the most resilient, when Volos is the most vulnerable, followed by Chryssoupolis, Kavala, which seems to be reasonable as Thessaloniki is the second largest city in Greece and concentrates more resources compared to the other cities. The administrative – institutional subsystem is not applicable in the selected areas, as they all belong to the same country, without any differentiation between the legislative and administrative tools and guidelines used in different Prefectures. The performance of each subsystem, as well as the performance of TCCFVI are presented in Fig. 8.

The scope of this paper is to apply the CCFVI methodology (Balica et al., 2009) in six (6) urban coastal areas in Greece that have industrial activities established in zones not dedicated for this purpose. Mostly, these industries remain in their initial position since the primary urbanisation/industrialization of the area. The challenges posed by climate change increase the importance of preparedness against coastal flooding risk, which is anticipated to be more strictly and regularly in the future because of climate change, unplanned rapid urbanization, change in land use pattern, and poor flood management. The application of the CCFVI in the above-mentioned areas provides useful insights in the opportunities, challenges, and constraints associated with the use of the CCFVI in coastal areas. First of all, it should be mentioned that CCFVI is a practical, easy to apply and trustworthy tool for getting an overview concerning the vulnerability of an area compared to others regarding coastal flooding. Its results are useful for policy formulation and public communication. But there are some considerations when the comparison is between areas that belong to the same country despite of the proposed applicability of the index in any spatial scale. In particular, the use of CCFVI in areas that belong to the same country weakens the impact of the administrative – institutional subsystem’s indicators, as the legislative and administrative tools and guidelines are the same for the whole country and therefore there is not any differentiation, especially in centrally planned countries, where the same policy is applied in all the regions of the country.

Moreover, in every subsystem there exist indicators that are not applicable or their values have not any contributing role in the calculation of the vulnerability of the studied areas. That depends on the hydro-geological, socioeconomic and administrative characteristics of the studied areas. For example, the indicator “number of cyclones” is not applicable for any of the areas as in Greece are an extremely rare meteorological phenomenon. An indicator like the “Exposure to waves” that calculates the difference of maximum wave runup in meters to ground level is better in terms of data availability and appropriateness. Smaller differences indicate higher vulnerability. Alternatively, the indicator “Wave overtopping” could also be chosen, as it calculates the overtopping volume of water from the coastal structures. In this case higher volumes indicate higher vulnerability. It should also be mentioned that in literature indicators like bathymetry, variations of the coastline, wave activity, and tidal range are usually preferred for the calculation of coastal vulnerability (Martínez-Graña et al., 2016).

Similarly, the indicator “Recovery Time” that indicates the time required for the area to recover from the coastal flood event, and to return to a functional operation mode. The longer the time needed, the higher the vulnerability of the area. This indicator should reflect the wealth of an area. Therefore, richer areas recover faster, due to the higher GDP/cap for example. But in the studied comparison all areas belong to the same country and although the areas might have different GDP/cap, this cannot affect the recovery time required, because in case of a natural hazard the country itself takes care of the damaged areas and therefore the GDP/cap of the country could only be indicative. Accordingly, the indicator “km of Drainage” cannot reflect the economic status of the area, as all the required infrastructure are subsidised by the country and it cannot indicate any difference in the vulnerability of the areas. As both indicators are combined with the wealth of the studied area, they could be replaced by indicators reflecting the economic status of the area, such as “Changes in the Gross Domestic Product (GDP)”, which is typically considered to be the most important measure of the economy’s current health, or “Unemployment Rate”, which measures the number of people looking for work as a percentage of the total labour force, or even “Consumer Price Index (CPI)” that reflects the increased cost of living. By measuring the costs of essential goods and service is how the CPI is calculated.In order to overcome difficulties in the application of CCFVI alternative indicators’ sets could be adopted, which would be more applicable or more relevant to the area or the spatial scale. Additionally, as a whole subsystem is not applicable for the areas chosen to be studied in this article, another subsystem that influences the vulnerability of the areas could be preferred. For example, in this case a component containing indicators that reflect the vulnerability of the area concerning the industrial activity within the urban network should be strongly considered to replace the administrative – institutional component, or even a subsystem related to environmental indicators could be applicable reflecting the vulnerability of the selected areas.
Moreover, as an alternative for the calculation of the selected areas’ vulnerability, a method like the one followed by the Environmental Vulnerability Index (EVI) could be adopted. The EVI method implies an average of fifty “smart indicators”, so called because of their ability to summarize environmental conditions and processes that are considered important. They are selected on the basis of their global applicability, ease of collection, ease of comprehension, and their ability to measure or be a proxy for a change with adverse consequences (Kaly, Pratt, and Mitchell 2004). Therefore, a set of indicators applicable, available and appropriate could be constructed, by combining indicators proposed by vulnerability calculation tools in order to succeed a representative result that would be useful in the hands of decision-makers.

It should also be mentioned that for the needs of this research the indicator “shelters” represents the number of hospitals and public healthcare services that exist in a range of 15km from the area’s centre per the extend of the area. The indicator “awareness and preparedness” is quantified only by using the number of the important past flood events that the selected area has experienced, because any educational and public awareness programmes aiming at helping the people living in coastal areas to understand the consequences and the restrictions of their actions towards coastal flood protection, and to be prepared for emergency situations are organised at national level and they do not reflect differences in the areas’ vulnerability.
Another point that is considered as important is the fact that the selected areas are very popular as touristic destinations and therefore the number of the visitors per year could be indicative for the area’s vulnerability. The higher number of visitors reflects higher vulnerability as in the area are more people unprepared in case of an emergency. Moreover, the resilience indicator “shelters” takes lower values which reflect higher vulnerability, because in the area exist less available shelters per capita.
Finally, it should be pointed out that the CCFVI methodology adopted for this article can only provide a primary estimation in order to lead the decision-making procedures to safer choices concerning the coastal flooding risk management policy. Further research is required to draw sounder conclusion, starting from weighting the indicators used and continuing with its validation and sensitivity analysis. As mentioned in relevant literature, validation of the conceptual and methodological construct of a composite index requires meaningful engagement with and significant input from stakeholders, experts on the geographic area or sector of interest, and experts on indicator and index design (Barnett et al., 2008). In cases when a more immediate weighting solution is required to strengthen the decision-making process objective weights could be calculated, deriving from CRITIC method for example (Diakoulaki et al., 1995), which is based on the quantification of two fundamental notions of Multi Criteria Decision Making (MCDM): the contrast intensity and the conflicting character of the evaluation criteria. The latter notion is of great importance in interfirm comparisons because the financial indices used are often highly correlated.

As vulnerability is considered one of the main components in flood risk management process, it is important to develop trustworthy tools for measuring it in order to bridge the gaps between the theoretical concepts of vulnerability and day-to-day decision making. Therefore, it is important to view vulnerability as a dynamic process. Within this process is important to adopt instruments that allow researchers to assess the past, current, and potential future areas and people at risk or vulnerable. The CCFVI methodology could be for the decision makers a useful tool in cases of an initial estimation of coastal flooding management problems, when corrective actions, such as adopting applicable indicators for the studying areas and adding important to the decision-making subsystem indicators, can be used.

Therefore, the creation of alternative subsystems that could replace non-applicable subsystems and sets of indicators that provide easy substitutes, when the appropriateness and the availability is an issue for the prechosen ones, should be further studied. As such substitutions can simplify the vulnerability calculation process. The alternatives provide better reflection of the resilience, as they are aligned to the selected area’s special characteristics and its scale. Therefore, such replacements, should be taken into consideration when CCFVI is applied. Finally, weighting the indicators used can lead to safer choices in decision making process. The adoption of weight for every indicator and every component that formulate the CCFVI, no matter the methodology that will be followed for the weighing procedure, should be preferred even for an initial estimation of the problem studied.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Adger W.N., Brooks, N., Bentham, G., Agnew, M., Eriksen, S., 2004, New indicators of vulnerability and adaptive capacity. Technical Report Number 7, Tyndall Centre for Climate Change Research. University of East Anglia, Norwich
Adger, W.N., Hughes, T.P., Folke, C., Carpenter, S.R., Rockstrom, J., 2005, Socialecological resilience to coastal disasters. Science 309 (5737), 1036–1039.

Balica, S. F., 2012, Applying the flood vulnerability index as a knowledge base for flood risk assessment, Ph.D Thessis, UNESCO-IHE Institute for Water Education (Netherlands); Delft University of Technology (Netherlands), Incl. bibl. (Dissertation submitted in fulfillment of the requirements of the Board for Doctorates of Delft University of Technology and of the Academic Board of the UNESCO-IHE Institute for Water Education for the Degree of Doctor). (Summary in Dutch), ISBN: 978-0-415-64157-9.

Balica, S.F., Wright, N.G, Meulen, V., 2012, A Flood Vulnerability Index for Coastal Cities and Its Use in Assessing Climate Change Impacts. Natural Hazards. 52. 10.1007/s11069-012-0234-1.

Balica, S.F., Douben, N., Wright, N.G., 2009, Flood vulnerability indices at varying spatial scales, Water Sci Technol, J 60(10):2571–2580, ISSN 0273-1223.

Bebbington, A., 2007, Social Movements and the Politicization of Chronic Poverty. Development and Change, 38: 793–818. doi:10.1111/j.1467-7660.2007. 00434.x
Birkmann, J., 2006, Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and Definitions. Measuring Vulnerability to Natural Hazards: towards disaster resilient societies, United Nations University Press, p. 9-54.

Birkmann, J., 2007, Risk and Vulnerability indicators at different scales: Applicability, usefulness and policy implications, Environmental Hazards, 7, 20-31.

Brooks, N., Adger, W.N., Kelly, P.M., 2005, The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation, Global Environmental Change, 15, 151-163.

Cardona, O.D., 2004, The need for rethinking the concepts of vulnerability and risk from a holistic perspective: a necessary review and criticism for effective risk management. In: Bankoff, G., Frerks, G., Hilhorst, D. (Eds.), Mapping Vulnerability: Disasters, Development and People, London, Earthscan Publishers.

Cardona, O.D., 2006, A system of indicators for disaster risk management in the Americas. Measuring Vulnerability to Natural Hazards—Towards Disaster Resilient Societies.

Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., Webb, J., 2008, A place-based model for understanding community resilience to natural disasters, Global Environmental Change, 18, p. 598-606.

Cutter, S.L., Boruff, B.J. and Shirley, W.L., 2003, Social Vulnerability to Environmental Hazards. Social Science Quarterly, 84, 242-261,

Diakoulaki, D., Mavrotas, G., Papayannakis, L., 1995, Determining objective weights in multiple criteria problems: The critic method, Computers & Operations Research 22 (7), 763-770, 1995. 404, 1995,, M., Chen, R.S., Deichmann, U., Lerner-Lam, A., Arnold, M., 2005. Natural Disaster Hotspots. A Global Risk Analysis. The World Bank, Hazard Management Unit, Washington, DC.

Dong, Z., Pan, Z., An, P., Wang, L., Zhang, J., He, D., Han, H., and Pan, F., 2015. A novel method for quantitatively evaluating agricultural vulnerability to climate change. Ecological Indicators. 48. 49–54. 10.1016/j.ecolind.2014.07.032.

Douben, K. J., 2006, Characteristics of River Floods and Flooding: A Global Overview, 1985-2003, Irrigation and Drainage, 55, 9-21., I., Castillo-Rodr?guez, J. T., Zechner, S., Jobstl, C., Perales-Momparler, S., and Petaccia, G., 2012, A quantitative flood risk analysis methodology for urban areas with integration of social research data, Natural Hazards and Earth System Sciences, 12, 2843–2863,

Fenster, M. S., and Dolan, R., 1996, Assessing the impact of tidal inlets on adjacent barrier island shorelines. J Coast Res 12:294–310.

Gallopin, G., and Stockholm Environment Institute, 1997, Branch points: Global scenarios and human choice, Stockholm: SEI.

IPCC, 2007, Climate change, impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York.

Kaly, U., Pratt, C. and Mitchell, J., 2004. The Environmental Vulnerability Index (EVI) 2004, Suva, Fiji: South Pacific Applied Geoscience Commission. SOPAC Tech. Rep. 384
Krestenitis, Y.N., Androulidakis, Y.S., Kombiadou, K.D., Makris, C.V., Baltikas, V.N., 2015, Storm surges in the Mediterranean Sea: Variability and trends under future climatic conditions, Dynamics of Atmospheres and Oceans, vol. 71, p. 56–82, DOI:
Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Hentenryck, P.V., Fowler, J., Cebrian, M., 2016. Rapid assessment of disaster damage using social media activity. Sci. Adv. 2, e1500779, DOI: 10.1126/sciadv.1500779
Lewis, J., 1999, Development in Disaster-prone Places: Studies of Vulnerability. Intermediate Technology Publications, London, UK.

Makgill, R. A., and Rennie, H. G., 2012, A Model for Integrated Coastal Management Legislation: A Principled Analysis of New Zealand’s Resource Management Act 1991 The International Journal of Marine and Coastal Law 27, 135–165, DOI: 10.1163/157180812X620667.

Martínez-Graña, A.?., Boski, T., Goy, J.L., Zazo, C., and Dabrio, C., 2016, Coastal-flood risk management in Central Algarve: Vulnerability and Flood Risk Indices (South Portugal). Ecological Indicators. Vol, 71. 302-316. 10.1016/j.ecolind.2016.07.021.

Mileti, D. and 136 contributing authors. 1999. Disasters by Design: A Reassessment of Natural Hazards in the United States, Joseph Henry Press, Washington, DC.

Nardo, N., Saiana, M., Saltelli, A., Tarantola, S., Hoffmann, A. and Giovannini, E., 2005, Handbook on constructing composite indicators: methodology and user guide, OECD Statistics Working Paper.

Peduzzi, P., 2006, The disaster risk index: overview of a quantitative approach. In: Birkmann, J. (Ed.), Measuring Vulnerability to Natural Hazards—Towards Disaster Resilient Societies, New York, United Nations University Press.

Pelling, M., 2004, Visions of Risk: A Review of International Indicators of Disaster Risk and its Management. UNDP—Bureau for Crisis Prevention and Recovery (BRCP), Geneva.

Prior, T., Roth, F., Maduz, L., and Scafetti, F., 2017, Mapping Social Vulnerability in Switzerland: A pilot study on Flooding in Zürich, Risk and Resilience Report, Center for Security Studies (CSS), ETH Zurich.
Schmidtlein, M. C., Deutsch, R., Piegorsch, W. W., and. Cutter, S. L., 2008, A Sensitivity Analysis of the Social Vulnerability Index, Risk Analysis 28 (4), 1099-1114, doi:10.1111/j.1539-6924.2008.01072. x.

UNEP (2002). GEO-3. Past, present and future perspectives‚ Earthscan‚ London. Link to PBL-website: Nations University, 2004, Two Billion Vulnerable To Floods By 2050; Number Expected To Double Or More In Two Generations, ScienceDaily, de Leon, J.C., 2006, Vulnerability – A Conceptual and Methodological Review, SOURCE – Publication Series of UNU-EHS, United Nations University.

Wamsler, C., 2008, Climate Change Impacts on Cities: Ignore, Mitigate or Adapt? Trialog, 97(2), 4-10. Water International, Volume 30, Number 1, Pages 58–68.

Wisner, B., Blaikie, P., Cannon, T., and Davis, I., 2004, At Risk: Natural Hazards, People’s Vulnerability and Disasters, 2nd ed. Routledge, London, UK.

Wu, F., Sun, Y., Sun, Z., Wu, S., Zhang, Q., 2017, Assessing agricultural system vulnerability to floods: A hybrid approach using energy and a landscape fragmentation index, Ecological Indicators,
Yohe, G., 2009, Toward an integrated framework derived from a risk-management approach to climate change: An editorial comment, Climatic Change, DOI 10.1007/s10584-009-9614-2.

Web References
European Environment Agency <>
WWF Hellas, web mapping application <>
Hellenic Statistical Authority <>
Ministry of culture and sports, Greece <>
Ministry of Health, Greece <>
Special Secretariat for Waters, Greece <>
National infrastructure for spatial information, Greece <>
Ministry of Environment and Energy, Greece <>

Post Author: admin