Who can you trust? Implications of institutional vulnerability in flood exposure along the Spanish Mediterranean coast

Environmental Science and Policy 76 (2017) 29–39

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Who can you trust? Implications of institutional vulnerability in flood exposure along the Spanish Mediterranean coast

MARK

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Francisco López-Martínez , Salvador Gil-Guirado, Alfredo Pérez-Morales Laboratorio de Cartografía y Análisis Geográfico Regional, Departamento de Geografía, Universidad de Murcia, 30001 Murcia, Spain

A R T I C L E I N F O

A B S T R A C T

Keywords: Floods Exposure Institutional vulnerability Spatial planning Flood-prone area

Over the last years, the Spanish Mediterranean coastal area has undergone significant increase in the number of floods and their consequential damages. However, according to climatic records, this trend is more related to an exposure multiplication than with the increase of extreme rainfall events. Within this framework, it is interesting to evaluate how the different local governments have influenced on urban growth in flood-prone areas through a deficient spatial planning, also to evaluate its possible relation with sociodemographic factors. The proposed methodology is based on two institutional vulnerability index related to the local spatial planning intersection with the hydrological modelling data, and a multiple linear regression of these index value and several sociodemographic parameters (population, tourists, housings, etc…). The final results demonstrate how local governments increase exposure and its relationship to population growth, foreign tourist and economic causes.

1. Introduction Recent decades have seen an increase in the intensity, frequency and economic losses related to floods in Europe (Barredo, 2007; Marchi et al., 2010). This situation has been accentuated in the Mediterranean region (Jonkman, 2005), especially in Spain and Italy (Llasat et al., 2010). In fact, in Spain floods are the natural hazard with the greatest territorial impact (480,000 ha are high probability floodable areas, SNCZI, 2015) and are responsible for great socio-economic losses (3400 million euros and 311 human deaths between 1995 and 2014, CCS, 2014). Some authors have suggested that the increase in the impact of floods is due more to socio-economic factors than to climate-related factors (Barredo et al., 2012; IPCC, 2012; Pérez et al., 2015a). In this context, the social factor involved in the risk equation established by Wisner et al. (2004), vulnerability, is particularly relevant with respect to changes in the flood risk, but is very difficult to measure because social and environmental issues are at stake (Gil-Guirado et al., 2016). Despite there are a multitude of definitions of vulnerability (e.g. Calvo, 2001; Wisner et al., 2004; Parker et al., 2009), we understand vulnerability “as the capacity of a society to deal with hazard” (IPPC, 2012). This meaning that vulnerability is an exclusively social concept (Fuchs, 2009) whose value is determined by a series of social factors (e.g. economy, politics, education, etc.), that vary considerably depending on the author (Appendix A). Among different vulnerability factors are a series of synergistic or antagonistic relationships, whose weighted

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consideration represent the final value of vulnerability (Wilches-Chaux, 1993). Given the difficulty of knowing to what extent each of the vulnerability factors influence final vulnerability (Calvo, 2001), a successful way of approaching their study is through the detailed analysis of each one. In this respect, there is no doubt concerning the role played by the different administrations responsible for guiding the capacity of adaptation to the environment hazards. Despite the main task of the different administrations is to limit the exposure of citizens to hazards (Giddens, 2002), there are some regions in where this situation is far from being the case (e.g. Thailand, Lebel et al., 2011; USA, Burby, 2006; the Netherlands, Jongman et al., 2014). By the way of example, the works of Fuchs et al. (2015, 2017) demonstrated how the current exposure level in the European Alps (Austria and Switzerland) does not depends exclusively on the environmental factor, because is also related to the economic activities and the different weaknesses of the established measures (structural and non-structural). Likewise, Pérez et al. (2015a) showed that, the increase of flood exposure in the Spanish Mediterranean coast, is positively correlated with periods of economic growth and legal permissiveness. According to this, we have to consider vulnerability as cause able to determine exposure (e.g. Adger, 2006; Parker et al., 2009; Wisner et al., 2004), instead of two independent values (e.g. Cardona et al., 2012; Smith and Petley, 2009). This vulnerability factor is called institutional vulnerability (hereinafter IV) (Raschky, 2008; Parker et al., 2009; Fuchs, 2009) and

Corresponding author. Present address: Laboratorio de Cartografía y Análisis Geográfico Regional, Departamento de Geografía, Universidad de Murcia, 30001 Murcia, Spain. E-mail addresses: fl[email protected] (F. López-Martínez), [email protected] (S. Gil-Guirado), [email protected] (A. Pérez-Morales).

http://dx.doi.org/10.1016/j.envsci.2017.06.004 Received 28 December 2016; Received in revised form 1 June 2017; Accepted 5 June 2017 1462-9011/ © 2017 Elsevier Ltd. All rights reserved.

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Currently this area has 6,312,997 inhabitants, 14% population of Spain (INE, 2015), that since the mid XX century has suffered a large influx of immigration from the interior of Spain. However the highest rates of population growth and real estate was recorded between 2000 and 2011 (INE, 2015), due to the arrival of many foreigners (the percentage of foreing population increased from 5.31% to 15.20%). From an hidrologyc point of view, the area has an irregular distribution of the size of the drainage basins where, with the exception of the large rivers present in the study area (Ebro, Júcar and Segura), the most common are ephemeral rivers (ramblas) responsible of flash floods after an intense rainfall (typical of the Mediterranean climate). However, this small water courses has had little social importance (Saurı́ et al., 2001). In Spain, despite the different spatial planning legislations (from the firs Land Law of 1956 to the currently in force Land Law of 2008) have gradually incorporated natural risk analysis, there is a lack of any state law framework concerning natural hazards. This legal loophole has led to a variety of FRM, each treating the problem from a different perspective (soil, water, environment, etc.) (Olcina, 2010a). This situation is very similar to other countries like Austria (Holub and Fuchs, 2009), where the lack of a national law has originated important differences between regions (Thaler et al., 2016). Conversely, other countries have developed a more responsible natural hazard management. For example, in France the Prevention of Predictable Natural Hazards Plan regulatory embeds the imposition of land use zoning and control measures in SP documents (Erdlenbruch et al., 2009). In the USA, the Federal Disaster Mitigation Act requires states to integrate hazard mitigation activities in land use planning or preparing standalone mitigation plans (Berke et al., 2012). Also, in the UK the Planning Policy Statements responsible of Flood Risk and Development considers flooding as mandatory in spatial planning (Pardoe et al., 2011). As regard preventive measures against the risk of flooding considered in Spain within SP, we can distinguish three periods: 1956–1998; 1998–2008; and 2008-future. The first period is characterized by an almost total lack of consideration of natural hazards in the law (Olcina, 2010b). The second one started with the land market liberalisation introduced by Land Law of 2008 and which resulted in the well known Spanish “housing boom”. This was the first time that natural hazards were considered in planning instruments, but by means of sectoral laws that failed to minimize the effects of floods (Pérez et al., 2015a). In the last period, the municipal plans adapted to the 2008 Land Law, where more restrictive construction measures concerning floods were established. However, as we mentioned above, the effect was not that expected, because the FRM adopted (mainly structural measures) have serious shortcomings and had not prevented the occupation of flood-prone areas, especially in the coastal municipalities of south-eastern Spain (Pérez et al., 2015b).

represents the sensitivity of public administrations to deal with hazards (Parker et al., 2009). Although from an economic point of view, organizations and institution are two facets of one and the same phenomenon (Commons, 1934), we understand institutions as “systems of established and prevalent social rules that structure social interactions” (Hodgson, 2006). However, organizations are a special kind of institution made up of groups of individuals bound together by some common purpose to achieve certain objectives, i.e. while institutions are the rules of the game the organizations are the players (North, 1994). In this regard, we define IV as “the inefficiency of the different authorities responsible for hazard management whose results imply an exposure increase on societies, i.e. amplifies hazard”. The IV involves institutions and organizations in charge of hazard management (e.g. governments, civil protection, warning systems, spatial planning) or related to it (e.g. risk communication, NGO’s, healthcare systems, education, research centres). IV is influenced by other vulnerability factors (Wilches-Chaux, 1993) and by several internal limitations (e.g. technical, legislative, staff) and external pressures (e.g. political, social, employment) where corruption is its greatest weakness (Wisner, 2000). Although there is no universally accepted methodology used by administrations to limit the risk of flooding, there is no doubt that spatial planning (SP) has a preventive role to play as non-structural measure of impact mitigation (Directive 2007/60/CE; Olcina, 2010b; Cardona et al., 2012). In this way, SP is a tool able to decrease the exposure to floodable areas to obtain a balanced development between the inhabitants and the managed space (Adger, 2006; Birkmann, 2006). In this sense, SP is more efficient than structural measures, because prevents the floodable areas occupation since the beginning and avoids the false sense of safety generated by technical means (Lane et al., 2011). However, despite the high degree of efficiency to mitigate the risk of flooding that SP is supposed to involve (Fleischhauer, 2006), SP has several limitations (Smith and Petley, 2009; Fuchs et al., 2015 Fuchs et al., 2015) and is affected by a series of obstacles that hinder its correct application (Sutanta et al., 2010). In this regard, in geographical areas such as the Spanish Mediterranean coast, huge economic incentives arising from land speculation have become an important vulnerability factor (Smith and Petley, 2009) that hinders the implementation of flood damage mitigation measures (Iglesias, 2007; Romero et al., 2012). The Spanish Mediterranean coastal area is one of the main tourist destinations in the world (WTO, 2016). More than 6 million people normally reside in this area and millions of tourists annually visit it (Boniface et al., 2006). This social dynamic, generated by “Sun and beach” tourism, has caused a tourist “boom” that multiplied the amount of buildings by six in only ten years (Sánchez, 2008). This concentration of people and buildings is related to a deficient SP which have not consider flood-prone areas and the adoption of engineering solutions as main flood risk management (FRM) (Pérez et al., 2015a; Saurı́ et al., 2001). As a consequence, this process has resulted in the so-called “coastalisation of risk” (Olcina, 2009). Thus, the objectives of this study are to: 1) quantify the IV to flood hazards 2) analyse and quantify the explanatory factors flooding exposure and 3) identify black spots where actual and potential IV levels compromise population security. To conduct the study, we selected as study area the municipalities of the Spanish Mediterranean coast, due to its great socio-economic importance and for being one of the areas most affected by floods in Europe (Schmidt-Thomé, 2006).

3. Data and methods To assess flood hazards and the institutional vulnerability (IV), we intersect by GIS the two sources that define exposure to the risk of flooding: spatial planning and flood extent area (Cardona et al., 2012). 3.1. Spatial planning (SP) In Spain, the central government can only dictate basic conditions, while the autonomous communities are responsible for the approval of norms for territorial planning and the municipalities for urban development. In this way, municipalities are officially responsible for the process of SP, however each Autonomous Community has its own criteria and denominations for both planning instruments and for categorizing the different municipal areas. According to these denominations, the municipalities classify in different categories all their territory and determine what use is applicable to each area.

2. Study area and legal framework The methodology was applied to all the Spanish Mediterranean coastal municipalities, from Águilas (Murcia) to Portbou (Girona) (Fig. 1). The study area covers 8358 km2 administratively divided into 137 municipalities, seven provinces (Alicante, Barcelona, Castellón, Gerona, Murcia, Tarragona and Valencia) and three autonomous communities (Catalonia, Comunidad Valenciana, and Región de Murcia). 30

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Fig. 1. Study area.

Among all, we consider T100 as reference for calculating IV because 1) is modelled for the total study area and 2) it covers a time scale that is adequate as regard planning instruments (medium flooding risk).

In this sense, the SP cartography of all the municipalities was provided by the respective organisms responsible for SP in each Autonomous Community: the Department of Land and Sustainability for Catalonia (GENCAT, 2015); the Valencia Institute of Cartography (TerraSIT, 2015) for Comunidad Valenciana; and the System of Territorial Information (SITMURCIA, 2015) for Región de Murcia. Due to the different land use denominations established by each Autonomous Community, we grouped it according to its constructivist characteristics in: (i) “Urban”, land already built on with the correct planning permission; (ii) “Urbanisable”, areas suitable for urban development depending on population growth and its needs; and (iii) “Non-urbanisable” land that is not suitable for urban development because of its particular characteristics (e.g. natural or cultural value, public infrastructures, etc.), because it is included in a protected area, or simply because it is deemed so by the planning authorities (Appendix B).

3.3. Institutional vulnerability assessment: methodological procedure The IV was calculated by a spatial intersection of T100 flood-prone areas with the different land categories of each municipality. This process quantifies the urban and urbanisable zones that may be affected by floods. “Urban zones” have been considered as subject to Present Institutional Vulnerability (hereinafter PIV) and “urbanisable zones” as subject to Future Institutional Vulnerability (hereinafter FIV). In other words, PIV shows the current percentage of constructed area in floodable zones, and FIV the percentage of future constructed area in floodable zones. In this sense, although we consider both aspects to be stable (e.g. Jongman et al., 2014; Pérez et al., 2015a) due to the lack of information, we understand that flood-prone areas where urbanization is permitted by current law reflect the FIV since there is no legal impediment to building on these exposed spaces. However, the projection of land consumption in each municipality is unknown and we can not determine when or if it will be occupied. The two above indices have been calculated for all the municipalities, Eqs. (1) and (2), and the results are specified at local, provincial and autonomous community levels.

3.2. Flooding extent area Since publication of the Floods Directive (2007/60/CE), most European countries have drawn up flood hazard maps as support tools for SP (Moel et al., 2009). Such maps show the surface of flood-prone areas after a specific or hypothetical flood event for a given return period (according to Directive, three as minimum). In Spain these flood hazard maps were adopted through the National System of Cartography of Flood-Prone Areas (SNCZI in its Spanish abbreviation). The SNCZI (2015) was drawn up by the Ministry of the Environment and Rural and Marine Medium for each river basin. However, for Catalonian river basins the flood prone areas were provided by the Catalonian Water Agency (ACA, 2015), which denominated the risk-prone zones as Flood-Prone Areas (ZI in Spanish). The ZI was made following a method that slightly differs (DTM resolution, return periods, hydrological and hydraulic model) with the SNCZI. Both the SNCZI and ZI have been modelled into five distinct hazard zones related with return periods (T) of 10, 50, 100 and 500 years.

n

∑ PIV =

Urban land in flood prone areasi

i =1

Flood prone areasi

× 100

(1)

n

∑ FIV =

Urbanisable land in flood prone areasi

i =1

Flood prone areasi

× 100

(2)

The results of both indices were combined into a new index 31

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community with the greatest PIV. The indicated results highlight a clear construction-linked gradient from north to south that broadly reflects the much more advanced urban development that occurred in Catalonia earlier than in the other two autonomous communities. This has determined the occupation of flood-prone areas that is much higher in urban areas, mainly due to residential tourism and demographic growth of a city like Barcelona (Morris and Dickinson, 1987). Of note is the fact that the highest PIV is that of the Barcelona municipality, because 100% of its flood-prone area is urbanized. Concerning regard the rest of the municipalities, they present an average PIV of 21.02% (s.d. = 23.28, median = 12.55), which reflects a serious current problem that affects a significant part of the urbanized space, where any solution will involve drastic and costly structural policies. On the other hand, regarding responsible municipalities, only 10 (7 belong to the Comunidad Valenciana) have been able to restrict or limit construction in flood-prone areas. In this sense, we should recognise the great efforts devoted by the regional administration in question to the preparation and implementation of a comprehensive plan against flash floods, PATRICOVA, which, while not reaching maximum efficiency, seems to have had some degree of success (Olcina, 2010b). The situation in terms of FIV seems to be a mirror image of all that has been commented above. In this case, the Región of Murcia has the highest FIV (Table 1B), followed by Comunidad Valenciana and Catalonia. While in Catalonia much of its area already have been built on, in the Región of Murcia and Comunidad Valenciana have tried to minimize this unevenness through a liberal land use policy which, according to the data, has permitted flood-prone areas to be classified as urbanisable. Amongst the causes that help to understand the magnitude of this phenomenon there are two very notable ones. Firstly, liberalization of the urbanisation process introduced by the Land Law of 1998 and some regional and municipal planning laws in the Mediterranean area (Jiménez, 2009). The impact of this has been reflected in an increase of developable land in flood-prone areas in the majority of municipalities within the study area. Indeed, only 14 municipalities (12.73%) of the 110 municipalities with flood hazard zones (FHZ) do not have FIV (see Fig. 2 and Appendix F of Supplementary material), the rest registering an average of 15.61% (s.d. = 17.03, median = 9.52). There are circumstances in some municipalities of the study area (for example, Torreblanca in Castellón) whereby the use of urban land has virtually exhausted this resource, and, what is worse, this increase has mostly

denominated Evolution of Institutional Vulnerability (hereinafter EIV), Eq. (3), which allows the variations in IV with time to be calculated. We also consider that EIV reflects the institutional sensitivity to nonstructural flood risk management (FRM) since the higher its value, the greater the exposure derived from the number of constructions permitted in the flood-prone areas.

EIV =

FIVi × 100 PIVi

(3)

The values calculated for the three indices were analyzed by a nonparametric test (Mann-Whitney U test and Kruskal-Wallis H test) or parametric test (Wilcoxon Signed-rank test and paired-t-test) to test significant differences (between municipalities, provinces and autonomous communities). All the tests were carried out by the RStudio, (2015) statistical software with α = 0.05 significance level. 3.4. Spatial-demographic causal relationships To understand the values of PIV and FIV, we made a multiple linear regression to analyze the link between the IV and one or more sociodemographic variables. All the variables used were extracted or calculated from the data provided by the Spanish national census of the population and housing from 1960 to 2011 (INE, 2015), except the mean price of housing, which was taken from the estate agent portal Fotocasa (2013). During the selection of variables some were discarded due to certain economic or time-related factors, or because of the difficulty of accessing them or unreliable data. As a result we finally used the variables contained in Appendix C in both models. 4. Results and discussion 4.1. Present Institutional Vulnerability (PIV) and Future Institutional Vulnerability (FIV) As regard PIV, while Catalonia and its three provinces have the highest values (Mann-Whitney U test and Kruskal-Wallis H test, pvalue < 0.05), the Región de Murcia has the lowest level (Table 1A). In addition, considering all the percentages at municipal level (Appendix E of Supplementary material), Catalonia is the autonomous

Table 1 Percentage PIV and FIV in the study area (S.A.) and at the Autonomous Community (AC) and provincial (Prov.) levels. Each panel represent the total flood-prone surface (A), the urban flood-prone surface (B) and the urbanisable flood-prone surface (C). A Study Area

% PIV (100 = S.A.)

AC

% PIV (100 = AC)

Province

% PIV (100 = Prov.)

% PIV (100 = S.A.)

Spanish Mediterranean coastal municipalities

6.16

Catalonia

1.18

Comunidad Valenciana

0.74

Región de Murcia

0.21

Barcelona Girona Tarragona Alicante Castellón Valencia Murcia

1.01 1.61 0.97 0.63 0.85 0.86 0.21

8.63 19.39 17.48 18.64 14.03 10.95 10.87

B Study Area

% FIV (100 = S.A.)

AC

% FIV (100 = AC)

Province

% FIV (100 = Prov.)

% FIV (100 = S.A.)

Spanish Mediterranean coastal municipalities

15.95

Catalonia

1.54

Comunidad Valenciana

1.78

Región de Murcia

1.80

Barcelona Girona Tarragona Alicante Castellón Valencia Murcia

1.30 2.03 1.34 1.10 2.23 2.76 1.80

4.30 9.43 9.34 12.63 14.18 13.50 36.61

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Fig. 2. Bar chart FHZ in each municipality (left) and percentage FHZ at different levels (right).

4.3. Spatial-demographic causal relationships

(92.97%) occurred at the expense of the FHZ. Secondly, the demand for housing on the part of European nationals, whose number grew in the study area by 477,325 between 2001 and 2011 (Pérez et al., 2015a).

The differences shown between the IV indices, point to a complex sociodemographic and socioeconomic dynamics that make it difficult to really understand what is happening. For this reason, the analysis has been simplified by means of a multiple linear regression model that combines demographic and economic variables. In the linear regression analysis, PIV and FIV were considered dependent variables and non-significant parameters were eliminated from the model according to their significance (Table 2). From the initial 23 independent variables (Appendix B), by isolating the most significant, we determined that PIV was related to 4 parameters and FIV with 6. As regard EIV, no model can be regarded as good (R2 = 0.1543) because planned urban development has followed no pattern related with the social and/or territorial dynamics of the municipality. This points to 1) the enormous influence over spatial planning of interests unconnected with the needs of society as a whole, and 2) how local managers have used land use norms as a tool designed to satisfy particular interests. As regards the models obtained for PIV and FIV, the F test was significant (p < 0.01) and the part of variance explained by the regression was, respectively, R2 = 0.5036 and R2 = 0.4076. Also, according to an influence analysis (Cook’s distance), several cases can be regarded as influential but not atypical (Cook’s distance > 1), and should be eliminated from the models. Concerning the model for the PIV, two groups of variables related to the internal dynamics of Spain can be established: movements of the population (TCP6011 and DP91) and housing (PMV0716 and TCV6001). Regarding this model, note the influence of the variable related with the evolution in the average house price (PMV0716), whose significance would supposedly be higher if we had access to the house values for the period known as the “Spanish housing bubble” (1998–2005) (Domínguez, 2009). As regards interpretation of the model, we observe a great amount of urban development in flood-prone areas as a result of a demographic growth attracted by a real estate boom of a speculative character (Bellod, 2007; Burriel, 2008). Joining the European Union in 1986 had a great effect on tourist towns such those in the study area, amplifying the effect (Harrison, 2006). The “planning” in many of these municipalities found little in the legislation then in force to limit urban expansion (there were no maps of floodprone areas), leading to the intensive occupation of the “first line” of the coast, even the mouths of ramblas. Later, even though the rate of urbanization slowed down in the 1980s, a fresh wave of housing

4.2. Evolution of institutional vulnerability (EIV) According to the EIV, the area built on in flood-prone zones will be significantly higher than today (Wilcoxon Signed-rank test, p-value > 0.05). As a result, the IV will increase in the study area as a whole (Appendix G of Supplementary material), since there will be an increase in the number of municipalities with a large area constructed in floodprone zones (Fig. 3). This situation has special importance in the Región de Murcia, where the FIV may exceed PIV by more than 850% (Fig. 2). Región de Murcia is followed at a distance by the Comunidad Valenciana and, lastly, Catalonia, where seems that the FRM have been correctly integrated into spatial planning and the occupation of flood-prone zones is minimal. However, as already mentioned, a large part of urban development has been in flood-prone areas, so that the FIV rate is low compared with the other autonomous communities. Despite the unequal growth in FIV in each autonomous community, if the results are considered at municipal level there is a significant increase in all three communities (Wilcoxon Signed-rank test and paired-t-test, p > 0.05). In general, the high to very high FIV values depicted for Catalonia seem to increase as we move southwards through Castellón, Alicante and Murcia. As regard the EIV, contrary to that seen for the provinces of Catalonia, where there are isolated increases in this parameter due to the high PIV (50% of Catalonian provinces shows a mean PIV value of 55%), the rest of the provinces shows a much higher value. This is especially the case in Murcia and Castellón, where, despite the low PIV, the FIV values represent a considerable increase in the exposed area. Unlike in the province of Valencia, plans against flooding have had little effect in these provinces (Olcina, 2010b; Pérez et al., 2015b). This situation takes on worrying proportions since, according to the mean rate of urban growth in flood-prone zones of the study area (8.9%/year) (Pérez et al., 2015a) and in the percentage of land destined for public use (24–33% of residential land since 2008 Land Law), in less than 20 years the built up area exposed flooding will increase by 50%, which may double in less than 40 years.

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Fig. 3. Weighted heatmap with the values of Present Institutional Vulnerability (PIV), Future Institutional Vulnerability (FIV) and its evolution (EIV). Due to the high values, EIV has been plotted on a logarithmic scale. To improve the graphical representation of the map, the coastal level has been extended 10 km inland.

interpretation seems to complete that for PIV, because, according to the result, the growth of flood-prone space declared urbanisable in each municipality seemed to reflect the clear interest of the local administrations to meet housing needs of the growing population arriving mostly from abroad. In this sense, far from organizing the new growth of towns in a way more adapted to the environment, the towns have continued growing in flood-prone land. In this regard, the variable (PURBINU), which has the highest positive coefficient, reflects how municipalities have not been able to integrate the variable flood-prone within their planning policy, repeating the mistakes that were made in

construction lasted until 2008 with much higher rates of construction then in the rest of the country. Regarding the FIV explanatory model, the most representative variables are related with the massive immigration associated with residential tourism reaching the Mediterranean coast. That is, those related with the arrival of a foreign population (DP11, PORPOBEXT11 and VPE9111), especially for reasons related with tourism (POBVIN14). In addition, this model also considers as explanatory variables the percentage of flood-prone urban surface (PURBINU), and even more, the average price of housing in recent years (PMV0716). The 34

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Table 2 Contributions of each parameter level to the PIV and FIV value. PIV Parameter

Estimate

Std. Error

P-value

Significancea

Intercept TCP6011 DP91 PMV0716 TCV6001

−20.7902 2.925e − 02 3.732e − 03 1.63e − 02 2.225e − 03

7.984919 0.010787 0.001169 0.005014 0.003128

< 0.05 < 0.01 < 0.01 < 0.01 < 0.05

* ** ** ** *

FIV Parameter

Estimate

Std. Error

P-value

Significancea

Intercept PURBINU DP11 PMV0716 POBVIN14 PORPOBEXT11 VPE9111

−36.32 0.9698 2.209e − 04 3.209e − 02 4.430e − 05 5.080e − 02 6.239e − 04

1.266e + 01 3.306e − 01 1.720e − 03 5.985e − 03 1.295e − 04 2.454e − 01

< 0.01 < 0.01 <1 < 0.001 <1 <1 <1

** **

a

***

Significance codes: P-value < 0.001 = «***», < 0.01 = «**», < 0.05 = «*», < 0.1 = «.», < 1 = « ».

delineating urban areas and including them within the flood-prone areas. In other words, the larger the urban zones in flood-prone areas, the more urbanisable area in the same. As regard the importance of each of the variables within the model, it can be concluded that: 1) for each increase of 31 €/m2 in the price of the housing, the FIV increases by 1%, 2) if the percentage of foreign population in 2011 were to double, on average there would be an increase of 5% in FIV, however if this occurred in the variation of foreign population between 1991 and 2011, the growth of FIV would be 6%, and 3) FIV has an identical response to the increase in floodable urban percentage.

the institutional vulnerability that characterizes this area is closely reflected in the historical frequency of flooding (Barriendos et al., 2014; Marchi et al., 2010). Third, the regression models obtained point to the close link between the PIV, FIV, and several sociodemographic variables. While the PIV is closely associated with the growth in population registered since the 1960s, the FIV is more related with the variation in the foreign population and the mean price of housing during the last property boom. In both cases the causal nexus of actual and future levels of IV can be identified as the speculation associated with previous and projected urban development levels (Raschky, 2008). In this sense, due to speculation is more related to an economic point of view than a social perspective, we can understand it as a kind of corruption which has more influence over spatial planning than sociodemographic variables. Fourth, the exclusive competences of the autonomous communities in Spatial Planning (similar to a federal structure), and the not harmonised national and regional laws have originated social inequalities in hazard management. In fact, the sectoral instruments for each autonomous community have not had the same effect. While in the Comunidad Valenciana the PATRICOVA has reduced by 7300 ha the flood-prone areas in Alicante (Olcina, 2010b), the equivalent instrument in the province of Murcia (Directrices y el Plan de Protección del Litoral) has had a minimal effect (Pérez et al., 2015b). In Catalonia, the reduction in exposed areas can be explained by the improved sensitivity of the respective legislation (Olcina et al., 2016; Serra-Llobet et al., 2016) although, according to Saurı́ et al. (2001), the management of flood risks by public administrations continues to centre on structural actions with low effectiveness as regards the minimization of economic losses. In accordance with these scenarios, it is hard for citizens to trust different authorities responsible for hazard management, especially administrations, which have demonstrated their inefficiency when it comes to limiting negative externalities that economic growth causes in society. In this context, self-protection against flood risk becomes a defense strategy to deal with the institutional neglect.

5. Conclusions From the findings, we can draw four important conclusions. First, although previous studies have evaluated the exposure to flooding (e.g. Jongman et al., 2012; Pérez et al., 2015a), there is still no high resolution methodology that permits evaluation of IV to floods. In this respect, this work introduces a new approach that uses the different local spatial planning instruments to solve the problems related with: 1) knowing which are has been authorized to be constructed by different local stakeholders and which not according to the land registry services (Jongman et al., 2014; Pérez et al., 2015a); 2) defining future previsions of growth and not only present growth (Feyen et al., 2012; Rojas et al., 2013); and 3) eliminating the problems of scale and precision that working at supranational levels (e.g. Corine Land Cover) involves (Jongman et al., 2012). Besides solving the above problems, the method provides a double improvement over other by 1) quantifying present and potential IV of the study area, and 2) identifying how IV influences flooding exposure. Second, the results show that territorial importance of floods has not been given due consideration, because current spatial planning has increased flooding exposure by over 250%. This situation reinforces the findings of Barredo et al. (2012) and Pérez et al. (2015a), who highlight that socio-economics factors amplify hazards in greater extent than climate-related factors. This situation will be especially important in the Región de Murcia, which has the highest FIV values. However, currently it is important to emphasise that the autonomous community of Catalonia and its three provinces have the highest PIV values. Indeed,

Acknowledgements The authors wish to thank the two anonymous reviewers for their helpful comments.

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Appendix A. Different vulnerability dimensions.

Source

Vulnerability dimensions

Short explanation

Wilches-Chaux (1993)

Environmental Physical Economic Social Political Technical Ideological Cultural Educational Ecological Institutional

Environmental limits (temperature, humidity, pressure, etc.) between which life is possible Spatial location and presence of anti-risk measures. Income levels. Internal cohesion level of a community. Autonomy to make decisions. Technological development of a society. Conception of the environment around us. Particular characteristics of society. Disaster Information Available. Self-adjustments of the planet to compensate for the effects of human beings. Breaking the connection between institutions, environment and needs of citizens.

Parker et al. (2009) Physical Systemic Social Economic Territorial Institutional Organizational Cultural Calvo, (1997)

Potential damage to structures, buildings, infrastructures, properties or support. Where and how an event might propagate through systems and susceptibility to an inability to function. Related to different levels of society. Loss of economic assets and productivity. Implies a unit of space and territoriality. Potential consequences of the critical shortcomings of institutions and institutional arrangements. Presence of social organizations. Loss of indigenous beliefs, customs, related artefacts and ways of life.

Economic conditions Social cohesion Law and political framework Technical means Cultural-educational “Media impact”

Income levels for dealing with disasters. Presence of organizations. Existence of laws.

Smith and Petley (2009)

Economic Social Political Environmental Geographical

Incomes and level of services. Personal characteristics. Existence of a centralized government. Unsustainable natural resource management. Distance to aid areas.

Fuchs (2009)

Structural Economic Institutional Social

Degree of loss resulting from the impact of a certain event on the elements at risk. Ability to recover exposed assets and values. Political system and related institutional structures. Personal characteristic of people.

IPPC (2012)

Environmental Social Economic

Related to natural system (vulnerability,impacts, mechanism and responses). Societal organization and collective aspects. Inability of affected people, communities, businesses, and governments to absorb or cushion the damage.

Wisner et al. (2004) Root causes Dynamic pressures Unsafe conditions

Defence of infrastructures. How much information people have. How disasters are treated in the media.

An interrelated set of widespread and general processes within a society and the world economy. Processes and activities that ‘translate’ the effects of root causes both temporally and spatially into unsafe conditions. Specific forms in which the vulnerability of a population is expressed in time and space in conjunction with a hazard.

Cutter et al. (2003) Byophisical Social

Areas exposed to hazard. Product of social inequalities, governs and their ability to respond.

Lebel et al. (2011)

Degree to which the system is affected by disturbance or stress. Short-term measures and resilience.

Sensitivity Response capacity

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Appendix B. Type and surface area of the different classes of land in Spanish Mediterranean coastal municipalities after classification.

Autonomous Community

Original category

Final category

Area (ha)

%

Catalonia

Urban: consolidated Urban: not consolidated Urbanisable: delimited Urbanisable not delimited Non urbanisable

Urban

39,370.80

18.27

Urbanisable

14,751.06

6.85

Non Urbanisable

161,375.41

74.89

Urban Historical Urbanisable Non urbanisable

Urban

35,124.56

10.79

Urbanisable Non Urbanisable

36,330.36 254,207.55

11.16 78.06

Urban Urban: consolidated Urban not consolidated Rural centre Urbanisable Urbanisable: sectored Urbanisable: not sectored Urbanisable: not sectored in any specialised way Urbanisable: programmed Urbanisable:not programmed Urbanisable: suitable for urbandevelopment Non urbanisable Non urbanisable: unsuitable Non urbanisable: protected Specific Protection General System

Urban

6327.25

2.15

Urbanisable

36,732.05

12.47

Non urbanisable

251.595.24

85.39

Comunidad Valenciana

Región de Murcia

Appendix C. Variables used in the linear regression.

Category

Variable name

Short description

Measurement

Independent variables

PIV FIV EIV

Present Institutional Vulnerability Future Institutional Vulnerability Evolution of Institutional Vulnerability

% % %

Constructive

PURBINU

Percentage of “Urban”land in flood-prone area: “Urban” land surface in flood-prone area with % regard to total “Urban” land in each municipality. Percentage of “Urbanisable”land in flood-prone area: “Urbanisable” land surface in flood-prone % area with regard to total “Urbanisable” land in each municipality.

PURBNZIN Social

POB11 TCP6001

DP91

Population in 2011: Number of inhabitants registered in each municipality in 2011 Population growth rate 1960–2001: Variation of the population census at the municipal level in 1960 compared to 2001. Population growth rate 960–2011: Variation of the population census at the municipal level in 1960 compared to 2011. Population density in 1991: Number of inhabitants counted in 1991 by m2 in municipality.

DP11

Population density in 2011: Number of inhabitants counted in 2011 by m2 in municipality.

VARDP

Variation in population density: Difference in number of inhabitants between 1991 and 2011

TCP6011

VARDPP POBVIN14

Variation in population density (%): % growth in population between 1991 and 2011. Population with some link to municipality: Number of non-residents who spend more than 14 nights in the municipality. According to National Institute of Statistics (INE), these people do not work or study in the municipality POBEXT91 Foreign population in 1991: Number of foreign inhabitants recorded in municipality in 1991. IPOBEXT9111 Increase in foreign population 1991–2011 VPE9111 Variation in foreign population 1991–2011 (%) POBEXT11 Foreign population 2011: Number of foreign inhabitants recorded in 2011. PORPOBEXT11 Percentage of foreign population: Number of foreign inhabitants recorded at municipal level as 37

N° personas % % Inhabitants/ m2 Inhabitants/ m2 Inhabitants/ m2 % N° people

N° people N° people % N° people %

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percentage of total number of inhabitants in 2011. Housing

TCV6001 TCV6011 VV11 PORVV11 VS11 PORVS11

PMV0716 VIV2011

Growth in number of houses 1960–2001: Variation in number of houses recorded at municipal level between 1960 and 2001. Growth in number of houses 1960–2011: Variation in number of houses recorded at municipal level between 1960 and 2011. Empty houses 2011: Number of empty houses recorded empty in 2011. Percentage of empty houses 2011: Number of empty houses at municipal level recorded as being empty in 2011 as percentage of total number of houses. Second homes 2011: Number of houses at municipal level recorded as not constituting habitual residence of one or more persons. Percentage of second homes 2011: Number of houses at municipal level recorded as not constituting habitual residence of one or more persons expressed as percentage of total number of houses. Mean cost of housing 2007–2016: Mean cost at municipal level of houses (per m2). NUMBER OF HOUSES 2011: Number of houses registered at municipal level in 2011.

% % N° houses % N° houses %

€/m2 N° houses

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.envsci.2017.06.004.

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