5.1.3.- Comprobación a deformación

Vulnerability assessments for coastal evolution analysis require a substantial amount of knowledge from different disciplines. Coastal vulnerability indices for vulnerability assessments are also disadvantaged by the lack of available data, coastal protection information and from the diversity of methods in use. When estimating risks, no method will identify the same hotspots (Hinkel 2008, Klein and Hinkel 2009). As a result, processes of quality data control, selection of indicators, weighting of variables, ranking of variables, construction of indices, interpretation, etc. are challenging (Moss et al., 2001; Schmidtlein et al., 2008).

It is very important to be able to compare with other studies at a national scale or if possible, internationally (Eakin and Luers, 2006). Besides, some show a lack of focus or theoretical and conceptual framework (Eriksen and Kelly, 2007; Wamsley et al 2015) that determine how robust they are towards validation.

At a European level, the MOVE project (2008) or EUROSION project (2004) made attempts to unify vulnerability methods and quality criteria requirements in order to create a general framework and methodology for assessment of vulnerability to natural hazards in Europe. Nonetheless, international standard methodologies have not yet been enforced. There is still a lack of coordination regarding approaches for coastal vulnerability assessments. If vulnerability assessments have nothing in common (approach, result, or data used, metrics), it is difficult to compare or distinguish trends (Wolters & Kuenzer, 2015).

Vulnerability assessments are scale-dependent in space and time (McLaughlin and cooper, 2010; Yoo et al., 2011) and these constraints should not be too broad or narrow (Pendleton el at., 2005; Dawson et al., 2009). Different tools and methods could address coastal vulnerability at different spatial and temporal scales depending on goals and context. Introducing these two factors is relevant as they determine the amount of

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time and the type of exposed coastal elements (susceptibility), and therefore their vulnerability (Bonetti et al., 2012).

CVI calculations are not predictive instruments and their information is limited by to short period of time or static (McLaughlin and Cooper, 2010). One of the current limitations of CVI and rank-based techniques applied to climate change is that they do not generally include climate impact model projections (Füssel, 2010). A coastal vulnerability index is useful in prioritising decisions. However, predictive tools are rather static and do not provide absolute predictions about the impacts of sea-level rise.

They locate the areas within a region most likely to be affected. The validity of the CVIs can be tested against observed shoreline changes within a particular time frame since variables are subjected to time-spatial restrictions (Abuodha and Woodroffe, 2006).

Coastal processes span from hours (tides) to millennia (tectonic) and threats (SLR) from short to long term. Hence it is difficult to differentiate between current and future vulnerability due to the lack of data on future projections of sensitivity and adaptive capacity (Schauser et al., 2010). Consequently, many studies are based on current vulnerability (less than 10 years) and do not account for future adaptation strategies (Masselink and Russell, 2013).

Uni-temporal studies are common. However, decision makers would benefit from multi-temporal assessments, evaluating what degree of vulnerability we are facing and identifying trends in this (Wolters and Kuenzer, 2015). When the hazard is supposed to change over time, the time horizon on which we are assessing vulnerability has to be specified (Wamsley et al., 2015). These vulnerability assessments could address current vulnerability (Wang et al., 2011a), or by introducing future scenarios (Thatcher et al., 2013). Temporal variability should be reflected by the variables used (Bonetti et al., 2012). Some studies introduce the barrier type to capture millennial scale trends of progradation or erosion (Abuodha and Woodroffe, 2010b).

Indices that consider large amounts of variability are more useful for long-term planning, and enhance resiliency. Sometimes some components of vulnerability reflect the current conditions (socioeconomic data) and others the future (sea-level projections).

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Economic conditions such as population, houses, and roads will change over time, but it is difficult to predict how these variables will change. If vulnerability analysis in one area is only based on current conditions it will somehow underestimate future vulnerability to sea-level rises (Wu et al., 2002); whereas if future predictions are included in CVI then the validity of the vulnerability map would be extended in time (Thatcher et al., 2013; Jimenez et al., 2017).

Several research studies have assessed the impacts of climate change on Mediterranean coasts at national to regional scale (Torresan et al., 2012; Jimenez et al., 2017) or local scale (Sanchez-Arcilla et al., 2011, 2016).

Indicator-based approaches constitute an efficient way to locate vulnerable areas at the local scale, which is the scale at which adaptation usually operates. Their metrics are developed for the purpose of their study and are consequently spatially scale-dependent (Wamsley et al., 2015; Wolters & Kuenzer, 2015).

Different scales respond to different purposes and priorities and therefore show different information. The spatial resolution increases at a local and regional scale; a greater level of detail (and less perspective) is required to distinguish between areas of vulnerability, and some information only becomes evident at that scale. Going to larger areas would also add information on some variables as perspective is gained. In addition, the gradient of a particular metric may differ depending on the scale of the analysis; some metrics might not be relevant at one scale but valuable at another. It could happen that some variables become obsolete at a particular resolution. Results cannot be easily compared across scales directly or sometimes from different variables from components at the same scale. However, index values can be normalised, so each value is relative to the full range of values for that scale (McLaughlin and Cooper, 2010).

If operating at national level, important information at the county level could be masked (Adger et al., 2004). Global scale methods do not apply directly to local areas, so local methods need to be adapted (Yoo et al., 2014). When comparing to wider areas the same metrics should apply (Hinkel, 2011). Therefore, studies are sometimes not comparable either internationally or with other areas nationally where similar CVI

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methods has not been employed. Thus, some authors advocate a national-scale approach (Abuodha and Woodroffe, 2006). A general view could be useful for general assessment e.g. implementing to assess help, distribute EU funds and allocate money per counties, and then refine the target and resources (ie: focusing on vulnerable structures) when higher resolution assessments becomes available. However other authors recommend operating at small scales and then aggregating data into a simplified larger scale as the other way around is not a possibility if high resolution data has not been compiled in the first place (McLaughlin and Cooper, 2010).