Manuscript prepared for Nat. Hazards Earth Syst. Sci.with version 5.0 of the LATEX class copernicus.cls.Date: 11 December 2015

Regional prioritisation of flood risk in mountainous areasM. C. Rogelis1, M. Werner1,2, N. Obregon3, and N. Wright4

1UNESCO-IHE, PO Box 3015, 2601DA Delft, The Netherlands2Deltares, PO Box 177, 2600MH Delft, The Netherlands3Universidad Javeriana, KR 7 No 40-62, Bogota, Colombia4School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK

Correspondence to: Maria Carolina Rogelis(c.rogelisprada@unesco-ihe.org)

Abstract. In this paper a method to identify mountainouswatersheds with the highest flood damage potential at the re-gional level is proposed. Through this, the watersheds to besubjected to more detailed risk studies can be prioritised inorder to establish appropriate flood risk management strate-5

gies. The prioritisation is carried out through an index com-posed of a qualitative indicator of vulnerability and a qualita-tive flash flood/debris flow susceptibility indicator. At the re-gional level vulnerability was assessed on the basis of a prin-cipal component analysis carried out with variables recog-10

nised in literature to contribute to vulnerability, using wa-tersheds as the unit of analysis. The area exposed was ob-tained from a simplified flood extent analysis at the regionallevel, which provided a mask where vulnerability variableswere extracted. The vulnerability indicator obtained from the15

principal component analysis was combined with an exist-ing susceptibility indicator, thus providing an index that al-lows the watersheds to be prioritised in support of flood riskmanagement at regional level. Results show that the com-ponents of vulnerability can be expressed in terms of three20

constituent indicators; (i) socio-economic fragility, which iscomposed of demography and lack of well-being; (ii) lack ofresilience and coping capacity, which is composed of lackof education, lack of preparedness and response capacity ,lack of rescue capacity, cohesiveness of the community; and25

(iii) physical exposure, which is composed of exposed in-frastructure and exposed population. A sensitivity analysisshows that the classification of vulnerability is robust for wa-tersheds with low and high values of the vulnerability indi-cator, while some watersheds with intermediate values of the30

indicator are sensitive to shifting between medium and highvulnerability.

1 Introduction

Flood risk represents the probability of negative conse-35

quences due to floods and emerges from the convolution offlood hazard and flood vulnerability (Schanze et al., 2006).Assessing flood risk can be carried out at national, regionalor local level (IWR, 2011), with the regional scale aimingat contributing to regional flood risk management policy and40

planning. Approaches used to assess flood risk vary widely.These include the assessment of hazard using model-basedhazard analyses and combining these with damage estima-tions to derive a representation of risk (Liu et al., 2014; Suand Kang, 2005), as well as indicator-based analyses that fo-45

cus on the assessment of vulnerability through composite in-dices (Chen et al., 2014; Safaripour et al., 2012; Greiving,2006). The resulting levels of risk obtained may subsequentlybe used to obtain grades of the risk categories (e.g. high,medium and low) that allow prioritisation, or ranking of areas50

for implementation of flood risk reduction measures, such asflood warning systems and guiding preparations for disasterprevention and response (Chen et al., 2014).

A risk analysis consists of an assessment of the hazard aswell as an analysis of the elements at risk. These two as-55

pects are linked via damage functions or loss models, whichquantitatively describe how hazard characteristics affect spe-cific elements at risk. This kind of damage or loss mod-elling, typically provides an estimate of the expected mone-tary losses (Seifert et al., 2009; Luna et al., 2014; van Westen60

et al., 2014; Mazzorana et al., 2012). However, more holisticapproaches go further, incorporating social, economic, cul-tural, institutional and educational aspects, and their inter-dependence (Fuchs, 2009). In most cases these are the un-derlying causes of the potential physical damage (Cardona,65

2003; Cardona et al., 2012; Birkmann et al., 2014). A holis-

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tic approach provides crucial information that supplementsflood risk assessments, informing decision makers on theparticular causes of significant losses from a given vulner-able group and providing tools to improve the social capac-70

ities of flood victims (Nkwunonwo et al., 2015). The needto include social, economic and environmental factors, aswell as physical in vulnerability assessments, is incorporatedin the Hyogo Framework for Action and emphasized in theSendai Framework for Disaster Risk Reduction 2015-2030,75

which establishes as a priority the need to understand disasterrisks in all its dimensions (United Nations General Assem-bly, 2015). However, the multi-dimensional nature of vulner-ability has been addressed by few studies (Papathoma-Kohleet al., 2011).80

The quantification of the physical dimension of vulner-ability can be carried out through empirical and analyticalmethods (Sterlacchini et al., 2014). However, when the mul-tiple dimensions of vulnerability are taken into account, chal-lenges arise in the measurement of aspects of vulnerability85

that can not be easily quantified. Birkmann (2006) suggeststhat indicators and indices can be used to measure vulnerabil-ity from a comprehensive and multidisciplinary perspective,capturing both direct physical impacts (exposure and suscep-tibility), and indirect impacts (socio-economic fragility and90

lack of resilience). The importance of indicators is rooted intheir potential use for risk management since they are usefultools for: (i) identifying and monitoring vulnerability overtime and space; (ii) developing an improved understanding ofthe processes underlying vulnerability, (iii) developing and95

prioritising strategies to reduce vulnerability; and for (iv) de-termining the effectiveness of those strategies (Rygel et al.,2006). However, developing, testing and implementing indi-cators to capture the complexity of vulnerability remains achallenge.100

The use of indices for vulnerability assessment has beenadopted by several authors, for example, Balica et al. (2012)describe the use of a Flood Vulnerability Index (FVI), anindicator-based methodology that aims to identify hotspotsrelated to flood events in different regions of the world.105

Muller et al. (2011) used indicators derived from geodata andcensus data to analyse the vulnerability to floods in a denseurban setting in Chile. A similar approach was followed byBarroca et al. (2006), organising the choice of vulnerabilityindicators and the integration from the point of view of var-110

ious stakeholders into a software tool. Cutter et al. (2003)constructed an index of social vulnerability to environmentalhazards at county-level for the United States. However, sev-eral aspects of the development of these indicators continueto demand research efforts, including: the selection of appro-115

priate variables that are capable of representing the sourcesof vulnerability in the specific study area; the determinationof the importance of each indicator; the availability of datato analyse and assess the indicators; the limitations in thescale of the analysis (geographic unit and timeframe); and120

the validation of the results (Muller et al., 2011). Since, no

variable has yet been identified against which to fully vali-date vulnerability indicators, an alternative approach to as-sess the robustness of indices is to identify the sensitivity ofhow changes in the construction of the index may lead to125

changes in the outcome (Schmidtlein et al., 2008).Vulnerability is closely tied to natural and man made en-

vironmental degradation at urban and rural levels (Cardona,2003; UNEP, 2003). At the same time the intensity or re-currence of flood hazard events can be partly determined by130

environmental degradation and human intervention in natu-ral ecosystems (Cardona et al., 2012). This implies that hu-man actions on the environment determine the constructionof risk, influencing the exposure and vulnerability as well asenhancing or reducing hazard. For example, the construction135

of a bridge can increase flood hazard upstream by narrowingthe width of the channel, increasing the resistance to flow andtherefore resulting in higher water levels that may inundate alarger area upstream.

The interaction between flood hazard and vulnerability is140

explored in small watersheds in a mountainous environment,where human-environment interactions that influence risklevels take place in a limited area. The hydrological responseof these watersheds is sensitive to anthropogenic interven-tions, such as land use change (Seethapathi et al., 2008).145

The consequence of the interaction between hazard andvulnerability in such small watersheds is that those at risk offlooding themselves play a crucial role in the processes thatenhance hazard, through modification of the natural envi-ronment. Unplanned urbanization, characterized by a lack of150

adequate infrastructure and socioeconomic issues (both con-tributors to vulnerability) may also result in environmentaldegradation, which increases the intensity of natural hazards(UNISDR, 2004). In the case of floods, such environmentaldegradation may lead to an increase in peak discharges, flood155

frequency and sediment load.In this paper a method to identify montane watersheds

with the highest flood damage potential at the regional levelis proposed. Through this, the watersheds to be subjectedto more detailed risk studies can be prioritised in order to160

establish appropriate flood risk management strategies. Themethod is demonstrated in the montane watersheds that sur-round the city of Bogota (Colombia), where floods typicallyoccur as flash floods and debris flows.

The prioritisation is carried out through an index com-165

posed of a qualitative indicator of vulnerability and a qual-itative indicator of the susceptibility of the watersheds to theoccurrence of flash floods/debris flows. Vulnerability is as-sessed through application of an indicator system that con-siders social, economic and physical aspects that are derived170

from the available data in the study area. This is subsequentlycombined with an indicator of flash flood/debris flow sus-ceptibility that is based on morphometry and land cover, andwas applied to the same area in a previous study (Rogelisand Werner, 2013). In the context of the flash flood/debris175

flow susceptibility indicator, susceptibility is considered as

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the spatial component of the hazard assessment, showing thedifferent likelihoods that flash floods and debris flow occurin the watersheds. In contrast, risk is defined as the combi-nation of the probability of an event and its negative conse-180

quences (UNISDR, 2009). The priority index can be consid-ered a proxy for risk, identifying potential for negative con-sequences but not including probability estimations.

The paper is structured as follows: (i) Section 2 reviewsthe conceptual definition of vulnerability as the foundation of185

the paper; (ii) Section 3 describes the study area, and the dataand methodology used; (iii) Section 4 presents the results ofthe analysis. This includes the construction of the indicatorsand the corresponding sensitivity analysis, as well as the pri-oritisation of watersheds; (iv) Section 5 interprets the results190

that lead to the final prioritisation; (v) The conclusions aresummarised in Section 6.

2 Conceptualization of Vulnerability

Several concepts of vulnerability can be identified, and thereis not a universal definition of this term (Thieken et al., 2006;195

Birkmann, 2006). Birkmann (2006) distinguishes at least sixdifferent schools of thinking regarding the conceptual andanalytical frameworks on how to systematise vulnerability.In these, the concept of exposure and its relation with vul-nerability, the inclusion of the coping capacity as part of vul-200

nerability, the differentiation between hazard dependent andhazard independent characteristics of vulnerability play animportant role. (Sterlacchini et al., 2014) identifies at leasttwo different perspectives: (i) one related to an engineeringand natural science overview; and (ii) a second one related to205

a social science approach.With relation to the first perspective (i), vulnerability is de-

fined as the expected degree of loss for an element at risk,occurring due to the impact of a defined hazardous event(Varnes, 1984; Fuchs, 2009; Holub et al., 2012). The rela-210

tionship between impact intensity and degree of loss is com-monly expressed in terms of a vulnerability curve or vulner-ability function (Totschnig and Fuchs, 2013), although alsosemi-quantitative and qualitative methods exist (Totschnigand Fuchs, 2013; Fuchs et al., 2007; Jakob et al., 2012;215

Kappes et al., 2012). The intensity criteria of torrent (steepstream) processes, encompassing clear water, hyperconcen-trated and debris flows, has been considered in terms of im-pact forces (Holub et al., 2012; Quan Luna et al., 2011; Huet al., 2012); deposit height (Mazzorana et al., 2012; Fuchs220

et al., 2012, 2007; Akbas et al., 2009; Totschnig et al., 2011;Lo et al., 2012; Papathoma-Kohle et al., 2012; Totschnigand Fuchs, 2013); kinematic viscosity (Quan Luna et al.,2011; Totschnig et al., 2011), flow depth (Jakob et al., 2013;Tsao et al., 2010; Totschnig and Fuchs, 2013); flow veloc-225

ity times flow depth (Totschnig and Fuchs, 2013); and ve-locity squared times flow depth (Jakob et al., 2012). Dif-ferent types of elements at risk will show different levels

of damage given the same intensity of hazard (Jha et al.,2012; Albano et al., 2014; Liu et al., 2014), therefore vul-230

nerability curves are developed for a particular type of ex-posed element (such as construction type, building dimen-sions or road access conditions). A limited number of vul-nerability curves for torrent processes have been proposed,and the efforts have been mainly oriented to residential build-235

ings (Totschnig and Fuchs, 2013). Since it can be difficultto extrapolate data gathered from place to place to differentbuilding types and contents (Papathoma-Kohle et al., 2011),different curves should be created for different geographicalareas and then applied to limited and relatively homogeneous240

regions (Luino et al., 2009; Jonkman et al., 2008; Fuchs et al.,2007) .

Regarding the second perspective (ii), social sciences de-fine vulnerability as the pre-event, inherent characteristics orqualities of social systems that create the potential for harm245

(Cutter et al., 2008). This definition is focused on the char-acteristics of a person or group and their situation than influ-ence their capacity to anticipate, cope with, resist and recoverfrom the impact of a hazard (Wisner et al., 2003). Social andplace inequalities are recognized as influencing vulnerability250

(Cutter et al., 2003). The term livelihood is highlighted andused to develop models of access to resources, like money,information, cultural inheritance or social networks, influ-encing people’s vulnerability (Hufschmidt et al., 2005).

Given the different perspectives of vulnerability it be-255

comes apparent that only by a multidimensional approach,the overall aim of reducing natural hazards risk can beachieved (Fuchs and Holub, 2012). Fuchs (2009) identifies astructural (physical) dimension of vulnerability that is com-plemented by economic, institutional and societal dimen-260

sions. In addition to these, Sterlacchini et al. (2014) identifya political dimension. Birkmann et al. (2014) and Birkmannet al. (2013) identify exposure, fragility and lack of resilienceas key causal factors of vulnerability, as well as physical, so-cial, ecological, economic, cultural and institutional dimen-265

sions.In this study, physical exposure (hard risk and considered

to be hazard dependent), socioeconomic fragility (soft riskand considered to be not hazard dependent) and lack of re-silience and coping capacity (soft risk and is mainly not haz-270

ard dependent) (Cardona, 2001) are used to group the vari-ables that determine vulnerability in the study area. In thispaper, the risk perception and the existence of a flood earlywarning, which are hazard dependent, are considered as as-pects influencing resilience since they influence the hazard275

knowledge of the communities at risk and the level of or-ganization to cope with floods. An analysis of physical vul-nerability through vulnerability curves is not incorporated,instead the expected degree of loss is assessed qualitativelythrough the consideration of physical exposure and factors280

that amplify the loss (socioeconomic fragility and lack of re-silience). This means the expected degree of loss depends on

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the extent of the flash floods/debris flows, and not on the in-tensity of those events.

The terminology and definitions that are used in this study285

are as follows:

– Vulnerability: propensity of exposed elements such asphysical or capital assets, as well as human beings andtheir livelihoods, to experience harm and suffer damageand loss when impacted by a single or compound hazard290

events (Birkmann et al., 2014).

– Exposure: people, property, systems, or other elementspresent in hazard zones that are thereby subject to po-tential losses (UNISDR, 2009).

– Fragility: predisposition of elements at risk to suffer295

harm (Birkmann et al., 2014).

– Lack of resilience and coping capacity: limited capaci-ties to cope or to recover in the face of adverse conse-quences (Birkmann et al., 2014).

3 Methods and Data300

3.1 Study Area

Bogota is the capital city of Colombia with 7 million inhab-itants and an urban area of approximately 385 km2. The cityis located on a plateau at an elevation of 2640 meters abovesea level and is surrounded by mountains from where several305

creeks drain to the Tunjuelo, Fucha and Juan Amarillo rivers.These rivers flow towards the Bogota River. Precipitation inthe city is characterised by a bimodal regime with mean an-nual precipitation ranging from 600 mm to 1200 mm (Bernalet al., 2007).310

Despite its economic output and growing character as aglobal city, Bogota suffers from social and economic inequal-ities, lack of affordable housing, and overcrowding. Statisticsindicate that there has been a significant growth in the pop-ulation, which also demonstrates the process of urban im-315

migration that the whole country is suffering not only dueto industrialization processes, but also due to violence andpoverty. This disorganised urbanisation process has pushedinformal settlers to build their homes in highly unstablezones and areas that can be subjected to inundation. Eigh-320

teen percent of the urban area has been occupied by infor-mal constructions, housing almost 1,400,000 persons. Thisis some 22% of the urban population of Bogota (Pacific Dis-aster Center, 2006).

Between 1951 and 1982, the lower (northern) part of the325

Tunjuelo basin (see Figure 1) was the most important areafor urban development in the city, being settled by the poor-est population of Bogota (Osorio, 2007). This growth hasbeen characterised by informality and lack of planning. Thischange in the land use caused loss of vegetation and erosion,330

which enhanced flood hazard (Osorio, 2007).

The urban development of the watersheds located in thehills to the east of Bogota (see Figure 1) has a different char-acteristic to that of the Tunjuelo basin. Not only has this takenplace through both informal settlements, but also includes335

exclusive residential developments (Buendıa, 2013). In addi-tion, protected forests cover most of the upper watersheds.

In this analysis the watersheds located in mountainous ter-rain that drain into the main stream of the Tunjuelo basin, aswell as the watersheds in the Eastern Hills were considered.340

The remaining part of the urban area of the city covers anarea that is predominantly flat, and is not considered in thisstudy. Table 1 shows the number of watersheds in the studyarea, as well as the most recent and severe flood events thathave been recorded.345

3.2 Methodology

The prioritisation of flood risk was carried out using water-sheds in the study area as units of analysis. The watersheddivides were delineated up to the confluence with the Tun-juelo River, or up to the confluence with the storm water350

system, whichever is applicable. First a delineation of areasexposed to flooding from these watersheds using simplifiedapproaches was carried out. Subsequently a vulnerability in-dicator was constructed based on a principal component anal-ysis of variables identified in the literature as contributing to355

vulnerability. A sensitivity analysis was undertaken to testthe robustness of the vulnerability indicator. From the vul-nerability indicator a category (high, medium and low vul-nerability) was obtained that was then combined with a cate-gorisation of flash flood/debris flow susceptibility previously360

generated in the study area to obtain a prioritisation category.The tool that was used to combine vulnerability and sus-ceptibility was a matrix that relates the susceptibility levelsand vulnerability levels producing as output a priority level.The combination matrix was constructed through the assess-365

ment of all possible matrices using as assessment criterionthe ”proportion correct”. In order to obtain the ”proportioncorrect” an independent classification of the watersheds wascarried out on the basis of the existing damage data.

A detailed explanation of the analysis is given in the fol-370

lowing subsections.

3.2.1 Delineation of exposure areas

Flood events in the watersheds considered in this study typ-ically occur as flash floods given their size and mountainousnature. Flash floods in such small, steep watersheds can fur-375

ther be conceptualized to occur as debris flows, hypercon-centrated flows or clear water flows (Hyndman and Hynd-man, 2008; Jakob et al., 2004; Costa, 1988). Costa (1988)differentiates: (i) clear water floods as newtonian, turbulentfluids with non-uniform concentration profiles and sediment380

concentrations of less than about 20% by volume and shearstrengths less than 10 N/m2; (ii) hyperconcentrated flows

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as having sediment concentrations ranging from 20 to 47%by volume and shear strengths lower than about 40 N/m2;and (iii) debris flows as being non-Newtonian visco-plastic385

or dilatant fluids with laminar flow and uniform concentra-tion profiles, with sediment concentrations ranging from 47to 77% by volume and shear strengths greater than about 40N/m2. Debris flow dominated areas can be subject to hy-perconcentrated flows as well as clear water floods (Larsen390

et al., 2001; Santo et al., 2015; Lavigne and Suwa, 2004), de-pending on the hydroclimatic conditions and the availabilityof sediments (Jakob, 2005), and occurrence of all types inthe same watersheds has been reported (Larsen et al., 2001;Santo et al., 2015). Therefore, the areas exposed to clear wa-395

ter floods and debris flows were combined. This providesa conservative delineation of the areas considered to be ex-posed to flooding.

Exposure areas were obtained from an analysis of the sus-ceptibility to flooding. Areas that potentially can be affected400

by clear water floods and debris flows were determined usingsimplified methods that provide a mask where the analysis ofexposed elements was carried out. The probability of occur-rence and magnitude are not considered in the analysis, sincethe scope of the simplified regional assessment is limited to405

assessing the susceptibility of the watersheds to flooding. Ar-eas prone to debris flows were previously identified by Ro-gelis and Werner (2013) through application of the ModifiedSingle Flow Direction model.

In order to delineate the areas prone to clear water floods,410

or floodplains, two geomorphic-based methods were testedusing a digital elevation model with a pixel size of 5 metresas an input, which was obtained from contours. Floodplainsare areas near stream channels shaped by the accumulatedeffects of floods of varying magnitudes and their associated415

geomorphological processes. These areas are also referred toas valley bottoms and riparian areas or buffers (Nardi et al.,2006).

The first approach is the multi-resolution valley bottomflatness (MRVBF) algorithm (Gallant and Dowling, 2003).420

The MRVBF algorithm identifies valley bottoms using aslope classification constrained on convergent area. The clas-sification algorithm is applied at multiple scales by progres-sive generalisation of the DEM, combined with progressivereduction of the slope class threshold. The results at different425

scales are then combined into a single index. The MRVBFindex utilises the flatness and lowness characteristics of val-ley bottoms. Flatness is measured by the inverse of slope, andlowness is measured by ranking the elevation with respect tothe surrounding area. The two measures, both scaled to the430

range 0 to 1, are combined by multiplication and could beinterpreted as membership functions of fuzzy sets. While theMRVBF is a continuous measure, it naturally divides intoclasses corresponding to the different resolutions and slopethresholds (Gallant and Dowling, 2003).435

In the second method considered, threshold buffers areused to delineate floodplains as areas contiguous to the

streams based on height above the stream level. Cells inthe digital elevation model adjacent to the streams that meetheight thresholds are included in the buffers (Cimmery,440

2010). Thresholds for the height of 1, 2, 3, 4, 5, 7 and 10metres were tested.

In order to evaluate the results of the MRVBF index andthe threshold buffers, flood maps for the study area wereused. These are available for only 9 of the 106 watersheds,445

and were developed in previous studies through hydraulicmodelling for return periods up to 100 years. The delineationof the flooded area for a return period of 100 years was usedin the nine watersheds to identify the suitability of the flood-plain delineation methods to be used in the whole study area.450

With respect to areas prone to debris flows, these were vali-dated with existing records in the study area by Rogelis andWerner (2013).

3.2.2 Choice of indicators and principal componentanalysis for vulnerability assessment455

In this study vulnerability in the areas identified as being ex-posed is assessed through the use of indicators. The complex-ity of vulnerability requires a transformation of available datato a set of important indicators that facilitate an estimation ofvulnerability (Birkmann, 2006). To this end, principal com-460

ponent analysis was applied to variables describing vulnera-bility in the study area in order to create composite indicators(Cutter et al., 2003). The variables were chosen by taking intoaccount their usefulness according to the literature, and werecalculated using the exposure areas as a mask.465

Table 2 shows the variables chosen to explain vulnerabil-ity in the study area. These are grouped in socio-economicfragility, lack of resilience and coping capacity and physi-cal exposure. The variables are classified according to theirsocial level (individual, household, community and institu-470

tional), hazard dependence and influence on vulnerability(increase or decrease). The third column specifies the spatialaggregation level of the available data. The three spatial lev-els considered are urban block, watershed and locality, wherethe locality corresponds to the 20 administrative units of the475

city. The data used to construct the indicators was obtainedfrom the census and reports published by the municipality.For each variable the values were normalised between theminimum and the maximum found in the study area. In thecase of variables that contribute to decreasing vulnerability480

a transformation was applied so a high variable value repre-sents high vulnerability for all variables.

In order to construct the composite indicators related tosocio-economic fragility and physical exposure, principalcomponent analysis (PCA) was applied on the corresponding485

variables shown in table 2. PCA reduces the dimensionalityof a data set consisting of a large number of interrelated vari-ables, while retaining as much as possible of the variationpresent in the data set. This is achieved by transforming to anew set of variables, the principal components (PCs), which490

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are uncorrelated (Jolliffe, 2002). The number of componentsto be retained from the PCA was chosen by considering fourcriteria: the Scree test acceleration factor, optimal coordi-nates (Cattell, 1966), the Kaiser’s eigenvalue-greater-than-one rule (Kaiser, 1960) and parallel analysis (Horn, 1965).495

Since the number of components may vary among these cri-teria, the interpretability was also taken into account whenselecting the components to be used in further analysis, witheach PC being considered an intermediate indicator. Sub-sequently a varimax rotation (Kaiser, 1958) was applied to500

minimise the number of individual indicators that have a highloading on the same principal component, thus obtaining asimpler structure with a clear pattern of loadings (Commis-sion, 2008). The intermediate indicators (PCs) were aggre-gated using a weight equal to the proportion of the explained505

variance in the data set (Commission, 2008) to provide anoverall indicator for socio-economic fragility and for physi-cal exposure.

PCA has the disadvantage that correlations do not neces-sarily represent the real influence of the individual indica-510

tors and variables on the phenomenon being measured (Com-mission, 2008). This can be addressed by combining PCAweights with an equal weighing scheme for those variableswhere PCA does not lead to interpretable results (Esty et al.,2006). In the construction of the lack of resilience and coping515

capacity indicator, this issue led to a separation of variablesin four groups:

– Robberies and participation: These were treated sepa-rately from the rest of the variables to maintain inter-pretability as a measure of cohesiveness of the commu-520

nity. Cohesiveness of the community was identified as afactor that influences the resilience since the degrada-tion of social networks limits the social organisation foremergency response (Ruiz-Perez and Gelabert Grimalt,2012). Since there are only two variables to measure525

this aspect of resilience, PCA was not applied, and theaverage of the variables was used instead.

– Risk perception and early warning: Risk perception de-pends on the occurrence of previous floods, thus it de-pends on hazard exclusively. The existence of early530

warning is manly an institutional and organizational is-sue. Therefore, an interpretation of correlation of thesevariables with other variables in the group of lack ofresilience and coping capacity is not possible. Thesevariables were considered separated intermediate indi-535

cators. Risk perception and early warning decrease thelack of coping capacity (Molinari et al., 2013), andtherefore an equal negative weight was assigned to theseindicators summing up to -0.2. This value was chosen sothat their combined influence is less than the individual540

weight of the other four indicators. The sensitivity ofthis subjective choice was tested. The effectiveness offlood early warning is closely related to the level of pre-paredness as well as the available time for implementa-

tion of appropriate actions (Molinari et al., 2013). Due545

to the flashy behaviour and configuration of the water-sheds in the study area, flood early warning actions aretargeted at reducing exposure and vulnerability and notat hazard reduction.

– Rescue personnel: this variable was initially used in the550

PCA with all lack of resilience and coping capacity vari-ables. However, it was found to increase with lack of re-silience and coping capacity. This implied that the sta-tistical behaviour of the variable did not represent its thereal influence on vulnerability. It was therefore treated555

independently.

– Level of education, illiteracy, access to information, in-frastructure/accessibility, hospital beds and health careHR: PCA was applied to these variables, since they ex-hibit high correlation and are interpretable in terms of560

their influence on vulnerability.

To combine all the lack of resilience and coping capacityintermediate indicators into a composite indicator, weightssumming up to 1 were assigned (see Section 4.3 for an ex-planation of the resulting intermediate indicators).565

The indicators corresponding to socio-economic fragility,lack of resilience and coping capacity and physical exposurewere combined, assigning equal weight to the three compo-nents, to obtain an overall vulnerability indicator. The water-sheds were subsequently categorised as being low, medium570

or high vulnerability based on the value of the vulnerabilityindicator and using equal intervals. This method of categori-sation was chosen to avoid dependence on the distribution ofthe data, so monitoring of evolution in time of vulnerabilitycan be carried out applying the same criteria.575

3.2.3 Sensitivity of the vulnerability indicator

The influence of all subjective choices applied in the con-struction of the indicators was analysed. This included bothchoices made in the application of PCA, and for the weight-ing scheme adopted for the factors contributing to resilience580

and total vulnerability.

1. For the application of PCA, sensitivity to the followingchoices was explored:

(a) Four alternatives for the number of components tobe retained were assessed as explained in Section585

3.2.2.

(b) Five different methods in addition to the vari-max rotation were considered: Unrotated solution;quatimax rotation (Carroll, 1953; Neuhaus, 1954);promax rotation (Hendrickson and White, 1964);590

oblimin (Carroll, 1957); simplimax (Kiers, 1994);and cluster (Harris and Kaiser, 1964).

2. For the weighting scheme

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(a) The weights used in the four groups of variablesthat describe lack of resilience and coping capacity595

were varied by ± 10%.

(b) The weights used to combine the three indicatorsthat result in the final vulnerability composite indi-cator were varied by ± 10%.

All possible combinations were assessed and the results in600

terms of the resulting vulnerability category (high, mediumand low) were compared in order to identify substantial dif-ferences as a result of the choices of subjective options.

3.2.4 Categories of recorded damage in the study area

A database of historical flood events compiled by the mu-605

nicipality was used to classify the watersheds in categories,depending on damages recorded in past flood events. Foreach of these events the database includes: date, location,injured people, human losses, evacuated people, number ofaffected houses and an indication of whether the flow depth610

was higher than 0.5 m or not. Unfortunately, no informationon economic losses is available and as the database only cov-ers the period from 2000 to 2012 it is not possible to carryout a frequency analysis. Complete records were only avail-able for 14 watersheds. The event with the highest impact for615

each watershed was chosen from the records. Subsequently,the 14 watersheds were ordered according to their highestimpact event. The criteria to sort the records and to sort thewatersheds according to impact from highest to lowest werethe following (in order of importance):620

1. Human losses

2. Injured people

3. Evacuated people

4. Number of affected houses

Watersheds with similar or equal impact were grouped, re-625

sulting in 11 groups. The groups were again sorted accord-ing to damage. A score from 0 to 10 was assigned, where ascore of 0 implies that no flood damage has been recordedin the watershed for a flood event, despite the occurrenceof flooding, while a score of 10 corresponds to watersheds630

where human losses or serious injuries have occurred (seeTable 3). The 11 groups were further classified into threecategories according to the emergency management organi-zation that was needed for the response: (i) low: the responsewas coordinated locally; (ii) medium: centralized coordina-635

tion is needed for response with deployment of resources ofmainly the emergency management agency; (iii) high: cen-tralized coordination is needed with an interistitutional re-sponse. This classification was made under the assumptionthat the more resources are needed for response the more se-640

vere the impacts are, allowing in this way a comparison withthree levels of priority classification.

3.2.5 Prioritisation of watersheds

Due to the regional character and scope of the method ap-plied in this study, a qualitative proxy for risk was used to645

prioritise the watersheds in the study area. A high priorityindicates watersheds where flood events will result in moresevere consequences. However, the concept of probability ofoccurrence of these is not involved in the analysis, since theanalysis of flood hazard is limited to susceptibility.650

In order to combine the vulnerability and susceptibility toderive a level of risk, a classification matrix was used. Thisis shown in figure 2. The columns indicate the classifica-tion of the vulnerability indicator and the rows the classifi-cation of the susceptibility indicator. Only two priority out-655

comes are well defined, these are the high and low degreesassigned to the corners of the matrix corresponding to highsusceptibility and high vulnerability and low susceptibilityand low vulnerability (cells a and i), since they correspondto the extreme conditions in the analysis. The priority out-660

comes in cells from b to h were considered unknown andto potentially correspond to any category (low, medium orhigh priority). To define the category for these cells, the pri-ority using all possible matrices (all possible combinationsof categories of cells b to c) was assessed for the watersheds665

for which flood records are available. Once, these watershedswere prioritised, a contingency table is constructed compar-ing the priority with the damage category (from table 3) fromwhich the ”proportion correct” is obtained. The classificationmatrix that results in the highest proportion correct (best fit)670

was used for the prioritisation of the whole study area.

4 Results

4.1 Exposure Areas

Figure 3 shows the results of the methods applied to iden-tify areas susceptible to flooding through clear water floods675

or debris flows. Figure 3-a shows the debris flow propaga-tion extent derived for the watersheds of the Tunjuelo basinand the Eastern Hills by Rogelis and Werner (2013). Sincethe method does not take into account the volume that canbe deposited on the fan, this shows the maximum potential680

distance that the debris flow could reach according to themorphology of the area, which is in general flat to the westof the Eastern Hills watersheds. A different behaviour canbe observed in the watersheds located in the Tunjuelo riverbasin where the marked topography and valley configuration685

restricts the propagation areas.Figure 3-b shows the results obtained from the MRVBF

index. The comparison of the index with the available floodmaps in the study area shows that values of the MRVBFhigher than 3 can be considered areas corresponding to valley690

bottoms. In areas of marked topography the index identifiesareas adjacent to the creeks in most cases and the larger scale

8 xxxxx: xxxx

valley bottoms. However, in flat areas the index unavoidablytakes high values and cannot be used to identify flood proneareas.695

Figure 3-c shows the result obtained from the use of bufferthresholds. The buffers that were obtained by applying thecriteria explained in Section 3.2.1, were compared with theavailable flood maps. Areas obtained for a depth criterion of3 meters were the closest to the flood delineation for a return700

period of 100 years, and this value was chosen as appropriatefor the study area.

In order to obtain the delineation of the exposure areas,the results of the debris flow propagation; the MRVBF indexand the buffers were combined. The results of all three meth-705

ods in flat areas does not allow for a correct identification offlood prone areas, and a criteria based on the available infor-mation and previous studies was needed to estimate a rea-sonable area of exposure. The resulting exposure areas areshown in Figure 4.710

4.2 Socio-economic fragility indicators

The results of the principal component analysis applying avarimax rotation are shown in table 4. Two principal compo-nents were retained as this allowed a clear interpretation to bemade for each of the components. The variables included in715

the first principal component are related to lack of well-being(PLofW ), while in the second these are related to the demog-raphy (Pdemog). The two principal components account for79 percent of the variance in the data with the first compo-nent explaining 80% of the variance (PVE) and the second720

20%.Using the factor loadings (correlation coefficients between

the PCs and the variables) obtained from the analysis (seetable 4) and scaling them to unity, the coefficients of eachindicator are shown in the following equations:725

PLofW = 0.10Whh+0.10UE+0.10PUBNI+

0.09Ho+0.11P +0.10Pho+0.09M+

0.10LE+0.08QLI +0.10HDI +0.04G

(1)

Pdemog = 0.32Age+0.20D+0.29PE12+0.19IS (2)

The impacts of the indicators imply that the higher thelack of well-being the higher the socio-economic fragility,730

and equally the higher the demography indicator the higherthe socio-economic fragility. Using the percentage of vari-ability explained (PVE) by each component, the compositeindicator for socio-economic fragility (Psoc−ec) is found as:

Psoc−ec = 0.8PLofW +0.2Pdemog (3)735

4.3 Lack of Resilience and coping capacity indicators

The loadings of the indicators representing lack of resilienceand coping capacity obtained from the PCA are shown in

table 5. Two principal components were used; the first corre-lated with variables related to the lack of education (PLEdu)740

and the second with variables related to lack of prepared-ness and response capacity (PLPrRCap). These account for97 percent of the variance in the data with the first compo-nent explaining 53% of the variance (PVE) and the second47%.745

Using the factor loadings obtained from the analysis andscaling them to unity, the coefficients of each indicator areshown in the following equations:

PLEdu = 0.33LEd+0.32I +0.35AI (4)750

PLPrRCap = 0.26IA+0.39Hb+0.35HRh (5)

In an initial analysis, the variable rescue personnel was in-cluded in the principal component analysis. Results showeda high negative correlation of this variable with lack of edu-cation, illiteracy and access to information. This may be due755

to more institutional effort being allocated to depressed areasthat are more often affected by emergency events in orderto strengthen the response capacity of the community. Alsocivil protection groups rely strongly on voluntary work thatseems to be more likely in areas with lower education levels.760

Since the consideration of rescue personnel changes theinterpretation of the principal component that groups the lackof education and access to information indicator, it was de-cided to exclude it from the PCA and to consider this variableas an independent indicator (Lack of Rescue Capacity).765

In the analysis of robberies and participation as variablesdescribing cohesiveness of the community, it was found thatthe increase in crime is correlated with the lack of participa-tion, describing the distrust of the community both of neigh-bours and of institutions. The corresponding composite indi-770

cator was calculated as the average of robberies and lack ofparticipation.

The equation of Lack of Resilience and coping capacity isshown in equation 6. Equal weight was assigned to the in-dicators reflecting Lack of Education, Lack of Preparedness775

and Response Capacity, Lack of Rescue Capacity (PLRc) andCohesiveness of the Community (PCC); and a weight of -0.1to Risk Perception (PRP ) and Early Warning (PFEW ).

PLRes = 0.25PLEdu +0.25PLPrRCap +0.25PLRc

0.25PCC − 0.1PRP − 0.1PFEW

(6)

Once the indicator of lack of resilience and coping capac-780

ity was obtained it was rescaled between 0 and 1.

4.4 Physical exposure indicators

The principal component analysis of the variables selectedfor physical exposure shows that these can be grouped intotwo principal components that explain 82% of the variability785

(exposed infrastructure - PEi and exposed population - PEp).The results of the analysis are shown in table 6.

xxxxx: xxxx 9

Using the factor loadings obtained from the analysis andscaling them to unity, the coefficients of each composite in-dicator are shown in the following equations:790

PEi = 0.32Ncb+0.37Niu+0.32Ncu (7)

PEp = 0.38Nru+0.33Pe+0.28Dp (8)

Using the percentage of variability explained (PVE) byeach indicator, the composite indicator of physical suscep-795

tibility is found to be:

Pps = 0.52PEi +0.48PEp (9)

4.5 Vulnerability indicator

The resulting vulnerability indicator was obtained throughthe equal-weighted average of the indicators for socio-800

economic fragility, lack of resilience and coping capacity,and physical exposure. Categories of low, medium and highvulnerability for each watershed were subsequently derivedbased on equal bins of the indicator value. The spatial distri-bution is shown in figure 5, as well as the spatial distribution805

of the three constituent indicators.Conditions of lack of well-being are shown to be concen-

trated in the south of the study area. The demographic con-ditions are more variable, showing low values (or better con-ditions) in the watersheds in the South, where the land use810

is rural. Low values also occur in the North, where the de-gree of urbanization is low due the more formal urbaniza-tion processes (see figure 5-a). The spatial distribution ofthe indicator of lack of resilience and coping capacity (fig-ure 5-b) shows that the highest values are concentrated in815

the south-west of the study area where the education levelsare lower and the road and health infrastructure poorer. Thesame spatial trend is exhibited by the lack of preparednessand response capacity. The south of the study area corre-sponds mainly to rural use, thus the physical exposure indi-820

cator shows low values (see figure 5-a). The highest valuesare concentrated in the centre of the area where the densityof population is high and the economic activities are located.

The spatial distribution of the overall indicator and the de-rived categories show that the high vulnerability watersheds825

are located in the centre of the study area and in the west.

4.6 Prioritisation of watersheds according to the qual-itative risk indicator and comparison with damagerecords

The ”proportion correct” of all possible matrices according830

to Section 3.2.5 (see figure 2) resulted in the optimum matrixshown in figure 6-a, the corresponding contingency matrix isshown in figure 6-b with a ”proportion correct” (PC) of 0.85.

The prioritisation level obtained from the application ofthe combination matrix to the total vulnerability indicator835

and the susceptibility indicator for each watershed is shown

in figure 7-a. The results were assigned to the watersheds de-lineated up to the discharge into the Tunjuelo River or intothe storm water system, in order to facilitate the visualisa-tion. The damage categorisation of the study area using the840

database with historical records according to table 3 is shownin figure 7-b with range categories classified as high, mediumand low. This shows that the most significant damages, corre-sponding to the highest scores for the impact of flood events,are concentrated in the central zone of the study area. The845

comparison between figure 7-a and figure 7-b shows that theindicators identify a similar spatial distribution of prioritylevels in the central zone of the study area that is consistentwith the distribution of recorded damage. This is reflected inthe ”proportion correct” of 0.85.850

4.7 Sensitivity analysis of the vulnerability indicator

Figure 8 shows the box plots of the values of the vulnerabilityindicator obtained from the sensitivity analysis in applicationof PCA as well as the weighting scheme as explained in Sec-tion 3.2.3. The values of the vulnerability indicator obtained855

from the proposed method were also plotted for reference.The most influential input factors correspond to the weightsused both in the construction of the lack of resilience indi-cator and in the construction of the total vulnerability indi-cator. The thick vertical bars for each watershed show the860

interquartile range of the total vulnerability indicator, withthe thin bars showing the range (min-max). While the rangeof the indicator for some watersheds is substantial, the sensi-tivity of the watersheds being classified differently in termsof low, medium or high vulnerability was evaluated through865

the number of watersheds for which the interquartile rangeintersects with the classification threshold. For seven water-sheds classified as of medium vulnerability the interquartilerange crosses the upper limits of classification of mediumvulnerability, while for four watersheds classified as of high870

vulnerability the range crosses that same threshold. For thelower threshold, only two watersheds classified as being oflow vulnerability are sensitive to crossing into the class ofmedium vulnerability.

5 Discussion875

5.1 Exposure areas

Existing flood hazard maps developed using hydraulic mod-els that were available for a limited set of the watersheds inthe study area were used to assess the suitability of the pro-posed simplified methods to identify flood prone areas and880

extend the flood exposure information over the entire studyarea. The areas exposed to debris flows obtained through theMSF propagation algorithm show a good representation ofthe recorded events (Rogelis and Werner, 2013). However, inthe eastern hills, where the streams flow towards a flat area,885

the results of the algorithms tend to overestimate the propa-

10 xxxxx: xxxx

gation areas since in these algorithms the flood extent is dom-inated purely by the morphology and the flood volume is notconsidered, which means there is no limitation to the floodextent (see figure 3).890

Each of the methods applied for flood plain delineationhas strengths and weaknesses, while the combination of theresults from these methods provides a consistent and conser-vative estimate of the exposure areas. The MRVBF index al-lows the identification of valley bottoms at several scales. In895

the mountainous areas, zones contiguous to the streams areidentified, and in areas of marked topography the results aresatisfactory, allowing a determination of a threshold of theindex to define flood prone areas. In the case of the buffers(see figure 3-c), a depth of 3 meters seems adequate to rep-900

resent the general behaviour of the streams. The combina-tion of the methods allowed the estimation of exposure areasbased on the morphology (low and flat areas), elevation dif-ference with the stream level (less than 3 meters) and capac-ity to propagate debris flows.905

5.2 Representativeness and relative importance of indi-cators

The principal component analysis of the variables used to ex-plain socio-economic fragility showed that the 16 variablesthat were chosen for the analysis could be grouped into two910

principal components strongly associated with the demogra-phy and the lack of well-being in the area. The latter wasfound to explain most of the variance in the data (80 % asshown in table 4).

The demography intermediate indicator describes the de-915

pendent population and the origin of the population. De-pendent population (children, elderly and disabled) has beenalso identified by other authors as an important descriptorof vulnerability (Cutter et al., 2003; Fekete, 2009), associ-ated to the limited capacity of this population to evacuate920

(Koks et al., 2015) and recover (Rygel et al., 2006). The ori-gin of the population (illegal settlements and % of populationin strata 1 and 2) shows the proportion of population result-ing mainly from forced migration due to both violence andpoverty (Beltran, 2008).925

The lack of well-being indicator is composed of 14strongly correlated variables that are commonly used tomeasure livelihood conditions. Poverty does not necessarilymean vulnerability, though the lack of economic resourcesis associated with the quality of construction of the houses,930

health and education, which are factors that influence the ca-pability to face an adverse event (Rygel et al., 2006). Thevariable “women-headed households” is correlated with theprincipal component related to lack of well-being as identi-fied by Barrenechea et al. (2003). Even if this condition of935

the families is not necessarily a criteria related to poverty,women-headed households with children are related to vul-nerability conditions. The woman in charge of the familyis responsible for the economic, affective and psychological

well-being of other persons, specially her children and el-940

derly, in addition to domestic tasks and the family income.This condition suggest more assistance during emergencyand recovery (Barrenechea et al., 2003).

In the case of the lack of resilience and coping capacity in-dicators, the PCA resulted in the intermediate indicators lack945

of education and lack of preparedness and response capacity.The former captures limitations in knowledge about hazardsin individuals (Muller et al., 2011) and the latter is linked tothe institutional capacity for response. Risk perception andearly warning are boolean indicators. Since risk perception is950

based on the occurrence or non-occurrence of floods, aspectssuch as specific knowledge of the population about their ex-posure are not included. In the case of flood early warning,the effectiveness of the systems is not considered. These areaspects that can be taken into account for future research and955

that can help to improve the lack of resilience and copingcapacity indicators.

Regarding the physical exposure, the method that was ap-plied does not involve hazard intensity explicitly and dif-ferent levels of physical fragility are not considered due to960

limitations in the available data. The indicators used to ex-press physical exposure imply that the more elements ex-posed the more damage, neglecting the variability in the de-gree of damage that the exposed elements may have. Otherregional indicator-based approaches have used physical char-965

acteristics of the exposed structures to differentiate levelsof damage according to structure type (Kappes et al., 2012)and economic values of the exposed elements (Liu and Lei,2003). This is a potential area of improvement of the indi-cator, since the degree of damage depends on the type and970

intensity of the hazard and the characteristics of the exposedelement. However, the development of indicators of phys-ical characteristics and economic values is highly data de-manding, therefore future applications could be aimed at ef-ficiently use existing information and apply innovative data975

collection methods at regional level for the improvement ofthe physical indicator.

5.3 Sensitivity of the vulnerability indicator

The interquartile ranges cross the thresholds between cate-gories of low, medium and high vulnerability only in the case980

of 13 watersheds (see figure 8). This means that only these 13watersheds are sensitive to the criteria selected for the anal-ysis. In 11 of these, the category changes between mediumvulnerability and high vulnerability and in the remaining twothe change is from low to medium vulnerability. Watersheds985

with values of the vulnerability indicator out of the inter-mediate ranges of the thresholds are robust to the change inthe modelling criteria. Clearly, these results are dependent onthe number of categories. While introducing more categoriesmay provide more information to differenciate watersheds,990

the identification of category of the watersheds may becomemore difficult due to the sensitivity to the results. Therefore,

xxxxx: xxxx 11

in order to preserve identifiability of the vulnerability cate-gory of the watersheds more than three categories could notbe used. Indicator-based regional studies that classify vulner-995

ability in 3 categories, have shown to provide useful informa-tion for flood risk management (Kappes et al., 2012; Liu andLi, 2015; Luino et al., 2012).

The impact on the proportion correct of a shift of categoryfor the 13 watersheds mentioned above can only be assesssed1000

for the 2 watersheds where flood records are available. Thisdoes not result in changes in the contingency matrix shownin figure 6-b. With respect to the assigning the priority tothe watersheds, only 7 (7% of the total) of the 13 water-sheds that showed sensitivity to a shift of the vulnerability1005

categories were found to be sensitive to a change in priority(high/medium), which reflects the robustness of the analysisusing the considered categories.

5.4 Usefulness of the prioritisation indicator

The resulting vulnerability-susceptibility combination ma-1010

trix shown in figure 6-a, shows that in the study area highpriorities are determined by high vulnerability conditions andmedium and high susceptibility. This would suggest that,high vulnerability is a determinant condition of priority, sinceareas with high vulnerability can only be assigned a low pri-1015

ority if the susceptibility to flash floods/debris flows is low.This also shows that the analysis of the indicators that com-pose the vulnerability index allows insight to be gained intothe drivers of high vulnerability conditions. Figure 5 showsthat high vulnerability watersheds are the result of:1020

– High socio-economic fragility and high lack of re-silience and coping capacity (west of the lower and mid-dle basin of the Tunjuelo river; and watershed most tothe south of the Eastern Hills).

– High socio-economic fragility and high physical expo-1025

sure (east of the middle basin of the Tunjuelo river).

– High physical exposure levels (south of the EasternHills)

This information is useful for regional allocation of re-sources for detailed flood risk analysis, with the advan-1030

tage that the data demand is low in comparison with otherindicator-based approaches (Kappes et al., 2012; Fekete,2009). Furthermore most weights are determined from a sta-tistical analysis with a low influence of subjective weights,which is an advantage over expert weighting where large1035

variations may occur depending on the expert’s perspective(Muller et al., 2011). However, more detailed flood risk man-agement decision-making cannot be informed by the level ofresolution used in this study. Studies where assessments arecarried out at the level of house units would be needed for1040

planning of mitigation measures, emergency planning andvulnerability reduction (Kappes et al., 2012). Although, theproposed procedure could be applied at that more detailed

level, this could not be done due to the availability of in-formation. This is a common problem in regional analyses1045

(Kappes et al., 2012) where collecting large amount of data athigh resolution is a challenge. Nevertheless, future advancesin collection of data could be incorporated in the proposedprocedure yielding results at finer resolutions. The challengenot only lies in collecting data of good quality at high res-1050

olution that can be transformed into indicators, but also inproducing data at the same pace as significant changes invariables that contribute to vulnerability take place in thestudy area. In this research, vulnerability was assessed stat-ically, however, there is an increasing need for analyses that1055

take into account the dynamic characteristics of vulnerability(Hufschmidt et al., 2005). Methods such as the one appliedin this study can provide a tool to explore these dynamicssince it can be adapted to different resolutions according tothe available data.1060

6 Conclusions

In this paper a method to identify mountainous watershedswith the highest flood damage potential at the regionallevel is proposed. Through this, the watersheds to be sub-jected to more detailed risk studies can be prioritised in or-1065

der to establish appropriate flood risk management strate-gies. The method is demonstrated in the steep, mountain-ous watersheds that surround the city of Bogota (Colom-bia), where floods typically occur as flash floods and de-bris flows. The prioritisation of the watersheds is obtained1070

through the combination of vulnerability with susceptibilityto flash floods/debris flows. The combination is carried outthrough a matrix that relates levels of vulnerability and sus-ceptibility with priority levels.

The analysis shows the interactions between drivers of vul-1075

nerability, and how the understanding of these drivers can beused to gain insight in the conditions that determine vulner-ability to floods in mountainous watersheds. Vulnerability isexpressed in terms of composite indicators; Socio-economicfragility, lack of resilience and coping capacity and physi-1080

cal exposure. Each of these composite indicators is formedby an underlying set of constituent indicators that reflect thebehaviour of highly correlated variables, and that representcharacteristics of the exposed elements. The combination ofthese three component indicators allowed the calculation of a1085

vulnerability indicator, from which a classification into high,medium and low vulnerability was obtained for the water-sheds of the study area. Tracing back the composite indi-cators that generate high vulnerability, provided an under-standing of the conditions of watersheds that are more criti-1090

cal, allowing these to be targeted for more detailed flood riskstudies. In the study area it is shown that those watershedswith high vulnerability are categorised to be of high priority,unless the susceptibility is low, indicating that in the vulner-ability is the main contributor to risk. Furthermore, the con-1095

12 xxxxx: xxxx

tributing components that determine high vulnerability couldbe identified spatially in the study area.

The developed methodology can be applied to other areas,although adaptation of the variables considered may be re-quired depending on the setting and the available data. The1100

proposed method is flexible to the availability of data, whichis an advantage for assessments in mountainous developingcities and when the evolution in time of variables that con-tribute to vulnerability is taken into account.

The results also demonstrate the need for a comprehensive1105

documentation of damage records, as well as the potential forimprovement of the method. Accordingly, further researchshould be focused on (i) the use of smaller units of analysisthan the watershed scale, which was used in this study; (ii)Improvement of physical exposure indicators incorporating1110

type of structures and economic losses; and (iii) incorpora-tion of more detailed information about risk perception andflood early warning.

Acknowledgements. This work was funded by the UNESCO-IHEPartnership Research Fund - UPARF in the framework of the1115

FORESEE project. We wish to express our gratitude to the Fondode Prevencion y Atencion de Emergencias de Bogota for providingthe flood event data for this analysis.

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16 xxxxx: xxxx

50

Km

500

KmStudy area - Bogotá

5

Km

Legend

Eastern Hills

Tunjuelo River basin

Watershed divides

Fig. 1. Location of the study areas

xxxxx: xxxx 17

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!"#$

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) * +

*+, % & $",-.

-(./&012"."3456/'"713+0

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Fig. 2. Initial matrix of priority

18 xxxxx: xxxx

Bogota River

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Bogota River

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Bogota River

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a) b) c)

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Kilometers

Legend

Main Rivers

Watersheds

Valley bottom

0 2 4 6 81

Kilometers

Legend

Main Rivers

Watersheds

Debris flow

Legend

Main Rivers

Watersheds

Buffer

Fig. 3. Clear water flood and debris flow susceptibility areas. Areas in dark grey in each map represent; a) debris flow extent (Rogelis andWerner, 2013); b) Valley bottoms identified using the the MRVBF index; c) Buffers. In the case of maps b and c, the flood prone areas extendin the direction of the arrows over the flat area.

xxxxx: xxxx 19

Bogota River

Tu

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r

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Juan A

marillo

River

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0 2 4 6 81

Kilometers

Legend

Main Rivers

Watersheds

Exposure areas

Fig. 4. Exposure areas

20 xxxxx: xxxx

Bogota River

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Bogota RiverTu

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River

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marillo

River

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Kilometers

0 2 4 6 81

Kilometers

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Kilometers

a) b) c) d)

Legend

0.00 -

0.20

0.21 -

0.40

0.41 -

0.60

0.61 -

0.80

0.81 -

1.00

Main Rivers Watersheds

Value of the indicators in figures a, b and c Vulnerability indicator in figure d

HIGH MEDIUM LOW No exposure

Fig. 5. a) Spatial distribution of the Socio-economic indicator; b) Spatial distribution of the resilience indicator; c) spatial distribution of thephysical exposure indicator; d) Spatial distribution of the total vulnerability indicator

xxxxx: xxxx 21

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Fig. 6. (a) Vulnerability–susceptibility combination matrix. (b) Contingency matrix.

22 xxxxx: xxxx

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Tu

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Legend

Main Rivers

Watersheds

Priority

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MEDIUM

LOW

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Damage Score

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Susceptibility

HIGH

MEDIUM

LOW

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Kilometers

a)

Fig. 7. (a) Susceptibility classification of the study area. (b) Prioritisation according to the qualitative risk indicator. (c) Damage categoriza-tion.

xxxxx: xxxx 23

Upper limit low vulnerability

Upper limit Medium vulnerability

LOW VULNERABILITY

MEDIUM VULNERABILITY

HIGH VULNERABILITY

0.2

0.4

0.6

30 9 8 5 1

22

11

12

27

13 3 4

14

25

20

28 6

29

26

21

24

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16

18

15

40

19

10

32

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31

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1017

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1031

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39

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1009

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36

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1007

1002

1016

1006

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1001

1003

1015

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Vuln

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Boxplot sensitivity results

Fig. 8. Sensitivity analysis of the vulnerability indicator. Note: The numbering of the watersheds in the Eastern Hills goes from 1 to 40 andin the Tunjuelo River Basin from 1000 to 1066.

24 xxxxx: xxxx

Table 1. Most severe recent flooding events in the study area

Watersheds

Study Area NumberAverage

Slope(%)

Area(km2)

Recent flooding events

Tunjuelo RiverBasin

66 12-40 0.2-57

The most severe events include:

– In May/1994 a debris flow affected 830 people and caused thedeath of 4 people in the north east of the basin (JICA, 2006).

– In November/2003 a hyperconcentrated flow took place in thenorth west of the Tunjuelo basin. 2 people were killed and 1535were affected. A similar event occurred at the same location inNovember/2004 without death toll (DPAE, 2003a,b).

Eastern Hills 40 21-59 0.2-33

The most sever events include:

– In May/2005 a hyperconcentrated flow occurred in the central partof the area affecting 2 houses (DPAE, 2005).

xxxxx: xxxx 25

Table 2. Variables used to construct vulnerability indicators

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26 xxxxx: xxxx

Table 3. Categories of recorded damage

Category Score DescriptionLow 0 No recorded damage in the watershed.

Low 1Events that affect 1 house without causing injuries orhuman loss and without the need of evacuation.

Low 2Events that affect 1 house without causing injuries orhuman loss and with the need of evacuation.

Low 3Events that affect up to 5 houses without causing in-juries or human loss, flood depth less than 0.5 m withevacuation of families.

Medium 4Events that affect up to 5 houses without causing in-juries or human loss, flood depth higher than 0.5 m withevacuation of families.

Medium 5Events that affect up to 10 houses without causing in-juries or human loss with evacuation of families.

Medium 6Events that affect 10-20 houses without causing injuriesor human loss with evacuation of families, flood depthless than 0.5 m.

High 7Events that affect 10-20 houses without causing injuriesor human loss with evacuation of families, flood depthhigher than 0.5 m.

High 8Events that affect 20-50 houses without causing injuriesor human loss with evacuation of families and possibil-ity of structural damage in the houses.

High 9Events that affect more than 50 houses without causinginjuries or human loss with evacuation of families andpossibility of structural damage in the houses.

High 10 Events that cause human losses or injuries.

xxxxx: xxxx 27

Table 4. Results of the principal component analysis for socio-economic fragility indicators.

Variable Symbol Loadings

Lack of Well-being (PVE=0.8)Women-headed households Whh 0.94

Unemployment UE 0.97Poor-Unsatisfied Basic Needs Index PUBNI 0.98

% Homeless Ho 0.92% Poor P 0.99

Persons per home Pho 0.94Mortality M 0.91

Life Expectancy LE 0.94Quality life index QLI 0.86

Human Development Index HDI 0.97Population Growth Rate G 0.57

Demography (PVE=0.2)% of Children and Elderly Age 0.84

% Disabled D 0.67% Population estrata 1 and 2 PE12 0.81

% Illegal settlements IS 0.64

Table 5. Results of the principal component analysis resilience in-dicators

Variable Symbol Loadings

Lack of Education (PVE=0.53)Level of Education LEd 0.94

Illiteracy I 0.96Access to information AI 0.93

Lack of Prep. and Resp. Capacity (PVE=0.47)Infrastructure/accessibiliy IA 0.80

Hospital beads Hb 0.97Health Care HR HRh 0.92

Table 6. Results of the principal component analysis physical sus-ceptibility indicators

Variable Symbol Loadings

Exposed infrastructure (PVE=0.52)Number of civic buildings Ncb 0.86Number of industrial units Niu 0.96Number of comercial units Ncu 0.85

Exposed population (PVE=0.48)Number of residential units Nru 0.91

Population exposed Pe 0.85Density of population Dp 0.78