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Applied Geography 39 (2013) 16e25

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Applied Geography

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Impact of the 2010e2011 La Niña phenomenon in Colombia, SouthAmerica: The human toll of an extreme weather event

N. Hoyos a,b,*, J. Escobar a,c, J.C. Restrepo d, A.M. Arango e, J.C. Ortiz d

aCenter for Tropical Paleoecology and Archaeology, Smithsonian Tropical Research Institute (STRI), PanamabCorporación Geológica ARES, Calle 44A No. 53-96, Bogotá, ColombiacUniversidad del Norte, Km 5 vía Puerto Colombia, Departamento de Ingeniería Civil y Ambiental, Barranquilla, ColombiadGrupo de Física Aplicada, Área de Océano y Atmósfera, Departamento de Física, Universidad del Norte, Km 5 vía Puerto Colombia, Barranquilla, Colombiae iMMAP, Bogota, Colombia

Keywords:ENSOExtreme weather eventsSpatial autocorrelationSpatial errorNatural hazardVulnerability

* Corresponding author. Corporación Geológica ABogotá, Colombia. Tel.: þ57 3105149269.

E-mail address: natahoyos2011@gmail.com (N. Ho

0143-6228/$ e see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.apgeog.2012.11.018

a b s t r a c t

The 2010e2011 La Niña (positive phase of El Niño) phenomenon affected four million Colombians, w9%of the total population, and caused economic losses of approximately US $7.8 billion, related todestruction of infrastructure, flooding of agricultural lands and payment of government subsidies. Weanalyzed the spatial patterns of effects on the population, measured as the number of affected persons ineach municipality normalized to the total municipal population for 2011, using global (Moran’s I index)and local (LISA) spatial autocorrelation indicators, and multiple regression analyses (OLS and ML spatialerror model). The spatial autocorrelation analysis revealed two regional clusters or “hotspots” with highautocorrelation values, in the lower Magdalena River Valley (Caribbean plains) and lower Atrato Valley(Pacific lowlands). The regression analyses emphasized the importance of the spatial component as wellas the variables related to hazard exposure and social vulnerability. Municipalities in “hotspots” show:(1) a high degree of flooding, as they are located on the Magdalena and Atrato River floodplains, and (2)high social vulnerability, suggested by low values of the ICV (national living conditions index).

� 2012 Elsevier Ltd. All rights reserved.

Introduction

Climate patterns have changed throughout Earth’s history. Sincethe late 1800s, these changes have been largely caused byincreasing amounts of anthropogenic greenhouse gases in theatmosphere. The average temperature of the planet has increased0.74 �C over the last century, and most of this increase has occurredin the last three decades (Arguez, 2007; IPCC, 2007). It is estimatedthat increases in the concentration of greenhouse gases will causeadditional warming of 1.1e6.4 �C by the end of this century (IPCC,2007). The increase in global average temperatures is expected tocause increases in extreme weather events, which will, in turn,have effects on ecosystems and society. Such events drive greaterchanges in natural and social systems than do average climateconditions as a consequence of damage to infrastructure and agri-cultural lands, diminished ecosystem function, and human death,

RES, Calle 44A No. 53-96,

yos).

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injury and displacement (Parmesan & Martens, 2008; Parmesan,Root, & Willig, 2000). Climate change, particularly extremeweather events, poses risks and challenges for society. Mostresearch, however, has addressed the climate component of climatechange, whereas its impact on human well-being remains poorlyunderstood (NRC, 2009). In social terms, effects of extreme eventsare evaluated by analyzing the vulnerability of exposed commu-nities. Impacts on socioeconomic systems are often amplified byfactors such as social inequality, disease and social conflict.Understanding vulnerability and how it relates to climate change,particularly extreme weather events, is an initial step in managingclimate change risks. Geographically explicit vulnerability analysisis critical to understand how interactions between the physicalenvironment and humans change over space and time (Emrich &Cutter, 2011; Montz & Tobin, 2011; Moser, 2010).

Colombia experienced a strong El Niño Southern Oscillation(ENSO) cold phase known as La Niña, from 2010 to 2011. Theweather event affected approximately four million people as ofSeptember 2011 and caused losses of more than US $7.8 billion, asa consequence of destruction of infrastructure, flooding of agri-cultural lands and payment of government subsidies (Redacción,

N. Hoyos et al. / Applied Geography 39 (2013) 16e25 17

2010a, 2011a). A wealth of data was generated by governmentagencies and non-governmental organizations on the effects of thisphenomenon. Furthermore, such information was used to developmitigation plans. Participating institutions included the NationalOffice for Disaster Risk Management (Unidad Nacional para laGestión del Riesgo de Desastrese UNGRD), National Department ofStatistics (Departamento Nacional de Estadística e DANE), NationalInstitute of Hydrology, Meteorology and Environmental Studies(Instituto de Hidrología, Meteorología y Estudios Ambientales e

IDEAM), the National Geographic Institute (Instituto GeográficoAgustín Codazzi e IGAC) and non-governmental entities such asiMMAP and the United Nation’s Office for the Coordination ofHumanitarian Affairs (OCHA). Although these institutions pre-sented their data in a spatial format (i.e. maps), rigorousgeographical analysis was not done, largely because of timeconstraints. In this study, we assessed the spatial patterns of ENSOeffects on the human population in Colombia, and explored therelationship between such patterns and physical geographic andsocioeconomic variables. We first summarize the effect of ENSO onColombian river flow dynamics and followwith a spatial analysis ofthe 2010e2011 La Niña event. We conclude with a discussion of ourfindings.

Climate and river discharge during ENSO

In Colombia, the annual hydrologic cycle is controlled by oscil-lation of the inter-tropical convergence zone, superimposed onregional patterns caused by orographic influence of the Andes,evapotranspiration in the Amazon Basin, continent-atmosphereinteractions and dynamics of the western Colombian windcurrents (Western Colombian Jet e Chocó Jet) (Mesa, Poveda, &Carvajal, 1997; Poveda, Jaramillo, Gil, Quiceno, & Mantilla, 2001;Poveda & Mesa, 2004) (Fig. 1). Over longer time scales, majorhydrologic anomalies are experienced during both phases of ENSO(Aceituno, 1988; Poveda, 2004; Poveda et al., 2001) and othermacro-climatic phenomena such as the North Atlantic Oscillation(NAO) and Pacific Decadal Oscillation (PDO) (Mesa, Poveda,&Carvajal, 1997; Poveda et al., 2002).

The ENSO warm phase (El Niño) causes droughts in the westernmargin of Central America, Mexico, the Amazon Basin, northernSouth America (i.e. Colombia and northeastern Brazil), whereas itproduces excess precipitation in the eastern region of CentralAmerica, and increased summer rainfall in the Paraná Basin and theAndes of Peru, Bolivia and Chile (Capel, 1999). In Colombia, ENSOhas a strong effect on precipitation, river discharge and soil mois-ture (Montealegre & Pabón, 1992; Poveda & Mesa, 1996; Povedaet al., 2001, 2002; Puertas & Carvajal, 2008; Restrepo & Kjerfve,2004). The warm phase is associated with an increase in theaverage air temperature, a decrease in soil moisture and evapo-transpiration, a decrease in rainfall and a consequent decrease inthe average flow of the rivers in the western, central and northernregions of the country (Poveda et al., 2001). The opposite pattern isobserved during the cold phase (La Niña), which is mainly char-acterized by intense and abundant rainfall, increased river flow andsubsequent flooding (Poveda & Mesa, 1996; Mesa et al., 1997;Poveda et al., 2001). ENSO events, however, differ in intensity andspatial extent, so their effects on hydro-climatology are event-specific (Poveda, 2004).

A common variable used to assess the strength of a particularENSO event is the Southern Oscillation Index (SOI). It is calculatedas the normalized difference in surface air pressure between Dar-win, Australia (Western Pacific) and Tahiti, French Polynesia(Eastern Pacific). A positive index points to low pressures in thewestern tropical Pacific and indicates the occurrence of the coldphase (La Niña). A negative index signals the presence of the warm

phase (El Niño). According to this index, there were at least 19 ElNiño and 17 La Niña events between 1950 and 2011 (NOAA, 2011).Because of their intensity and duration, the warm events in 1957e1958 (8 months), 1965e1966 (12 months), 1972e1973 (10 months),1976e1978 (18 months), 1982e1983 (14 months), 1986e1987 (16months), 1991e1992 (17 months), 1997e1998 (12 months) and2009e2010 (11 months) are notable. Strong cold events took placein 1954e1957 (20 months), 1970e1971 (14months), 1973e1974 (13months), 1975e1976 (12 months), 1988e1989 (14 months), 1998e2000 (24 months), 2007e2008 (10 months) and 2010e2011 (10months) (Fig. 2). Climatic, hydrological and oceanographic distur-bances related to these events had dramatic global socioeconomicand environmental repercussions (Capel, 1999).

In Colombia, the 1982e1983 ENSO stimulated scientific andacademic interest because of its environmental impacts, particu-larly in the marine sector (Alvarado, Duque, Flórez, & Ramírez,1986). Interest only became widespread after the 1991e1992event, which caused a large decrease in precipitation and Andeanriver streamflows, and led to a collapse of the national hydropowersystem (Mesa et al., 1997; Montealegre & Pabón, 1992). The rela-tionship between ENSO and river flow in Colombia was studied byMesa et al. (1997) and Restrepo & Kjerfve (2000). They showed thatENSO has an earlier and stronger effect on rivers in western,northern and central Colombia, in contrast to a later and reducedeffect on rivers in the eastern and southeastern regions of thecountry. For instance, ENSO explains up to 64% of the inter-annualvariability in discharge of the Magdalena River, the main riverdraining the Colombian Andes (Restrepo & Kjerfve, 2000). Abruptchanges in river discharge have occurred during the past 12 years,and all were related with ENSO cold conditions (Fig. 3a). Waveletanalysis, however, reveals that the contribution of ENSO to flowvariability has not been constant over time (Fig. 3b). Caribbean riverdischarge also reflects the effect of ENSO (Fig. 4). Nevertheless, it isdifficult to separate the influence of climate variability from that ofanthropogenic disturbance (Restrepo & Restrepo, 2005).

The 2010e2011 ENSO cold event was one of the most intense, inboth duration and magnitude (Fig. 2). In 2010, there was a rapidtransition between the warm and cold phases of ENSO. Completionof the 2009e2010 warm event was marked by negative SOIanomalies during the first quarter of 2010. Beginning in July, thepositive anomalies were consolidated, which initiated the coldevent and lasted for 18 months, until December 2011. During thatperiod, the anomalies ranged from 1.9 to 5.2. The only comparableanomalies were observed in the cold events of 1970e1971, 1975e1976 and 2007e2008.

Methods

For our spatial analysis, we used the number of individuals ineach municipality reported as affected by the UNGRD, as ofSeptember 2011. We normalized by the total municipal populationin 2011, estimated by extrapolation from the 2005 National Censusby the National Department of Statistics (DANE). A value of 1 meansthat all (100%) of the municipality’s inhabitants were affected,whereas a value of 0.5 means that 50% were affected, and so on. Bygovernment standards, the term “affected” included (1) individualswhowere deceased, missing, or suffered direct material loss and/orinjury, and (2) individuals who suffered indirect or secondaryimpacts, such as not being able to work.

We compiled disaster-related data, as well as socioeconomic,hydrological, geomorphological and administrative data fromvarious sources (Table 1). Editing and analysis was conducted usingArcGIS (ESRI), Geoda (Anselin, 2005) and NCSS (Hintze, 2007).Spatial autocorrelation analysis was accomplished using global(Moran’s I) and local (LISA) indicators (Moran, 1948; Anselin, 1995).

Fig. 2. Southern Oscillation Index (SOI) anomalies for the 1951e2010 period. The thin line represents the raw data (NOAA, 2011), the thick line represents data smoothed bya low-pass filter. Light boxes represent El Niño events,dark boxes represent La Niña events.

Fig. 1. Major physiograhic regions and rivers of Colombia, and relevant cities mentioned in the text. (1) Magdalena River, (2) Cauca River, (3) Sinú River, (4) Atrato River, (5)Putumayo River, (6) Western Cordillera, (7) Central Cordillera, (8) Eastern Cordillera, (9) Eastern plains or Llanos, (10) Amazon region. Elevation data from shuttle radar topographymission (USGS 2006). Basemap data from the national geographic database (sigotn.igac.gov.co).

N. Hoyos et al. / Applied Geography 39 (2013) 16e2518

Fig. 3. Average discharge for the Magdalena River. (a) Standardized monthly discharge and southern oscillation index (SOI) with El Niño events shown in light boxes and La Niñaevents in dark boxes, and (b) Morletwavelet spectrum and scale average variance of 2e8 year band (1941e2010).

N. Hoyos et al. / Applied Geography 39 (2013) 16e25 19

Once the statistical significance of the spatial patterns was estab-lished, we performed a regression analysis, using the normalizednumber of affected individuals as the dependent variable, andrelevant socioeconomic and environmental factors as explanatoryvariables. For the latter, we used variables that (1) were publiclyavailable, (2) could be aggregated at the municipal level and (3) hadbeen measured, whenever possible, close to the time of our periodof interest (2010e2011). Socioeconomic variables included: (1)population density, measured as the estimated population in 2011divided by the municipality area, (2) the 2005 living conditionsindex (ICV), which is a measure of the possession of physical goods(access to public services and housing characteristics), humancapital (average years of education of household heads and childrenmore than 12 years old, and school attendance) and householdcomposition (overcrowding and number of children less than 6years old) (DNP, 1999), (3) the 2005 water supply and sewercoverage, and (4) the 2010 municipal performance index, which isa measure of local compliance with development goals, adminis-trative capacity and fiscal performance (DNP, 2010). The followingphysical environmental variables were considered: (1) percent ofthe total municipal area subject to flooding i.e. floodplains, lowalluvial terraces, eolian lowlands subject to seasonal flooding,overflow swamp lowlands, deltas and coastal areas (Flórez et al.,2010), (2) annual maximum peak discharge for 2, 5, 10, 20, 50and 100 year return periods (each municipality was assigned themaximum value within its area of jurisdiction), and (3) the average

monthly precipitation for the months of June, July and August(rainy season in the eastern Llanos and Amazon) and October andNovember (rainy season in the Andean and Caribbean regions).Each municipality was assigned the average value within its area ofjurisdiction.

We used two regression analysis techniques to assess theimportance of the spatial component, a multiple linear regressionmodel with coefficients estimated by the method of ordinary leastsquares (OLS) and a spatial autoregressive model with spatial errordependence, which estimates the coefficients by the maximumlikelihood method (ML). The first model assumes spatially inde-pendent observations, whereas the latter includes a spatialcomponent because it assumes that model errors are spatiallycorrelated. The original datawere modified as follows.We assigneda value of 1.0 to municipalities with anomalous values (>1.0) of thedependent variable (normalized number of affected individuals).Values >1.0 imply that affected individuals outnumber the totalpopulation for a municipality. We therefore re-scaled those valuesto the maximum possible, i.e. the total population affected. Withrespect to the independent variables, municipalities with missingdata and “island” municipalities, i.e. those without neighbors,were eliminated. After these modifications, we had 1,090 munici-palities that were included in the regression analyses. Finally, thestatistical distribution of each variable was assessed and, ifnecessary, transformations were performed to obtain a normaldistribution.

Fig. 4. Discharge patterns for eight rivers in the Colombian Caribbean region (Restrepo et al., in preparation). Mean annual discharge represented by the solid line, long-term trendin dashed line, shaded area indicates change as identified by the Pettit test. Z is the standardized variable of ManneKendall test for significant long-term trends (significant trends at90% confidence level when Z > Z(1 � a/2) ¼ 1.77).

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For the regression analyses, we followed the methods of Anselin(2005) and Hinze (2007). Briefly, we used techniques to identifyvariables with significant predictive power. Then we performedOLS regression with the selected variables and looked for spatialdependence of errors. Finally, we calculated the most appropriatespatial autoregressive model according to the indices of spatialdependence.

Results

Regional “hotspots” for affected individuals (raw and normal-ized values) include municipalities on the Pacific and Caribbeancoasts, in the lower Magdalena Valley and a few in the Andes(Fig. 5). Thirty-seven municipalities had anomalous normalizedvalues, >1.0. The most extreme cases were observed in somemunicipalities from the Pacific and Caribbean states, where thenumber of affected individuals was nearly twice the total pop-ulation. We believe this was a consequence of inaccurate

population projections from the 2005 Census, or incorrect regis-tration of individuals affected by flooding, with many individualsregistered multiple times. The normalized map fails to show somemunicipalities where a large number of individuals were affected,but they represent only a small percentage of the total municipal-ity’s population. This phenomenon was particularly noticeable instate capitals (e.g. Riohacha, Montería, Valledupar, Cúcuta, Medel-lín, Cali and Florence), as well as the national capital, Bogotá.

Global and local spatial autocorrelation indices were significant.For example, Moran’s I index for global spatial autocorrelation(0.42, a ¼ 0.05, n ¼ 1123, Rook contiguity matrix type) indicatessignificant positive spatial autocorrelation. Similarly, local indica-tors of spatial association (LISA) point to the existence of tworegional “hotspots” in the lower Magdalena River Valley (61municipalities, 5.43% of total) and the Atrato River Valley (13municipalities, 1.16% of the total) (Fig. 6). Other smaller “hotpspots”were observed along the southern Pacific coast (11 municipalities,0.98% of the total) and on the northern Eastern Cordillera, close to

Table 1Relevant characteristics of spatial data.

Group Name Source Temporal resolution Spatial resolution

Basemap data Municipalities IGACa 2009 MunicipalPopulation DANEb 2011 (projected) Municipal

Disaster data Individuals affected UNGRDc April 2010eSeptember 2011 MunicipalExplanatory variables Municipal area (% of total)

subject to floodingFlórez et al., 2010 NA 1:500,000

Annual maximum discharge(m3 s�1) (return periods of 2,5, 10, 20, 50 and 100 years)

HidroSIGd >25 years 12000 (w3.7 km)

Average monthly rainfall forJune, July, August, Octoberand November (mm)

HidroSIGd >25 years 30000 (w9.3 km)

Population density(individuals km�2)

DANEb 2011 Municipal

Index of living conditionsICVe (%)

DNPf 2005 Municipal

Municipal Performancee (%) DNPf 2010 MunicipalAqueduct coverage (%) DANEb 2005 MunicipalSewer coverage (%) DANEb 2005 Municipal

a National geographic institute.b National department of statistics.c National office for disaster risk management.d School of Geosciences and Environment, National University of Colombia, Medellin , v. 3.1 Beta.e See text for details.f National planning department.

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the Venezuelan border (6 municipalities, 0.53% of the total). Clus-ters of low values are located on the Eastern plains (Orinoquía),Amazon and southern Eastern Cordillera eastern foothills (45municipalities, 5.34% of the total). Less extensive, low-value clus-ters are apparent in the central Eastern Cordillera (35 municipali-ties, 3.12% of the total), and northern Central Cordillera (78municipalities, 6.95% of the total).

We selected the regression model that had both good predictivepower and the smallest possible number of independent variables.

Fig. 5. (a) Raw number of individuals affected by the 2010e2011 La Niña, and (b) normalized2011 population). Municipalities with anomalous values are shown with a thick black outli

Table 2 shows the relevant characteristics of the selected model,a spatial autoregressive model with spatial error dependence, incomparison with an equivalent multiple linear regression model.Socioeconomic variables (living conditions index) as well as envi-ronmental physical variables (percent of themunicipality subject toflooding and average June precipitation) were selected as signifi-cant explanatory variables (p < 0.01). Some other variablesexhibited high correlation with the selected variables and werediscarded because of multi-collinearity. For example, potable water

number of individuals affected by the 2010e2011 La Niña (raw number divided by totalne. All data are aggregated at the municipal level.

Fig. 6. Clusters of positive spatial autocorrelation for the normalized number ofaffected individuals. High values are shown in red (dark gray), whereas low values areshown in blue (intermediate gray). Areas with negative spatial autocorrelation areshown in light gray. (For interpretation of the references to color in this figure legend,the reader is referred to the web version of this article.)

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and sewer coverage, as well as population density, showeda significant positive correlationwith the index of living conditions.Although both models (classical and spatial) were similar in termsof selected variables and regression coefficients, we selected the

Table 2Relevant characteristics of selected regression models (n ¼ 1,090). Variablessignificant at p < 0.01.

Variable/mode1 Multiple linearregression (OLS)

Spatial error model(MLE)

Coefficient t-value Coefficient z-value

Living conditionsindex

�0.010599 �16.18605 �0.009156 �12.60932

% municipal landsubjectto flooding (x0.5)

0.299605 11.4672 0.239139 6.89489

Average Junerainfall (log)

�0.179308 �6.06699 �0.124084 �2.64768

la N/A 0.566601 16.71821Adjusted R2 0.272 N/APseudo R2 N/A 0.448Log likelihood 86.216 198.781Akaike criterion �164.432 �389.563Schwarz criterion �144.456 �369.59Residuals spatial

autocorrelation(Moran I index)

0.32 �0.02

a Spatial autoregressive coefficient.

spatial error model because it had: (1) better performance indica-tors (log-likelihood, Akaike and Schwarz criteria), (2) a highlysignificant spatial autoregressive coefficient (l), and (3) residualsthat were not spatially autocorrelated.

Discussion

This study was framed within the context of vulnerability andnatural hazards research. As these terms are widely used, we followthe definitions of Cutter & Finch (2008) and UNISDR (2009). Naturalhazard refers to a “natural process or phenomenon that may causeloss of life, injury or other health impacts, property damage, loss oflivelihoods and services, social and economic disruption, or envi-ronmental damage” (UNISDR, 2009). On the other hand, vulnera-bility is broadly defined as the potential for loss and is a function ofexposure, sensitivity and resilience (Cutter & Finch, 2008; Wood,Burton, & Cutter, 2010). Exposure refers to the frequency, severityand extent of a specific hazard (Emrich & Cutter 2011). Sensitivity(or social vulnerability) refers to the ability of a community toprotect itself from future events and depends on the social,economic and demographic characteristics that make it susceptibleto loss (Cutter, Boruff, & Shirley, 2003; Emrich & Cutter, 2011). Theresilience of a community is defined as its ability to resist, absorb,adapt and recover during and after an event (Cutter & Finch, 2008;UNISDR, 2009; Wood et al., 2010). The 2010e2011 La Niña wasa multi-hazard event, as it was associated with the occurrence offloods, landslides, windstorms, lightning and landslides. Floods andlandslides were by far the most common and damagingphenomena. For instance, for the SeptembereDecember of 2011rainy season, a total of 1,107 weather-related events were reportedby UNGRD, of which 684 (62%) were floods and 321 (29%) werelandslides. In the same period, 182 individuals were reported deadas a result of landslides (172 or 95%) and floods (10) (UNGRD, 2011).

In terms of vulnerability, the variables included in our modelrepresent exposure, sensitivity and resilience. For example, thepercent of municipal land subject to flooding is an indication of thedegree of exposure (Fig. 7). The spatial error model indicates, aswould be expected, a positive relationship between the area subjectto flooding and normalized number of affected individuals. Theextent of flooding for the 2010e2011 La Niña can be assessed fromofficial reports comparing the extent of seasonal floods during“regular” conditions, with those during the 2010e2011 La Niña, forthe most critical regions, i.e. the Caribbean, Pacific and easternAndean foothills (Instituto Geográfico Agustín Codazzi IGAC,Instituto de hidrología, meteorología y estudios ambientalesIDEAM, & Departamento Administrativo Nacional de EstadísticaDANE, 2011). Numbers show that during the rainy seasons of2010e2011, the flooded areas in those regions nearly doubledrelative to the baseline year (2001). Specifically, it is estimated thatseasonal flooding affects 1,212,965 ha in the eastern Andean foot-hills, Caribbean lowlands, lower Magdalena Valley, and lower Sinúand Atrato River Basins. In comparison, during the 2010e2011 LaNiña, 1,642,108 ha of additional land were flooded, primarily in thelower Magdalena River Basin and the lower Sinú River Basin (IGACet al., 2011).

We propose, however, that the living conditions index (ICV)represents, at least partially, the concepts of sensitivity and resil-ience (Fig. 7). Because of its multidimensional nature, this indexprovided a better representation of social vulnerability than inde-pendent variables such as water supply and sewers, which werealso redundant according to multicollinearity indicators. Regionalstudies on social vulnerability indicate that its spatial and temporalvariability are related to variables that measure socioeconomicstatus, age, commercial, industrial and housing developments,rurality, race, gender and employment (Cutter & Finch, 2008;

Fig. 7. (a) Spatial distribution of life conditions index (ICV) at the municipal level (2005), and (b) Spatial distribution of areas subject to flooding as percentage of total municipalarea (flood-prone areas from Flórez et al., 2010). Municipalities with hatch pattern were not included in the regression analysis as they had missing data or were island polygons.

N. Hoyos et al. / Applied Geography 39 (2013) 16e25 23

Emrich & Cutter, 2011; Lazarus, 2011). The relationship is notstraightforward and should be interpreted within the broadercontext of social and economic policies (Lazarus, 2011). Forinstance, studies in the United States indicate that increased socialvulnerability is driven by poverty, ethnicity, rurality and gender(Cutter & Finch, 2008; Emrich & Cutter, 2011). By comparison,research from Sri Lanka shows that relations between gender,ethnicity and vulnerability (measured as coping capacity) areplace-dependent (Lazarus, 2011). In our case, higher vulnerability,as indicated by ICV scores, is associated with restricted access topublic services, limited education, poor home construction mate-rials, and overcrowding. Because the ICV score is a compoundindex, it is not possible to assess the relative importance of eachfactor. Geographically, low ICV scores are found mostly in rural,sparsely populated areas in the Caribbean lowlands, Pacific coast,Llanos and Amazon (Fig. 7). Furthermore, resilience to naturaldisasters is also multidimensional and integrates ecological, social,economic, institutional and infrastructure variables (Cutter & Finch,2008). Recent studies and policies on disaster-risk reductionemphasize the importance of resilience as a tool to reduce thevulnerability of communities exposed to natural hazards (UNISDR,2010). In our study, the spatial regression model shows a negativerelationship between ICV and the normalized number of affectedindividuals. This result is in agreement with the above findingsfrom other studies.

Regarding the precipitation variable included in our model(average June rainfall), we believe it represents the contrastingregimes of the Andean/Caribbean and Llanos/Amazon regions.Under normal conditions, the Andean and Caribbean regions arepredominantly dry in June, whereas wet conditions prevail over theLlanos and Amazon regions. The spatial autocorrelation analysisshows that most municipalities in the Llanos and Amazon wereaffected little by the 2010e2011 La Niña. We believe this pattern

was a consequence of: (1) lower population density and (2) reduceddischarge response to La Niña, as it is both delayed and of smallermagnitude than in the Andean and Caribbean regions (Poveda et al.,2001). Although it seems counterintuitive, it is for this reason thatthe precipitation variable has a negative relationship with thenormalized number of affected individuals.

It is useful to analyze specific cases where our model grosslyunderestimates the number of affected people. For this analysis,we focused on municipalities with residuals that were >2 stan-dard deviations (þ2 std dev) from the regression line (Fig. 8).There were 43 municipalities (3.9% of the total) for which themodel underestimated the true number of affected individuals.Poor prediction in these municipalities was related to severalfactors. The first factor is flooding by small rivers, which was notconsidered in our scale of analysis. This was the case for somemunicipalities in the Pacific region and Eastern Cordillera (Bagadó,2011; OCHA, 2010; Redacción, 2010b, 2011b, 2011c). The secondfactor is the occurrence of hazards other than flooding, such aslandslides, storms, and mud and debris flows, which were notincluded in our model. Examples include municipalities in thenorthern Eastern Cordillera, and central Western Cordillera(Redacción, 2010c, 2010d). The third factor is related to munici-palities that, despite having considerable areas that are flood-prone, had low percent values for this variable because the totalmunicipal area was very large. This situation was observed insouthern Colombia, along the Putumayo River (Redacción, 2010e;Salamanca, 2011). Finally, there were several municipalities wherethe model performed poorly even though they had a large fractionof their area (>30%) in the flood-prone lowlands of the Magdalena,Cauca and Sinú Rivers. (Alzate, 2010; Redacción, 2010f, 2010g,2011d). These cases require further investigation to understandwhat specific factors in each municipality accounted for the highnumber of affected individuals.

Fig. 8. Municipalities where the normalized number of affected individuals wasgrossly underpredicted (residuals > 2.0 std dev). Light shaded polygons representflood-prone areas from Flórez et al. (2010). Elevation data from shuttle radar topog-raphy mission (USGS 2006).

N. Hoyos et al. / Applied Geography 39 (2013) 16e2524

Conclusions

Our analysis showed that Colombians affected by the 2010e2011 La Niña were clustered in two regional “hotspots,” in thelower Magdalena River Valley and Pacific regions. Areas wherepeople were less affected (“coldspots”) were concentrated in theLlanos and Amazon region. Our regression model emphasizes theimportance of the spatial component. Conceptually, this meansthat values at one municipality are related to values at neighboringmunicipalities. The model also emphasizes the role of variablestraditionally used to assess natural hazards, such as hazardexposure and social vulnerability. The high numbers of peopleaffected in municipalities of the lower Magdalena and Atrato RiverBasins point to the importance of both high hazard exposure andhigh social vulnerability. In these municipalities, social vulnera-bility was amplified by internal armed conflict (Colombia SSH,2011).

The scale of our analysis precluded modeling predominantlylocal threats such as mud or debris flows, windstorms and floodingby smaller, local streams. In addition, although publicly accessibledata proved useful, we caution that the most complete, availablesocioeconomic data came from the 2005 National Census. There-fore, there was a 6e7 year gap between the collection of demo-graphic data and occurrence of the 2010e2011 La Niña. Finally,considering that floods were the predominant hazard and had thegreatest impact, we believe it is necessary to address integrated

flood-risk management (Schelfault et al., 2011). This perspectiveemphasizes the reduction of vulnerability by strengthening theresilience of at-risk communities. It is predicated on the belief thatfloods cannot be controlled by structural measures alone (Dixonet al., 2006), and that social vulnerability plays a critical role incommunity recovery (Finch, Emrich, & Cutter, 2010). Our studyindicates that future strategies to mitigate the impacts of climateevents such as the 2010e2011 La Niña should include compre-hensivemeasures to reduce the social vulnerability of communitiesand thereby increase their resilience. As such, the importance ofupdating socioeconomic data related to social vulnerability isunderscored.

Acknowledgments

We thank Dr. Mark Brenner for proof reading the article andthe comments provided by the editor and two anonymousreviewers.

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