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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, South America: 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 a Center for Tropical Paleoecology and Archaeology, Smithsonian Tropical Research Institute (STRI), Panama b Corporación Geológica ARES, Calle 44A No. 53-96, Bogotá, Colombia c Universidad del Norte, Km 5 vía Puerto Colombia, Departamento de Ingeniería Civil y Ambiental, Barranquilla, Colombia d Grupo de Física Aplicada, Área de Océano y Atmósfera, Departamento de Física, Universidad del Norte, Km 5 vía Puerto Colombia, Barranquilla, Colombia e iMMAP, Bogota, Colombia

abstract

Keywords: The 2010e2011 La Niña (positive phase of El Niño) phenomenon affected four million Colombians, w9% ENSO of the total population, and caused economic losses of approximately US $7.8 billion, related to Extreme weather events destruction of infrastructure, flooding of agricultural lands and payment of government subsidies. We Spatial autocorrelation analyzed the spatial patterns of effects on the population, measured as the number of affected persons in Spatial error ’ Natural hazard each municipality normalized to the total municipal population for 2011, using global (Moran s I index) Vulnerability and local (LISA) spatial autocorrelation indicators, and multiple regression analyses (OLS and ML spatial error model). The spatial autocorrelation analysis revealed two regional clusters or “hotspots” with high autocorrelation 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 well as 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). Ó 2012 Elsevier Ltd. All rights reserved.

Introduction injury and displacement (Parmesan & Martens, 2008; Parmesan, Root, & Willig, 2000). Climate change, particularly extreme Climate patterns have changed throughout Earth’s history. Since weather events, poses risks and challenges for society. Most the late 1800s, these changes have been largely caused by research, however, has addressed the climate component of climate increasing amounts of anthropogenic greenhouse gases in the change, whereas its impact on human well-being remains poorly atmosphere. The average temperature of the planet has increased understood (NRC, 2009). In social terms, effects of extreme events 0.74 C over the last century, and most of this increase has occurred are evaluated by analyzing the vulnerability of exposed commu- in the last three decades (Arguez, 2007; IPCC, 2007). It is estimated nities. Impacts on socioeconomic systems are often amplified by that increases in the concentration of greenhouse gases will cause factors such as social inequality, disease and social conflict. additional warming of 1.1e6.4 C by the end of this century (IPCC, Understanding vulnerability and how it relates to climate change, 2007). The increase in global average temperatures is expected to particularly extreme weather events, is an initial step in managing cause increases in extreme weather events, which will, in turn, climate change risks. Geographically explicit vulnerability analysis have effects on ecosystems and society. Such events drive greater is critical to understand how interactions between the physical changes in natural and social systems than do average climate environment and humans change over space and time (Emrich & conditions as a consequence of damage to infrastructure and agri- Cutter, 2011; Montz & Tobin, 2011; Moser, 2010). cultural lands, diminished ecosystem function, and human death, Colombia experienced a strong El Niño Southern Oscillation (ENSO) cold phase known as La Niña, from 2010 to 2011. The weather event affected approximately four million people as of September 2011 and caused losses of more than US $7.8 billion, as * Corresponding author. Corporación Geológica ARES, Calle 44A No. 53-96, a consequence of destruction of infrastructure, flooding of agri- Bogotá, Colombia. Tel.: þ57 3105149269. E-mail address: [email protected] (N. Hoyos). cultural lands and payment of government subsidies (Redacción,

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2010a, 2011a). A wealth of data was generated by government phase (El Niño). According to this index, there were at least 19 El agencies and non-governmental organizations on the effects of this Niño and 17 La Niña events between 1950 and 2011 (NOAA, 2011). phenomenon. Furthermore, such information was used to develop Because of their intensity and duration, the warm events in 1957e mitigation plans. Participating institutions included the National 1958 (8 months), 1965e1966 (12 months), 1972e1973 (10 months), Office for Disaster Risk Management (Unidad Nacional para la 1976e1978 (18 months), 1982e1983 (14 months), 1986e1987 (16 Gestión del Riesgo de Desastres e UNGRD), National Department of months), 1991e1992 (17 months), 1997e1998 (12 months) and Statistics (Departamento Nacional de Estadística e DANE), National 2009e2010 (11 months) are notable. Strong cold events took place Institute of Hydrology, Meteorology and Environmental Studies in 1954e1957 (20 months), 1970e1971 (14 months), 1973e1974 (13 (Instituto de Hidrología, Meteorología y Estudios Ambientales e months), 1975e1976 (12 months), 1988e1989 (14 months), 1998e IDEAM), the National Geographic Institute (Instituto Geográfico 2000 (24 months), 2007e2008 (10 months) and 2010e2011 (10 Agustín Codazzi e IGAC) and non-governmental entities such as months) (Fig. 2). Climatic, hydrological and oceanographic distur- iMMAP and the United Nation’sOffice for the Coordination of bances related to these events had dramatic global socioeconomic Humanitarian Affairs (OCHA). Although these institutions pre- and environmental repercussions (Capel, 1999). sented their data in a spatial format (i.e. maps), rigorous In Colombia, the 1982e1983 ENSO stimulated scientific and geographical analysis was not done, largely because of time academic interest because of its environmental impacts, particu- constraints. In this study, we assessed the spatial patterns of ENSO larly in the marine sector (Alvarado, Duque, Flórez, & Ramírez, effects on the human population in Colombia, and explored the 1986). Interest only became widespread after the 1991e1992 relationship between such patterns and physical geographic and event, which caused a large decrease in precipitation and Andean socioeconomic variables. We first summarize the effect of ENSO on river streamflows, and led to a collapse of the national hydropower Colombian river flow dynamics and follow with a spatial analysis of system (Mesa et al., 1997; Montealegre & Pabón, 1992). The rela- the 2010e2011 La Niña event. We conclude with a discussion of our tionship between ENSO and river flow in Colombia was studied by findings. Mesa et al. (1997) and Restrepo & Kjerfve (2000). They showed that ENSO has an earlier and stronger effect on rivers in western, Climate and river discharge during ENSO northern and central Colombia, in contrast to a later and reduced effect on rivers in the eastern and southeastern regions of the In Colombia, the annual hydrologic cycle is controlled by oscil- country. For instance, ENSO explains up to 64% of the inter-annual lation of the inter-tropical convergence zone, superimposed on variability in discharge of the Magdalena River, the main river regional patterns caused by orographic influence of the Andes, draining the Colombian Andes (Restrepo & Kjerfve, 2000). Abrupt evapotranspiration in the Amazon Basin, continent-atmosphere changes in river discharge have occurred during the past 12 years, interactions and dynamics of the western Colombian wind and all were related with ENSO cold conditions (Fig. 3a). Wavelet currents (Western Colombian Jet e Chocó Jet) (Mesa, Poveda, & analysis, however, reveals that the contribution of ENSO to flow Carvajal, 1997; Poveda, Jaramillo, Gil, Quiceno, & Mantilla, 2001; variability has not been constant over time (Fig. 3b). Caribbean river Poveda & Mesa, 2004)(Fig. 1). Over longer time scales, major discharge also reflects the effect of ENSO (Fig. 4). Nevertheless, it is hydrologic anomalies are experienced during both phases of ENSO difficult to separate the influence of climate variability from that of (Aceituno, 1988; Poveda, 2004; Poveda et al., 2001) and other anthropogenic disturbance (Restrepo & Restrepo, 2005). macro-climatic phenomena such as the North Atlantic Oscillation The 2010e2011 ENSO cold event was one of the most intense, in (NAO) and Pacific Decadal Oscillation (PDO) (Mesa, Poveda, both duration and magnitude (Fig. 2). In 2010, there was a rapid &Carvajal, 1997; Poveda et al., 2002). transition between the warm and cold phases of ENSO. Completion The ENSO warm phase (El Niño) causes droughts in the western of the 2009e2010 warm event was marked by negative SOI margin of Central America, Mexico, the Amazon Basin, northern anomalies during the first quarter of 2010. Beginning in July, the South America (i.e. Colombia and northeastern Brazil), whereas it positive anomalies were consolidated, which initiated the cold produces excess precipitation in the eastern region of Central event and lasted for 18 months, until December 2011. During that America, and increased summer rainfall in the Paraná Basin and the period, the anomalies ranged from 1.9 to 5.2. The only comparable Andes of Peru, Bolivia and Chile (Capel, 1999). In Colombia, ENSO anomalies were observed in the cold events of 1970e1971, 1975e has a strong effect on precipitation, river discharge and soil mois- 1976 and 2007e2008. ture (Montealegre & Pabón, 1992; Poveda & Mesa, 1996; Poveda et al., 2001, 2002; Puertas & Carvajal, 2008; Restrepo & Kjerfve, Methods 2004). The warm phase is associated with an increase in the average air temperature, a decrease in soil moisture and evapo- For our spatial analysis, we used the number of individuals in transpiration, a decrease in rainfall and a consequent decrease in each municipality reported as affected by the UNGRD, as of the average flow of the rivers in the western, central and northern September 2011. We normalized by the total municipal population regions of the country (Poveda et al., 2001). The opposite pattern is in 2011, estimated by extrapolation from the 2005 National Census observed during the cold phase (La Niña), which is mainly char- by the National Department of Statistics (DANE). A value of 1 means acterized by intense and abundant rainfall, increased river flow and that all (100%) of the municipality’s inhabitants were affected, subsequent flooding (Poveda & Mesa, 1996; Mesa et al., 1997; whereas a value of 0.5 means that 50% were affected, and so on. By Poveda et al., 2001). ENSO events, however, differ in intensity and government standards, the term “affected” included (1) individuals spatial extent, so their effects on hydro-climatology are event- who were deceased, missing, or suffered direct material loss and/or specific(Poveda, 2004). injury, and (2) individuals who suffered indirect or secondary A common variable used to assess the strength of a particular impacts, such as not being able to work. ENSO event is the Southern Oscillation Index (SOI). It is calculated We compiled disaster-related data, as well as socioeconomic, as the normalized difference in surface air pressure between Dar- hydrological, geomorphological and administrative data from win, Australia (Western Pacific) and Tahiti, French Polynesia various sources (Table 1). Editing and analysis was conducted using (Eastern Pacific). A positive index points to low pressures in the ArcGIS (ESRI), Geoda (Anselin, 2005) and NCSS (Hintze, 2007). western tropical Pacific and indicates the occurrence of the cold Spatial autocorrelation analysis was accomplished using global phase (La Niña). A negative index signals the presence of the warm (Moran’s I) and local (LISA) indicators (Moran, 1948; Anselin, 1995). Author's personal copy

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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 topography mission (USGS 2006). Basemap data from the national geographic database (sigotn.igac.gov.co).

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 by a low-pass ﬁlter. Light boxes represent El Niño events,dark boxes represent La Niña events. Author's personal copy

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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 Niña events in dark boxes, and (b) Morletwavelet spectrum and scale average variance of 2e8 year band (1941e2010).

Once the statistical significance of the spatial patterns was estab- monthly precipitation for the months of June, July and August lished, we performed a regression analysis, using the normalized (rainy season in the eastern Llanos and Amazon) and October and number of affected individuals as the dependent variable, and November (rainy season in the Andean and Caribbean regions). relevant socioeconomic and environmental factors as explanatory Each municipality was assigned the average value within its area of variables. For the latter, we used variables that (1) were publicly jurisdiction. available, (2) could be aggregated at the municipal level and (3) had We used two regression analysis techniques to assess the been measured, whenever possible, close to the time of our period importance of the spatial component, a multiple linear regression of interest (2010e2011). Socioeconomic variables included: (1) model with coefficients estimated by the method of ordinary least population density, measured as the estimated population in 2011 squares (OLS) and a spatial autoregressive model with spatial error divided by the municipality area, (2) the 2005 living conditions dependence, which estimates the coefficients by the maximum index (ICV), which is a measure of the possession of physical goods likelihood method (ML). The first model assumes spatially inde- (access to public services and housing characteristics), human pendent observations, whereas the latter includes a spatial capital (average years of education of household heads and children component because it assumes that model errors are spatially more than 12 years old, and school attendance) and household correlated. The original data were modified as follows. We assigned composition (overcrowding and number of children less than 6 a value of 1.0 to municipalities with anomalous values (>1.0) of the years old) (DNP, 1999), (3) the 2005 water supply and sewer dependent variable (normalized number of affected individuals). coverage, and (4) the 2010 municipal performance index, which is Values >1.0 imply that affected individuals outnumber the total a measure of local compliance with development goals, adminis- population for a municipality. We therefore re-scaled those values trative capacity and fiscal performance (DNP, 2010). The following to the maximum possible, i.e. the total population affected. With physical environmental variables were considered: (1) percent of respect to the independent variables, municipalities with missing the total municipal area subject to flooding i.e. floodplains, low data and “island” municipalities, i.e. those without neighbors, alluvial terraces, eolian lowlands subject to seasonal flooding, were eliminated. After these modifications, we had 1,090 munici- overflow swamp lowlands, deltas and coastal areas (Flórez et al., palities that were included in the regression analyses. Finally, the 2010), (2) annual maximum peak discharge for 2, 5, 10, 20, 50 statistical distribution of each variable was assessed and, if and 100 year return periods (each municipality was assigned the necessary, transformations were performed to obtain a normal maximum value within its area of jurisdiction), and (3) the average distribution. Author's personal copy

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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 trend in 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 at

90% conﬁdence level when Z > Z(1 a/2) ¼ 1.77).

For the regression analyses, we followed the methods of Anselin population projections from the 2005 Census, or incorrect regis- (2005) and Hinze (2007). Briefly, we used techniques to identify tration of individuals affected by flooding, with many individuals variables with significant predictive power. Then we performed registered multiple times. The normalized map fails to show some OLS regression with the selected variables and looked for spatial municipalities where a large number of individuals were affected, dependence of errors. Finally, we calculated the most appropriate but they represent only a small percentage of the total municipal- spatial autoregressive model according to the indices of spatial ity’s population. This phenomenon was particularly noticeable in dependence. state capitals (e.g. Riohacha, Montería, Valledupar, Cúcuta, Medel- lín, Cali and Florence), as well as the national capital, Bogotá. Results Global and local spatial autocorrelation indices were significant. For example, Moran’s I index for global spatial autocorrelation Regional “hotspots” for affected individuals (raw and normal- (0.42, a ¼ 0.05, n ¼ 1123, Rook contiguity matrix type) indicates ized values) include municipalities on the Pacific and Caribbean significant positive spatial autocorrelation. Similarly, local indica- coasts, in the lower Magdalena Valley and a few in the Andes tors of spatial association (LISA) point to the existence of two (Fig. 5). Thirty-seven municipalities had anomalous normalized regional “hotspots” in the lower Magdalena River Valley (61 values, >1.0. The most extreme cases were observed in some municipalities, 5.43% of total) and the Atrato River Valley (13 municipalities from the Pacific and Caribbean states, where the municipalities, 1.16% of the total) (Fig. 6). Other smaller “hotpspots” number of affected individuals was nearly twice the total pop- were observed along the southern Pacific coast (11 municipalities, ulation. We believe this was a consequence of inaccurate 0.98% of the total) and on the northern Eastern Cordillera, close to Author's personal copy

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Table 1 Relevant characteristics of spatial data.

Group Name Source Temporal resolution Spatial resolution Basemap data Municipalities IGACa 2009 Municipal Population DANEb 2011 (projected) Municipal Disaster data Individuals affected UNGRDc April 2010eSeptember 2011 Municipal Explanatory variables Municipal area (% of total) Flórez et al., 2010 NA 1:500,000 subject to ﬂooding Annual maximum discharge HidroSIGd >25 years 12000 (w3.7 km) (m3 s 1) (return periods of 2, 5, 10, 20, 50 and 100 years) Average monthly rainfall for HidroSIGd >25 years 30000 (w9.3 km) June, July, August, October and November (mm) Population density DANEb 2011 Municipal (individuals km 2) Index of living conditions DNPf 2005 Municipal ICVe (%) Municipal Performancee (%) DNPf 2010 Municipal Aqueduct coverage (%) DANEb 2005 Municipal Sewer 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. the Venezuelan border (6 municipalities, 0.53% of the total). Clus- Table 2 shows the relevant characteristics of the selected model, ters of low values are located on the Eastern plains (Orinoquía), a spatial autoregressive model with spatial error dependence, in Amazon and southern Eastern Cordillera eastern foothills (45 comparison with an equivalent multiple linear regression model. municipalities, 5.34% of the total). Less extensive, low-value clus- Socioeconomic variables (living conditions index) as well as envi- ters are apparent in the central Eastern Cordillera (35 municipali- ronmental physical variables (percent of the municipality subject to ties, 3.12% of the total), and northern Central Cordillera (78 flooding and average June precipitation) were selected as signifi- municipalities, 6.95% of the total). cant explanatory variables (p < 0.01). Some other variables We selected the regression model that had both good predictive exhibited high correlation with the selected variables and were power and the smallest possible number of independent variables. discarded because of multi-collinearity. For example, potable water

Fig. 5. (a) Raw number of individuals affected by the 2010e2011 La Niña, and (b) normalized number of individuals affected by the 2010e2011 La Niña (raw number divided by total 2011 population). Municipalities with anomalous values are shown with a thick black outline. All data are aggregated at the municipal level. Author's personal copy

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spatial error model because it had: (1) better performance indicators (log-likelihood, Akaike and Schwarz criteria), (2) a highly signiﬁcant spatial autoregressive coefﬁcient (l), and (3) residuals that were not spatially autocorrelated.

Discussion

This study was framed within the context of vulnerability and natural hazards research. As these terms are widely used, we follow the definitions of Cutter & Finch (2008) and UNISDR (2009). Natural hazard refers to a “natural process or phenomenon that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage” (UNISDR, 2009). On the other hand, vulnerability is broadly defined as the potential for loss and is a function of exposure, sensitivity and resilience (Cutter & Finch, 2008; Wood, Burton, & Cutter, 2010). Exposure refers to the frequency, severity and extent of a specific hazard (Emrich & Cutter 2011). Sensitivity (or social vulnerability) refers to the ability of a community to protect itself from future events and depends on the social, economic and demographic characteristics that make it susceptible to loss (Cutter, Boruff, & Shirley, 2003; Emrich & Cutter, 2011). The resilience 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 was a multi-hazard event, as it was associated with the occurrence of floods, landslides, windstorms, lightning and landslides. Floods and landslides were by far the most common and damaging phenomena. For instance, for the SeptembereDecember of 2011 rainy season, a total of 1,107 weather-related events were reported by UNGRD, of which 684 (62%) were floods and 321 (29%) were landslides. In the same period, 182 individuals were reported dead as a result of landslides (172 or 95%) and floods (10) (UNGRD, 2011). Fig. 6. Clusters of positive spatial autocorrelation for the normalized number of In terms of vulnerability, the variables included in our model affected individuals. High values are shown in red (dark gray), whereas low values are represent exposure, sensitivity and resilience. For example, the shown in blue (intermediate gray). Areas with negative spatial autocorrelation are percent of municipal land subject to flooding is an indication of the fi shown in light gray. (For interpretation of the references to color in this gure legend, degree of exposure (Fig. 7). The spatial error model indicates, as the reader is referred to the web version of this article.) would be expected, a positive relationship between the area subject to flooding and normalized number of affected individuals. The and sewer coverage, as well as population density, showed extent of flooding for the 2010e2011 La Niña can be assessed from a significant positive correlation with the index of living conditions. official reports comparing the extent of seasonal floods during Although both models (classical and spatial) were similar in terms “regular” conditions, with those during the 2010e2011 La Niña, for of selected variables and regression coefficients, we selected the the most critical regions, i.e. the Caribbean, Pacific and eastern Andean foothills (Instituto Geográfico Agustín Codazzi IGAC,

Table 2 Instituto de hidrología, meteorología y estudios ambientales Relevant characteristics of selected regression models (n ¼ 1,090). Variables IDEAM, & Departamento Administrativo Nacional de Estadística significant at p < 0.01. DANE, 2011). Numbers show that during the rainy seasons of e fl Variable/mode1 Multiple linear Spatial error model 2010 2011, the ooded areas in those regions nearly doubled regression (OLS) (MLE) relative to the baseline year (2001). Specifically, it is estimated that seasonal flooding affects 1,212,965 ha in the eastern Andean foot- Coefficient t-value Coefficient z-value hills, Caribbean lowlands, lower Magdalena Valley, and lower Sinú Living conditions 0.010599 16.18605 0.009156 12.60932 e index and Atrato River Basins. In comparison, during the 2010 2011 La % municipal land 0.299605 11.4672 0.239139 6.89489 Niña, 1,642,108 ha of additional land were flooded, primarily in the subject lower Magdalena River Basin and the lower Sinú River Basin (IGAC 0.5 to flooding (x ) et al., 2011). Average June 0.179308 6.06699 0.124084 2.64768 We propose, however, that the living conditions index (ICV) rainfall (log) la N/A 0.566601 16.71821 represents, at least partially, the concepts of sensitivity and resil- Adjusted R2 0.272 N/A ience (Fig. 7). Because of its multidimensional nature, this index Pseudo R2 N/A 0.448 provided a better representation of social vulnerability than inde- Log likelihood 86.216 198.781 pendent variables such as water supply and sewers, which were Akaike criterion 164.432 389.563 Schwarz criterion 144.456 369.59 also redundant according to multicollinearity indicators. Regional Residuals spatial 0.32 0.02 studies on social vulnerability indicate that its spatial and temporal autocorrelation variability are related to variables that measure socioeconomic (Moran I index) status, age, commercial, industrial and housing developments, a Spatial autoregressive coefficient. rurality, race, gender and employment (Cutter & Finch, 2008; Author's personal copy

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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 municipal area (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.

Emrich & Cutter, 2011; Lazarus, 2011). The relationship is not was a consequence of: (1) lower population density and (2) reduced straightforward and should be interpreted within the broader discharge response to La Niña, as it is both delayed and of smaller context of social and economic policies (Lazarus, 2011). For magnitude than in the Andean and Caribbean regions (Poveda et al., instance, studies in the United States indicate that increased social 2001). Although it seems counterintuitive, it is for this reason that vulnerability is driven by poverty, ethnicity, rurality and gender the precipitation variable has a negative relationship with the (Cutter & Finch, 2008; Emrich & Cutter, 2011). By comparison, normalized number of affected individuals. research from Sri Lanka shows that relations between gender, It is useful to analyze specific cases where our model grossly ethnicity and vulnerability (measured as coping capacity) are underestimates the number of affected people. For this analysis, place-dependent (Lazarus, 2011). In our case, higher vulnerability, we focused on municipalities with residuals that were >2 stan- as indicated by ICV scores, is associated with restricted access to dard deviations (þ2 std dev) from the regression line (Fig. 8). public services, limited education, poor home construction mate- There were 43 municipalities (3.9% of the total) for which the rials, and overcrowding. Because the ICV score is a compound model underestimated the true number of affected individuals. index, it is not possible to assess the relative importance of each Poor prediction in these municipalities was related to several factor. Geographically, low ICV scores are found mostly in rural, factors. The first factor is flooding by small rivers, which was not sparsely populated areas in the Caribbean lowlands, Pacific coast, considered in our scale of analysis. This was the case for some Llanos and Amazon (Fig. 7). Furthermore, resilience to natural municipalities in the Pacific region and Eastern Cordillera (Bagadó, disasters is also multidimensional and integrates ecological, social, 2011; OCHA, 2010; Redacción, 2010b, 2011b, 2011c). The second economic, institutional and infrastructure variables (Cutter & Finch, factor is the occurrence of hazards other than flooding, such as 2008). Recent studies and policies on disaster-risk reduction landslides, storms, and mud and debris flows, which were not emphasize the importance of resilience as a tool to reduce the included in our model. Examples include municipalities in the vulnerability of communities exposed to natural hazards (UNISDR, northern Eastern Cordillera, and central Western Cordillera 2010). In our study, the spatial regression model shows a negative (Redacción, 2010c, 2010d). The third factor is related to munici- relationship between ICV and the normalized number of affected palities that, despite having considerable areas that are flood- individuals. This result is in agreement with the above findings prone, had low percent values for this variable because the total from other studies. municipal area was very large. This situation was observed in Regarding the precipitation variable included in our model southern Colombia, along the Putumayo River (Redacción, 2010e; (average June rainfall), we believe it represents the contrasting Salamanca, 2011). Finally, there were several municipalities where regimes of the Andean/Caribbean and Llanos/Amazon regions. the model performed poorly even though they had a large fraction Under normal conditions, the Andean and Caribbean regions are of their area (>30%) in the flood-prone lowlands of the Magdalena, predominantly dry in June, whereas wet conditions prevail over the Cauca and Sinú Rivers. (Alzate, 2010; Redacción, 2010f, 2010g, Llanos and Amazon regions. The spatial autocorrelation analysis 2011d). These cases require further investigation to understand shows that most municipalities in the Llanos and Amazon were what specific factors in each municipality accounted for the high affected little by the 2010e2011 La Niña. We believe this pattern number of affected individuals. Author's personal copy

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ﬂood-risk management (Schelfault et al., 2011). This perspective emphasizes the reduction of vulnerability by strengthening the resilience of at-risk communities. It is predicated on the belief that ﬂoods cannot be controlled by structural measures alone (Dixon et al., 2006), and that social vulnerability plays a critical role in community recovery (Finch, Emrich, & Cutter, 2010). Our study indicates that future strategies to mitigate the impacts of climate events such as the 2010e2011 La Niña should include compre- hensive measures to reduce the social vulnerability of communities and thereby increase their resilience. As such, the importance of updating socioeconomic data related to social vulnerability is underscored.

Acknowledgments

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

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