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2 Applied Geography 39 (2013) 16e25 Contents lists available at SciVerse ScienceDirect Applied Geography journal homepage: 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 , 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: ENSO Extreme weather events Spatial autocorrelation Spatial error Natural hazard Vulnerability 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 to destruction of infrastructure, flooding of agricultural lands and payment of government subsidies. We analyzed the spatial patterns of effects on the population, measured as the number of affected persons in each 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 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 Climate patterns have changed throughout Earth s history. Since the late 1800s, these changes have been largely caused by increasing amounts of anthropogenic greenhouse gases in the atmosphere. The average temperature of the planet has increased 0.74 C over the last century, and most of this increase has occurred in the last three decades (Arguez, 2007; IPCC, 2007). It is estimated that increases in the concentration of greenhouse gases will cause additional warming of 1.1e6.4 C by the end of this century (IPCC, 2007). The increase in global average temperatures is expected to cause increases in extreme weather events, which will, in turn, have effects on ecosystems and society. Such events drive greater changes in natural and social systems than do average climate conditions as a consequence of damage to infrastructure and agricultural lands, diminished ecosystem function, and human death, * Corresponding author. Corporación Geológica ARES, Calle 44A No , Bogotá, Colombia. Tel.: þ address: (N. Hoyos). injury and displacement (Parmesan & Martens, 2008; Parmesan, Root, & Willig, 2000). Climate change, particularly extreme weather events, poses risks and challenges for society. Most research, however, has addressed the climate component of climate change, whereas its impact on human well-being remains poorly understood (NRC, 2009). In social terms, effects of extreme events are evaluated by analyzing the vulnerability of exposed communities. Impacts on socioeconomic systems are often amplified by factors 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 managing climate change risks. Geographically explicit vulnerability analysis is critical to understand how interactions between the physical environment 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 The weather event affected approximately four million people as of September 2011 and caused losses of more than US $7.8 billion, as a consequence of destruction of infrastructure, flooding of agricultural lands and payment of government subsidies (Redacción, /$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.

3 N. Hoyos et al. / Applied Geography 39 (2013) 16e a, 2011a). A wealth of data was generated by government agencies and non-governmental organizations on the effects of this phenomenon. Furthermore, such information was used to develop mitigation plans. Participating institutions included the National Office for Disaster Risk Management (Unidad Nacional para la Gestión del Riesgo de Desastres e UNGRD), National Department of Statistics (Departamento Nacional de Estadística e DANE), National Institute of Hydrology, Meteorology and Environmental Studies (Instituto de Hidrología, Meteorología y Estudios Ambientales e IDEAM), the National Geographic Institute (Instituto Geográfico Agustín Codazzi e IGAC) and non-governmental entities such as immap and the United Nation s Office for the Coordination of Humanitarian Affairs (OCHA). Although these institutions presented their data in a spatial format (i.e. maps), rigorous geographical analysis was not done, largely because of time constraints. In this study, we assessed the spatial patterns of ENSO effects on the human population in Colombia, and explored the relationship between such patterns and physical geographic and socioeconomic variables. We first summarize the effect of ENSO on Colombian river flow dynamics and follow with a spatial analysis of the 2010e2011 La Niña event. We conclude with a discussion of our findings. Climate and river discharge during ENSO In Colombia, the annual hydrologic cycle is controlled by oscillation of the inter-tropical convergence zone, superimposed on regional patterns caused by orographic influence of the Andes, evapotranspiration in the Amazon Basin, continent-atmosphere interactions and dynamics of the western Colombian wind currents (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, major hydrologic anomalies are experienced during both phases of ENSO (Aceituno, 1988; Poveda, 2004; Poveda et al., 2001) and other macro-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 western margin of Central America, Mexico, the Amazon Basin, northern South America (i.e. Colombia and northeastern Brazil), whereas it produces excess precipitation in the eastern region of Central America, and increased summer rainfall in the Paraná Basin and the Andes of Peru, Bolivia and Chile (Capel, 1999). In Colombia, ENSO has a strong effect on precipitation, river discharge and soil moisture (Montealegre & Pabón, 1992; Poveda & Mesa, 1996; Poveda et al., 2001, 2002; Puertas & Carvajal, 2008; Restrepo & Kjerfve, 2004). The warm phase is associated with an increase in the average air temperature, a decrease in soil moisture and evapotranspiration, a decrease in rainfall and a consequent decrease in the average flow of the rivers in the western, central and northern regions of the country (Poveda et al., 2001). The opposite pattern is observed during the cold phase (La Niña), which is mainly characterized by intense and abundant rainfall, increased river flow and subsequent flooding (Poveda & Mesa, 1996; Mesa et al., 1997; Poveda et al., 2001). ENSO events, however, differ in intensity and spatial extent, so their effects on hydro-climatology are eventspecific (Poveda, 2004). A common variable used to assess the strength of a particular ENSO event is the Southern Oscillation Index (SOI). It is calculated as the normalized difference in surface air pressure between Darwin, Australia (Western Pacific) and Tahiti, French Polynesia (Eastern Pacific). A positive index points to low pressures in the western tropical Pacific and indicates the occurrence of the cold phase (La Niña). A negative index signals the presence of the warm phase (El Niño). According to this index, there were at least 19 El Niño and 17 La Niña events between 1950 and 2011 (NOAA, 2011). Because of their intensity and duration, the warm events in 1957e 1958 (8 months), 1965e1966 (12 months), 1972e1973 (10 months), 1976e1978 (18 months), 1982e1983 (14 months), 1986e1987 (16 months), 1991e1992 (17 months), 1997e1998 (12 months) and 2009e2010 (11 months) are notable. Strong cold events took place in 1954e1957 (20 months), 1970e1971 (14 months), 1973e1974 (13 months), 1975e1976 (12 months), 1988e1989 (14 months), 1998e 2000 (24 months), 2007e2008 (10 months) and 2010e2011 (10 months) (Fig. 2). Climatic, hydrological and oceanographic disturbances related to these events had dramatic global socioeconomic and environmental repercussions (Capel, 1999). In Colombia, the 1982e1983 ENSO stimulated scientific and academic interest because of its environmental impacts, particularly in the marine sector (Alvarado, Duque, Flórez, & Ramírez, 1986). Interest only became widespread after the 1991e1992 event, which caused a large decrease in precipitation and Andean river streamflows, and led to a collapse of the national hydropower system (Mesa et al., 1997; Montealegre & Pabón, 1992). The relationship between ENSO and river flow in Colombia was studied by Mesa et al. (1997) and Restrepo & Kjerfve (2000). They showed that ENSO has an earlier and stronger effect on rivers in western, northern and central Colombia, in contrast to a later and reduced effect on rivers in the eastern and southeastern regions of the country. For instance, ENSO explains up to 64% of the inter-annual variability in discharge of the Magdalena River, the main river draining the Colombian Andes (Restrepo & Kjerfve, 2000). Abrupt changes in river discharge have occurred during the past 12 years, and all were related with ENSO cold conditions (Fig. 3a). Wavelet analysis, however, reveals that the contribution of ENSO to flow variability has not been constant over time (Fig. 3b). Caribbean river discharge also reflects the effect of ENSO (Fig. 4). Nevertheless, it is difficult to separate the influence of climate variability from that of anthropogenic disturbance (Restrepo & Restrepo, 2005). The 2010e2011 ENSO cold event was one of the most intense, in both duration and magnitude (Fig. 2). In 2010, there was a rapid transition between the warm and cold phases of ENSO. Completion of the 2009e2010 warm event was marked by negative SOI anomalies during the first quarter of Beginning in July, the positive anomalies were consolidated, which initiated the cold event and lasted for 18 months, until December During that period, the anomalies ranged from 1.9 to 5.2. The only comparable anomalies were observed in the cold events of 1970e1971, 1975e 1976 and 2007e2008. Methods For our spatial analysis, we used the number of individuals in each municipality reported as affected by the UNGRD, as of September We normalized by the total municipal population in 2011, estimated by extrapolation from the 2005 National Census by the National Department of Statistics (DANE). A value of 1 means that all (100%) of the municipality s inhabitants were affected, whereas a value of 0.5 means that 50% were affected, and so on. By government standards, the term affected included (1) individuals who were deceased, missing, or suffered direct material loss and/or injury, and (2) individuals who suffered indirect or secondary impacts, such as not being able to work. We compiled disaster-related data, as well as socioeconomic, hydrological, geomorphological and administrative data from various sources (Table 1). Editing and analysis was conducted using ArcGIS (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).

4 18 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 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.

5 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 19 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 established, we performed a regression analysis, using the normalized number of affected individuals as the dependent variable, and relevant socioeconomic and environmental factors as explanatory variables. For the latter, we used variables that (1) were publicly available, (2) could be aggregated at the municipal level and (3) had been measured, whenever possible, close to the time of our period of interest (2010e2011). Socioeconomic variables included: (1) population density, measured as the estimated population in 2011 divided by the municipality area, (2) the 2005 living conditions index (ICV), which is a measure of the possession of physical goods (access to public services and housing characteristics), human capital (average years of education of household heads and children more than 12 years old, and school attendance) and household composition (overcrowding and number of children less than 6 years old) (DNP, 1999), (3) the 2005 water supply and sewer coverage, and (4) the 2010 municipal performance index, which is a measure of local compliance with development goals, administrative capacity and fiscal performance (DNP, 2010). The following physical environmental variables were considered: (1) percent of the total municipal area subject to flooding i.e. floodplains, low alluvial 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, 50 and 100 year return periods (each municipality was assigned the maximum 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 and November (rainy season in the Andean and Caribbean regions). Each municipality was assigned the average value within its area of jurisdiction. We used two regression analysis techniques to assess the importance of the spatial component, a multiple linear regression model with coefficients estimated by the method of ordinary least squares (OLS) and a spatial autoregressive model with spatial error dependence, which estimates the coefficients by the maximum likelihood method (ML). The first model assumes spatially independent observations, whereas the latter includes a spatial component because it assumes that model errors are spatially correlated. The original data were modified as follows. We assigned a value of 1.0 to municipalities with anomalous values (>1.0) of the dependent variable (normalized number of affected individuals). Values >1.0 imply that affected individuals outnumber the total population for a municipality. We therefore re-scaled those values to the maximum possible, i.e. the total population affected. With respect to the independent variables, municipalities with missing data and island municipalities, i.e. those without neighbors, were eliminated. After these modifications, we had 1,090 municipalities that were included in the regression analyses. Finally, the statistical distribution of each variable was assessed and, if necessary, transformations were performed to obtain a normal distribution.

6 20 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 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 (2005) and Hinze (2007). Briefly, we used techniques to identify variables with significant predictive power. Then we performed OLS regression with the selected variables and looked for spatial dependence of errors. Finally, we calculated the most appropriate spatial autoregressive model according to the indices of spatial dependence. Results Regional hotspots for affected individuals (raw and normalized values) include municipalities on the Pacific and Caribbean coasts, in the lower Magdalena Valley and a few in the Andes (Fig. 5). Thirty-seven municipalities had anomalous normalized values, >1.0. The most extreme cases were observed in some municipalities from the Pacific and Caribbean states, where the number of affected individuals was nearly twice the total population. We believe this was a consequence of inaccurate population projections from the 2005 Census, or incorrect registration of individuals affected by flooding, with many individuals registered multiple times. The normalized map fails to show some municipalities where a large number of individuals were affected, but they represent only a small percentage of the total municipality s population. This phenomenon was particularly noticeable in state capitals (e.g. Riohacha, Montería, Valledupar, Cúcuta, Medellín, 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) indicates significant positive spatial autocorrelation. Similarly, local indicators of spatial association (LISA) point to the existence of two regional hotspots in the lower Magdalena River Valley (61 municipalities, 5.43% of total) and the Atrato River Valley (13 municipalities, 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

7 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 21 Table 1 Relevant characteristics of spatial data. Group Name Source Temporal resolution Spatial resolution Basemap data Municipalities IGAC a 2009 Municipal Population DANE b 2011 (projected) Municipal Disaster data Individuals affected UNGRD c April 2010eSeptember 2011 Municipal Explanatory variables Municipal area (% of total) Flórez et al., 2010 NA 1:500,000 subject to flooding Annual maximum discharge HidroSIG d >25 years (w3.7 km) (m 3 s 1 ) (return periods of 2, 5, 10, 20, 50 and 100 years) Average monthly rainfall for HidroSIG d >25 years (w9.3 km) June, July, August, October and November (mm) Population density DANE b 2011 Municipal (individuals km 2 ) Index of living conditions DNP f 2005 Municipal ICV e (%) Municipal Performance e (%) DNP f 2010 Municipal Aqueduct coverage (%) DANE b 2005 Municipal Sewer coverage (%) DANE b 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). Clusters of low values are located on the Eastern plains (Orinoquía), Amazon and southern Eastern Cordillera eastern foothills (45 municipalities, 5.34% of the total). Less extensive, low-value clusters are apparent in the central Eastern Cordillera (35 municipalities, 3.12% of the total), and northern Central Cordillera (78 municipalities, 6.95% of the total). We selected the regression model that had both good predictive power and the smallest possible number of independent variables. Table 2 shows the relevant characteristics of the selected model, a spatial autoregressive model with spatial error dependence, in comparison with an equivalent multiple linear regression model. Socioeconomic variables (living conditions index) as well as environmental physical variables (percent of the municipality subject to flooding and average June precipitation) were selected as significant explanatory variables (p < 0.01). Some other variables exhibited high correlation with the selected variables and were 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.

8 22 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 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 Fig. 6. Clusters of positive spatial autocorrelation for the normalized number of affected individuals. High values are shown in red (dark gray), whereas low values are shown in blue (intermediate gray). Areas with negative spatial autocorrelation are shown 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.) and sewer coverage, as well as population density, showed a significant positive correlation with the index of living conditions. Although both models (classical and spatial) were similar in terms of selected variables and regression coefficients, we selected the Table 2 Relevant characteristics of selected regression models (n ¼ 1,090). Variables significant at p < Variable/mode1 Multiple linear regression (OLS) Spatial error model (MLE) Coefficient t-value Coefficient z-value Living conditions index % municipal land subject to flooding (x 0.5 ) Average June rainfall (log) l a N/A Adjusted R N/A Pseudo R 2 N/A Log likelihood Akaike criterion Schwarz criterion Residuals spatial autocorrelation (Moran I index) a Spatial autoregressive coefficient. 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). In terms of vulnerability, the variables included in our model represent exposure, sensitivity and resilience. For example, the percent of municipal land subject to flooding is an indication of the degree of exposure (Fig. 7). The spatial error model indicates, as would be expected, a positive relationship between the area subject to flooding and normalized number of affected individuals. The extent of flooding for the 2010e2011 La Niña can be assessed from official reports comparing the extent of seasonal floods during regular conditions, with those during the 2010e2011 La Niña, for the most critical regions, i.e. the Caribbean, Pacific and eastern Andean foothills (Instituto Geográfico Agustín Codazzi IGAC, Instituto de hidrología, meteorología y estudios ambientales IDEAM, & Departamento Administrativo Nacional de Estadística DANE, 2011). Numbers show that during the rainy seasons of 2010e2011, the flooded areas in those regions nearly doubled relative to the baseline year (2001). Specifically, it is estimated that seasonal flooding affects 1,212,965 ha in the eastern Andean foothills, Caribbean lowlands, lower Magdalena Valley, and lower Sinú and Atrato River Basins. In comparison, during the 2010e2011 La Niña, 1,642,108 ha of additional land were flooded, primarily in the lower Magdalena River Basin and the lower Sinú River Basin (IGAC et al., 2011). We propose, however, that the living conditions index (ICV) represents, at least partially, the concepts of sensitivity and resilience (Fig. 7). Because of its multidimensional nature, this index provided a better representation of social vulnerability than independent variables such as water supply and sewers, which were also redundant according to multicollinearity indicators. Regional studies on social vulnerability indicate that its spatial and temporal variability are related to variables that measure socioeconomic status, age, commercial, industrial and housing developments, rurality, race, gender and employment (Cutter & Finch, 2008;

9 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 23 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 straightforward and should be interpreted within the broader context of social and economic policies (Lazarus, 2011). For instance, studies in the United States indicate that increased social vulnerability 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) are place-dependent (Lazarus, 2011). In our case, higher vulnerability, as indicated by ICV scores, is associated with restricted access to public services, limited education, poor home construction materials, and overcrowding. Because the ICV score is a compound index, it is not possible to assess the relative importance of each factor. 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 natural disasters is also multidimensional and integrates ecological, social, economic, institutional and infrastructure variables (Cutter & Finch, 2008). Recent studies and policies on disaster-risk reduction emphasize the importance of resilience as a tool to reduce the vulnerability of communities exposed to natural hazards (UNISDR, 2010). In our study, the spatial regression model shows a negative relationship between ICV and the normalized number of affected individuals. This result is in agreement with the above findings from other studies. Regarding the precipitation variable included in our model (average June rainfall), we believe it represents the contrasting regimes of the Andean/Caribbean and Llanos/Amazon regions. Under normal conditions, the Andean and Caribbean regions are predominantly dry in June, whereas wet conditions prevail over the Llanos and Amazon regions. The spatial autocorrelation analysis shows that most municipalities in the Llanos and Amazon were affected little by the 2010e2011 La Niña. We believe this pattern was a consequence of: (1) lower population density and (2) reduced discharge response to La Niña, as it is both delayed and of smaller magnitude than in the Andean and Caribbean regions (Poveda et al., 2001). Although it seems counterintuitive, it is for this reason that the precipitation variable has a negative relationship with the normalized number of affected individuals. It is useful to analyze specific cases where our model grossly underestimates the number of affected people. For this analysis, we focused on municipalities with residuals that were >2 standard deviations (þ2 std dev) from the regression line (Fig. 8). There were 43 municipalities (3.9% of the total) for which the model underestimated the true number of affected individuals. Poor prediction in these municipalities was related to several factors. The first factor is flooding by small rivers, which was not considered in our scale of analysis. This was the case for some municipalities in the Pacific region and Eastern Cordillera (Bagadó, 2011; OCHA, 2010; Redacción, 2010b, 2011b, 2011c). The second factor is the occurrence of hazards other than flooding, such as landslides, storms, and mud and debris flows, which were not included in our model. Examples include municipalities in the northern Eastern Cordillera, and central Western Cordillera (Redacción, 2010c, 2010d). The third factor is related to municipalities that, despite having considerable areas that are floodprone, had low percent values for this variable because the total municipal area was very large. This situation was observed in southern Colombia, along the Putumayo River (Redacción, 2010e; Salamanca, 2011). Finally, there were several municipalities where the model performed poorly even though they had a large fraction of 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 understand what specific factors in each municipality accounted for the high number of affected individuals.

10 24 N. Hoyos et al. / Applied Geography 39 (2013) 16e25 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 comprehensive 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. References Fig. 8. Municipalities where the normalized number of affected individuals was grossly underpredicted (residuals > 2.0 std dev). Light shaded polygons represent flood-prone areas from Flórez et al. (2010). Elevation data from shuttle radar topography mission (USGS 2006). Conclusions Our analysis showed that Colombians affected by the 2010e 2011 La Niña were clustered in two regional hotspots, in the lower Magdalena River Valley and Pacific regions. Areas where people were less affected ( coldspots ) were concentrated in the Llanos and Amazon region. Our regression model emphasizes the importance of the spatial component. Conceptually, this means that values at one municipality are related to values at neighboring municipalities. The model also emphasizes the role of variables traditionally used to assess natural hazards, such as hazard exposure and social vulnerability. The high numbers of people affected in municipalities of the lower Magdalena and Atrato River Basins point to the importance of both high hazard exposure and high social vulnerability. In these municipalities, social vulnerability was amplified by internal armed conflict (Colombia SSH, 2011). The scale of our analysis precluded modeling predominantly local threats such as mud or debris flows, windstorms and flooding by smaller, local streams. In addition, although publicly accessible data proved useful, we caution that the most complete, available socioeconomic data came from the 2005 National Census. Therefore, there was a 6e7 year gap between the collection of demographic data and occurrence of the 2010e2011 La Niña. Finally, considering that floods were the predominant hazard and had the greatest impact, we believe it is necessary to address integrated Aceituno, P. (1988). On the functioning of the Southern Oscillation in the South American sector, part I. 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