Junín is a department located in the Pampas’ section of the province of Buenos Aires, Argentina. It is the most populous settlement in the region, crossed by several water bodies (such as the Salado River and the Mar Chiquita, Gomez and Carpincho basins). It has a population of over 90 000 inhabitants, most of them settled in the urban area.

Deepening the study developed in the Lab "Residual Risk and Resilience" we study this town and its response to the increasing phenomena of precipitation and subsequent flooding under a tripartite framework: Exposition, Sensitivity and Vulnerability.

Vulnerability can be defined as "the degree to which a particular man-environment system experiences damage or impact due to a disturbance or threat" (Turner, Kasperson, & Polsky, 2003). The critical factors that frame vulnerability is the degree of exposure to stressors, the ability to anticipate the system, i.e. its adaptability, and the capability to cope with the damage and recover after the occurrence of the event; in other words, its resilience that is linked to the previous concept.

In this Lab a methodology to generate the following strategies to mitigate risk through a chain of analysis such as that shown in Figure 1 was developed, in order to achieve a simple decision-making process that is effective and consistent with the developed of the problems faced by the territory. The study was performed at the farm level upon the occurrence of a flood.


Figure 1. Risk-mitigation strategies based on a complex analysis of vulnerability.

As a result of climate change (http://www.ipcc.ch/), flooding is an increasingly common problem in the area. Particularly in the case of Junín, these events are not only associated with a rural consequences, as it also affects the urban area in a similar way. Furthermore, the policies that are taking place to mitigate the effects of major extreme events are based on ex-post measures, such as emergency and relief, disregarding an agenda of ex-ante based measures based on principles of risk transfer and prevention..



Figure 2. Flooded area from 1997 to 2005

From data collected for flooding events (as shown in Figure 2), recurrence of flood maps were made using a function to know the probability of occurrence of extreme rainfall events.


Figure 3. Flooded (exposure) and affected (sensitivity) areas for recurrences between 2 and 500 years.

Then, based on the images of Figure 3, we can generate new ones - maps of sensitivity - (Figure 4) in which the Sensitivity Index of each allotment, for various periods of recurrence is displayed. The sensitivity was defined as the relative area affected weighted by the return period of event.

Then, combining the preset data, the Resilience Index is defined based on the calculation of the ratio of the area of the allotment and profitable unit (500 ha of productive land). This indicator provides information about the ability to absorb and withstand the consequences of a flood, as seen in Figure 5. The greater the productivity of the parcel, the bigger the index will be and the greater the resilience of the field is.

Figure 4. Sensitivity Index per plot for the department of Junín.


Figure 5. Plot Resilience Index per allotment for of Junín.

Therefore, from the parameters developed it is possible to characterize the economic vulnerability of each plot by means of the Vulnerability Index, in which the non-affected and productive area is of each allotment is evaluated for subsequent recurrences. Consequently, a vulnerability map like the one in Figure 6 is obtained, varying according to the frequency considered.


Figure 6. Economic Vulnerability Index per plot for Junín.

From the analysis of Junín at an allotment level, it was possible to characterize in detail the exposure, sensitivity, resilience and finally, the vulnerability of the area. This tool is useful to comprehend and define long-term impacts, due to both climate change and to seasonal variations; its implementation will lead to improved management of water resources.

The vulnerability analysis is a fundamental pillar in decision-making process. It allows us to know in detail the region and consequently generate more individualized policies to suit the particular needs of the residents. As a result, in BerecoLabs we believe that it is possible to focus on planning strategies based on ex-ante measures that seek to increase the adaptative capacity rather than alleviate the consequences after the event has taken place.