40
E. A. Agosta et al.
A crucial aspect is to have an appropriate measure describing the hazard and
the vulnerability to determine the extent of flood risks. In this context, we interpret
risk as the state of susceptibility to harm from exposure to stresses associated with
environmental and social change and from the absence of capacity to adapt (Adger
2006). Thus, flood risk is the combination of flood hazard and people exposed to it. In
Argentina, there are no detailed geographical records of flood events and their properties such as extension, intensity, and duration; so, their variability cannot be assessed.
Hence, no detailed flood hazard maps are available yet at high geographical resolution.
2 A few engineering studies modeling river dynamics and potential flooding have
been carried out, albeit representative of small portions of urban areas (e.g., Saurral
et al. 2008). Besides, there are no accessible household surveys to extract information
about flooding occurrence or their potential impacts on households’ welfare.
Figure 3 presents the mean of the MFM index over the period 1970–2009 for each
municipality in Argentina. The map can be interpreted as the structural flooding risk
in every municipality. A municipality with an index value of 0 is riskless, at least
in terms of flooding, and when the index reaches a maximum of 30, it points to
municipalities which are extremely risky in terms of the number of events, duration,
material damages, and affected people. Note that ESA is the region where flood risks
show the highest figures in Argentina, especially those areas along the Paraná River.
For example, those municipalities along the river in the provinces of Santa Fe, Entre
Ríos, and Corrientes have a mean MFM index value four times higher than the rest
of the municipalities in the same provinces.
Interdecadal variations in the MFM index suggest that the level of hazard has
declined between the 1980s and 2000s, the last decade for which data exist, but
without reaching previous values of the 1970s (Fig. 4, all municipalities, blue bars).
While the all-municipality mean MFM index in ESA in the 1970s and the 2000s
was 9.81 and 11.46, respectively, the decadal average index in the 1980s was a
staggering 17.39. This could be interpreted as that flood risks have diminished in the
region because either local populations have adapted (i.e., by migrating or building
resilient infrastructure) or the occurrence of floods has diminished, or a combination
of both. This diminution in flood risk is evident for ESA, when excluding the Buenos
Aires metropolitan area, known as the Grand Buenos Aires (GBA). The long-term
MFM index variation in ESA without GBA (non-GBA) shows an overall negative
trend (Fig. 4, red bars and dashed curve). In contrast, the GBA subregion shows
a positive trend, since there is a net rise in the MFM index in the 2000s (Fig. 4,
GBA, green bars and dashed curve). In the following sections, we will try to further
elucidate what factors (climatic features or population exposure) are influencing the
observed decadal variation of the MFM index.
2 One way to generate detailed flood mapping for the entire ESA is using remote sensing, such as
LANDSAT imagery. Such an endeavor is beyond the scope of this paper, which is left for future
research.
E. A. Agosta et al.
A crucial aspect is to have an appropriate measure describing the hazard and
the vulnerability to determine the extent of flood risks. In this context, we interpret
risk as the state of susceptibility to harm from exposure to stresses associated with
environmental and social change and from the absence of capacity to adapt (Adger
2006). Thus, flood risk is the combination of flood hazard and people exposed to it. In
Argentina, there are no detailed geographical records of flood events and their properties such as extension, intensity, and duration; so, their variability cannot be assessed.
Hence, no detailed flood hazard maps are available yet at high geographical resolution.
2 A few engineering studies modeling river dynamics and potential flooding have
been carried out, albeit representative of small portions of urban areas (e.g., Saurral
et al. 2008). Besides, there are no accessible household surveys to extract information
about flooding occurrence or their potential impacts on households’ welfare.
Figure 3 presents the mean of the MFM index over the period 1970–2009 for each
municipality in Argentina. The map can be interpreted as the structural flooding risk
in every municipality. A municipality with an index value of 0 is riskless, at least
in terms of flooding, and when the index reaches a maximum of 30, it points to
municipalities which are extremely risky in terms of the number of events, duration,
material damages, and affected people. Note that ESA is the region where flood risks
show the highest figures in Argentina, especially those areas along the Paraná River.
For example, those municipalities along the river in the provinces of Santa Fe, Entre
Ríos, and Corrientes have a mean MFM index value four times higher than the rest
of the municipalities in the same provinces.
Interdecadal variations in the MFM index suggest that the level of hazard has
declined between the 1980s and 2000s, the last decade for which data exist, but
without reaching previous values of the 1970s (Fig. 4, all municipalities, blue bars).
While the all-municipality mean MFM index in ESA in the 1970s and the 2000s
was 9.81 and 11.46, respectively, the decadal average index in the 1980s was a
staggering 17.39. This could be interpreted as that flood risks have diminished in the
region because either local populations have adapted (i.e., by migrating or building
resilient infrastructure) or the occurrence of floods has diminished, or a combination
of both. This diminution in flood risk is evident for ESA, when excluding the Buenos
Aires metropolitan area, known as the Grand Buenos Aires (GBA). The long-term
MFM index variation in ESA without GBA (non-GBA) shows an overall negative
trend (Fig. 4, red bars and dashed curve). In contrast, the GBA subregion shows
a positive trend, since there is a net rise in the MFM index in the 2000s (Fig. 4,
GBA, green bars and dashed curve). In the following sections, we will try to further
elucidate what factors (climatic features or population exposure) are influencing the
observed decadal variation of the MFM index.
2 One way to generate detailed flood mapping for the entire ESA is using remote sensing, such as
LANDSAT imagery. Such an endeavor is beyond the scope of this paper, which is left for future
research.