A conceptual framework for reducing water vulnerability to extreme events by
risk transferring schemes: elements of a case study in the Pampas system,
Argentina
Pablo Bereciartua
Abstract
There is evidence about the increasing economic losses from extreme natural events
during the last decades. These facts, thought to be triggered by environmental changes
coupled with inefficient management and policies, highlight particularly exposed and
vulnerable regions worldwide. Argentina faces several challenges associated with global
environmental change and climate variability, especially related to water resources
management including extreme floods and droughts. At the same time, the country's
production capacity (i.e. natural resource-based commodities) and future development
opportunities are closely tied to the sustainable development of its natural resource
endowments. Given that vulnerability is registered not only by exposure to hazards
(perturbations and stresses), but also resides in the sensitivity and resilience of the
system experiencing such hazards, Argentina will need to improve its water
management capacities to reduce its vulnerability to climate variability and change.
By focusing in a case study at the Province of Buenos Aires, this paper describes main
characteristics of the vulnerability to extreme floods in the region. The work presents a
classification of the area in three sub regions according to its vulnerability profiles and
later on makes the case for the need of a risk transferring policy at the regional level i.e.
a public-private insurance system, in order to address the significant flood residual risks
in the area. Along this reasoning, the paper develops a Monte Carlo simulation exercise
to evaluate the design of a public insurance fund for the region. The paper finishes with
a set of conclusions and suggestions for future work.
Key words: global change, water managment, risks transferring, insurance, Argentina
Acknowledgments. I would to acknowledge the collaboration of Lic. Pierre Bitte in the
image processing for this project.
1
CONTENTS
1. Introduction
2. Analysis framework
3. Case study: extreme floods events in the Pampas System
3.1. Environmental modeling: macro vulnerability regions
3.1.1. Three flooding vulnerability regions for the Pampas System within the
Province of Buenos Aires
3.2. Socio economic consequences of floods
3.2.1. Economic losses associated with extreme flood events
3.2.2. Expected losses – risk curve
3.2.3. Scale of the socio economic impact: macro economic and inter sector
impacts
3.3. Policy options
3.3.1. Residual risk versus risk that can be mitigated
3.3.2. Financing of extreme event losses
3.3.3. The development of an insurance system for flood event in the Pampas
3.3.4. Evaluation of a fund to implement a risk transferring system for the region
4. Conclusions and future work
5. References
Appendixes
Appendix 1. Methodology for the evaluation of direct economic losses due to flooding
events in the Pampas system.
Appendix 2. Analysis of extreme water events for the region
Appendix 3. Some simplified macro economic impact models for the Pampas System
Appendix 4. Estimation of the primes for the flooding insurance system exercise.
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1. Introduction
Several studies show that the socio economic impacts of extreme events have been
growing steadily during the last decades (IADB ). In parallel there is a need body work
of academic knowledge and methodologies on vulnerability (see Turner et al. 2003 as
example), which practical purpose is to help to better define policies targeted to
reducing the vulnerability status of regions to environmental stressors.
For the purpose of policy making, there is a group of questions that are common to any
case analysis and that need to be addressed in order to define policies (i.e. understood as
a set of actions and rules sustained during a certain period of time) that have chances to
improve the vulnerability status of a given region.
In this work we consider three basic issues that seem to be common to any policy
analysis and design:

Scale of the problem. What is the scale of the problem and whether it can be
solved at the system level and/or the agent level?

Risk mitigation versus risk transferring. How is the risk profile to be addressed
and how much of the risk can be mitigated in cost efficient ways versus how
much of the risk is residual risk?

Financing of the costs. How is the cost associated to the impacts of the extreme
events allocated among the involved actors? How is the financing cost allocated
in time? and, What are cost efficient and socially fair ways of allocating the
costs?
The rest of the document presents in section two, a conceptual framework to develop
risk management systems to deal with flood extreme events at regional scale. The
section three describes the main elements for the case study of extreme flood events in
the Pampas system in Argentina. The analysis starts by diving the region three sub
regions according to its vulnerability profile and later on makes the case for the need of
a risk transferring policy at the regional level i.e. a public-private insurance system, to
address the significant flood residual risks in the area. In particular, it presents a Monte
Carlo simulation exercise to evaluate the design of an public insurance fund to cover the
such a system. The paper finishes with a section with conclusions and suggestions for
future work.
2. Analysis framework
A proper design of risk management system to deal with extreme events must cover all
the significant dimensions of the problem i.e. environmental, socio economics, policy
options, and participatory decision making. Figure 1 presents a general framework
proposed to perform environmental vulnerability study for extreme flood events at
regional scale and suggests a comprehensive procedure to design a implement efficient
and sustainable policies.
3
This framework complements the conceptual approach presented in the vulnerability
frameworks (Turner et al. 2003, Bereciartua 2005) by providing a certain number of
phases in order to implement such a vulnerability analysis for the case of region
management of extreme environmental events at a regional i.e. extreme floods.
Environmental
Model
Socio economic
consequences
Policy
options
•Extreme
weather analysis
•Hydraulic /
hydrology and
environmental
models
•Flood
inundation maps
•Loss – risk curves
•Scale of the
consequences (i.e.
system or agent):
macro, meso or
micro economic
•Risk management
versus risk
transferring
•Structural versus
non structural
solutions
•Financing needs
(i.e. regional funds
vs market
solutions)
Participatory
Decisionmaking
•Institutional
setup and legal
frame (i.e. who is
responsible)
•Public-private
partnerships
Figure 1. Conceptual analysis framework to study policies for dealing with extreme
flood events at regional scale.
The conceptual model consists of four phases. The firs phase is an environmental
modeling exercise, that on the basis of the available information and proper models and
technologies (i.e. extreme weather analysis, hydraulics and hydrology models and flood
inundation maps and GIS), results in an assessment of the spatial and temporal
probabilistic distribution of selected stressors in the system. It should be noticed that
under a scenario of climate change the traditional analysis based on past information
should be revised to account for newer trends in the climatic variables.
The next phase deals with the socio economic consequences derived from impacts
associated to the chosen extreme events. An issues of particular importance, is the
assessment of the scale of the problem. This has to do with the size of the impacts in
relation to the characteristics of the region i.e. whether the impact is economic wise
significant at a national or regional level.
This assessment can be evaluated with macro economic models and regional economic
models, such as input-output models for example. This information is useful to
understand what part of the problem affects the system as a whole and what part of it
affects particular groups of actors within the system. For the former case type of
problems, it is clear that the system will need external help and support to deal with the
problem, in other words the problem the solution of the problem needs to be scaled up
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to a level that might exceed the spatial dimension of the impacts i.e. it may need to be
solved as a national problem instead of a regional one.
For the cases of problems that are limited to certain actors within a region, the problem
can be studies and resolved at the regional level, and in many cases will have to do with
schemes of social distribution of the losses.
The other important output of this phase is the risk profile of the studied region to the
selected stressor. It can be summarized in an economic losses – risk curve that later on
is useful to define the expected economic solicitations to the region.
The third phase the policy options phase. The purpose of this phase of the analysis is to
define a policy, understood as a set of actions sustained during a period of time that
have high chance of dealing with the losses associated with extreme events in an
economic efficient and socially fair fashion. For the case of extreme flood events, we
find that there are three issues that are of most importance: 1. a clear definition between
risks that can be mitigated and residual risks, 2. a complementation between structural
and non-structural measures, and 3. an analysis of the financial dimension of the
different policies.
For example, traditional engineering approaches tend to choose structural measures that
mitigate risks, such as infrastructure works. However in many flood cases, there are
significant residual risks that can not be mitigated at cost efficient levels and that are
better suited for risk transferring schemes. As we will show later on in this paper, that is
the case of the flooding problems at the Province of Buenos Aires in Argentina.
An important component of any policy analysis and design is the financial dimension of
the policy. By the financial dimension we are referring to definitions regarding how the
costs are allocated among the involved stakeholders, who pays for each cost, and what
is the bottom line impact of those decisions in the final cost of the event and in the final
impact of the event on the regional economic processes.
Lastly, there is four phase that must attend the implementation issues and that must
allow for participatory models of decision making. This last phase must consider the
institutional dimensions of the policies under analysis, and must deal with devising
partnerships among stakeholders that warranty the success of the selected policy.
The approach integrated in the sense that although there is a natural phasing for the
generation of information i.e. it is first needed information on the environmental process
to be able to evaluate alternative policies, the final outcome will most probably imply
several feedbacks to each phase until the a better policy is designed and selected.
3. Case study: extreme floods events in the Pampas System
Subtropical Argentina is one of the world areas that experimented stronger climate
changes during the twentieth century as they were reflected in annual average rainfall
and an annual river flows between 1995 and 1991, the increment of average annual
rainfall went from 10% to 30% in different regions. Two regions showed the higher
positive changes, the northwestern region of the Province of Buenos Aires Province,
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and the south of the Province of Corrientes (Barros et al. 2000), . In both cases, rainfall
increments were over 400 mm in terms of average annual records. This trend meant that
the agriculture frontier moved west (approx. 300 km), south (approx. 200 km) and north
(approx. 200 km), at the same time several low lands became permanent lagoons and
wetlands.
There seems to be a trend of regional higher climate variability during the last decades.
This is reflected in higher floods and lower low flows in the Parana river since the late
70’s. At the same time, the increment in the river flows is higher than the increase in
average rainfall which might be showing impacts of changes in land use patterns i.e.
expansion of the agricultural frontier. The hydrological amplification of the rainfall
pattern is typical of areas with mild slopes found in Argentina. In this sense, this region
is expected to have a higher hydrological vulnerability to climate variability and climate
change than other regions. In fact, it can be shown that regional extreme floods and their
economic losses have been increasing during the last decades.
Given the increasing economic losses in the region due to extreme flood events and the
fact that the land use patterns are difficult to change, there is a need to device regional
strategies for adaptation to the experimented climate variability and potential climate
change. This analysis follows an initial description of the problem and conceptual
analysis within the vulnerability framework (Bereciartua, 2005).
3.1 .Environmental modeling: macro vulnerability regions
In order to understand the flooding potential of the region at a macro scale two sources
of information have been used. An implementation of the index of agricultural
production capacity for the area, and a processing of satellite images to assess the macro
potential of flooding at each location.
The lands of region have been classified according to their agricultural production
capacity. This is achieved through the use of an index of agricultural production
capacity (ICAP) that combines the characteristics of the soil following the system of
soil taxonomy (National Resource Conservation Service, USDA 1999, 2nd Edition). The
methodology was originally developed by FAO and has been adopted by the National
Institute of Agricultural Technology (INTA, Atlas de Suelos de la República Argentina
INTA PNUD SAGyP, Arg. 85/019). It considers eleven characteristics at each location:
macro climate, drainage, superficial texture, sub superficial texture, ionic exchange
capacity, organic mater, effective depth, salinity, sodicity, current erosion and potential
of erosion. Figure 2 presents the result of this exercise for the project area.
The flooding potential at the macroscopic level is available from work done through
processing of the satellite images from the high resolution Landsat 5 and 7 (Institute of
Water and Climate, National Institute of Agricultural Technology). This work is
directed specifically to the risks of water excess and deficit in relation to agricultural
production and has been done only by the analysis of the images and without taking into
account historic data records (i.e. rainfall). In this sense, it is useful to mention that the
analysis does not show the probability of flooding but it gives a classification of the
land according to the observed frequency of flooding. The probability of flooding is
related not only to the rainfall data but also to the conditions at the soil and the time
6
after previous rainfall events for example. To be able to get that information it necessary
to make an analysis a higher level of spatial detail (i.e. at the property level), which is
not the purpose of our present work. This type of analysis is presented later on in this
document.
Figure 3 presents the results of the analysis and gives an overview picture of the spatial
distribution of the risk of water flooding at different location throughout the project
area.
Figure 2. Map of the index of Agricultural Capacity (ICAP) of the project region.
Source: Office of Agricultural Risk, original work INTA. Legend: darker green shows
higher index of agricultural production, whereas lighter green shows areas with lower
index of agricultural production values.
It can be notice that a first analysis of this information can serve to identify areas with
high potential for vulnerability to water risks i.e. areas with high flooding potential and
high index of agricultural capacity.
3.1.1. Three flooding vulnerability regions for the Pampas System within the
Province of Buenos Aires
The Pampas System extends for over 60 millions hectares representing 22% of
Argentina’s continental surface. Out of it something close to 38 millions hectares
represent the humid pampas and are the focus of this study.
In particular, and for the rest of the work, this project studies the part of the humid
Pampas System that falls in the territory of the Province of Buenos Aires. On the basis
of the above presented information and other data sources, we divided the region in
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three sub regions according to their vulnerability profile. Figure 4 shows a map with the
spatial extension of each of them and following is a brief description of each one.
Figure 3. Map of the macroscopic water risk for the project zone. Source: Office of
Agricultural Risk, original work INTA. Legend: dark blue shows areas with permanent
surface water (i.e. lakes or lagoons), lighter blues show areas with flooding occurrence
in lower frequency, green areas are locations with no flooding risk, and yellow areas
identify zones with water deficit risk.
Region 1
This region extends over the northwestern area of the Buenos Aires Province and
coincides with the geological region know as wavy pampa. The area includes 31
counties (partidos) with and average surface extension of approximately 400,000 has
each. It is one of the regions with higher levels of economic development in the country
both in agriculture and in industrial activities. There is abundant water supply and
groundwater resources are key to the system. The rural population is approximately
7.6% of the total population and it is decreasing. The percentage is low but the absolute
value is relatively high comparing with other regions. The urban population is spread in
several small cities that located close to the transportation lines (railway and highways).
Most of the population have fresh water supply.
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1
3
2
Figure 4. Map with the defined three flooding vulnerability regions for the Pampas
System within the Province of Buenos Aires.
Region 2
This region extends over the central part of the Province of Buenos Aires and it
coincides with the geological region known as depressed pampa (Salado-Vallimanca
valley) and it includes 42 counties. One of the main regional problems is the water
resources management due to the alternated humid and dry periods and the existence of
saline groundwater. Major infrastructure projects have been built in the past to deal with
this problem and several are currently in design an in construction. The main economic
activity is live stock and some agriculture which are not intense in human resources.
However over 20% of the population in the region is rural although with a decreasing
trend. The urban population is not well developed and there are no major urban centers.
Several counties show positive emigration. There some households with problems of
fresh water supply mostly in the lower watershed.
Region 3
This regions extends over the eastern part of the Buenos Aires Province and in the
geological region known as sandy pampa. It is integrated by 9 counties. It presents some
limitations regarding surface and groundwater supply. For the former there is no well
developed drainage system and for the latter there is excess of salinity. There is an
important percentage of rural population although it is not that significant in absolute
terms. The population tends to spread between rural housings and urban small towns.
There few important urban centers. The main economic activity is live stock and some
agriculture.
Table 1 shows the population distribution in each sub regions.
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Population
Region 1
Region 2
Region 3
Total regions
Total province
Province
2.252.343
902.429
137.583
3.292.355
13.827.203
Percentage
16,29%
6,53%
1,00%
23,81%
Table 1. Population by vulnerability region.
3.2. Socio economic consequences of floods
Due to its extension and homogeneity, the humid pampas are comparable with the
American flats areas, or the Russian or Ucranian steppes, or the China flats, though with
a template climate. However in many senses it is a unique feature as it concentrates very
fertile soils with mild slopes, sufficient rainfall with no dry season, and a not excessive
different temperature for each season.
This region is key to the economy of Argentina, concentrating the higher concentration
of manufacturing industries and population i.e. 37% of total population, which reaches
70% when considering the Buenos Aires City metropolitan area. The area is suited to
produce almost any primary agricultural product, it has a potential for expanding the
agricultural frontier, good average climate conditions, and it is possible to do two
harvests per year. The area classifies as the world eighth food producer and fifth food
exporter.
It is not a surprise that the region had a high share in the national gross product and
exports. Table 2 shows a data series of the total exports of the Province of Buenos Aires
and its share in the national exports. Table 3 presents a break up of those exports values
in categories for year 2002. It can be noticed from those data that agriculturally based
exports represent more than 35 % of the total.
The agricultural sector is also significant in terms of employment by giving jobs to
close to a million people. The share of the agricultural employment in the national
employment is around 11%. The sector is composed of more than 100,000 private
farming companies.
It is useful to note that between 1990 and 2002 the total area under farming production
grew 41% whereas the total output grew 81%. This represents an increment of
productivity in the order of 30% that is mostly due to the green revolution and the use of
better production technology.
Year Total exports from the
Province of Buenos Aires MM u$s
1998 10,490.7
1999 8,572.2
2000 9,969.6
2001 10,091.9
10
Share of the total country exports
39.7%
36.8%
36.5%
38%
2002 9,229.1
35.9%
Table 2. Export performance of the Province of Buenos Aires and the country. Source:
Secretary of Industry, Argentina. It can be noticed that the Province of Buenos Aires
accounts for over one third of the country’s export performance. The project region is
closely overlap with the area of higher productivity of that province and therefore is
responsible of a high contribution to the country’s export performance.
Year 2002
Primary products
Manufactures of Agricultural
Origin
Manufactures of Industrial
Origin
Combustibles and Energy
Total
Total exports from the
Province of Buenos Aires
MM u$s
1,370.4
2,030.3
Share of the total
exports
4,606.5
49.9%
1,221.9
9,229.1
13.2%
100%
14.8%
22%
Table 3. Export break up for the Province of Buenos Aires and the country for year
2002. Source: Secretary of Industry, Argentina. It can be noticed that both primary
products and manufactures of agricultural origin account for close to a third of the
country’s export performance. These two categories are directly affected by water
related risks.
In sum, given the importance of the region to the economy of the country, it is expected
that the socio economic impacts of extreme flood events should managed with
according criteria of cost efficiency and social equity.
3.2.1. Economic losses associated with extreme flood events
The extreme flood events in the pampas cause economic losses that can be classified as
direct (i.e. loss of agricultural production, damage to infrastructure, soil degradation)
and indirect (i.e. inter industry regional impacts, differed financial burden due to
financing, social distress).
Furthermore in Table 4 we present an analysis of the sources of economic losses
grouped by social actors or stakeholders (i.e. public sector governments at different
levels, private sector, regional and international financial institutions) involved in the ex
ante and ex post phases of a flood extreme event in the pampas.
Social Actors
National
government
Economic Losses
Reduction in the tax income and increase in the tax expenses due to
tax subsidies (Law 22.913). Allocation of higher amounts of funds for
the event mitigation and for the reconstruction of infrastructure.
Increase in financial burden (capital + interests) due to new loans for
dealing with the extra expenses. Reduction of the funds for
agricultural emergency. Opportunity costs for allocating the available
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Provincial
government
Municipal
government
International
organisms
Banks and
financial
institutions
Farmers
Rural
population
Urban
population in
rural areas
funds to these purposes.
Reduction in the tax income and increase in the tax expenses due to
tax subsidies (Law 22.913). Allocation of higher amounts of funds for
the event mitigation and for the reconstruction of infrastructure.
Increase in financial burden (capital + interests) due to new loans for
dealing with the extra expenses. Reduction of the funds for
agricultural emergency. Opportunity costs for allocating the available
funds to this purposes.
Reduction in the tax incomes. Increase of expenses due to evacuation
and emergency costs. Institutional resources allocated to the
coordination of the emergency action in situ.
Risk of default on loans allocated to build hydraulic infrastructure
and to help economic and social recovery in the ex post phase.
Losses due to payment delays, debts re negotiated, reduction of
payments. Subsidies applied to the market interest rates on loans to
affected farmers. Deterioration of the financial loan portfolios in the
region by higher default risks.
Losses due to the selling of cattle at prices below market. Losses due
to plantations affected in total or partially. Increment of production
costs due to deteriorated infrastructure (i.e. rural roads). Losses due to
increment of low lands and wetlands. Losses due to sanitation
impacts (i.e. on plants and cattle). Negative impact on the property
value. Reduction of funding for maintenance and for the next
agricultural campaign.
Losses due to impact on properties and on health. Temporal reduction
of salaries due to less work in the area. Resources affected at
replacing things lost or damaged.
Losses due to impact on properties and on health. Difficulties to go to
work. Resources affected at replacing things lost or damaged.
Table 4. Social actors and economic losses associated with extreme flood events in the
pampas system.
3.2.2. Expected losses – risk curves
With the purpose of evaluating the risk of economic losses associated with different
flooding extreme events, two analysis were developed: an estimation of the economic
losses occurred for a given extreme flooding event i.e. the 1998 flood, for which there
was available information, and an estimation of the probability the extreme events.
For the estimation of the direct economic losses we did an in depth analysis of the 1998
flood this was achieved by estimating the level of agricultural production of the area
(exposed value) and estimating the size of the flooding effects due to losses in three
main dimensions: 1. the agricultural production affected, 2. the infrastructure damage
and 3. social sector impact. Further information is presented in Appendix 1. This
approach was later on generalized for other couple of events in order to have a “sample”
of events with different level of recurrence. As a preliminary conclusion the annual
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average direct losses from flooding events in the Pampas System are estimated in the
order of 300 MM u$s.
The estimation of the probability of the extreme events was done through the extreme
value distribution analysis of rainfall series data at Junin, a location close to the center
of the project area. Even though this is an indirect way of estimating the probability of
the event we considered this a reasonable assumption for the purpose of this work. The
results of this analysis are presented in the Appendix 2.
The combination of both analysis resulted in a curve of expected losses – risk as it is
presented in Figure 5.
Figure 5. Expected losses – risk curve for the project area. This curve results from
fitting an extreme value distribution the estimations of the direct economic losses
associated with recent extreme water events in the region.
3.2.3. Scale of the socio economic impact: macro economic and inter sector
impacts
As mentioned before, one of the key issues to understand the socio economic impacts of
the floods in the area is not only to assess the size of the direct economic losses but
what are the consequences of the event on the macro economic relation in the region i.e.
production at related industrial sectors and impact on the economic growth dynamic for
the region. To this end we implemented some simplified macro economic models and
an input-output model for the region.
The implementation of several simplified macro economic impact models to the area
(i.e. model of Abdala Bertrand Chapter 7 and model ICOR) showed that although the
annual average estimated direct losses due to flooding events in the area are in the order
of 300 MM u$s and they can reach values of close to 1 KM u$s, they do not represent a
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major impact to the national GNP (i.e. losses < 1% of the GNP) neither the provincial
government for the vast majority of the expected cases. A summary of the
implementations of two of the macro economic simplified models is presented in the
appendix 3.
On the same track we did an analysis of the governmental budgets (national and for the
Province of Buenos Aires) to see whether the impact of the extreme floods was
noticeable at that level. Our conclusion is that it was not possible to identify any major
impacts associated with the occurrence of the extreme floods in this macro level.
In order to check for indirect extreme events effects we did a broad implementation of a
simplified Input-Output model for the intra effects of a flooding in the area. The
analysis followed a similar approach to the one presented in Abdala Chapter 5 and
Appendix D and it showed that there are small inter-sectors regional multipliers to the
consequences of a flooding (unpublished).
In conclusion our work shows that the impacts of floods in the pampas system have
small macro economic consequences for the country and even for the Province of
Buenos Aires.
3.3. Policy Options
At the system level the goals of risk management system for the regions must have two
main objectives: economic efficient and socially equity. The economic efficiency, in
turn, is related to two issues: 1. the relative amount of incurred economic losses, and 2.
the perturbation that the event generates on the regional economic processes.
The economic losses generated by an extreme event are not only related to the specific
characteristics of the event itself but are also consequence of the risk management
system in place at the region. In fact this is one of the key ideas behind the concept of
vulnerability i.e. that a region vulnerability is the result of both the exposure of capital
to a certain stressors and the capacity to response to negative outcomes. In this sense,
the international experience and the vulnerability framework of analysis, help to show
that the economic losses associated to a given extreme event are reduced when:

There is a proper balance between risk mitigation measures (i.e. measures that
tend to reduce the capital exposure in a given region such as infrastructure) and
risk transferring measures (i.e. measures that tend to transfer the residual risk to
a private market, in many cases this is only possible through a public private
partnership that allows governmental intervention in cases of extreme low
probability events).

There is a proper balance between ex ante and ex post measures. The principle is
that ex ante measures tend to have a higher net direct cost to the system but it
reduces the level of the “perturbation” of the extreme event losses on the
economic processes. In other words, ex ante measures are usually more
expensive that the net expected direct costs of extreme events but in the overall
give sustainability to the economic processes i.e. the production activity tends to
recover faster and there less needs for diverting funds from other uses at the
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moment of the emergency. This diversion of the funds in emergency cases also
allows for lack of transparency and possibilities of corruption which always
result in higher economic costs to the system.
3.3.1. Residual risk versus risk that can be mitigated
The specific geomorphic and hydraulic characteristics of the pampas result in a system
with high levels of flood residual risks. The area is extremely flat and extended, the
natural geomorphic features of the watershed are those of a naturally under developed
drainage network.
Even when there several macro infrastructure projects, i.e. several hundred million
dollars projects, under development in the region at the present, they will provide a
drainage network to some regions but will only slightly reduce the remaining residual
risk. In other words, many of the infrastructure projects will help to reduce the flood
risk exposure of some areas, but due to particular characteristics of the region i.e. lack
of hydraulic slope, large extensions of land will remain affected by the “residual” flood
risk.
Therefore a risk management system for the region will need a scheme for risk
transferring, such as an insurance system for flooding events, to deal in a cost efficient
and socially fair fashion with the residual risk remaining in the region.
3.3.2. Financing of extreme events losses
One of the key issues for defining a policy is how to allocate the costs and the benefits.
This issue is closely related to the scheme that is selected to finance the losses of an
extreme event.
Lending
institution /
actors
IADB
Time of the
loan
Kind of action and interest rates
Before /
After
World Bank
Before /
After
Public Banks
Meanwhile /
After
Private Banks
Meanwhile /
After
The loans given beforehand are usually directed to
build infrastructure to reduce regional vulnerability.
Fixed and/or variable rates range 3 – 5%.
The loans given beforehand are usually directed to
build infrastructure to reduce regional vulnerability.
Fixed and/or variable rates range 3 – 5%.
Refinancing. The government subsidizes the market
interest rates through the public banks: 25 % of the
market rates for areas declared “in emergency” and
50% of the market rates for areas declared “in
disaster”. According to the law, an area is “in
emergency” when over 50% of its production capacity
is affected and is “in disaster” when over 80% of its
production capacity is affected.
The farming production is activity is financed with
periods in coincidence with the income for selling.
15
National /
Provincial
Government
Farmers
After
Before /
Meanwhile /
After
The government provides money to help repairing
infrastructural losses and provides delays in certain
taxes.
To a certain level, the farmers provide self financing to
extreme events by using savings when available to pay
or extra costs and/or reduce than expected incomes.
Table 5. Summary information on social institutions / actors and their participation in
the financing of losses for extreme flood events.
3.3.3. The development of an insurance system for flood event in the Pampas
The implementation of an insurance system for flood events in the Pampas System is
justified by the next facts:



The recent high growth rate of the agricultural industry in the Pampas is based in
an increment of the direct costs mainly due to higher used of technology,
fertilizers and GMO (genetically modified organisms). The higher use of
technology results in sustained higher productivity but to the expense of higher
exposed capital.
Even when there is an efficient spatial and temporal distribution of plantations,
there are emergencies due to extreme events. The existence of a specific
insurance system could help a faster recovery of the production activity.
Additionally over 50% of the extensive agricultural production in the pampas
system is done by companies that rent the land; therefore the larger part of their
operating capital is exposed. The financial security of these companies is based
on the success of the harvest.
3.3.4. Evaluation of a fund to implement a risk transferring system for the region
After some broad evaluations and some contacts with insurance companies operating in
the region and with private and public actors we concluded that there are low chances
that a fully private insurance system can develop in the area. On the contrary, it seemed
possible for several of the stake holders to develop this kind of a system when given
two key issues: 1. the participation of the public sector in cooperation with the private
sector (public-private partnerships), mainly in relation to the design and management of
an specific insurance fund targeted to cover low probability catastrophic extreme events
losses, and 2. the availability of better spatial risk profiles based to better qualify and
quantify the probability of events occurrence at different locations. The latter can result
in the definition of areas of different level of water risk, much in the same way as the
vulnerability regions we presented in this work but at a finer spatial scale.
With the purpose of evaluating alternative insurance systems for the region, we
developed a Monte Carlo simulation model to design an insurance fund. The model
solves a cash flow scheme for a fund and it allows for different combination of: initial
fund size, temporal progression of the income due to insurance primes in the region,
different level of taxes income from the region to cover the extreme event. For a given
16
set of conditions, the model performs several thousand runs, i.e. 10,000, sampling the
events in random fashion from the expected losses – risk curve presented beforehand.
Figure 6 shows the result of one of the simulations performed. The purpose of this
simulation was to estimate after how many years the fund achieves self sustainability
with a level of statistical significance of 95%. The simulation assumes that the fund will
have two sources of income: a governmental tax for floods and the insurance primes,
and that it will have to cover the in excess expenses that the private insurance system
will not be responsible for. For further information on how were modeled the insurance
prime income for each region see Appendix 4.
As mentioned before, the probability occurrence of the extreme events follows a
random sampling from the expected losses – risk curve developed for the region. The
simulation shows that after 23 years, with a size of u$s 2,469 M, the fund reaches self
sustainability. From that point on, the government would be able to stop the flood tax
system and the fund should be able to cover for the in excess losses due to floods at the
selected level of statistical significance.
To the fund
Primes
Taxes
2,700.25
220.01
2,664.76
220.01
220.01
2,548.86
220.01
2,527.14
220.01
220.01
220.01
2,507.39
23
2,489.44
22
2,473.11
21
220.01
309.05
17
220.01
309.05
220.01
309.05
20
2,458.28
2,206.70
220.01309.05
-78
1,978.00
-78
1,770.08
-78
220.01309.05
1,581.07
19
18
-293
220.01 309.05
220.01 309.05
220.01309.05
1,409.24
17
-299
1,253.03
1,111.03
16
-78
15
-92
309.05
209.61
220.01 309.05
981.93
864.57
14
-78
13
-160
12
309.05
309.05
11
199.21
171.01
10
757.87
660.88
-29
9
-57
-15
572.71
156.91
309.05
13
309.05
309.05
142.81
128.53
492.55
8
419.67
7
-1
56
71
128
309.05
108.54
353.43
309.05
85.69
6
293.20
88
309.05
309.05
71.41
54.27
5
238.45
188.68
45.7
143.43
4
-119
3
309.05
113
309.05
2
-116
102.30 37.13
309.05
309.05
30.91
22.85
1
64.9028.56
105
Fund for 30 years
100%
95%
90%
85%
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
-25%
24
25
26
27
28
29
30
Remaining
Figure 6. Monte Carlo simulation output for the designed of a regional fund for extreme
water events. Under a set of particular conditions chosen for the exercise, the simulation
shows that the flood insurance fund for the pampas system becomes self sustaining after
the 23rd year in operation.
4. Conclusions and suggestions for future work
This work presented a first analysis of the vulnerability of the Pampas System to
extreme flood events according to the vulnerability framework.
Following the analysis approach presented in the section two of this paper, we were able
to arrive to the following preliminary conclusions:
17



That the pampas system has is a rather extended area and it is possible to
identify sub areas with differentiated vulnerability profiles. The differences are
borne both in the geomorphic conditions in the area and to a less extent in the
climate conditions, and in the socio-economic differences among regions due to
the production profiles of each regions and the distribution of population and
cities in the space.
That the system has a particular balance between risks that can be mitigated and
residual risk that are not possible to be mitigated at cost efficient levels.
Although it seems to be the case that there is a need for large scale hydraulic
infrastructure across the region to improve the drainage capacities in the area, it
is clear that this measures will help to reduce the exposure of capital to the
floods (i.e. mitigate) but will not reduce a large and spatially distribute residual
risk.
That the presence of large and spatially distributed residual flood risks in the
area justifies the analysis of a risk transferring management system i.e. a publicprivate insurance system to dealing in cost efficient and socially fair ways with
the residual risks.
On this basis, the paper suggests that the region needs a comprehensive design of a
disaster risk management system. There is increasing international experience and
conceptual approaches the designing those types of systems (i.e. P. Freeman et al.
IADB, 2003). Along those lines, this work proposes a public-private insurance system
as a risk transferring and management approach for dealing with the residual risk across
the region. Furthermore it develops and presents a Monte Carlo simulation model to
analysis and design a public fund to cover the system against low probability extreme
events.
Our preliminary simulation results show that it is possible to design a fund that reach
self sustainability after several year of operation with a back up from the government.
Even when these types of analysis are preliminary they still help to show that the
policies could be useful.
There are several interesting lines for future work. At this moment it seems particularly
attractive to highlight two of them:

To further develop the approach proposed in this work. This implies to refine the
analysis with further detail and to implement similar analysis such as the ones
presented in this work on the basis of more information i.e. the simulation
model.

To complement the presented analysis at the system level with an analysis at the
finer spatial level. This implies to do an analysis at the county level (i.e.
Partidos) studying the specific conditions at each farm. We have started this
analysis for the country of Junin, a district of close to 400 thousand hectares,
located in the center of the vulnerability sub region 2. Preliminary results are
available
18
5. References
Barros V., Castañeda E. and Doyle M. (2000). Precipitation trends in Southern South
America, east of the Andes: An indication of climate variability. In: Southern
Hemisphere Paleo- and Neoclimates: Key Sites, Methods, Data and Models, Springer,
pp. 187-208.
Bereciartua P. (2005). Vulnerability to global environmental changes in Argentina:
opportunities for upgrading regional water resources management strategies. Water
Science & Technology Vol 51 No 5 pp 97–103 © IWA Publishing 2005
Climate change: a glossary by the Intergovernmental Panel on Climate Change (1995).
(http://www.ipcc.ch/pub/gloss.pdf) .
El Cambio Climático y sus Consecuencias Territoriales (2003). Tomos I y II. Cámara
Argentina de la Construcción (CAA).
Freeman P., Martin L., Linnerooth-Bayer J., Mechler R., Pflug G. and Warner K.
(2002). Disaster Risk Management. National Systems for the Comprehensive
Management of Disaster Risk. Financial Strategies for Natural Disaster Reconstruction.
Inter-American Development Bank.
Guenni L., A. Hernandez, M. Fillipone (2003). Modeling Population Vulnerability and
Risk to Extreme Rainfall Events in Venezuela. Acta Cientifica Venezolana, Vol. 54,
Suplemento No. 1: 2-12.
Linerooth-Bayer J., Field M.J. and Verheyen R. (2003). Insurance-Related Actions and
Risks Assessment in the Context of the UNFCCC. Background paper for the UNFCCC
workshops.
Mechler R. (2003). Natural Disaster Risk Management and Financing Disaster Losses
in Developing Countries. PhD Dissertation, Universität Fridericiana zu Karlsruhe.
Pollner J.D. (2001). Managing catastrophic disaster risk using alternative risk financing
and pooled insurance structures. World Bank technical paper; no. 495.
Salado Basin Master Plan (2001). Salado Master Plan Unit Governement of the
Province of Buenos Aires and Sir Halcrow Ltd.
Turner II B. L., Kasperson R.E., Matson P.A., McCarthy J.J., Corell R.W., Christensen
L., Eckley N., Kasperson J.X., Luers A., Martello M.L., Polsky C., Pulsipher A. and
Schiller A. (2003). A framework for vulnerability analysis in sustainability science.
PNAS, Vol. 100, no. 14., pp. 8074-8079.
Wilbanks T.J., Kane S.M. and Leiby,P.N.(2003). Possible responses to global climate
change: Integrating mitigation and adaptation. Environment 45:30-37
World Bank (2001). Inundaciones en el Área Metropolitana de Buenos Aires, Kreimer
A., Kullock D., Valdés J. (ed.), Disaster Risk Management Series, Working Paper N 3.
19
Appendices
20
Appendix 1. Methodology for the evaluation of direct economic losses due to
flooding events in the Pampas system.
The direct economic losses due to an extreme water event (i.e. flooding) in the project
area were estimated according to the following procedure:
1. Estimation of the production value exposed. This is made up of the total amount
of production at risk (i.e. agricultural production in the area for the given year)
2. Estimation of economic losses as the sum of three components:
a. Percentage of agricultural production lost due to flooded areas, areas
with excess of water and areas with difficult of impossible transportation
access
b. Losses due to infrastructure damage i.e. roads, bridges, railways that are
damaged
c. Losses to the social sector. This item is the most subjective to evaluate
and is estimated through items such as amount of evacuated people, days
without schooling, among others.
3. Estimation of losses cited in the media i.e. newspapers and performed by
different institutions related to the region i.e. Minister of Production, Province of
Buenos Aires, or National Institute of Agricultural Technology.
4. Areas classified under Agricultural Emergency Law (Law 22.913). It is a federal
law that declares an area “in emergency” when its production capacity is
affected in more than 50% and an area “in disaster” when its production capacity
is affected in more that 80%. The implementation of this law is an indirect way
of estimating the magnitude of the impact of a given extreme water event.
5. The sum up of each component estimated in the previous point 2 adds-up to the
a first assessment of the overall economic losses. This evaluation is
complemented with the information listed in points 3 and 4.
This is the methodology that was implemented to evaluate the consequences of the
1998 flooding.
21
Appendix 2. Analysis of extreme water events for the region
The Generalized Extreme Value (GEV) distribution is given by,
1 / 

 

 x    
G( x)  exp  1   
 
   

 

with parameters μ, σ (<0) and ξ, μ and σ are the location and scale parameters
respectively, and ξ is the shape parameter. A value of ξ > 0 corresponds to a Frechet
distribution, a value of ξ < 0 corresponds to a Weibull distribution, and a value of ξ  0
leads to the Gumbel family with distribution:

  x    
G( x)  exp  exp  
 
    

with - ∞ < x < ∞. Having a set of annual maxima and fitting the GEV distribution it is
possible to calculate the extreme quantile estimates,
xp   



1   log( 1  p)


where G ( x p )  1  p . In other words, x p is known as the return level corresponding to a
return period of 1/p (Guenni et al. 2003).
The following figures show the result of fitting extreme value distributions to the
rainfall data record series for 1960 to 2004 at the location of Junin, Province of Buenos
Aires. According this analysis the distribution with best data fit is the Weibull
distribution.
22
Figure. Analysis of extreme rainfall events at the location of Junin, Province of Buenos
Aires for the data series record 1960 to 2004.
23
Appendix 3. Some simplified macro economic impact models for the Pampas
System
It is stressed that these models over simplify the analysis but are nonetheless tools to
have broad results that can be useful to understand some of the impacts of these type of
extreme events.
1) a simplified macro model, Albala Bertrand, Chapter 7 and Appendix f
The following analysis corresponds to the 1998 flooding consequences. It assumes
values at 2003 u$s.
L= u$s 359,304 M
K= u$s 70,618
W= Production= u$s 288,686 M
∆K= Loss in K + loss in production W= u$s 359,304 M= L
c= ∆K/∆Y= 1.65
∆Y= ∆K/c= 217,656 M
∆Y/Y= 1/c x L/Y= 0.606 0.085= 0.05159
∆Y/Y= 5.16%
y= 0.085/1.65= 5.16%
This results in a loss of 5.16% of the agricultural product growth in the Province of
Buenos Aires.
Amount of GRP (gross regional product) lost at the end of the first year after the
flooding:
R: gross product per capita
r: gross product growth rate
p: population growth rate
1+R= (1+r)/(1+p)
or
R= (r-p)/(1+p)
Then,
R= r-p= $1.282
R= average gross product per capita series 1993 to 2001= 3.22%
GNP= $ 4,220,525 M
r: average growth of GNP for series 1993 to 2001= 4.75%
p= 0.5%
I= flooding losses rate= 0.085
Then,
L= total loss= L1 (capital loss) + L0 (production loss)
24
L= 0.0167 + 0.0683= 0.0858
Therefore the total lost can be divided in L1= 1.67% and L0= 6.83%.
The rate of growth of the regional economy to avoid a loss in the rate of growth of the
regional income per capita as a consequence of the flooding is:
R= r –I –p
r= 12.22%
2) Incremental Capital-Output Ratio (ICOR)
The model measures the productivity of additional capital. In this sense, the amount of
investment needed to recover the level of production pre disaster is the amount of
capital destroyed. So the model ICOR can be presented as,
ICOR = Investment/∆GNP
Using ∆ GNP = ∆ loss in capital, then ICOR = Investment / ∆ loss in capital
In other words,
Current ICOR + growth rate = target growth for next year
After a disaster, some funds are directed to re construction and therefore,
ICOR’ = ICOR + reconstruction investment / ∆ GNP
It is assumed that for Argentina public investment represents 30% of total investment.
The total investment will then need to be financed either through a reduction in
spending or external financing.
GNP = gross product
C= spending
Ip= private investment
Ig= public investment
G= governmental spending
BP= balance of payments
GNP = C + Ip + Ig + G + BP
After the disaster,
Ip’ + Ig’ = Ip + Ig + investment in reconstruction
If the goal is to maintain the same level of growth after the disaster,
GNP = C’ + Ip + Ig + investment in reconstruction + G’ + BP’
25
An the cost of reconstruction can be estimated as,
Reconstruction investment = (C – C’) + (G- G’) + (BP’ – BP)
Results of the implementation of the model for the case of a 1998 flooding on the 2005
growth estimates
Variable
Total spending
Private spending
Public spending
Total investment
Investment in construction
Investment in public construction
Investment in private construction
Investment in durable goods
Balance of payments
Exports
Imports
Amount (year 2005 in u$s 1993)
229,008
193,657
35,350
47,053
28,414
8,524.2
19,889.8
18,639
6,837
36,646
29,809
Source: Economic outlook 2004-2005 Orlando J. Ferreres & Asociados, according to
their moderate scenario.
The main hypothesis behind the model is that total investment is desegregated in
investment and durable goods. The government does not invest in the latter.
GNP = 229,008 + 47,053 + 6,837 = 282,898
Then for the case of the 1998 flooding occurring in 2004, in order to maintain the
growth rate in 2005 and using the ICOR model, the investment rate should be estimated
as,
Ip’ + Ig + = Ip + Ig + investment in reconstruction
Assuming a lost in capital due to the flooding of $ 70,618 M, the 30% of it would be
public sector in re investment in infrastructure $ 21,185.4 M and to the private sector $
49,432.6 M which have to be added to the current investment.
Therefore the accounts for 2004 would look as,
Variable
Total spending
Private spending
Public spending
Total investment
Investment in construction
Investment in public construction
Investment in private construction
Investment in durable goods
26
Amount (year 2005 in u$s 1993)
221,235
186,235
35,000
44,986.62
27,434.19
8,245.2
19,189.1
17,552.43
Balance of payments
Exports
Imports
7,200
34,224
27,024
The new GNP would be estimated as,
GNP = 221,235 + 8,245.09 + 19,189.1 + 17,552.43 + 7,200 = 273,421.62
Therefore the variation of the GNP is 0.0167% which is a very small one.
27
Appendix 4. Estimation of the primes for the flooding insurance system exercise.
The estimation of the insurance primes for the pampas system should take into account:




The specific climatic risk. A spatial characterization and division of the territory
according to the specific climatic events under consideration i.e. floods.
The exposed capital. A spatial characterization and division of the territory
according to the specific production technology, including kind of production,
level of technology, use of fertilizers, etc.
The vulnerability profiles of each sub region. This is the summary information
needed and results from the couple consideration of both previous set of
information i.e. the specific climate risk and the specific exposed capital at each
location.
The level of covered risks i.e. the maximum event that will be covered by the
insurance system.
According to the vulnerability profiles for each of the three sub regions presented in this
paper and the review of information regarding the current insurance system in the
region we have assumed the following values for a potential insurance system to flood
extreme events.
Zone 1
50%
Maximum
% of insured
hectares
Maximum
50%
% of insured
capital
$ per
$ 71
hectares
insured
$ per each $ $ 1
100 insured
of capital
Zone 2
80%
Zone 3
70%
80%
70%
$ 175
$ 131
$ 1.5
$ 1.15
Table. Summary information on the insurance prime levels per vulnerability regions
used for the Monte Carlo simulation exercise.
The maximum values for the insurance primes used for the Monte Carlo simulation
were picked by specifically considering the vulnerability profile of each sub region of
the system:

Region 1. It shows a lower vulnerability to climate events in comparison to the
other two regions. The hydrologic recurrence of floods in this region is lower
than in the other two regions, whereas the level of production and exposed
capital is higher. The prime level is therefore lower per production and per
exposed capital than the primes for the other two regions. However the income
per primes in absolute values is expected to be higher than in the other regions.
28

Region 2. In this zone the vulnerability is higher than in the other two regions,
thus the expected relative prime is higher than in the other regions. However
because the relative level of production and exposed capital is smaller, therefore
the expected income due to primes in absolute terms is expected to be smaller
than in the other regions. It is expected that the farmers in this regions would be
the most interested in signing for flood insurance.

Region 3. This region shows a vulnerability profile that falls in between of the
profiles of the other two regions.
29