Author’s Accepted Manuscript
Re-thinking socio-economic impact assessments of
disasters: the 2015 flood in Rio Branco, Brazilian
Amazon
Dorien Irene Dolman, Irving Foster Brown, Liana
O. Anderson, Jeroen Frank Warner, Victor
Marchezini, George Luiz Perreira Santos
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https://doi.org/10.1016/j.ijdrr.2018.04.024
IJDRR880
To appear in: International Journal of Disaster Risk Reduction
Received date: 30 November 2017
Revised date: 23 April 2018
Accepted date: 25 April 2018
Cite this article as: Dorien Irene Dolman, Irving Foster Brown, Liana O.
Anderson, Jeroen Frank Warner, Victor Marchezini and George Luiz Perreira
Santos, Re-thinking socio-economic impact assessments of disasters: the 2015
flood in Rio Branco, Brazilian Amazon, International Journal of Disaster Risk
Reduction, https://doi.org/10.1016/j.ijdrr.2018.04.024
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Re-thinking socio-economic impact assessments of disasters: the 2015 flood in Rio Branco,
Brazilian Amazon
Dorien Irene Dolman1, Irving Foster Brown2,3, Liana O. Anderson4,5, Jeroen Frank
Warner , Victor Marchezini5, George Luiz Perreira Santos6
1
Corresponding author: Dorien Dolman, doriendolman@gmail.com, +31 (0)6 52673757
1
Social Sciences Group, Wageningen University and Research, Hollandseweg 1, 6706 KN,
Wageningen, the Netherlands
2
Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540-1644, USA
3
Federal University of Acre –UFAC, Campus Universitário - BR 364, Km 04 - Distrito industrial
- Rio Branco – Acre, Brazil, CEP: 69.920-900
4
School of Geography and the Environment, University of Oxford, Oxford, UK
5
National Centre for Monitoring and Early Warning of Natural Disasters - Cemaden São Jose dos
Campos, São Paulo, Brazil
6
Municipal Civil Defense of Rio Branco, Rua Rui Barbosa, 285 – Centro, Rio Branco, Acre,
Brazil, CEP: 69.900-901
Available ORCID numbers of authors:
L.O.A. – ORCID number: 0000-0001-9545-5136
D.I.D – ORCID number : 0000-0002-3362-7163
V.M – ORCID number: 0000-0002-1974-0960
I.F.B. ORCID number: 0000-0003-1877-0866
Abstract
The impact of water-related disasters has become more acute in cities of the Amazon Basin.
Socio-economic impact assessments have a key role in improving sustainable mitigation projects
in order to increase resilience and reduce societal vulnerability. This paper reviews the current
state of loss assessments and then explores how to improve estimates for the 2015 flood affecting
the city of Rio Branco, Brazil, located on the headwaters of the Amazon Basin. Prevailing
models, loss assessments, and databases are not applicable in this Amazonian context due to the
lack of detailed cost administration, low levels of human and financial capital, and limited
insurance coverage. This paper uses uncertainty ranges of the costs of water-related disasters to
provide an assessment of the total impact. Our estimate ranges from 60 to 200 million USD of
losses and damage solely due to this flood event, compared to the official estimate of 98 million
USD. As floods in Rio Branco are recurrent nearly annually, the cumulative losses over the years
may be significantly higher. Our study illustrates the need for improving impact assessments in
order to increase the knowledge on the actual costs of flood disasters and avoiding silent
impoverishment. Outcomes of impact assessments can show the necessity of mitigation activities
which will reduce vulnerability of societies.
Keywords: floods, socio-economic impact assessments, uncertainty, Amazon, Brazil
1
1. Introduction
Over the past decade more than 1.5 billion people have been affected by major and
reported disasters (United Nations, 2015, EM-DAT, 2016). The impact of floods has grown over
time: total worldwide economic damage of floods in the last decade is 228 times higher than that
reported for 1955-1965 (EM-DAT, 2016). Economic impact consists of losses of and damages to
belongings, agriculture, income, services, and buildings. Social impacts include aspects such as
increased disease morbidity, violence, traumatic memories, as well as non-monetary recovery
efforts. This trend is expected to continue. Climate change, social vulnerability population
growth and land-use change are likely to worsen the socioeconomic and environmental impact of
natural hazards (Helmer and Hilhorst, 2006; Raddatz, 2009; Poussin et al., 2013; Koks and
Thissen, 2015; Raddatz, 2009; De Groeve et al., 2015).
Sustainable strategies of disaster mitigation require an in-depth understanding of the
dynamics of disasters (Koks and Thissen, 2015; United Nations, 2015). This can be improved by
conducting socio-economic impact assessments in the aftermath of disasters (Green et al., 2011).
Loss assessments can reveal weaknesses and identify opportunities for reform (De Groeve et al.,
2014), especially in developing countries where vulnerabilities are higher and institutional
capacities need to be strengthened.
Although the DesInventar database offers a wider inclusion criterion for disaster types –
such as sub-national or small-scale disasters – and data categories that go beyond mortality and
economic losses, as done for El Salvador’s 2001 earthquake (Muñoz et al, 2017), the coverage of
losses in diverse sectors still remains patchy and arbitrary in its scope (Zaidi, 2018). This
patchiness includes the indirect disaster impacts in the short and long-term recovery periods. In
Brazil, for instance, the lack of loss estimation data limits understanding as to how the different
sectors are affected during and in the long-term disaster recovery. This article doesn´t address all
of these gaps, but provides some insights and findings to improve loss estimation and to reduce
institutional vulnerability, taking Rio Branco city in Amazonian Brazil, as a case study
Much improvement in the data collection could be made during and after floods. This is
especially relevant in contexts of uncertainty, with limited administration by institutions, lack of
human and financial resources, and no developed insurance system – in other words, in contexts
of institutional vulnerability where formal institutions (constitutions, regulations, bureaucracy,
rule of law, codes) and informal institutions (culture, traditions, norms) are too weak to provide
disaster risk management measures (Lassa, 2010).
To remedy (to a degree) such shortcomings, the present contribution presents a method to
calculate the impact of natural hazards which could contribute to an increased understanding of
the relevance of data collection in the immediate aftermath of disasters. We developed this
method in the context of a flood-prone city in Amazonian Brazil.
In Brazil the estimated average of annual Brazilian economic losses due to the four major floods
between 2008 and 2011 was 1.4 billion USD (World Bank, 2014). Loss assessments are officially
based on rapid assessments during the natural hazard event (World Bank, 2014) as a precursor for
financial aid. It is not possible to fully quantify the total socio-economic impacts as these rapid
assessments are by their very nature preliminary. This is a limitation that many developing
countries face due to the lack of human, financial and infrastructure resources to conduct detailed
2
post-hoc analysis. In addition, other drivers contribute to the increase of the social and economic
damages. Although the Brazilian Human Development Index (HDI) is above average at 0.754
(no. 79 in the world1), a high degree of social inequality in vulnerability still exists. The Brazilian
Disaster Risk Indicators (DRIB-Index) and Social Vulnerability Index (SOVI) also show that the
highest vulnerability urban centers are located in the North (Amazon) and Northeast Regions
(Almeida, Welle and Birkmann, 2016; Hummell, Cutter and Emrich, 2016). These vulnerable
regions are exposed to recurrent floods, droughts and forest fires, which are predicted to intensify
in the future (Malhi et al., 2009).
Rio Branco, the capital of the state of Acre in Brazil´s southwestern Amazon (Figure 1) is
an example of an area experiencing extreme events with increased frequency (Araujo Lambert,
2015, Espinoza et al., 2014; Ovando et al., 2016). Since 1988, the city has been flooded,
operationally defined as river levels exceeding 14.0 meters, in the years 1988, 1997, 2006, 2009,
2010, 2011, 2012, 2013, 2014, and 2015 (Rio Branco Civil Defense, 2016). The 2015 flood was
the most severe in the recent history of the city. During 2015, the Acre river, which bisects the
city of Rio Branco, was above flood stage for 32 consecutive days and reached a level of 18.4
meters on 4 March 2015 (Civil Defense of Rio Branco, personal communication, June 5th, 2016).
On the 5th of March, the National Civil Defense declared a State of Public Calamity. The Civil
Defense of Rio Branco estimated that the flood affected 100,000 persons, about a third of the
city´s population. In the aftermath of this long-term flood of 32 consecutive days no official
socio-economic impact was conducted. Although hazard maps were drawn up in many
municipalities after the 2015 flood, there is a dearth of data regarding municipal vulnerability
assessment. Moreover, in the aftermath of disasters, the voices of affected people are not heard,
and their losses and damages are missing in official loss estimation carried out by national
governments (Marchezini, 2015). Without these data is not possible to analyze the spatial
patterns of losses in affected areas, as is done in the DesInventar database.
Figure 1: Location of the city of Rio Branco Acre State, Brazil within the Amazon Basin. The
dark line depicts the Acre river and the shaded area is the Acre River Basin. Courtesy of Sonaira
Souza.
1
http://hdr.undp.org/en/countries/profiles/BRA
3
This paper explores challenges to socio-economic impact assessments of long-term floods
in developing countries, focusing on the institutional context in Brazil, one of the largest and
most diverse developing countries. First, we present the current knowledge on disaster models,
assessments, and databases and identify gaps. Second, we review the state of the art of impact
assessments and the implications for the case study in Rio Branco. Third, we present the
outcomes of the multisector socio-economic impact assessment of the 2015 long-term flood.
Finally, we discuss future low-cost improvements to these assessments.
2. Socio-economic impact assessments
2.1 Models, assessments, and databases
Decision makers require accurate data on disaster losses of previous events in order to
take effective actions about research priorities, planning decisions, disaster assistance,
reconstruction activities, and policy evaluations (Downton and Pielke, 2005). We identified three
key variables associated with floods and disasters to be improved in order to increase accuracy of
impacts and losses quantification: flood models, loss assessments and databases on water-related
disasters impacts.
Flood damage estimates via mathematical models represent a significant amount of
literature (Egorova et al., 2008; FEMA, 2011; Koks and Thissen, 2015). These models are useful
for strategic decisions on flood mitigation, as they provide the estimated impacted area, and are
aimed at disaster risk reduction (FEMA, 2011). An example of a widely-used flood model is
HAZUS-RM developed by the Federal Emergency Management Agency (FEMA), an agency of
the United States Department of Homeland Security (FEMA, 2011). This model uses census
information to estimate potential damage on public facilities, transportation, and buildings.
Current literature analyzes the reasons for inaccuracy (Handmer, 2013), describes the
types of costs (Meyer et al., 2013), and provides examples of socio-economic impact assessments
of floods (Chatterton et al., 2007). In particular, the inaccuracy of flood loss assessments can be
explained by the complexity of loss assessments, variations in approaches, lack of knowledge on
data, divergent interests of stakeholders, variation in funds, expertise, and time available
(Handmer, 2013).
The CONHAZ program (Costs of Natural Hazards, Green et al., 2011) has developed a
terminology of cost types: direct, business interruption, indirect, intangible, and risk mitigation.
The terminology is useful as each type of cost requires a different assessment. One example of a
flood loss assessment, Chatterton et al. (2007), documents well which aspects can be included in
loss assessments in other contexts.
Over the past decades the number of databases on disaster losses has increased.
According to De Groeve et al. (2014), fifteen of the twenty EU member states which participated
in their research have a national disaster database. Several international databases on disaster
losses have been created such as EM-DAT (Emergency Events Database), NATHAN (Natural
Hazards Assessments Network), SHELDUS (Storm Event Database) (Gall et al., 2009) and the
DesInventar (Disaster Information Management System). The database which contains
information on disasters in Brazil is the Integrated System on Information of Disasters (S2ID)
which was created in 2012 (Federal University of Santa Catharina - Brazil, 2012).
4
2.2 Gaps in current literature
Significant improvements have been made in disaster models, assessments, and databases
but challenges remain (Meyer et al., 2013). We however identified the following aspects as major
challenges: (1) model application, (2) assessments made in a context in which detailed
administration and financing is lacking, and (3) discrepancies in disaster loss databases. It is
important to overcome these barriers in order to improve mitigation projects for future disasters,
such as floods (Merz et al., 2010). Many of these factors can be seen in the following analysis of
the 2015 flood in Rio Branco, Acre, Brazil.
Models such as HAZUS are useful to predict disaster impacts but cannot replace loss
assessments conducted in the aftermath of disasters (FEMA, 2011; Koks and Thissen, 2015). In
practice size and impact of disasters often differ from model predictions. Flood models face
technical implications due to their inflexibility and dependency on high quality data as input, and
organizational implications due to difficulties of exchanging results with decision makers
(Leskens, 2014). Hence, in order to verify the accuracy of disaster models as a suitable tool for
decision makers, it is important to evaluate and calibrate flood models by comparing these with
actual impacts calculated with a post-disaster assessment.
Loss assessments often focus on contexts which consist of detailed administration of costs
by local and national authorities, sufficient human and financial capital, and a developed
insurance system, such as Chatterton et al. (2007) observed for Great Britain. Such methods are
not applicable in the context of Brazil where the previous mentioned aspects are not available for
loss assessments (Szlafsztein, 2012). Researchers emphasize the need for research, procedures,
and guidelines which define a systematic and standardized data collection on flood losses. Those
will contribute to reduction of inaccuracy in estimations of impacts of water-related disasters
(Elber et al., 2010; De Groeve et al., 2014; United Nations, 2015; Gall et al., 2009; Meyer et al.,
2012).
Databases on disasters are sometimes inconsistent as they lack data, standardized methods
and explication of methods for data collection (Gall et al., 2009; Meyer et al., 2009). The
accuracy of data of EM-DAT is questionable because the website lacks the following
information: aspects included, methods of calculation, and sources. This lack of meta-data makes
verification of the data impossible (Gall et al., 2009). The database on all disasters in Brazil on
the website “System of Information on Disasters” (S2ID) is based on “Forms of Information of
Disasters” (FIDE). FIDE is a tool of the Brazilian Civil Defense through which local branches
apply to request funds from the Federal Civil Defense. While it is an appropriate tool for data
collection in the midst of disasters, the reliability of this data is not very high as it is collected in a
chaotic situation. In addition, the evaluations teams of civil defense are not well trained to apply
it without subjectivities and the deadline for data collection is only 10 days after the State of
Public Calamity was confirmed (National Civil Defense).
Therefore, the data presented in FIDE contains several rough estimations and is incomplete.
Hence, the accuracy of the S2ID database is limited (World Bank, 2014). As a consequence,
extrapolations of disaster losses have considerable error embedded in them, especially in longterm flood events.
3. Methodology
5
3.1 Data collection
Field data collection for this study occurred in Rio Branco between May and July 2016 a
year after the flood. In this period 29 semi-structured interviews were conducted with 18
employees of 14 municipal and two state governmental departments and 11 with families that
represents different vulnerable neighborhoods affected by the flood of 2015. Listening to the
voices of people in the frontline helps us better understand their losses and should be an
important step in planning community-based mitigation strategies. The sample of 11 families is
small but it provided an estimate of the loss of belongings, given that the types of their different
personal and domestic assets (stove, refrigerator) are not measured in FIDE. The sample included
respondents of different: backgrounds, financial situations, and of five different neighborhoods of
Rio Branco. When available, reports from governmental departments were used (see
Supplemental Online Material-SOM, Table S-1). The governmental costs were estimated by
circulating a questionnaire to governmental departments of Rio Branco and Acre states. Of the
sixteen local authorities involved in the response to the flood nine filled out the questionnaire
(SOM). As the response was not complete, some costs were calculated with the method of
uncertainty ranges, described below. Based on the costs acquired through the semi-structured
interviews and the questionnaire we estimated the total loss.
3.2 Uncertainty analysis
The calculation of uncertainty has the potential to generate a range of estimates of costs of
disasters. It provides a means for decision-makers to decide whether more resources are merited
to reduce the ranges of cost uncertainty. The case study of Rio Branco was carried out in a
context of limited administration by institutions, lack of human and financial resources, and no
developed insurance system. Therefore, methods such as the one described by CONHAZ (Meyer
et al., 2013) and Chatterton et al. (2007) are not applicable.
In many calculations of costs, the final value is the product of two or more variables. For
example, if the average loss per person is 100 US dollars then for an affected community of 1,000
persons, the cost would be 100,000 USD (100 USD/person x 1,000 persons). But both variables
are subject to uncertainty which is propagated to the final product. There are several ways of
dealing with this uncertainty, from Monte Carlo simulation to a percent of the estimated value..
In the case of Rio Branco, some estimations (Tables 1 and S.2) were calculated using the Form of
Information on Disasters (FIDE). FIDE is based on rapid assessments executed by different
municipal departments during the disasters. In the case of Rio Branco experts of the local
government carried out the data collection during the 32 days of flooding (Civil Defense of Rio
Branco, personal communication, June 5, 2016).
As FIDE is a rapid assessment, conducted in a chaotic situation during floods, it generally
underestimates the total cost. For example, in the FIDE of the 2015 floods in Rio Branco the
number of people affected was estimated to be 71,000 (National Civil Defense, 2015) while in
the aftermath this number turned out to be approximately 100,000 according to the Municipal
Civil Defense of Rio Branco, a difference of over 30%. Nonetheless, for some variables impacted
by the flood FIDE represents the only data available. This resulted in a significant underestimate
of the losses and costs of the long-term flood event.
6
The FIDE value served as an ´anchor´ (Morgan and Henrion, 1990) and de facto became
the best estimate of the cost. As a result, we were systematically underestimating the damages
associated with the flooding initially, creating “silent” impoverishment of the state, as many of
the damages are not reported. In order to emphasize the range of possible values, we eschewed
the use of a single value and generated maxima and minima, using a simple percentage as a
propagation tool. The application of uncertainty ranges is described in literature such as the costs
of the 2007 floods in England (Chatterton et al., 2007). Chatteron et al. apply for their estimates
a degree of confidence as presented in Penning-Rowsell et al.’s (2005) Multi-Coloured Manual.
Penning-Roswell et al. divide uncertainties into the following categories of confidence:
best breed, limiting assumptions, gross assumptions, and heroic assumptions (Chatterton et al.,
2007; Penning-Rowsell, 2005). “Best breed” are assumptions for which no better data is available
and hence an uncertainty of +/- 10% is applied. Limiting assumptions are those which have
known deficiencies and are replaced as soon as better data is available. Hence, an uncertainty of
+/- 20% is applied. Gross assumptions are data which are not an outcome of the respective
researchers but deducted from other sources and therefore a range of 25-35% uncertainty is used
(Chatterton et al., 2007; Penning-Rowsell, 2005). For heroic assumptions, Chatterton et al., do
not present a specified uncertainty range. Penning-Rowsell describes heroic assumptions as: “No
data sources available or yet found; data based on educated guesses” Penning-Rowsell, Table 3.3
on p.18). In the case of Rio Branco uncertainties are mostly considered heroic assumptions (Eq.
1, 2 and 3) for aggregating costs. We developed the following equation to calculate uncertainties.
(Eq.1)
(
(
)
)
(
(
)
)
(Eq. 2)
(Eq. 3)
Where
is the aggregated cost,
is the Minimum aggregated cost,
is the
Maximum aggregated cost, is the cost per unit and
is the total number of units. With a use
of two variables and 50% uncertainties, there is a nine-fold range (2.25/0.25) between maximum
and minimum estimates, nearly an order of magnitude.
3.3 Data Analysis
3.3.1 Impact on families affected by the 2015 flood
The losses were assessed based on information provided by respondents in the semistructured interviews and by the calculation of uncertainty ranges. Families suffered from the
following losses: belongings, agriculture, income, decrease in services and damages to buildings.
The methods to estimate these losses are described and summarized in Table S-2.
First, we identified the typology of basic belongings that families have and estimated their
loss. In FIDE only the damage to housing was included, not the losses of belongings. In
England, it is relatively easy to calculate losses of belongings as people claim these losses with
insurance companies (Chatterton et al., 2007). In Brazil, few people have home insurance, and
even if they have one, it would not cover losses from “natural” disasters as this specific condition
is not well developed in the country. Hence, the method proposed by Chatterton et al. (2007) is
not applicable. The average loss per family was estimated based on eleven semi-structured
7
interviews in which respondents of different neighborhoods were asked which belongings they
lost. This sample was used to identify the typology of basic belongings lost, but we know that
point out the diversity of these items are difficult, as well as to quantify how useful they are in the
daily activities of each family, especially during disaster recovery phase when other types of
social suffering usually arise. Respondents stated they lost micro-waves, tables, wardrobes,
clothes, gas stoves, etc. By estimation of the average value of these belongings the loss of each
family of the sample was calculated. The average loss per family was multiplied with the
estimated amount of 20,000 affected families (Table 1, Table S.2).
Second, we explored the agricultural losses as the flood destroyed many crops. In this
case, there is no range of uncertainty as the Municipal Department of Agriculture and Forests
conducted an extensive survey in the aftermath of the flood. All information on losses of
agriculture was collected from the official report published of the survey, no range of uncertainty
is applied as it was considered to have 100% coverage.. This respective department executed an
extensive recovery program with which they supported the affected the families. Hence, the
amount of public funds spent on recovery was deducted from the total agricultural loss.
Third, we estimated the income loss as many affected people could not work during the
32 days of overflow of the Rio Acre. These data are not included in FIDE and hence is an
innovative contribution of our method. This absence is due to various reasons: inaccessibility of
roads, necessity to watch over their houses, flooded workplaces, temporary stay at family distant
from work, illness, etc. We considered if one of the models or assessments described above
would be applicable to calculate income loss. For example, the HAZUS-MH MR model
estimates wage losses based on the Social Accounting Matrix, which represents flows of all
economic transactions that take place within an economy (FEMA, 2011).
A Social Accounting Matrix for Brazil exists but it does not include incomes of the
informal sector. This sector is quite substantial in Brazil; according to Henley et al.(2009), it
ranges from 40 to 63%. As there is a large informal sector in Rio Branco, the Social Accounting
Matrix is not representative for all incomes and hence HAZUS-RM is not applicable. Our study
was based on the assumption that 100,000 people were affected and that approximately one fifth
is employed and has an income of 243 USD2 per month. For this article the exchange rate applied
is 1 USD equals 3.14 Brazilian Reals. Hence, the average wage was multiplied with one fifth of
the affected people (Table 1, Table S.2).
Fourth, we estimated the loss of services as this affected families as either consumers or
employees. Offices, shops, and warehouses were flooded which influenced the sale of products
and services. Meyer et al. (2013) define losses of services as business interruption costs. Methods
described in the CONHAZ report to calculate the losses of services are not applicable in Rio
Branco due to lack of information on added value of all products, history of product outputs or
direct damages of all services affected. Therefore, we estimated the losses of services, with the
data in FIDE as a point of departure. In this report, we estimated that the damageto services
amounted to 20.8 million USD in case 71,000 persons had been affected which was raised
proportionally for the 100,000 persons.
2
According to the 2010 population census the average nominal urban income per capita per month in the state Acre
was 765,49 Reals. This equals 243 USD.
8
Fifth, we estimated the damage to buildings as approximately 29,300 buildings were
affected by the flood according to the State Civil Defense of Acre (Coronel Batista, personal
communication, June 7th, 2016). According to the Civil Municipal Defense of Rio Branco, few
houses were destroyed completely but many houses had partial losses and families had high costs
of reconstruction. The HAZUS-RM model estimates the losses of housing by the average loss
and weighing it with the area of inundation (FEMA, 2011). Unfortunately, such a map is absent
in Rio Branco. Therefore, the uncertainty range of damages to buildings was calculated based on
the data in FIDE (Table, S.2). In this document the damages to buildings were estimated to be 4.5
million USD if 71,000 persons had been affected.
3.3.2 Governmental costs
Municipal departments of Rio Branco and state departments of Acre incurred high costs
in providing response to the 2015 flood. Municipal and state governmental departments
supported the Civil Defense in evacuation of people, construction of temporary shelters,
distribution of food, provision of healthcare, and cleaning of the city. Governmental expenditures
are divided by the governmental body paying the expenses: (1) National Civil Defense, (2)
federal ministries, and (3) resources of municipal and state departments. The funds liberated by
the National Civil Defense were taken from data of the Financial Department of Rio Branco. The
funds liberated to the Civil Defense of Acre were collected in a semi-structured interview with
their treasurer. These are reported actual costs and therefore no degree of uncertainty was
applied.
The federal ministries of health and education liberated funds for recovery projects of the
municipal and state departments. The funds received by the municipal departments were
collected during semi-structured interviews. The funds received by the Educational Department
of Acre were estimated with the methods of calculation of uncertainty ranges based on the data of
FIDE. In FIDE it was estimated that the recovery of twelvedamaged schools would cost 3.8
million USD.
The expenditures which were not reimbursed by any funds were paid with own resources
of the respective departments of Rio Branco and Acre. These data are not included in FIDE and
hence are an innovative contribution of our method. Although requested multiple times not all
governmental departments reported their costs and therefore an uncertainty range was calculated
for the lacking information. The expenditures of the nine departments which filled the
questionnaire were summed,of this amount the average was calculated. Hence, the average was
multiplied by seven to estimate the own resources spend by the departments which did not fill the
questionnaire (Table 1, Table S.3).
An innovative aspect of this research was the calculation of opportunity costs of the
departments of Rio Branco and Acre. These data are included in our method and not in FIDE.
Opportunity costs, or alternative choices, are defined as: “The evaluation placed on the most
highly valued option of the rejected alternatives or opportunities” (Buchanan, 1991, p.520). The
opportunity costs of disasters are numerous as schools are closed, institutions function slower,
and employees were diverted from their daily tasks. In prevailing models, assessments, and
databases on disasters little attention is devoted to opportunity costs (Chatterton et al., 2007;
Meyer et al., 2013). Hence, in this study the opportunity costs of employees of municipal and
state governmental departments, who provided relief during the flood, were calculated. The
opportunity costs of the nine departments which filled the questionnaire were calculated by
9
multiplying average wage with the average days involved and the number of employees. The
opportunity costs of the departments which did not fill the questionnaire were estimated with the
averages of wages, number of workers, and days involved of the departments which filled the
questionnaire (Table S.3).
4. Results and discussion
4.1 Economic impact on families in Rio Branco due to the 2015 flood
The 2015 flood was the most severe in the recorded history of Rio Branco. Local
authorities constructed 29 temporary shelters which accommodated 11,000 people for 32 days,
which is approximately 11% of the affected population. This is a sub estimate of those displaced
as many sought shelter with relatives and friends or remained in their houses in order to protect
their belongings from robbery. The flood affected 53 neighborhoods of Rio Branco, 165
kilometers of roads, and 29,300 buildings (SitGeo, 2016; Colonel Batista, Civil Defense of Acre,
personal communication, June 8th 2016). Many people were affected as they still resided in zones
at risk due to socio-environmental reasons.
Of the affected people, three pathways of relocation were identified by the department of
Social Assistance of Rio Branco (D. Araujo, personal communication, June 7th 2016): (1)
relocation to public shelters, (2) relocation to reside temporarily with family members outside the
flood zone, and (3) remaining in their houses in order to protect their belongings from robbery.
The most important impacts of the flood on affected families were: losses, diseases, violence,
traumatic memories, and difficulties to recover. The losses of families are divided in: belongings,
agriculture, income, services, and damages to buildings (Table 1).
Families lost belongings during the flood either during the transport from their houses or
as these remained in their houses during the flood. Many residents did not leave their houses
before these flooded because (1) some did not believe the early warnings, (2) others did not want
to leave, or (3) did not receive logistic support. The neighborhoods affected by the flood were
among the poorest of Rio Branco and hence the impact of losses was relatively greater. Our
results suggest that the average loss per family was 3,800 Reals, approximately 1,172 USD,
which revealed part of their social economic vulnerability once their income is 243 USD per
month. With the method of calculation of uncertainty ranges we estimate that the uncertainty
range of the total losses of belongings of families: between 6 and 56 million USD.
The 2015 flood also affected 1,500 families of small farmers. The actual agricultural
losses were approximately 9 million USD according to the report provided by the Municipal
Department of Agriculture and Forests (SEAPROF, 2015). From these costs the amount of the
recovery program was deducted which result in a total agricultural loss of 4.3 million USD.
This study estimated that between 1.6 and 14.1 million USD were associated with
incomes loss during the flood event. Moreover, our estimates related to the flood impact on the
services reveals that between 14.4 and 43 million USD were lost. Finally we estimated that the
total cost for families for reconstruction of damages to their homes is between 3.1 and 9.4 USD.
The total economic loss of families affected by the 2015 loss is in the range of 29.7 and 127.7
million USD.
10
Table 1: Uncertainty ranges of total losses of families affected by the flood of 2015 in Rio
Branco.
Losses of families
(20,000 families)
Belongings
Agriculture
Income
Services
Damages to housing
Total
Minimum loss in
million USD
6.0
4.3
1.6
14.4
3.1
29.4
Average loss Maximum loss in million
in million
USD
USD
34.2
56.4
4.3
4.3
7.9
14.1
28.7
43.0
6.3
9.4
78.7
127.7
4.2 Social impacts
In addition to the economic losses, floods have social impacts. The most severe social
impacts were on health, security, memories, and subsequent well-being. Interviewees reported an
increase in illnesses, anxiety about violence and traumatic memories. Also, the recovery after the
flood was difficult. Social impacts are considered intangible (Meyer et al., 2013) as no monetary
value is attributed to them
Health of inhabitants of zones at risk was impacted and many people suffered from
diseases. Unfortunately, quantitative data on most health impacts are lacking which limits
assessments. Data which are available for Rio Branco are the number of diagnoses done in the
temporary shelters. According to O. Almeida (personal communication, June 9th, 2016,
department of Health of Rio Branco), more than 3,000 disease diagnoses occurred in the
temporary shelters of which 20% were infections of the upper respiratory tracts.
Anxiety about violence increased during the flood of Rio Branco. In the semi-structured
interviews affected people mentioned they were afraid that their belongings would be robbed
during their absence. This fear was based on stories of previous floods of houses being robbed.
These robberies occurred during the stay in temporary shelters or with family members. Although
there was an increased police surveillance during the flood, some people organized themselves in
groups within their neighborhood to guard their houses. Others decided to remain in their flooded
houses in order to prevent robbery. Unfortunately, there is no specific data on the robberies and
crime rates in Rio Branco during the flood. Data is available on the number of crimes in the
largest temporary shelter, “Parque do Exposição”. The chief of the military police of Rio Branco,
(C. Maciel, personal communication, June 20th, 2016), stated that in the largest shelter 96 crimes
were registered such as drug trafficking, threats, domestic violence, and robbery. Current data
available on violence in the temporary shelters during the 2015 floods does not demonstrate an
increase as there is no data available to compare it with. Those crime rates and the anxiety
expressed by respondents of the semi-structured interviews does justify the necessity to explore
more on this topic. Several studies confirm an increase of child abuse, sexual violence, and other
interpersonal violence during disasters (Rezaeian, 2013). Therefore it is important to include the
impact of violence in socio-economic impact assessments.
11
People affected by floods also suffer from traumatic memories. Respondents expressed
sentiments such as fear and stress resulted from the chaotic disruption of their private and public
routines, the loss of home and usual family roles, and the moral dimension of labor. They felt
stressed because they faced losses, diseases, and anxiety of violence. Emotions which were
expressed by respondents are: desperation, helplessness, fear, and sadness. Moreover, some
people described traumatic memories of encounters with wild animals, such as snakes and
alligators close to their houses. Traumatic memories have an important impact on lives of
affected people and pose several challenges to the “resilience imperative” in contexts of social
abandonment (Marchezini, 2015).
Finally, we explored how the affected families experienced the recovery after the flood.
D. Araujo, department of Social Assistance (personal communication, June 7th 2016), explained
how returning to homes after the flood was accompanied by traumatic memories for some
residents due to losses of belongings and damages to their houses. The cleaning of houses was
laborious and some people stated that a bad smell remained for more than three weeks.
According to K.S. Carvalho (personal communication, June 16th, 2016), chief of the waste
collection department, in the weeks after the flood 18,000 tons of trash were collected in the
impacted neighborhoods, which is equal to 85 days of normal trash collection. About 55% of the
respondents stated they felt not being fully recovered more than a year after the flood. Asked
what recovery meant to them, they stated that they still did not possess the financial resources to
purchase the assets they lost during the flood. The total loss for affected families was between 30
and 128 million USD.
4.3 Governmental costs for the flood of 2015
The total governmental costs are presented in Table 2. The National Civil Defense
transferred 3.6 million USD to municipal and state Civil Defense agencies to help the provision
of relief during the flood. The funds were mainly used for construction of temporary shelters,
provision of meals in the shelters, fuel, chemical toilets, and reconstruction activities. The federal
ministries of education and health supported the respective departments in Rio Branco and Acre
in the reconstruction of damaged schools and healthcare centers. The Rio Branco Health
Department stated they received 1.6 million USD, and the education department 0.3 million
USD. The expenditures of the Acre Education Department are estimated as these were not
collected during the fieldwork. The total funds liberated by federal ministries are between 3.7 and
7.2 million USD. Therefore, about 5% to 25% of the total governmental costs were covered by
federal ministries.
Table 2: Total governmental costs to the flood in Rio Branco, 2015
Costs
National Civil Defense
Federal Ministries
Governmental
departments of Acre
and Rio Branco: own
resources
Governmental
departments of Acre
Minimum costs in
million USD
3.6
3.4
14.2
4.5
Average costs in
million USD
3.6
5.3
21.9
13.8
Maximum costs in
million USD
3.6
7.2
29.7
23
12
and Rio Branco:
opportunity costs
Total
25.7
44.6
63.5
The governmental costs of the flood of 2015 in Rio Branco were between 25.7 and 63.5
million USD. In comparison, the total annual budget of the municipality of Rio Branco in 2015
was 250 million USD and the GDP per capita of Rio Branco in 2015 was 7,125 USD (IBGE,
2015).
5. Discussion: re-thinking socio-economic impact assessments
The uncertainty calculation method of disaster impacts shows that the total costs of the
2015 flood in Rio Branco were between 60 to 200 million USD. The estimation of the losses
done with the FIDE is within this range; 97.9 million USD – about 40 million USD above our
lower estimate and about 100 million USD below our highest estimate. Hence, the estimation of
FIDE is not in the middle of the uncertainty range but rather closer to our lower estimate. As the
floods in Rio Branco are recurrent the cumulative losses over the years must be even higher. Our
method is useful in contexts of absence of: (1) detailed administration of costs by local and
national authorities, (2) sufficient human and financial capital, and (3) a developed insurance
system. The goal of this method is to raise awareness of local authorities on the need of
improvement of socio-economic impact assessments. If authorities would feel that the range is
too wide for directing policy, then these ranges serve to motivate local and state governments to
collect more accurate data in the future. Moreover presentation of our highest estimates could
make governmental actors realize the financial benefits of investing in mitigation projects rather
than focusing on disaster relief. . This would benefit the inhabitants of Rio Branco as
governmental actors might implement projects to mitigate suffering of the population.
Based on the presentation of our preliminary results governmental actors started to
discuss what kind of mitigation activities would be most economically viable in order to reduce
vulnerability of the population of Rio Branco. Our method of calculation of uncertainty ranges is
an appropriate complement to the FIDE method. FIDE is useful to collect data in the midst of an
emergency whereas our method is more inclusive. In addition to the costs included in FIDE our
method includes: losses of belongings, losses of income, governmental expenditures and
opportunity costs. However, several aspects of this method should be considered.
First, better estimates would reduce the range of uncertainty. Due to the lack of data in
calculations of the costs of the flood in Rio Branco, we applied 50%, which led to a three-fold
range for one variable and a nine-fold range for two multiplied variables. Not to use heroic
assumptions, as described by Chatterton et al. (2007) would have meant systematically
underestimating damages which would lead to unacknowledged “silent” impoverishment.
Second, the method of calculation of uncertainty ranges should be used with the aim of
increasing awareness. Presentation of uncertainty ranges is useful to enhance awareness of the
need to improve socio-economic assessments which will contribute to a decrease of uncertainty
in the future. Instead of presenting estimates as exact data, as done in FIDE, our method stresses
the necessity to have more accurate data by showing an uncertainty range. Our results indicated
that local authorities can improve assessments by: (1) carrying out post-disaster surveys, (2)
improve data storage and dissemination, and (3) better administration of own resources. Surveys
13
can be conducted during and in the aftermath of a disaster to gather data of the socio-economic
impact on affected families (Handmer, 2013). These surveys are useful to obtain information on
losses (belongings, agriculture, income, losses of services, and damage to buildings), traumatic
memories, and difficulties to recover.
Contrary to the current method of FIDE, socio-economic impacts data should be gathered
in the aftermath of a disaster and with involvement of the affected people. Once the situation is
stable, it is recommended to start the surveys as experiences are still vivid in people’s memories.
Considering the lack of human and financial resources we recommend selecting a sample size
that reduces uncertainty to an acceptable level for policy decisions. Authorities can collect and
share data on intangible, or social, impacts with the local Civil Defense. The health department
can gather data of the increase in diseases in the entire city instead of only in the temporary
shelters. The security department can collect information on the increased amount of violence as
a result of floods. Municipal and state governmental departments can improve the administration
of their own resources, both general expenditures and opportunity costs. Execution of surveys,
data collection on social impacts, and improved administration would lead to a decreased
uncertainty of the socio-economic impact.
Third, current literature on impact assessments elaborates on economic losses but rarely
discusses valorization of social impacts of disasters of diseases, violence, traumatic memories,
and difficulties to recover (Chatterton et al., 2007; Meyer et al., Green et al., 2011; Elmer et al.,
2010). It is important to include social impacts during floods as these are often underestimated. In
Rio Branco after the 2015 floods, we found these to be serious and durable. Improved knowledge
on social impacts could contribute to more adequate projects of local governments to respond to
these impacts in the long-term disaster recovery process. Research can contribute to improving
socio-economic impact assessments, particularly in a context of a lack of detailed administration,
insurance system, and human and financial resources. Furthermore, more research is needed on
assessment of impact of intangible costs of floods. Socio-economic impact assessments should
include impacts on health, violence, traumatic memories, and difficulties to recover. Scholars
should explore how to devote more attention to social impacts in disaster models, assessments,
and databases. Perhaps citizen science approaches (Marchezini et al, 2017) can contribute to
build a strategy for collecting crowdsourcing data about social impacts, especially during longduration floods. Researchers from different fields of knowledge and policymakers need to discuss
together what to do to face the challenges from the frontline of the disasters.
Fourth, a form should be designed with the participation of civil society and academia in
order to be applied to assess the multidimensional socio-economic impacts in the aftermath of
disasters in Brazil. The entire database (S2ID) of disasters in Brazil is based on the FIDE forms
which are filled out during the emergency phase, and doesn’t capture the impacts of a disaster.
Hence, a form should be developed to assess the impact of disasters in order to improve data
collection of disasters in Brazil. One way to facilitate this would be a legal requirement for a
post-disaster evaluation for those municipalities and states that received federal funding. Training
should be provided to local authorities on how to conduct surveys, which data to collect, and how
to administrate their own resources effectively. The development of a standardized form of cost
and training on its use will enhance the capacity to execute effective socio-economic impact
assessments.
14
6. Conclusion
This paper presented a comprehensive assessment of socio-economic costs of the 2015
flood in Rio Branco, in Brazil´s western Amazon. This type of analysis is currently lacking in the
literature and high uncertainties of the actual cost of climatic extremes, such as floods, are not
fully quantified. The flood of 2015 had a severe impact on affected families in Rio Branco:
losses, diseases, anxiety of increased violence, traumatic memories, and difficulties of recovery.
The method of calculation of uncertainty ranges of the impact of the floods shows that the total
costs of the flood in Rio Branco are between 60 and 200 million USD. In addition, the method of
calculation of uncertainty presented this study provides a range of costs that could be used for the
local government in order to assess the pros and cons of mitigation strategies for the future. Rio
Branco, such as most of South American cities, lacks in detailed administrative information of
costs by local and national authorities, due to insufficient human and financial capital, and no
developed insurance system.
Presentation of these results can lead to the raise of awareness by local authorities for the
need to improve assessments by conducting surveys, storage and dissemination, and
administration of finances. This could benefit populations living in risk-prone areas as an
improved administration of data stresses the need for support. Data on previous floods motivates
governmental actors to reconsider their policies and to understand the possible benefits of
investing in mitigation rather than relief projects. Hence, we conclude that socio-economic
impact assessments can be useful to improve sustainable mitigation projects in order to decrease
future impacts of floods.
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