Future scenarios in the former oil capital: coastal flooding and social vulnerability in Macaé, RJ

Abstract

The accelerated urban growth in Macaé had important consequences on socio-spatial organization, especially about housing spaces that became increasingly difficult to be accessed by the low-income population. The most devalued lands, such as mangroves and floodplains, were occupied by the low-income population. The proposal highlighted in this project focuses directly on the problem of rising sea levels and flooding in the urban space of Macaé, which is of social interest. A simulation of future scenarios with sea level rise above the current one, allowing the identification of areas flooded by marine transgression on a time scale of 100 years (for the year 2100). For this, the rate was chosen for the simulation: the greenhouse gas scenario RCP8.5, as given in IPCC's Fifth Assessment Report (AR5) of 2014. A radiative forcing that corresponds to more than 700 ppm CO2-eq, but less than 1500 ppm, the projected increase is 1 m to more than 3 m (medium confidence) and more than 3 m (medium confidence). This assessment is based on the average confidence in the contribution from thermal expansion and low confidence in the modeled contribution modeled contribution of the ice sheets. Therefore, the climate change-induced global mean sea level rise is caused by thermal expansion of ocean water and ocean mass gain, the latter being mainly due to a decrease in land ice mass. The estimated sea-level rise used for the projection of this study is 2.15, as proposed by Grinsted et al. in 2009.

Introduction

Approximately 3.5 billion people in the world, that is, more than 50% of the world's population, live in cities, and this number continues to grow (Puppim de Oliveira et al., 2011). If the current pace continues, it is predicted that between 2000 and 2030, the planet's urban coverage will increase by 1.2 million square kilometers, which represents a 200% growth compared to the current area, for an increase in its world urban population of about 70% (Fragkias & Seto, 2012). Current changes in climate expose coastal cities to sea-level rise, changes in storm frequency and intensity, and increased precipitation. Coastal areas are the most heavily exploited locations, where the urban expansion largely alters the natural landscape. However, as land-sea interfaces, these regions are characterized by various conflicts between anthropic pressures and natural sustainability. The disorderly urban expansion with the multiplication of irregular settlements, especially in areas of natural risk, contributes significantly to intensifying environmental and social vulnerabilities to risks such as erosion and coastal flooding. To face this problem, integrated actions between different sectors of society and based on a deep knowledge of current and projected scenarios will be indispensable.

The city of Macaé went through an industrial boom in the oil sector, especially at the end of the 1990s. According to 2012 IBGE data, this development increased the city population, which reached 217,951,000 people. The gross domestic product (GDP) per capita is over R$50,000. Due to this situation, ten percent of its population is foreigners, and every two years the city hosts the Brasil Offshore Fair, the world's third largest event of the sector. Despite the name “Bacia de Campos” (considered the largest oil reserve on the Brazilian continental shelf), Macaé is where Petrobrasoffshore facilities and companies are located, with 276 industries in 2011. The basin produces 80% of Brazil's oil production and 47% of its natural gas, which earned Macaé the nickname “National Oil Capital” by the media and experts. (Macaé City Hall) In 2000, the number of inhabitants jumped to 132,431. According to data from the last demographic census conducted in 2010 by IBGE, the total population of Macaé is 206,728 inhabitants, more than four times the number of inhabitants who lived in the city in the seventies. Today, the estimated number of inhabitants for the year 2018 is 25,163,118, which is about 22% more than the number obtained in the 2010 census (Fig. 1).

Fig. 1.

Data on Macaé's population between 1970 and 2018.

Source: IBGE (2023)

Over time, the municipality of Macaé began to concentrate more people in its agglomeration, where were located the small and large offices of the oil sector, the main service establishments and the port, which became one of the busiest in the country. Thus, Monié and Binsztok (2012) point out that more than 95% of the municipality's population now lives in an area of about 20% of the official territory.

Formation of human settlements in potentially fragile and at-risk areas

There was “the formation of several human settlements with low and high purchasing power in the city of Macaé” (Jeronymo et al., 2017) and many of these settlements contributed to the expansion of the segregation process space in the urban area. Have emerged in areas at risk and/or fragile ecosystems, areas of restingas, mangroves, Atlantic forest, very close to waterways, and are often prone to flooding (Fig. 2).

Fig. 2.

Location of subnormal agglomerations in the Municipality of Macaé

Urban areas frequently experience flooding, particularly when there is a disordered occupancy of the floodplains. As a result, homes and the people who live in them become victims of natural disasters caused by the river rise.

It is possible to predict the dangers of flooding and, as a result, to minimize damage, both to people and property. This is accomplished by monitoring the daily evolution of meteorological conditions and the rivers. However, because this predictability is not currently incorporated into a comprehensive risk management strategy, it does not lessen the susceptibility of riverbank villages and the urban periphery to floods and flood-related calamities.

Materials and methods

Simulation of future scenarios with sea level above the current

Several SLR models are used for exposure assessment, and each has benefits and drawbacks that are tailored to specific management objectives (Mcleod et al., 2010). The Bathtub model was used in this study because it has been widely used and accepted by coastal communities as well as major public agencies (such as NOAA, 2022) to simulate the effects of SLR. In addition, the simplicity of this model and the availability of a high-quality digital elevation model (DEM) facilitated modeling studies to analyze SLR scenarios (Figs. 3 and 4).

Fig. 3.

Methodological model

Fig. 4.

Classified raster

In order to generate the DEM containing the representation of the existing landforms, was possible thanks to the extraction of information such as the elevation points (https://www.ibge.gov.br/geociencias/modelos-digitais-de-superficie/modelos-digitais-de-superficie.html) the contour lines; photogrammetric flight orthophotos, on a scale of 1:25,000 made available by the IBGE, for the spatial representation of the terrain; and digital land use map of the municipality of Macaé, at a scale of 1:25,000, made available by the National Institute for the Environment (Instituto Estadual do Ambiente—INEA).

The simulation of sea-level rise was carried out with ArcGis 10.5 software using IBGE orthophotos. Considering the effects of melting of small glaciers and ice caps associated with temperature increase, an elevation of 2.15 m was adopted, as proposed by Grinsted et al. (2009). Using this model, the impact of sea-level rise was assessed on the surface. The land cover and neighborhood shapefiles were overlaid on the model and the flooded area was depicted, also allowing the verification of exposed areas. We decided to consider the neighborhoods considered as urban in the municipality of Macae, since these are located in the coastal part of the municipality and have a higher population than the rural areas. It is worth mentioning that the limits of the neighborhoods are already officially stipulated by the IBGE. (Neighborhood SHPs obtained https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_de_setores_censitarios__divisos_intramunicipais/2020/Malha_de_setores_(shp)_by_UFs/RJ/).

Social vulnerability index

Adaptive capacity analysis, in contrast to exposure analysis, concentrates on the socioeconomic view of vulnerability. As several terrible extreme occurrences painfully demonstrate, different people frequently experience the same danger in various ways; the short- and long-term effects of hazards vary across socioeconomic and geographic gradients. According to the social science literature, “social vulnerability” is the core cause of these varying experiences and reactions to risk. Social vulnerability is a term that encapsulates a variety of social, cultural, economic, political, and institutional variables that influence how different people perceive dangers (Adger, 2006; Birkmann et al., 2013; Turner et al., 2003).

The socioeconomic vulnerability factors are often heavily influenced by the variables chosen for the research by Cutter et al. (2003), where the socioeconomic and demographic profile is based on census data and a relative indicator is built that approximates vulnerability.

We used a set of 8 variables that characterized the social, economic and demographic conditions of the urban neighborhoods of Macaé. Data were obtained from the 2010 IBGE population survey; this was due to Ministry of Health guidelines related to the public health emergency caused by COVID-19; the IBGE has decided to postpone the completion of the demographic census 2020 to 2021.

Poverty and income

The least wealthy and developed people often more vulnerable to natural disasters and have a harder time recovering from them. The poor are more frequently impacted by natural disasters because, for two major reasons, they frequently have to dwell in high-risk locations. First, riskier locations could be more attractive if they provide employment possibilities, immediate utilities or amenities, better productivity, and higher salaries (Hallegatte et al., 2020). Second, due mostly to a lack of protective infrastructure, the poor gain less from hazard protection.

Population density

A large population concentrated in one place means that not only would more people be impacted by a disaster but also that they would find it more difficult to evacuate or be rescued, making them more susceptible to natural disasters (Cova & Church, 1997; Cutter et al., 2000).

Race

Similar to how the physical terrain varies, the social landscape of inequality has widened the gap between the affluent and the poor in this nation and increased the social vulnerability of our citizens, particularly to coastal disasters. In order to plan for future catastrophes, we must consider both the natural environment and the social environment in the establishment of mitigation strategies, as evidenced by strained racial relations and the clear differential reaction to disaster (Vadi, 2007).

Through lack of access to resources, cultural inequalities, and the economic, social, and political marginalization that is frequently linked to racial inequities, race contributes to social vulnerability. Our race component recognizes race as a sign of social vulnerability, especially among the Afro and Indigenous populations.

Age

The ages at either end of the age range make it more difficult to move safely. When daycare facilities are impacted, parents spend time and money caring for their children, and older individuals may experience mobility issues that increase the cost of care and reduce resilience (Cutter et al., 2000).

Education

Higher socioeconomic position is correlated with better levels of education, which also correlates with higher lifetime incomes. A lower level of education restricts access to recovery knowledge and the capacity to comprehend warning messages (Table 1) (Heinz Center, 2000).

Table 1.

Ranking of Variables used in the Social Vulnerability Index in Macaé

Variable	Unit	Very low	Low	Moderate	High	Very high
		1	2	3	4	5
Average household income (2010)	R$	> 4000	4000–3200	3200–2000	2000–1200	1200 >
Poverty	% (Income of 1/2 minimum wage or less)	< 0.2	0.2–0.6	0.6–1	1–2.9	3 <
Population density	Inhabitants/km2	< 5.8	5.8–12.5	12.5–35	35–80	80 <
Total Population	%	< 1000	1000–4000	4000–8000	8000–15,000	15,000 <
< 5 years old	%	< 6	6–7	7–8	8–9	10 <
> 65 years old	%	< 3	3–4.5	4.5–6.5	6.5–9	10 <
Race	% (Black, Brown or Native)	< 40	40–50	50–60	60–70	70 <
Literacy	% (Illiterate)	< 2	2–3	3–5	5–10	10 <

Each variable is examined and modified in accordance with the standards that support its inclusion in the analysis order of importance. Using the following formula, we first normalized each values throughout the range of units:

$$Z_i=\frac{X_i-min\left(x\right)}{max\left(x\right)-min\left(x\right)}$$

where Z_i is the ith normalized value of variable Z, x_i is the ith value of variable Z, and min(x) and max(x) are the minimum and maximum values of variable Z. Rescaling was done so that a value of 1 indicated the highest vulnerability and vice versa. Such an approach required returning to normalized values of certain variables. This applied to average household income, where the higher value indicated the lower vulnerability.

We calculated the SV index for each neighborhood by aggregating these variables. They are combined with the rest and through these criteria obtain a quantitative result that allows you to select or find the best territorial alternative; in this part of the process, the Map Algebra function is important in combining these variables. We assumed no weights for the different factors, which means that they have the same importance in the overall sum and the same contribution to the overall vulnerability. The vulnerability range used was based on that of the Institute for Applied Economic Research-IPEA (2023).

Adaptation and mitigation

This study, which uses a literature review, is qualitative. Contrary to popular belief in academics that adaptation should be a planned process, most current coping strategies used in coastal regions are ad hoc, retroactive initiatives rather than long-term strategies. The creation of early warning systems, the building of levees, and the creation of new building rules are examples of adaptive and mitigation measures that are anticipatory, function in human systems, and act via the public sector. Early warning systems, hazard insurance, enhanced drainage systems, and desalination projects are a few examples of technical adaptations that protect (levees, embankments), allow removal (remote places, relocation), and accommodate (accommodation projects) (UNFCCC, 2006).

Fundamentally, adaptation seems to occur in stages throughout time, with early initiatives addressing coastal erosion and flooding and progressively more difficult and ambitious plans being developed as financial capacity and technical competence rise. The current work focuses on methods that have either been used in the past or are now being used globally (See the last section of the paper for bibliography).

Results

Exposure to sea-level rise

The flooded areas were quantified in hectares (ha): where it was verified in the total area of the municipality (121,685.75 ha) a floodable area equivalent to 6977.43 ha (6%). Thanks to the land cover map, it was possible to quantify the total area and the flooded area by class as well as their percentages in relation to the total flooded area. A map was then drawn showing the flooded areas for each land cover class (Fig. 5).

Fig. 5.

Representation of the flooded area in the Municipality of Macaé, with an overlay of the shapefile of land use classes—simulation of 2.15 m above sea level

In relation to the total flooded area, the percentage of flooding was equivalent to 6%. It is important to point out that Macaé has an almost predominant geomorphology of steep mountains, isolated mountains, and hills. In agreement with this fact, the results indicated that the land use classes with the highest percentage of highest inundation were wetlands, pastures and mangroves, but also areas of medium and high density urban occupation, which are all present in areas with flatter and lower relief. The simulation of sea level rise of around 2.15 m, compared with the geomorphology map, highlights the predominant floods in the fluvial and marine fluvial plains. Low-lying areas are more exposed to sea-level rise.

The simulation of sea level rise based on the study by Grinsted et al. (2009) made it possible to verify the percentage of flooding in Macaé, even expressing a low percentage (due to its geomorphology with a predominance of mountain ranges and hills) According to the land use map, there is an intense urban concentration in the plain areas. The loss of urban areas (Table 2) of 199.68 and 716.41 ha, respectively, should be considered alarming. This form of coastal flooding brought on by sea-level rise will affect a broad spectrum of people and land usage. The primary contributing element to the observed trend is the physiographic features of the city. For various causes, flooding would worsen near the shore, destroying beachfront houses and making inland properties susceptible to storm surges. The coastal region would be physically and economically impacted by the anticipated inundation (Fig. 6 and Table 3).

Table 2.

Flood survey (area and percentage) in the municipality of Macaé by land use class—simulation of 2.15 m above sea level

Soil type	Total area (ha)	Flooded area (ha)	Flooded area by type (%)	Flooded area (%)
Rocky outcrop	441.02	0.00	0	0
Water	431.40	197.78	46	3
Wet land	1708.28	1548.74	91	22
Sand barrier	130.65	11.29	9	0
Forest	40,151.79	55.09	0	1
Mangrove	106.57	85.72	80	1
High density urban occupation	742.37	199.68	27	3
Medium-density urban occupation	3180.06	716.41	23	10
Pasture	60,727.75	2011.50	3	29
Pasture in floodplain	11,876.46	2133.94	18	31
Sand Bank	146.34	11.29	8	0
Secondary vegetation in the early stage	2043.08	6.00	0	0
Total	121,685.7599	6977.431565	6	100

Fig. 6.

Representation of the flooded area in the Municipality of Macaé, with an overlay of the shapefile of the urban area by neighborhoods—simulation of 2.15 m above sea level

Table 3.

Flood survey (area and percentage) in the urban space of Macaé by neighborhood—simulation of 2.15 m above sea level

Neighborhood	Total area (ha)	Flooded area (ha)	Flooding by Neighborhood (%)	Flooded area (%)
Ajuda	1278.73	586.96	46	14
Aroeira	307.80	145.68	47	4
Barra	657.94	569.96	87	14
Botafogo	196.83	184.46	94	5
Cabiunas	1627.36	84.61	5	2
Cajueiros	28.64	3.64	13	0
Cavaleiros	115.98	24.08	21	1
Centro	118.86	17.39	15	0
Gloria	499.34	130.63	26	3
Granja dos Cavaleiros	206.92	1.51	1	0
Imbetida	115.07	23.55	20	1
Imboassica	5013.84	123.48	2	3
Lagoa	868.77	215.18	25	5
Lago mar	430.82	22.43	5	1
Miramar	67.71	3.00	4	0
Parque Aeroporto	2095.34	1428.03	68	35
Praia Campista	60.96	12.59	21	0
Riviera Fluminense	134.25	38.70	29	1
Sao Jose do Barreto	186.87	63.85	34	2
Vale encantado	305.85	30.88	10	1
Virgem santa	357.07	298.25	84	7
Visconde de Ara	109.89	50.81	46	1
Total	14,784.84	4059.66	27	100

By observing the image, it can be concluded that the occupation of the Midwest and North areas took place mainly on the mangroves, wetlands, and sandbanks. Urban densification has been caused not only by the installation of low-income human settlements but also by industrial enterprises and high-income housing, such as the island of Caieira. Also note the densification of dwellings in Nova Malvinas, Nova Holanda, Nova Esperança, and the invasion of the Piracema farm above Nova Esperança. Factors such as increasing population, urbanization, increased risk, and there is evidence that population densities in coastal areas around the world are increasing (Merkens et al., 2018), resulting in losses greater due to flooding and other natural disasters (Jonkman & Dawson, 2012; Jonkman et al., 2012). The historical and recent process of land use and land use in the city of Macaé has taken place and still takes place, for the most part, in risk areas and fragile ecosystems (Fig. 7).

Fig. 7.

Aerial view of the Macaé River estuary. Photo: SECOM Archive (2006)

The following table contains the neighborhoods most affected by flooding, considering those that were affected by at least 45–50% of the area (Table 4):

Table 4.

Neighborhoods with at least or close to 50% of flooding

Neighborhood	Total area (ha)	Flooded area (ha)	Flooding by neighborhood (%)	Flooded area (%)
Botafogo	196.83	184.46	94	5
Barra	657.94	569.96	87	14
Virgem santa	357.07	298.25	84	7
Parque Aeroporto	2095.34	1428.03	68	35
Aroeira	307.80	145.68	47	4
Visconde de Ara	109.89	50.81	46	1
Ajuda	1278.73	586.96	46	14

Social vulnerability index

Vulnerability assessment has received increasing attention in the scientific community as a guidance tool to reduce the impacts of natural hazards and to encourage the community to be resilient to disasters. However, academic communities have yet to decide on a single definition of vulnerability due to its complexity. Therefore, such complexity impedes the creation of a general set of stable socio- and demographic matrices to measure vulnerability at various levels (Cutter & Finch, 2008; Kuhlicke et al., 2011). The indicator selection is an important factor in designing the SI and is subject to data availability (Tapsell et al., 2010) (Fig. 8).

Fig. 8.

Social vulnerability by neighborhood in Macaé

The map shows the spatial distribution pattern of SOVI components that regulate neighborhoods, whether they are the most vulnerable or the least vulnerable. The figure shows the spatial distribution of VS based on the SoVIs of 22 selected neighborhoods of Macaé. The neighborhood with an index greater than 0.5 is classified as the most vulnerable, and the neighborhood with an index lower than −0.2 is classified as the least vulnerable. Many of these neighborhoods (Botafogo, Lago mar, Barra, Cabuiunas, Ajuda, Aroeira, Cajueiros, Visconde de Ara, Parque Aeroporto) fall into the high to very high category. These neighborhoods have high vulnerability scores due to urbanization, poor occupation and infrastructure, poverty, presence of vulnerable groups, and population density. Considering these neighborhoods and exposing them to our SLR result, we have our risk result (Fig. 9 and Table 5):

Fig. 9.

Areas most at risk in Macaé

Table 5.

Neighborhoods at risk

Adaptation and mitigation alternatives in coastal urban areas

On any time scale, no method of shoreline stabilization can completely stop coastal erosion (Cooper & Pilkey, 2012). The alternative selected, the resources at hand, the design parameters, and the particulars of the eroding region all affect the degree of protection. In theory, all available coastal defense measures require maintenance on a nearly regular basis, and they all call for solutions to reduce the harmful effects on the environment (MOCZM, 2013).

SLR responses include laws, plans of action, and other methods designed to lower risk and boost resistance to SLR. The following are some examples of these strategies: safeguarding the coastline, accepting the effects of SLR, withdrawing from the coast, pushing into the ocean by developing offshore, and ecosystem-based adaptation:

Protection By preventing inland propagation and other consequences of mean or exceptional sea level, it lowers coastal risks and impacts. To prevent flooding, erosion and saltwater intrusion, this includes I strong protection such as seawalls, breakwaters, barriers, and dams (Rangel-Buitrago et al., 2018) sediment-based protection such as beach and shore nourishment, dunes (also as soft structures), and iii) ecosystem-based adaptation. These responses offer a combination of protection benefits and progress based on sustainable management, conservation, and restoration of ecosystems (van Wesenbeeck et al., 2017). Examples include protecting or restoring coastal habitats such as mangroves, reefs, and wetland areas.

Accommodation Comprises a variety of biophysical and institutional solutions that lessen the vulnerability of coastal people, human activities, ecosystems, and the built environment, allowing coastal zones to remain habitable despite rising hazard occurrence levels. Developing regulations, elevating a house (for example, on stilts), moving valuables to higher levels, and building floating homes and gardens are all adaptations for erosion and floods (Haithuan et al., 2016).

In general, many flood prevention methods are used for houses of all kinds, from single homes for complexes. Both “floating house” and “amphibious dwelling” are regularly used words. SLR is viewed as an opportunity rather than a problem for communities and structures constructed on permanent but unique foundations that may float during a flood under the new flood adaptation concept known as “living on water.” They are a cutting-edge architectural adaptation solution that guaranties people to remain safe indoors while addressing climate change issues.

Coastal retreat Relocates vulnerable persons, possessions, and human activities outside the danger zone of the shore. These three forms are among them: I Migration, which is a person's voluntary permanent or semi-permanent movement that lasts at least a year (Adger et al., 2007; Islam & Khan, 2018) Displacement is the involuntary and unplanned migration of people because of environmental effects or political or military instability. Resettlement, also known as controlled retreat or managed realignment, is a practice that is often started, supervised, and carried out by governments at all levels, from the national to the local (Hino et al., 2017).

According to Koerth et al. (2017), most adaptation actions that occur in coastal areas are gradual and often include “accommodation” or “protection” against flood danger. On the other hand, emigration or leaving coastal areas would be a more transformative type of adaptation. A well-planned retirement is still quite uncommon. Siders and colleagues stated in recent research published in Science that withdrawal is now neither widespread nor frequently carried out with broad communal interests in mind. Instead, it seldom happens and when it does, it's usually unjust.

Conclusions

Various complex interacting mechanisms that collectively configure and concentrate exposure and susceptibility to climate change and SLR along the coast have caused and continue to cause major shifts in coastal settlement patterns throughout the twentieth century. Demographic expansion and changes, urbanization and rural exodus, the development of tourism, and the displacement or (re)installation of certain ethnic groups are all examples of these processes. In Macaé, the implementation of a policy of urban and real estate extension to accommodate these new inhabitants did not cause any concern. Unable to afford property on the formal market, the target areas for this population were mangroves and sandbars near the central area of the city. Mangroves and restingas are legally designated as important conservation sites. However, without access to capitalized space, the occupying population views its own survival or spatial demands as more important than the environmental conservation.

Effective solutions for mitigating or reducing erosion on beaches and thus minimizing the impacts of intense and extreme events, now and in the future, are nature-based (long-term) adaptation measures and hard protection. We are talking about public policies with the consideration of coastal and climatic risks, environmental education programs to raise awareness of the dangers of the ocean, the involvement of local communities in decision-making and monitoring of beaches, the creation buffer zones to minimize the impacts of extreme events and sea level rise (a frontal strip without buildings, which includes the removal and/or displacement of anthropogenic structures and the restoration of frontal dunes), as well as the restoration of beaches with nature-based solutions (artificial feeding/fattening/widening with sand brought from outside the beach). These are either referred to as 'in situ adaptation measures', in the form of protection, hard engineering measures, or soft sediment and ecosystem-based measures that could significantly extend habitability. However, this is unlikely to be a feasible solution for those located in fragile ecosystems and at-risk areas with a high vulnerable SOVI score. If these measures are not properly applied, relocation measures could be unavoidable.

The coastal region would be at danger of flooding with the expected inundation of this magnitude (2.15 m SLR), which will have a significant impact on various enterprises close to the coastline. Depending on how our SOVI index revealed the most susceptible populations, different amounts of damage have been done to this coastal city. The hinterlands will be impacted by changes, and urban planning will need to address various foreseeable issues, such as adaptation plans. The current work unequivocally establishes that digital elevation analysis is beneficial for quantifying future flood scenarios in the coastal zone. The maps created using this method act as a broad indication to increase local awareness and develop efficient coastal management measures.

Declarations

Conflict of interest

I, Fábio Ferreira Dias declare that I will make the data of the present research available and that there are no conflicts of interest.