Coastal Protection Service Delivery in Northeast Brazil in Face of
Coastal Dynamics
Nadia Selene Zamboni1, Maria de Fátima Alves de Matos2, Venerando Eustáquio Amaro2,
Mattheus da Cunha Prudêncio 2, Gregory M. Verutes3 and Adriana Rosa Carvalho1 1 FEME (http://feme-group.blogspot.com.br). Ecology Department, Universidade Federal de
Rio Grande do Norte. Av. Salgado Filho, s/n, Natal, 59078-970. Brazil,
(nselenezamboni@gmail.com), phone: 55-84-3215-3441, fax: 55-84-3211-9205.
2 Laboratório de Geotecnologias Aplicadas, Modelagens Costeira e Oceânica (GNOMO). Civil
Engineering Department, Complexo Tecnológico de Engenharia, Universidade Federal de Rio Grande do Norte, Campus Universitário Lagoa Nova, 1524, 59072-970, Natal, Brazil
3Faculty of Political and Social Sciences, Universidade de Santiago de Compostela, Campus
Vida, Avda. Dr. Ángel Echeverri, s/n, 15782 Santiago de Compostela, A Coruña, Spain
Abstract
Coastal and estuarine ecosystems such as mangroves, provide coastal protection service,
safeguarding coastal communities and their economies. However, the effects of sea-level
rise can affect mangrove’s defense role, by increasing coastal vulnerability and economic
costs. In order to understand more about vulnerability of coastal areas for conservation
planning, the objective of this study was to evaluate the vulnerability and the temporal
changes in coastal landscape by measuring the erosion/accretion rates in the shoreline and
the economic losses and costs from long-term retreats in the coastline and mangroves
loss. This study was carried out along the western coast of South Atlantic, in the Brazilian
northeast region. Coastal vulnerability was modeled using the Integrated Valuation of
Environmental Services and Tradeoffs (InVEST) software and the multitemporal analysis
of shoreline was developed using the Digital Shoreline Analysis System (DSAS)
software. Finally, the economic losses produced by those shoreline changes were
estimated. Results show that one third of coastline is under erosion process and sea-level
rise estimates will likely increase the prediction of losing 630,000 m2 of shoreline in the
coastal areas from sea-level rise effects, while protecting a fourth part of the total exposed
population. Probably this proportion could increase to half the population if total
mangrove area were lost, causing costs of more than USD6,1billions (BRL32,4 billions).
Conservation strategies through the application of ecosystem-based coastal adaptation planning supported by vulnerabilities temporal assessments are necessary in order to avoid mangroves downsizing and preserve coastal ecosystem service.
Keywords: Coastal vulnerability; Ecosystem services; Shoreline erosion; InVEST; Climate change.
Introduction
Mangroves are one of the most productive ecosystems worldwide and provide
several ecosystem services valued in US$33,000-57,000 ha−1.yr−1 within the national
economies of developing countries that host these ecosystems (UNEP 2014). Some of the
goods and services they afford include food, raw material and medicinal resources (Mitra
2020). They also support nurseries for fish and invertebrates, biodiversity and nutrient
cycling; they allow water regulation, prevention of seawater intrusion, and erosion
control; and they give rise to tourism and recreation (Barbier et al. 2011; Oudenhoven et
al. 2015).
Coastal protection is a service that stands out considerably in these systems, since
mangroves represent an important buffer from floods, erosion and natural threats to
coastal populations (Kathiresan and Rajendran 2005; Chatenoux and Peduzzi 2007; Das
and Vincent 2009) by dissipating waves and currents (Massel et al. 1999; Quartel et al.
2007; Gedan et al. 2011; Suzuki et al. 2012). Through their complex aerial root structure,
they trap sediment contributing to sediment retention and delta aggradation (Mazda et al.
2006; Adame et al. 2010; Kumara et al. 2010; Bao 2011), that mitigate both storm surges
and sea-level rise (SLR) effects (such as coastline erosion and inundation) (Koch et al.
2009; Barbier et al. 2011; Das and Crépin 2013), and ultimately protect physical property,
local economic activities and lives in coastal areas, by reducing exposure to coastal
hazards (Arkema et al. 2013).
Despite the numerous benefits that mangrove forests afford, they are constantly
suffering from the effects of climate change and human threats (FAO 2007). About 25%
of the world´s mangroves have been lost due to human activities that involve filling, direct
removal and clearing, or through other influences coming from urbanization, pollution,
al. 2010; Friess and Webb 2013; Rogers et al. 2016). Among the climate related factors,
SLR affects mangroves in the long term (Lafever et al. 2007; Kassakian et al. 2017), since
the existence of these ecosystems depend on the duration and frequency of upstream
sediment supply, tidal inundation and height above sea level, which are generally altered
by climate change (Spanger-Siegfried et al. 2014).
Global mean SLR have been accelerating in average about 3 mm.year-1 during the
last quarter century (Nerem et al. 2018; Bamber et al. 2019), and predictions for all four
Representative Concentration Pathway (RCP) scenarios throughout the 21st century, is
an increase of rates that will exceed those of the last decades (IPCC 2013). In face of this
rise, areas of coastal plains, wetlands or river deltas are more vulnerable to huge
horizontal transgression; saline intrusion, that leads to contamination of freshwater
aquifers and coastal erosion; and alteration of tides amplification, storm surges and waves
(Wright et al. 2019). In addition, under higher rates of SLR, local wetland loss rates are
expected to be high and landward migration is expected to become the main strategy for
coastal wetland adaptation to SLR (Borchert et al. 2018). However, on highly developed
coastal areas this is no longer possible, and mangroves may suffer from “coastal squeeze”
(Primavera et al. 2019).
Mangrove loss exposes coastal areas to coastal hazards, leading to a reduction in
habitat quality, and with this, loss of biodiversity and ecosystem services, that ultimately
impact negatively human welfare (Barbier et al. 2008; Polidoro et al. 2010), and generate
monetary losses. Within this context, decision makers are increasingly aware of the need
to implement both adaptation and risk reduction measures to avoid losses and damages
and a suite of other approaches within comprehensive risk management frameworks to
address losses and damages that are not averted (Mechler et al. 2019). Therefore, there is
functioning, in order to comprehend the influence of ecosystem services losses and
damages and specific policies over human well-being detriment (Geest et al. 2019).
Studies for disaster risk reduction planning and climate adaptation, usually include
not only future changes in risk from extreme events, but also future changes in hazard,
exposure and vulnerability as important drivers of future risks, on the basis of physical
modelling and scenarios (Bouwer 2019). In this way is possible to indicate what impacts
are expected for the near and more distant future (Bouwer 2019), and thus look for
vulnerability reduction strategies and adaptative responses. Besides, quantifying
economic losses of resources, goods and services (example: traded in markets), is another
way to analyze ecosystem services losses and damages (Serdeczny 2019).
Since the understanding on vulnerability of coastal areas may trigger conservation
or restoration planning for safeguarding coastal protection service, the aim of this study
is to evaluate vulnerability and temporal changes in coastal landscape by measuring the
erosion/accretion rates in the shoreline. Estimates consider the changes when mangrove
is kept or eliminated and the economic losses and costs from long-term retreats in the
coastline and mangrove areas.
The method used produced useful information to foresee effects of sea level rise
on coastal protection service provided by mangroves and to support future adaptation
strategies to SLR and its economic impact on worldwide mangrove shoreline areas.
Materials and Methods
Study Area
The assessment was carried out along 70 km of western coast of South Atlantic, in
the Brazilian northeast region, located in the state of Rio Grande do Norte. The entire area
do Mangue. Approximately 7.4% of the area is within the Ponta do Tubarão Sustainable
Development Reserve, specifically located in Macau and Guamaré municipalities into a
seasonally dry tropical forest biome (UTM Zone 24 South/WGS84 Datum 5° 02' 29" S,
36° 47' 51" W and 5° 05' 55" S, 36° 09' 47" W) (Fig. 1). Total population in the whole
area reaches 48,734 inhabitants (IBGE, 2011).
This coastal region has an annual rainfall lower than 650 mm and a mean annual
temperature slightly higher than 26.5ºC, also classified as a semi-arid region (Caatinga’s
morphoclimatic domain) (Ab´Sáber 1977; Alvares et al. 2013). The trade winds blow
from SE, NE, and E, influenced by the oscillations of the Intertropical Convergence Zone
(Franco et al. 2012). The action of winds (with average speeds of 9.0 m/s to 12.2 m/s),
waves (heights between 1.25 m and 2.09 m) and currents (from 0.17 m/s to 1.1 m/s in W
direction), modify the conformation of the coastline and promotes intense coastal
processes (Chaves et al. 2006; Matos et al. 2013).
Geology of the region is composed of lithological units of the Potiguar Basin, and
also shows current sediments from alluvial, fluvial-estuarine, fluvial-marine, beach and
coastal wind farms (Bezerra et al. 2009). The intense erosive processes displace barrier
islands and sandy spurs, provides migration of dune fields, and the opening and closing
of tidal channels in estuarine areas (Amaro and Araújo 2008; Santos and Amaro 2013).
Conversely, transport and deposition of sediments by semidiurnal tidal regime ranging
from 0.43 cm during neap tides to 2.9 m, during spring tides may counteract erosion
process (Franco et al. 2012; Santos et al. 2014). Besides, since the barrier islands system
is delimited by the Alfonso Bezerra and Carnaubais regional failure systems, its evolution
is also determined by tectonic movements (Rios and Amaro 2012).
The landscape is formed by fixed and mobile dunes, inter-dune depression, coastal
reaching the area arrive with less energy and bring fine particles in suspension that
accumulates, forming substrate on which the mangroves develop (Macedo 1995). Main
vegetation species are Rhizhophora mangle L., Laguncularia racemosa (L.) Gaertn,
Avicennia germinans (L.) growing in coastal stretches, which are indirectly linked to the
sea, as well as within lagoons and/or along tidal channels. These mangrove species are
protected from open sea waves and coastal currents by the barrier islands and sandy spurs
(or peninsulas).
Figure 1. Mangrove areas location within the western coast of South Atlantic, in the Brazilian northeast region (includes the towns of Galinhos, Guamaré, Diogo Lopes, Macau and Porto do Mangue; and the protected area Ponta do Tubarão Sustainable Development Reserve)
The main traditional economic activities developed in the region are artisanal
fishing, selective logging for charcoal production or construction (fences, houses and
boats), subsistence agriculture and livestock production (goat, sheep and cattle) raised
the area include oil and natural gas extraction by private companies, sea salt production,
shrimp farming, and recently, wind energy farming that rapidly spread during the last few
decades (Amaro and Araújo 2008; Boori and Amaro 2010; Amaro and Júnior 2012). Most
of these activities lead to significant loss of mangrove area and increased their coastal
vulnerability (Guimarães et al. 2010; Santos et al. 2014; Ferreira et al. 2015; Ferreira and
Lacerda 2016).
Despite the great pressure of human activities in the region, there is the Ponta do
Tubarão Sustainable Development Reserve, as was mentioned above. This protected area
was defined as category VI by IUCN (International Union of Conservation Nature) and
was created to avoid the advance of destructive companies (Dias et al. 2007). Local
people remain living into reserve boundaries and practicing managed traditional
livelihood, if considered sustainable by local management committee (BRASIL 2000).
The areas along this coastline are subject to floods caused by sea level rise, strong
erosion and marine transgression, due to the lack of terrestrial sediments which are being
blocked by constructions in the urban areas (Tabosa et al. 2002; Diniz 2013). Altogether
the anthropogenic impacts and erosion of seaward mangrove fringes, increasing saline
intrusion and increasing wind driven sand transport, change mangroves cover along the
coast reducing and even eliminating these forests (Lacerda and Marins 2002; Maia and
Lima 2004; Neves and Muehe 2008).
Coastal Vulnerability Modelling
In order to understand the role of coastal protection provided by mangroves in face
of sea level rise we applied the Coastal Vulnerability (CV) model of the Integrated
Valuation of Environmental Services and Tradeoffs (InVEST- Version 3.7.0) software
InVEST is an open-source software tool created by the Natural Capital Project,
which allows quantify and spatially map a wide range of ecosystem services (Sharp et al.
2018). Specifically, the CV model works as a decision support tool that produces a
qualitative index of coastal exposure to erosion and inundation.
The index combines ranks of several bio-geophysical variables to represent natural
biological and geomorphic characteristics in the region, the amount of expected net sea-
level rise and the relative wind and wave forcing associated with storm. By coupling the
qualitative index with population information, the model underlines areas along a given
coastline where humans are most vulnerable to coastal hazards, consequently detaching
habitats of higher potential to provide coastal protection (Arkema et al. 2013; Hopper and
Meixler 2016; Sharp et al. 2018; Sathiya Bama et al. 2020).
The variables used as inputs to qualitatively assess the potential of coastal
protection from mangroves and to run the model were: geomorphology (shoreline type),
relief, bathymetry, natural habitats, wind and wave exposure obtained from WAVE-
WATCHIII (Tolman 2009), sea level rise (in this study we considered erosion/accretion
rates) and local population. Surge potential was not included in the model, since there are
no records available to the region.
On the basis combination of criteria defined by the model [see Gornitz et al. (1990)
and Hammar-Klose and Thieler (2001)], and by the authors, the level of coastal exposure
was rated from very low exposure (rating=1) to very high exposure (rating=5; Table 1).
By the rankings, the model estimates by geometric mean, the exposure or vulnerability
index to each shoreline segment considered (Sharp et al. 2018).
The shoreline change (erosion/accretion) rates obtained, together with the other bio-
geophysical variables, were used to estimate the coastal exposure index (coastal
mangroves. Therefore, we were able to identify areas likely to be affected by
erosion/inundation, and recognize the role of mangroves as coastal protectors.
Table 1. List of the bio-geophysical variables and exposure ranks used as inputs in the coastal vulnerability modeling of mangrove shoreline
Acquisition of input data for Coastal Vulnerability Modelling
Chief input data to be obtained were geomorphology, natural habitats, relief, wave
exposure and sea level change. They were acquired as follows:
1. Geomorphology: Shoreline type
Different coastal geomorphology shoreline types were identified by using Google
Earth imagery and through validation in fieldwork conducted by the team of the
Laboratory of Applied Geotechnologies, Coastal and Oceanic Modeling (GNOMO team)
at Federal University of Rio Grande do Norte (UFRN). Specifically, natural substrates
mainly constituted by muddy and/or sandy substrates (such as beach, mud flat, delta,
lagoon, estuary and beach-barrier) and man-made structures built of rocks or concrete
(such as revetment and small seawalls) were recorded. Rank Very low
(1) Low (2) Moderate (3) High (4) Very high (5) Geomorphology Small seawalls Revetment Lagoon Estuary Sand beach Beach barrier Mud flat Delta Natural Habitats Mangroves
Coral reefs Dunes
Relief 0 to 20 quantile 21 to 40 quantile 41 to 60 quantile 61 to 80 quantile 81 to 100 quantile Wave Exposure 0 to 20 quantile 21 to 40 quantile 41 to 60 quantile 61 to 80 quantile 81 to 100 quantile Sea Level Rise > 10 m/year 2 to 10
m/year
2 to -2 m/year
-2 to -10
2. Natural Habitats
Along the coastline assessed, ecosystems included in the CV model were dunes,
mangroves and inorganic carbonate reefs habitats. These habitats were ranked based on
the model criteria, that considers reefs, mangroves and sand dunes as fixed stiff habitats
that penetrate the water column and are most effective in protecting coastal communities
(Sharp et al. 2018). Sand dunes however are prone to displacement by the frequency of
coastal processes like waves, currents, tides and winds (Castro and Ramos 2006) .
Land Use and Land Cover (LULC) classes were identified and mapped in the study
region using multispectral optical image—Landsat 8-OLI (dated on 2017 and spatial
resolution of 30 m), obtained from United States Geological Survey (USGS) Earth
Explorer (https://earthexplorer.usgs.gov/). Applying ENVI v.5.3® software, LULC
mapping was corrected, calibrated, georeferenced and submitted to Digital Image
Processing (DPI) to identify the distribution of the different natural habitats. Following
the DPI, segmentation and unsupervised classification were applied over the LULC map
using ArcGis v.10.3 software (ESRI, 2014). All LULC classes were validated “in situ”
by one research team members and one member of GNOMO Lab.
3. Relief
For the purpose of this assessment, relief is defined as the average elevation of the
coastal land area that is within a radius for (default = 5 km) from each shore segment of
the discretized shoreline (Sharp et al. 2018). According to the model´s ranking criteria,
sites at higher elevations over sea-level were at a lower risk of being inundated than areas
at lower elevations. Information on coastal relief was obtained by the combination of
topographic and bathymetric surveys, creating a new topographic-bathymetric Digital
Elevation Model (DEM). Topographic data has spatial resolution of 30 m and was locally
of 50 m was collected from Nautical Chart n.720 of Directorate of Hydrography and
Navigation of the Brazilian Navy (see Matos et al. 2013).
4. Wave Exposure
The amplitude and periodic variations of the water level and the wind fetch
conditions (Jackson and Nordstrom 2014) control the degree of coastline exposure to
waves, defining the potential of erosion or flooding on the shoreline. Coasts opened to
the ocean receive winds blowing over a very large distance or fetch, generating larger
waves. Sheltered beaches have low energy transfer and are protected from the direct
impact of high energy swells or longer period waves (Hegge et al. 1996; Goodfellow and
Stephenson 2005; Sharp et al. 2018).
Following the model´s ranking criteria, the discrimination between exposed and
sheltered areas was estimated from the average height of 10% of the highest waves,
calculated from every 16 cardinal directions of a given stretch of coast (Sharp et al. 2018).
Wave heights were obtained from a globally available wave dataset compiled from 8
years of model hindcast re-analysis results, the WAVEWATCH III (National
Oceanographic and Atmospheric Administration – NOAA; Tolman, 2009). The final
relative ranks were assigned based on the full distribution of wave power values observed
along the area.
5. Sea Level Change
Given the scarcity of local tide gage data to meaningful establish rates of sea level
rise in the study area, we estimate the shoreline change rates (erosion/accretion) as a
measure of variations in the coast as a consequence of sea level rise. The variation of sea
level rise and the future forecast for it (until 2026) were based on historic trends of
erosion/deposition of sediments along the coastline. The four steps methodological
i) Image selection, acquisition and digital processing
Images were obtained from United States Geological Survey (USGS) Earth
Explorer (https://earthexplorer.usgs.gov/) and the National Institute of Space Research
(INPE, http://www.inpe.br/). We selected multispectral optical images the Landsat 8-OLI
(2017) and the Landsat 5-TM (2007 and 1999 respectively) with 30 m of spatial
resolution. Following the standard correction, calibration and georeferencing of each
image using the ENVI v.5.3® software, DPI techniques were applied to expands contrasts
among pixels and thus improve the highlight between shorelines. The colored
compositions in the Red-Green-Blue (RGB) color system used were: R4G3B2; R5G3B2
and R5G4B2 for Landsat 8-OLI and R3G2B1; R4G2B1 and R4G3B1 for Landsat 5-TM.
ii) Obtention of the shorelines
Once the DPI techniques have been applied, a manual vectorization was performed
to detect the shorelines. Every visual assessment of the shorelines was analyzed for their
correspondence with the terrain reality, according to “in situ” recognition and expert
knowledge.
iii) Multitemporal Analysis of the shorelines:
The Digital Shoreline Analysis System (DSAS - Version 5.0), one free available
software that works with ArcGis extension, was used to calculate the statistical details of
the coastlines using multiple historic coastline positions (Himmelstoss et al. 2018). It
allows the estimate of rates of coastline change, by calculating the statistics of erosion
and accretion in the study area. Three shorelines referring to each multitemporal satellite
image for the years 1999, 2007 and 2017 were used. Baselines based on the average
direction of all shorelines were created in parallel with these shorelines to prevent false