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Article

Impact of Climate Change on the Tourism Potential of Northeastern Brazil: Trend Analysis and Future Perspectives

by
Ayobami Badiru
1,*,
Lívia Humaire
2,
Lucas Suassuna de Albuquerque Wanderley
3 and
Andreas Matzarakis
1,4
1
Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, 79085 Freiburg im Breisgau, Germany
2
Center for Anthropology and Health, University of Coimbra, CC Martim de Freitas, 3000-456 Coimbra, Portugal
3
Federal Institute of Education, Sciences and Technology of Alagoas, Coruripe 57230-000, Brazil
4
Democritus University of Thrace, GR-69100 Komotini, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5290; https://doi.org/10.3390/su17125290
Submission received: 25 April 2025 / Revised: 30 May 2025 / Accepted: 2 June 2025 / Published: 7 June 2025
(This article belongs to the Special Issue Resident Well-Being and Sustainable Tourism Development)

Abstract

:
This study aims to assess the impacts of climate change on the tourism potential of Northeastern Brazil by analyzing historical trends and future climate projections, identifying climate risks, and proposing spatially targeted adaptation strategies. Historical daily climate data from the BR-DWGD and future projections from the MPI-ESM1-2-LR model under the SSP2 4.5 scenario were used to evaluate extremes in temperature and precipitation. Principal component analysis and spatial cluster analysis were applied to identify five climatically homogeneous zones across the region. Results indicate generalized warming trends and intensifying rainfall extremes, particularly in coastal clusters where tourism infrastructure is concentrated. Inland zones, especially those with semi-arid climates, exhibit rising temperatures, prolonged droughts, and increasing water scarcity. These differentiated climatic patterns pose risks to infrastructure, ecosystem services, and the overall sustainability of tourism. In response, the study proposes adaptation measures tailored to each zone, including improved drainage systems, sustainable cooling technologies, rainwater harvesting, and diversification of tourism activities. Emphasis is placed on community-based governance to enhance social equity and resilience. The findings highlight the relevance of spatialized climate analysis for guiding adaptation planning and supporting a more inclusive and climate-resilient tourism sector in the region.

1. Introduction

The Brazilian Northeast (BNE) is a macro-region of Brazil located within tropical climate domains [1]. Humid and subhumid climates are predominant along the eastern and northwestern coast of this region, and semi-arid climates are frequently observed in the interior [2]. The region experiences alternating dry and wet climate cycles over years and decades but major drought events have historically impacted the region [1,3], causing severe impacts on the most vulnerable populations [4]. Conversely, intense rainfall also has detrimental effects on climate-related risk in this region, e.g., urban communities living in at-risk areas such as hillsides and river floodplains [1,5].
Recent climate change has posed significant challenges for BNE, vulnerable areas already affected by climate variability must now cope with the increasing frequency and intensity of extreme weather events, such as heat waves, droughts, and heavy rainfall [3,6]. From a socioeconomic perspective, one of the defining characteristics of the BNE is the unequal distribution of resources and services, with a social structure that fails to ensure a dignified standard of living for a large portion of its population [4]. This scenario further complicates efforts to mitigate the effects of climate change.
The economic structure of the region is marked by industrial concentration in major state capitals, with the service sector playing a significant role in economic dynamics [7]. Agriculture is predominantly practiced with low technological input, except in certain agribusiness hubs specializing in irrigated fruit farming, livestock, soybean cultivation, and sugarcane production [8]. Tourism is also an important economic activity in the region [9]. Along the coast, warm-water beaches drive the tourism sector during the summer, while in the interior, the main attractions include mountainous areas, plateaus, and the banks of the São Francisco River.
Despite its high tourism potential for local development, economic activities in the Northeast are highly vulnerable to the effects of climate change [9,10]. Short- and medium-term projections indicate an increase in heat waves, greater water scarcity in the interior, more frequent heavy rainfall, coastal flooding, and rising sea levels along the coastline [1,11]. Therefore, this study aims to assess the impact of climate change on the tourism potential of BNE by analyzing historical climate trends and future projections, identifying areas of climate risk and proposing opportunities for sustainable adaptation.
To achieve this, the next section describes the study area, data, and methodological approach used. Section 3 presents the results of the climatic assessment, identifying patterns of thermal and hydrological extremes across distinct climate zones and tourism clusters. Section 4 discusses the implications of these findings for sustainable tourism, with a focus on climate-related risks and adaptive strategies tailored to each cluster. Finally, Section 5 outlines the main conclusions and presents future directions for research and climate-resilient tourism planning in the region.

2. Materials and Methods

2.1. Study Area

The study area comprises the BNE, as shown in Figure 1. This region includes the nine states of Alagoas, Bahia, Ceará, Maranhão, Paraíba, Pernambuco, Piauí, Rio Grande do Norte, and Sergipe, and spans approximately 1,561,177 square kilometers. The BNE is home to more than 57 million people and comprises 18.27% of Brazil’s total land area [12].
The BNE has historically exhibited the lowest development indicators in the country compared to other regions. These include metrics such as the Human Development Index (HDI), per capita income, and access to infrastructure. The region also faces significant climate vulnerability due to its physical characteristics, including the presence of the most densely populated semi-arid region in the world [13]. This region is marked by prolonged droughts, irregular rainfall patterns, and limited water availability, heavily influenced by atmospheric and oceanic dynamics, and by its diverse climatic conditions, ranging from humid tropical zones in coastal areas to semi-arid climates in the interior.

2.2. Dataset

This study uses two climate datasets covering different time periods: historical data from 1961 to 2019 obtained from the Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14] and future climate projections from 2015 to 2100 based on the MPI-ESM1-2-LR global climate model under the SSP2-4.5 scenario, as provided by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The characteristics and processing steps of each dataset are detailed below.
For the historical analysis, daily data on evapotranspiration (ETo), precipitation (pr), relative humidity (RH), global radiation (Rs), maximum temperature (Tmax), minimum temperature (Tmin), and wind speed (u2) from the BR-DWGD program were utilized. The data were provided in NetCDF format with a spatial resolution of 0.1° × 0.1°, a temporal series from 1 January 1991 to 31 July 2020 and daily resolution.
Climate indices used to analyze future projections were derived from daily temperature and precipitation data simulated by the MPI-ESM1-2-LR, under the SSP2-4.5 scenario. Seven indices were computed according to the standardized definitions established by the Expert Team on Climate Change Detection and Indices (ETCCDI): daily temperature range (dtr), maximum one-day precipitation (rx1day), maximum five-day precipitation (rx5day), lowest annual maximum temperature (txn), highest annual minimum temperature (tnx), lowest annual minimum temperature (tnn), and highest annual maximum temperature (txx). These indices, as adopted by the IPCC [15], are methodologically appropriate for assessing climate extremes in tourism-sensitive sectors, including infrastructure, water security, and public health.
MPI-ESM1-2-LR data were obtained in NetCDF format, with a spatial resolution of 250 × 250 km, monthly temporal resolution, and coverage from 1 January 2015 to 31 December 2100. The selection of the MPI-ESM1-2-LR model under the SSP2-4.5 scenario was based on its extensive use in climate research and its compatibility with regional assessments in Brazil. The SSP2-4.5 pathway reflects an intermediate socio-environmental trajectory, balancing development and mitigation efforts, and is consistent with Brazil’s current climate policy framework. Additionally, the model has been employed in previous studies on Northeast Brazil [16], demonstrating suitable performance in simulating tropical climate dynamics and precipitation extremes. As with any global climate model, the MPI-ESM1-2-LR presents inherent limitations. In this case, the analysis is limited to the regional scale, without microclimatic or site-specific detail due to the model’s spatial resolution and absence of localized forcings.
Vector data in shapefile format, representing the official geographic boundaries of BNE, were obtained from the Brazilian Institute of Geography and Statistics (IBGE) and used to produce thematic maps related to the analyzed climate indices. All datasets mentioned in this study were spatially cropped to match the extent of the study area, ensuring consistency across geographic references.

2.3. Methodological Procedure

The methodological procedure followed an applied and interdisciplinary approach, structured in three main steps, which are presented in the following sections: (a) climate data processing and analysis; (b) regional vulnerability assessment; and (c) development of adaptation strategies.

2.3.1. Climate Data Processing and Analysis

This step involved three main components. First, historical trend analysis was conducted using BR-DWGD time series to identify patterns of climatic variability and extremes over time. Second, climate change projections were examined based on simulations from the MPI-ESM1-2-LR model, focusing on annual changes in variables relevant to tourism, such as average temperatures, heat indices, and precipitation shifts. Finally, climate hotspots were identified by overlaying climate maps in a Geographic Information System (GIS), with emphasis on areas of particular significance to tourism activities.

2.3.2. Zonal Risk Assessment

The adaptation strategies proposed in this study are grounded in an integrated assessment of climate risks, derived from the identification of homogeneous climatic zones and their specific vulnerabilities. These zones were delineated through two spatial statistical methods, cluster analysis and principal component analysis (PCA), applied to historical BR-DWGD climate data. This approach enabled the classification of areas with similar climatic behavior, forming the basis for a regionally differentiated analysis.
Following the spatial statistical delimitation, the risk assessment was conducted for each zone, considering official guidelines such as Brazil’s National Adaptation Plan to Climate Change (PNA), the Paris Agreement, and the United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action), SDG 11 (Sustainable Cities and Communities), and SDG 15 (Life on Land). The results provided the analytical foundation for the formulation of adaptation measures aligned with the specific risks and characteristics of each climatic zone.

2.3.3. Proposing Adaptation Strategies

Based on zonal risk assessment, adaptation strategies were formulated through a qualitative synthesis of regional vulnerabilities and sectoral climate risks identified in previous steps. Drawing from the zonal risk assessment, the process considered bioclimatic characteristics, exposure to specific hazards (e.g., heatwaves, droughts, and floods), and the socioeconomic profiles of tourism clusters. Strategies were designed to be spatially differentiated, context-sensitive, and aligned with local priorities. This stage also incorporated the official guidelines referenced in the previous section, to ensure policy coherence and normative relevance. The development of measures emphasized cross-sectoral integration and long-term resilience, with particular attention to community participation, aiming to ensure that adaptation efforts are inclusive, equitable, and grounded in local realities.

3. Results

3.1. Historical Data: Climate Data Processing and Analysis (1961–2019)

The historical series of temperature and relative humidity data from 1961 to 2019 (Figure 2) reveals statistically significant trends of increasing maximum temperature (p = 9.361 × 10−7), increasing minimum temperature (p = 9.837 × 10−14), and decreasing relative humidity (p = 2.833 × 10−7). These patterns align with broader regional warming trends observed in the BNE [1,6,16] and have direct implications for the sustainability of tourism in the region.
The rising maximum temperatures indicate an intensification of heat extremes, which may exacerbate evaporation rates, soil moisture deficits, and atmospheric dryness, reinforcing semi-arid conditions [11]. Simultaneously, the increase in minimum temperatures suggests reduced nighttime cooling, prolonging heat stress, and altering local thermal regimes [16]. The declining relative humidity further emphasizes a shift toward drier conditions, particularly in regions already prone to drought events, with potential repercussions for water resource management and outdoor-dependent activities [6]. While coastal areas still benefit from maritime influences, these trends suggest a growing need for adaptation strategies in sectors sensitive to climatic variability, including urban infrastructure and environmental management [17]. To further understand these climate dynamics, the spatial distribution of the median values of the seven climatic variables will now be examined, providing a comprehensive view of regional patterns and their potential implications.
The spatial distribution of median maximum temperatures (Figure 3) reveals a pattern of higher values over lowland depressions in the interior of the region and lower values over uplands and plateaus. In these low-elevation continental areas, weather types associated with atmospheric subsidence contribute to the occurrence of extreme temperatures, intensified by adiabatic compression—particularly in the valleys of major river systems [16]. In parts of the interior of Ceará, Rio Grande do Norte, Paraíba, Maranhão, Piauí, and Bahia, median values of temperature extremes may exceed 34 °C. Conversely, in elevated zones such as the Borborema Plateau, the southwestern highlands of Bahia, and the Chapada Diamantina, median maximum temperatures may remain below 26 °C.
With respect to the median values of minimum temperatures (Figure 3), values above 24 °C are concentrated in interior depressions and along the coastal plains of the region. In these areas, where absolute minimum temperatures are relatively high, the maritime air influence along the coast contributes to thermal regulation by reducing diurnal temperature variation. In the continental interior, the higher minimums are associated with intense surface heating in areas of low elevation (below 300 m). Conversely, in upland areas and plateaus exceeding 600 m in elevation, absolute minimum temperatures exhibit median values equal to or below 18 °C.
Therefore, the spatial pattern of temperature extremes results from the interaction between synoptic-scale atmospheric systems and climatic factors, with topography playing a key role [2]. The interaction of tropical air masses with the trade wind belt tends to generate more humid and mild weather conditions, particularly along the eastern coastline and windward slopes of mountain ranges. In contrast, excessively hot and dry conditions prevail in the continental interior, especially in lowland depressions where semi-arid climates dominate [2,11]. These climatic and topographic features largely explain the behavior of thermal extremes in the region.
Median values of relative humidity and evapotranspiration are inversely related (Figure 4). The eastern portion of the region and the windward slopes of mountain ranges exhibit high median relative humidity values (>75%), because of the frequent presence of the Atlantic tropical air mass, driven by the trade winds that blow year-round from the ocean toward the continent 31 May 2025 10:39:00 AM. The maritime air associated with this air mass enhances moisture availability and cloudiness, leading to lower evapotranspiration rates when compared to the region’s interior [2].
This effect is particularly evident in the southeastern and eastern areas of Bahia, where the combined influence of maritime conditions and topography results in more humid environments and lower evaporation rates (<4.0 mm/day). In the northwest of the region, which includes areas in the northern part of the state of Maranhão, the greater influence of the Intertropical Convergence Zone (ITCZ) and the humid air masses formed over the Amazon rainforest result in high median humidity values [18], resembling the behavior of this variable and evapotranspiration along the region’s eastern coast.
The central belt of the region, which connects the lowland areas with semi-arid climates from the state of Bahia to Rio Grande do Norte, exhibits lower median relative humidity values (<60%) and higher median evapotranspiration rates (>5.5 mm/day). In these areas, stable weather conditions—driven by hot and dry continental air masses—promote low humidity levels and high potential evaporation rates.
The analysis of mean daily precipitation values (Figure 5) reveals a marked interannual variability in rainfall, with more intense wet peaks occurring up to the 1980s and increasingly frequent and severe dry cycles from the 1990s onward. During the wettest years, average precipitation exceeded 3.5 mm/day, while in the driest years it dropped below 2.0 mm/day. A decline in annual averages is observed starting in the 1990s. Notably, the major drought that extended from 2012 to 2017 stands out, presenting the highest cumulative deficits in the data series. Investigating the climatic features of the 2012–2016 drought, ref. [19] classified it as the most severe event on record.
In the regional context, the decrease in rainfall combined with rising maximum temperatures indicates a potential increase in aridity [11,16]. Sub-regions of the Brazilian Northeast that already experience high annual water deficits may face scenarios of intensified water scarcity [16].
The observed climatic trends in the Brazilian Northeast, marked by rising temperatures, declining humidity, and shifting precipitation patterns, underscore the increasing environmental challenges faced by the region [1]. These changes not only intensify hydric stress and thermal extremes [6,16] but also pose risks to sectors heavily reliant on climate stability, such as sustainable tourism [17]. As visitor experience and local economies are closely tied to environmental conditions [9,17], understanding these spatial climate patterns is essential for strategic planning. The following chapter explores Zonal Risk Assessment and climate change adaptation measures, providing insights into how regions can mitigate vulnerabilities and build climate resilience.

3.2. Zonal Risk Assessment and Climate Change Projection Trends

PCA and Spatial Cluster Analysis were applied to median and extreme values of seven key climatic variables for the period 1991–2019 to identify climatically homogeneous zones within the Brazilian Northeast and support the development of region-specific adaptation strategies aligned with sustainable tourism. Figure 6 presents the principal components retained based on the 95% cumulative variance threshold, along with the proportion of variance explained by each component.
Based on these components, the cluster analysis grouped the region into five climatically homogeneous zones. Figure 7 presents both the spatial distribution of these zones and the trends of seven extreme climate indices, from ETCCDI data, within each cluster, revealing distinct patterns of climatic variability across the Brazilian Northeast.
These zones reflect distinct geographic, features across the region, with a territorial basis for assessing regionalized climate trends and risks. Cluster 1, represented in light green, includes areas along the coastline and spans seven states. It concentrates the region’s highest population density, including six state capitals: Fortaleza, Natal, João Pessoa, Recife, and Maceió, and Aracaju. This cluster exhibits significant climatic variation between coastal areas and its western portion. Humid and sub-humid climates occur along the coastal strip, while dry semi-arid climates are found in the interior, particularly in lowland depressions and leeward slopes. This climatic configuration is associated with the influence of the trade winds within the domain of the Atlantic tropical air mass, which generates humid weather conditions along the coast and dry conditions in the interior [2].
The climate is humid, semi-humid and semiarid In this cluster, the rainfall regime is [2], influenced by the Intertropical Convergence Zone (ITCZ), Easterly Wave Disturbances (EWD), and trade winds [20], resulting in frequent rainfall and high temperatures. Additionally, the ITCZ plays an important role during the austral summer and autumn, enhancing humidity and precipitation. The vegetation consists of rainforests along the coast and in elevated terrains and seasonal dry forests, locally known as “Caatinga,” from approximately 50 km inland. The vegetation is primarily composed of Atlantic Forest remnants, transitional Agreste formations and dry forest (Caatinga).
Cluster 2 (C2), shown in beige, spans parts of Maranhão, Piauí, western and southern Bahia, and a small portion of Ceará. It is characterized by transitional climatic conditions, where sub-humid and semiarid climates are influenced by South American monsoon regime [21,22], with seasonal rainfall. During the summer, the expansion of humid air masses over the continent favors the occurrence of intense rainfall, while in winter, dry air expands, producing hot and stable weather conditions throughout the days. the interaction of multiple air masses, particularly during the summer, when the presence of tropical air and the ITCZ becomes more active. These interactions favor rainfall in parts of the cluster, with generally higher precipitation in southern Bahia and lower totals in southern Maranhão and Piauí.
Cluster 3 (C3), in orange, corresponds to the Sertão, the semi-arid heart of the region and the most densely populated semi-arid zone in the world [13]. This cluster is marked by high temperatures, low humidity, and recurrent droughts [2,3]. The semiarid climate has its origin related to the general atmospheric circulation, with dry air masses formed by the subsiding branches of the Hadley and Walker circulation cells, in association with regional landscape factors, especially topography, which results in drier climates in depressions and leeward slopes and less dry conditions in mountain ranges or elevated residual massifs [23]. Its leeward location relative to the coastal orographic barrier limits moisture penetration. The South Atlantic Subtropical High (SASH), also inhibits cloud formation and rainfall, reinforcing arid conditions. The dominant vegetation is Caatinga [24].
Cluster 4 (C4), represented in light yellow, encompasses most of Maranhão and western coastal areas of Ceará and Piauí. It occupies a transitional space between the Amazon and the semi-arid Northeast, with strong influence from the (ITCZ) and the South Atlantic Convergence Zone (SACZ) on the rainfall regime [2]. This cluster presents a rich mosaic of vegetation, including Amazon rainforest remnants, Cerrado savannas, Babaçu palm forests (formerly covering 36% of the state), floodplains, mangroves, and aquatic vegetation adapted to humid soils [20].
Cluster 5 (C5), shown in dark green, covers the southern and southeastern regions of Bahia. It is characterized by a humid to semiarid-humid tropical climate under the influence of the tropical Atlantic air mass (mTa1) and the South America monsoon regime, an unstable system associated with cloudiness and rainfall [2]. Vegetation in this zone includes Atlantic Forest remnants and transitional formations toward the semi-arid interior.

4. Discussion

4.1. Climate Change and Sustainable Tourism in the BNE: Hazards and Risks

Tourism in BNE is one of the region’s main economic activities [9], driven by its diverse landscapes, tropical climate, and rich cultural heritage [25,26]. Nevertheless, climate change is altering environmental conditions, directly affecting the attractiveness of tourist destinations [17,27] as international studies have also demonstrated shifts in tourist preferences and mobility patterns in response to climatic stressors [28,29]. Based on the analysis of the geographic characteristics of the BNE and the climate trends presented earlier, the main climate hazards and tourism-related risks were identified for each cluster and are listed in Table 1.
The high population density in C1, which includes the main metropolitan areas of BNE, accentuates its environmental vulnerability and consequently threatens its tourism potential. Furthermore, the areas of highest risk for tourism in Brazil are precisely the coastal zones and mountains [27]; this cluster draws attention as it possesses an extensive coastal strip and a significant portion of the Borborema Plateau [30]. In C1, coastal erosion is one of the major climate-related risks to tourism, due to the intensification of extreme precipitation events [17], as indicated by the positive trend in daily precipitation intensification (rx1day and rx5day) in this cluster.
The danger of coastal erosion combined with the warming of the Sea Surface Temperature (SST) [17,31] and the rise in Mean Sea Level (MSL) [31], leads to the gradual loss of tourist beach strips. The retreat of the coastline also compromises hotel infrastructure, restaurants, and tourist facilities located on the coastal fronts of urban areas [5]. These risks, coupled with ocean acidification, compromise the environmental quality and marine fauna and flora, consequently reducing the attractiveness of these places.
These same extreme events are associated with landslides in urbanized areas [5], increasing the risk for tourists and resident populations. Increased precipitation, with intense rainfall events, can cause urban flooding, hindering access to airports, hotels, and tourist attractions [17,32].
The rise in minimum and maximum temperatures (tnn and tnx) can make beaches and outdoor activities less attractive during the day, leading to a decrease in tourists staying in these areas. Similarly, the rise in minimum and maximum temperatures increases the demand for air conditioning systems, which raises operational costs in hotels, tourist spots, and establishments [27].
Additionally, the negative impacts of heatwaves on health [15] discourage tourists and affect the well-being of workers in the tourism sector [33]. The loss of terrestrial biodiversity, alongside marine, in coastal ecosystems can affect ecotourism activities like hiking and visiting natural parks. Long-term risks to cultural heritage in coastal zones, where historical sites might be threatened, need consideration. Furthermore, the food security of coastal communities reliant on fisheries is at risk due to changing marine conditions [10,17], impacting local economies and tourism.
In C2, the minimal variation in diurnal temperature range (dtr) along with the rise in both minimum and maximum temperatures contribute to both diurnal and nocturnal thermal discomfort [1]. This increase in temperatures demands cooling infrastructures, such as air conditioning in hotels, tourist centers, and airports, to manage the discomfort [27]. Also, heat waves intensified by higher local temperatures can cause damage to rural and ecotourism infrastructure due to occasional intense rain events [5,27].
The slight positive trend in extreme rainfall events (rx1day and rx5day) suggests a pattern oscillating between intense rainy periods and prolonged droughts, that can quickly overwhelm local infrastructure and disrupt tourist activities [9,17]. This variability accelerates the degradation of ecological trails and protected natural areas, posing significant challenges to environmental conservation and tourism sustainability [27]. Risks of interruptions to access to tourist destinations during periods of extreme rain and intense droughts are heightened, impacting the flow and satisfaction of visitors.
The variability in climate conditions, fluctuating between extremes of heavy rains and prolonged droughts, reduces the attractiveness of community-based and ecotourism [15]. These climatic inconsistencies can deter tourists, particularly those interested in environmental and sustainable travel, due to the less predictable and often harsh environmental conditions. Another critical impact in this cluster, driven by the rising temperatures and the cyclical nature of the droughts, is a risk to local biodiversity, which affects natural attractions and ecotourism routes. The scarcity of water and concerns about its potability during prolonged dry periods [4,25] further strain the natural environment and tourism activities that rely on vibrant ecosystems.
Regarding C3, the increasing trend in minimum and maximum temperatures (tnn and tnx) may restrict outdoor tourism activities during peak heat hours. Severe heat waves increase health risks for tourists and locals alike [17]. A slight increase in extreme rainfall events (rx1day and rx5day) indicates cycles of intense rains and prolonged droughts, which cause rapid erosion and damage access routes to tourist sites. Drought conditions compromise water availability and quality for lodges, hotels, and resorts, affecting agriculture-based and culinary tourism [6,15,27]. Reduced water flow in ecotourism sites like waterfalls and rivers impacts the tourist experience, while increased fire risks during dry periods and a loss of local biodiversity (Caatinga) diminish the region’s ecological and cultural appeal [32,33].
C4 extends through the coast of the states of Maranhão, Piauí, and western Ceará, mostly in a transitional area between the Northeast and the Amazon. This cluster is influenced by the ITCZ and showcases diverse vegetation, including Babaçu palm forests, mangroves, and Cerrado. The climatic changes pose significant threats to these coastal ecosystems and mangroves, impacting nature tourism and wildlife observation [27,32]. The increasing trend in minimum and maximum temperatures may lead to prolonged periods of intense heat, directly influencing thermal discomfort and heightening the demand for air conditioning, which is critical for maintaining tourist comfort and safety. Additionally, the rise in extreme rainfall events (rx1day) suggests a higher frequency of severe rainfalls concentrated in a single day, escalating the risk of urban and rural flooding, particularly in poorly drained coastal areas [1,32].
The critical risks to tourism in this area include severe damage to coastal tourism infrastructure due to coastal erosion and sea level rise [31]. These events undermine the structural integrity of beachfront tourist facilities, from hotels to restaurants [10]. Moreover, the economic impacts are profound, with damages requiring significant financial resources for repair and future flood defenses, influencing the overall investment climate and insurance costs for the region. Intensified climatic extremes reduce the touristic appeal of natural areas, crucial for ecotourism, and community-based tourism [33] by altering landscapes and affecting biodiversity, which can deter tourists looking for authentic natural experiences.
C5, situated in the southern and southeastern regions of Bahia, is characterized by a humid and semi-humid tropical climate heavily influenced by the Atlantic tropical air mass (mTa1) and SACZ, which results in frequent intense rainfall. This area faces significant climatic hazards, such as heat waves, intense rainfall leading to punctual flooding, coastal erosion, sea level rise, and warming of sea surface temperatures [2]. These factors pose severe threats to the local tourism industry. The warming ocean temperatures and rising sea levels are particularly detrimental to coral reefs and mangrove ecosystems, reducing the appeal for ecotourism and beach activities. Furthermore, increased heat waves and higher temperatures can lead to discomfort among tourists and residents alike, reducing the attractiveness of the region’s beaches and potentially shortening visit durations [10].
The escalation in intense rainfall events heightens the risk of severe flooding and coastal erosion, which can damage critical infrastructure such as roads, bridges, and tourist facilities, leading to costly repairs and undermining the region’s tourism potential [17,32]. Additionally, the area’s increased susceptibility to wildfires during dry periods poses a risk to its Atlantic Forest and natural reserves, further threatening its ecological tourism appeal. Together, these challenges call for robust management and resilience planning to preserve the tourism sector in C5 against the backdrop of changing climatic conditions [17,25,32].
The analysis of extreme climate indices across the five climatic zones of Northeast Brazil reveals a consistent warming trend, underscoring the intensification of thermal stress in the region. All clusters show increasing trends in both maximum and minimum temperature indices (TXx, TXn, TNx, and TNn), with Clusters 3 and 4 experiencing particularly pronounced nighttime warming. This reduced nocturnal cooling indicates higher levels of thermal discomfort [1] and poses a challenge to health and comfort standards crucial for tourism activities [17,33]. While the diurnal temperature range (DTR) remains stable in Cluster 1, it presents a slight narrowing in the other clusters, suggesting increased heat retention across days and nights.
Results indicated a growing intensity and concentration of rainfall events, particularly in the coastal and transitional zones [6]. Rainfall extremes also show important variations; the maximum five-day rainfall (Rx5day) presents a generalized upward trend across all clusters, with Clusters 1, 2, and 3 being the most affected. The maximum one-day rainfall (Rx1day) trends, although more spatially variable, indicate a slight increase in most clusters, except for a marginal decrease in Cluster 4. These patterns suggest not only more intense rainfall events but also their growing concentration over shorter periods, particularly in the coastal and transitional zones [1], which are key regions for regional tourism [10]. These spatially differentiated climate trends carry implications for sustainable tourism planning. Rising temperatures and more erratic precipitation regimes compromise infrastructure, reduce tourist comfort, and intensify the vulnerability of both visitors and local populations to climate-related hazards [10,17,33].
The observed increase in temperature extremes across all climatic clusters, especially the consistent rise in nighttime temperatures (TNn and TNx) in Clusters 3 and Cluster 4, suggests a growing discomfort for both tourists and tourism workers [17,28]. Reduced nocturnal cooling and prolonged exposure to heat may discourage outdoor activities, strain energy systems, and compromise health and well-being, particularly in destinations without adequate thermal infrastructure [17,27,28]. The intensification and concentration of rainfall events, especially the rise in five-day maximum rainfall (Rx5day) in Clusters 1, Cluster 2, and Cluster 3, heighten the risk of urban flooding, infrastructure disruption, and damage to natural attractions [10,17]. In coastal clusters, such conditions threaten beach tourism, heritage sites, and marine ecosystems [10]. In more arid or semi-arid zones, irregular rainfall may aggravate water scarcity, impacting both community livelihoods and water-dependent tourist services [17,27].
These climate-driven hazards challenge core pillars of sustainable tourism: environmental preservation, economic viability, and social inclusion [34]. Rising thermal stress can shorten tourist seasons, shift travel patterns, and increase operational costs for local businesses [28,35]. At the same time, populations under climate risk employed in tourism may face greater exposure to climate risks without adequate protections or support. These pressures jeopardize not only the quality of the tourism experience but also the equitable development of the sector in the long term [17].
Understanding the regionalized nature of these impacts is key to designing effective adaptation strategies. The next section explores concrete, place-based adaptation measures tailored to each cluster’s vulnerabilities. From infrastructure innovation to emergency planning, these strategies aim to support climate-resilient tourism practices that safeguard both visitors and local communities, ensuring the continuity and sustainability of tourism in a changing climate.
The implications of these climate trends will be further explored in the next section, which focuses on targeted adaptation measures for each tourist cluster. By aligning technical responses with local vulnerabilities and potential, these measures aim to enhance the resilience of the tourism sector while ensuring that Northeast Brazil remains an attractive, safe, and sustainable destination in the face of ongoing climate change.

4.2. Adaptation Measures for Climate-Related Risks in Sustainable Tourism in the BNE

In the face of increasing climate variability and extreme weather events, it is imperative to implement targeted adaptation measures across tourist clusters in Northeast Brazil [1,15]. This subchapter explores a range of strategies designed to mitigate the impacts of climate-related risks such as coastal erosion, heatwaves, droughts, and flooding [29,36,37]. By detailing the integration of sustainable infrastructures, innovative water management techniques, and proactive planning initiatives [35], we aim to enhance the resilience and sustainability of these vital tourist destinations [29,32,33]. These measures not only safeguard the physical and economic well-being of the areas [25,29] but also ensure a comfortable and secure experience for both tourists and residents [17,37].
In clusters 1, 2, and 4, which encompass coastal regions, cities such as Recife, Fortaleza, and São Luís, frequently face flooding during intense rainfall periods. In these contexts, the implementation of robust urban and coastal drainage systems is crucial [36]. This includes traditional infrastructures like drainage canals and pumps, as well as innovative solutions such as permeable pavements and rain gardens [17,37]. These measures are vital for maintaining the operationality and attractiveness of tourist areas, ensuring uninterrupted tourist activities and providing a safe experience for visitors [29].
As temperatures rise, particularly relevant in clusters like 2, 3, and 4, promoting the use of sustainable cooling systems is essential. Investing in solar-powered cooling technologies for hotels and restaurants and increasing natural shading areas through the planting of native trees and green structures in public spaces and beaches effectively reduce energy consumption and operational costs while mitigating urban heat island effects [37,38]. This approach enhances thermal comfort for tourists and residents alike [10].
In inland landscapes, particularly within the semi-arid or sub-humid regions of clusters 1, 2, 3, and 5, such as the Sertão of Pernambuco and the Cariri region in Ceará, rainwater harvesting and storage systems are crucial. These systems ensure water availability during prolonged droughts, supporting not only the local population and agriculture but also tourist activities that depend on water resources [15,39]. This strategy is vital for the long-term sustainability of these regions, enabling them to continue attracting tourists under adverse conditions.
For clusters exposed to prolonged droughts and other extreme climatic events, such as around the Chapada Diamantina area, developing emergency infrastructure is crucial. This includes providing resources such as potable water, temporary shelters, and establishing emergency protocols and early warning systems to ensure the safety and well-being of tourists and local workers during crises [15,19,37].
In response to climate variability, as indicated for clusters 1 and 5, where cities like Salvador and João Pessoa are located, developing tourist attractions that are not exclusively dependent on outdoor conditions is a smart adaptive measure. Museums, cultural centers, and promoting local cuisine not only diversify available tourist options but also offer attractive alternatives during adverse weather periods, helping maintain a steady flow of visitors [27,37].
Implementing continuous climate monitoring systems and training tourism professionals are essential, especially in areas prone to extreme weather events like the Alagoas coast in cluster 1 [15,19]. These proactive measures minimize the negative impacts on tourism and ensure that professionals are prepared to handle adverse conditions, maintaining service quality and tourist safety [37].
For mountainous and coastal areas prone to erosion and landslides in clusters 1 and 5, such as in the coastal region of Pernambuco, Alagoas e Bahia, reforestation and slope protection are critical measures [10,15]. These practices contribute not only to soil stabilization and improving water infiltration but also to enhancing the visual landscape of tourist areas, thereby supporting ecotourism development and reinforcing long-term environmental conservation goals [37].
To ensure that all climate-related risks are properly addressed and adaptation measures are effectively implemented, it is essential to integrate communication and education strategies that keep local communities, tourism professionals, and visitors informed about climate hazards and available responses [15,37]. Adaptive and participatory planning allows strategies to evolve as new data, technologies, and governance models emerge. Rather than placing faith in public–private partnerships—which frequently concentrate power and economic benefits among external or elite actors—it is crucial to promote community-based governance structures and public-community collaborations that emphasize local agency, equitable participation, and the fair redistribution of tourism benefits [40]. When rooted in inclusive planning and genuine community empowerment, tourism can contribute to poverty alleviation, fair income distribution, and dignified work opportunities [41]—particularly in regions historically marginalized by conventional tourism models, such as Northeastern Brazil. In this context, tourism is not merely a vehicle for economic growth, but a transformative force capable of addressing structural inequalities and enhancing local capacities for self-determined development [41]. This is especially relevant for the tourism clusters analyzed in this study, where social vulnerability intersects with environmental risks [27]. Repositioned through regenerative frameworks, tourism can help restore ecosystems, support local economies, and empower communities through participatory and equitable governance arrangements—reinforcing social justice and redefining tourism as a catalyst for inclusive and sustainable development [37,40,41].

5. Conclusions

This study was undertaken with the objective of assessing the impact of climate change on the tourism potential of Northeastern Brazil by analyzing historical climate trends and future projections. This assessment aimed to identify specific areas of vulnerability within tourism clusters and to propose opportunities for sustainable adaptation strategies. Through our detailed analysis, five climatically homogeneous zones within Northeastern Brazil were delineated, accounting for 95% of the explained variance in regional climate data. This precision allowed for targeted adaptation measures tailored to the specific climate challenges of each zone.
The recurring climate hazards identified across the analyzed clusters include rising temperatures, sea-level rise, and increasingly intense precipitation events [15,19,35]. These dynamics are particularly critical in coastal regions, where tourism infrastructure and natural ecosystems are highly exposed to climatic stressors [29,37]. In response, this study has outlined a series of targeted adaptation measures:
  • Enhanced Coastal Defenses: in zones facing the threat of sea-level rise and coastal erosion, the construction of sea walls and dune stabilization projects were prioritized to protect critical tourist infrastructure [31,37].
  • Urban Heat Management: for urban tourist clusters experiencing increased heat, the implementation of green roofs, walls, and the expansion of urban forestry were recommended to mitigate heat stress and enhance urban livability [36,38,42].
  • Improved Water Management and Flood Resilience: enhanced drainage systems and water-sensitive urban design features like rain gardens and permeable pavements were suggested for zones prone to increased precipitation and flooding risks [1,19,29].
  • Water Scarcity Solutions: in drier inland zones, developing robust systems for water capture and storage is crucial to ensure a sustainable water supply for ecological and human needs, supporting the continuity of tourism activities [15,17,39].
  • Emergency Preparedness and Early Warning Systems: in areas prone to extreme climate events, establishing early warning mechanisms, emergency protocols, and access to temporary shelters are essential to protect both tourists and tourism workers, as well as local residents [19,36,37].
Community-Based Governance and Regenerative Tourism: Promoting inclusive governance models and regenerative tourism approaches helps strengthen local agency, distribute benefits equitably, and align adaptation with social and environmental justice goals. This includes the diversification of tourism products—such as indoor cultural attractions and culinary tourism—and the implementation of training programs that enhance the adaptive capacity of local communities and tourism professionals [27,37,40,41].
The importance of this paper lies in its systematic approach to linking climate data with practical adaptation strategies, providing a foundational guide for policymakers, stakeholders in the tourism industry, and community planners. This strategic framework not only informs ongoing and future adaptation efforts but also serves as a critical tool for enhancing resilience in Northeastern Brazil’s tourism sector.
For future studies, continued refinement of climate projection models is recommended to enhance the precision of adaptation strategies. Further research should also explore the socioeconomic impacts of these adaptations, assessing their effectiveness in sustaining local economies and the environmental resources upon which tourism heavily relies. Comparative studies with other global tourism destinations facing similar climate challenges could provide additional insights into shared vulnerabilities and transferable solutions, enriching the global dialog on sustainable tourism in the face of climate change.

Author Contributions

Conceptualization, A.B. and A.M.; methodology, A.B. and A.M.; formal analysis, A.B., L.S.d.A.W. and L.H.; writing—original draft preparation, A.B., L.S.d.A.W. and L.H.; writing—review and editing, A.B., L.S.d.A.W. and L.H.; visualization, A.B.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded financial support from the National Council for Scientific and Technological Development (CNPq), (Grant number: 200151/2025-8) and by the Alexander von Humboldt Foundation through the International Climate Protection Fellowship.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors thank the National Council for Scientific and Technological Development and the International Climate Protection Fellowship of the Alexan-der von Humboldt Foundation.

Acknowledgments

The authors thank the International Climate Protection Fellowship of the Alexander von Humboldt Foundation.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location map of the Northeast region of Brazil, showing the territorial boundaries of its states (in orange) and neighboring regions (in shades of gray).
Figure 1. Location map of the Northeast region of Brazil, showing the territorial boundaries of its states (in orange) and neighboring regions (in shades of gray).
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Figure 2. Time series of maximum temperature, minimum temperature, and relative humidity, along with their respective trends, from 1961 to 2019 in the Northeast region of Brazil. Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
Figure 2. Time series of maximum temperature, minimum temperature, and relative humidity, along with their respective trends, from 1961 to 2019 in the Northeast region of Brazil. Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
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Figure 3. Median values of maximum temperature (left) and minimum temperature (right) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
Figure 3. Median values of maximum temperature (left) and minimum temperature (right) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
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Figure 4. Median values of relative humidity (left) and evapotranspiration (right) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
Figure 4. Median values of relative humidity (left) and evapotranspiration (right) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
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Figure 5. Median values of global solar radiation (left), wind speed (right), and precipitation (bottom) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
Figure 5. Median values of global solar radiation (left), wind speed (right), and precipitation (bottom) from 1961 to 2019, spatially distributed for the Northeast region of Brazil (BNE). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
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Figure 6. Explained variance and cumulative variance from PCA applied to seven climatic variables in the Brazilian Northeast (1991–2019). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
Figure 6. Explained variance and cumulative variance from PCA applied to seven climatic variables in the Brazilian Northeast (1991–2019). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14].
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Figure 7. Spatial limits of the five climatically homogeneous zones identified through Principal Component Analysis (PCA) and Spatial Cluster Analysis and the trends of seven indices within each cluster: diurnal temperature range (dtr), maximum 1-day precipitation (rx1day), maximum 1-day precipitation (rx1day), minimum value of daily minimum temperature (tnn), maximum value of daily minimum temperature (tnx), minimum value of daily maximum temperature (txn), and maximum value of daily maximum temperature (txx). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14] and Expert Team on Climate Change Detection and Indices (ETCCDI).
Figure 7. Spatial limits of the five climatically homogeneous zones identified through Principal Component Analysis (PCA) and Spatial Cluster Analysis and the trends of seven indices within each cluster: diurnal temperature range (dtr), maximum 1-day precipitation (rx1day), maximum 1-day precipitation (rx1day), minimum value of daily minimum temperature (tnn), maximum value of daily minimum temperature (tnx), minimum value of daily maximum temperature (txn), and maximum value of daily maximum temperature (txx). Data source: Brazilian Daily Weather Gridded Dataset (BR-DWGD) [14] and Expert Team on Climate Change Detection and Indices (ETCCDI).
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Table 1. Main climate hazards and associated tourism risks across the five climatically homogeneous zones (clusters) in the Brazilian Northeast region. Identified hazards include extreme events and gradual climate trends, while risks describe potential negative impacts on regional tourism.
Table 1. Main climate hazards and associated tourism risks across the five climatically homogeneous zones (clusters) in the Brazilian Northeast region. Identified hazards include extreme events and gradual climate trends, while risks describe potential negative impacts on regional tourism.
ClusterMain HazardsRisks for Tourism
Cluster 1 (Coastal Zone)Coastal erosion; heatwaves, extreme rainfall; urban flooding; coastal flooding; sea level rise; sea surface temperature warming (impacting reefs and coastal ecosystems); increases in minimum and maximum air temperatures; ocean acidification; severe droughts in inland areas.Severe damage to urban tourism and hotel infrastructure; recurring disruptions of tourism services due to extreme rainfall events and urban flooding; coastline retreat and beach loss; reduced tourist attractiveness of urban beaches and resorts; decline in the environmental quality of beaches and marine ecosystems; public health issues and thermal discomfort for tourists in urban centers caused by heatwaves; increased operational costs in the tourism sector; salinization of coastal aquifers; water supply crises, particularly in inland municipalities.
Cluster 2 (Western zone)Heat waves; extreme/intense rain; prolonged droughts.Damage to rural and ecotourism infrastructure due to occasional intense rainfall events; risk of access disruptions to tourist destinations during periods of extreme rainfall and intensified droughts; reduced attractiveness of community-based and ecotourism activities due to climate variability (alternating extremes of drought and heavy rain); risk to local biodiversity, affecting natural attractions and ecotourism routes; increased risk of wildfires during prolonged dry periods.
Cluster 3 (Sertão/Semi-arid zone)Severe heatwaves; prolonged droughts; isolated intense rainfall (isolated but destructive events).Critical reduction in water availability, directly affecting rural and cultural tourism; increased health risks for tourists and local communities due to intense heatwaves; increased risk of localized extreme rainfall events, leading to accelerated erosion and damage to access routes to tourist sites; gradual loss of tourism appeal due to environmental degradation and decreased thermal comfort; increased risk of wildfires during prolonged dry periods; loss of local biodiversity (Caatinga), reducing ecotourism and cultural attractiveness.
Cluster 4Intense heatwaves; extreme rainfall (occasional flooding, especially in flood-prone areas); marked climate variability (shifts in dry and wet season cycles); coastal erosion; sea level rise; sea surface temperature warming (impacting reefs and coastal ecosystems).Loss or degradation of coastal tourism infrastructure (due to coastal erosion and sea level rise); damage to infrastructure caused by extreme rainfall events and occasional flooding in urban and rural areas; reduction in the tourism potential of natural areas due to intensified climate extremes, affecting ecotourism and community-based tourism; direct economic impact from damage to tourist destinations vulnerable to climate variability; severe damage to coastal tourism infrastructure due to coastal erosion and sea level rise.
Cluster 5 (South/Southeast of Bahia)Heatwaves; intense rainfall and localized flooding; coastal erosion; sea level rise; sea surface temperature warming (impacting reefs and coastal ecosystems).Loss of coastal and marine biodiversity (reefs, mangroves), reducing the attractiveness of ecotourism and beach tourism; increased risk of wildfires affecting tourist attractions in Atlantic Forest areas and nature reserves; impacts on tourism infrastructure due to coastal erosion, extreme rainfall, and localized flooding; gradual decline in environmental quality of beaches and reduced thermal comfort due to the combination of heatwaves and sea surface temperature rise.
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Badiru, A.; Humaire, L.; Wanderley, L.S.d.A.; Matzarakis, A. Impact of Climate Change on the Tourism Potential of Northeastern Brazil: Trend Analysis and Future Perspectives. Sustainability 2025, 17, 5290. https://doi.org/10.3390/su17125290

AMA Style

Badiru A, Humaire L, Wanderley LSdA, Matzarakis A. Impact of Climate Change on the Tourism Potential of Northeastern Brazil: Trend Analysis and Future Perspectives. Sustainability. 2025; 17(12):5290. https://doi.org/10.3390/su17125290

Chicago/Turabian Style

Badiru, Ayobami, Lívia Humaire, Lucas Suassuna de Albuquerque Wanderley, and Andreas Matzarakis. 2025. "Impact of Climate Change on the Tourism Potential of Northeastern Brazil: Trend Analysis and Future Perspectives" Sustainability 17, no. 12: 5290. https://doi.org/10.3390/su17125290

APA Style

Badiru, A., Humaire, L., Wanderley, L. S. d. A., & Matzarakis, A. (2025). Impact of Climate Change on the Tourism Potential of Northeastern Brazil: Trend Analysis and Future Perspectives. Sustainability, 17(12), 5290. https://doi.org/10.3390/su17125290

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