Escalating Climate Disasters in the Amazon (2006-2022): Vulnerabilities and Compound Risks

Escalating Climate Disasters in the Amazon (2006-2022): Vulnerabilities and Compound Risks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Escalating Climate Disasters in the Amazon (2006-2022): Vulnerabilities and Compound Risks Patricia Pinho, Rafaella Silvestrini, Martha Fellows, Letícia Perez, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5045887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The Amazon, a global biodiversity hotspot and home to ~30 million inhabitants, including numerous Indigenous ethnies, is increasingly vulnerable to climate impacts. Despite global evidence of climate-related disasters, the Brazilian Amazon's exposure remains underexplored. This study elucidates the spatiotemporal dynamics of socioeconomic vulnerabilities to climate disasters, including floods, droughts, heatwaves, fires, and landslides. From 2006 to 2010, an annual average of 540,000 people was affected by climate disasters in the Brazilian Amazon; by 2018-2022, this number surged to 1.78 million annually, a 229% increase. Financial losses rose 377%, from $132.8 million per year (2006-2010) to $634.2 million per year (2018-2022). Smaller municipalities with populations under 50,000, home to 61% of the Amazon’s Indigenous people, experienced an average economic growth loss of 9.58% from 2002 to 2020, along with lower Social Progress Index scores. Our findings highlight compound risks, revealing the urgent need for justice in adaptation for risk reduction. Addressing these vulnerabilities with justice and equity at the forefront is essential to mitigate further social and cultural losses in the Amazon. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Environmental social sciences/Climate-change impacts/Governance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In the globally significant Brazilian Amazon, comprehending the confluence of risks, exposure, and vulnerabilities is central to alleviating human experiences in the face of climate change disasters. This region, the world’s largest tract of tropical forest, is central to global climate stability 1 , 2 , with high biological and cultural diversity 3 . Its collapse is one of the major ecological tipping points under increasing global temperatures and greenhouse gas emissions 4 . For over two decades, the Brazilian Amazon has been severely impacted by extreme droughts and heatwaves, floods, and fires of intense magnitude and frequency 5 , 6 . These events have often occurred consecutively, sometimes overlapping, posing significant challenges to both the local people and institutional adaptation efforts 7 , 8 . Global climate disaster data already underscore the acute impacts faced by hundreds of millions in the Global South, who confront substantial challenges in preventing climate change related loss and damage and protecting livelihoods, health, and food security, often compounded by limited adaptive capacities 9 – 11 . However, the impacts on the health, food security, livelihoods and cultural heritage of Amazon people, especially Indigenous groups, are profound, irreversible but still poorly understood 12 , 13 . Research on unquantifiable, non-economic losses and damages (NELD), including values, social norms and culture, to which damage is irreversible 14 has demonstrated a devastating impact on these groups, particularly through the erosion of livelihoods, ecological knowledge, and place-of-birth attachment, alongside increasing migration 15 . Disasters are also affecting children's biological and educational development causing intergenerational disadvantages within Brazilian society 16 , while migration is affecting Indigenous people's emotional health through perceived depressive symptoms, distress and suicides 15 . These exposures and vulnerabilities arise not only from poverty but also from various dimensions of inequality, including ethnicity, which distinctly shapes local experiences of climate disasters 17 . Despite the critical need emerging from the little existing research available, there remains an urgent gap in understanding and data on how climate disasters specifically affect human populations, their livelihoods, and regional economies in the Amazon 11 , 12 . This paper advances understanding of social vulnerability and exposure to climate disasters in the Brazilian Amazon by employing a compound risk perspective 18 to comprehensively assess socioeconomic impacts and underlying vulnerabilities. We delineate the spatiotemporal patterns of climate-related disasters—including floods, landslides, droughts, heatwaves and fires—over a period of twenty-two years (2000–2022) and conduct a municipal-level analysis to identify the demographics most affected, quantifying both economic and non-economic losses and damages. Our findings highlight the uneven burden of climate disasters, with smaller municipalities and Indigenous populations bearing the brunt of the impacts, underscoring the importance of a justice framing in managing climate impacts. Our comprehensive assessment provides an important foundation for developing effective policy interventions in critical systems like the Amazon that integrate considerations of justice with economic development, human rights, and climate risks, offering actionable insights for policymakers committed to enhancing resilience. Without addressing exposure, vulnerability, and the limits to adaptation among other structural constraints faced by the Amazonian municipalities and Indigenous populations, compound risks beyond the 1.5°C warming target set by the Paris Agreement will be increasingly challenging for adaptation 10 . Results 1. Shifting Climate Disasters Climate-related disasters in the Brazilian Amazon have shown a pronounced increase and shift in prevalence over the two-decade period in this study. Initially dominated by wet events, the region has seen a substantial increase in droughts, heatwaves, and fires since 2005 (Fig. 1a, b). These disasters, defined as significant disruptions caused by hazardous events interacting with population's exposure, vulnerability, and capacity, have led to extensive human, material, economic, and ecological losses. The frequency of wet events, including various types of floods and intense rainfall, increased by 124% from the mid-2000s (2006–2010) to 2018–2022. When comparing the mean annual values from 2000–2005 to 2018–2022, wet events have risen more than fivefold, and landslides have increased by 189% over the same mid-2000s period, showing an eightfold surge. Similarly, drought and heatwave occurrences have tripled when comparing the early 2000s (2000–2005) to 2018–2022, with a 15% increase noted when comparing the mid-2000s (2006–2010) to 2018–2022. Fire events, in particular, have surged by 409% compared to the mid-2000s, resulting in a more than nine fold increase from 2000–2005 to 2018–2022 (Fig. 1b). Other climatic disasters also exhibited significant increases over these periods. Spatial analysis indicates that while wet events are uniformly distributed across the municipalities of the Brazilian Amazon, droughts predominantly affect the northern (RR), central (AM), and southern and southeastern municipalities (MT, TO, and MA) (Fig. 1a). Fire disasters are primarily concentrated in the municipalities of RR, MT, AC, and TO. Conversely, landslides and other disaster types do not exhibit a clear pattern of distribution across the region. Figure 1 here Figure 1: Number of climate disasters along municipalities in the Brazilian Amazon from 2000–2022. a) Spatial distribution of total number of disasters by municipality and disaster typology from 2000 to 2022; b) Temporal distribution of disasters over the years in all municipalities. c) Total number of disasters by climate typology in 2000–2022 (Data Source: Brazilian Atlas of Disasters). 2. Disaster Exposures on the Rise: A Growing Concern for People Our analysis from 2018 to 2022 reveals a significant increase in the number of individuals affected by disasters, with 1.78 million people, or 6.44% of the region's population, now exposed annually. This marks a dramatic rise from the 2000–2004 period, when only 2,316 people, or 0.11% of the population, were exposed (Fig. 2a). Compared with the 2006–2010 period, 541,180 people, or 2% of the population, were exposed annually. Wet events accounted for the majority of this exposure, affecting 62.08% of the population, followed by droughts and heatwaves (13.65%), fires (11.73%), landslides (8.7%), and other events (3.84%) (Fig. 2a, b). In years with extreme conditions, wet disasters impacted over 75% of the population in 408 municipalities, accounting for 53% of the region, and were evenly distributed throughout the Region (Fig. 2b). Severe droughts, heatwaves, and fire events affected more than 75% of the population in 130 municipalities, about 17% of the region, with these incidents primarily concentrated in the states of Acre (AC), Mato Grosso (MT), the southwest of Pará (PA), and Roraima (RR) (Fig. 2b). Figure 2 here Figure 2: Population exposed to disasters in municipalities of the Brazilian Amazon over the period 2000–2002. a) Temporal distribution of number of people affected by disaster typology (millions/year) and percentage of population exposed considering all disasters; b) Maximum annual percentage of affected people between 2000–2022 and its corresponding disaster typology by municipality. 3. Substantial Economic Impacts from Climate Disasters Economic losses from climate-related disasters in the Brazilian Amazon reveal a striking increase over two decades. From 2000 to 2022, the total financial damages were substantial, totaling approximately $ 5.78 billion USD. The annual mean losses have surged from $ 32.8 million USD during 2006–2010 to $ 634.2 million USD in the period 2018–2022, marking an increase of 3.7 times (Fig. 3a). Breaking down the losses by disaster type, wet events were the most costly, accounting for 84.78% of total losses ( $ 4.9 billion USD). Droughts and heatwaves followed, incurring 12.1% of the losses ( $ 700 million USD), with landslides, fires, and other climate disasters contributing less significantly (Fig. 3b). Wet disasters dominated losses, particularly in farming (agriculture and livestock), which amounted to $ 2.77 billion USD (57% of losses from wet events). Infrastructure and housing were also heavily affected, with damages of $ 962 million USD and $ 447 million USD, respectively. Health-related impacts, including damage to healthcare facilities and services, totaled $ 53 million USD. Droughts and heatwave disasters primarily impacted farming, accounting for $ 650.5 million USD (97% of losses from these events). Health-related costs amounted to $ 5.87 million USD. Likewise, fire disasters resulted in $ 64.2 million USD in losses, predominantly affecting farming (92%), with health-related losses totaling $ 0.58 million USD. Landslide disasters caused significant damage to public infrastructure ( $ 46.5 million USD). Across all disaster types, farming sustained 60.3% of total losses ( $ 3.5 billion USD). Damages to public infrastructure and housing were also notable for wet disasters, totaling $ 1.1 billion USD and $ 470 USD, 45%) and housing ( $ 18.9 million USD, 18%), with health-related costs of $ 1.77 million USD. Landslide disasters resulted in total losses of $ 101 million USD, with 45% ( $ 46.5 million USD) attributed to infrastructure damages and 18% ( $ 18.9 million USD) to housing (Fig. 3a). Figure 3 here Figure 3: Asset losses due to climate disasters in the Brazilian Legal Amazon. a) Total losses from 2000 to 2022, categorized by disaster type and affected sectors. b) Annual economic losses by disaster type. Data source: Brazilian Digital Atlas of Disasters. 4. Small Municipalities and Indigenous Peoples are disproportionately impacted Our analysis reveals a striking disparity in the impact of climate-related disasters across different-sized municipalities and Indigenous populations within the Brazilian Amazon. The smallest municipalities, defined as those with populations under 50,000 suffered the most severe economic consequences of climate disasters, with losses averaging 9.58% of their economic growth from 2002 to 2020. In contrast, municipalities with populations exceeding 50,000 and 300,000 saw economic losses constituting 3.8% and 1.3% of their economic growth, respectively (Fig. 4d). Municipal population size can be considered as a vulnerability, because in Brazil, municipalities with less than 20,000 people are not required to have a city master plan to guide the city's development. Often master plans indicate risk areas that should not be occupied. Smaller municipalities also show consistently higher numbers of climate disasters and lower Social Progress Index (SPI) scores compared to larger cities, while host a greater proportion of Indigenous residents (Fig. 4a, b, c). Our statistical analysis indicates significant correlations between smaller municipalities and the number of Indigenous inhabitants, with a Kendall-tau of -0.063 and Spearman of 0.025 for SPI, and a Kendall-tau of 0.106 and Spearman of 0.16 for the proportion of Indigenous populations. These findings underscore the vulnerability of smaller, and indigenous-rich municipalities to climate disasters and highlight the urgent need for targeted adaptation and resilience strategies to reduce these risks for these populations. Figure 4 here Figure 4. Vulnerability to climate disasters based on municipalities' population size. Vulnerability is represented on the X-axis by population size and on the Y-axis as follows: a) number of climate disasters; b) Social Progress Index; c) Indigenous population; and d) Loss to Economic Growth ratio. 5. Compound Risks Across the Brazilian Amazon Our study highlights significant variations in the multidimensional components of exposure and vulnerability to climate risks across municipalities in the Brazilian Amazon. We have identified distinct levels of compound risk based on the exposure of people and assets and the resultant economic impact. At the highest level of risk, defined by a "red rectangle," nine municipalities, each with a population under 20,000 people, experienced the most severe effects (Fig. 5a, b). In these areas, disasters affected over 50% of the population, and total economic losses exceeded 50% of their economic growth from 2002–2020 (Fig. 5a). This category includes municipalities located across various states: four in Amazonas (AM), two in Maranhão (MA), and one each in Mato Grosso (MT), Pará (PA), and Tocantins (TO) (Fig. 5a, b). The "orange rectangle" represents the second highest level of risk, comprising 187 municipalities (24.2% of the region) where disasters affected more than 50% of the population, yet the economic losses were less than 50% of the economic growth. This group is geographically diverse, with 119 municipalities (15% of the total) having populations under 20,000 (Fig. 5a, b). The "green rectangle" indicates a medium level of risk and includes just two municipalities located in eastern Amazonas, along the Amazon River, with populations under 50,000. Here, the climate disasters affected less than 50% of the population, but the economic losses totaled more than 50% of their growth between 2002 and 2020. The lowest level of risk, defined by a "blue rectangle," includes 575 municipalities (74.5% of the region). In these areas, climate disasters affected less than 50% of the population, and the economic losses were less than 50% of their economic growth. A majority, 355 municipalities (50%), have populations less than 20,000 and are spread across the Amazon. Figure 5 here Figure 5: Compound risk in each municipality, analyzing both population exposure in the year of the most extreme disaster and total economic loss in relation to economic growth. a) Scatterplot used to classify municipalities into 4 different levels of risk: red (high/high), grouping municipalities in which more than 50% of the population was affected in the most extreme disaster year and in which total economic losses surpassed 50% of municipality economic growth; yellow (high/low), grouping municipalities in which more than 50% of population was affected in the most extreme disaster year, but total economic losses were less than 50% of municipality economic growth; green (low/high), comprising municipalities with less than 50% people affected in the most extreme year and with total losses of more than 50% of economic growth; blue (low/low), comprising municipalities with less than 50% people affected in the most extreme year and with total losses of less than 50% of economic growth. b) map showing the spatial distribution of compound risk (classification obtained in a). Discussion Over a twenty-two-year period our study reveals that the frequency, intensity, number of people exposed and economic losses have all increased substantially across the Brazilian Amazon. Almost 2 million people are now annually exposed to climate-related disasters, including floods, droughts, heatwaves, fires, and landslides. Financial damages from these events have escalated with average annual losses increasing more than 4-fold in comparison to the 2000–2005 period. Flood events inflicted the most severe economic damage, affecting agriculture, livestock, infrastructure, housing, and healthcare facilities, leading to widespread economic losses across essential sectors. Similarly, severe droughts, heatwaves, and fires are exacerbating social challenges by causing physical isolation, disrupting access to education and healthcare, increasing food and fuel costs, worsening health conditions, and heightening conflict and violence 7 , 11 , 19 . The historical pattern of disasters in the Amazon has shifted from predominantly wet events to an increasing frequency of droughts, heatwaves, and fires, with droughts occurring earlier than anticipated 11 , 20 These hazards profoundly impact the region’s climate-sensitive livelihoods, such as agriculture, livestock, fisheries, and extractivism, as well as the already precarious basic services and infrastructure 7 , 9 , 11 . Indigenous people and local communities, dependent on natural resources and river systems are particularly vulnerable to the escalating severity and frequency of these climate disruptions 21 – 24 .The spatiotemporal exposure of municipalities and populations in the Amazon is closely linked to social vulnerability distribution, including low social development, particularly in terms of water, sanitation, health and education, indicating critically low capacities to meet basic needs 25 , 26 . These areas also coincide with the highest proportion of Indigenous inhabitants, highlighting a significant overlap between social inequalities, Indigenous populations and climate disasters 9 . The intersections of ethnicity and race, as well as age, gender, socioeconomic class, significantly influence the climate risks and differential impacts experienced by these groups 10 , 27 , 28 . The historical and ongoing colonial processes that create social, economic, and political inequalities, and marginalize Indigenous people and their livelihoods, drive the unevenness of opportunities to manage climate disasters 10 , 29 . Most municipalities in the Amazon (~ 61%) originated from Indigenous settlements emerging from the colonial exploitation of natural resources in their territories 30 . This colonial dominance has persisted and is a key driver of unequal climate impacts in the Global South more broadly 29 . Amazon municipalities are detached from centers of power, resources and decision-making, leading to ill-preparedness to plan for and respond to floods, droughts and their impacts. Large number of municipalities are located up to 2,820 km from state capitals and rely directly on major river systems to meet essential needs, compromising timely responses during extreme events 23 . Livelihoods in municipalities with larger Indigenous populations are often informal, invisible, and do not benefit from information sharing linked to hazard monitoring for risk reduction 24 . The official data of the National Center for Monitoring and Early Warning of Natural Disasters (Cemaden) monitors the Amazon’s larger urban centers in relation to floods and landslides, but it does not monitor droughts and heatwaves that affect mostly vulnerable municipalities and people 31 . Thus, data on smaller size municipalities are largely unavailable for Amazon 32 reinforcing vulnerability in these areas. Globally, Indigenous peoples and forest-dwelling communities in urban centers are often missing from national poverty assessments, and are frequently excluded from planning, including adaptation planning, despite potentially representing a small fraction of the population 33–353 . It is highly worrisome that these vulnerable Indigenous municipalities are unable to establish prevention, preparedness, response and recovery actions given data, financial and human capital constraints 25 , 36 . Without a master plan, municipalities are not required to respond to the federal survey on disaster public policies 37 , making it more difficult to establish adaptation measures 38 , yet the National Civil Defense and Protection Policy (PNPDEC) attributes the responsibility to the municipalities to map risk areas, identify threats, evaluate susceptibilities, vulnerabilities, and plan for disaster risk relief and relocation 30 . Higher governance levels continue to impose colonial conditions on poor municipalities in the Amazon, limiting their access to financial relief and assistance to affected populations 23 , 24 . Consequently, losses experienced by the Indigenous people and their livelihoods in affected municipalities are likely to be more severe than is shown by the data. Compound risks in the Amazon are increasing losses over the years, leading to the silent impoverishment of people and municipalities, increasing the adaptation gap. Addressing these multifaceted challenges requires a justice lens 39 , integrating Indigenous and local knowledge into attribution 40 , vulnerability, and adaptation science 41 . By incorporating Indigenous and local knowledge, we can better assess vulnerabilities, capacities, and losses from extreme events, enhancing monitoring and early warning systems, and ensuring equitable access to resources, recovery, rehabilitation, and compensation for inclusive and effective adaptation 35 . At the same time, careful and fair relocation of the affected population is necessary to avoid NELD due to breakdown of social networks, livelihoods and cultural practices. Yet, NELD evaluation and policy mechanisms remain an under-assessed area of academic investigation and policy practice in the Amazon and Global South 42 , 43 . Conclusion Inter-related forms of invisibility and social and spatial marginalization, neglected multi-dimensional social development and uneven climatic monitoring and forecasting, demand an urgent justice lens to be placed on climate related disasters in the Amazon. Justice enables us to draw attention to structural inequalities to reorient efforts to reduce risks 44 , in a context in which our data has shown smaller municipalities and Indigenous groups who are highly and directly dependent on natural resources are bearing the greater burden of exposure and vulnerability when climate disasters hit. Justice in the Brazilian Amazon needs to address the historical colonial legacy of exclusion, alongside unequal benefits and access to resources, opportunities, socioeconomic support, and wealth, that hinder their agency and adaptive capacity 45 . At-risk municipalities, the government and private sector are accountable and need to provide what is absent (e.g. accessible transportation, access to health systems, water, and food supplies, schools, and social support) including early warning systems, presenting a need for distributive justice 9 , 46 .Justice actions need to permeate the private sector through socioecological and climate related safeguards, primarily linked to activities exploiting Amazonia’s natural resources, significantly reducing risks in the near future. The Brazilian Amazon also needs a similar and urgent inclusion into article 8 of the Paris Agreement establishing the mechanisms by which Annex I countries should support adaptation finance to build resilience and reduce loss and damage to affected populations in the Global South. Climate change impacts are happening faster than previously anticipated, and responses are highly inefficient and spatially differentiated, highlighting an urgent need for legal and integrated mechanisms to be in place to avert losses and damages. In the absence of effective and urgent adaptation, these populations face a growing risk burden as increased inland flooding, droughts, forest fires, and heatwaves compromise sustainable development 9 . Methods The disaster risk evaluation employed here was elaborated in accordance with the disaster risk components outlined in the AR6-IPCC 9 , 18 . " Hazards " focus on the identification and quantification, in space and time, of climatic extreme events or disturbances directly related to them, such as fires and landslides, in the Amazon region. " Exposure " analyzed the population, infrastructure, housing, agriculture and public services exposed to the hazards. "Vulnerability," assessed the social and demographic conditions of the affected population and municipalities, including the financial capacity of municipalities to respond to disasters. "Compound risk" was obtained by analyzing the joint impact of the three previous risk components, allowing the identification of the municipalities that faced the highest or lowest disaster-related risks. All analyses were conducted at the municipality level for the Brazilian Amazon for the period 2000–2022. The following sections describe the methods and datasets employed for each disaster risk component. 1. Hazards Climatic extreme events data were obtained from the Brazilian Atlas of Disasters 47 which documented 5010 disaster events between 2000 and 2022 in the Brazilian Amazon, with 4792 of these occurrences being related to climatic extremes. The Atlas classifies disasters into sixteen categories, eleven of which are associated with climate-related hazards, such as different types of floods, droughts, and extreme heat conditions. In this study, these eleven original categories were consolidated into five major hazard typologies relevant to the Brazilian Amazon: (1) Wet (different kinds of floods and intense rain); (2) Drought and Heatwaves; (3) Fire; (4) Landslides; (5) Other climate-related extremes (gales, tornadoes, cold waves and hazel). The total annual number of climate-related hazards reported for the Amazon was computed between 2000 and 2022. Subsequently, the spatial and temporal distributions of these observed hazards were examined. The spatial distribution was established by mapping the total number of occurrences (2000–2022) for each of the 772 municipalities. The temporal distribution was evaluated by analyzing the annual frequency of climate disasters across the entire Brazilian Legal Amazon for each of the five hazard disaster typologies. 2. Exposure Exposure was evaluated according to two numerical indicators: human population and asset exposure. 2.1 Human population exposure Human population exposure data used here is the annual number of people affected by disasters available at the Brazilian Atlas of disasters 48 . The yearly total population data per municipality was obtained from the IBGE (2023) 48 and is based on decennial census counts (years 2000, 2010, and 2022) or on population size estimates (years 2001–2009; 2011–2021). We calculated the proportion of the population affected by disasters by dividing the annual number of affected people by the total population of each municipality for the corresponding year and disaster typology. To observe the temporal distribution of population exposure, we plotted the number of people affected by hazard type and the percentage of population affected yearly. This allowed us to identify, for each municipality, the disaster typology that affected the highest percentage of population affected throughout the years, with the resultant map showing the typology with highest population exposure across all years. 2.2 Asset exposure Asset exposure data were sourced from the Brazilian Atlas of Disasters 47 , which documents financial losses associated with each climate-related disaster. Losses resulting from disasters are classified into several categories, including private and public infrastructure damage (reclassified into health, education, community centers, construction works, housing and other) and private and public losses (divided into farming, industries, and services, including water supply and public health assistance). For each disaster typology, losses pertaining to the above-mentioned assets categories were gathered by municipality, annually, for the period 2000–2022. Monetary values were made available originally in Brazilian currency (Reais) updated to monetary values for 2022 and were converted into 2022 dollars using the conversion rate of 1 US dollar = 0.19 reais 49 . In addition to presenting the annual distribution of losses over time and by hazard typology, we have also demonstrated the allocation of total losses among asset categories for each risk typology. 3. Vulnerability The vulnerability component was composed of two variables, social and demographic characteristics and the response capacity of the municipalities. 3.1. Social and demographic characteristics We analyzed the social characteristics of the population using the Social Progress Index (SPI) 50 and the Indigenous population within each municipality 48 . The SPI comprehensively measures the social and environmental performance of territories by integrating globally relevant human development indicators alongside indicators adapted for the context of the Amazonia region, encompassing aspects like maternal mortality rate, access to water, school enrollment, deforestation rate, early pregnancy in childhood and adolescence, and violence. The Indigenous population was measured in the last census in 2022 by IBGE 48 . We calculated the percentage of Indigenous people in relation to the total municipality population and measured the correlation between population exposure (as estimated in section 2.1 ) and the SPI and the percentage of Indigenous people. We used the non-parametric correlation indices of Spearman ranking correlation and Kendall’s tau since the maximum percentage of people affected is not normally distributed. Both tests were performed considering a 95% level of confidence. 3.2. Response capacity The response capacity of a municipality is determined by its economic condition to recover from a disaster and the presence of master plans that guide the city’s occupation process, avoiding the occupation of high-risk areas. As such it merges social, demographic and political characteristics into a single variable. The economic capacity of each municipality to recover from disasters was evaluated by the ratio between total losses from 2000 to 2022 (in USD) and the economic growth attained between 2002 and 2020 (hereafter referred to as "Loss/EconGrowth ". The economic growth for each municipality was calculated as the difference in Gross Domestic Product (GDP) between 2020 and 2002. It was not possible to calculate the economic growth between the same years as the losses (2000–2022) due to the availability of GDP data, which only spans from 2002 to 2020 51 Another aspect of response capacity is the existence of a master plan in the municipality. In Brazil, only municipalities with more than 20,000 people are required to have a city master plan. The master plan guides urban management regarding the city's development, indicating risk areas, such as flooding and landslide risk areas that should not be occupied. We further examined the relationship between the number of climate disasters and the social and demographic characteristics of the population (SPI and Indigenous People) across different classes of municipality population size. Specifically, we divided municipalities into four classes: 20,000–50,000 people; >50,000-300,000 people; and > 300,000 people. 4. Compound risk To classify municipalities according to their multidimensional compound risk, we employed a dispersion diagram that visually represents the interplay between hazard, exposure, and response capacity. In this diagram, the X-axis displays the maximum percentage of the population affected between 2000 and 2022, capturing both observed hazards and population exposure. The Y-axis represents economic response capacity, quantified as the ratio of total disaster-related losses to economic growth (Loss/EconGrowth, see section 3.1 ). Municipalities are depicted as colored dots, with each color corresponding to a class of population size (see section 3.2 ). In this visualization, municipalities with high population exposure and low economic response capacity—indicating those most severely impacted—are concentrated in the upper right quadrant. The distribution of colors within this quadrant provides further insights into the prevalence of master plans among these municipalities. References Malhi Y et al (1979) Climate Change, Deforestation, and the Fate of the Amazon. Science 319, 169–172 (2008) O’Neill BC et al (2017) IPCC reasons for concern regarding climate change risks. Nat Clim Chang 7:28–37 Myers N, Mittermeler RA, Mittermeler CG, Fonseca D, G. A. B., Kent J (2000) Biodiversity hotspots for conservation priorities. Nature. 10.1038/35002501 O’Neill B et al (2022) Key Risks Across Sectors and Regions. In: Pörtner H-O et al (eds) Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA Marengo JA, Espinoza JC (2016) Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int J Climatol 36:1033–1050 Aragão LEOC et al (2018) 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat Commun. 10.1038/s41467-017-02771-y Pinho PF, Marengo JA, Smith MS (2015) Complex socio-ecological dynamics driven by extreme events in the Amazon. Reg Environ Change 15 Maru YT, Smith S, Sparrow M, Pinho A (2014) Dube, O. P. A linked vulnerability and resilience framework for adaptation pathways in remote disadvantaged communities. Glob Environ Change 28:337–350 Birkmann J et al (2022) IPCC WGII Sixth Assessment Report, Chap. 8: Poverty, Livelihoods, and Sustainable Development Roy J et al (2018) Sustainable Development, Poverty Eradication and Reducing Inequalities. in An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, (eds. Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. M.-O., C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, A. & Waterfield, T.) 435–558 Berrang-Ford L et al (2021) A systematic global stocktake of evidence on human adaptation to climate change. Nat Clim Chang 11:989–1000 Lapola DM et al (2018) Limiting the high impacts of Amazon forest dieback with no-regrets science and policy action. Proceedings of the National Academy of Sciences 115, 11671–11679 Brondizio ES, Moran EF (2008) Human dimensions of climate change: the vulnerability of small farmers in the Amazon. Philosophical Trans Royal Soc B: Biol Sci 363:1803–1809 Boyd E, James RA, Jones RG, Young HR, Otto FE (2017) L. A typology of loss and damage perspectives. Nat Clim Chang 7:723–729 McNamara KE, Jackson G (2019) Loss and damage: A review of the literature and directions for future research. Wiley Interdiscip Rev Clim Change 10:e564 Chacón-Montalván EA et al (2021) Rainfall variability and adverse birth outcomes in Amazonia. Nat Sustain 4:583–594 Tallman PS (2019) Water insecurity and mental health in the Amazon: Economic and ecological drivers of distress. Economic Anthropol 6:304–316 Pinho PF et al (2014) Ecosystem protection and poverty alleviation in the tropics: Perspective from a historical evolution of policy-making in the Brazilian Amazon. Ecosyst Serv 8:97–109 Lapola DM et al (1979) The drivers and impacts of Amazon forest degradation. Science 379, (2023) Simpson NP et al (2021) A framework for complex climate change risk assessment. One Earth 4:489–501 Möller V, van Diemen R, Matthews JBR, Méndez C (2022) & S. Semenov. IPCC 2022- Annex II: Glossary Brondízio ES, de Lima ACB, Schramski S, Adams C (2016) Social and health dimensions of climate change in the Amazon. Annals of Human Biology vol. 43 405–414 Preprint at https://doi.org/10.1080/03014460.2016.1193222 Nyantakyi-Frimpong H, Bezner-Kerr R (2015) The relative importance of climate change in the context of multiple stressors in semi-arid Ghana. Glob Environ Change 32:40–56 Schipper ELF et al (2023) Cambridge University Press, Cambridge,. Climate Resilient Development Pathways. in Climate Change 2022 – Impacts, Adaptation and Vulnerability (ed. H.-O. Pörtner.) 2655–2808 10.1017/9781009325844.027 Sealey-Huggins L (2017) 1.5°C to stay alive’: climate change, imperialism and justice for the Caribbean. Third World Q 38:2444–2463 Rufat S, Tate E, Burton CG, Maroof AS (2015) Social vulnerability to floods: Review of case studies and implications for measurement. Int J Disaster Risk Reduct 14:470–486 Sultana F (2021) Critical climate justice. Geogr J. 10.1111/geoj.12417 Dolman DI et al (2018) Re-thinking socio-economic impact assessments of disasters: The 2015 flood in Rio Branco, Brazilian Amazon. Int J Disaster Risk Reduct 31:212–219 Parry L et al (2018) Social Vulnerability to Climatic Shocks Is Shaped by Urban Accessibility. Ann Am Assoc Geogr 108:125–143 Parry L et al (2019) The (in)visible health risks of climate change. Soc Sci Med 241:112448 CEMADEN. Centro Nacional de Monitoramento e Alertas de Desastres Naturais. (2022) de Oliveira G et al (1979) Smoke pollution’s impacts in Amazonia. Science 369, 634.2–635 (2020) Carr-Hill R (2013) Missing Millions and Measuring Development Progress. World Dev 46:30–44 Galappaththi EK, Ford JD, Bennett EM (2020) Climate change and adaptation to social-ecological change: the case of indigenous people and culture-based fisheries in Sri Lanka. Clim Change 162:279–300 Mansur AV, Brondizio ES, Roy S, de Miranda Araújo Soares PP, Newton A (2018) Adapting to urban challenges in the Amazon: flood risk and infrastructure deficiencies in Belém, Brazil. Reg Environ Change 18:1411–1426 IBGE (2021) IBGE CIDADES. https://cidades.ibge.gov.br/ IBGE. Perfil dos municípios brasileiros (Munic 2017). Coordenação de População e Indicadores Sociais 106 (2017) Saito SM et al (2020) Disaster risk areas in Brazil: outcomes from an intra-urban scale analysis. Int J Disaster Resil Built Environ ahead-of-p Schlosberg D, Collins LB, Niemeyer S (2017) Adaptation policy and community discourse: risk, vulnerability, and just transformation. Env Polit 26:413–437 Bellprat O, Guemas V, Doblas-Reyes F, Donat MG (2019) Towards reliable extreme weather and climate event attribution. Nat Commun 10:1732 Ford JD et al (2020) The Resilience of Indigenous Peoples to Environmental Change. One Earth 2:532–543 Tschakert P et al (2017) Climate change and loss, as if people mattered: values, places, and experiences. Wiley Interdisciplinary Reviews: Climate Change vol. 8 Preprint at https://doi.org/10.1002/wcc.476 Serdeczny OM, Bauer S, Huq S (2018) Non-economic losses from climate change: opportunities for policy-oriented research. Clim Dev 10:97–101 Thomas A, Serdeczny O, Pringle P (2020) Loss and damage research for the global stocktake. Nat Clim Chang. 10.1038/s41558-020-0807-z Boyd E et al (2021) Loss and damage from climate change: A new climate justice agenda. One Earth 4:1365–1370 Tanner T et al (2015) Livelihood resilience in the face of climate change. Nat Clim Chang 5 BRAZIL (2023) Digital Atlas of Disasters in Brazil. Brasília. Ministry of Integration and Regional Development. Secretariat of Protection and Civil Defense. Federal University of Santa Catarina. Center for Studies and Research in Engineering and Civil Defense IPEA (2023) Commercial exchange rate for purchase: Brazilian real (R $ )/ US dollar (US $ )- average. http://www.ipeadata.gov.br/ExibeSerie.aspx?serid=38590&module=M . Accessed in 31/10/2023 Santos D, Verissimo A, Seifer P, Mosaner M (2021) Indice de Progresso Social Na Amazônia Brasileira- IPS IBGE, Population CENSUS, and Population Estimates. (2023). accessed in 10/31/2023. Avalilable at https://www.ibge.gov.br/en/statistics/social/population.html Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5045887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361278611,"identity":"a01d2a2a-ff1a-4cd8-b8b3-d9655a93e66a","order_by":0,"name":"Patricia Pinho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYLCCBAYJIMnYwMzAYMMDFzXAqZ4ZRUsakVqQmIcRPFxazNnPH/vwoMZCnkG6ue1xQcV5GXP20wkMH/fUMphLH8CqxbInmXlGwjEJwwaZg+3GM87c5rHsyd3AOOPZcQbLvgSsWgwOJDMzJDZIMDZIJLZJ87bd5jE4kLuBmefAMQaDM9gdZnD+MViLPVTLOR6D828JaLkBsSURquUAj8ENsC01OLVYznhszAD0S3KbzME2aZ4zyTyWM95uODjjwAGgp3CEGH/iY8YfNXW2/dLtz6R5KuzszflzNz74cKBOzpwHuxZ44LNJIIkcAEYQDg3I8SWBKlKHS8coGAWjYBSMPAAAqZpZFQ5o45cAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4214-485X","institution":"Amazon Environmental Research Institute (IPAM)","correspondingAuthor":true,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Pinho","suffix":""},{"id":361278613,"identity":"9513491c-e1ff-4d50-aa93-b8d01c344070","order_by":1,"name":"Rafaella Silvestrini","email":"","orcid":"","institution":"Instituto de Pesquisa Ambiental da Amazônia (IPAM)","correspondingAuthor":false,"prefix":"","firstName":"Rafaella","middleName":"","lastName":"Silvestrini","suffix":""},{"id":361278616,"identity":"1467cbcf-3c7e-48d2-aaa0-fbd1fbdf6498","order_by":2,"name":"Martha Fellows","email":"","orcid":"","institution":"Amazon Environmental Research Institute (IPAM)","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"","lastName":"Fellows","suffix":""},{"id":361278617,"identity":"db790080-f957-4fbb-892e-b33286f2c2e8","order_by":3,"name":"Letícia Perez","email":"","orcid":"https://orcid.org/0000-0002-6784-3964","institution":"National Institute for Space Research (INPE)","correspondingAuthor":false,"prefix":"","firstName":"Letícia","middleName":"","lastName":"Perez","suffix":""},{"id":361278618,"identity":"6f0e5957-1da6-49e4-8c7b-e7d0299ce179","order_by":4,"name":"Ane Alencar","email":"","orcid":"https://orcid.org/0000-0001-5605-7469","institution":"IPAM","correspondingAuthor":false,"prefix":"","firstName":"Ane","middleName":"","lastName":"Alencar","suffix":""},{"id":361278621,"identity":"443d1ab9-44db-4a5f-be0d-5a5e8118ec9e","order_by":5,"name":"Carol Guyot","email":"","orcid":"","institution":"Amazon Environmental Research Institute (IPAM)","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"","lastName":"Guyot","suffix":""},{"id":361278623,"identity":"776204fc-42f9-40f9-9a61-0c5b07be26ec","order_by":6,"name":"Paulo Moutinho","email":"","orcid":"https://orcid.org/0000-0003-0596-1823","institution":"Amazon Environmental Research Institute - IPAM Amazonia","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"","lastName":"Moutinho","suffix":""},{"id":361278624,"identity":"0d223aa7-b02a-40cc-9e7c-e4766709fcd0","order_by":7,"name":"David Lapola","email":"","orcid":"https://orcid.org/0000-0002-2654-7835","institution":"University of Campinas (UNICAMP)","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Lapola","suffix":""},{"id":361278626,"identity":"20d0df8e-3459-4e28-ba1d-3984ec2757b8","order_by":8,"name":"Lindsay Stringer","email":"","orcid":"https://orcid.org/0000-0003-0017-1654","institution":"University of York","correspondingAuthor":false,"prefix":"","firstName":"Lindsay","middleName":"","lastName":"Stringer","suffix":""}],"badges":[],"createdAt":"2024-09-06 19:00:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5045887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5045887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-66603-0","type":"published","date":"2025-11-23T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65831426,"identity":"2861faf7-83e0-4dd0-bb2a-066e589d5987","added_by":"auto","created_at":"2024-10-03 09:39:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1080900,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of climate disasters along municipalities in the Brazilian Amazon from 2000-2022. a) Spatial distribution of total number of disasters by municipality and disaster typology from 2000 to 2022; b) Temporal distribution of disasters over the years in all municipalities. c) Total number of disasters by climate typology in 2000-2022 (Data Source: Brazilian Atlas of Disasters).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/9926a52940779e57de16b83b.png"},{"id":65830431,"identity":"abc30c0b-7a35-4ed5-b97b-c782341861a9","added_by":"auto","created_at":"2024-10-03 09:31:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":551015,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation exposed to disasters in municipalities of the Brazilian Amazon over the period 2000-2002. a) Temporal distribution of number of people affected by disaster typology (millions/year) and percentage of population exposed considering all disasters; b) Maximum annual percentage of affected people between 2000-2022 and its corresponding disaster typology by municipality.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/037951ad31c174850438a44c.png"},{"id":65831701,"identity":"30bf84b8-7614-436a-89d7-147c66a1fc4b","added_by":"auto","created_at":"2024-10-03 09:47:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268636,"visible":true,"origin":"","legend":"\u003cp\u003eAsset losses due to climate disasters in the Brazilian Legal Amazon. a) Total losses from 2000 to 2022, categorized by disaster type and affected sectors. b) Annual economic losses by disaster type. Data source: Brazilian Digital Atlas of Disasters.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/deb827b474787b832872d840.png"},{"id":65830430,"identity":"a8a8bc33-18ee-4431-858a-07095757a8c2","added_by":"auto","created_at":"2024-10-03 09:31:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109082,"visible":true,"origin":"","legend":"\u003cp\u003eVulnerability to climate disasters based on municipalities' population size. Vulnerability is represented on the X-axis by population size and on the Y-axis as follows: a) number of climate disasters; b) Social Progress Index; c) Indigenous population; and d) Loss to Economic Growth ratio.\u003c/p\u003e","description":"","filename":"Fig41.png","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/c38a890e1740bd6d4167c99c.png"},{"id":65830432,"identity":"bdb3a99b-2e03-492e-b0ee-0c3af6f3af0f","added_by":"auto","created_at":"2024-10-03 09:31:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1095205,"visible":true,"origin":"","legend":"\u003cp\u003eCompound risk in each municipality, analyzing both population exposure in the year of the most extreme disaster and total economic loss in relation to economic growth. a) Scatterplot used to classify municipalities into 4 different levels of risk\u003c/p\u003e","description":"","filename":"Fig51.png","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/939655cfaf9854bd5fecb722.png"},{"id":99211850,"identity":"a24f9a50-4715-46d2-9ee8-7e47d981e5ce","added_by":"auto","created_at":"2025-12-30 08:10:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2732106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5045887/v1/d0ad17d4-9fd3-4365-b032-f578c931db7c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Escalating Climate Disasters in the Amazon (2006-2022): Vulnerabilities and Compound Risks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the globally significant Brazilian Amazon, comprehending the confluence of risks, exposure, and vulnerabilities is central to alleviating human experiences in the face of climate change disasters. This region, the world\u0026rsquo;s largest tract of tropical forest, is central to global climate stability \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with high biological and cultural diversity \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Its collapse is one of the major ecological tipping points under increasing global temperatures and greenhouse gas emissions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. For over two decades, the Brazilian Amazon has been severely impacted by extreme droughts and heatwaves, floods, and fires of intense magnitude and frequency \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These events have often occurred consecutively, sometimes overlapping, posing significant challenges to both the local people and institutional adaptation efforts \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal climate disaster data already underscore the acute impacts faced by hundreds of millions in the Global South, who confront substantial challenges in preventing climate change related loss and damage and protecting livelihoods, health, and food security, often compounded by limited adaptive capacities \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, the impacts on the health, food security, livelihoods and cultural heritage of Amazon people, especially Indigenous groups, are profound, irreversible but still poorly understood\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Research on unquantifiable, non-economic losses and damages (NELD), including values, social norms and culture, to which damage is irreversible \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e has demonstrated a devastating impact on these groups, particularly through the erosion of livelihoods, ecological knowledge, and place-of-birth attachment, alongside increasing migration\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Disasters are also affecting children's biological and educational development causing intergenerational disadvantages within Brazilian society \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, while migration is affecting Indigenous people's emotional health through perceived depressive symptoms, distress and suicides \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These exposures and vulnerabilities arise not only from poverty but also from various dimensions of inequality, including ethnicity, which distinctly shapes local experiences of climate disasters \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Despite the critical need emerging from the little existing research available, there remains an urgent gap in understanding and data on how climate disasters specifically affect human populations, their livelihoods, and regional economies in the Amazon \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis paper advances understanding of social vulnerability and exposure to climate disasters in the Brazilian Amazon by employing a compound risk perspective\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e to comprehensively assess socioeconomic impacts and underlying vulnerabilities. We delineate the spatiotemporal patterns of climate-related disasters\u0026mdash;including floods, landslides, droughts, heatwaves and fires\u0026mdash;over a period of twenty-two years (2000\u0026ndash;2022) and conduct a municipal-level analysis to identify the demographics most affected, quantifying both economic and non-economic losses and damages. Our findings highlight the uneven burden of climate disasters, with smaller municipalities and Indigenous populations bearing the brunt of the impacts, underscoring the importance of a justice framing in managing climate impacts. Our comprehensive assessment provides an important foundation for developing effective policy interventions in critical systems like the Amazon that integrate considerations of justice with economic development, human rights, and climate risks, offering actionable insights for policymakers committed to enhancing resilience. Without addressing exposure, vulnerability, and the limits to adaptation among other structural constraints faced by the Amazonian municipalities and Indigenous populations, compound risks beyond the 1.5\u0026deg;C warming target set by the Paris Agreement will be increasingly challenging for adaptation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":" \u003cp\u003e1. Shifting Climate Disasters\u003c/p\u003e \u003cp\u003eClimate-related disasters in the Brazilian Amazon have shown a pronounced increase and shift in prevalence over the two-decade period in this study. Initially dominated by wet events, the region has seen a substantial increase in droughts, heatwaves, and fires since 2005 (Fig.\u0026nbsp;1a, b). These disasters, defined as significant disruptions caused by hazardous events interacting with population's exposure, vulnerability, and capacity, have led to extensive human, material, economic, and ecological losses. The frequency of wet events, including various types of floods and intense rainfall, increased by 124% from the mid-2000s (2006\u0026ndash;2010) to 2018\u0026ndash;2022. When comparing the mean annual values from 2000\u0026ndash;2005 to 2018\u0026ndash;2022, wet events have risen more than fivefold, and landslides have increased by 189% over the same mid-2000s period, showing an eightfold surge. Similarly, drought and heatwave occurrences have tripled when comparing the early 2000s (2000\u0026ndash;2005) to 2018\u0026ndash;2022, with a 15% increase noted when comparing the mid-2000s (2006\u0026ndash;2010) to 2018\u0026ndash;2022. Fire events, in particular, have surged by 409% compared to the mid-2000s, resulting in a more than nine fold increase from 2000\u0026ndash;2005 to 2018\u0026ndash;2022 (Fig.\u0026nbsp;1b). Other climatic disasters also exhibited significant increases over these periods.\u003c/p\u003e \u003cp\u003eSpatial analysis indicates that while wet events are uniformly distributed across the municipalities of the Brazilian Amazon, droughts predominantly affect the northern (RR), central (AM), and southern and southeastern municipalities (MT, TO, and MA) (Fig.\u0026nbsp;1a). Fire disasters are primarily concentrated in the municipalities of RR, MT, AC, and TO. Conversely, landslides and other disaster types do not exhibit a clear pattern of distribution across the region.\u003c/p\u003e \u003cp\u003eFigure 1 here\u003c/p\u003e \u003cp\u003eFigure 1: Number of climate disasters along municipalities in the Brazilian Amazon from 2000\u0026ndash;2022. a) Spatial distribution of total number of disasters by municipality and disaster typology from 2000 to 2022; b) Temporal distribution of disasters over the years in all municipalities. c) Total number of disasters by climate typology in 2000\u0026ndash;2022 (Data Source: Brazilian Atlas of Disasters).\u003c/p\u003e \u003cp\u003e2. Disaster Exposures on the Rise: A Growing Concern for People\u003c/p\u003e \u003cp\u003eOur analysis from 2018 to 2022 reveals a significant increase in the number of individuals affected by disasters, with 1.78\u0026nbsp;million people, or 6.44% of the region's population, now exposed annually. This marks a dramatic rise from the 2000\u0026ndash;2004 period, when only 2,316 people, or 0.11% of the population, were exposed (Fig.\u0026nbsp;2a). Compared with the 2006\u0026ndash;2010 period, 541,180 people, or 2% of the population, were exposed annually.\u003c/p\u003e \u003cp\u003eWet events accounted for the majority of this exposure, affecting 62.08% of the population, followed by droughts and heatwaves (13.65%), fires (11.73%), landslides (8.7%), and other events (3.84%) (Fig.\u0026nbsp;2a, b). In years with extreme conditions, wet disasters impacted over 75% of the population in 408 municipalities, accounting for 53% of the region, and were evenly distributed throughout the Region (Fig.\u0026nbsp;2b). Severe droughts, heatwaves, and fire events affected more than 75% of the population in 130 municipalities, about 17% of the region, with these incidents primarily concentrated in the states of Acre (AC), Mato Grosso (MT), the southwest of Par\u0026aacute; (PA), and Roraima (RR) (Fig.\u0026nbsp;2b).\u003c/p\u003e \u003cp\u003eFigure 2 here\u003c/p\u003e \u003cp\u003eFigure 2: Population exposed to disasters in municipalities of the Brazilian Amazon over the period 2000\u0026ndash;2002. a) Temporal distribution of number of people affected by disaster typology (millions/year) and percentage of population exposed considering all disasters; b) Maximum annual percentage of affected people between 2000\u0026ndash;2022 and its corresponding disaster typology by municipality.\u003c/p\u003e \u003cp\u003e3. Substantial Economic Impacts from Climate Disasters\u003c/p\u003e \u003cp\u003e Economic losses from climate-related disasters in the Brazilian Amazon reveal a striking increase over two decades. From 2000 to 2022, the total financial damages were substantial, totaling approximately \u003cspan\u003e$\u003c/span\u003e5.78\u0026nbsp;billion USD. The annual mean losses have surged from \u003cspan\u003e$\u003c/span\u003e32.8\u0026nbsp;million USD during 2006\u0026ndash;2010 to \u003cspan\u003e$\u003c/span\u003e634.2\u0026nbsp;million USD in the period 2018\u0026ndash;2022, marking an increase of 3.7 times (Fig.\u0026nbsp;3a). Breaking down the losses by disaster type, wet events were the most costly, accounting for 84.78% of total losses (\u003cspan\u003e$\u003c/span\u003e4.9\u0026nbsp;billion USD). Droughts and heatwaves followed, incurring 12.1% of the losses (\u003cspan\u003e$\u003c/span\u003e700\u0026nbsp;million USD), with landslides, fires, and other climate disasters contributing less significantly (Fig.\u0026nbsp;3b). Wet disasters dominated losses, particularly in farming (agriculture and livestock), which amounted to \u003cspan\u003e$\u003c/span\u003e2.77\u0026nbsp;billion USD (57% of losses from wet events). Infrastructure and housing were also heavily affected, with damages of \u003cspan\u003e$\u003c/span\u003e962\u0026nbsp;million USD and \u003cspan\u003e$\u003c/span\u003e447\u0026nbsp;million USD, respectively. Health-related impacts, including damage to healthcare facilities and services, totaled \u003cspan\u003e$\u003c/span\u003e53\u0026nbsp;million USD. Droughts and heatwave disasters primarily impacted farming, accounting for \u003cspan\u003e$\u003c/span\u003e650.5\u0026nbsp;million USD (97% of losses from these events). Health-related costs amounted to \u003cspan\u003e$\u003c/span\u003e5.87\u0026nbsp;million USD. Likewise, fire disasters resulted in \u003cspan\u003e$\u003c/span\u003e64.2\u0026nbsp;million USD in losses, predominantly affecting farming (92%), with health-related losses totaling \u003cspan\u003e$\u003c/span\u003e0.58\u0026nbsp;million USD. Landslide disasters caused significant damage to public infrastructure (\u003cspan\u003e$\u003c/span\u003e46.5\u0026nbsp;million USD). Across all disaster types, farming sustained 60.3% of total losses (\u003cspan\u003e$\u003c/span\u003e3.5\u0026nbsp;billion USD). Damages to public infrastructure and housing were also notable for wet disasters, totaling \u003cspan\u003e$\u003c/span\u003e1.1\u0026nbsp;billion USD and \u003cspan\u003e$\u003c/span\u003e470 USD, 45%) and housing (\u003cspan\u003e$\u003c/span\u003e18.9\u0026nbsp;million USD, 18%), with health-related costs of \u003cspan\u003e$\u003c/span\u003e1.77\u0026nbsp;million USD. Landslide disasters resulted in total losses of \u003cspan\u003e$\u003c/span\u003e101\u0026nbsp;million USD, with 45% (\u003cspan\u003e$\u003c/span\u003e46.5\u0026nbsp;million USD) attributed to infrastructure damages and 18% (\u003cspan\u003e$\u003c/span\u003e18.9\u0026nbsp;million USD) to housing (Fig.\u0026nbsp;3a).\u003c/p\u003e \u003cp\u003eFigure 3 here\u003c/p\u003e \u003cp\u003eFigure 3: Asset losses due to climate disasters in the Brazilian Legal Amazon. a) Total losses from 2000 to 2022, categorized by disaster type and affected sectors. b) Annual economic losses by disaster type. Data source: Brazilian Digital Atlas of Disasters.\u003c/p\u003e \u003cp\u003e4. Small Municipalities and Indigenous Peoples are disproportionately impacted\u003c/p\u003e \u003cp\u003eOur analysis reveals a striking disparity in the impact of climate-related disasters across different-sized municipalities and Indigenous populations within the Brazilian Amazon. The smallest municipalities, defined as those with populations under 50,000 suffered the most severe economic consequences of climate disasters, with losses averaging 9.58% of their economic growth from 2002 to 2020. In contrast, municipalities with populations exceeding 50,000 and 300,000 saw economic losses constituting 3.8% and 1.3% of their economic growth, respectively (Fig.\u0026nbsp;4d).\u003c/p\u003e \u003cp\u003eMunicipal population size can be considered as a vulnerability, because in Brazil, municipalities with less than 20,000 people are not required to have a city master plan to guide the city's development. Often master plans indicate risk areas that should not be occupied. Smaller municipalities also show consistently higher numbers of climate disasters and lower Social Progress Index (SPI) scores compared to larger cities, while host a greater proportion of Indigenous residents (Fig.\u0026nbsp;4a, b, c).\u003c/p\u003e \u003cp\u003eOur statistical analysis indicates significant correlations between smaller municipalities and the number of Indigenous inhabitants, with a Kendall-tau of -0.063 and Spearman of 0.025 for SPI, and a Kendall-tau of 0.106 and Spearman of 0.16 for the proportion of Indigenous populations. These findings underscore the vulnerability of smaller, and indigenous-rich municipalities to climate disasters and highlight the urgent need for targeted adaptation and resilience strategies to reduce these risks for these populations.\u003c/p\u003e \u003cp\u003eFigure 4 here\u003c/p\u003e \u003cp\u003eFigure 4. Vulnerability to climate disasters based on municipalities' population size. Vulnerability is represented on the X-axis by population size and on the Y-axis as follows: a) number of climate disasters; b) Social Progress Index; c) Indigenous population; and d) Loss to Economic Growth ratio.\u003c/p\u003e \u003cp\u003e5. Compound Risks Across the Brazilian Amazon\u003c/p\u003e \u003cp\u003eOur study highlights significant variations in the multidimensional components of exposure and vulnerability to climate risks across municipalities in the Brazilian Amazon. We have identified distinct levels of compound risk based on the exposure of people and assets and the resultant economic impact. At the highest level of risk, defined by a \"red rectangle,\" nine municipalities, each with a population under 20,000 people, experienced the most severe effects (Fig.\u0026nbsp;5a, b). In these areas, disasters affected over 50% of the population, and total economic losses exceeded 50% of their economic growth from 2002\u0026ndash;2020 (Fig.\u0026nbsp;5a). This category includes municipalities located across various states: four in Amazonas (AM), two in Maranh\u0026atilde;o (MA), and one each in Mato Grosso (MT), Par\u0026aacute; (PA), and Tocantins (TO) (Fig.\u0026nbsp;5a, b). The \"orange rectangle\" represents the second highest level of risk, comprising 187 municipalities (24.2% of the region) where disasters affected more than 50% of the population, yet the economic losses were less than 50% of the economic growth. This group is geographically diverse, with 119 municipalities (15% of the total) having populations under 20,000 (Fig.\u0026nbsp;5a, b). The \"green rectangle\" indicates a medium level of risk and includes just two municipalities located in eastern Amazonas, along the Amazon River, with populations under 50,000. Here, the climate disasters affected less than 50% of the population, but the economic losses totaled more than 50% of their growth between 2002 and 2020. The lowest level of risk, defined by a \"blue rectangle,\" includes 575 municipalities (74.5% of the region). In these areas, climate disasters affected less than 50% of the population, and the economic losses were less than 50% of their economic growth. A majority, 355 municipalities (50%), have populations less than 20,000 and are spread across the Amazon.\u003c/p\u003e \u003cp\u003eFigure 5 here\u003c/p\u003e \u003cp\u003eFigure 5: Compound risk in each municipality, analyzing both population exposure in the year of the most extreme disaster and total economic loss in relation to economic growth. a) Scatterplot used to classify municipalities into 4 different levels of risk: red (high/high), grouping municipalities in which more than 50% of the population was affected in the most extreme disaster year and in which total economic losses surpassed 50% of municipality economic growth; yellow (high/low), grouping municipalities in which more than 50% of population was affected in the most extreme disaster year, but total economic losses were less than 50% of municipality economic growth; green (low/high), comprising municipalities with less than 50% people affected in the most extreme year and with total losses of more than 50% of economic growth; blue (low/low), comprising municipalities with less than 50% people affected in the most extreme year and with total losses of less than 50% of economic growth. b) map showing the spatial distribution of compound risk (classification obtained in a).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver a twenty-two-year period our study reveals that the frequency, intensity, number of people exposed and economic losses have all increased substantially across the Brazilian Amazon. Almost 2\u0026nbsp;million people are now annually exposed to climate-related disasters, including floods, droughts, heatwaves, fires, and landslides. Financial damages from these events have escalated with average annual losses increasing more than 4-fold in comparison to the 2000\u0026ndash;2005 period. Flood events inflicted the most severe economic damage, affecting agriculture, livestock, infrastructure, housing, and healthcare facilities, leading to widespread economic losses across essential sectors. Similarly, severe droughts, heatwaves, and fires are exacerbating social challenges by causing physical isolation, disrupting access to education and healthcare, increasing food and fuel costs, worsening health conditions, and heightening conflict and violence\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The historical pattern of disasters in the Amazon has shifted from predominantly wet events to an increasing frequency of droughts, heatwaves, and fires, with droughts occurring earlier than anticipated \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese hazards profoundly impact the region\u0026rsquo;s climate-sensitive livelihoods, such as agriculture, livestock, fisheries, and extractivism, as well as the already precarious basic services and infrastructure\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Indigenous people and local communities, dependent on natural resources and river systems are particularly vulnerable to the escalating severity and frequency of these climate disruptions\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e .The spatiotemporal exposure of municipalities and populations in the Amazon is closely linked to social vulnerability distribution, including low social development, particularly in terms of water, sanitation, health and education, indicating critically low capacities to meet basic needs\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These areas also coincide with the highest proportion of Indigenous inhabitants, highlighting a significant overlap between social inequalities, Indigenous populations and climate disasters \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The intersections of ethnicity and race, as well as age, gender, socioeconomic class, significantly influence the climate risks and differential impacts experienced by these groups \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The historical and ongoing colonial processes that create social, economic, and political inequalities, and marginalize Indigenous people and their livelihoods, drive the unevenness of opportunities to manage climate disasters \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Most municipalities in the Amazon (~\u0026thinsp;61%) originated from Indigenous settlements emerging from the colonial exploitation of natural resources in their territories\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This colonial dominance has persisted and is a key driver of unequal climate impacts in the Global South more broadly \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmazon municipalities are detached from centers of power, resources and decision-making, leading to ill-preparedness to plan for and respond to floods, droughts and their impacts. Large number of municipalities are located up to 2,820 km from state capitals and rely directly on major river systems to meet essential needs, compromising timely responses during extreme events \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Livelihoods in municipalities with larger Indigenous populations are often informal, invisible, and do not benefit from information sharing linked to hazard monitoring for risk reduction\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The official data of the National Center for Monitoring and Early Warning of Natural Disasters (Cemaden) monitors the Amazon\u0026rsquo;s larger urban centers in relation to floods and landslides, but it does not monitor droughts and heatwaves that affect mostly vulnerable municipalities and people\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThus, data on smaller size municipalities are largely unavailable for Amazon\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e reinforcing vulnerability in these areas. Globally, Indigenous peoples and forest-dwelling communities in urban centers are often missing from national poverty assessments, and are frequently excluded from planning, including adaptation planning, despite potentially representing a small fraction of the population \u003csup\u003e33\u0026ndash;353\u003c/sup\u003e. It is highly worrisome that these vulnerable Indigenous municipalities are unable to establish prevention, preparedness, response and recovery actions given data, financial and human capital constraints \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Without a master plan, municipalities are not required to respond to the federal survey on disaster public policies \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, making it more difficult to establish adaptation measures \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, yet the National Civil Defense and Protection Policy (PNPDEC) attributes the responsibility to the municipalities to map risk areas, identify threats, evaluate susceptibilities, vulnerabilities, and plan for disaster risk relief and relocation \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Higher governance levels continue to impose colonial conditions on poor municipalities in the Amazon, limiting their access to financial relief and assistance to affected populations\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Consequently, losses experienced by the Indigenous people and their livelihoods in affected municipalities are likely to be more severe than is shown by the data.\u003c/p\u003e \u003cp\u003eCompound risks in the Amazon are increasing losses over the years, leading to the silent impoverishment of people and municipalities, increasing the adaptation gap. Addressing these multifaceted challenges requires a justice lens \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, integrating Indigenous and local knowledge into attribution\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, vulnerability, and adaptation science \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. By incorporating Indigenous and local knowledge, we can better assess vulnerabilities, capacities, and losses from extreme events, enhancing monitoring and early warning systems, and ensuring equitable access to resources, recovery, rehabilitation, and compensation for inclusive and effective adaptation \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. At the same time, careful and fair relocation of the affected population is necessary to avoid NELD due to breakdown of social networks, livelihoods and cultural practices. Yet, NELD evaluation and policy mechanisms remain an under-assessed area of academic investigation and policy practice in the Amazon and Global South \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInter-related forms of invisibility and social and spatial marginalization, neglected multi-dimensional social development and uneven climatic monitoring and forecasting, demand an urgent justice lens to be placed on climate related disasters in the Amazon. Justice enables us to draw attention to structural inequalities to reorient efforts to reduce risks \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, in a context in which our data has shown smaller municipalities and Indigenous groups who are highly and directly dependent on natural resources are bearing the greater burden of exposure and vulnerability when climate disasters hit. Justice in the Brazilian Amazon needs to address the historical colonial legacy of exclusion, alongside unequal benefits and access to resources, opportunities, socioeconomic support, and wealth, that hinder their agency and adaptive capacity\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. At-risk municipalities, the government and private sector are accountable and need to provide what is absent (e.g. accessible transportation, access to health systems, water, and food supplies, schools, and social support) including early warning systems, presenting a need for distributive justice\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.Justice actions need to permeate the private sector through socioecological and climate related safeguards, primarily linked to activities exploiting Amazonia\u0026rsquo;s natural resources, significantly reducing risks in the near future. The Brazilian Amazon also needs a similar and urgent inclusion into article 8 of the Paris Agreement establishing the mechanisms by which Annex I countries should support adaptation finance to build resilience and reduce loss and damage to affected populations in the Global South. Climate change impacts are happening faster than previously anticipated, and responses are highly inefficient and spatially differentiated, highlighting an urgent need for legal and integrated mechanisms to be in place to avert losses and damages. In the absence of effective and urgent adaptation, these populations face a growing risk burden as increased inland flooding, droughts, forest fires, and heatwaves compromise sustainable development \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe disaster risk evaluation employed here was elaborated in accordance with the disaster risk components outlined in the AR6-IPCC\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. \"\u003cem\u003eHazards\u003c/em\u003e\" focus on the identification and quantification, in space and time, of climatic extreme events or disturbances directly related to them, such as fires and landslides, in the Amazon region. \"\u003cem\u003eExposure\u003c/em\u003e\" analyzed the population, infrastructure, housing, agriculture and public services exposed to the hazards. \"Vulnerability,\" assessed the social and demographic conditions of the affected population and municipalities, including the financial capacity of municipalities to respond to disasters. \"Compound risk\" was obtained by analyzing the joint impact of the three previous risk components, allowing the identification of the municipalities that faced the highest or lowest disaster-related risks.\u003c/p\u003e \u003cp\u003eAll analyses were conducted at the municipality level for the Brazilian Amazon for the period 2000\u0026ndash;2022. The following sections describe the methods and datasets employed for each disaster risk component.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1. Hazards\u003c/h2\u003e \u003cp\u003eClimatic extreme events data were obtained from the Brazilian Atlas of Disasters\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e which documented 5010 disaster events between 2000 and 2022 in the Brazilian Amazon, with 4792 of these occurrences being related to climatic extremes. The Atlas classifies disasters into sixteen categories, eleven of which are associated with climate-related hazards, such as different types of floods, droughts, and extreme heat conditions. In this study, these eleven original categories were consolidated into five major hazard typologies relevant to the Brazilian Amazon: (1) Wet (different kinds of floods and intense rain); (2) Drought and Heatwaves; (3) Fire; (4) Landslides; (5) Other climate-related extremes (gales, tornadoes, cold waves and hazel).\u003c/p\u003e \u003cp\u003eThe total annual number of climate-related hazards reported for the Amazon was computed between 2000 and 2022. Subsequently, the spatial and temporal distributions of these observed hazards were examined. The spatial distribution was established by mapping the total number of occurrences (2000\u0026ndash;2022) for each of the 772 municipalities. The temporal distribution was evaluated by analyzing the annual frequency of climate disasters across the entire Brazilian Legal Amazon for each of the five hazard disaster typologies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Exposure\u003c/h3\u003e\n\u003cp\u003eExposure was evaluated according to two numerical indicators: human population and asset exposure.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Human population exposure\u003c/h2\u003e \u003cp\u003eHuman population exposure data used here is the annual number of people affected by disasters available at the Brazilian Atlas of disasters\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The yearly total population data per municipality was obtained from the IBGE (2023)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and is based on decennial census counts (years 2000, 2010, and 2022) or on population size estimates (years 2001\u0026ndash;2009; 2011\u0026ndash;2021).\u003c/p\u003e \u003cp\u003eWe calculated the proportion of the population affected by disasters by dividing the annual number of affected people by the total population of each municipality for the corresponding year and disaster typology. To observe the temporal distribution of population exposure, we plotted the number of people affected by hazard type and the percentage of population affected yearly. This allowed us to identify, for each municipality, the disaster typology that affected the highest percentage of population affected throughout the years, with the resultant map showing the typology with highest population exposure across all years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Asset exposure\u003c/h3\u003e\n\u003cp\u003eAsset exposure data were sourced from the Brazilian Atlas of Disasters\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which documents financial losses associated with each climate-related disaster. Losses resulting from disasters are classified into several categories, including private and public infrastructure damage (reclassified into health, education, community centers, construction works, housing and other) and private and public losses (divided into farming, industries, and services, including water supply and public health assistance). For each disaster typology, losses pertaining to the above-mentioned assets categories were gathered by municipality, annually, for the period 2000\u0026ndash;2022. Monetary values were made available originally in Brazilian currency (Reais) updated to monetary values for 2022 and were converted into 2022 dollars using the conversion rate of 1 US dollar\u0026thinsp;=\u0026thinsp;0.19 reais \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to presenting the annual distribution of losses over time and by hazard typology, we have also demonstrated the allocation of total losses among asset categories for each risk typology.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3. Vulnerability\u003c/h2\u003e \u003cp\u003eThe vulnerability component was composed of two variables, social and demographic characteristics and the response capacity of the municipalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Social and demographic characteristics\u003c/h2\u003e \u003cp\u003eWe analyzed the social characteristics of the population using the Social Progress Index (SPI) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and the Indigenous population within each municipality\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The SPI comprehensively measures the social and environmental performance of territories by integrating globally relevant human development indicators alongside indicators adapted for the context of the Amazonia region, encompassing aspects like maternal mortality rate, access to water, school enrollment, deforestation rate, early pregnancy in childhood and adolescence, and violence. The Indigenous population was measured in the last census in 2022 by IBGE \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. We calculated the percentage of Indigenous people in relation to the total municipality population and measured the correlation between population exposure (as estimated in section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e) and the SPI and the percentage of Indigenous people. We used the non-parametric correlation indices of Spearman ranking correlation and Kendall\u0026rsquo;s tau since the maximum percentage of people affected is not normally distributed. Both tests were performed considering a 95% level of confidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Response capacity\u003c/h2\u003e \u003cp\u003eThe response capacity of a municipality is determined by its economic condition to recover from a disaster and the presence of master plans that guide the city\u0026rsquo;s occupation process, avoiding the occupation of high-risk areas. As such it merges social, demographic and political characteristics into a single variable.\u003c/p\u003e \u003cp\u003eThe economic capacity of each municipality to recover from disasters was evaluated by the ratio between total losses from 2000 to 2022 (in USD) and the economic growth attained between 2002 and 2020 (hereafter referred to as \"Loss/EconGrowth \". The economic growth for each municipality was calculated as the difference in Gross Domestic Product (GDP) between 2020 and 2002. It was not possible to calculate the economic growth between the same years as the losses (2000\u0026ndash;2022) due to the availability of GDP data, which only spans from 2002 to 2020\u003csup\u003e51\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAnother aspect of response capacity is the existence of a master plan in the municipality. In Brazil, only municipalities with more than 20,000 people are required to have a city master plan. The master plan guides urban management regarding the city's development, indicating risk areas, such as flooding and landslide risk areas that should not be occupied.\u003c/p\u003e \u003cp\u003eWe further examined the relationship between the number of climate disasters and the social and demographic characteristics of the population (SPI and Indigenous People) across different classes of municipality population size. Specifically, we divided municipalities into four classes: \u0026lt;=20,000 people; \u0026gt;20,000\u0026ndash;50,000 people; \u0026gt;50,000-300,000 people; and \u0026gt;\u0026thinsp;300,000 people.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4. Compound risk\u003c/h2\u003e \u003cp\u003eTo classify municipalities according to their multidimensional compound risk, we employed a dispersion diagram that visually represents the interplay between hazard, exposure, and response capacity. In this diagram, the X-axis displays the maximum percentage of the population affected between 2000 and 2022, capturing both observed hazards and population exposure. The Y-axis represents economic response capacity, quantified as the ratio of total disaster-related losses to economic growth (Loss/EconGrowth, see section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMunicipalities are depicted as colored dots, with each color corresponding to a class of population size (see section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e). In this visualization, municipalities with high population exposure and low economic response capacity\u0026mdash;indicating those most severely impacted\u0026mdash;are concentrated in the upper right quadrant. The distribution of colors within this quadrant provides further insights into the prevalence of master plans among these municipalities.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMalhi Y et al (1979) Climate Change, Deforestation, and the Fate of the Amazon. Science 319, 169\u0026ndash;172 (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neill BC et al (2017) IPCC reasons for concern regarding climate change risks. Nat Clim Chang 7:28\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyers N, Mittermeler RA, Mittermeler CG, Fonseca D, G. A. B., Kent J (2000) Biodiversity hotspots for conservation priorities. Nature. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/35002501\u003c/span\u003e\u003cspan address=\"10.1038/35002501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neill B et al (2022) Key Risks Across Sectors and Regions. In: P\u0026ouml;rtner H-O et al (eds) Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarengo JA, Espinoza JC (2016) Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int J Climatol 36:1033\u0026ndash;1050\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArag\u0026atilde;o LEOC et al (2018) 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat Commun. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-017-02771-y\u003c/span\u003e\u003cspan address=\"10.1038/s41467-017-02771-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinho PF, Marengo JA, Smith MS (2015) Complex socio-ecological dynamics driven by extreme events in the Amazon. Reg Environ Change 15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaru YT, Smith S, Sparrow M, Pinho A (2014) Dube, O. P. A linked vulnerability and resilience framework for adaptation pathways in remote disadvantaged communities. Glob Environ Change 28:337\u0026ndash;350\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirkmann J et al (2022) IPCC WGII Sixth Assessment Report, Chap. 8: Poverty, Livelihoods, and Sustainable Development\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy J et al (2018) Sustainable Development, Poverty Eradication and Reducing Inequalities. in An IPCC Special Report on the impacts of global warming of 1.5\u0026deg;C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, (eds. Masson-Delmotte, V., P. Zhai, H.-O. P\u0026ouml;rtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. M.-O., C. P\u0026eacute;an, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, A. \u0026amp; Waterfield, T.) 435\u0026ndash;558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerrang-Ford L et al (2021) A systematic global stocktake of evidence on human adaptation to climate change. Nat Clim Chang 11:989\u0026ndash;1000\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapola DM et al (2018) Limiting the high impacts of Amazon forest dieback with no-regrets science and policy action. Proceedings of the National Academy of Sciences 115, 11671\u0026ndash;11679\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrondizio ES, Moran EF (2008) Human dimensions of climate change: the vulnerability of small farmers in the Amazon. Philosophical Trans Royal Soc B: Biol Sci 363:1803\u0026ndash;1809\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyd E, James RA, Jones RG, Young HR, Otto FE (2017) L. A typology of loss and damage perspectives. Nat Clim Chang 7:723\u0026ndash;729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNamara KE, Jackson G (2019) Loss and damage: A review of the literature and directions for future research. Wiley Interdiscip Rev Clim Change 10:e564\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChac\u0026oacute;n-Montalv\u0026aacute;n EA et al (2021) Rainfall variability and adverse birth outcomes in Amazonia. Nat Sustain 4:583\u0026ndash;594\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTallman PS (2019) Water insecurity and mental health in the Amazon: Economic and ecological drivers of distress. Economic Anthropol 6:304\u0026ndash;316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinho PF et al (2014) Ecosystem protection and poverty alleviation in the tropics: Perspective from a historical evolution of policy-making in the Brazilian Amazon. Ecosyst Serv 8:97\u0026ndash;109\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapola DM et al (1979) The drivers and impacts of Amazon forest degradation. Science 379, (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimpson NP et al (2021) A framework for complex climate change risk assessment. One Earth 4:489\u0026ndash;501\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026ouml;ller V, van Diemen R, Matthews JBR, M\u0026eacute;ndez C (2022) \u0026amp; S. Semenov. IPCC 2022- Annex II: Glossary\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrond\u0026iacute;zio ES, de Lima ACB, Schramski S, Adams C (2016) Social and health dimensions of climate change in the Amazon. Annals of Human Biology vol. 43 405\u0026ndash;414 Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03014460.2016.1193222\u003c/span\u003e\u003cspan address=\"10.1080/03014460.2016.1193222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyantakyi-Frimpong H, Bezner-Kerr R (2015) The relative importance of climate change in the context of multiple stressors in semi-arid Ghana. Glob Environ Change 32:40\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchipper ELF et al (2023) Cambridge University Press, Cambridge,. Climate Resilient Development Pathways. in Climate Change 2022 \u0026ndash; Impacts, Adaptation and Vulnerability (ed. H.-O. P\u0026ouml;rtner.) 2655\u0026ndash;2808 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009325844.027\u003c/span\u003e\u003cspan address=\"10.1017/9781009325844.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSealey-Huggins L (2017) 1.5\u0026deg;C to stay alive\u0026rsquo;: climate change, imperialism and justice for the Caribbean. Third World Q 38:2444\u0026ndash;2463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRufat S, Tate E, Burton CG, Maroof AS (2015) Social vulnerability to floods: Review of case studies and implications for measurement. Int J Disaster Risk Reduct 14:470\u0026ndash;486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana F (2021) Critical climate justice. Geogr J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/geoj.12417\u003c/span\u003e\u003cspan address=\"10.1111/geoj.12417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolman DI et al (2018) Re-thinking socio-economic impact assessments of disasters: The 2015 flood in Rio Branco, Brazilian Amazon. Int J Disaster Risk Reduct 31:212\u0026ndash;219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParry L et al (2018) Social Vulnerability to Climatic Shocks Is Shaped by Urban Accessibility. Ann Am Assoc Geogr 108:125\u0026ndash;143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParry L et al (2019) The (in)visible health risks of climate change. Soc Sci Med 241:112448\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCEMADEN. Centro Nacional de Monitoramento e Alertas de Desastres Naturais. (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira G et al (1979) Smoke pollution\u0026rsquo;s impacts in Amazonia. Science 369, 634.2\u0026ndash;635 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarr-Hill R (2013) Missing Millions and Measuring Development Progress. World Dev 46:30\u0026ndash;44\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalappaththi EK, Ford JD, Bennett EM (2020) Climate change and adaptation to social-ecological change: the case of indigenous people and culture-based fisheries in Sri Lanka. Clim Change 162:279\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansur AV, Brondizio ES, Roy S, de Miranda Ara\u0026uacute;jo Soares PP, Newton A (2018) Adapting to urban challenges in the Amazon: flood risk and infrastructure deficiencies in Bel\u0026eacute;m, Brazil. Reg Environ Change 18:1411\u0026ndash;1426\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBGE (2021) IBGE CIDADES. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cidades.ibge.gov.br/\u003c/span\u003e\u003cspan address=\"https://cidades.ibge.gov.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBGE. Perfil dos munic\u0026iacute;pios brasileiros (Munic 2017). Coordena\u0026ccedil;\u0026atilde;o de Popula\u0026ccedil;\u0026atilde;o e Indicadores Sociais 106 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito SM et al (2020) Disaster risk areas in Brazil: outcomes from an intra-urban scale analysis. Int J Disaster Resil Built Environ ahead-of-p\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlosberg D, Collins LB, Niemeyer S (2017) Adaptation policy and community discourse: risk, vulnerability, and just transformation. Env Polit 26:413\u0026ndash;437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellprat O, Guemas V, Doblas-Reyes F, Donat MG (2019) Towards reliable extreme weather and climate event attribution. Nat Commun 10:1732\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord JD et al (2020) The Resilience of Indigenous Peoples to Environmental Change. One Earth 2:532\u0026ndash;543\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTschakert P et al (2017) Climate change and loss, as if people mattered: values, places, and experiences. Wiley Interdisciplinary Reviews: Climate Change vol. 8 Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wcc.476\u003c/span\u003e\u003cspan address=\"10.1002/wcc.476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerdeczny OM, Bauer S, Huq S (2018) Non-economic losses from climate change: opportunities for policy-oriented research. Clim Dev 10:97\u0026ndash;101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas A, Serdeczny O, Pringle P (2020) Loss and damage research for the global stocktake. Nat Clim Chang. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41558-020-0807-z\u003c/span\u003e\u003cspan address=\"10.1038/s41558-020-0807-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyd E et al (2021) Loss and damage from climate change: A new climate justice agenda. One Earth 4:1365\u0026ndash;1370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanner T et al (2015) Livelihood resilience in the face of climate change. Nat Clim Chang 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRAZIL (2023) Digital Atlas of Disasters in Brazil. Bras\u0026iacute;lia. Ministry of Integration and Regional Development. Secretariat of Protection and Civil Defense. Federal University of Santa Catarina. Center for Studies and Research in Engineering and Civil Defense\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPEA (2023) Commercial exchange rate for purchase: Brazilian real (R\u003cspan\u003e$\u003c/span\u003e)/ US dollar (US\u003cspan\u003e$\u003c/span\u003e)- average. \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttp://www.ipeadata.gov.br/ExibeSerie.aspx?serid=38590\u0026amp;module=M\u003c/span\u003e \u003cspan address=\"http://www.ipeadata.gov.br/ExibeSerie.aspx?serid=38590\u0026amp;module=M\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e. Accessed in 31/10/2023\u003c/span\u003e \u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos D, Verissimo A, Seifer P, Mosaner M (2021) Indice de Progresso Social Na Amaz\u0026ocirc;nia Brasileira- IPS\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBGE, Population CENSUS, and Population Estimates. (2023). accessed in 10/31/2023. Avalilable at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibge.gov.br/en/statistics/social/population.html\u003c/span\u003e\u003cspan address=\"https://www.ibge.gov.br/en/statistics/social/population.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5045887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5045887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Amazon, a global biodiversity hotspot and home to ~30 million inhabitants, including numerous Indigenous ethnies, is increasingly vulnerable to climate impacts. Despite global evidence of climate-related disasters, the Brazilian Amazon's exposure remains underexplored. This study elucidates the spatiotemporal dynamics of socioeconomic vulnerabilities to climate disasters, including floods, droughts, heatwaves, fires, and landslides. From 2006 to 2010, an annual average of 540,000 people was affected by climate disasters in the Brazilian Amazon; by 2018-2022, this number surged to 1.78 million annually, a 229% increase. Financial losses rose 377%, from $132.8 million per year (2006-2010) to $634.2 million per year (2018-2022). Smaller municipalities with populations under 50,000, home to 61% of the Amazon’s Indigenous people, experienced an average economic growth loss of 9.58% from 2002 to 2020, along with lower Social Progress Index scores. Our findings highlight compound risks, revealing the urgent need for justice in adaptation for risk reduction. Addressing these vulnerabilities with justice and equity at the forefront is essential to mitigate further social and cultural losses in the Amazon.","manuscriptTitle":"Escalating Climate Disasters in the Amazon (2006-2022): Vulnerabilities and Compound Risks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-03 09:31:45","doi":"10.21203/rs.3.rs-5045887/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1443a238-7b30-4799-baba-aed36a232c6e","owner":[],"postedDate":"October 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38438943,"name":"Earth and environmental sciences/Natural hazards"},{"id":38438945,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change impacts/Governance"}],"tags":[],"updatedAt":"2025-12-30T08:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-5045887","link":"https://doi.org/10.1038/s41467-025-66603-0","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-11-23 05:00:00","publishedOnDateReadable":"November 23rd, 2025"},"versionCreatedAt":"2024-10-03 09:31:45","video":"","vorDoi":"10.1038/s41467-025-66603-0","vorDoiUrl":"https://doi.org/10.1038/s41467-025-66603-0","workflowStages":[]},"version":"v1","identity":"rs-5045887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5045887","identity":"rs-5045887","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}