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Open Access

Peer-reviewed

Research Article

Keep your head above water: Explaining disparities in local drinking water bills

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Environmental Finance Center, School of Government, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

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Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Environmental Science and Policy, University of California Davis, Davis, California, United States of America

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation UCLA Department of Urban Planning, UCLA Luskin Center for Innovation, Los Angeles, California, United States of America

  • Ahmed Rachid El-Khattabi, 
  • Kyra Gmoser-Daskalakis, 
  • Gregory Pierce

PLOS

  • Published: December 21, 2023
  • https://doi.org/10.1371/journal.pwat.0000190
  • Reader Comments

Table 1

Rising water bills across the U.S. underscore the need to understand the factors that contribute to disparities in local system bills. In this paper, we examine residential water bill amounts from 1,720 systems in four states in different regions of the U.S. (Arizona, Georgia, New Hampshire and Wisconsin) to (1) examine how local system bills at a constant consumption level (4,000 gallons per month or 15.14m3) for drinking water vary within and across states, as well as within combined metropolitan statistical areas (MSAs), and (2) study the relationship between local system bills and system-level characteristics. We find a high degree of similarity in median bill amounts between states but substantial variation within them at the MSA and local system scale. Our multivariate analysis suggests that municipally-owned systems are more likely to have lower water bills relative to for-profit systems, while factors such as purchasing water and having neighboring systems with high bills significantly correlate with higher water bills. Though we find that water systems with high levels of poverty tend to have higher water bills, our results also suggest that local systems that serve populations with higher levels of income inequality and higher proportions of non-White population tend to have lower water bills. These findings point to future research and data needs to better inform federal, state and local water affordability policy and underline the importance of examining and addressing water affordability at local scales.

Citation: El-Khattabi AR, Gmoser-Daskalakis K, Pierce G (2023) Keep your head above water: Explaining disparities in local drinking water bills. PLOS Water 2(12): e0000190. https://doi.org/10.1371/journal.pwat.0000190

Editor: Dil Bahadur Rahut, Asian Development Bank Institute, JAPAN

Received: May 13, 2023; Accepted: November 16, 2023; Published: December 21, 2023

Copyright: © 2023 El-Khattabi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: We compile our dataset using data from American Community Survey, Safe Drinking Water Information System, and UNC’s Environmental Finance Center’s database. We are temporarily storing our compiled database one the following github repository: https://github.com/arelkhattabi/Disparities-in-Local-Drinking-Water-Bills/blob/main/FinalData_3-7-22_RaceRecode.csv .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Over the past decade, rising residential drinking water bills across the U.S. have raised concerns about households’ ability to access safe and affordable drinking water services. These concerns have been accentuated further by the COVID-19 pandemic, which has laid bare many of the stark economic inequalities related to accessing water services. In the policy arena, concerns over high water bills have prompted regulators, policy makers and advocates to contextualize bill amounts in water system needs assessments. Local water systems are very fragmented compared to other utility providers; water bills have historically been, and largely remain, a local matter with some state oversight. Understanding local system-level characteristics that contribute to variation in water bill amounts can help inform household water affordability policies and address broader environmental justice efforts.

In this paper, we (1) examine how bill amounts at a constant consumption level (4,000 gallons per month or 15.14 m 3 ) for drinking water vary within and across states and by sub-state region—defined as combined metropolitan statistical areas (MSAs)—and (2) study the relationship between bills and system-level characteristics. We use the most recent data available on residential water bills from four states in different areas of the U.S. (Arizona, Georgia, New Hampshire and Wisconsin). We select these four states in part because of consistency in the most recent year of billing data available in states, consistency in available time periods of data across the states, metro area representation, and the presence of rate data for both drinking water and wastewater. These data are uniquely compiled and made available by the University of North Carolina’s Environmental Finance Center (UNC EFC). Notably, the 1,721 water systems with available billing data in the UNC EFC database represents a near census of water systems with greater than 500 connections operating in the four selected states.

Contribution to scholarship

Our study contributes to the literature by examining how bills for drinking water vary within and across states and by sub-state region, defined in terms of combined metropolitan statistical areas (MSAs). Comparisons using these geographic units allow us to account for potential similarities in regulatory environment, climate, water source availability, and other factors that influence bill amounts. To our knowledge, only one previous study, Thorsten, Eskaf and Hughes (2009), takes a similar data collection approach with a near census of systems with available billing data across one entire state in the U.S [ 1 ]. Notably, they find that water systems’ bills are significantly and positively correlated with bill amounts charged by other nearby systems. Chica-Olmo, González-Gómez and Guardiola (2013) take a similar study approach in Southern Spain and find similar results (2009) [ 1 , 2 ]. This finding is somewhat intuitive; one might expect relative parity in price levels for an essential service such as drinking water, as is supported for other household staple goods and services.

Our use of a cross-state sample of systems with available billing data complements work by Teodoro and Saywitz, who report trends in drinking water and sewer affordability based on a nationally-representative sample of household water bills, and additional work in certain states by Teodoro [ 3 – 5 ]. Other recent studies analyzing water bills also identify multi-state or national trends in drinking water affordability over time [ 6 – 8 ]. Each of these studies has a slightly different focus and contribution to the literature; we discuss our findings in the context of these studies. One drawback of this literature is that studies either examine affordability in very large systems or use a convenience sample of systems.

Our study differs from other recent analyses in at least three key respects. First, our dataset of systems, as described below, represents nearly all systems that report billing data in four regionally diverse states across the U.S. where the most extensive data are available. This is valuable given that most studies which examine variation in water bills focus predominantly on large systems or small geographies, or use very limited data [ 9 ]. For instance, previous comparative bill work in North America is largely limited to individual metro areas—Los Angeles, Chicago, and British Columbia [ 10 – 12 ].

Second, we focus on explaining disparities in bill amounts rather than exceedances of any specifically defined affordability thresholds. We focus on bills to relate water affordability to the broader concept of water poverty, which provides a more holistic assessment of water access [ 13 ]. Previous literature acknowledges that water affordability is shaped by local community contexts and social inequalities [ 13 , 14 ]. In particular, bill amounts interact with city, community, and household factors to influence water affordability and access [ 14 ]. We further differentiate our analysis from past studies by identifying bill amounts that are outliers at the low end and the high end of the state and MSA distributions, the former of which could suggest low system financial capacity and the latter of which could suggest customer affordability concerns. We use 4,000 gallons per month as our primary billing comparison point to capture a relatively modest level of household consumption. Prior research on affordability and conservation has also used this threshold for household consumption [ 7 , 15 ].

Third, in addition to using available system characteristics, we collect data on water quality violations from the Safe Drinking Water Information System (SDWIS), available from the US Environmental Protection Agency, and customer base characteristics from the U.S. Census to jointly explore the relationship between these factors and billing levels for water to an extent beyond that in existing studies. Several studies have shown a relationship between race-ethnicity and water quality outcomes [ 16 , 17 ]. The relationship between a system’s technical, managerial, and financial (TMF) capacity, including its ability to respond to regulatory water quality violations, however, is much discussed and assumed in affordability conversations, but rarely documented empirically [ 18 ].

Preview of findings and policy implications

In this study, we find notable local-level variation in bills that differs based on system characteristics. We find that municipally-owned systems are more likely to have lower water bills relative to for-profit systems. Our results also suggest that systems that serve populations with higher levels of income inequality and higher proportions of Non-White population typically have lower water bills. Additionally, factors such as purchasing water and having neighboring systems with higher water bills significantly correlate with higher water bills.

Although our findings have implications for state and national drinking water system assessment efforts, the extent to which these results are generalizable across the U.S. is unclear. Collecting and analyzing bill data from additional states will be essential to assess national trends. Notwithstanding, this study fills a gap in the literature by comparing bill amounts across and within states, and examining the factors that drive variation in water bills across state boundaries [ 9 ]. It also calls attention to some system-level characteristics that drive some of the observed variation in bills. As such, the findings of this study provide insight for water system TMF capacity, local economic capacity, and affordability policies. This study also identifies future research and data collection needs to make those efforts more robust.

We use data from UNC EFC’s rates dashboard as our primary source of data. We combine these data with several other data sources. Notably, we collect additional data on water quality and demographics available from the U.S. EPA and U.S. Census Bureau, respectively. The matching process resulted in some reductions in the total water systems for which data was available, as detailed in Table 1 below. To obtain system-level information on water bills, water quality, and sociodemographics, three sources were combined (see subsections below). Systems lacking data from the source at each step (UNC EFC rate data, SDWIS water quality, and U.S. Census demographics) were removed from analysis, resulting in the final sample. A total of 1,721 systems in the four states contained monthly water or sewer bill data at the 4,000-gallon consumption level in the UNC EFC database—removing systems with only sewer bill data resulted in 1,637 systems. These systems were then matched to water quality violation data available in the U.S. EPA SDWIS database. A total of 1,558 systems had available water quality violation data; the final dataset sample contains water bill, water quality, system structure, and customer sociodemographic data for these 1,558 systems. These systems were matched to U.S. Census demographic data based on the “primary Census Designated Place served” listed in the UNC EFC database. If no primary Census Designated Place (CDP) was available, the primary county served was identified from the SDWIS database. Merging census data with the UNC EFC data resulted in 1,373 systems with sociodemographic data at the CDP level and 185 systems with county-level sociodemographic data.

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https://doi.org/10.1371/journal.pwat.0000190.t001

Water rates data

The UNC EFC dashboards are a unique and valuable set of water bill data. First, they are carefully compiled using a refined and well-tested data collection and standardization method. UNC EFC compiles these data primarily for benchmarking and comparison purposes between local systems in a given state. UNC EFC explicitly advises users to “compare [bills] with caution. High rates may be justified and necessary to protect public health.” Though we acknowledge the validity of this statement, we also recognize that high rates may create affordability challenges for low-income households and in turn create different public health risks [ 19 ]. Second, even compared to other credible state-level and national-bill-level surveys (such as surveys conducted by AWWA-Raftelis and Circle of Blue) and dashboards (Duke’s Nicholas Institute), the UNC EFC dashboards provide a near census of all systems with available billing data in a given state. The data contained in UNC EFC’s dashboards effectively represent 80–90% coverage of all systems serving 500 or more people and historically have achieved the highest response rate of any such effort. The actual number of community water systems in each state is larger than the number reported in each UNC EFC dashboard (AZ = 742; WI = 1,034; GA = 1,725; NH = 710). This equates to 40% of all systems, but 86% of non “very small” systems serve a population of 500+ (GA = 1,100; WI = 541; AZ = 434; NH = 581). The 500-customer population threshold is the cutoff for EPA’s “very small” system designation. Third, the dashboards also provide bill amounts for multiple levels of consumption; this provides a more accurate and consistent estimate of bills than estimates from water systems or household self-reporting. Lastly, they merge in additional valuable contextual information from several sources. This information includes system size, billing cycles, rate structures, and CDP.

As of late 2021, the UNC EFC currently had rates dashboards available for 18 states. All the dashboards employ very consistent, albeit not uniform, methodologies and some contain multiple years of data [ 20 ]. For the purposes of this study, we focus on data for Arizona, Georgia, New Hampshire, and Wisconsin (see Fig 1 ). The UNC EFC made available a near-standardized Excel spreadsheet version of data for each state analyzed, with the exception of Wisconsin. In each spreadsheet, data on most variables available in the online dashboards are available for each system, with one key exception. For Wisconsin, collecting data from the dashboard required manual scraping from PDF forms into Excel.

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We focus our analyses on documenting the variation in bill totals at 4,000 gallons of consumption. We examine this variation within and across states and adjacent communities, defined in terms of metropolitan statistical areas (MSAs).

With respect to rate structures, a flat fee or charge refers to systems charging customers a single monthly amount regardless of water use. Uniform rates charge a single volumetric rate per unit of consumption (e.g., the rate is multiplied by monthly consumption). Uniform rates differ from block or tiered rates, in which the volumetric rate changes based on which ‘block’ of consumption a customer falls in. Increasing-block rates occur when the volumetric rate increases for customers in higher consumption levels, while decreasing block rates charge lower rates for customers with higher water usage. Many systems have bills that include a combination of flat fixed charges and volumetric charges. The flat fixed charge is often called a base charge in this situation, as it is assessed even for customers with no water usage, with volumetric charges (whether uniform or block rates) then included on top of this base charge.

There are clear differences in water rate structures for systems in each state. In Arizona, 73% of systems use increasing-block rate structures, whereas 78% of Wisconsin systems use decreasing-block rate structures, and 75% of New Hampshire systems use uniform rate structures. In Georgia, there is a more even split between uniform rate structures (47%) and increasing-block rates (48%). Flat and other rate structures are uncommon in all four states.

The bill amounts in the UNC EFC dataset reflect monthly-equivalent water bills for the various consumption points. Given that systems use billing structures with differing frequencies, the actual bills customers face in each system may be different. Notwithstanding, the monthly equivalents provide accurate estimates of the amount of monthly expenditure necessary for a household for comparative purposes.

There are significant differences in the method that water systems use for determining fixed charges across states. The majority of systems in Arizona calculate base pricing by meter size (67%), although some use constant pricing (32%). New Hampshire systems show a more even distribution between constant base pricing (48%), pricing by meter size (33%) and no base pricing (19%). Most systems in both Georgia and Wisconsin use constant base prices (84% and 99.7% respectively).

The median water bills at typical consumption points of 4,000 gallons per month are strikingly similar across states. Arizona, New Hampshire, and Wisconsin have nearly identical median water bills ranging $31-$33 whereas Georgia has consistently lower median bills at around $23.

The UNC EFC data also includes key data on water system characteristics, including how each system sources its water. Water systems are classified into three categories: groundwater, surface water, or purchased water. EFC obtains this information from the U.S. EPA’s publicly-available SDWIS database. The U.S. EPA categorizes systems as surface water if any of their sources are surface water. Groundwater is the most common water source for water systems across all four states (85% in Arizona, 63% in Georgia, 67% in New Hampshire, 89% in Wisconsin). Purchased water and other sources are uncommon in Arizona (9%) and Wisconsin (4%) but more prevalent in Georgia (19%) and New Hampshire (26%).

The UNC EFC data also include information on ownership structure. System ownership includes four categories: for-profit, municipal, other government, and other. For-profit refers to private systems including investor-owned systems which, depending on the state and size of the system, may or may not be regulated by a state or state-level public systems commission. Municipal refers to city-owned and operated water or sewer systems; while “other government” refers to special districts, county authorities, or joint powers authorities, depending on the state. “Other” encompasses all other system types, particularly mutual water companies that do not fit in the for-profit, municipal, or other government categories. Not-for-profit (e.g., mutual and cooperative water companies), as classified in the EFC database, only accounted for 24 systems total. Due to the very small size of this category compared to the overall sample, these were combined with the 3 systems classified as ‘other’ into a single other category. The states exhibit similar makeups of ownership type diversity, except for Arizona. Over 50% of all systems in Georgia, New Hampshire, and Wisconsin are municipal; in contrast, 69% of systems in Arizona are for-profit and only 3% are municipal (see Fig A in S1 Text ).

Water quality data

Given the lack of readily available water quality data from state-level databases, we queried the U.S. EPA’s publicly-available SDWIS search function by state [ 21 ]. We then use the system names provided in the EFC database to identify and scrape data system by system [ 1 , 8 ]. We only consider and scrape violation records data between the years of 2009 and 2020. To determine year of violation, we use the compliance period start date in the SDWIS data. As with rate data, SDWIS data on water violations and system characteristics must also be compared with caution, based on potential missing or incorrectly classified data [ 22 ].

For each system (referred to as community water systems in SDWIS) in each of the four states (1,558 systems- see Table 1 ), we collect and record each violation as an individual row in a spreadsheet to allow for maximum flexibility in analysis by violation type/time period per system. For analytical purposes, we define health-based compliance shortcomings (our primary water quality category of interest) in terms of Maximum Contaminant Level (MCL) violations, Lead Copper Rule exceedances, and treatment technique violations. We also include an additional measure of water quality compliance shortcomings that includes all “monitoring and reporting,” “notification”, and other miscellaneous violation types. The difference between compliance start and end dates captures length of time out of compliance during the time period of interest. But this was not used as a variable of interest in analysis given variability and degree of missingness in the quality of compliance date entry. While the original intention of the effort was to code the full detail of each violation, it quickly became impossible given some systems (especially in Arizona) have dozens of monitoring and reporting violations; we only coded full detail of each violation for systems with less than 5 violations. Combining these data allows us to study water quality in relation to other system characteristics which may be explored in future studies. In Supplementary Text 1, we provide bivariate correlations between water quality data and system-level characteristics. In this study, however, the primary outcome of interest is the residential monthly bill for water services.

In terms of health violations, most systems (1,158 of 1,558 systems) incurred no primary health-based violations from 2009 to 2020. Resulting health-based violation averages vary from 1 to 2 violations per system for all states, with the lowest average of 1.05 violations per system in Georgia and the highest average of 2.17 violations in Arizona. However, the median number of violations for systems in each state is 0, with several outliers (a maximum of 290 for one system in Wisconsin). Further analysis would be required to ascertain the extent of data limitations from SDWIS violation data. Observed differences in water quality compliance data across states may in part reflect actual compliance variation but also may reflect potential inconsistencies in state programs monitoring compliance or in the way in which violation data were coded and entered between states. Efforts were made to clean data to ensure unique violation entries, but SDWIS data should also be used with caution [ 22 ].

Demographic data

We obtain demographic data for each water system using CDP boundaries as the relevant geographic unit. Though geographic information system (GIS) shapefiles for water systems currently represent the best currently available approach (albeit still imperfect) to approximating water system boundaries, shapefiles are only available for the state of Arizona among the four states analyzed [ 23 ]. We explore our choice of using CDP boundaries relative to other potential approaches using shapefiles for Arizona (see Supplementary Text 2 for boundary comparisons). This approach follows Berazher et al. in their use of CDP boundaries for water systems in North Carolina [ 24 ]. We explored the possibility of using zip codes and geocoding address information but found these approaches to be inferior to our selected method. Using a single valid address for each system (i.e., geocoding) is not a reliable strategy given that the public-facing SDWIS is missing any address for some systems, and some of the addresses provided are out of county or even out of state P.O. boxes [ 22 ].

In addition to better approximating geographic boundaries, our use of CDP boundaries confers two additional benefits. First, the choice of CDP boundaries is consistent with collection efforts by the UNC EFC for some of its state dashboards, as well as other recent studies which evaluated similar alternatives [ 24 ]. Second, using CDP boundaries allows us to compile and approximately match income and race-ethnicity data from the U.S. Census to characterize the socioeconomic status of customer bases, as well approximate each system’s total population, the proportion of population which is Non-White, its median household income, the proportion of its population under 100%, 150%, and 200% of poverty level, and the Gini Inequality Index, which is a measure of income inequality.

We collect socioeconomic data from the U.S. Census (American Community Survey 2015–2019, 5-year estimates) at both the CDP and county-levels: race, ethnicity, median household income, and poverty status. The ACS data for these variables were downloaded using the National Historical Geographic Information System (NHGIS) database [ 25 ]. Using these data, we create the following five variables: Non-White population proportion, Hispanic/Latino population proportion, median household income, and proportion of the population under 100% and 200% of the federal poverty level (FPL). We match these census variables to systems using the system’s primary CDP, as listed on the UNC EFC dashboard or SDWIS. If the EFC dashboard did not provide a primary CDP/county or one that did not match census data (144 systems), the CDP was obtained from SDWIS. In the event the SDWIS CDP did not match available census data (35 cities), the primary county was used.

Census demographic data was matched to systems based on the primary location served. Where CDPs could not be obtained (no primary CDP served was noted in the UNC EFC data), we supplemented these data with the primary county served from SDWIS codes. We also ran a sensitivity test which excludes systems with county-matched data. We recognize substantial shortcomings with this approach; there is no perfect way to match system customer base demographic data to system boundaries. Any method to attribute population characteristics from the census to small water systems is likely to have a high degree of inaccuracy, given that the smallest census geography at which population characteristic data are available (the block group, serving between 600 and 3,000 people) is larger than any very small systems. For very small and some small systems, only manually-collected socioeconomic characteristic survey data will be sufficient.

3. Methodology and hypotheses

Regression model specifications.

Our analysis provides basic descriptive statistics across states to look for general state-level and regional trends for water bills at the 4,000-gallon consumption level. We then estimate a linear model to examine what system-level characteristics most significantly influence water bills. We estimate our model using ordinary least squares linear regression to shed light on the contribution of different system characteristics, including ownership type, rate structure, and water source type to a system’s monthly water bill.

water billing system research paper

We acknowledge that essential indoor needs vary by household size; and some studies find indoor use to be greater in some U.S. metro areas than others [ 26 ]. Notwithstanding, our choice of 4,000 gallons is consistent with the range presented in affordability literature which examines bills for “reasonable” levels of consumption. Such previous studies examine consumption levels ranging from 3,000 gallons in North Carolina (Thorsten, et. al, 2009), to 3,740 gallons nationally (5 CCF, commonly used in AWWA-Raftelis surveys), 4,488 gallons in California (Pierce, Chow and DeShazo, 2020), to up to 6,200 gallons for municipalities across the U.S. (Teodoro and Saywitz, 2020) [ 1 , 4 , 19 ].

The vector X i represents the following system-level characteristics to explain variation in bills:

  • ○ total number of health-based violations (2009–2020)
  • ○ total number of monitoring and reporting violations (2009–2020)
  • ○ proportion of major racial ethnic groups
  • ○ median household income,
  • ○ proportion of population below federal poverty level
  • nearest neighbors’ cost (i.e., average bill for all other systems in the county of the system and systems in the counties contiguous to the system’s county)
  • system ownership type: municipal, other government type, private, or other
  • system customer base size (i.e., approximated system service population)
  • rate structure type: flat, increasing block, other block or uniform volumetric
  • presence of a free monthly water allowance (i.e., binary variable where a 1 indicates system provides a baseline water allocation with the fixed charge)
  • type of water source: groundwater, surface water, or purchased

Each of the model specifications includes data on system-level characteristics hypothesized to affect water bills, based in part on similar studies cited in a recent U.S. Government Accountability Office (GAO) report [ 9 ]. Despite limited previous research on drivers of variation in bills, we draw on prior studies to generate hypotheses on the expected influence of the system-level variables on system bills.

Hypothesis 1 : A review of multiple rate studies by the GAO suggests that public or quasi-public system ownership types (e.g., municipal, other government) will correlate with lower bills than for-profit systems [ 8 , 9 ]. We anticipate that larger system customer base size will correlate with lower bill amounts based on potential economies of scale [ 10 ].

Hypothesis 2 : We anticipate that higher nearest neighboring system bills will lead to higher bills, as local similarities to some extent reflect cost and political similarities [ 1 , 2 ].

Hypothesis 3 : We expect that purchased water sources will also lead to higher bills due to the costs of purchasing raw water which may be reflected in the bill price for customers, compared to systems with their own ground or surface water source rights. However, we note that we do not have data on source water quality, which may also impact bills. In particular, levels of salinity or contaminants may increase treatment costs [ 27 ]. Geographic or topographical factors can also influence costs of purchased water delivery [ 9 ].

Hypothesis 4 : While limited, prior research in Michigan and North Carolina found higher bills faced by minority populations; if these trends extend beyond these states we would expect higher proportions of Non-White customer populations to correlate with higher water bills [ 28 , 29 ].

Hypothesis 5 : Finally, we expect household bills to be lower for systems that provide a free baseline allocation of water each month, as customers are not charged for this initial allocation that is a portion of the 4,000 gallons.

The vector γ i represents geographic controls to assess intra-state variation. Including these fixed effects allows us to examine the influence of system-level characteristics on water costs while taking into account variation due to state location (state-level cost variation). We also estimate an additional specification which includes an additional set of fixed effects that represent local areas. As previously noted, we define local areas in terms of MSAs, using combined statistical areas where applicable. The last term in our model, ϵ i , represents an error term that captures any remaining unexplained variation.

4. Cross-section variation in system level water bills

In this section, we explore cross-sectional trends in system-level deviation of water bills from the central tendency of local, state and national comparator groups described above. We define “local” in terms of metropolitan statistical areas (MSAs). Where applicable, we use combined statistical areas to account for arbitrary boundaries that separate contiguous MSAs. A little fewer than 25% of all systems considered in this study are not located within an MSA—we label these systems as “non-MSA” for the purposes of analysis and categorize them in a single group of rural systems within each state.

To gain a better understanding of the percentage of systems with high or low water bills, we compute the percentage of bills at “extreme values” at different spatial scales. We define relatively high bill values as above 200% of the average of the comparator group (e.g., MSA, state, or all systems) and relatively low bill values as below 50% of the average of the comparator group. We computed and compared deviations from central tendency within a metro area using medians as the reference point, and the results were largely similar. We also considered a method looking at top and bottom deciles within a metro area but found the artificial bounds this poses on the range of deviation to be less helpful than our primary method.

As shown in Table 2 , these ratios are remarkably consistent, irrespective of using the mean bill of the local area, individual states, or the four combined states as the point of comparison. For instance, 8.4% of systems have water bills below 50% of the average of their MSA, whereas 9.1% have water bills below 50% of their state average, and 9.7% have water bills below 50% of the 4-state average. In fact, the distribution of relatively low and high values at each scale is fairly similar to a bottom-top decile approach for identifying values of potential concern.

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On the other hand, we document major differences in the distribution of relatively low and high bills using the local area as a comparison group, both across states and by ownership type. As shown in Table 3 , bill totals for systems in Wisconsin show the least variance within local areas by far. This difference may reflect the central regulation of ratemaking for all systems by the state’s public service commission, a practice which does not occur in other states [ 30 ]. Georgia has the highest proportion of systems with relatively low bill values but the second lowest proportion of relatively high values. Arizona and New Hampshire have similarly large proportions of relatively high bill values. New Hampshire has the second largest proportion of relatively low values.

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https://doi.org/10.1371/journal.pwat.0000190.t003

Across the three main ownership types, clear trends emerge, especially in terms of relatively high bills. Municipalities are much less likely to have high bills compared to local neighbors and are slightly less likely to have relatively low values as well.

Fig 2 highlights the variation across metro areas for water bills at 4,000 gallons of monthly consumption to gain a better understanding of the distribution of water bills. The spread of each MSA is plotted and visually confirms the findings of on state-level and metro area-level variation in water bills. Despite similar median water bills across states, systems in Georgia have consistently lower mean bills compared to other states. Georgia also exhibits the fewest outlier systems with extreme bills within metro areas. Meanwhile, Arizona demonstrates the most spread of water bills both within and between metro areas compared to other states.

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Notes: Data for the figure is derived from UNC EFC (2021).

https://doi.org/10.1371/journal.pwat.0000190.g002

In this section, we present the results from estimating our regression model to shed light on factors that explain variation in water bills at the system-level. We estimate two specifications that include different sets of fixed effects; the first specification includes controls for the state where a water system is located while the second controls for the MSA in which the system is located.

In our primary set of results, presented in Tables 4 and 5 , we exclude systems with socio-demographic characteristics that are not matched to CDP areas (i.e., County information). In Table 4 we estimate a version of our model using the federal poverty limit as a measure of poverty whereas in Table 5 we use Gini index to capture income inequality. In Supplementary Text 3, we estimate several additional versions of our model as sensitivity checks, including versions of the model that include all water systems. The results from these additional analyses support our main set of results in Tables 4 and 5 . In Supplementary Text 4, we estimate our main model restricting the data to water systems in Arizona to explore the effect of matching systems to CDP (Table A in S4 Text ) as compared to water system boundaries (Table B in S4 Text ). The results of these regressions qualitatively support those shown in the Tables 4 and 5 , though point to the need to better ascertain water system boundaries.

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We find that several factors we include in the model are significant in explaining variation in the cost of water. Significant correlates include nearest neighbors’ bills, ownership type (municipal as compared to for-profit), monthly allowances, service population, income inequality, percentage of Non-White population, and source water (purchased as compared to the baseline of groundwater). Significant correlates do not vary between the model controlling for state location and the model controlling for metro area location, except with respect to the presence of tiered rate structure.

Influence of system characteristics

In support of Hypotheses 2 and 5, water bills are significantly higher among systems where neighboring systems charge more for water and significantly lower for systems that provide a baseline monthly allocation of water [ 1 , 2 ]. Table 4 demonstrates that, when controlling for state location (fixed effects) and holding all else constant, a $1 increase in the average monthly bill for 4,000 gallons by neighboring systems will predict a $0.80 increase in average monthly bill for a system. Meanwhile, the presence of a baseline monthly allocation of water (some water provided without charge) predicts a decrease of $4.73 for a system’s monthly bill level for 4,000 gallons, holding all else constant. These effects are slightly stronger when the local MSA of a system is controlled for instead of the state in Table 5 ($0.96 increase and $4.94 decrease for an increase in neighboring water bills or a monthly allowance respectively). Additionally, municipal systems tend to levy significantly lower water bills than for-profit systems (the baseline of for-profit systems is not included as a variable in the model to enable comparison) [ 6 ]. The “other” category for government ownership type was not a significant correlate in the model compared to the for-profit system type baseline. In support of Hypothesis 1, a larger service population is associated with significantly lower bills. We also find that water systems that purchase water sources have significantly higher bills than systems with groundwater sources, which supports Hypothesis 3. This is demonstrated by the values in Table 4 and 5 ; all else constant, when a system purchases water as its main source, the model predicts an increase in the monthly water bill for 4,000 gallons of $1.78 or $2.14 depending on fixed effects (controlling for a system’s state or MSA respectively).

The presence of a tiered water rate structure is also significantly negatively correlated with bill amounts in the model. While further research is required to assess the reasons behind this trend, this does follow prior research that finds increasing block rates for water may improve water affordability [ 31 ]. Often increasing block rates include a ‘lifeline’ or lower rate for lower levels of consumption, thus modest levels of usage (e.g. our selected threshold of 4,000 gallons) may result in lower bills than the same usage under certain flat or decreasing block rate structures [ 32 ]. In the regression models, the use of an increasing block rate structure predicts a system will have a $2.44 or $2.85 decrease in monthly bill when controlling for state or MSA system location respectively (all else constant).

Influence of demographic variables

However, higher income inequality, which is highly correlated with a greater proportion of Non-White population (see Supplementary Text 1), is significantly associated with lower bill amounts. Higher Non-White population in our model is significantly associated with lower bills, contrary to our expectation (Hypothesis 4) after controlling for other factors. Table 4 demonstrates that, holding all else constant, a 1% increase in the proportion of Non-White population served by a water system predicts a decrease of $6.38 or $6.27 in the system’s monthly water bill for 4,000 gallons, when controlling for the system’s state and MSA respectively.

Influence of water quality and monitoring variables

We did not find evidence of water quality violations, either health or procedural, influencing bill totals. We do note, however, that the proportion of Hispanic/Latino residents served by a system is significantly positively correlated with more health violations (see results of bivariate correlations in Table 1 in S1 Text ). This finding echoes existing research regarding potential inequities in water quality by race and ethnicity [ 23 ]. Similarly, we do not find significant associations between health-violations and system size (i.e., service population). The relationship is positive and non-significant, despite evidence that smaller system size predicts more health violations than much larger systems [ 10 , 33 ]. Further research is required to better assess the relationship between water quality and bills; as noted above, there may be differences in how systems and regulators monitor, report, and enforce water quality violations among states which may impact the data and ultimate correlations.

6. Discussion

Review of results and contributions.

In this study, we compile data on water system bills from four states to analyze variation in local bills at modest levels of consumption (4,000 gallons). We then examine characteristics of systems and water bills across the four states selected for analysis: Arizona, Georgia, New Hampshire, and Wisconsin. Though median bill amounts for drinking water are similar across all four states, we find major differences in bills within regional and local areas, influenced by system-specific characteristics.

We find large variation in bill amounts at each geographic scale that we analyzed, including within MSAs. Variation in water bills across systems can in part be explained by system ownership type, size, water source, rate structure, and the bills of neighbors, as well as race-ethnicity and income inequality of customers. Municipal ownership type, larger service population, and a baseline monthly allowance of water all predict systems with lower bills, supporting hypotheses from existing studies. Additionally, we find that, as hypothesized, higher bills of nearby systems and using purchased water as the main water source predicts higher bills for a system. Our results suggest that water systems with high levels of poverty have higher water bills. Counter to our expectations, including evidence from previous studies, our results also suggest that local systems that serve populations with higher levels of income inequality and higher proportions of non-White population typically have lower water bills. These relationships, however, appear to be relatively weak statistically, and thus deserve further scrutiny. Overall, our finding that some system characteristics are strongly correlated with water bills has important implications for policy interventions in the context of evaluating systems’ TMF capacity, as well as customer affordability, and community environmental justice outcomes.

Limitations and future directions

Much of the variation in bills, however, remains unexplained in our modeling. One potential reason for the unexplained variation is limited water system-specific data. Comparisons with existing available data sources require ‘caution’ [ 20 , 22 ]. We could not capture all drivers of water costs nor could we consider historic drivers of water rates since our bill data is derived from a single point in time. Other potentially interesting variables, suggested by Teodoro and Saywitz and the UNC EFC, for example, are not readily available for collection and matching at the local system level but may be collectible for pilot analyses for individual or small groups of states [ 4 , 34 ]. These include system capital replacement rates; receipt of general fund, state, or federal assistance; physical and uncollectible non-revenue water levels; customer class composition ratios; staffing levels and compensation; and quality treatment requirements. Recent efforts by researchers have resulted in the Municipal Drinking Water Database (MDWD) which may prove useful for capturing such variables at the water system level [ 35 ]. The MDWD contains variables on water system revenues, expenditures, and capital outlay, although initial review finds numerous systems in the UNC EFC database missing from the MDWD. A null finding regarding water quality compliance and bills, despite multiple specifications, also deserves further exploration, particularly to understand links between water affordability and access to safe, clean water. With respect to water quality, another avenue for future research would include a comparison of pre and post pandemic situations as several recent studies have observed that water quality improved during periods when lockdown measures were in place [ 36 – 40 ].

Our study expands upon previous work examining variation in water bills at the state and metro-specific levels and provides additional insight which can inform future policy efforts. This study does not, however, provide definitive answers on the best metrics to compare intra-MSA bill variation at the low or high end, or factors contributing to this variance. Local water bill amounts have historically been, and largely remain, a local matter with some state oversight. While we find consistent averages across states, we find very high levels of bill variation at the local and regional scales. Significant variation in bill amounts, as opposed to water quality—where the Safe Drinking Water Act imposes common minimum standards—is inevitable and may be justifiable. Most of the correlates of high bills which we identify are not conducive to direct policy reforms. Notwithstanding, extreme disparities may justify policy efforts from states to promote consolidation, evaluate TMF capacity, provide customer assistance, or implement rate revision guardrails. Regardless of the extent to which policymakers seek to intervene to address local bill disparities, understanding this variation is an important step in the context of affordability and larger environmental justice efforts.

7. Conclusion

Overall, our analysis helps fill an ongoing gap in national studies of water bills that consider the influence of system-level, local, and regional factors on water system TMF and customer affordability, as well as helps inform ongoing efforts to expand data collection and needs analysis efforts nationally. Broad implications of the study include, first, that lifeline rates and inclining block rates can help address water affordability challenges. Second, when developing a state or federal low-income water affordability or assistance program, policy makers must account in their program design for the huge variability in local water bills. Third, consolidation of small water systems may, in appropriate cases and with robust community engagement and buy-in, help reduce bills for essential water service. Fourth, when privatization of a publicly-owned water system is under consideration, there must be a careful consideration of impacts on water bill affordability. Fifth, state oversight of local water rates may contribute to greater equity in water expenditures across communities. Sixth, understanding bill amounts can help state and federal agencies prioritize communities for receipt of financial and technical assistance.

Last, but not least, the results from this study motivate an effort to expand the states included in data collection and analysis. We selected the four states based on data collection method and timing consistency, as well as geographic diversity within the U.S. However, the four states we examine differ in terms of climate and geography and have substantially different metropolitan area profiles. More importantly, these states cannot be considered representative of the entire U.S. Though expanding to include additional states with UNC EFC-collected data appears feasible, it appears highly unlikely that other national association and states’ ad hoc system bill collection efforts, such as in California and New Jersey, will be consistent, accurate, or extensive enough to be included in a master dataset. A unified database on residential household system bills would be useful, perhaps by combining rates dashboard efforts by the UNC EFC—which still offer by far the most coverage, reliability and flexibility of any multi-state source—with more recent efforts such as those at Duke’s School of the Environment [ 7 ]. Reforms to address SDWIS data and include bills may also be a unique opportunity to create a more comprehensive database of water systems [ 22 ]. While we recognize several ongoing efforts may yield great progress along these fronts, in terms of improved understanding of water system structures and governance, ongoing and future national and multi-state analyses could greatly benefit from better national system spatial location information and more efficient ways to incorporate SDWIS data [ 9 ].

Supporting information

S1 text. water system ownership and correlations with local factors..

https://doi.org/10.1371/journal.pwat.0000190.s001

S2 Text. Comparison of methods to match water system boundaries.

https://doi.org/10.1371/journal.pwat.0000190.s002

S3 Text. Multivariate regression sensitivity analyses.

https://doi.org/10.1371/journal.pwat.0000190.s003

S4 Text. Arizona only models.

https://doi.org/10.1371/journal.pwat.0000190.s004

Acknowledgments

The authors declare no conflicts of interest for this article. The authors would like to thank the efforts of the University of North Carolina Chapel Hill Environmental Finance Center to compile water system rate data in its Utility Rates Dashboards which made this analysis possible.

  • View Article
  • Google Scholar
  • 9. U.S. Government Accountability Office (GAO). Private Water Systems: Actions Needed to Enhance Ownership Data. U.S. Government Accountability Office; 2021 [cited 2023 Sept 24]. Available from: https://www.gao.gov/assets/gao-21-291.pdf
  • 12. Honey-Rosés J.; Gill D., Pareja C. British Columbia Municipal Water Survey 2016. Water Planning Lab. School of Community and Regional Planning, University of British Columbia; 2016 March [cited 2023 Sept 24]. Available from: http://hdl.handle.net/2429/57077
  • PubMed/NCBI
  • 30. University of North Carolina Environmental Finance Center (UNC EFC). Navigating Legal Pathways to Rate-Funded Customer Assistance Programs. UNC EFC; 2017 [cited 2023 Sept 24]. Available from: https://efcnetwork.org/navigating-legal-pathways-to-rate-funded-customer-assistance-programs/
  • 36. Arif M, Kumar R. Reduction in water pollution in Yamuna river due to lockdown under COVID-19 pandemic.

water billing system research paper

AUTOMATED WATER BILLING SYSTEM OF HINUNANGAN MUNICIPALITY

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water billing system research paper

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A water billing system is an automated system that makes the complex task of billing easy, fast and accurate. This paper presents the development of an Automated Water Billing System for the local government unit of Hinunangan in the province of Southern Leyte, Philippines. The system constitutes two applications - Desktop and Mobile Reader.  The System Development Life Cycle (SDLC) framework was adopted in the development process utilizing the Waterfall Model. Survey and interviews were also done as a supplementary technique of the fact-finding. The purposive sampling method was employed in the selection of respondents, and a researcher-made questionnaire was used in the systems evaluation. Weighted mean was used as a statistical treatment. Based on the results of the study, the developed system contributes a greater advantage in providing and delivering better operation, reporting, and services to the clientele.

The copyright holder is the Innovative Technology and Management Journal, Eastern Visayas State University, Tacloban City, Philippines.

College of Agriculture, Foods and Environmental Sciences Southern Leyte State University Hinunangan, Southern Leyte

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Home ⇛ pulsar ⇛ vol. 2 no. 1 (2013), electronic water bill monitoring system.

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This study aimed to develop a device that could enable consumers to monitor their water consumption. The device, comprising of a water digital flow meter sensor, Liquid Crystal Displays (LCDs), a keypad, and microcontrollers, would serve as a monitoring system that aids user in keeping track of their water consumption. The efficiency of its individual feature’s performance and as a whole was evaluated. The design project employed experimental development method and descriptive approach in assessing the device’s performance. The device was tested for nine days and was evaluated by 11 respondents, which were comprised of 10 water consumers and one water district personnel. Every observation in testing the device and statistical evaluation were constantly recorded. The recorded data were then analyzed to come up with a result as to how well did the device work. After conducting a thorough research, from device testing to evaluation, the researchers concluded that the device is effective in performing its task. Nevertheless, certain factors still need to be considered in further improving the device and for it to reach its maximum potential.

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The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern 1 , 2 , 3 . For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system 1 . Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

The Amazon forest is a complex system of interconnected species, ecosystems and human cultures that contributes to the well-being of people globally 1 . The Amazon forest holds more than 10% of Earth’s terrestrial biodiversity, stores an amount of carbon equivalent to 15–20 years of global CO 2 emissions (150–200 Pg C), and has a net cooling effect (from evapotranspiration) that helps to stabilize the Earth’s climate 1 , 2 , 3 . The forest contributes up to 50% of rainfall in the region and is crucial for moisture supply across South America 4 , allowing other biomes and economic activities to thrive in regions that would otherwise be more arid, such as the Pantanal wetlands and the La Plata river basin 1 . Large parts of the Amazon forest, however, are projected to experience mass mortality events due to climatic and land use-related disturbances in the coming decades 5 , 6 , potentially accelerating climate change through carbon emissions and feedbacks with the climate system 2 , 3 . These impacts would also involve irreversible loss of biodiversity, socioeconomic and cultural values 1 , 7 , 8 , 9 . The Amazon is home to more than 40 million people, including 2.2 million Indigenous peoples of more than 300 ethnicities, as well as afrodescendent and local traditional communities 1 . Indigenous peoples and local communities (IPLCs) would be harmed by forest loss in terms of their livelihoods, lifeways and knowledge systems that inspire societies globally 1 , 7 , 9 .

Understanding the risk of such catastrophic behaviour requires addressing complex factors that shape ecosystem resilience 10 . A major question is whether a large-scale collapse of the Amazon forest system could actually happen within the twenty-first century, and if this would be associated with a particular tipping point. Here we synthesize evidence from paleorecords, observational data and modelling studies of critical drivers of stress on the system. We assess potential thresholds of those drivers and the main feedbacks that could push the Amazon forest towards a tipping point. From examples of disturbed forests across the Amazon, we analyse the most plausible ecosystem trajectories that may lead to alternative stable states 10 . Moreover, inspired by the framework of ‘planetary boundaries’ 11 , we identify climatic and land use boundaries that reveal a safe operating space for the Amazon forest system in the Anthropocene epoch 12 .

Theory and concepts

Over time, environmental conditions fluctuate and may cause stress on ecosystems (for example, lack of water for plants). When stressing conditions intensify, some ecosystems may change their equilibrium state gradually, whereas others may shift abruptly between alternative stable states 10 . A ‘tipping point’ is the critical threshold value of an environmental stressing condition at which a small disturbance may cause an abrupt shift in the ecosystem state 2 , 3 , 13 , 14 , accelerated by positive feedbacks 15 (see Extended Data Table 1 ). This type of behaviour in which the system gets into a phase of self-reinforcing (runaway) change is often referred to as ‘critical transition’ 16 . As ecosystems approach a tipping point, they often lose resilience while still remaining close to equilibrium 17 . Thus, monitoring changes in ecosystem resilience and in key environmental conditions may enable societies to manage and avoid critical transitions. We adopt the concept of ‘ecological resilience’ 18 (hereafter ‘resilience’), which refers to the ability of an ecosystem to persist with similar structure, functioning and interactions, despite disturbances that push it to an alternative stable state. The possibility that alternative stable states (or bistability) may exist in a system has important implications, because the crossing of tipping points may be irreversible for the time scales that matter to societies 10 . Tropical terrestrial ecosystems are a well-known case in which critical transitions between alternative stable states may occur (Extended Data Fig. 1 ).

Past dynamics

The Amazon system has been mostly covered by forest throughout the Cenozoic era 19 (for 65 million years). Seven million years ago, the Amazon river began to drain the massive wetlands that covered most of the western Amazon, allowing forests to expand over grasslands in that region. More recently, during the drier and cooler conditions of the Last Glacial Maximum 20 (LGM) (around 21,000 years ago) and of the mid-Holocene epoch 21 (around 6,000 years ago), forests persisted even when humans were already present in the landscape 22 . Nonetheless, savannas expanded in peripheral parts of the southern Amazon basin during the LGM and mid-Holocene 23 , as well as in the northeastern Amazon during the early Holocene (around 11,000 years ago), probably influenced by drier climatic conditions and fires ignited by humans 24 , 25 . Throughout the core of the Amazon forest biome, patches of white-sand savanna also expanded in the past 20,000–7,000 years, driven by sediment deposition along ancient rivers 26 , and more recently (around 800 years ago) owing to Indigenous fires 27 . However, during the past 3,000 years, forests have been mostly expanding over savanna in the southern Amazon driven by increasingly wet conditions 28 .

Although palaeorecords suggest that a large-scale Amazon forest collapse did not occur within the past 65 million years 19 , they indicate that savannas expanded locally, particularly in the more seasonal peripheral regions when fires ignited by humans were frequent 23 , 24 . Patches of white-sand savanna also expanded within the Amazon forest owing to geomorphological dynamics and fires 26 , 27 . Past drought periods were usually associated with much lower atmospheric CO 2 concentrations, which may have reduced water-use efficiency of trees 29 (that is, trees assimilated less carbon during transpiration). However, these periods also coincided with cooler temperatures 20 , 21 , which probably reduced water demand by trees 30 . Past drier climatic conditions were therefore very different from the current climatic conditions, in which observed warming trends may exacerbate drought impacts on the forest by exposing trees to unprecedented levels of water stress 31 , 32 .

Global change impacts on forest resilience

Satellite observations from across the Amazon suggest that forest resilience has been decreasing since the early 2000s 33 , possibly as a result of global changes. In this section, we synthesize three global change impacts that vary spatially and temporally across the Amazon system, affecting forest resilience and the risk of critical transitions.

Regional climatic conditions

Within the twenty-first century, global warming may cause long-term changes in Amazonian climatic conditions 2 . Human greenhouse gas emissions continue to intensify global warming, but the warming rate also depends on feedbacks in the climate system that remain uncertain 2 , 3 . Recent climate models of the 6th phase of the Coupled Model Intercomparison Project (CMIP6) agree that in the coming decades, rainfall conditions will become more seasonal in the eastern and southern Amazonian regions, and temperatures will become higher across the entire Amazon 1 , 2 . By 2050, models project that a significant increase in the number of consecutive dry days by 10−30 days and in annual maximum temperatures by 2–4 °C, depending on the greenhouse gas emission scenario 2 . These climatic conditions could expose the forest to unprecedented levels of vapour pressure deficit 31 and consequently water stress 30 .

Satellite observations of climatic variability 31 confirm model projections 2 , showing that since the early 1980s, the Amazonian region has been warming significantly at an average rate of 0.27 °C per decade during the dry season, with the highest rates of up to 0.6 °C per decade in the centre and southeast of the biome (Fig. 1a ). Only a few small areas in the west of the biome are significantly cooling by around 0.1 °C per decade (Fig. 1a ). Dry season mean temperature is now more than 2 °C higher than it was 40 years ago in large parts of the central and southeastern Amazon. If trends continue, these areas could potentially warm by over 4 °C by 2050. Maximum temperatures during the dry season follow a similar trend, rising across most of the biome (Extended Data Fig. 2 ), exposing the forest 34 and local peoples 35 to potentially unbearable heat. Rising temperatures will increase thermal stress, potentially reducing forest productivity and carbon storage capacity 36 and causing widespread leaf damage 34 .

figure 1

a , Changes in the dry season (July–October) mean temperature reveal widespread warming, estimated using simple regressions between time and temperature observed between 1981 and 2020 (with P  < 0.1). b , Potential ecosystem stability classes estimated for year 2050, adapted from current stability classes (Extended Data Fig. 1b ) by considering only areas with significant regression slopes between time and annual rainfall observed from 1981 through 2020 (with P  < 0.1) (see Extended Data Fig. 3 for areas with significant changes). c , Repeated extreme drought events between 2001–2018 (adapted from ref. 39 ). d , Road network from where illegal deforestation and degradation may spread. e , Protected areas and Indigenous territories reduce deforestation and fire disturbances. f , Ecosystem transition potential (the possibility of forest shifting into an alternative structural or compositional state) across the Amazon biome by year 2050 inferred from compounding disturbances ( a – d ) and high-governance areas ( e ). We excluded accumulated deforestation until 2020 and savannas. Transition potential rises with compounding disturbances and varies as follows: less than 0 (in blue) as low; between 1 and 2 as moderate (in yellow); more than 2 as high (orange–red). Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a , multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b ), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels within 10 km from a road; (5) areas with higher governance within protected areas and Indigenous territories, with −1 for pixels inside these areas. For more details, see  Methods .

Since the early 1980s, rainfall conditions have also changed 31 . Peripheral and central parts of the Amazon forest are drying significantly, such as in the southern Bolivian Amazon, where annual rainfall reduced by up to 20 mm yr −1 (Extended Data Fig. 3a ). By contrast, parts of the western and eastern Amazon forest are becoming wetter, with annual rainfall increasing by up to 20 mm yr −1 . If these trends continue, ecosystem stability (as in Extended Data Fig. 1 ) will probably change in parts of the Amazon by 2050, reshaping forest resilience to disturbances (Fig. 1b and Extended Data Fig. 3b ). For example, 6% of the biome may change from stable forest to a bistable regime in parts of the southern and central Amazon. Another 3% of the biome may pass the critical threshold in annual rainfall into stable savanna in the southern Bolivian Amazon. Bistable areas covering 8% of the biome may turn into stable forest in the western Amazon (Peru and Bolivia), thus becoming more resilient to disturbances. For comparison with satellite observations, we used projections of ecosystem stability by 2050 based on CMIP6 model ensembles for a low (SSP2–4.5) and a high (SSP5–8.5) greenhouse gas emission scenario (Extended Data Fig. 4 and Supplementary Table 1 ). An ensemble with the 5 coupled models that include a dynamic vegetation module indicates that 18–27% of the biome may transition from stable forest to bistable and that 2–6% may transition to stable savanna (depending on the scenario), mostly in the northeastern Amazon. However, an ensemble with all 33 models suggests that 35–41% of the biome could become bistable, including large areas of the southern Amazon. The difference between both ensembles is possibly related to the forest–rainfall feedback included in the five coupled models, which increases total annual rainfall and therefore the stable forest area along the southern Amazon, but only when deforestation is not included in the simulations 4 , 37 . Nonetheless, both model ensembles agree that bistable regions will expand deeper into the Amazon, increasing the risk of critical transitions due to disturbances (as implied by the existence of alternative stable states; Extended Data Fig. 1 ).

Disturbance regimes

Within the remaining Amazon forest area, 17% has been degraded by human disturbances 38 , such as logging, edge effects and understory fires, but if we consider also the impacts from repeated extreme drought events in the past decades, 38% of the Amazon could be degraded 39 . Increasing rainfall variability is causing extreme drought events to become more widespread and frequent across the Amazon (Fig. 1c ), together with extreme wet events and convective storms that result in more windthrow disturbances 40 . Drought regimes are intensifying across the region 41 , possibly due to deforestation 42 that continues to expand within the system (Extended Data Fig. 5 ). As a result, new fire regimes are burning larger forest areas 43 , emitting more carbon to the atmosphere 44 and forcing IPLCs to readapt 45 . Road networks (Fig. 1d ) facilitate illegal activities, promoting more deforestation, logging and fire spread throughout the core of the Amazon forest 38 , 39 . The impacts of these pervasive disturbances on biodiversity and on IPLCs will probably affect ecosystem adaptability (Box 1 ), and consequently forest resilience to global changes.

Currently, 86% of the Amazon biome may be in a stable forest state (Extended Data Fig. 1b ), but some of these stable forests are showing signs of fragility 33 . For instance, field evidence from long-term monitoring sites across the Amazon shows that tree mortality rates are increasing in most sites, reducing carbon storage 46 , while favouring the replacement by drought-affiliated species 47 . Aircraft measurements of vertical carbon flux between the forest and atmosphere reveal how southeastern forests are already emitting more carbon than they absorb, probably because of deforestation and fire 48 .

As bistable forests expand deeper into the system (Fig. 1b and Extended Data Fig. 4 ), the distribution of compounding disturbances may indicate where ecosystem transitions are more likely to occur in the coming decades (Fig. 1f ). For this, we combined spatial information on warming and drying trends, repeated extreme drought events, together with road networks, as proxy for future deforestation and degradation 38 , 39 . We also included protected areas and Indigenous territories as areas with high forest governance, where deforestation and fire regimes are among the lowest within the Amazon 49 (Fig. 1e ). This simple additive approach does not consider synergies between compounding disturbances that could trigger unexpected ecosystem transitions. However, by exploring only these factors affecting forest resilience and simplifying the enormous Amazonian complexity, we aimed to produce a simple and comprehensive map that can be useful for guiding future governance. We found that 10% of the Amazon forest biome has a relatively high transition potential (more than 2 disturbance types; Fig. 1f ), including bistable forests that could transition into a low tree cover state near savannas of Guyana, Venezuela, Colombia and Peru, as well as stable forests that could transition into alternative compositional states within the central Amazon, such as along the BR319 and Trans-Amazonian highways. Smaller areas with high transition potential were found scattered within deforestation frontiers, where most forests have been carved by roads 50 , 51 . Moreover, 47% of the biome has a moderate transition potential (more than 1 disturbance type; Fig. 1f ), including relatively remote parts of the central Amazon where warming trends and repeated extreme drought events overlap (Fig. 1a,c ). By contrast, large remote areas covering 53% of the biome have low transition potential, mostly reflecting the distribution of protected areas and Indigenous territories (Fig. 1e ). If these estimates, however, considered projections from CMIP6 models and their relatively broader areas of bistability (Extended Data Fig. 4 ), the proportion of the Amazon forest that could transition into a low tree cover state would be much larger.

Box 1 Ecosystem adaptability

We define ‘ecosystem adaptability’ as the capacity of an ecosystem to reorganize and persist in the face of environmental changes. In the past, many internal mechanisms have probably contributed to ecosystem adaptability, allowing Amazonian forests to persist during times of climate change. In this section we synthesize two of these internal mechanisms, which are now being undermined by global change.

Biodiversity

Amazonian forests are home to more than 15,000 tree species, of which 1% are dominant and the other 99% are mostly rare 107 . A single forest hectare in the central and northwestern Amazon can contain more than 300 tree species (Extended Data Fig. 7a ). Such tremendous tree species diversity can increase forest resilience by different mechanisms. Tree species complementarity increases carbon storage, accelerating forest recovery after disturbances 108 . Tree functional diversity increases forest adaptability to climate chance by offering various possibilities of functioning 99 . Rare species provide ‘ecological redundancy’, increasing opportunities for replacement of lost functions when dominant species disappear 109 . Diverse forests are also more likely to resist severe disturbances owing to ‘response diversity’ 110 —that is, some species may die, while others persist. For instance, in the rainy western Amazon, drought-resistant species are rare but present within tree communities 111 , implying that they could replace the dominant drought-sensitive species in a drier future. Diversity of other organisms, such as frugivores and pollinators, also increases forest resilience by stabilizing ecological networks 15 , 112 . Considering that half of Amazonian tree species are estimated to become threatened (IUCN Red list) by 2050 owing to climate change, deforestation and degradation 8 , biodiversity losses could contribute to further reducing forest resilience.

Indigenous peoples and local communities

Globally, Indigenous peoples and local communities (IPLCs) have a key role in maintaining ecosystems resilient to global change 113 . Humans have been present in the Amazon for at least 12,000 years 114 and extensively managing landscapes for 6,000 years 22 . Through diverse ecosystem management practices, humans built thousands of earthworks and ‘Amazon Dark Earth’ sites, and domesticated plants and landscapes across the Amazon forest 115 , 116 . By creating new cultural niches, humans partly modified the Amazonian flora 117 , 118 , increasing their food security even during times of past climate change 119 , 120 without the need for large-scale deforestation 117 . Today, IPLCs have diverse ecological knowledge about Amazonian plants, animals and landscapes, which allows them to quickly identify and respond to environmental changes with mitigation and adaptation practices 68 , 69 . IPLCs defend their territories against illegal deforestation and land use disturbances 49 , 113 , and they also promote forest restoration by expanding diverse agroforestry systems 121 , 122 . Amazonian regions with the highest linguistic diversity (a proxy for ecological knowledge diversity 123 ) are found in peripheral parts of the system, particularly in the north-west (Extended Data Fig. 7b ). However, consistent loss of Amazonian languages is causing an irreversible disruption of ecological knowledge systems, mostly driven by road construction 7 . Continued loss of ecological knowledge will undermine the capacity of IPLCs to manage and protect Amazonian forests, further reducing their resilience to global changes 9 .

CO 2 fertilization

Rising atmospheric CO 2 concentrations are expected to increase the photosynthetic rates of trees, accelerating forest growth and biomass accumulation on a global scale 52 . In addition, CO 2 may reduce water stress by increasing tree water-use efficiency 29 . As result, a ‘CO 2 fertilization effect’ could increase forest resilience to climatic variability 53 , 54 . However, observations from across the Amazon 46 suggest that CO 2 -driven accelerations of tree growth may have contributed to increasing tree mortality rates (trees grow faster but also die earlier), which could eventually neutralize the forest carbon sink in the coming decades 55 . Moreover, increases in tree water-use efficiency may reduce forest transpiration and consequently atmospheric moisture flow across the Amazon 53 , 56 , potentially reducing forest resilience in the southwest of the biome 4 , 37 . Experimental evidence suggests that CO 2 fertilization also depends on soil nutrient availability, particularly nitrogen and phosphorus 57 , 58 . Thus, it is possible that in the fertile soils of the western Amazon and Várzea floodplains, forests may gain resilience from increasing atmospheric CO 2 (depending on how it affects tree mortality rates), whereas on the weathered (nutrient-poor) soils across most of the Amazon basin 59 , forests might not respond to atmospheric CO 2 increase, particularly on eroded soils within deforestation frontiers 60 . In sum, owing to multiple interacting factors, potential responses of Amazonian forests to CO 2 fertilization are still poorly understood. Forest responses depend on scale, with resilience possibly increasing at the local scale on relatively more fertile soils, but decreasing at the regional scale due to reduced atmospheric moisture flow.

Local versus systemic transition

Environmental heterogeneity.

Environmental heterogeneity can reduce the risk of systemic transition (large-scale forest collapse) because when stressing conditions intensify (for example, rainfall declines), heterogeneous forests may transition gradually (first the less resilient forest patches, followed by the more resilient ones), compared to homogeneous forests that may transition more abruptly 17 (all forests transition in synchrony). Amazonian forests are heterogeneous in their resilience to disturbances, which may have contributed to buffering large-scale transitions in the past 37 , 61 , 62 . At the regional scale, a fundamental heterogeneity factor is rainfall and how it translates into water stress. Northwestern forests rarely experience water stress, which makes them relatively more resilient than southeastern forests that may experience water stress in the dry season, and therefore are more likely to shift into a low tree cover state. As a result of low exposure to water deficit, most northwestern forests have trees with low drought resistance and could suffer massive mortality if suddenly exposed to severe water stress 32 . However, this scenario seems unlikely to occur in the near future (Fig. 1 ). By contrast, most seasonal forest trees have various strategies to cope with water deficit owing to evolutionary and adaptive responses to historical drought events 32 , 63 . These strategies may allow seasonal forests to resist current levels of rainfall fluctuations 32 , but seasonal forests are also closer to the critical rainfall thresholds (Extended Data Fig. 1 ) and may experience unprecedented water stress in the coming decades (Fig. 1 ).

Other key heterogeneity factors (Extended Data Fig. 6 ) include topography, which determines plant access to groundwater 64 , and seasonal flooding, which increases forest vulnerability to wildfires 65 . Future changes in rainfall regimes will probably affect hydrological regimes 66 , exposing plateau (hilltop) forests to unprecedented water stress, and floodplain forests to extended floods, droughts and wildfires. Soil fertility is another heterogeneity factor that may affect forest resilience 59 , and which may be undermined by disturbances that cause topsoil erosion 60 . Moreover, as human disturbances intensify throughout the Amazon (Fig. 1 ), the spread of invasive grasses and fires can make the system increasingly homogeneous. Effects of heterogeneity on Amazon forest resilience have been poorly investigated so far (but see refs. 37 , 61 , 62 ) and many questions remain open, such as how much heterogeneity exists in the system and whether it can mitigate a systemic transition.

Sources of connectivity

Connectivity across Amazonian landscapes and regions can contribute to synchronize forest dynamics, causing different forests to behave more similarly 17 . Depending on the processes involved, connectivity can either increase or decrease the risk of systemic transition 17 . For instance, connectivity may facilitate forest recovery after disturbances through seed dispersal, but also it may spread disturbances, such as fire. In the Amazon, an important source of connectivity enhancing forest resilience is atmospheric moisture flow westward (Fig. 2 ), partly maintained by forest evapotranspiration 4 , 37 , 67 . Another example of connectivity that may increase social-ecological resilience is knowledge exchange among IPLCs about how to adapt to global change 68 , 69 (see Box 1 ). However, complex systems such as the Amazon can be particularly vulnerable to sources of connectivity that spread disturbances and increase the risk of systemic transition 70 . For instance, roads carving through the forest are well-known sources of illegal activities, such as logging and burning, which increase forest flammability 38 , 39 .

figure 2

Brazil holds 60% of the Amazon forest biome and has a major responsibility towards its neighbouring countries in the west. Brazil is the largest supplier of rainfall to western Amazonian countries. Up to one-third of the total annual rainfall in Amazonian territories of Bolivia, Peru, Colombia and Ecuador depends on water originating from Brazil’s portion of the Amazon forest. This international connectivity illustrates how policies related to deforestation, especially in the Brazilian Amazon, will affect the climate in other countries. Arrow widths are proportional to the percentage of the annual rainfall received by each country within their Amazonian areas. We only show flows with percentages higher than 10% (see  Methods for details).

Five critical drivers of water stress

Global warming.

Most CMIP6 models agree that a large-scale dieback of the Amazon is unlikely in response to global warming above pre-industrial levels 2 , but this ecosystem response is based on certain assumptions, such as a large CO 2 -fertilization effect 53 . Forests across the Amazon are already responding with increasing tree mortality rates that are not simulated by these models 46 , possibly because of compounding disturbance regimes (Fig. 1 ). Nonetheless, a few global climate models 3 , 14 , 71 , 72 , 73 , 74 indicate a broad range for a potential critical threshold in global warming between 2 and 6 °C (Fig. 3a ). These contrasting results can be explained by general differences between numerical models and their representation of the complex Amazonian system. While some models with dynamic vegetation indicate local-scale tipping events in peripheral parts of the Amazon 5 , 6 , other models suggest an increase in biomass and forest cover (for example, in refs. 53 , 54 ). For instance, a study found that when considering only climatic variability, a large-scale Amazon forest dieback is unlikely, even under a high greenhouse gas emission scenario 75 . However, most updated CMIP6 models agree that droughts in the Amazon region will increase in length and intensity, and that exceptionally hot droughts will become more common 2 , creating conditions that will probably boost other types of disturbances, such as large and destructive forest fires 76 , 77 . To avoid broad-scale ecosystem transitions due to synergies between climatic and land use disturbances (Fig. 3b ), we suggest a safe boundary for the Amazon forest at 1.5 °C for global warming above pre-industrial levels, in concert with the Paris Agreement goals.

figure 3

a , Five critical drivers of water stress on Amazonian forests affect (directly or indirectly) the underlying tipping point of the system. For each driver, we indicate potential critical thresholds and safe boundaries that define a safe operating space for keeping the Amazon forest resilient 11 , 12 . We followed the precautionary principle and considered the most conservative thresholds within the ranges, when confidence was low. b , Conceptual model showing how the five drivers may interact (arrows indicate positive effects) and how these interactions may strengthen a positive feedback between water stress and forest loss. These emerging positive feedback loops could accelerate a systemic transition of the Amazon forest 15 . At global scales, driver 1 (global warming) intensifies with greenhouse gas emissions, including emissions from deforestation. At local scales, driver 5 (accumulated deforestation) intensifies with land use changes. Drivers 2 to 4 (regional rainfall conditions) intensify in response to drivers 1 and 5. The intensification of these drivers may cause widespread tree mortality for instance because of extreme droughts and fires 76 . Water stress affects vegetation resilience globally 79 , 104 , but other stressors, such as heat stress 34 , 36 , may also have a role. In the coming decades, these five drivers could change at different rates, with some approaching a critical threshold faster than others. Therefore, monitoring them separately can provide vital information to guide mitigation and adaptation strategies.

Annual rainfall

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold between 1,000 and 1,250 mm of annual rainfall 78 , 79 . On the basis of our reanalysis using tree cover data from the Amazon basin (Extended Data Fig. 1a ), we confirm a potential threshold at 1,000 mm of annual rainfall (Fig. 3a ), below which forests become rare and unstable. Between 1,000 and 1,800 mm of annual rainfall, high and low tree cover ecosystems exist in the Amazon as two alternative stable states (see Extended Data Table 2 for uncertainty ranges). Within the bistability range in annual rainfall conditions, forests are relatively more likely to collapse when severely disturbed, when compared to forests in areas with annual rainfall above 1,800 mm (Extended Data Fig. 1a ). For floodplain ecosystems covering 14% of the forest biome, a different critical threshold has been estimated at 1,500 mm of annual rainfall 65 , implying that floodplain forests may be the first to collapse in a drier future. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary in annual rainfall conditions at 1,800 mm.

Rainfall seasonality intensity

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold in rainfall seasonality intensity at −400 mm of the maximum cumulative water deficit 37 , 80 (MCWD). Our reanalysis of the Amazon basin (Extended Data Fig. 1c ) confirms the critical threshold at approximately −450 mm in the MCWD (Fig. 3a ), and suggests a bistability range between approximately −350 and −450 mm (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with MCWD below −350 mm. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of MCWD at −350 mm.

Dry season length

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold at 7 months of dry season length 79 (DSL). Our reanalysis of the Amazon basin (Extended Data Fig. 1d ) suggests a critical threshold at eight months of DSL (Fig. 3a ), with a bistability range between approximately five and eight months (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with DSL below five months. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of DSL at five months.

Accumulated deforestation

A potential vegetation model 81 found a critical threshold at 20% of accumulated deforestation (Fig. 3a ) by simulating Amazon forest responses to different scenarios of accumulated deforestation (with associated fire events) and of greenhouse gas emissions, and by considering a CO 2 fertilization effect of 25% of the maximum photosynthetic assimilation rate. Beyond 20% deforestation, forest mortality accelerated, causing large reductions in regional rainfall and consequently an ecosystem transition of 50−60% of the Amazon, depending on the emissions scenario. Another study using a climate-vegetation model found that with accumulated deforestation of 30−50%, rainfall in non-deforested areas downwind would decline 67 by 40% (ref.  67 ), potentially causing more forest loss 4 , 37 . Other more recent models incorporating fire disturbances support a potential broad-scale transition of the Amazon forest, simulating a biomass loss of 30–40% under a high-emission scenario 5 , 82 (SSP5–8.5 at 4 °C). The Amazon biome has already lost 13% of its original forest area due to deforestation 83 (or 15% of the biome if we consider also young secondary forests 83 that provide limited contribution to moisture flow 84 ). Among the remaining old-growth forests, at least 38% have been degraded by land use disturbances and repeated extreme droughts 39 , with impacts on moisture recycling that are still uncertain. Therefore, to avoid broad-scale ecosystem transitions due to runaway forest loss (Fig. 3b ), we suggest a safe boundary of accumulated deforestation of 10% of the original forest biome cover, which requires ending large-scale deforestation and restoring at least 5% of the biome.

Three alternative ecosystem trajectories

Degraded forest.

In stable forest regions of the Amazon with annual rainfall above 1,800 mm (Extended Data Fig. 1b ), forest cover usually recovers within a few years or decades after disturbances, yet forest composition and functioning may remain degraded for decades or centuries 84 , 85 , 86 , 87 . Estimates from across the Amazon indicate that approximately 30% of areas previously deforested are in a secondary forest state 83 (covering 4% of the biome). An additional 38% of the forest biome has been damaged by extreme droughts, fires, logging and edge effects 38 , 39 . These forests may naturally regrow through forest succession, yet because of feedbacks 15 , succession can become arrested, keeping forests persistently degraded (Fig. 4 ). Different types of degraded forests have been identified in the Amazon, each one associated with a particular group of dominant opportunistic plants. For instance, Vismia forests are common in old abandoned pastures managed with fire 85 , and are relatively stable, because Vismia trees favour recruitment of Vismia seedlings in detriment of other tree species 88 , 89 . Liana forests can also be relatively stable, because lianas self-perpetuate by causing physical damage to trees, allowing lianas to remain at high density 90 , 91 . Liana forests are expected to expand with increasing aridity, disturbance regimes and CO 2 fertilization 90 . Guadua bamboo forests are common in the southwestern Amazon 92 , 93 . Similar to lianas, bamboos self-perpetuate by causing physical damage to trees and have been expanding over burnt forests in the region 92 . Degraded forests are usually dominated by native opportunistic species, and their increasing expansion over disturbed forests could affect Amazonian functioning and resilience in the future.

figure 4

From examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories related to the types of disturbances, feedbacks and local environmental conditions. These alternative trajectories may be irreversible or transient depending on the strength of the novel interactions 15 . Particular combinations of interactions (arrows show positive effects described in the literature) may form feedback loops 15 that propel the ecosystem through these trajectories. In the ‘degraded forest’ trajectory, feedbacks often involve competition between trees and other opportunistic plants 85 , 90 , 92 , as well as interactions between deforestation, fire and seed limitation 84 , 87 , 105 . At the landscape scale, secondary forests are more likely to be cleared than mature forests, thus keeping forests persistently young and landscapes fragmented 83 . In the ‘degraded open-canopy ecosystem’ trajectory, feedbacks involve interactions among low tree cover and fire 97 , soil erosion 60 , seed limitation 105 , invasive grasses and opportunistic plants 96 . At the regional scale, a self-reinforcing feedback between forest loss and reduced atmospheric moisture flow may increase the resilience of these open-canopy degraded ecosystems 42 . In the ‘white-sand savanna’ trajectory, the main feedbacks result from interactions among low tree cover and fire, soil erosion, and seed limitation 106 . Bottom left, floodplain forest transition to white-sand savanna after repeated fires (photo credit: Bernardo Flores); bottom centre, forest transition to degraded open-canopy ecosystem after repeated fires (photo credit: Paulo Brando); bottom right, forest transition to Vismia degraded forest after slash-and-burn agriculture (photo credit: Catarina Jakovac).

White-sand savanna

White-sand savannas are ancient ecosystems that occur in patches within the Amazon forest biome, particularly in seasonally waterlogged or flooded areas 94 . Their origin has been attributed to geomorphological dynamics and past Indigenous fires 26 , 27 , 94 . In a remote landscape far from large agricultural frontiers, within a stable forest region of the Amazon (Extended Data Fig. 1b ), satellite and field evidence revealed that white-sand savannas are expanding where floodplain forests were repeatedly disturbed by fires 95 . After fire, the topsoil of burnt forests changes from clayey to sandy, favouring the establishment of savanna trees and native herbaceous plants 95 . Shifts from forest to white-sand savanna (Fig. 4 ) are probably stable (that is, the ecosystem is unlikely to recover back to forest within centuries), based on the relatively long persistence of these savannas in the landscape 94 . Although these ecosystem transitions have been confirmed only in the Negro river basin (central Amazon), floodplain forests in other parts of the Amazon were shown to be particularly vulnerable to collapse 45 , 64 , 65 .

Degraded open-canopy ecosystem

In bistable regions of the Amazon forest with annual rainfall below 1,800 mm (Extended Data Fig. 1b ), shifts to degraded open-canopy ecosystems are relatively common after repeated disturbances by fire 45 , 96 . The ecosystem often becomes dominated by fire-tolerant tree and palm species, together with alien invasive grasses and opportunistic herbaceous plants 96 , 97 , such as vines and ferns. Estimates from the southern Amazon indicate that 5−6% of the landscape has already shifted into degraded open-canopy ecosystems due to deforestation and fires 45 , 96 . It is still unclear, however, whether degraded open-canopy ecosystems are stable or transient (Fig. 4 ). Palaeorecords from the northern Amazon 98 show that burnt forests may spend centuries in a degraded open-canopy state before they eventually shift into a savanna. Today, invasion by alien flammable grasses is a novel stabilizing mechanism 96 , 97 , but the long-term persistence of these grasses in the ecosystem is also uncertain.

Prospects for modelling Amazon forest dynamics

Several aspects of the Amazon forest system may help improve earth system models (ESMs) to more accurately simulate ecosystem dynamics and feedbacks with the climate system. Simulating individual trees can improve the representation of growth and mortality dynamics, which ultimately affect forest dynamics (for example, refs. 61 , 62 , 99 ). Significant effects on simulation results may emerge from increasing plant functional diversity, representation of key physiological trade-offs and other features that determine water stress on plants, and also allowing for community adjustment to environmental heterogeneity and global change 32 , 55 , 62 , 99 . For now, most ESMs do not simulate a dynamic vegetation cover (Supplementary Table 1 ) and biomes are represented based on few plant functional types, basically simulating monocultures on the biome level. In reality, tree community adaptation to a heterogenous and dynamic environment feeds into the whole-system dynamics, and not covering such aspects makes a true Amazon tipping assessment more challenging.

Our findings also indicate that Amazon forest resilience is affected by compounding disturbances (Fig. 1 ). ESMs need to include different disturbance scenarios and potential synergies for creating more realistic patterns of disturbance regimes. For instance, logging and edge effects can make a forest patch more flammable 39 , but these disturbances are often not captured by ESMs. Improvements in the ability of ESMs to predict future climatic conditions are also required. One way is to identify emergent constraints 100 , lowering ESMs variations in their projections of the Amazonian climate. Also, fully coupled ESMs simulations are needed to allow estimates of land-atmosphere feedbacks, which may adjust climatic and ecosystem responses. Another way to improve our understanding of the critical thresholds for Amazonian resilience and how these link to climatic conditions and to greenhouse gas concentrations is through factorial simulations with ESMs. In sum, although our study may not deliver a set of reliable and comprehensive equations to parameterize processes impacting Amazon forest dynamics, required for implementation in ESMs, we highlight many of the missing modelled processes.

Implications for governance

Forest resilience is changing across the Amazon as disturbance regimes intensify (Fig. 1 ). Although most recent models agree that a large-scale collapse of the Amazon forest is unlikely within the twenty-first century 2 , our findings suggest that interactions and synergies among different disturbances (for example, frequent extreme hot droughts and forest fires) could trigger unexpected ecosystem transitions even in remote and central parts of the system 101 . In 2012, Davidson et al. 102 demonstrated how the Amazon basin was experiencing a transition to a ‘disturbance-dominated regime’ related to climatic and land use changes, even though at the time, annual deforestation rates were declining owing to new forms of governance 103 . Recent policy and approaches to Amazon development, however, accelerated deforestation that reached 13,000 km 2 in the Brazilian Amazon in 2021 ( http://terrabrasilis.dpi.inpe.br ). The southeastern region has already turned into a source of greenhouse gases to the atmosphere 48 . The consequences of losing the Amazon forest, or even parts of it, imply that we must follow a precautionary approach—that is, we must take actions that contribute to maintain the Amazon forest within safe boundaries 12 . Keeping the Amazon forest resilient depends firstly on humanity’s ability to stop greenhouse gas emissions, mitigating the impacts of global warming on regional climatic conditions 2 . At the local scale, two practical and effective actions need to be addressed to reinforce forest–rainfall feedbacks that are crucial for the resilience of the Amazon forest 4 , 37 : (1) ending deforestation and forest degradation; and (2) promoting forest restoration in degraded areas. Expanding protected areas and Indigenous territories can largely contribute to these actions. Our findings suggest a list of thresholds, disturbances and feedbacks that, if well managed, can help maintain the Amazon forest within a safe operating space for future generations.

Our study site was the area of the Amazon basin, considering large areas of tropical savanna biome along the northern portion of the Brazilian Cerrado, the Gran Savana in Venezuela and the Llanos de Moxos in Bolivia, as well as the Orinoco basin to the north, and eastern parts of the Andes to the west. The area includes also high Andean landscapes with puna and paramo ecosystems. We chose this contour to allow better communication with the MapBiomas Amazonian Project (2022; https://amazonia.mapbiomas.org ). For specific interpretation of our results, we considered the contour of the current extension of the Amazon forest biome, which excludes surrounding tropical savanna biomes.

We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) data (MOD44B version 6; https://lpdaac.usgs.gov/products/mod44bv006/ ) for the year 2001 at 250-m resolution 124 to reanalyse tree cover distributions within the Amazon basin, refining estimates of bistability ranges and critical thresholds in rainfall conditions from previous studies. Although MODIS VCF can contain errors within lower tree cover ranges and should not be used to test for bistability between grasslands and savannas 125 , the dataset is relatively robust for assessing bistability within the tree cover range of forests and savannas 126 , as also shown by low uncertainty (standard deviation of tree cover estimates) across the Amazon (Extended Data Fig. 8 ).

We used the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS; https://www.chc.ucsb.edu/data/chirps ) 127 to estimate mean annual rainfall and rainfall seasonality for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.05° spatial resolution.

We used the Climatic Research Unit (CRU; https://www.uea.ac.uk/groups-and-centres/climatic-research-unit ) 128 to estimate mean annual temperature for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.5° spatial resolution.

To mask deforested areas until 2020, we used information from the MapBiomas Amazonia Project (2022), collection 3, of Amazonian Annual Land Cover and Land Use Map Series ( https://amazonia.mapbiomas.org ).

To assess forest fire distribution across the Amazon forest biome and in relation to road networks, we used burnt area fire data obtained from the AQUA sensor onboard the MODIS satellite. Only active fires with a confidence level of 80% or higher were selected. The data are derived from MODIS MCD14ML (collection 6) 129 , available in Fire Information for Resource Management System (FIRMS). The data were adjusted to a spatial resolution of 1 km.

Potential analysis

Using potential analysis 130 , an empirical stability landscape was constructed based on spatial distributions of tree cover (excluding areas deforested until 2020; https://amazonia.mapbiomas.org ) against mean annual precipitation, MCWD and DSL. Here we followed the methodology of Hirota et al. 104 . For bins of each of the variables, the probability density of tree cover was determined using the MATLAB function ksdensity. Local maxima of the resulting probability density function are considered to be stable equilibria, in which local maxima below a threshold value of 0.005 were ignored. Based on sensitivity tests (see below), we chose the intermediate values of the sensitivity parameter for each analysis, which resulted in the critical thresholds most similar to the ones previously published in the literature.

Sensitivity tests of the potential analysis

We smoothed the densities of tree cover with the MATLAB kernel smoothing function ksdensity. Following Hirota et al. 104 , we used a flexible bandwidth ( h ) according to Silverman’s rule of thumb 131 : h  = 1.06 σn 1/5 , where σ is the standard deviation of the tree cover distribution and n is the number of points. To ignore small bumps in the frequency distributions, we used a dimensionless sensitivity parameter. This parameter filters out weak modes in the distributions such that a higher value implies a stricter criterion to detect a significant mode. In the manuscript, we used a value of 0.005. For different values of this sensitivity parameter, we here test the estimated critical thresholds and bistability ranges (Extended Data Table 2 ). We inferred stable and unstable states of tree cover (minima and maxima in the potentials) for moving windows of the climatic variables. For mean annual precipitation, we used increments of 10 mm yr −1 between 0 and 3500 mm yr −1 . For dry season length, we used increments of 0.1 months between 0 and 12 months. For MCWD, we used increments of 10 mm between −800 mm and 0 mm.

Transition potential

We quantified a relative ecosystem transition potential across the Amazon forest biome (excluding accumulated deforestation; https://amazonia.mapbiomas.org ) to produce a simple spatial measure that can be useful for governance. For this, we combined information per pixel, at 5 km resolution, about different disturbances related to climatic and human disturbances, as well as high-governance areas within protected areas and Indigenous territories. We used values of significant slopes of the dry season (July–October) mean temperature between 1981 and 2020 ( P  < 0.1), estimated using simple linear regressions (at 0.5° resolution from CRU) (Fig. 1a ). Ecosystem stability classes (stable forest, bistable and stable savanna as in Extended Data Fig. 1 ) were estimated using simple linear regression slopes of annual rainfall between 1981 and 2020 ( P  < 0.1) (at 0.05° resolution from CHIRPS), which we extrapolated to 2050 (Fig. 1b and Extended Data Fig. 3 ). Distribution of areas affected by repeated extreme drought events (Fig. 1c ) were defined when the time series (2001–2018) of the MCWD reached two standard deviation anomalies from historical mean. Extreme droughts were obtained from Lapola et al. 39 , based on Climatic Research Unit gridded Time Series (CRU TS 4.0) datasets for precipitation and evapotranspiration. The network of roads (paved and unpaved) across the Amazon forest biome (Fig. 1d ) was obtained from the Amazon Network of Georeferenced Socio-Environmental Information (RAISG; https://geo2.socioambiental.org/raisg ). Protected areas (PAs) and Indigenous territories (Fig. 1e ) were also obtained from RAISG, and include both sustainable-use and restricted-use protected areas managed by national or sub-national governments, together with officially recognized and proposed Indigenous territories. We combined these different disturbance layers by adding a value for each layer in the following way: (1) slopes of dry season temperature change (as in Fig. 1a , multiplied by 10, thus between −0.1 and +0.6); (2) ecosystem stability classes estimated for year 2050 (as in Fig. 1b ), with 0 for stable forest, +1 for bistable and +2 for stable savanna; (3) accumulated impacts from repeated extreme drought events (from 0 to 5 events), with +0.2 for each event; (4) road-related human impacts, with +1 for pixels within 10 km from a road; and (5) protected areas and Indigenous territories as areas with lower exposure to human (land use) disturbances, such as deforestation and forest fires, with −1 for pixels inside these areas. The sum of these layers revealed relative spatial variation in ecosystem transition potential by 2050 across the Amazon (Fig. 1f ), ranging from −1 (low potential) to 4 (very high potential).

Atmospheric moisture tracking

To determine the atmospheric moisture flows between the Amazonian countries, we use the Lagrangian atmospheric moisture tracking model UTrack 132 . The model tracks the atmospheric trajectories of parcels of moisture, updates their coordinates at each time step of 0.1 h and allocates moisture to a target location in case of precipitation. For each millimetre of evapotranspiration, 100 parcels are released into the atmosphere. Their trajectories are forced with evaporation, precipitation, and wind speed estimates from the ERA5 reanalysis product at 0.25° horizontal resolution for 25 atmospheric layers 133 . Here we use the runs from Tuinenburg et al. 134 , who published monthly climatological mean (2008–2017) moisture flows between each pair of 0.5° grid cells on Earth. We aggregated these monthly flows, resulting in mean annual moisture flows between all Amazonian countries during 2008–2017. For more details of the model runs, we refer to Tuinenburg and Staal 132 and Tuinenburg et al. 134 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting the findings of this study are openly available and their sources are presented in the Methods.

Science Panel for the Amazon. Amazon Assessment Report 2021 (2021); www.theamazonwewant.org/amazon-assessment-report-2021/ .

IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) https://www.ipcc.ch/report/ar6/wg1/#FullReport (Cambridge Univ. Press, 2021).

Armstrong McKay, D. et al. Exceeding 1.5 °C global warming could trigger multiple climate tipping points. Science 377 , abn7950 (2022).

Article   Google Scholar  

Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8 , 539–543 (2018).

Article   ADS   Google Scholar  

Cano, I. M. et al. Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios. Proc. Natl Acad. Sci. USA 119 , e2203200119 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Parry, I. M., Ritchie, P. D. L. & Cox, P. M. Evidence of localised Amazon rainforest dieback in CMIP6 models. Earth Syst. Dynam. 13 , 1667–1675 (2022).

Bromham, L. et al. Global predictors of language endangerment and the future of linguistic diversity. Nat. Ecol. Evol. 6 , 163–173 (2022).

Article   PubMed   Google Scholar  

Gomes, V. H. F., Vieira, I. C. G., Salomão, R. P. & ter Steege, H. Amazonian tree species threatened by deforestation and climate change. Nat. Clim. Change 9 , 547–553 (2019).

Cámara-Leret, R., Fortuna, M. A. & Bascompte, J. Indigenous knowledge networks in the face of global change. Proc. Natl Acad. Sci. USA 116 , 9913–9918 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413 , 591–596 (2001).

Article   ADS   CAS   PubMed   Google Scholar  

Rockstrom, J. et al. A safe operating space for humanity. Nature 461 , 472–475 (2009).

Article   ADS   PubMed   Google Scholar  

Scheffer, M. et al. Creating a safe operating space for iconic ecosystems. Science 347 , 1317–1319 (2015).

van Nes, E. H. et al. What do you mean, ‘tipping point’? Trends Ecol. Evol. 31 , 902–904 (2016).

Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105 , 1786–1793 (2008).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Flores, B. M. & Staal, A. Feedback in tropical forests of the Anthropocene. Global Change Biol. 28 , 5041–5061 (2022).

Article   CAS   Google Scholar  

Scheffer, M. Critical Transitions in Nature and Society (Princeton Univ. Press, 2009).

Scheffer, M. et al. Anticipating critical transitions. Science 338 , 344–348 (2012).

Holling, C. S. Engineering Resilience versus Ecological Resilience (National Academy Press, 1996).

Hoorn, C. et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330 , 927–931 (2010).

Wang, X. et al. Hydroclimate changes across the Amazon lowlands over the past 45,000 years. Nature 541 , 204–207 (2017).

Kukla, T. et al. The resilience of Amazon tree cover to past and present drying. Global Planet. Change 202 , 103520 (2021).

Clement, C. R. et al. Disentangling domestication from food production systems in the neotropics. Quaternary 4 , 4 (2021).

Mayle, F. E. & Power, M. J. Impact of a drier Early–Mid-Holocene climate upon Amazonian forests. Phil. Trans. R. Soc. B 363 , 1829–1838 (2008).

Article   PubMed   PubMed Central   Google Scholar  

Montoya, E. & Rull, V. Gran Sabana fires (SE Venezuela): a paleoecological perspective. Quat. Sci. Rev. 30 , 3430–3444 (2011).

Rull, V., Montoya, E., Vegas-Vilarrúbia, T. & Ballesteros, T. New insights on palaeofires and savannisation in northern South America. Quat. Sci. Rev. 122 , 158–165 (2015).

Rossetti, D. F. et al. Unfolding long-term Late Pleistocene-Holocene disturbances of forest communities in the southwestern Amazonian lowlands. Ecosphere 9 , e02457 (2018).

Prance, G. T. & Schubart, H. O. R. Notes on the vegetation of Amazonia I. A preliminary note on the origin of the open white sand campinas of the lower Rio Negro. Brittonia 30 , 60 (1978).

Wright, J. L. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and central Brazilian savannas. Glob. Change Biol. 27 , 136–150 (2021).

Article   ADS   CAS   Google Scholar  

van der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO 2 fertilization but water-use efficiency increased. Nat. Geosci. 8 , 24–28 (2015).

Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6 , 1225–1230 (2020).

Article   CAS   PubMed   Google Scholar  

Marengo, J. A., Jimenez, J. C., Espinoza, J.-C., Cunha, A. P. & Aragão, L. E. O. Increased climate pressure on the agricultural frontier in the Eastern Amazonia–Cerrado transition zone. Sci. Rep. 12 , 457 (2022).

Tavares, J. V. et al. Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature 617 , 111–117 (2023).

Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12 , 271–278 (2022).

Doughty, C. E. et al. Tropical forests are approaching critical temperature thresholds. Nature 621 , 105–111 (2023).

Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117 , 11350–11355 (2020).

Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368 , 869–874 (2020).

Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8 , 14681 (2017).

Bullock, E. L., Woodcock, C. E., Souza, C. Jr & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biol. 26 , 2956–2969 (2020).

Lapola, D. M. et al. The drivers and impacts of Amazon forest degradation. Science 379 , eabp8622 (2023).

Feng, Y., Negrón-Juárez, R. I., Romps, D. M. & Chambers, J. Q. Amazon windthrow disturbances are likely to increase with storm frequency under global warming. Nat. Commun. 14 , 101 (2023).

Anderson, L. O. et al. Vulnerability of Amazonian forests to repeated droughts. Phil. Trans. R. Soc. B 373 , 20170411 (2018).

Staal, A. et al. Feedback between drought and deforestation in the Amazon. Environ. Res. Lett. 15 , 044024 (2020).

Alencar, A. A., Brando, P. M., Asner, G. P. & Putz, F. E. Landscape fragmentation, severe drought, and the new Amazon forest fire regime. Ecol. Appl. 25 , 1493–1505 (2015).

Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9 , 536 (2018).

Silvério, D. V. et al. Intensification of fire regimes and forest loss in the Território Indígena do Xingu. Environ. Res. Lett. 17 , 045012 (2022).

Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519 , 344–348 (2015).

Esquivel‐Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25 , 39–56 (2019).

Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595 , 388–393 (2021).

Nepstad, D. et al. Inhibition of Amazon deforestation and fire by parks and Indigenous lands: inhibition of Amazon deforestation and fire. Conserv. Biol. 20 , 65–73 (2006).

Botelho, J., Costa, S. C. P., Ribeiro, J. G. & Souza, C. M. Mapping roads in the Brazilian Amazon with artificial intelligence and Sentinel-2. Remote Sensing 14 , 3625 (2022).

Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369 , 1378–1382 (2020).

Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free‐air CO 2 enrichment (FACE)? A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO 2 . New Phytol. 165 , 351–372 (2005).

Kooperman, G. J. et al. Forest response to rising CO 2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8 , 434–440 (2018).

Lapola, D. M., Oyama, M. D. & Nobre, C. A. Exploring the range of climate biome projections for tropical South America: the role of CO 2 fertilization and seasonality: future biome distribution in South America. Global Biogeochem. Cycles 23 , https://doi.org/10.1029/2008GB003357 (2009).

Brienen, R. J. W. et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs. Nat. Commun. 11 , 4241 (2020).

Lammertsma, E. I. et al. Global CO 2 rise leads to reduced maximum stomatal conductance in Florida vegetation. Proc. Natl Acad. Sci. USA 108 , 4035–4040 (2011).

Terrer, C. et al. Nitrogen and phosphorus constrain the CO 2 fertilization of global plant biomass. Nat. Clim. Change 9 , 684–689 (2019).

Ellsworth, D. S. et al. Elevated CO 2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Change 7 , 279–282 (2017).

Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9 , 2203–2246 (2012).

Flores, B. M. et al. Soil erosion as a resilience drain in disturbed tropical forests. Plant Soil https://doi.org/10.1007/s11104-019-04097-8 (2020).

Longo, M. et al. Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. New Phytol. 219 , 914–931 (2018).

Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl Acad. Sci. USA 113 , 793–797 (2016).

Staver, A. C. et al. Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire. Ecol. Lett. 23 , 99–106 (2020).

Mattos, C. R. C. et al. Double stress of waterlogging and drought drives forest–savanna coexistence. Proc. Natl Acad. Sci. USA 120 , e2301255120 (2023).

Flores, B. M. et al. Floodplains as an Achilles’ heel of Amazonian forest resilience. Proc. Natl Acad. Sci. USA 114 , 4442–4446 (2017).

Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int. J. Climatol. 36 , 1033–1050 (2016).

Boers, N., Marwan, N., Barbosa, H. M. J. & Kurths, J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 7 , 41489 (2017).

Alexander, C. et al. Linking Indigenous and scientific knowledge of climate change. BioScience 61 , 477–484 (2011).

Ford, J. D. et al. The resilience of Indigenous peoples to environmental change. One Earth 2 , 532–543 (2020).

Cooper, G. S., Willcock, S. & Dearing, J. A. Regime shifts occur disproportionately faster in larger ecosystems. Nat. Commun. 11 , 1175 (2020).

Drijfhout, S. et al. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proc. Natl Acad. Sci. USA 112 , E5777–E5786 (2015).

Salazar, L. F. & Nobre, C. A. Climate change and thresholds of biome shifts in Amazonia: climate change and Amazon biome shift. Geophys. Res. Lett. 37 , https://doi.org/10.1029/2010GL043538 (2010).

Jones, C., Lowe, J., Liddicoat, S. & Betts, R. Committed terrestrial ecosystem changes due to climate change. Nat. Geosci. 2 , 484–487 (2009).

Schellnhuber, H. J., Rahmstorf, S. & Winkelmann, R. Why the right climate target was agreed in Paris. Nat. Clim. Change 6 , 649–653 (2016).

Chai, Y. et al. Constraining Amazonian land surface temperature sensitivity to precipitation and the probability of forest dieback. npj Clim. Atmos. Sci. 4 , 6 (2021).

Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl Acad. Sci. USA 111 , 6347–6352 (2014).

Berenguer, E. et al. Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests. Proc. Natl Acad. Sci. USA 118 , e2019377118 (2021).

Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11 , 4978 (2020).

Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334 , 230–232 (2011).

Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106 , 20610–20615 (2009).

Nobre, C. A. et al. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl Acad. Sci. USA 113 , 10759–10768 (2016).

Burton, C. et al. South American fires and their impacts on ecosystems increase with continued emissions. Clim. Resil. Sustain. 1 , e8 (2022).

Google Scholar  

Smith, C. C. et al. Old-growth forest loss and secondary forest recovery across Amazonian countries. Environ. Res. Lett. 16 , 085009 (2021).

Brando, P. M. et al. Prolonged tropical forest degradation due to compounding disturbances: Implications for CO 2 and H 2 O fluxes. Glob. Change Biol. 25 , 2855–2868 (2019).

Mesquita, R. C. G., Ickes, K., Ganade, G. & Williamson, G. B. Alternative successional pathways in the Amazon Basin: successional pathways in the Amazon. J. Ecol. 89 , 528–537 (2001).

Jakovac, C. C., Peña-Claros, M., Kuyper, T. W. & Bongers, F. Loss of secondary-forest resilience by land-use intensification in the Amazon. J. Ecol. 103 , 67–77 (2015).

Barlow, J. & Peres, C. A. Fire-mediated dieback and compositional cascade in an Amazonian forest. Phil. Trans. R. Soc. B 363 , 1787–1794 (2008).

Jakovac, A. C. C., Bentos, T. V., Mesquita, R. C. G. & Williamson, G. B. Age and light effects on seedling growth in two alternative secondary successions in central Amazonia. Plant Ecol. Divers. 7 , 349–358 (2014).

Mazzochini, G. G. & Camargo, J. L. C. Understory plant interactions along a successional gradient in Central Amazon. Plant Soil https://doi.org/10.1007/s11104-019-04100-2 (2020).

Schnitzer, S. A. & Bongers, F. Increasing liana abundance and biomass in tropical forests: emerging patterns and putative mechanisms: Increasing lianas in tropical forests. Ecology Letters 14 , 397–406 (2011).

Tymen, B. et al. Evidence for arrested succession in a liana-infested Amazonian forest. J Ecol 104 , 149–159 (2016).

da Silva, S. S. et al. Increasing bamboo dominance in southwestern Amazon forests following intensification of drought-mediated fires. For. Ecol. Manag. 490 , 119139 (2021).

Carvalho, A. Lde et al. Bamboo-dominated forests of the southwest Amazon: detection, spatial extent, life cycle length and flowering waves. PLoS ONE 8 , e54852 (2013).

Adeney, J. M., Christensen, N. L., Vicentini, A. & Cohn‐Haft, M. White‐sand ecosystems in Amazonia. Biotropica 48 , 7–23 (2016).

Flores, B. M. & Holmgren, M. White-sand savannas expand at the core of the Amazon after forest wildfires. Ecosystems 24 , 1624–1637 (2021).

Veldman, J. W. & Putz, F. E. Grass-dominated vegetation, not species-diverse natural savanna, replaces degraded tropical forests on the southern edge of the Amazon Basin. Biol. Conserv. 144 , 1419–1429 (2011).

Silvério, D. V. et al. Testing the Amazon savannization hypothesis: fire effects on invasion of a neotropical forest by native cerrado and exotic pasture grasses. Phil. Trans. R. Soc. B 368 , 20120427 (2013).

Rull, V. A palynological record of a secondary succession after fire in the Gran Sabana, Venezuela. J. Quat. Sci. 14 , 137–152 (1999).

Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6 , 1032–1036 (2016).

Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9 , 269–278 (2019).

Willcock, S., Cooper, G. S., Addy, J. & Dearing, J. A. Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers. Nat. Sustain 6 , 1331–1342 (2023).

Davidson, E. A. et al. The Amazon basin in transition. Nature 481 , 321–328 (2012).

Hecht, S. B. From eco-catastrophe to zero deforestation? Interdisciplinarities, politics, environmentalisms and reduced clearing in Amazonia. Envir. Conserv. 39 , 4–19 (2012).

Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334 , 232–235 (2011).

Hawes, J. E. et al. A large‐scale assessment of plant dispersal mode and seed traits across human‐modified Amazonian forests. J. Ecol. 108 , 1373–1385 (2020).

Flores, B. M. & Holmgren, M. Why forest fails to recover after repeated wildfires in Amazonian floodplains? Experimental evidence on tree recruitment limitation. J. Ecol. 109 , 3473–3486 (2021).

ter Steege, H. et al. Biased-corrected richness estimates for the Amazonian tree flora. Sci. Rep. 10 , 10130 (2020).

Poorter, L. et al. Diversity enhances carbon storage in tropical forests: Carbon storage in tropical forests. Global Ecol. Biogeogr. 24 , 1314–1328 (2015).

Walker, B., Kinzig, A. & Langridge, J. Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2 , 95–113 (1999).

Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1 , 488–494 (2003).

Esquivel-Muelbert, A. et al. Seasonal drought limits tree species across the Neotropics. Ecography 40 , 618–629 (2017).

Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333 , 301–306 (2011).

Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1 , 369–374 (2018).

Morcote-Ríos, G., Aceituno, F. J., Iriarte, J., Robinson, M. & Chaparro-Cárdenas, J. L. Colonisation and early peopling of the Colombian Amazon during the Late Pleistocene and the Early Holocene: new evidence from La Serranía La Lindosa. Quat. Int. 578 , 5–19 (2021).

Levis, C. et al. How people domesticated Amazonian forests. Front. Ecol. Evol. 5 , 171 (2018).

Clement, C. R. et al. The domestication of Amazonia before European conquest. Proc. R. Soc. B. 282 , 20150813 (2015).

Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 355 , 925–931 (2017).

Coelho, S. D. et al. Eighty-four per cent of all Amazonian arboreal plant individuals are useful to humans. PLoS ONE 16 , e0257875 (2021).

de Souza, J. G. et al. Climate change and cultural resilience in late pre-Columbian Amazonia. Nat. Ecol. Evol. 3 , 1007–1017 (2019).

Furquim, L. P. et al. Facing change through diversity: resilience and diversification of plant management strategies during the Mid to Late Holocene Transition at the Monte Castelo shellmound, SW Amazonia. Quaternary 4 , 8 (2021).

Schmidt, M. V. C. et al. Indigenous knowledge and forest succession management in the Brazilian Amazon: contributions to reforestation of degraded areas. Front. For. Glob. Change 4 , 605925 (2021).

Tomioka Nilsson, M. S. & Fearnside, P. M. Yanomami mobility and its effects on the forest landscape. Hum. Ecol. 39 , 235–256 (2011).

Cámara-Leret, R. & Bascompte, J. Language extinction triggers the loss of unique medicinal knowledge. Proc. Natl Acad. Sci. USA 118 , e2103683118 (2021).

DiMiceli, C. et al. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006. https://doi.org/10.5067/MODIS/MOD44B.006 (2015).

Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digital Earth 6 , 427–448 (2013).

Staver, A. C. & Hansen, M. C. Analysis of stable states in global savannas: is the CART pulling the horse? – a comment. Global Ecol. Biogeogr. 24 , 985–987 (2015).

Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2 , 150066 (2015).

Mitchell, T. D. & Jones, P. D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25 , 693–712 (2005).

Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178 , 31–41 (2016).

Livina, V. N., Kwasniok, F. & Lenton, T. M. Potential analysis reveals changing number of climate states during the last 60 kyr. Clim. Past 6 , 77–82 (2010).

Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC Taylor & Francis Group, 1998).

Tuinenburg, O. A. & Staal, A. Tracking the global flows of atmospheric moisture and associated uncertainties. Hydrol. Earth Syst. Sci. 24 , 2419–2435 (2020).

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020).

Tuinenburg, O. A., Theeuwen, J. J. E. & Staal, A. High-resolution global atmospheric moisture connections from evaporation to precipitation. Earth Syst. Sci. Data 12 , 3177–3188 (2020).

Oliveira, R. S. et al. Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro‐topographic gradients. New Phytol. 221 , 1457–1465 (2019).

Mattos, C. R. C. et al. Rainfall and topographic position determine tree embolism resistance in Amazônia and Cerrado sites. Environ. Res. Lett. 18 , 114009 (2023).

NASA JPL. NASA Shuttle Radar Topography Mission Global 1 arc second. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 (2013).

Hess, L. L. et al. Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar. Wetlands 35 , 745–756 (2015).

Eberhard, D. M., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World . (SIL International, 2021).

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Acknowledgements

This work was inspired by the Science Panel for the Amazon (SPA) initiative ( https://www.theamazonwewant.org/ ) that produced the first Amazon Assessment Report (2021). The authors thank C. Smith for providing deforestation rates data used in Extended Data Fig. 5b . B.M.F. and M.H. were supported by Instituto Serrapilheira (Serra-1709-18983) and C.J. (R-2111-40341). A.S. acknowledges funding from the Dutch Research Council (NWO) under the Talent Program Grant VI.Veni.202.170. R.A.B. and D.M.L. were supported by the AmazonFACE programme funded by the UK Foreign, Commonwealth and Development Office (FCDO) and Brazilian Ministry of Science, Technology and Innovation (MCTI). R.A.B. was additionally supported by the Met Office Climate Science for Service Partnership (CSSP) Brazil project funded by the UK Department for Science, Innovation and Technology (DSIT), and D.M.L. was additionally supported by FAPESP (grant no. 2020/08940-6) and CNPq (grant no. 309074/2021-5). C.L. thanks CNPq (proc. 159440/2018-1 and 400369/2021-4) and Brazil LAB (Princeton University) for postdoctoral fellowships. A.E.-M. is supported by the UKRI TreeScapes MEMBRA (NE/V021346/1), the Royal Society (RGS\R1\221115), the ERC TreeMort project (758873) and the CESAB Syntreesys project. R.S.O. received a CNPq productivity scholarship and funding from NERC-FAPESP 2019/07773-1. S.B.H. is supported by the Geneva Graduate Institute research funds, and UCLA’s committee on research. J.A.M. is supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq grant 465501/2014-1; FAPESP grants 2014/50848-9, the National Coordination for Higher Education and Training (CAPES) grant 88887.136402-00INCT. L.S.B. received FAPESP grant 2013/50531-0. D.N. and N.B. acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820970. N.B. has received further funding from the Volkswagen foundation, the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 956170, as well as from the German Federal Ministry of Education and Research under grant no. 01LS2001A.

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Graduate Program in Ecology, Federal University of Santa Catarina, Florianopolis, Brazil

Bernardo M. Flores, Carolina Levis & Marina Hirota

Geosciences Barcelona, Spanish National Research Council, Barcelona, Spain

Encarni Montoya

Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

Boris Sakschewski, Da Nian & Niklas Boers

Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil

Nathália Nascimento & Carlos A. Nobre

Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands

Met Office Hadley Centre, Exeter, UK

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School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

Adriane Esquível-Muelbert

Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK

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Catarina Jakovac

Department of Plant Biology, University of Campinas, Campinas, Brazil

Rafael S. Oliveira & Marina Hirota

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Luskin School for Public Affairs and Institute of the Environment, University of California, Los Angeles, CA, USA

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Naturalis Biodiversity Center, Leiden, The Netherlands

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Contributions

B.M.F. and M.H. conceived the study. B.M.F. reviewed the literature, with inputs from all authors. B.M.F., M.H., N.N., A.S., C.L., D.N, H.t.S. and C.R.C.M. assembled datasets. M.H. analysed temperature and rainfall trends. B.M.F. and N.N. produced the maps in main figures and calculated transition potential. A.S. performed potential analysis and atmospheric moisture tracking. B.M.F. produced the figures and wrote the manuscript, with substantial inputs from all authors. B.S. wrote the first version of the ‘Prospects for modelling Amazon forest dynamics’ section, with inputs from B.M.F and M.H.

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Correspondence to Bernardo M. Flores or Marina Hirota .

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Extended data figures and tables

Extended data fig. 1 alternative stable states in amazonian tree cover relative to rainfall conditions..

Potential analysis of tree cover distributions across rainfall gradients in the Amazon basin suggest the existence of critical thresholds and alternative stable states in the system. For this, we excluded accumulated deforestation until 2020 and included large areas of tropical savanna biome in the periphery of the Amazon basin (see  Methods ). Solid black lines indicate two stable equilibria. Small grey arrows indicate the direction towards equilibrium. (a) The overlap between ~ 1,000 and 1,800 mm of annual rainfall suggests that two alternative stable states may exist (bistability): a high tree cover state ~ 80 % (forests), and a low tree cover state ~ 20% (savannas). Tree cover around 50 % is rare, indicating an unstable state. Below 1,000 mm of annual rainfall, forests are rare, indicating a potential critical threshold for abrupt forest transition into a low tree cover state 79 , 104 (arrow 1). Between 1,000 and 1,800 mm of annual rainfall, the existence of alternative stable states implies that forests can shift to a low tree cover stable state in response to disturbances (arrow 2). Above 1,800 mm of annual rainfall, low tree cover becomes rare, indicating a potential critical threshold for an abrupt transition into a high tree cover state. In this stable forest state, forests are expected to always recover after disturbances (arrow 3), although composition may change 47 , 85 . (b) Currently, the stable savanna state covers 1 % of the Amazon forest biome, bistable areas cover 13 % of the biome (less than previous analysis using broader geographical ranges 78 ) and the stable forest state covers 86 % of the biome. Similar analyses using the maximum cumulative water deficit (c) and the dry season length (d) also suggest the existence of critical thresholds and alternative stable states. When combined, these critical thresholds in rainfall conditions could result in a tipping point of the Amazon forest in terms of water stress, but other factors may play a role, such as groundwater availability 64 . MODIS VCF may contain some level of uncertainty for low tree cover values, as shown by the standard deviation of tree cover estimates across the Amazon (Extended Data Fig. 8 ). However, the dataset is relatively robust for assessing bistability within the tree cover range between forest and savanna 126 .

Extended Data Fig. 2 Changes in dry-season temperatures across the Amazon basin.

(a) Dry season temperature averaged from mean annual data observed between 1981 and 2010. (b) Changes in dry season mean temperature based on the difference between the projected future (2021−2050) and observed historical (1981−2010) climatologies. Future climatology was obtained from the estimated slopes using historical CRU data 128 (shown in Fig. 1a ). (c, d) Changes in the distributions of dry season mean and maximum temperatures for the Amazon basin. (e) Correlation between dry-season mean and maximum temperatures observed (1981–2010) across the Amazon basin ( r  = 0.95).

Extended Data Fig. 3 Changes in annual precipitation and ecosystem stability across the Amazon forest biome.

(a) Slopes of annual rainfall change between 1981 and 2020 estimated using simple regressions (only areas with significant slopes, p  < 0.1). (b) Changes in ecosystem stability classes projected for year 2050, based on significant slopes in (a) and critical thresholds in annual rainfall conditions estimated in Extended Data Fig. 1 . Data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), at 0.05° spatial resolution 127 .

Extended Data Fig. 4 Changes in ecosystem stability by 2050 across the Amazon based on annual rainfall projected by CMIP6 models.

(a) Changes in stability classes estimated using an ensemble with the five CMIP6 models that include vegetation modules (coupled for climate-vegetation feedbacks) for two emission scenarios (Shared Socio-economic Pathways - SSPs). (b) Changes in stability classes estimated using an ensemble with all 33 CMIP6 models for the same emission scenarios. Stability changes may occur between stable forest (F), stable savanna (S) and bistable (B) classes, based on the bistability range of 1,000 – 1,800 mm in annual rainfall, estimated from current rainfall conditions (see Extended Data Fig. 1 ). Projections are based on climate models from the 6 th Phase of the Coupled Model Intercomparison Project (CMIP6). SSP2-4.5 is a low-emission scenario of future global warming and SSP5-8.5 is a high-emission scenario. The five coupled models analysed separately in (a) were: EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-LR, TaiESM1 and UKESM1-0-LL (Supplementary Information Table 1 ).

Extended Data Fig. 5 Deforestation continues to expand within the Amazon forest system.

(a) Map highlighting deforestation and fire activity between 2012 and 2021, a period when environmental governance began to weaken again, as indicated by increasing rates of annual deforestation in (b). In (b), annual deforestation rates for the entire Amazon biome were adapted with permission from Smith et al. 83 .

Extended Data Fig. 6 Environmental heterogeneity in the Amazon forest system.

Heterogeneity involves myriad factors, but two in particular, related to water availability, were shown to contribute to landscape-scale heterogeneity in forest resilience; topography shapes fine-scale variations of forest drought-tolerance 135 , 136 , and floodplains may reduce forest resilience by increasing vulnerability to wildfires 65 . Datasets: topography is shown by the Shuttle Radar Topography Mission (SRTM; https://earthexplorer.usgs.gov/ ) 137 at 90 m resolution; floodplains and uplands are separated with the Amazon wetlands mask 138 at 90 m resolution.

Extended Data Fig. 7 The Amazon is biologically and culturally diverse.

(a) Tree species richness and (b) language richness illustrate how biological and cultural diversity varies across the Amazon. Diverse tree communities and human cultures contribute to increasing forest resilience in various ways that are being undermined by land-use and climatic changes. Datasets: (a) Amazon Tree Diversity Network (ATDN, https://atdn.myspecies.info ). (b) World Language Mapping System (WLMS) obtained under license from Ethnologue 139 .

Extended Data Fig. 8 Uncertainty of the MODIS VCF dataset across the Amazon basin.

Map shows standard deviation (SD) of tree cover estimates from MODIS VCF 124 . We masked deforested areas until 2020 using the MapBiomas Amazonia Project (2022; https://amazonia.mapbiomas.org ).

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