COMPREHENSIVE DAM FAILURE IMPACT FRAMEWORK
by
Fatemehossadat Mirhosseini
M.Sc., Azad University of Kerman, 2014

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF APPLIED SCIENCE
IN
ENGINEERING

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
August 2025
© Fatemehossadat Mirhosseini, 2025

ABSTRACT
While dams offer substantial benefits, they also present considerable risks in the event of failure,
particularly in light of increasing climate change concerns. Policymakers and risk assessors are
therefore intensifying efforts to enhance risk assessment practices and implement preventative
measures. A comprehensive understanding of the impacts associated with dam failures is critical
to improving these efforts and supporting evidence-based policy development. Although previous
studies have examined various impacts and some have attempted to integrate them within the
context of sustainability, a unified and realistic framework capturing both the short-term and longterm consequences, along with sustainability pillars interdependencies, has remained absent. To
address this gap, this study was conducted in three stages. In the first stage, the initial version of
the impact framework based on a systematic review of the literature was developed. In the second
stage, the framework was enhanced and expanded using artificial intelligence and data mining
techniques to ensure depth, accuracy, and relevance. In the third stage, the framework was
validated through a real-world case study: the Fundão Dam failure in Brazil. The resulting
comprehensive framework enables systematic comparison and analysis of dam failure impacts,
highlights under-researched areas, and provides a practical tool for decision-makers to prioritize
interventions and formulate targeted policies grounded in the significance of each impact.

ii

TABLE OF CONTENTS

ABSTRACT ................................................................................................................................... ii
TABLE OF CONTENT ............................................................................................................... iii
LIST OF TABLES ........................................................................................................................ v
LIST OF FIGURES ..................................................................................................................... vi
ACKNOWLEDGEMENTS ....................................................................................................... vii
CHAPTER 1: INTRODUCTION ................................................................................................ 1
I.

BACKGROUND ................................................................................................................... 1

II.

GAP ANALYSIS AND RESEARCH GOAL ................................................................. 3

III.

THESIS STRUCTURE .................................................................................................... 5

CHAPTER 2: DAM FAILURE IMPACT ASSESSMENT FRAMEWORK .......................... 7
ABSTRACT ................................................................................................................................... 7
2.1. INTRODUCTION.............................................................................................................. 8
2.2

METHODOLOGY ....................................................................................................... 9

2.3

RESULTS AND DISCUSSION ................................................................................. 16

2.3.1 AI TOOLS UTILIZED IN THIS STUDY ............................................................... 23
2.3.2. STATISTICAL ANALYSIS ................................................................................... 24
2.4. CONCLUSION AND FUTURE WORK ....................................................................... 30
CHAPTER 3: ADVANCING THE UNDERSTANDING OF DAM FAILURE IMPACTS:
FRAMEWORK VALIDATION WITH FUNDÃO DAM ....................................................... 32
ABSTRACT ................................................................................................................................. 32
3.1.

INTRODUCTION........................................................................................................... 33

3.2.

METHODOLOGY ......................................................................................................... 36

3.2.1 CASE STUDY ................................................................................................................ 36
3.2.2 METHODOLOGICAL APPROACH .......................................................................... 38
3.3.

RESULTS AND DISCUSSION ..................................................................................... 42

3.3.1. DAM FAILURE IMPACTS SCORES........................................................................ 51
3.4.

CONCLUSION AND FUTURE WORK ...................................................................... 52

CHAPTER 4: THESIS CONCLUSION ................................................................................... 54
4.1. DECLARATION.............................................................................................................. 56
REFERENCES ............................................................................................................................ 57
APPENDIX A .............................................................................................................................. 73
APPENDIX B ............................................................................................................................ 109
iii

APPENDIX C ............................................................................................................................ 113

iv

LIST OF TABLES
Table 1 Articles analysed in the first stage ................................................................................... 19
Table 2 References added alongside the Fundão LACTEC reports ............................................. 43
Table 3 Classes with available data .............................................................................................. 46
Table 4 Classes with no data……………………………………………………………………..46
Table 5 % of classes with available data………………………………………………………...47
Table 6 % of available indices data……………………………………………………………...47
Table 7 Summary of impact scores for Fundão dam failure ......................................................... 48
Table 8 Short-term and long-term impact scores of Fundão dam failure ..................................... 52
Table 9 Categories impact scores of Fundão dam failure………………………………………..52

v

LIST OF FIGURES
Figure 1 Impacts of dam failure .................................................................................................... 22
Figure 2 AI comparison ................................................................................................................ 24
Figure 3 Indicators statistics……………………………………………………………………..26
Figure 4 Comprehensive dam impacts framework………………………………………………29
Figure 5 Rio Doce Basin and the location of Fundao dam………………………………………38
Figure 6 Available Fundão impact indicators data ....................................................................... 45

vi

ACKNOWLEDGEMENTS
First and foremost, I would like to express my deepest gratitude to God for granting me the
strength, patience, and perseverance to complete this work despite the many challenges and
difficulties faced throughout the journey. I sincerely hope that the outcome of this research can
contribute meaningfully to the advancement of scientific knowledge and academic understanding
in the field. I am profoundly grateful to my supervisor, Professor Mauricio Dziedzic, for his
continuous support, valuable guidance, and encouragement throughout this study. His expertise,
constructive feedback, and unwavering commitment played a vital role in shaping the direction
and quality of this research. I would like to express my sincere gratitude to my committee
members, Dr. Steve Helle and Dr. Wenbo Zheng, for their valuable guidance and support.
I would also like to extend my appreciation to the University of Northern British Columbia
(UNBC) and all the professors who have taught and inspired me during my academic program.
Their knowledge and dedication have had a lasting impact on my academic development.
I am grateful to Afrin Naz, B.Tech student at NIT Trichy, for her valuable assistance in analyzing
AI tools and extracting indicators from the reviewed articles. Finally, I wish to thank all those who
supported me along the way, my dear Ehsan, friends, colleagues, and anyone who provided help,
motivation, or assistance, directly or indirectly. Your contributions, no matter how big or small,
are sincerely appreciated and will not be forgotten.

vii

CHAPTER 1: INTRODUCTION

I.
BACKGROUND
There are primarily two types of dams: water dams and tailings dams. The construction of dams
serves various purposes, including flood control, irrigation, hydropower generation, water supply
provision, recreational activities, and containment of environmentally hazardous sediments
resulting from mineral extraction processes (Limin Zhang 2016; Rana et al. 2022). While dams
offer numerous benefits, they also bring about potential risks to the environment, economy, and
human life. Dam failures represent one of the most catastrophic non-natural disasters, often
resulting from uncontrolled water release triggered by natural events, such as extreme weather and
earthquakes, structural deficiencies, or equipment malfunctions (Ramirez et al. 2022; Rana et al.
2022; Xiong. Y. 2011). Around 300 out of 36,000 large dams listed in the World Register of dams
have experienced accidents, as reported by the International Commission on Large Dams (ICOLD
2024). The inundation zone is the region impacted by the flow resulting from a dam break (FEMA
2013). One of the most prevalent disasters worldwide, comprising 34% of total catastrophes, is
floods. The frequency of flood occurrences, including those resulting from dam failures, is on the
rise and believed to be significantly due to the impacts of climate change and intensified
precipitation patterns (Zhang et al. 2022). Despite an increase in the number of tailings dams in
recent years, the number of tailings dams’ failure has remained relatively constant since 1966. The
most common causes of tailings dam failure are overtopping, construction quality issues, poor
management, weather hazards such as heavy rain and natural disasters such as earthquakes(Limin
Zhang 2016; Rana et al. 2022; Xiong. Y. 2011). The repercussions of dam failures encompass
environmental, social, and economic impacts (Zhang et al. 2022; Ge et al. 2019).
1

Notably, floods triggered by dam failures have accounted for over 40,000 deaths globally since
1965. Emphasizing safety as a fundamental principle, engineers design dams with the aim of
preventing such catastrophic events. In light of the increased awareness of dam failures since 1966,
and their consequences, coupled with the changing climate, the safety of dams and effective risk
management have become growing concerns (Rana et al. 2022).
Assessing the consequences of dam failure is crucial as it can offer valuable insights and
recommendations for enhancing dam safety. By understanding and mitigating the potential
consequences, it becomes possible to prevent or minimize the impacts of dam failures, thereby
safeguarding lives, infrastructure, and the environment (Ji et al. 2021). Different methods of
evaluating the impacts of dam failures were employed over time (EL Bilali et al. 2022). Numerous
studies have delved into the examination of the social, environmental, and economic repercussions
of dam breaks. For instance, Islam and Murakami (2021) conducted research on the environmental
impacts of mine tailings dam failures spanning from 1915 to 2020. Ji et al. (2021) explored the
environmental, social, and economic impacts of dam breaks, utilizing indicators such as drinking
water pollution, loss of life, and building damage. Many of these indicators, reflecting the impacts
of dam failures, are complex and sometimes difficult to measure. Additionally, some of them are
connected, such as the loss of life and economic ramifications (Ji et al. 2021). Kibler (2012)
proposed a Dam Assessment Model considering political, social, economic and environmental
impacts of dams but not the impacts of dam failures. Scarpelin et al. (2022) considered some
interactions between socioeconomic and environmental impacts of dam failure and proposed a
framework for a dam failure in Brazil. Gu et al. (2020) suggested a framework including some
factors related to social and environmental impacts after failure. Zhang et al. (2022) proposed a
framework for the environmental impacts after dam failure. Aqilah et al. (2024) considered the

2

dynamic aspect and interconnection between environmental, social, and monetary values in a
framework regarding the flood risk management after dam failure. The analysis was based on the
Triple Bottom Line (TBL) and sustainable Development Goals (SDG). Also, some researchers
worked on frameworks for a specific topic in one of those 3 main impacts. For instance, Wu et al.
(2019) and Mahmoud, Wang, and Jin (2020) suggested frameworks focusing on loss of life after
dam failure.

II.

RESEARCH GAP ANALYSIS AND GOAL

While numerous publications have explored various aspects of dam failures and some studies
proposed frameworks including the impacts after failure, there are some limitations such as each
study employs distinct impact indicators based on different criteria, making it challenging to
establish consistent comparisons. Some studies aim to cover all potential impacts of dam failures
but often rely on a limited set of indicators, with little attention to how these impacts interact.
There is still no comprehensive approach that provides a complete “big picture” of all impact
classes and indicators. Comprehensive assessments of both short- and long-term impacts based on
sustainability criteria are largely lacking.
Existing frameworks have several notable limitations. Most lack an explicit time structure and do
not clearly differentiate between short-term and long-term impacts. Also, many focus on only one
or two categories of impacts (e.g., a mix of social and environmental), excluding others and
providing limited category coverage. Even when all three categories are considered, only a few
impact classes within each are included, with minimal explanation that each class may have
multiple indices and indicators. In most impact cases, only a few indicators are listed, and the
specific impact indicators, indices, and classes requiring data collection are not clearly identified
or listed.
3

Many frameworks are designed specifically for mining dams or water dams, limiting
generalizability. In addition, some frameworks rely on a single indicator to represent an entire
category of impacts, and they over-simplify the indicators. Moreover, none explicitly address the
impacts associated with dam removal after failure. Minimal consideration of interconnections of
impacts is the other limitation, and rarely do existing frameworks examine how different impact
categories influence and interact with each other.
Sustainable development originated from environmental concerns and has been clearly defined
from the start, with quantitative indicators playing a key role in its framework (Hák, Janoušková,
and Moldan 2016) . In 2015, after numerous efforts to promote sustainability, global leaders came
together to adopt the 2030 Agenda for Sustainable Development, a landmark commitment to
uphold human rights and well-being while ensuring a sustainable planet. This agenda includes 17
Sustainable Development Goals (SDGs), which address the social, environmental, and economic
dimensions of sustainability (United Nations 2022). The framework developed in this study aligns
with these dimensions by encompassing all three categories of impacts—social, environmental,
and economic—thereby supporting the objectives of the SDGs.
This thesis addresses a critical gap in dam failure research: the lack of a comprehensive, systematic
framework that integrates all known impacts of dam failures from a sustainability perspective.
Such a framework is vital not only for capturing the full range of consequences but also for guiding
researchers, policymakers, and practitioners. It enables comparative analysis of different impact
types and supports meaningful comparisons between dam failure events by identifying the data
needed for robust risk assessment and informed decision-making. The framework developed in
this research is not specific to any particular type of dam, such as water or tailings dams. It was
built using data from a comprehensive database of dam failure impacts, aiming to include all

4

potential impacts that could occur following any type of dam failure—whether water, tailings, or
others.
The Dam Failure Impact Framework developed in this study was created through a three-phase
process. Accordingly, the thesis is organized into four chapters.

III. THESIS STRUCTURE
The structure of this thesis is as follows:
•

Chapter 1 introduces the thesis and outlines the main research objectives.

•

Chapter 2 details the development of the Dam Failure Impact Framework in two stages.

Stage one involved a comprehensive literature review to establish the initial framework. In stage
two, the framework was expanded using artificial intelligence and data mining techniques to
identify, organize, and categorize indicators with greater accuracy. This stage included
contributions from Afrin Naz, a B.Tech student from NIT Trichy, through the 2024 Mitacs
Globalink Research Internship. This chapter is an updated version of a paper published by the
Canadian Dam Association (CDA) in 2024.
•

Chapter 3 validates the framework through a case study of the Fundão Dam failure in

Brazil. This expanded version builds on a paper accepted for presentation at the CDA 2025
conference.
•

Chapter 4 summarizes the key findings, discusses limitations, and proposes directions for

future research.
Chapters 2 and 3 are presented as standalone studies, each with its own methodology, analysis,
and conclusions, rather than a unified approach across the thesis.

5

Together, these chapters represent a comprehensive effort to develop, refine, and validate a robust
framework for assessing dam failure impacts. The structure reflects the sequential progression of
the research and aims to support future work in this field.
All results and supporting materials are included in Appendices A, B, and C to ensure transparency
and enable further analysis.

6

CHAPTER 2: DAM FAILURE IMPACT ASSESSMENT FRAMEWORK

Fatemehossadat Mirhosseini, M.Sc. student at UNBC
Afrin Naz, Btech student, NIT Trichy
Mauricio Dziedzic, Chair, School of Engineering, UNBC

ABSTRACT
While previous studies have explored the environmental, social, and economic impacts of dam
failures, they often use inconsistent criteria and isolated indicators, making it difficult to compare
results or conduct comprehensive assessments. Additionally, many focus mainly on short-term
effects, overlooking long-term consequences and the interplay between different impact types.
This study addresses these gaps in two stages. First, a broad literature review identified key impact
areas and established the initial structure of an impact assessment framework based on
sustainability principles. In the second stage, artificial intelligence (AI) and data mining techniques
were applied to extract a more comprehensive set of impact indicators from the literature. Various
AI tools were evaluated to determine the most effective methods for indicator extraction and
classification, and the identified indicators were integrated into the framework. The resulting Dam
Failure Impact Framework provides a clear, holistic tool for assessing both short- and long-term
impacts. It supports policymakers, engineers, and researchers by enabling more informed
evaluations of environmental, economic, and social consequences, ultimately contributing to better
decision-making.

7

2.1. INTRODUCTION
The rapid growth of dam construction and increased development in downstream areas have
contributed to a significant global rise in potential high-hazard dams (FEMA 2013). In the United
States, the number of such dams grew from 14,726 in 2015 to 15,600 in 2021, according to the
American Society of Civil Engineers (ASCE 2021). The National Inventory of Dams (NID) 2022
update further reported a sharp increase to 17,387 high-hazard potential dams (FEMA 2025).
Recent dam failures have underscored the severe consequences of such incidents for human life,
the economy, and the environment. The 2019 Brumadinho dam collapse in Brazil claimed 270
lives and caused widespread environmental damage, contaminating over 300 kilometers of rivers
with toxic mud (Czajkowski et al. 2023). Similarly, the 2018 Xe-Pian Xe-Namnoy dam failure in
Laos resulted in 71 deaths and affected more than 14,000 people (Baird 2021).
Recent research has increasingly focused on developing frameworks to assess dam break impacts
and risks. For example, a review of 179 relevant studies led to a proposed framework centered on
three key impact areas: social, economic, and environmental. This framework incorporates factors
such as flood duration, depth, and inundation area, with the potential for future expansion to
include additional variables (Aqilah et al. 2024).
Other studies have introduced innovative approaches to dam safety assessment. One
comprehensive study applied a multi-criteria decision-making method to evaluate dam risk,
integrating structural integrity, hydrological conditions, and potential downstream impacts (Zhang
et al. 2022). Together, these studies highlight the need for comprehensive and flexible frameworks
to effectively assess and manage dam break risks.
A Systematic Literature Review (SLR) is a structured method used to identify, evaluate, and
organize existing research in a specific field. Its main goal is to compile all relevant studies to
identify research gaps and reduce bias in knowledge synthesis. While highly valuable, the SLR
8

process is often time-consuming and complex, sometimes taking over a year to complete the stages
of identification, screening, and analysis. To streamline this process, various tools have been
developed, among them, artificial intelligence (AI) has recently emerged as a powerful aid
(Bolaños et al. 2024).
In this study, a comprehensive dam impact framework was developed by combining insights from
the literature with AI and data mining techniques to enable a more robust and complete assessment.
AI refers to the simulation of human cognitive functions—such as learning, reasoning, and
problem-solving—by machines. One of its key strengths is the ability to learn from data and
improve performance over time (Fitria 2021). Although AI originated in the mid-1950s, its early
development was limited by data processing constraints and the complexity of mimicking human
thought. Recent technological advances have renewed interest in AI, driving its widespread
adoption across sectors as a tool for improving efficiency, gaining competitive advantages, and
enhancing performance (Venkatesh 2022).
Data mining, in simple terms, involves extracting meaningful and useful information from large
datasets. It helps structure and organize data into clear, analyzable patterns, making it easier to
interpret and apply (Sinha 2018).
The aim of this study is to apply SLR, AI, and data mining techniques to analyze existing
information from both academic and non-academic sources. The research was conducted in two
stages, described in detail in the methodology section.

2.2
METHODOLOGY
In the first stage of this study, a comprehensive literature review was conducted to examine the
impacts of dam failures. Searches were performed using Google Scholar and ScienceDirect. The
initial search used broad keywords such as “dam failure impacts” and “dam breach impacts,” with
9

article selection based on abstract review. A more focused search followed, targeting
sustainability-related impacts using keywords like “environmental impacts of dam failure,” “social
impacts of dam failure,” “economic impacts of dam break,” and “impacts framework.” Additional
keywords included “dam break and water quality,” “dam break flood,” “loss of life due to dam
failure,” “water dam failure,” and “tailings dam failure.” Only English-language publications were
considered.
The abstract-based screening yielded 140 articles. After full-text review, studies that focused
solely on dam structure or flood mapping were excluded, resulting in 62 articles for analysis. These
were categorized into seven groups based on the type of impacts addressed:
1.

Social impacts

2.

Environmental impacts

3.

Economic impacts

4.

Economic and social impacts

5.

Environmental and social impacts

6.

Environmental and economic impacts

7.

Environmental, social, and economic impacts

No publication date restrictions were applied, though most articles were recent. Few studies
offered a comprehensive framework that integrated all aspects of sustainability or aligned with the
Sustainable Development Goals (SDGs). Most research was case-specific, focused on particular
geographic regions.
Based on this review, a general framework was developed by identifying common impact themes,
classes, and patterns found in the literature. This initial structure laid the foundation for a more
detailed framework by organizing dam failure impacts into thematic categories.

10

After developing a general framework based on a comprehensive literature review of dam failure
impacts—covering environmental, social, and economic dimensions—an integrated structure was
created to account for both short-term and long-term consequences within each category. This
served as the initial version of the Dam Failure Impact Framework.
To refine and expand this framework, more detailed research was needed to identify specific
impact indicators within each category. The preliminary framework provided a category-based
classification, which was enhanced in Stage Two by incorporating relevant indicators from the
literature.
To ensure comprehensive coverage, the original set of sources was revisited, and additional
systematic searches were conducted using the same keyword strategy as in Stage One. Search
terms included: “dam failure impacts,” “dam breach impacts,” “environmental impacts of dam
failure,” “social impacts of dam failure,” “economic impacts of dam break,” “impacts framework,”
“dam break and water quality,” “dam break flood,” “loss of life due to dam failure,” “water dam
failure,” and “tailings dam failure.”
In addition to peer-reviewed academic publications, grey literature—such as industry reports,
government documents, and other non-academic sources—was included through targeted searches
on Google to broaden the study’s scope.
Given the large volume of literature and the potential for future expansion of the framework, this
study explored the use of data analytics tools to efficiently extract relevant indicators from
academic texts. This led to an investigation of artificial intelligence (AI) tools and their suitability
for academic research, specifically in the context of dam failure impact analysis.
With AI increasingly applied across disciplines, the study aimed to assess its potential to
systematically identify impact indicators from scholarly sources. However, concerns about the

11

reliability and accuracy of AI-generated results required a comparative evaluation of multiple
tools. The focus was on each tool’s ability to extract precise, contextually relevant indicators based
on user-defined queries—effectively testing their academic research capabilities in this domain.
A total of 42 AI tools were initially identified for potential use. The first round of screening
excluded tools that did not support PDF uploads in their free versions, as PDF is the standard
format for academic articles. This reduced the list to 20 tools.
In the second round, tools with excessive limitations—such as word count caps, slow response
times, low response relevance, or restricted feature access—were eliminated. The goal was to
ensure adequate functionality without requiring paid subscriptions. Selected tools were tested
using random academic articles uploaded manually. Tools that produced inaccurate, irrelevant, or
overly verbose responses, even after prompts were refined, were excluded.
Ultimately, 11 AI tools were retained for detailed comparison. These were assessed for accuracy,
contextual awareness, and responsiveness. The final recommended tools for indicator extraction
in dam failure research are: PopAi, ChatGPT, Perplexity, PDF.ai, ChatPDF, Sharly, TextCortex,
AvidNotes, LightPDF, Humata, and ChatDoc.
To evaluate and compare the performance of AI tools in extracting impact indicators from
academic literature, a random sample of 11 articles was selected. Key indicators were manually
extracted from these articles to serve as a benchmark.
A set of 12 standardized questions was then developed to test each AI tool’s ability to identify and
present relevant indicators. These questions were refined for clarity, consistency, and grammatical
accuracy to ensure optimal processing by the AI tools and to support the generation of accurate
and comprehensive responses.

12

Each AI tool was prompted with the finalized questions for all 11 articles, and their outputs were
compared to the manually extracted benchmarks. The initial evaluation used qualitative analysis
to assess the accuracy and completeness of responses. To improve objectivity and reduce bias, the
analysis then shifted to a quantitative approach, measuring each tool’s performance by the number
of correctly identified indicators.
The questions guided the AI tools to categorize impacts into environmental, economic, and social
domains and to distinguish between direct and indirect indicators. Additional questions requested
information on indicators used in cited research, enhancing the depth and relevance of the extracted
data. This approach maximized the tools' effectiveness in producing a detailed and accurate
inventory of dam failure impact indicators.
The 12 questions used for evaluation were:
1-In this paper, are impacts of dam failure assessed or an assessment method proposed?
a. If yes:
2-What categories of impacts?
3-Which direct impact indicators/indices of dam failure were used to calculate the target impact
category to achieve the goal of the author?
4-Which indirect dam failure impact indicators/indices were used to calculate direct
indicators/indices to show the impacts after dam failure?
5-Which direct indicators and/or indices are used or proposed to assess environmental impacts of
dam failure?
6-Which indirect indicators and/or indices are used or proposed to assess environmental impacts
of dam failure?

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7-Which direct indicators and/or indices are used or proposed to assess economic impacts of dam
failure?
8-Which indirect indicators and/or indices are used or proposed to assess economic impacts of
dam failure?
9-Which direct indicators and/or indices are used or proposed to assess social impacts of dam
failure?
10-Which indirect indicators and/or indices are used or proposed to assess social impacts of dam
failure?
11-What are the calculated indirect social, economic and environmental impact caused by direct
impacts of dam failure?
12-Can you provide details on the indicators used in the cited research papers for dam break impact
assessment? Mentioned in the literature review part.
To ensure that AI responses were limited strictly to the content of the uploaded article, a
standardized prompt was also included: “Only list the indicators that are mentioned in the paper.”
Before recording any data, indicators were divided into two categories: breach parameters and
impact indicators. Breach parameters describe the physical characteristics of the dam failure itself
(e.g., breach size and rate), while impact indicators reflect the broader consequences—
environmental, social, or economic—resulting from the failure. This distinction allowed for a more
structured and targeted analysis. As the study focuses on consequences rather than failure
mechanics, breach parameters were excluded from further analysis.
Following the comparative evaluation, two AI tools that most closely matched the manually
extracted indicators were selected for ongoing use. These tools were considered the most reliable
for automated indicator extraction. Using two tools allowed for cross-validation, increasing

14

confidence in the results and improving accuracy. Subscriptions were acquired to unlock full
functionality, and the analysis proceeded using both tools in parallel.
After extracting all relevant impact indicators, data mining techniques were applied to refine and
consolidate the results into a final set. This involved a data cleaning process to remove irrelevant
or non-measurable entries, such as structural or breach-related terms and vague phrases not
representing concrete impacts.
Each remaining indicator was assigned a reference number linking it to its source article to ensure
traceability. Duplicate or overlapping indicators with similar wording and meaning were then
merged into unified entries. For instance, 44 mentions of "life loss," "fatalities," "deaths," and
"missing

persons"

were

consolidated

into

a

single

indicator

labeled

“life

loss/fatalities/deaths/missing persons,” with all associated reference numbers listed in the final
table for transparency.
Next, similar indicators were grouped into indices representing specific impact types, making the
framework more organized and interpretable. Indicators that did not align with others remained as
standalone entries. These indicators and indices were then grouped into broader classes based on
common themes and assigned to one of the three primary domains: environmental, economic, or
social. Each class was further labeled as short-term or long-term, enabling temporal differentiation
within the framework.
All relevant data—indicators, indices, classes, and categories—are compiled in a comprehensive
table (Appendix A). This table reflects the hierarchical structure of the framework and includes
additional attributes to enhance usability, such as:
•

The proposed unit of measurement (from the source or defined by the study’s authors if

unspecified),

15

•

The method of calculation or the event/condition that triggered the indicator,

•

And whether each indicator reflects a direct or indirect impact.

Indicators were classified as indirect if they resulted from a chain of events rather than stemming
directly from the dam failure itself.

2.3

RESULTS AND DISCUSSION

In the first stage, 62 articles were reviewed to examine dam failure impacts across three main
categories: environmental, social, and economic. Only seven articles addressed all three categories
comprehensively or explored the interconnections among them—particularly by incorporating
monetary valuations of environmental and social impacts.
For example, Czajkowski et al. (2023) evaluated environmental and cultural/heritage damage
using the Contingent Valuation (CV) method. This survey-based approach estimated a lowerbound average willingness-to-pay of USD 137 among 5,195 Brazilians to prevent a similar
incident, equating to a total damage valuation of USD 7.69 billion. Ji et al. (2021) analyzed
economic losses alongside environmental impacts and fatalities. Fernandes et al. (2016) examined
socioeconomic and environmental consequences, including effects on landscapes, habitats,
fisheries, and public health. Sánchez et al. (2018) classified impacts into biophysical and socioeconomic-cultural aspects, including indirect effects like mine closures and job losses. Azam and
(Li 2010) compared dam failures before and after 2000, focusing on environmental pollution,
infrastructure damage, public health, and fatalities. Scarpelin et al. (2022) provided integrated cost
estimates of environmental, social, and economic impacts. Aqilah et al. (2024) evaluated both
direct and indirect impacts in Malaysia and calculated monetary losses across all three categories.

16

Only two studies focused exclusively on economic impacts. Kulkarni (2016)

assessed

rehabilitation costs for affected areas, while Muchanga (2023) analyzed income loss, reduced
working capital, and business disruption in Zambia.
Four articles explored both environmental and social impacts. Rana et al. (2022) reported rising
environmental consequences of tailings dam failures since 2014, while fatalities decreased. Silva
Rotta et al. (2020) used satellite imagery and soil moisture indices to assess suspended particulate
matter and land use. Gu et al. (2020) applied a fuzzy evaluation model to assess social and
environmental impacts of earth-rock dam failures. Guimarães et al. (2023) examined global dam
failures, noting their effects on water access, aquatic life, and legal reforms.
Environmental impacts were the most frequently studied, appearing in 29 articles. Examples
include: Glotov et al. (2018) : groundwater contamination and harm to river ecosystems; Zhang et
al. (2022): plant impacts across multiple species; Hatje et al. (2017): toxic metal contamination in
Brazil’s Doce River; Aires et al. (2018): post-failure land use and vegetation loss.
Social impacts were addressed in 19 articles, with a primary focus on life loss. Several studies
applied modeling approaches to estimate fatalities: EL Bilali et al. (2022) combined Monte Carlo
Simulation, HEC-RAS 2D, and HEC-LifeSim in Morocco. Cavalheiro Paulelli et al. (2023)
examined human health by analyzing urine samples one year after a dam break. Shandro et al.
(2017) uniquely focused on short-term effects on First Nations communities, using indicators such
as loss of traditional fishing, emotional stress, and administrative burdens.
Only one article, Ge et al. (2019) analyzed combined social and economic impacts, assessing life
loss and economic damage. Notably, no articles examined environmental and economic impacts
together.

17

Seven articles proposed frameworks for assessing dam failure impacts, with varying degrees of
comprehensiveness across sustainability dimensions. Two frameworks addressed all three impact
categories: Aqilah et al. (2024) assessed social (household, health, education, water/sanitation,
livestock, cultural losses), environmental (morphology, water quality, biodiversity), and economic
(property damage, lost labor, capital loss) aspects, with monetary valuation.
Scarpelin et al. (2022): used energy accounting to quantify monetary losses from Fundão’s dam
failure, including ecological degradation, social disruption, and landscape changes. Other
framework studies included Ge et al. (2019) who proposed risk factors such as dam height,
reservoir capacity, population at risk, and industry vulnerability. Ji et al. (2021) distinguished
between direct (life, economic, environmental) and indirect (social) impacts. Zhang et al. (2022)
focused on environmental indicators like geomorphic changes, pollution, and biodiversity loss.
Mahmoud, Wang, and Jin (2020) developed a life loss framework based on hazard, exposure, and
rescue capacity. Gu et al. (2020) proposed a social-environmental framework addressing people at
risk, infrastructure, cultural heritage, and ecological effects.
While recent frameworks address multiple impact categories, they often omit certain classes,
overlook long-term impacts, or fail to consider post-failure dam removal effects.
In response, this study proposes a new framework that builds on previous classifications and aligns
with Sustainable Development by addressing all three pillars—environmental, social, and
economic—over both the short and long term.

Table 1 summarizes the reviewed articles, indicating the types of impacts covered and whether a
framework was proposed.

18

Table 1 Articles analysed in the first stage

Articles

(Czajkowski
et al. 2023)
(G.
W.
Fernandes et
al. 2016)
(Ji et al. 2021)
(Sánchez et al.
2018)
(Azam and Li
2010)
(Scarpelin et
al. 2022)
(Aqilah et al.
2024)
(Kulkarni
2016)
(Muchanga
2023)
(Rana et al.
2022)
(Silva Rotta et
al. 2020)
(Gu et al.
2020)
(Guimarães et
al. 2023)
(Glotov et al.
2018)
(Zhang et al.
2022)
(Hatje et al.
2017)
(Aires et al.
2018)
(Lines et al.
2023)
(Santos et al.
2023)

Env.,
Economic Social
Environmental Social
Social and
economic impacts
and
impacts
impacts economic
and
env.
impacts
social
impacts
impacts
X

Impact
framework

X
X
X

X

X
X

X

X

X
X
X
X
X
X

X

X
X
X

X

X
X
X
X

19

(C. E. D.
Vieira et al.
2022)
(SantosGonzález et al.
2021)
(Islam
and
Murakami
2021)
(dos Santos
Vergilio et al.
2021)
(Zhang et al.
2021)
(Kütter et al.
2023)
(Wu et al.
2019)
(OliveiraFilho et al.
2023)
(Kossoff et al.
2014)
(Ramirez et al.
2022)
(Moraga,
Gurkan, and
Sebnem
Duzgun 2020)
(Nikl 2016)
(Ge, Li, et al.
2020)
(Mendes et al.
2023)
(Thompson et
al. 2020)
(Wang
and
Zhou 2010)
(Costa et al.
2022)
(De Biasi et al.
2023)
(Quaresma et
al. 2020)
(Nascimento
et al. 2022)

X
X
X
X
X
X
X
X
X
X
X

X
X
X
X
X
X
X
X
X
20

(Macklin et al.
2003)
(L. Fernandes
et al. 2022)
(P. I. N. de
Almeida et al.
2023)
(EL Bilali et
al. 2022a)
(Mahmoud,
Wang, and Jin
2020)
(Peng
and
Zhang 2012)
(Jiao, Li, and
Ma 2022)
(Cavalheiro
Paulelli et al.
2023)
(Huang et al.
2017)
(Ge et al.
2022)
(Ge 2021)
(Shandro et al.
2016)
(Cavalheiro
Paulelli et al.
2022)
(Luo 2009)
(Hsiao et al.
2021)
(de Oliveira et
al. 2022)
(Lumbroso et
al. 2021)
(Liu 2011)
(Faiqa
Norkhairi,
Thiruchelvam,
and
Hasini
2018)
(Shandro et al.
2017)
(Buch et al.
2024)

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

X
X
21

X

(DOĞAN et
al. 2014)
(Ge et al.
2019)

X
X

X

The proposed dam failure impact framework developed in Stage One is divided into two parts:
short-term and long-term impacts. Each part includes social, environmental, and economic
categories, further broken down into multiple classes.
The initial version of the framework (Figure 1) also accounts for the impacts of dam removal
following a failure—an area receiving growing attention. For example, Martinez et al. (2018)
analyzed the environmental footprint of dam removal, including on-site fossil fuel use and indirect
energy consumption. Jumani et al. (2023)

proposed a framework integrating removal

opportunities with hydro-ecological and socio-cultural variables.
The impact categories and their classes in this study—spanning both short- and long-term effects—
are derived from both explicit categories and inferred themes identified across the reviewed
literature.

Figure 1 Impacts of dam failure

22

In Stage Two, a total of 88 documents were analyzed to explore dam failure impacts in greater
detail. This included 72 academic publications and 16 grey literature sources, such as technical
reports, government documents, and other non-academic studies.
Incorporating both academic and grey literature ensured a more comprehensive understanding by
combining peer-reviewed scientific findings with professional insights. Together, these sources
formed the basis for extracting relevant impact indicators, which were later used to refine and
structure the study’s overall impact framework.

2.3.1 AI TOOLS UTILIZED IN THIS STUDY
The results of the AI tool comparison are shown in Figures 2. A quantitative evaluation was
performed to measure each tool's accuracy in extracting relevant impact indicators, using a
reference set of 101 manually identified indicators.
ChatDoc achieved the highest accuracy, correctly identifying 81 indicators for a match rate of
80.2%. Perplexity ranked second, with 72 matched indicators, yielding a 71.3% match rate. These
findings demonstrate that both tools are capable of recognizing complex, context-specific
information, with ChatDoc providing more comprehensive results.
This comparison underscores the potential of AI tools to support academic research while also
reinforcing the need for manual verification to ensure completeness and accuracy in critical
assessments.

23

MATCHE D IMPACT
IN DICATORS

59

72

53

44

67

81
53

69

53

66

23

Figure 2 AI comparison

2.3.2. STATISTICAL ANALYSIS
ChatDoc and Perplexity were used to extract impact indicators from a total of 88 sources. Through
this process, the AI tools identified 817 impact indicators along with their corresponding units.
These indicators were compiled into a comprehensive table, organized by category—
environmental, social, or economic—to support structured analysis. All source references are
listed in the study’s References section.
In this study, short-term impacts are defined as those occurring within the first five years after a
dam failure, while long-term impacts refer to those manifesting from year five onward, in
alignment with the Canadian Dam Association’s consequence classification guidelines (CDA
2016a).
After data cleaning, removal of duplicates, and classification into indices and thematic classes, an
initial set of 460 indicators was structured as:
•

80 short-term economic indicators

•

45 long-term economic indicators

•

80 short-term social indicators
24

•

48 long-term social indicators

•

103 short-term environmental indicators

•

104 long-term environmental indicators

Some indicators appeared in both short- and long-term categories due to variations in how authors
interpreted or discussed impact timelines. However, to count unique indicators in the framework
(some used in both short- and long-term categories), repeated ones are counted only once. The
counts are as follows::
•

80 short-term economic (unchanged)

•

28 long-term economic (after removing 17 duplicates)

•

80 short-term social (unchanged)

•

27 long-term social (after removing 21 duplicates)

•

103 short-term environmental (unchanged)

•

10 long-term environmental (after removing 94 duplicates)

This results in a final total of 328 unique indicators (non-repetitive indicators) in the framework
(Figure 3). Many of these indicators appeared in multiple sources, reflecting their broad relevance
and recognition across the literature.

25

Framework Indicator Counts by Category
120
100
80
60
40
20
0
Economic

Short-Term

Long-Term
(Before)

Duplicates
Removed

Long-Term
(After)

80

45

17

28

Social

80

48

21

27

Environmental

103

104

94

10

Economic

Social

Environmental

Figure 3 Indicators statistics

The framework is the result of a comprehensive literature review on the classification of post-dam
failure impacts. It distinguishes between short- and long-term effects, while also accounting for
dam removal impacts. Structured around the three pillars of sustainability—environmental,
economic, and social—it organizes impacts into thematic classes, each containing both individual
indicators and composite indices for integrated assessment. The impact indicators in this study,
including short-term and long-term impacts, are based on both directly considered by authors and
those indirectly referred to in the reviewed articles. The indicators and indices selected for the
developed framework are based on the literature review, where they were either explicitly
mentioned or could be inferred from interpretation of the reviewed studies.
In the environmental dimension, short-term impacts are divided into four main classes. The first
class, Geology, includes indicators related to changes in soil and sediment (1-1), and land use and
land cover (1-2). The second class, Ecology, covers a range of indicators including biochemical
changes (2-1), impacts on flora (2-2), and impacts on fauna (2-3), which are further divided into
26

terrestrial fauna (2-3-1) and aquatic fauna (2-3-2). Water-related impacts are addressed through
subcategories on water quality (2-4-1) and water resource availability (2-4-2). The third class
covers energy use and carbon emissions resulting from emergency operations and reconstruction.
The fourth class includes secondary environmental impacts arising from social and economic
disruptions. In the long term, the environmental structure remains largely consistent with the shortterm framework, excluding only the energy use and carbon emission class.
The economic dimension follows a similarly structured classification across both timeframes. In
the short term, the first class includes immediate economic losses such as service disruptions (11), business interruptions (1-2), economic downturns in the affected area (1-3), benefit losses (14), property loss (1-5), and infrastructure damage (1-6). The second class captures emergency
response and rehabilitation costs, including maintenance and restoration (2-1), alternative supply
arrangements (2-2), and evacuation or disaster logistics (2-3). The third class accounts for
secondary economic impacts stemming from environmental and social consequences, such as
heritage loss (3-1), environmental restoration costs (3-2), and impacts on agriculture and fisheries
(3-3). Long-term economic indicators include property and infrastructure rehabilitation (1-1 and
1-2), adaptation and recovery efforts (2-1 and 2-2), long-term economic trends (4), and extended
consequences from environmental and social impacts (5), which include environmental
degradation (5-1), health-related costs (5-2), and long-term heritage loss (5-3), all reflecting
ongoing economic vulnerability.
The social dimension addresses human-centered impacts of dam failure. Short-term social impacts
are grouped under immediate losses, including fatalities and life loss (1-1), casualties (1-2), loss
of livelihoods (1-3), cultural asset loss (1-4), displacement (1-5), opportunity loss (1-6), and
community resilience (1-7). The second class includes the disruption of social services. The third

27

class focuses on health and mental health impacts, covering mental issues (3-1) and physical health
problems (3-2). The fourth class represents secondary social impacts driven by environmental and
economic effects. In the long term, the social dimension captures broader structural changes,
including social cohesion (1), access to services (2), long-term livelihood recovery (3), and chronic
physical and mental health challenges (4). It also accounts for long-term vulnerabilities resulting
from environmental and economic consequences (5), offering a more complete picture of societal
burden. The full list of supporting references is provided in Appendix B.
The indicators and indices in this framework can be quantified through various established or
emerging methodologies, and their calculation remains an important area for future research.
While some indicators have defined measurement approaches in the literature, others require
further development or adaptation. Once quantified, indicators can be scored using standardized
scales to enable consistent comparison and evaluation across different cases. This framework
serves as a reference for researchers and practitioners by identifying key indicators relevant to dam
failure impact assessments. It is also designed to be flexible—new indicators can be added, and
existing ones refined over time to reflect updated knowledge, local priorities, or changes in policy.
Its adaptability ensures relevance across diverse assessment contexts while maintaining alignment
with evidence-based classifications.
While each classification includes numerous indicators, Figure 4 presents only the classification
levels and the associated indices, which themselves encompass multiple indicators. Due to the
large volume of data, individual indicators are not displayed in the figure. Instead, a full table of
indicators—including units and, where applicable, calculation methods sourced from the
literature—is provided in Appendix A. This appendix offers the complete dataset underlying the
framework and supports future analysis.

28

Figure 4 Comprehensive dam impacts framework

29

2.4. CONCLUSION AND FUTURE WORK
Dams provide significant benefits but also pose serious risks to downstream communities,
ecosystems, and economies. Dam failures are among the most destructive non-natural disasters,
often caused by earthquakes, structural weaknesses, or equipment malfunctions that lead to
uncontrolled water release.
The global increase in dam construction, along with intensified development in downstream areas,
has led to a sharp rise in high-hazard potential dams. In response, researchers have conducted
numerous studies to better understand and mitigate the consequences of dam failures. While some
of these studies proposed frameworks for impact analysis, many failed to account for important
factors—particularly the distinction between short- and long-term impacts. Although various
models have been developed to assess environmental, social, and economic effects, comprehensive
studies addressing all three domains remain limited.
Many existing studies rely on narrow sets of indicators, assumptions, or data availability, and often
focus on specific dams or dam types. Some assessments even use single indicators to represent
entire impact categories. As a result, a systematic framework capable of capturing the full range
of dam failure impacts and enabling cross-event comparisons had not yet been developed.
This research addresses that gap through a two-phase process to develop a comprehensive Dam
Failure Impact Assessment Framework. In Phase One, a foundational structure was built through
a systematic literature review. Impacts were categorized as short-term or long-term across three
main domains: economic, social, and environmental. Consideration was also given to impacts
associated with dam removal. This phase aimed to conceptually map how dam failure
consequences evolve over time and how they are commonly discussed in scholarly and
professional literature.

30

In Phase Two, the framework was expanded to include a complete set of indicators under each
category and index. The goal was to build the most inclusive and representative framework
possible. To achieve this, AI-assisted indicator extraction and data mining techniques were applied
to academic and grey literature, enabling the identification, classification, and refinement of
hundreds of relevant indicators. The result is a comprehensive, data-informed framework for
assessing the diverse consequences of dam failure.
The framework is both academically rigorous and feasible to apply. It is designed to support a
wide range of stakeholders—researchers, policymakers, emergency planners, and dam safety
engineers—in conducting thorough risk assessments, developing mitigation strategies, and
guiding policy and research efforts. Its transparent structure helps identify well-studied impact
areas and highlight those requiring further exploration. Additionally, it supports cross-comparison
of indicators, allowing users to evaluate trade-offs and synergies among different impact types.
Importantly, the framework is dynamic and adaptable. It can be customized to suit local, regional,
or sector-specific needs, and new indicators can be added as data and knowledge evolve. Future
work should focus on developing a scoring system to quantify the severity of each indicator and a
weighting method to assess their relative importance.
Finally, applying the framework to real-world case studies is essential for validation and
improvement. Case study applications will demonstrate its practical utility and help refine its
accuracy and relevance across diverse contexts. Each application offers feedback that can
strengthen the framework, ensuring it remains current, evidence-based, and fit for purpose.

31

CHAPTER 3: ADVANCING THE UNDERSTANDING OF DAM FAILURE IMPACTS:
FRAMEWORK VALIDATION WITH FUNDÃO DAM
Fatemehossadat Mirhosseini, M.Sc. student at UNBC
Maurício Dziedzic, Chair, School of Engineering, UNBC

ABSTRACT
The Rio Doce Basin, Brazil’s fifth-largest hydrographic basin, covers 83,400 km² across the states
of Minas Gerais and Espírito Santo. It includes 230 municipalities and is home to 3.5 million
people. In 2015, the catastrophic failure of the Fundão Dam released an initial 33 million m³ of
mining tailings, which flowed through the collapsed Santarém Dam, contaminating the Gualaxo
do Norte and Carmo Rivers before reaching the Atlantic Ocean. Heavy rains worsened the disaster,
increasing the total volume to 44 million m³. The event affected approximately 1.4 million people
across 40 municipalities, becoming the largest socio-environmental disaster in Brazil’s history.
Building on the author’s earlier work, which proposed a comprehensive framework for assessing
short- and long-term dam failure impacts, this article applies and refines that framework using data
from the Fundão Dam failure. The results help identify impact areas that require further research
and data collection.
Applying the framework to this event showed that while environmental impacts have been widely
studied—and remain an area for continued research—economic and social impacts require
significantly more in-depth investigation. Impact indicator values from the Fundão Dam failure
were normalized within the framework to support weighting and prioritization, as well as
comparison with other such events. This process helps guide policy decisions based on impact
significance and highlights key research gaps.

32

3.1. INTRODUCTION
The rising demand for raw materials to support infrastructure and human development has led to
a significant increase in natural resource extraction, particularly through mining. Today, mining is
one of the most widespread human activities globally. As a consequence, the volume of mining
waste—especially tailings—has grown substantially. Tailings are fine-grained waste materials
generated during ore beneficiation, often mixed with water or chemicals to form slurry-like
suspensions that require secure handling and storage. In 2010 alone, global tailings production was
estimated at approximately 14 billion tonnes. The most common method for managing tailings is
storage in engineered structures known as tailings storage facilities or tailings dams. These dams
are the primary means of isolating tailings to prevent environmental contamination. However, their
construction, operation, and long-term stability are increasingly concerning, especially as mining
activities become larger in scale and intensity. Although mining remains economically vital for
many countries and regions, tailings storage poses serious risks. When tailings dams fail, they can
release millions of cubic meters of toxic materials into surrounding ecosystems, often containing
heavy metals and hazardous substances. This may lead to severe land degradation, water
contamination, ecosystem disruption, and direct threats to communities that depend on local
natural resources for their livelihoods. In coastal areas, tailings can accumulate and cause longterm contamination, resulting in extremely high environmental recovery costs. Major failures have
occurred in Bolivia, Spain, South Africa, Italy, Romania, and Brazil, leading to both environmental
devastation and significant social and economic consequences. These repeated incidents have
intensified global concern over the safety and regulatory oversight of tailings storage facilities.
The frequency and scale of these disasters highlight the urgent need for improved monitoring,
stronger design standards, and more rigorous regulatory frameworks to ensure the long-term

33

stability of these structures(L. A. DA Silva Junior and Santos 2023; A. P. V. da Silva et al. 2022;
Gomes et al. 2017; Camêlo et al. 2024; Czajkowski et al. 2023; Aires et al. 2018; Bonecker et al.
2022; C. A. da Silva Junior et al. 2018; Lyra 2019; C. C. Pereira et al. 2024; Nascimento et al.
2022; dos Santos Vergilio et al. 2021) .
Mining has played a central role in Brazil’s economic and territorial development since the 17th
century. In modern times, iron ore has become Brazil’s most valuable mineral resource. The
Mariana and Ouro Preto regions are central to iron ore production, continuing a long-standing
tradition of mineral exploitation (Nogueira et al. 2021) .
Brazil is currently the world’s second-largest iron ore producer. In 2020, iron ore accounted for
82% of Brazil’s mining exports and 9.3% of the country’s total exports. The mining sector also
generates substantial employment. In 2019, Brazil recorded the highest percentage of direct mining
jobs, with Minas Gerais accounting for 31.6% of all such positions—3.5 times more than in
mineral processing and nearly 11 times more than in the broader mineral supply chain. These
figures highlight the sector’s economic importance and its role in sustaining local livelihoods.
However, the growth of the mining industry—especially in iron ore—has also brought serious
socio-environmental challenges, particularly related to tailings dam safety. This risk has been
highlighted by recent failures in historically significant mining areas (Frachini et al. 2021; Motta
and Borges 2021; Cardoso et al. 2022).
One of the most catastrophic failures was the Fundão Dam disaster. While numerous studies have
examined its impacts, most focus on specific aspects such as environmental contamination or
isolated economic and social losses. Few studies have adopted a multi-dimensional sustainability
approach that integrates the full range of impacts (Marta-Almeida et al. 2016; Carmo et al. 2017;
Gomes et al. 2017; D. de C. Silva et al. 2018; C. A. da Silva Junior et al. 2018; Aguiar et al. 2020;

34

Dadalto et al. 2020; Henrique de Moura, Bruno Rocha e Cruz, and De Genaro Chiroli 2020;
Matsunaga 2020; K. I. C. Vieira et al. 2020; Nogueira et al. 2021; H. A. Almeida et al. 2022a;
Bonecker et al. 2022; Cardoso et al. 2022; Daros et al. 2022; Euclydes, Pereira, and Pintodafonseca
2022; Evangelista et al. 2022; Merçon et al. 2022; Scarpelin et al. 2022; Miranda et al. 2024; Palma
et al. 2024; L. Fernandes et al. 2022).
This gap highlights the need for holistic frameworks that can capture the full spectrum of
consequences from large-scale tailings dam failures like Fundão. In response to the absence of an
integrated framework capable of organizing all available data and clarifying which impact areas
are most affected or understudied, this study applies the comprehensive impact assessment
framework to the Fundão case. The framework builds on the model introduced in Chapter 2 and
incorporates the complete range of sustainability dimensions—environmental, social, and
economic.
As part of this study, the proposed framework was applied and validated using data from the
Fundão disaster, marking the first comprehensive assessment model adapted to a large-scale
tailings dam failure. By systematically organizing and evaluating the disaster’s impacts, the
framework aims to provide a holistic view, identify knowledge gaps, prioritize research needs, and
support a more complete understanding of the event’s aftermath.
Ultimately, this work lays the foundation for developing a full impact framework for the Fundão
case. It offers actionable insights for policymakers and stakeholders, supporting the creation of
more effective public policies and contributing to the design of integrated risk management
strategies for tailings dams in Brazil and beyond.

35

3.2. METHODOLOGY
3.2.1 CASE STUDY
The Rio Doce Basin is the fifth-largest hydrographic basin in Brazil, covering an area of 83,400
km² across the southeastern states of Minas Gerais and Espírito Santo. Minas Gerais accounts for
86% of the basin’s drainage area. The basin is bordered by the Paraíba do Sul basin and Espírito
Santo’s southern coastal basins to the south, the Rio Grande basin to the southwest, the São
Francisco basin to the west, the Jequitinhonha basin to the north and northwest, and Espírito
Santo’s northern coastal basins to the northeast. Within Minas Gerais, the basin is divided into six
water resource management units, corresponding to the sub-basins of the Piranga, Piracicaba,
Santo Antônio, Suaçuí, Caratinga, and Manhuaçu rivers, each with its own River Basin
Committee. The basin is vital to the region, providing water for domestic, industrial, agricultural,
and energy production uses. It spans 230 municipalities with a population of 3.5 million people,
of which 209 rely exclusively on surface water—eight drawing directly from the Doce River
(Lactec 2020a; Alkimin De Lacerda, Bastos, and Graf De Miranda 2017) .
Population density is highest in municipalities such as Ipatinga, Governador Valadares, Aimorés,
Colatina, and Linhares. The region also hosts a major mining complex, including three reservoirs
related to iron ore processing: the Fundão and Germano dams for tailings storage, and the Santarém
Dam, which serves both as a water reservoir for industrial use and as a secondary containment
system for overflow from the tailings dams (Lactec 2020a; Alkimin De Lacerda, Bastos, and Graf
De Miranda 2017).
The basin generally experiences high temperatures year-round. While the Doce River is
significantly affected by droughts, the coastal region of Espírito Santo receives significant rainfall.
However, the basin is also prone to flooding, especially in low-lying urban areas during intense
36

rainy seasons. Irregular land occupation and reduced vegetation cover—98% of the basin lies
within the critically endangered Atlantic Forest biome—further contribute to its vulnerability
(Alkimin De Lacerda, Bastos, and Graf De Miranda 2017).
The region supports a wide range of economic activities, including agriculture (notably coffee,
sugar cane, and livestock), agroindustry (sugar and ethanol production), and the timber industry
(pulp and paper, reforestation). Trade and services have developed to support local industrial and
energy sectors. The basin contains 10 hydroelectric power plants and hosts Latin America’s largest
steel complex. On the Espírito Santo coast, 18 ports facilitate trade, supported by major highways
and the Vitória-Minas Railway, which connects Belo Horizonte to Vitória and is one of the few
Brazilian railways that also transport passengers (Alkimin De Lacerda, Bastos, and Graf De
Miranda 2017).
On November 5, 2015, the Fundão tailings dam, operated by Samarco Minerações S.A., collapsed
catastrophically. Approximately 44 million cubic meters of mining waste were released, initially
contaminating the Fundão and Santarém streams, then flowing into the Gualaxo do Norte, Carmo,
and Doce rivers. Over the course of 17 days, the waste traveled more than 650 km from Minas
Gerais to the Atlantic Ocean, depositing sediment along the way and resulting in the deaths of 19
people. The disaster is considered one of Brazil’s worst environmental and social tragedies,
affecting an estimated 1.4 million people across multiple municipalities. Due to its scale and
impact, it has been classified as a "very large-scale disaster," with extensive and long-lasting
consequences (Lactec 2020b). Figure 5 presents the geographic location of the Fundão Dam within
the surrounding region.

37

Figure 5 Rio Doce Basin and the location of Fundao dam (Palú 2019)

3.2.2 METHODOLOGICAL APPROACH
A dam failure impact framework—detailing classes, indices, and indicators—was introduced in
Chapter Two to systematically categorize the consequences of dam failures. This framework
organizes impacts into short-term and long-term temporal phases, each encompassing three core
categories: environmental, social, and economic. Within each category, various classes are
defined, grouping related indices and indicators. To apply the proposed framework to the Fundão
Dam failure, post-disaster reports were used as primary sources of impact data. These reports
provided structured evaluations of the disaster’s socio-environmental and economic consequences
(Alkimin De Lacerda, Bastos, and Graf De Miranda 2017; Bastos et al. 2017; Lactec 2017; 2018;
38

2020a; 2020b; 2020c; 2020d; 2020e; 2020f; n.d.; Alkimin De Lacerda 2021; Bastos and Horizonte
-Mg 2021b).
Lactec, one of Brazil’s leading research and innovation centers, conducted the impact assessments
under the Preliminary Adjustment Agreement signed by the Federal Public Prosecutor’s Office
(MPF), Samarco Mineração S.A., Vale S.A., and BHP Billiton Brasil Ltda. Lactec was tasked with
diagnosing socio-environmental damages from the Fundão Dam collapse, particularly within the
Doce River Basin and surrounding coastal zone (Lactec 2020b).
A total of thirteen reports were analyzed:
•

Three Baseline Reports (Bastos et al. 2017; Alkimin De Lacerda, Bastos, and Graf De
Miranda 2017; Lactec 2017)

•

Three Economic Impact Assessment Reports (Lactec 2020a; 2018; Alkimin De Lacerda
2021)

•

Seven Socio-Environmental Impact Assessment Reports (Lactec 2020b; 2020d; 2020c;
2020e; 2020f; Bastos and Horizonte -Mg 2021b; Lactec Institutes 2020)

The socio-environmental reports examined impacts on terrestrial and aquatic ecosystems, marine
environments, and cultural heritage (Lactec 2020b). Economic reports focused on how
environmental and social damages translated into broader economic effects on communities,
industries, and the regional economy (Alkimin De Lacerda 2021) .
All indicators with available pre- and post-failure data were extracted and assigned to the relevant
category, class, and index in the framework. For economic indicators, original Lactec-defined
weighted scales were noted, but not adopted. Instead, a new, objective scaling system was
developed to quantify the percentage change between pre- and post-failure values. Change is
defined as the difference between the value of the indicator before and after the dam failure. This

39

system measured the degree of change in each indicator by comparing its pre-failure and postfailure values, without assigning any judgment regarding the severity of impact. This scale ranges
from 1 to 10, based on percentage change:
1 = 0–10% change, 2 = 10–20% change, 3 = 20–30% change, 4 = 30–40% change, 5 = 40–50%
change, 6 = 50–60% change, 7 = 60–70% change, 8 = 70–80% change, 9 = 80–90% change, 10 =
>90% change

This approach ensured a purely data-driven analysis, without applying qualitative labels (e.g.,
“severe” or “minor”). The exception was for loss of life, a key social impact, which was scaled
according to the Canadian Dam Association CDA (2016a) classification:
1-none, 2-Low potential for multiple loss of life, 3- 10 or fewer, 4-100 or fewer, 5-more than 100
This classification was specifically designed to reflect the loss of life, ensuring consistency with
established dam safety guidelines while maintaining compatibility with the broader impact
assessment framework.
For long-term impact assessment, the percentage of change scaling used for short-term impacts
was deemed insufficient to accurately reflect the extent of change over time. Therefore, a different
scaling system was developed, focusing on the estimated recovery time for each indicator. Since
short-term impacts after a dam failure are considered within the first five years, long-term impact
classifications begin from the fifth year onward. This classification is based on the recommended
timeframes for impacts in CDA consequence classifications(CDA 2016b). To better capture all
impacts in the medium- and long-term periods and provide more precise scoring, a three-section
scale was proposed to assess impacts after the short-term period.
This scaling system is:

40

1 – Recovery within 5 to 10 years, 2 – Recovery within 10 to 25 years, 3 – Recovery taking more
than 25 years.
To enrich the dataset, a literature review was conducted using Google Scholar and ScienceDirect
with the search terms “Fundão Dam” and “Impacts of Fundão Dam failure.” Articles were
reviewed for inclusion based on relevance and whether they contained impact indicators not
covered in the Lactec reports. Duplicates, inaccessible files, or studies lacking indicators were
excluded. These additional sources contributed further before-and-after data for the framework.
Once all relevant indicators were collected and scaled, normalization was applied to bring all
values into a standardized (0,1) range (Gopal, Patro, and Kumar Sahu 2015). Indicators were
averaged within each index to produce normalized index scores. These were then averaged within
their respective classes, and subsequently within each category to generate final category scores.
Equations 1 to 3 illustrate the calculation process used to derive the overall impact score for each
category.

Equation 1 𝐼𝑆𝑜 =Ave [𝐼𝑆𝑠ℎ ; 𝐼𝑆𝐿 ]
Where:
𝐼𝑆𝑜 is overall impact score in a category
𝐼𝑆𝑠ℎ is the short-term impact score
𝐼𝑆𝐿 is the long-term impact score

Equation 2 𝐼𝑆𝑠ℎ =1/𝐶 ∑𝐶𝑐=1 (𝐴𝐼𝐶 (𝐴𝐽𝐼 ))
Equation 3 𝐼𝑆𝐿 =1/𝐶 ∑𝐶𝑐=1 (𝐴𝐼𝐶 (𝐴𝐽𝐼 ))
Where:
41

𝐴𝐽𝐼 is the average of indicator scores within each index
𝐴𝐼𝐶 (𝐴𝐽𝐼 ) is the average of index scores within each class
C is total number of classes in the category
c: is the index representing each class in the summation
Equations 2 and 3 follow the same hierarchical structure, with differences only in the temporal
scope (short-term vs. long-term).

3.3.RESULTS AND DISCUSSION
A total of 95 academic publications related to the Fundão Dam were initially identified. After
applying exclusion criteria (duplicates, inaccessible files, no indicators), 14 articles were selected
for inclusion in the study. From these, 31 indicators were extracted, of which 26 were integrated
into the framework: 3 as short-term economic, 9 as short-term social, and 14 as short-term
environmental indicators.
109 indicators were identified in the Lactec reports: 10 short-term and 6 long-term economic; 9
short-term and 3 long-term social; and 69 short-term and 5 long-term environmental indicators. Of
these, 82 were incorporated into the framework. Indicators were excluded if data were missing for
either the pre- or post-disaster period, or if they lacked clarity for interpretation. Ultimately, the
framework includes 110 indicators:
•

Economic: 12 short-term and 6 long-term

•

Social: 27 short-term and 3 long-term

•

Environmental: 57 short-term and 5 long-term

Figure 6 illustrates this distribution, and Table 2 provides a detailed list of the included articles,
the number of indicators used, and their corresponding sections within this study.

42

Table 2 References added alongside the Fundão LACTEC reports

Articles
(C. A. da Silva
Junior et al.,
2018)
(L. A. DA Silva
Junior & Santos,
2023)
(Aires et al. 2018)
(Vieira et al.,
2020)
(G. W. Fernandes
et al. 2016)
(Matsunaga,
2020)
(Nunes et al.
2022)
(Quadra et al.
2019)
(Cavalheiro
Paulelli et al.
2022)
(Motta and
Borges 2021)
(Coimbra,
Alcântara, and de
Souza Filho 2020)
(Almeida et al.,
2022)
(W. G. Pereira et
al. 2024)
(Quaresma et al.
2020)
(Lactec 2020b;
2020d; 2020c;
2020e; 2020f;
Lactec Institutes
2020; Bastos and
Horizonte -Mg
2021a)
Lactec 2020a;
2018; Alkimin De
Lacerda, Bastos,

Number
Economic Social
of
indicators
1
2

Environmental
X

X

4
1

X
X

1

X

1

X

3

X

1

X

5

X

1

X

1
1

X
X

1

X

1

X

69

15

X

X

43

X

and Graf De
Miranda 2021)
In several cases, complete sets of indicators for a given index—as originally defined in the
framework—were not fully available for the Fundão case. When only one indicator was accessible
for a specific index, that single indicator was used to represent the index in the Fundão framework.
Once the indicators and indices were organized, the Fundão Dam Failure Impact Framework was
applied to guide the calculation of average normalized values for broader impact categories. These
categories, defined in the original Dam Failure Impact Framework, represent high-level themes
that group related indicators and indices across short- and long-term impacts within the social,
economic, and environmental domains.
Due to data constraints, not all classes from the original framework are represented in the Fundão
case. Only those for which indicator data was available were included. Each classification
incorporates all relevant and accessible indices and indicators specific to Fundão.
To standardize results and allow comparison, the framework uses average normalized values for
each class by aggregating the normalized scores of all related indicators and indices. These
averages offer a consolidated measure of impact severity and help highlight which classes were
most affected by the Fundão dam failure.

44

5

12

6
27

57
3

Short-term economic (12)

Long-term economic (6)

Short-term social (27)

Long-term social (3)

Short-term environmental (57)

Long-term environmental (5)

Figure 6 Available Fundão impact indicators data

Table 3 and 4 presents the framework classes for which data was available and not available
respectively, in the case of the Fundão dam failure. In the economic short-term impact category,
data was available for Classes 1 and 3 of the original framework: (1) Immediate Economic Loss
and (3) Secondary Impacts from Environmental and Social Impacts. For the economic long-term
category, data was available only for Class 5: Secondary Impacts from Environmental and Social
Impacts.
In the social short-term category, data was available for Classes 1, 2, and 3: (1) Immediate Loss,
(2) Social Services, and (3) Health and Mental Issues. In the social long-term category, only Class
5 was represented: Secondary Impacts from Environmental and Economic Impacts.
For the environmental domain, both the short-term and long-term categories included data for
Classes 1 and 2: (1) Geology and (2) Ecology, as defined in the developed framework in Chapter
Two.

45

Table 3 Classes with available data

Category
Short-term Economic
Long-term Economic
Short-term Social
Long-term Social
Short-term
Environmental
Long-term
Environmental

Classes with available data
1-immediate economic loss,
3-secondary impacts from environment and social impacts
5-secondary impacts from environment and social impacts
1-immediate loss,
2-social service,
3-health impact and mental issues
5-secondary impacts from env and economic impacts
1-geology, 2-ecology
1-geology, 2-ecology
Table 4 Classes with no data

Category

Classes without data

Short-term Economic

2- emergency response and rehabilitation

Long-term Economic

1- property and infrastructure rehabilitation, 2- adaptation
and recovery, 3- research and regulation change, 4- longterm economic trend

Short-term Social

4-secondary impacts from environment and economic
impacts

Long-term Social

1-society cohesion, 2-social service, 3-lon-term livelihood,
4-chronic health and mental issues
3-energy use and carbon generation, 4-secondary impacts
from social and economic impacts

Short-term
Environmental
Long-term
Environmental

3-secondary impacts from social and economic impacts

Tables 5 and 6 show the percentage of data availability for each class, the percentage of available
indices within each class, the total percentage of available classes, and the total percentage of
available indices for the Fundão Dam failure within the framework.

46

Table 5 % of Classes with available data

Category

% of classes with data

Short-term Economic

66.67

Long-term Economic

20.00

Short-term Social

75.00

Long-term Social

20.00

Short-term
Environmental

50.00

Long-term
Environmental

66.67

Total % Of Available
Classes

46.33

Table 6 % of available indices data

Category

Fundão Classes

Short-term
Economic

1-Immediate economic loss

Long-term
Economic
Short-term
Social

Long-term
Social
Short-term
Environmental

% of indices
with data
50.00

3-Secondary impacts
From environmental and social impacts
5-Secondary impacts from
environment and social impacts

66.67

1-Immediate loss

28.57

2-Social service

100

3-Health impact and mental issues

100

5-Secondary impacts from
environmental
and economic impacts
1-Geology

100

2-Ecology

100

1-Geology

50.00
47

66.67

100

Long-term
2-Ecology
40.00
Environmental
Total % Of
33.53 %
Available
Indices
Following this classification, the average normalized impact values are calculated for each major
temporal and thematic category within the overall dam failure impact framework. Table 7 displays
the scores for indices, classes, and categories specific to the Fundão case. The complete dataset
and detailed tables are provided in Appendix C.
Table 7 Summary of impact scores for Fundão dam failure

Impact list
ECONOMIC SHORT
TERM
1-Immediate economic
loss
Disruption of local
businesses index
Property loss and damage
index
Infrastructure loss and
damage index
3-Secondary impacts
from
environment and social
impacts
Heritage loss and damage
index
Environmental restoration
index
ECONOMIC LONG
TERM
5-Secondary impacts
from
environment and social
impacts
Long-term environmental
damage index
Long term heritage loss
and damage index

INDEX
SCORE

CLASS
SCORE

CATEGORY
SCORE
0.46

0.24
0
0.5
0.22
0.63
0.63
0.62
0.95
0.95
0.90
1
48

SOCIAL SHORT TERM
1-Immediate loss
Deaths
Loss of cultural assets
index
2-Social service
Service supply index
3-Health impact and
mental issues
Mental issues index
Social unrest index
Health problems index
SOCIAL LONG TERM
5-Secondary impacts
from env
and economic impacts
Loss of cultural assets
index
ENV SHORT TERM
1-Geology
Contamination of soils and
sediments index
Damage to sediments and
sediments index
Soil environments change
index
Erosion and displacement
impact index
Land use/ land cover index
2-Ecology
Biochemical impact index
Flora index
Terrestrial fauna index
Aquatic fauna index
Water quality index
Water resource index
ENV LONG TERM
1-Geology
Soil and sediment index
2-Ecology
Flora index
Aqua fauna index
Terrestrial fauna index

0.42
0.49
0.75
0.22
0.39
0.39
0.39
0.72
0
0.44
1
1
1
0.48
0.52
0.65
0.28
0.67
0.67
0.37
0.43
0.22
0.37
0.35
0.62
0.83
0.17
0.92
1
1
0.83
0.50
1
1

49

Although some data is available for specific impact classes, significant gaps remain—especially
in areas essential for a comprehensive assessment. In the short-term economic impact category,
Class 2 (Emergency Response and Dam Rehabilitation) lacks data, limiting insights into
immediate economic consequences and recovery efforts. For long-term economic impacts, data is
missing in four of the five classes. Only Class 5 (Secondary Impacts from Environmental and
Social Changes) contains partial data, while critical areas such as Property and Infrastructure
Rehabilitation, Adaptation and Recovery, Research and Regulatory Change, and Long-Term
Economic Trends remain unrepresented. These gaps hinder analysis of sustained economic
consequences and institutional responses over time.
Short-term social impact data is similarly limited. While some direct impacts are documented,
there is no data on secondary social effects resulting from environmental and economic
disruptions, making it difficult to capture the broader societal ripple effects. Long-term social
impact data is sparse, with only Class 5 (Secondary Impacts from Environmental and Economic
Changes) containing limited information. Key classes—such as Long-Term Livelihood and
Employment, Community and Social Cohesion, Access to Social Services, and Chronic Health
and Mental Health Impacts—lack any data, preventing a full understanding of prolonged social
consequences.
In the short-term environmental category, data is missing for key areas such as Energy Use and
Carbon Generation, and Secondary Environmental Impacts from Social and Economic Disruption.
This restricts evaluation of the disaster’s broader environmental footprint. Likewise, in the longterm environmental category, no data was available for Secondary Environmental Impacts from
Social and Economic Changes, limiting assessment of how human activity and policy shaped
environmental recovery.

50

Overall, these data limitations highlight the need for improved data collection, standardized
reporting, and collaboration among researchers and institutions. In particular, the lower availability
of indicators in the social and economic domains—compared to the environmental category—
underscores the importance of enhancing data collection efforts in these areas to ensure a more
balanced and accurate assessment of dam failure impacts.

3.3.1. DAM FAILURE IMPACTS SCORES
After incorporating the Fundão Dam failure data into the proposed dam failure impact framework,
a single aggregated score was calculated for each impact category—environmental, social, and
economic. This involved analyzing individual indicators within each category and consolidating
them into an overall impact score. Initially, the assessment was conducted without applying any
weighting, ensuring an unbiased representation. However, the framework allows for adjustments
by introducing weighting factors based on indicator relevance and data availability. Exploring
alternative weighting systems is recommended to perform a sensitivity analysis and develop a
standardized approach for scoring across all indicators.
Based on the available data, social impacts attained the highest score, followed by environmental
and then economic impacts. In this assessment, all indicators and categories were treated with
equal importance. The total normalized impact score of the Fundão Dam failure was calculated as
0.70 on a scale from 0 to 1, where 1 represents the maximum possible impact. This score is based
on the available data for the case study and reflects the aggregated environmental, social, and
economic impacts identified. While the value should be interpreted in light of data availability and
potential information gaps, it can serve as a reference point or benchmark for assessing the relative
magnitude and distribution of impacts in other dam failure scenarios using the same framework.
51

Final impact scores are summarized in Tables 8 and 9, with values rounded to two decimal places
(see Appendix C).
Table 8 Short-term and long-term impact scores of Fundão dam failure

Category
Short-term Economic Impact
Long-term Economic Impact
Short-term Social Impact
Long-term Social Impact
Short-term Environmental
Impact
Long-term Environmental
Impact

Normalized Impact
Score
0.43
0.95
0.42
1
0.48
0.91

Table 9 Categories impact scores of Fundão dam failure

Category

Normalized Impact
Score
0.69
0.71
0.70
0.70

Economic Impact
Social Impact
Environmental Impact
Total Failure Impact

3.4.CONCLUSION AND FUTURE WORK
Building on previous work that developed a comprehensive framework for assessing both
immediate and long-term impacts of dam failures, this study applies the framework to the Fundão
Dam disaster. The primary goal is to collect, consolidate, and systematically organize all available
data related to the Fundão failure within this structured evaluation model. This approach enables
a side-by-side comparison of impact indicators across environmental, social, and economic
categories, offering a holistic understanding of the disaster’s consequences.

52

This report also demonstrates the functionality of the framework by systematically aggregating
impact data, allowing for the identification of data gaps and the distribution of impacts across
various dimensions.
In 2015, the mining complex in Mariana, Minas Gerais, Brazil, experienced a catastrophic event
when the Fundão Dam collapsed, releasing 33 million cubic meters of mining waste. The waste
traveled downstream through the Santarém Dam, contaminating the Gualaxo do Norte and Carmo
Rivers, and eventually reaching the Atlantic Ocean. Heavy rainfall further worsened the situation,
increasing the total volume of tailings to 44 million cubic meters.
By mapping the available data into the framework, this study assesses the scale and distribution of
impacts and identifies critical gaps in information. This process helps determine which
categories—whether environmental degradation, social disruption, or economic loss—require
further research, targeted data collection, or increased attention from experts and policymakers. In
doing so, the study not only evaluates documented impacts but also highlights areas of uncertainty,
promoting a more data-driven response to future disasters.
Furthermore, the research underscores the need for a robust and adaptable indicator system that
reflects the relative importance of each impact class, and category. Such a system should be
tailored to the specific context of the dam, region, or sector, enhancing the framework’s utility for
comparative analysis, disaster planning, and impact mitigation.

53

CHAPTER 4: THESIS CONCLUSION
While dams provide significant societal benefits—such as water supply, hydroelectric power,
flood control, and irrigation—they also pose serious risks when not properly designed, maintained,
or operated. Structural deficiencies, poor maintenance, human error, and the growing influence of
climate change all contribute to the potential for dam failures. When such failures occur, the
consequences can be catastrophic, including loss of life, extensive environmental damage, and
severe economic disruption.
To mitigate these risks, it is essential to enforce robust policies and regulations for inspection,
maintenance, and emergency planning. Despite ongoing efforts by scientists, engineers, and
policymakers to enhance dam safety and promote best practices, dam failures continue to happen
worldwide. A key challenge remains: understanding the full scope of potential impacts and their
interconnections to support more effective risk assessments and policy development.
Although numerous studies have examined the consequences of individual dam failures, there has
been a lack of a unified, systematic framework to comprehensively assess these impacts—
particularly from a sustainability perspective that considers environmental, social, and economic
dimensions. This thesis addresses that gap by proposing an integrated impact assessment
framework for dam failure events. The framework compiles and organizes indicators from existing
literature into a cohesive structure to support broader understanding and informed decisionmaking. Designed to be both adaptable and expandable, the framework allows researchers and
practitioners to tailor it to different contexts and incorporate additional indicators. By offering a
more complete picture of dam failure consequences, the framework aims to improve planning, risk
mitigation, and policy development to help prevent future disasters.

54

Recognizing the increasing role of artificial intelligence (AI) in research, this study also explores
how AI can support dam failure analysis. AI tools were used to streamline data collection,
classification, and indicator extraction, enhancing the overall efficiency of framework
development. To validate its real-world applicability, the framework was applied to the 2015
Fundão Dam failure in Brazil, one of the most devastating such events in history. This case study
demonstrated the framework’s ability to capture and organize diverse impacts and underscored its
potential for broader application in risk assessment and policy-making.
Applying the framework also highlighted data gaps and areas requiring further research. By
systematically mapping the impacts, the framework helps identify underrepresented dimensions
and informs future data collection, policy development, and research priorities.
It is important to acknowledge that different failure scenarios can result in varying impact profiles.
The failure mode of a dam is a critical factor influencing the type and extent of post-failure impacts.
Similarly, the available response time following a failure plays a significant role in shaping the
severity and scope of those impacts. Although these factors are not explicitly included within the
framework, they are closely related to the magnitude and distribution of impacts observed after a
dam failure, and the impacts under different failure modes can be compared by using the
framework.

Despite its contributions, the study has several limitations. First, while AI tools improved
efficiency, their use carries a risk of misclassification, omission, or incomplete data due to inherent
limitations in automated extraction. Second, methods for calculating impact indicators vary in
geographic scope, assumptions, and measurement techniques, which can affect comparability
across studies. Additionally, in cases with multiple data sources, only one dataset was used for

55

each indicator, potentially introducing bias. Uneven data availability across the environmental,
social, and economic dimensions also complicates cross-category comparisons and may lead to
misleading conclusions.
To address these limitations, future research should focus on developing standardized weighting
methods to reflect the relative importance of indicators based on context and stakeholder needs.
Incorporating multiple data sources per indicator would improve reliability, while regular updates
and classification refinements would ensure the framework remains current and relevant as new
insights emerge.
4.1. DECLARATION
The ChatGPT AI tool was employed for reviewing the text on this paper.

56

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72

APPENDIX A

The following tables present all dam failure impact indicators, including influencing
factors, and any associated units of measurement.
•

Blue highlight means author added to the list based on understanding from literature

•

Red statement means author added to the list

•

First column showing the indicator number representing the reference

•

D represent direct and I represent indirect and referred to the direct indicator derived from
by” I from.”

•

Same color sections mean the group of one index

*Some of indicators include two or more words, but to prevent duplication of indicator in
different indices and classifications, each indicator is classified in one group, for example
an indicator is “fatalities and casualties, only it is considered in fatalities index.
* Some indicators are in both short term and long-term classifications.

73

1- Economic Impact Indicators:

Direct or indirect

Indicator no

indicator name

Subcategory (how the indicator can be calculated or due
to)

unit

Short term (up to 5 years)
1-IMMEDIATE ECONOMIC LOSS
Service Disruptions INDEX
736
194
666

D
D
D

15

D

395,345
667

D
D

668

D

756,581,780

D

32,282

D

72
241,289,523

D
D

606

D

663

D

303

D

12o,95,633,3
47

D

Electricity Supply Loss
water supply loss
Lost irrigation water
supply
Economic loss due to
disruption in water
supply and other water
uses
Ecosystem service loss
Lost municipal &
industrial water supply
Lost hydropower
generation
Disruption to transport
service

-Loss of megawatts
Water quality

$
$
Water supply: acre-feet/$
$

$
acre-feet/$
MWh (megawatt hours)
and $

DISRUPTION OF LOCAL BUSINESSES INDEX
Disruption of local
businesses
Business closures
Business interruption

$

241- due to downtime of mining and processing operations
523- Loss of Economic Activity,revenue loss

Decrease in local
business revenues
Initial changes in
industry
value due to alterations
in final demand.
Economic loss in local
agriculture
Impacts on local
fisheries and agriculture

$
$
$
$

due to flooding of farmland, leading to loss of livelihood (measured
in currency based on agricultural outputs
120-access and output losses

$
$

95- Reduction in fish biomass

323,788,531,
512,564

D

Loss of agricultural
productivity

81,442,802,3
74
128

D

733

D

270
688

D
D

Loss of income from
fishing and recreation
Local revenue loss from
tourism, fishing, and
agriculture
Variations in
governmental revenue
from oil production
Loss of Revenue
monthly sales, monthly
customers, LOSS

D

347- Environmental Impacts affecting fish populations and water
quality
323-Economic dependency on agriculture (e.g., percentage of
income derived from farming
565- crop yield loss
81-LOSS OF INFRASTRUCTURE AND FACILITIES

$

$
$

Closure of oil ports and
disruption of local economies

$

$
$

74

41

D

40
359

D
D

290

I from Disruption of
local businesses index
I from Disruption of
local businesses index

Local Tax Revenue Loss

419

D

297

D

Economic downturn in
affected areas (e.g.,
unemployment rates)
Interruption Economic
Activities

25

D

255

D

741

capital loss of
production
labor reduction
Effects on fisheries and
commercial activities
linked
to changes in
biodiversity and fish
stock dynamics.

$
$/NUMBER
$

Decrease in tax collections due to property damage and business
closures

Debt Repayment Issues

ECONOMIC DOWNTURN INDEX IN AFFECTED AREAS

629

$

Economic value
loss
loss of resources
Economic
downtime due to
infrastructure
damage

$

$

172,566,417

D

Economic downturn in
local industries

661

D

Benefit Losses: The loss
of future benefits

$

BENEFIT LOSSES INDEX

39

D

Dam benefit losses
(agriculture, recreation)

12,68,782,22
8,305,354,38
7,416,539,
669,676,702,
38,628,280,4
97
157,182,703,
555, 580,
721

D

property
destruction/damage

D

Property loss/destroyed

D

Damage to Residential
and Basic Security
Facilities

766

D

100
522

D
D

770

D

Property damage
incurred due to floods
and debris flows
Impact on properties,
Damage to public
facilities and homes.
recovery costs for
affected properties

-Lost flood control benefits ($)
-Reductions in tourism (%)
-Changes in employment (number of jobs)

Lost benefits: $ (dollars)
Recreation visits: number
of visits

PROPERTY LOSS AND DAMAGE INDEX
NUMBER OR PERCENT

75

499,812

D

Changes in local
property values
Increased costs of living

44

I from Property loss
and damage INDEX

675,623,731

D

impacts on
infrastructure

727

D

impacts on roads,
buildings, and essential
services

23,50,153,
262,284,322,
355,424,500,
511,769,642,
704,724,778,
63,103,159,2
03,486,554,5
77,73,508

D

Infrastructure damage

189,737,762,
809,396
648

D

Infrastructure loss

D

614

D

Damage to roads,
railways, utilities
damage to
infrastructure (roads,
accommodation)

719

D

183

D

650

D

619

D

48

I from Infrastructure
loss and damage INDEX

INFRASTRUCTURE LOSS AND DAMAGE INDEX

686,380,161

D

604
755

D
D

731-Damage to roads, bridges, and
communication networks.disruption of electricity and
communication lines

724-Flood wave height: Measurement of flood intensity
(e.g., maximum height of flood waves).
Inundation area: Geographic area inundated by flood waters.,Flow
progression over time,Water depth and extent of flooding,Water
depth and extent of flooding,Time of wave arrival at key cities

724- Flood Wave Height:
Meters (m).
Inundation Area: Square
kilometers (km²).
Initial Lake Level: Meters
(m)

778- Measured in terms of length of roads and railways washed out,
and the number
of bridges destroyed.

778- LENGTH AND
NUMBER

577- Flood Inundation Depth,Flood Inundation Area

159-number ,%

Area of production facilities affected, Area of inundated land (e.g.,
farmland,
infrastructure),Flow rate of released water, volume of tailings
released.

Area (square metres), flow
rate released water (cubic
meter per second),
damaged area (square
meters), no. of affected
population (count),
saturation line depth
(meter), pressure
distribution (pascal)

Damage to
Infrastructure (including
road, traffic, and
communication
facilities)
important facilities
affected
Damage to commercial
areas
Costs associated with
infrastructure repair
and loss of productivity
Production cost change

$

2-EMERGENCY RESPONSE AND REHABILITATION
IMPLICATIONS OF MAINTENANCE AND RESTORATION INDEX
Cost of repair or
686-number of months closed
replacement
Immediate repair costs
restoration or
compensation efforts
except environment
and social

$

ALTERNATIVE SUPPLY INDEX

76

207

D

196

D

alternative water supply
methods (e.g., water
tank trucks
Temporary water
treatment

D

$

alternative services

EVACUATIONS AND OTHER DISASTER RESPONSE INDEX
195,268,478,
498,530,634,
672,814
726,154,712,

D

emergency responses

D

Cost of potential
evacuations/
Emergency evacuations
Community
displacement and
health-related costs
Insurance Claims

$

391

288,190,810,
423
46

D

671
698

D

People NUMBER

Total value claimed by affected property owners
D

temporary classroom
setup
Temporary structures

$
$
$

3-SECONDARY IMPACTS FROM ENVIRONMENT AND SOCIAL IMPACTS
HERITAGE LOSS AND DAMAGE INDEX
78

D

Damages to heritage
sites along the Doce
River

COUNTS

79

D

Losses related to local
cultural assets
Food Shortages

COUNTS

D
740
I from Food Shortages

Increased costs of living
due to resource scarcity

D

Environmental
restoration
Economic losses
associated with
environmental pollution
Cleaning-up costs

-percentage reduction
in agricultural output
Or % increasing of the price

$

420

ENVIRONMENTAL RESTORATION
670
D
52
43,163,302,5
69,586,638,7
84,242

D

$

586-Land price, clean up budget
D

47

Health costs(postdisaster disease costs,
and psychological
impact-related
workdays lost)

$

ECONOMIC CONSEQUENCES FOR LOCAL AGRICULTURE AND FISHING INDUSTRIES
572

D

592

D

Economic consequences
for local agriculture
and fishing industries
due to contaminated
resources
potential crop yield
losses DUE TO Changes
in agricultural soil
quality

$

77

359

D

Effects on fisheries and
commercial activities
linked
to changes in
biodiversity and fish
stock dynamics.

D

commercial fishery
yields due to
impacts on fish
populations.

Direct or indirect

Indicator name

655

$

Subcategory (how the indicator can be calculated or
due to)

Indicator no

unit

Long term (more than 5 years)
1-PROPERTY AND INFRASTRUCTURE REHABILITATION
PROPERTY LOSS AND DAMAGE INDEX
12,68,782,2
28,305,354,
387,416,53
9,
669,676,70
2,38,628,28
0,497,
157,182,70
3,555, 580,
721

D

Property destruction
/damage

D

Property loss/destroyed

D

Damage to Residential
and Basic Security
Facilities

766

D

100
522

D

Property damage
incurred due to floods
and debris flows
Impact on properties,

770

D

NUMBER OR PERCENT

D

D
709,499,771,812

Damage to public
facilities and homes.
recovery costs for
affected properties
Changes in local
property values

$

INFRASTRUCTURE LOSS AND DAMAGE INDEX
675,623,
731
727

D

impacts on
infrastructure

D

impacts on roads,
buildings, and essential
services

23,50,15
3,
262,284,

D

Infrastructure damage

731-Damage to roads, bridges, and
communication networks.disruption of electricity and
communication lines

724-Flood wave height: Measurement of flood intensity
(e.g., maximum height of flood waves).

78

724- Flood Wave Height:
Meters (m).
Inundation Area: Square
kilometers (km²).

322,355,
424,500,
511,769,
642,704,
724,778,
63,103,1
59,203,4
86,554,5
77,73,50
8
189,737,
762,809,
396
648

Inundation area: Geographic area inundated by flood waters.,Flow
progression over time,Water depth and extent of flooding,Water
depth and extent of flooding,Time of wave arrival at key cities
778- Measured in terms of length of roads and railways washed out,
and the number
of bridges destroyed.

Initial Lake Level: Meters
(m)
778- LENGTH AND
NUMBER
159-number ,%

577- Flood Inundation Depth,Flood Inundation Area

D

Infrastructure loss

D

Damage to roads,
railways, utilities
damage to
infrastructure (roads,
accommodation)

614

D

719

D

183

D

650

D

619

D

Area of production facilities affected, Area of inundated land (e.g.,
farmland,
infrastructure),Flow rate of released water, volume of tailings
released.

Damage to
Infrastructure (including
road, traffic, and
communication
facilities)
important facilities
affected
Damage to commercial
areas
Costs associated with
infrastructure repair
and loss of productivity

Area (square metres), flow
rate released water (cubic
meter per second),
damaged area (square
meters), no. of affected
population (count),
saturation line depth
(meter), pressure
distribution (pascal)

$

2-ADAPTATION AND RECOVERY
SOCIETY ADAPTATION
325

D

605

D

692

D

Welfare(Effects on local
communities and
welfare)
Compensation paid to
victims
Worker’s migration

I from Worker’s
migration

increasing wages and
products costs

$
$
NUMBER
/cost

RESTORATION AND ECONOMIC RECOVERY INDEX
574
746
710
139
540
13
389
557
261

D

Restoration and
economic recovery
except for
environmental and
social in long term
746-years to rebuild infrastructure)
139-Financial estimates for ecosystem recovery.

79

$

591

D

673
651

D

344,585

D

Costs of remediation
and potential fines
related to failure
Dam
repair/replacement

$

3-RESEARCH AND REGULATION CHANGE

200

D

Remediation study and
research(financial
feasibility and cost
estimation of
remediation efforts)
Adaptation strategies
cost

.
*$
$

4-LONG TERM ECONOMIC TREND
ECONOMIC TREND index
33
250
544

D

Long-term economic
decline

432

D

811

D

664

D

425,122,164

D

256,556

D

130

D

747

D

715,772

D

Alteration of economic
flow
Long-term Economic
Loss in Affected Areas
Changes in regional
economic output
Changes in local
economic activity
impacts on local
industries and business
Long-term market value
decline of local
resources
changes in investment
levels
insurance costs

$

164-due to water contamination

$

$

5-SECONDARY IMPACTS FROM ENVIRONMENT AND SOCIAL IMPACTS
LONG-TERM ENVIRONMENTAL DAMAGE INDEX
247

D

258

D

254,597,662

D

670

D

314

D

196

D

590

D

449, 165

D

Long term
environmental cleanup
and restoration
Economic assessments
of long-term
environmental damage
costs associated with
remediation
Environmental
restoration costs
impact local economies
reliant on resources
water treatment

$

LONG-TERM HEALTH IMPCT INDEX
long-term costs
associated with HM
exposure
Long term Cost of
Health Care

$/number

LONG TERM HERITAGE LOSS AND DAMAGE INDEX
78

D

Damages to heritage
sites along the Doce
River

COUNTS

80

D

79

Losses related to local
cultural assets

COUNTS

2- Social Impact Indicators:
Indicator
no

Direct or Indirect

Indicator name
Subcategory (how the indicator can be calculated or due to)

unit

Short term (up to 5 years)
1-IMMEDIATE LOSS
FATALITIES , LIFE LOSS, DEATHS
8,158,202,2
18,317,390,
436,594,63
9,728,757,2
0,69,98,143
,152,173,21
6,276,277,3
52,366,404,
506,562,62
1,640,742,7
74,781,53,6
0,99,220
233,260,71
8,295,299,3
04,536,188,
527,418

D

Fatalities
(fatalities /missing
persons./Loss of
life/DEATHS)

594-i<Volume of tailings released (m³)
Distance traveled by tailings (km)
98-I<Population at Risk (PAR): The number of individuals living in
flood-prone areas below the dam.
Base Fatality Rate (FATBASE): Average fatalities per population at
risk.
Flood Severity Metrics: Flood depth and velocity at different points
of the inundation area, impacting the lethality of the flood.
Warning Time
143- I <Understanding of Dam Breach ,Warning Time,Population at
Risk
152-I<Population at risk,Evacuation potential (RP, RE, RS),Shelter
Rate(The proportion of the
un-evacuated population that successfully shelters in
buildings.),preparedness rate
216-I<Severity of Dam Break Flood (SF),Population at Risk
(PR),Evacuation Conditions (EC),Understanding of Dam Break
(UB),Warning Time (TW),Weather During Dam Break (WB),Dam
Break Mode (MB),Water Storage (SW),Building Vulnerability
(VB),Dam Break Time (TB),Average Distance from Affected Area to
Dam (DD)
276-I<Population at Risk (PAR) - The number of people exposed to
critical flood depth.
Severity Degree (SD) - A measure indicating the destructive impact
of the flood on residents and structures.
Warning Time (WT) - The time available for residents to evacuate
before the flood reaches them.
Understanding Degree (UD) - The public’s understanding of the flood
situation, influencing their ability to respond effectively.
352-I<Flood Severity: This is assessed by looking at the depth of
water, velocity, and inundation extent.
Warning Time: The effectiveness and timing of the warning systems
implemented.
Fatality Rates: Based on observational data from previous dam
failures, primarily expressed in fatalities per number of people at
risk.Public Awareness and Understanding of Flood Severity: Gauges
how well the population comprehends the risks and severity of
flooding.
Evacuation Rates: Derived from the effectiveness of warning
systems and preparedness of people in risky zones.
Flood depth and velocity
Road network data
Building locations/types
Population distribution data
366-I<Hydraulic conditions
Building characteristics

81

numbers
NUMBER, METER,
HOURS
Fatality rates: Fraction
(no unit)
Warning time: Minutes
Flood depth: Meters (m)
Flood velocity: Meters
per second (m/s)
Depth x velocity (DV):
Square meters per
second (m^2/s)
Population: Number of
people (no unit)
Dam height: Meters (m)
or Feet (ft)
Water volume: Cubic
meters (m^3) or Acre
feet
Depth: Meters (explicitly
stated).
Velocity: Meters/second
(explicitly stated).
-Population at Risk Individuals (unit
explicitly stated).
Flood Severity Degree m²/s (square meters per
second) for flood
dynamics.
Warning Time - Seconds
(s).
Public Comprehension
Degree - A qualitative
scale (not explicitly
quantified).
Water Depth - Meters
(m).
Flow Velocity - Meters
per second (m/s)
mortality rate , flood
severity (scale), property
damage (usd),

Temporal variables
Population dynamics,
population at risk, warning time, and flood severity.population
distribution, human behavior, and demographic characteristics (age,
gender) are considered indirect indicators influencing the direct
impact of dam failure.
506-I<Population at Risk,Annual Probability of Dam Failure,Dam
Height(M),
Social vulnerability parameters
640-I< floodwater depth and
velocity conditions,Population at Risk
781-I< flow depth, velocity of mudflow
99- I<Warning Time: The duration residents could potentially receive
a warning before flooding occurs.
Demographic Data: Population density and characteristics of the
affected area.
233-I<Population at risk downstream
295-I<Population at Risk - Number of individuals at risk due to dam
failure.
Flood Severity Degree - Assessed through flood metrics like flow
velocity and water depth.
Warning Time - Duration before the event that the population is
notified.
Public Comprehension of Risks - Understanding level of the
population regarding threats from dam failure.
299-I<Depth and velocity of mudflow (measured in meters and
meters/second respectively),
Extent of pollutant dispersion downstream,
304-I<Evacuation Condition (EC): Measures how well the population
can evacuate before floodwaters arrive.
Warning Time (TW): The amount of advance notice provided to
communities at risk.
Population at Risk (PR): The number of individuals living in areas that
may be affected by the dam break
Flood Severity (SF),Flood Depth (FD)
Flood Velocity (FV)
Understanding of Dam Break (UB)
Building Vulnerability (VB)
Height of dam (HD) - meters
Severity of dam-break flood (SF) - m2/s
Dam break mode (MB) - qualitative
Water storage (SW) - 104 m3
Exposure factors:
Dam breach time (TB) - qualitative (day/night)
Weather at breach (WB) - qualitative
Building vulnerability (VB) - qualitative
Average distance from affected area to dam (DD) - km
Population-related factors:
Population at risk (PR) - number of people
Understanding of dam break (UB) - qualitative
Rescue capability factors:
Warning time (TW) - minutes
Evacuation condition (EC) - qualitative
536-I<flood severity, warning time, understanding of dam failure,
and building vulnerability,
188-I<population density, warning time, and understanding of risks

CASUALTIES
813
786
170
298
507
230
278
729

D

Casualties

number

82

300
608
382

LOSS OF LIVELIHOODS index
392
24
412
560
785
406
320
6,174
96
743

D

Disruption of
livelihoods

D
D
D

Quality of life changes
community livelihoods
Impact on Livelihoods:

123

D

267

D

336

D

760

D

299,335

D

Changes in livelihood
security metrics
loss of livelihood
observed after
displacement.
Reduced working
capital among
community members
disruptions to local
economies and
livelihoods
lost income

21
138
410
722
184
647

D

Heritage loss or damage

414

D

175

D

793,446

D

249

296

I from Impacts on
Traditional Land Use
and resources
D

450

D

16
22
70
131
219
248
263
283
306
319
332
370
377

D

$
412- related to fishing and water supply
785-Economic loss in local agriculture Affected communities face
-loss of income due to displacement and regional instability.

**$
NUMBER

Changes in local fisheries
Affected communities face
loss of income due to displacement and regional instability.

LOSS OF CULTURAL ASSETS index

138-Destruction of historical sites and community identity.
D

Impacts on
archaeological/cultural
sites
Cultural heritage
preservation metrics
Loss of cultural relics
and art treasures
Impacts on Traditional
Land Use and resources
Loss of livelihoods
linked to agriculture
and fishing
Interruption of Social
activities
Shifts in cultural
practices

number
722-Cultural relics:
Dimensionless 0-100
scale

793-Qualitative
indicators.

PEOPLE DISPLACEMENTS

People
displacement(People
displacement/
* Number of
evacuations/Number of
people
affected/evacuated)

-Number of individuals evacuated
306-The number of individuals forced to relocate due to dam failure.
723- <Population exposed to different flood depths (number of
people)
201-water supply disruptions

83

number

407
417
470
487
494
509
524
558
582
595
617
632
677
730
723
779
805
166
201
711
264
54
541
773
630
790
683
759
501
714

D

Changes in population
density in impacted
areas
missing school days

45

I from People
displacement

31,776

I from People
displacement

Loss of educational
opportunities

129
331
397
565
665
291
607,18,687

D

employment rates

682
5
641
656

D

438
795

D

376

D

Loss of Tourism
Revenue

D

Community Resilience
capacity
(Community Resilience
Metrics and capacity to
recover/ Changes in
community

number
Days
Or % of children miss
school
% of children miss
education

LOSS OF OPPORTUNITIES

129-%
-number
D

D

Job losses in affected
regions
Loss of recreational
opportunities
loss of recreational
fishing activities.
Alteration of Fishing
Practices

%/number

5-electricity loss

-$

438-Direct measures would be the reported decrease in traditional
fishing practices.
*348-Community reliance on healthy aquatic systems

795-Frequency changes
(number of
participants).

COMMUNITY RESILIENCE INDEX
137
292
415
758
815

137-community capacity to recover post-disaster.
292-Measures of community preparedness and recovery postdisaster.

84

231-TIME NEED FOR
RECOVERY

231
324
341
369
532
474

preparedness and
emergency response
capacity)

D
645
D
660

775
D

80

Public resistance to
rebuilding efforts
Governance
Consequences
Changes in community
preparedness and
emergency response
capacity
Preparedness
Funding(willingness
to pay (usd) to
prevent future
incidents)

2-SOCIAL SERVICE
SERVICE SUPPLY INDEX
409
649
433

D

Disruption of water
supply

D

Access to clean water
and sanitation facility
counts
<Power

132,4,198,4
13,118
D
281

Time duration for power is interrupts
D

571
D
285
471,485,34
2,140
513
690

D

735
D
333
D
716,525
D

464,680,62
4
550
362
439

Limitations on local
populations accessing
river water for
consumption and
agriculture
Utility Service
Disruption Duration
ecosystem services

hours
140-Evaluations of lost services like water purification and habitat
provision.

D
D

576
458,135

198- water supply disruptions

Number
4-number of livelihoods
dependent on water.
NUMBER OF FAMILIES
AFFECTED

D

D
D

Losses in services
Access to medical
facilities
Access to services postdisaster (e.g., clean
water, health services)
Loss of community
services
Disruption of
Transportation
Networks
Loss of access to
fisheries resources
(Potential implications
on local fisheries
and ecosystem services,
/ Loss of access to
fisheries resources)
Impact on fisheries
availability of food and
income for local
communities/food
security

number

-Flood Inundation Depth(m),
-Flood Inundation Area(km²)

458-health status of fish communities
135-number

-number

550-Reduced fish populations
439-environmental degradation

550-Trophic Position
Units
Inference: May need to
be inferred from
ecological studies.

85

Species Diversity
Units: Count (number of
species).
Stable Isotope Ratios:
Units: δ13C and δ15N
(per mil, ‰).
796

D

734

D

533

D

505,631

D

446

D

Access to Traditional
Food Sources: Reflects
stress on dietary
practices
The extent of shelter
availability and access
to basic necessities.
Access to Recovery
Services

796-environmental degradation

number
Measured by the
percentage of affected
population receiving aid

Access to emergency
services
loss of access to
traditional land use and
resources

3-HEALTH IMPACT AND MENTAL ISSUES
MENTAL ISSUES INDEX
167
293
421
610
232
543
400
293
30

D

791

D

437
794
561

D

371
659
816
510
713

D

Mental health
impacts/issues

-qualitative measures like interviews that assess perceived stress
related to the incident.
D

D

D
575

D

658

D

720

D

36

D

Health impacts due to
displacement
psychological effects
due to fatalities
Emotional Stress Level

Due to displacement

qualitative measures like interviews that assess perceived stress
related to the incident.

psychological distress
among affected
populations
psychological impacts

Mental Health impacts
from injuries
public perception and
fear regarding the
safety of local resources
emotional impacts
relative to
loss of fishing
livelihoods.
Social Unrest and
Turmoil (triggered by
panic and loss of life)
Social morale decline

number

number

number

-due to displacement and loss of life

PHYSICAL HEALTH PROBLEMS index
252

D

NUMBER)

Public health outcomes
linked to contamination
events

86

37
488

D

Community well-being

4-SECONDARY IMPACTS FROM ENV AND ECONOMIC
IMPACTS
361

D

405

D

D

Impacts on community
health related to
environmental
degradation
The toxicity level of
mud impacting local
communities
Health problems arising
from tailings exposure./

434

number

-WATER QUALITY

-number
-cases per population
-PEOPLE NUMBER

-Disability-Adjusted Life Years (DALY),
-Measured for As (Arsenic),
Cd (Cadmium), and Pb (Lead) immediately post-failure
-Loss of access to clean water resources
- exposure to toxic metals (Aluminum, Arsenic, Mercury, Nickel).
-Blood metal concentrations of toxic elements (Al, As, Hg, Ni) in
participants’ blood samples,
-Dietary habits and sources of water as factors contributing to
exposure risks,
-Source of food (e.g., seafood consumption linked to metal
exposure).
-Water source as a potential source of contamination,
-Community demographics and lifestyle.
Previous health status linked to exposure.
-Aluminum (Al), Arsenic (As), Cadmium (Cd), Cobalt (Co), Copper
(Cu), Mercury (Hg), Manganese (Mn), Nickel (Ni), Lead (Pb),
Selenium (Se), and Zinc (Zn) ,smoking habits,
-Health conditions reported by participants: Mental disorders,
malaise, skin lesions, gastrointestinal disorders, and bone diseases
were documented.

315

D

789
7

D

584

D

Health risks:
Accumulation of metals
in the human body.
.health issues from
pollution exposure
health risks from heavy
metal (HM) exposure.

7- Loss of access to clean water resources

Disability-Adjusted Life Years (DALY),Measured for As (Arsenic),
Cd (Cadmium), and Pb (Lead) immediately post-failure

224
364
191
199
365
748
55
495
559

D

Health impact

364-exposure to toxic metals (Aluminum, Arsenic, Mercury,
Nickel).Blood metal concentrations of toxic elements (Al, As, Hg, Ni)
in participants’ blood samples,Dietary habits and sources of water as
factors contributing to exposure risks,Source of food (e.g., seafood
consumption linked to metal exposure).Water source as a potential
source of contamination,Community demographics and
lifestyle.Previous health status linked to exposure.Aluminum (Al),
Arsenic (As), Cadmium (Cd), Cobalt (Co), Copper (Cu), Mercury (Hg),
Manganese (Mn), Nickel (Ni), Lead (Pb), Selenium (Se), and Zinc (Zn)
,smoking habits,Health conditions reported by participants: Mental

87

364-(ug/l)
191-number
365-ng/mL

disorders, malaise, skin lesions, gastrointestinal disorders, and bone
diseases were documented.

199-lowered water quality.
365-urinary levels of toxic metals/metalloids and oxidative stress
indicators (specifically, DNA damage),Toxic Metal Levels: Urinary
concentrations of metals/metalloids such as arsenic (As), cadmium
(Cd), mercury (Hg), nickel (Ni), and lead (Pb).Oxidative Stress
Biomarker: Levels of 8-hydroxy-2′-deoxyguanosine (8OHdG).

55-TAILINGS RELEASE,
657

D

464
151
466
447

D

568

D

732

D

(Shandro
2017)

D

Indicator
no

Direct or Indirect

D

Public health risks
linked to
contaminated water
sources previously used
for drinking
Humanistic Ecological
Environment
health risks associated
with the contamination
of water and fish,
Potential health
implications for local
populations due to
contaminated water
and soil.
waterborne diseases
due to contaminated
water sources and fish
Difficult access to safe
water due to
deterioration of water
quality

indicator name

cases per population

The prevalence of stagnant water
as vectors for disease spread

Subcategory (how the indicator can be calculated or due
to)

unit

Long term (more than 5 years)
1-SOCIETY COHESION
441,
797
691

D

D
689
294
17
301
422
443
609
29
334
399
475
542

D

administrative burdens
on community
leadership
changes in community
leadership, and
migration of workers
Community Cohesion
294-Changes in community dynamics and relationships post-event
-crime rates, poverty levels
787,301-Due to Public perception and fear related to mining and
dam infrastructure,
443-Inter-community tensions
-Community Displacement/

88

-number

787
429,519,61
6,266,496,3
56

-Casualties
D

Social disruption

D

Social capital CHANGE
in affected communities
long-term community
fragmentation
social coherence
impacts
Increased vulnerability
and loss of community
cohesion
competition for fishing
resources
Social Value and
Recreational Loss
Changes in public
perception and values
Changes in community
demographics

356- Community Displacement/Casualties

124
D
192
D
803
D
489
801
444
641

D
D

238

D

761
426
251
660

D

749
168

displacement
Intra-community and Inter-community Tension

-Economic losses from damage
to properties, public utilities, and other critical facilities

-number
251-DUE TO MIGRATION

D

D

Governance
Consequences
Social stability
Population decreases or
migration trends

-%

crime rates, poverty levels)
number

2-SOCIAL SERVICE
571

D

409
649
433
132,4,198,4
13,118

D

471,485,34
2,140
513
690
716,525

D

Access to clean water
and sanitation facility
counts
ecosystem services

D

Losses in services

D

296

D

576

D

458,135

D

550
362
439

D

Loss of community
services
Interruption of Social
activities
Disruption of
Transportation
Networks
Loss of access to
fisheries resources
(Potential implications
on local fisheries
and ecosystem services,
/ Loss of access to
fisheries resources)
availability of food and
income for local
communities/food
security

D

Limitations on local
populations accessing
river water for
consumption and
agriculture
Disruption of water
supply
198- water supply disruptions

Number
4-number of livelihoods
dependent on water.

140-Evaluations of lost services like water purification and habitat
provision.

-Flood Inundation Depth(m),
-Flood Inundation Area(km²)
458-health status of fish communities
135-number

-number

550-Reduced fish populations
439-environmental degradation

550-Trophic Position
Units
Inference: May need to
be inferred from
ecological studies.
Species Diversity

89

Units: Count (number of
species).
Stable Isotope Ratios:
Units: δ13C and δ15N
(per mil, ‰).
796

D

446

D

Access to Traditional
Food Sources: Reflects
stress on dietary
practices
loss of access to
traditional land use and
resources

796-environmental degradation

3-LON-TERM
LIVELIHOOD
618

D

321
611
253
545

D
D

440

D

502

D

445

D

252

D

584

D

long-term housing
insecurity
Increased poverty
Long-term employment
rate and increased
unemployment

$
NUMBER, %

4-CHRONIC HEALTH AND MENTAL ISSUES
Changes in Dietary
Patterns
Long-term psychological
effects
Emotional trauma
related to perceived
threats to salmon
health
Public health outcomes
linked to contamination
events
health risks from heavy
metal (HM) exposure.

Disability-Adjusted Life Years (DALY),Measured for As (Arsenic),
Cd (Cadmium), and Pb (Lead) immediately post-failure

589,600,71,
215,162

D

134

I from : long-term
health impacts, Changes
in Dietary Patterns ,
Long-term psychological
effects

long-term health
impacts

71- displacement and loss of life
215-WATER CONTAMINATION
162-derived from toxic element exposure

Changes in social wellbeing scales

number

number

5-SECONDARY IMPACTS FROM ENV AND ECONOMIC IMPACTS
732

D

D
434

waterborne diseases
due to contaminated
water sources and fish
Health problems arising
from tailings exposure.

The prevalence of stagnant water
as vectors for disease spread

-WATER QUALITY
-Disability-Adjusted Life Years (DALY),
-Measured for As (Arsenic),
Cd (Cadmium), and Pb (Lead) immediately post-failure
-Loss of access to clean water resources
- exposure to toxic metals (Aluminum, Arsenic, Mercury, Nickel).

90

-number
-cases per population
-PEOPLE NUMBER

-Blood metal concentrations of toxic elements (Al, As, Hg, Ni) in
participants’ blood samples,
-Dietary habits and sources of water as factors contributing to
exposure risks,
-Source of food (e.g., seafood consumption linked to metal
exposure).
-Water source as a potential source of contamination,
-Community demographics and lifestyle.
Previous health status linked to exposure.
-Aluminum (Al), Arsenic (As), Cadmium (Cd), Cobalt (Co), Copper
(Cu), Mercury (Hg), Manganese (Mn), Nickel (Ni), Lead (Pb),
Selenium (Se), and Zinc (Zn) ,smoking habits,
-Health conditions reported by participants: Mental disorders,
malaise, skin lesions, gastrointestinal disorders, and bone diseases
were documented.
789
7

D

.health issues from
pollution exposure

657,133

D

568

D

315

D

361

D

447

D

464
151
466
(Shandro
2017)

D

Public health risks
linked to
contaminated water
sources previously used
for drinking
Potential health
implications for local
populations due to
contaminated water
and soil.
Health risks:
Accumulation of metals
in the human body.
Impacts on community
health related to
environmental
degradation
health risks associated
with the contamination
of water and fish,
Humanistic Ecological
Environment

Fundao
damfailure,
LACTAC
report

D

D

7- Loss of access to clean water resources

cases per population

number

Difficult access to safe
water due to
deterioration of water
quality
LOSS OF CULTURAL
ASSETS index

3- Environmental Impact Indicators:
91

Direct or Indirect

indicator name

Indicator no

Subcategory (how the indicator can be calculated or due
to)

unit

Short term (up to 5 years)
1-GEOLOGY
1-1-SOIL AND SEDIMENT INDEX
D
240
209
567
461

Contamination of soils
and sediments

91,210

D

sediment quality

236
67,350

D
D

sediment toxicity
Metals and Arsenic
Concentration in
sediment
Suspended Sediment
Loads
Injuries to sediments,
watercourse opacity,
and oxygenation
Alterations in bottom
sediments
soil contamination
levels
SEDIMENT Metal
Concentrations:
Measured in dissolved,
Channel erosion and
sediment yield.

208

D

82

D

205

D

259
588
104,88

D

378

D

147,461

D

312

D

D

428

D

765

D

121,338,477

D

767

D

697
57,61,265,492,
529,695,708,75
0,808

D
D

209-Iron,Arsenic,Mercury
567-Measurements of elements like Iron (Fe), Aluminum (Al), and
Manganese (Mn) in water, sediment, and soil
samples.Concentrations of heavy metals and minerals in sediment
and soil samples downstream of the dam failure.
91-(e.g., turbidity and sediment load).
210-rate of waste discharge,Fe, As, Hg

Erosion and
sedimentation rates
Volume of sediment
mobilized into nearby
water bodies
sedimentation,
Soil erosion impact

91-mg/L
210-mg/kg ; ug/l

mg/kg
350-mg/kg
mg/kg ; ug/l

$ per m3 of tailings released
µg/L or mg/L
(mg/kg)

Chromium (Cr), Nickel (Ni), Cadmium (Cd), and Mercury (Hg)

Flood Frequency: Events per specified time.

(mg-kg)
channel erosion (meters),
sediment yeild
(percentage)

-Fine sand, coarse sand, very fine sand, manganese (Mn), and iron
(Fe) fractions.
-Distance from B1 dam: Used to understand the spread and
distribution of tailings.
-Redox potential: Influencing the release of metals like manganese.
-Hydraulic features: Such as the presence of the Igarapé
-thermoelectric plant weir, which acts as a barrier to tailings
migration.
-Historical mining activities and agricultural practices

Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese:
mg/L
Total calcium: mg/L
Fine sand, coarse sand,
very fine sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value
(normalizednormalized)

Soil environment
change
sediment
contamination

Suppressed stocks in
the mud passage and
deposition areas
(MPDA)
Magnitude of mud and
debris flows

240-(mg/kg)
209-mg/kg ; ug/l
567-water
conc(micrograms per liter),
sediment and soil conc.
(%)

Peak Outflow Rates: m³/s (cubic meters per second)
Flood Volume Released: m³ (cubic meters)
Erosion Rate: m/year (meters per year),Rainfall Intensity
338-Volume of dam outwash material released (M m³),
Sedimentation depth in impacted water bodies (m)
Peak Outflow Rates: m³/s (cubic meters per second)
Flood Volume Released: m³ (cubic meters)
Erosion Rate: m/year (meters per year),Rainfall Intensity

121-%
338-(M m³)

750-Peak discharge (m3/s)
Flow depths (m)
Flow velocities (m/s)
Depth-velocity product (m2/s)

57,61-M2
265-meters/year
529-m/y,
695-meters per year

92

66,136,503,553
,751,379

D

sediment displacement

D

sediment dynamics in
the landscape

777

D

372

D

changes in creek flow
characteristics,
sediment
entrainment, and
debris flow dynamics.
changes in sediment
dynamics in coastal
marine environments

427

Inundation extent (mapped)
Tailings deposition extent (m)
751-Peak discharge (m3/s)
Flow depths (m)
Flow velocities (m/s)
Depth-velocity product (m2/s)
Inundation extent (mapped)
Tailings deposition extent (m)
427-Hydraulic parameters including flow, stage, shear stress, stream
power.
Peak discharge value (13,000 m³/s) ,Historical context of sediment
connectivity before the dam failure, considering landform changes
and sediment deposition patterns.Morphometric analysis of
landforms, including gravel bars, sand beds, and plunge
pools.Sediment source and yield relationships, historical patterns of
sediment connectivity, and effects on underlying geological
structures.

86-tailings contamination(Heavy Metal Concentrations)

PEAK FLOW M^3/S, FLOW
DEPTH (METRES), flow
velocity (m/s), bedrock
channel erosion depth (m),
plunge pool area (m^2),
sediment volume (m^30,
sediment deposit thickness
(cm)
86-(ppm) or milligrams per
liter (mg/L
516- Microbial Diversity
Indices:
Typically measured in
indices such as Shannon or
Simpson index
(normalized).

heavy metal contamination in sediments

%, mg/kg

Suspended Particulate Matter (SPM) Concentration,Changes in
sediment grain size,
Sediment Bulk Density,Local wind and wave conditions affecting
sediment dispersal mechanisms.Turbidity
86
516

I from contamination of
sediment and soil

microbial communities
CHANGE

515

I from contamination of
sediment and soil
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion
I from sediment quality,
sediment displacement,
erosion

Bacterial and Archaeal
Taxa Abundance
potential changes in
river geometry

62

83

145,176,459

206

385,479,752

768

102,211,373,70
0
456

M2
427-PEAK FLOW M^3/S,
FLOW
DEPTH (METRES), flow
velocity (m/s), bedrock
channel erosion depth (m),
plunge pool area (m^2),
sediment volume (m^30,
sediment deposit thickness
(cm)

Changes in riparian
morphology
River Morphology

176-meter

Changes in fluvial
dynamics
geomorphological
consequences

479- erosion potential (m)

Changes in watershed
hydrology
hydrological and
geomorphological
changes
Morphological damage

1-2-LAND USE/ LAND COVER INDEX
93

D

land use and land cover
change

1
316
368
403
430
538
520

1-km^2
316-Area Affected:
Hectares (ha)

316- river, forest, clear water, agricultural land, built-up areas,
grassland, and mine/tailings.
403-Normalized Difference Vegetation Index (NDVI)
-Emergy accounting

403-NDVI:
Dimensionless index (no
explicit units stated).
430-emergy accounting
(solar energy),
total emergy-based dollar
value

430-Emergy accounting
- acres
705

D

329
678,701

D
D

578

D

699

I from land use /land
cover index

Land destruction due to
debris flows
Land degradation
Negative impacts to
wetland systems
Impact on Agricultural
Land
changes in land
development patterns

hectares or acres
ha

Flood Inundation Depth,Flood Inundation Area

2-ECOLOGY
2-1-BIOCHEMICAL INDEX
517
105
126
340

D

Effects on
Biogeochemical Cycles
and biology

517-Changes in microbial communities that could impact organic
matter recycling and nutrient cycling
105- Assessed through cytotoxic, genotoxic,
340-Changes in fish populations (e.g., Rainbow Trout)
Habitat availability for spawning species, Benthic organism
community composition over time
Plant test species growth metrics post-incident

106

D

93

D

273,360,798,54
7,185,222
455

I from ECOLOGY
I from ECOLOGY

mutagenic effects in
various organisms and
changes in the
mitotic index.
loss of genetic
variability and biomass
reduction
ecological
consequences
Trophic diversity change

105-%
340-number

185-AREA

2-2-FLORA
D

Vegetation
loss/mortality

3
142
156
212
739
472

156-quantitative assessment of vegetation

764,77

D

148, 42, 462

D

481

D

Changes to riparian
vegetation
Vegetation Cover
change
Plant Biomass Loss (PB)

3-km^2 or %
142-ha
739-Number of species
472-number
ha

M2
Plant Height, Timing of Flood, and Other Factors ,Flood depth,
velocity, and
duration,Flood timing

94

plant biomass loss (kg/ha)

313

D

the vigor of riparian
forests

<Normalized Difference Vegetation Index (NDVI).
Distance from B1 dam: Used to understand the spread and
distribution of tailings.
Redox potential: Influencing the release of metals like manganese.
Hydraulic features: Such as the presence of the Igarapé
thermoelectric plant weir, which acts as a barrier to tailings
migration.
101

D

Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese:
mg/L
Total calcium: mg/L
Fine sand, coarse sand,
very fine sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value
(normalized)

Impact on plantations

2-3-FAUNA
2-3-1-TERRESTRIAL FAUNA
D

Terrestrial fauna loss

28
286
271
116
694
583
643

D

Wildlife habitat loss

58

D

35
115
141
226
330
394
482
807
92
275
521
548
149
463
535
551
598
84

D

603

D

245

D

75

D

754

D

127

D

186,
563

D

739

739- Immediate impact on wildlife,
such as crocodiles, hippos, and fish due to water changes.
Water flow and volume (cubic meters)

739- NUMBER OF species

Number
694-hectars
D

Loss or deterioration of
habitats,
specifically for species
listed as blue-listed or
red-listed
Terrestrial ecosystem
impact index
Biodiversity loss

number
Pollution

141-<Assessments of species richness and population stability
482-Species Richness, Survival Time of Animals, and Other Factors
,Flood depth, velocity, and duration,Migration ability of animals
274-Changes in species composition and diversity due to
environmental disturbances
521- due to flooding
524-Measurements of diversity indices (e.g., Shannon-Wiener
diversity index).
Environmental Quality Parameters:
Evaluation of affected water and habitat characteristics impacting
fish health and populations.,Long-term changes in ecosystem health
D

Changes in the wildlife
food chain
Changes in local wildlife
populations
Disruption of
ecosystems and
habitats
Terrestrial wildlife
impact

Number
482-species richness
(count)

COUNTS,Estimates for
recovery times
for injured species and
ecosystems

Changes in habitat
quality
cumulative effects on
species
habitat destruction

number
number

95

622
625
685
817
707
97

D

76

D

Genetic diversity indices
(number of alleles,
allelic richness,
observed and expected
heterozygosity)
Birds

D

Aquatic fauna loss

635

D

127

D

90

D

337

D

temporary loss of fish
habitats
cumulative effects on
species
Histopathological
damage in aquatic
organisms,
marine benthic
macrofauna affected by
the tailings mud

452
453
14

D

(number of alleles, allelic richness, observed and expected
heterozygosity)Inbreeding coefficient (Fis)
M-ratio (indicator of population bottlenecks)
Population differentiation (Jost's D index)
Genetic structure (STRUCTURE and DAPC analyses)

COUNT AND RATIO
COUNTS,Estimates for
recovery times
for injured species and
ecosystems

2-3-2-AQUATIC FAUNA
COUNTS,Estimates for
recovery times
for injured species and
ecosystems

74

Ecotoxicological impacts
on fish health

number

metal bioaccumulation.((disease prevalence, tissue damage)
337-Iron Mineralogical Set (IMS) Index, Species Abundance,
Richness, and Diversity,Sediment Characteristics: Such as grain size
and composition, which influence macrofauna composition.
Concentration of Metal(oid)s: Observed with the presence of
contaminants (V, Al, Ba, etc.) from the tailings mud.
IMS Index: Used to trace the presence of Fundão dam tailings mud.
Sediment Variables: Including granulometry (average grain size),
total organic matter (TOM), total organic carbon (TOC), total
nitrogen (TN), and calcium carbonate (CaCO₃).
Metal(oid)s Concentrations: Including iron (Fe), aluminum (Al),
manganese (Mn), arsenic (As), silver (Ag), barium (Ba), cadmium
(Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb),
and vanadium (V).
Organic Pollutants: Including DDTs, PCBs, PAHs, and sterols.
452-DNA damage
Lipid peroxidation (LPO)
Protein carbonyls (PCO)
Histological damage in gills and liver
Metallothioneins concentration
Activity of superoxide dismutase, Seasonal variations in metal
concentrations in the water and sediments
Trophic levels of fish communities
Environmental conditions (rainy vs. dry seasons),Integrated
Biomarker Response (IBR) index
Integrated Metal Bioaccumulation (IMB) index,Changes in
community structure (e.g., microbial community)
Changes in zooplankton diversity
453-heavy metal concentration
14-due to increased turbidity and contamination by heavy metals.

679, 652,85

D

Potential mortality of
mussel and fish
populations

457,799

D

health status of fish
communities

652-Growth and survival rates
of aquatic organisms like freshwater scuds and mayfly larvae,
Changes in food web dynamics due to the decline in populations of
key species
457-physiological alterations due to metal exposure and
seasonal variations in bioaccumulation.

96

Heavy Metal
Concentrations: mg/kg
Lipid Peroxidation:
nmol/mg protein
DNA damage: quantified
through comet assays or
similar measures (exact
units not specified)
Other biochemical
indicators: often expressed
as activity units (e.g., U/mg
protein)

85-number

799-esions and other indicators of contamination.
753

D

125,171,349,37
5,411,
357

D

59
546
637
279
725

D

D

loss or deterioration of
critical fish or wildlife
habitat
Changes in fish
populations
macrobenthic
crustacean diversity and
abundance

number
Species Richness: Number of different species recorded.
Species Abundance: Total number of individuals observed within a
species.
Environmental Variables: Parameters such as
turbidity and pH levels that are directly correlated to species
composition.
species richness (number), abundance(no. of individuals), shannon
wiener index (no dimension), turbidity (NTU), ph , temperature,
salinity

Aquatic ecosystem
impact

59-POLUTION
546-changes in fish populations:trophic structure of fish
communities,Habitat Alteration,Changes in water quality
and habitat structure
279-Measures such as fish populations and plant health
725-Flood wave height: Measurement of flood intensity
(e.g., maximum height of flood waves).
Inundation area: Geographic area inundated by flood waters.,Flow
progression over time,Water depth and extent of flooding,Water
depth and extent of flooding,Time of wave arrival at key cities
65

D

CHANGE aquatic
ecosystems (freshwater,
marine, and coastal)

546-Trophic Position
Units: Not explicitly stated
in the summary.
Inference: May need to be
inferred from ecological
studies.
Species Diversity
Units: Count (number of
species).
Stable Isotope Ratios:
Units: δ13C and δ15N (per
mil, ‰).
725-Flood Wave Height:
Meters (m).
Inundation Area: Square
kilometers (km²).
Initial Lake Level: Meters
(m)

HEAVY METAL AND arsenic INCREASE

2-4-WATER
2-4-1- WATER QUALITY INDEX
26
2
56
94
108
113
117
187
193
204
225
269
272
311
328
343
358
401
451
467
468
504
528
537
552

D

Water quality
degradation

94- turbidity, -CONTAMINTATION
108-suspended particulate matter (SPM),
113A-lkalinity
-Conductivity
-Chloride content,
-Total phosphorus
-Total solids
-Biochemical oxygen demand (BOD)
-Escherichia coli
-Nitrate
-Ph
117-HEAVY METAL
187- pollutant levels and sedimentation
193- alterations in solids, metals, and metalloids’
concentration,water pH levels
,204- solids, metals, metalloids
225- pollutant concentration
272- due to increased pH, metal concentrations(As, Ba, Cd, Cr, Cu,
Fe, Mn, Ni, Pb, Zn) and turbidity,suspended solids, and electrical
conductivity,Coliform levels,Factors associated with anthropogenic
influences ,dissolved oxygen

97

2,108-NTU
94- mg/L
113-conductivity (uS/cm,
micrisiemmens per cm), pH (ph units),
-dissolved oxygen (mg/l)
-mg/L
-us/cm
mg/l)
- µg/L or mg/L
-mg/kg ; ug/l
pH: no units
117,187- mg/l
193-204,- µg/L or mg/L
272- mg/L
pH: no units
Electrical conductivity:
μS/cm (inferred)
Turbidity: NTU
Total suspended solids:
mg/L

573
579
339
626
653
706
744
783
792
800
804
111

311- Chlorophyll-a, flow rate, total arsenic, dissolved manganese,
and total calcium.
Distance from B1 dam: Used to understand the spread and
distribution of tailings.
Redox potential: Influencing the release of metals like manganese.
Hydraulic features: Such as the presence of the Igarapé
thermoelectric plant weir, which acts as a barrier to tailings
migration.
Historical mining activities and agricultural practices
343- Comparison of pre- and post-event turbidity levels (NTU),
Plankton community structure metrics,copper levels, turbidity)
Fish mortality rates
401- Suspended Particulate Matter (SPM): mg/L
451- Water turbidity (measured in NTU)
Iron levels (measured in µg/L or mg/L)
Mortality rates of zebrafish embryos (expressed as
percentages),Microbial colony-forming units (CFU)
Rates of potential pathogenic microbial growth
Dissolved Metals: Concentrations of metals like iron and mercury
were measured, with iron levels increasing significantly
downstream.
Microbial Abundance:
467- Flood Velocity: Calculated through numerical simulations.
Flood Depth: Obtained from simulations,COD(Chemical Oxygen
Demand), TP(Total Phosphorus), TN(Total Nitrogen), and pH levels in
monitoring sites,Pollutant Concentration
468- Flood Duration,Velocity Changes
528- pH, turbidity
537- turbidity, chemical pollutants
573- turbidity, pH, total dissolved solids, and dissolved oxygen,
metal concentration
653-Concentrations of heavy metals
,Toxicity levels of copper
339- Metal concentrations in various media (mg/L or µg/L)
Toxicity testing results on sediment and water (general toxicity
scale)
744&783- Extent of pollutant dispersion downstream
792- concentrations of metals like manganese,aluminum,
phosphorus,Chlorophyll-a, flow rate, total arsenic, and total calcium.
Sediment Quality Indicators: Fine sand, coarse sand, very fine sand,
manganese (Mn), and iron (Fe) fractions. and
changes in the riparian vegetation index (NDVI)

Dissolved oxygen: mg/L
Nutrients: mg/L
Contamination factor: no
units
311- Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese:
mg/L
Total calcium: mg/L
Fine sand, coarse sand,
very fine sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value
(normalized)
343- mg/L for metal
concentrations and NTU
for turbidity.
451- turbidity (ntu),
dissolved metals (mg/l),
microbial colony units
(cfu/ml), zebrafish
mortality (%)
467- H: No unit;
dimensionless.
COD: mg/L (milligrams per
liter).
TP (Total Phosphorus):
mg/L.
TN (Total Nitrogen): mg/L.
,468,537,552,706- mg/L
792- Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese:
mg/L
Total calcium: mg/L
Fine sand, coarse sand,
very fine sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value
(normalized)
111-mg/l)

111-Chloride Content,

87,112
107

D
D

109
110
217,353,480,60
1,693,150,114,
636,383,239,64

D
D
D

Turbidity
suspended particulate
matter (SPM)
Alkalinity,
Conductivity,
water pollution

87- NTU

217-Gray Water Footprint (WF)
480-Point Source (PS) and Non-Point Source (NPS) pollution,Flood
depth, velocity, and duration
601-Concentration of heavy metals in water (mg/L)

98

(mg/l)
us/cm
217-cubic mters
480-mg/L
693-ppm for contaminants
114-mg/l, m³ for volume

,599,388,214,1
55,11

114-(e.g., heavy metals in water and sediment), TAILING RELEASE

239&64&388-mg/l
214-mg/kg ; ug/l
155- mg/dm^3

239-concentrations of toxic metals like As, Cu, Pb, Zn
64-Cd, Cu, Cr, Fe, Hg, Mn, Pb, Zn, and arsenic)
214-Concentration of Toxic Metals
11- km2
155-heavy metals, cyanide concentrations
476

D

351

D

Maximum Water
Surface Elevation
Dissolved oxygen
Biophysical impacts on
the river system

408
460,146
246

D
D

34

D

89

D

86
516

I from contamination of
water

milligrams per liter (mg/L)
water quality, freshwater and saltwater biota, fish community
analysis, Historical land use data (e.g., deforestation rates)
Climate change models ,habitat disruption

Water Environment
Transboundary
migration of effluent in
rivers
Contamination of water
bodies
Change Water quality
parameters
(temperature,
conductivity, pH,
dissolved oxygen)
microbial communities
CHANGE

Km2

86-tailings contamination(Heavy Metal Concentrations)

water temperatute
(degree celsius), electrical
conductivity (uS/cm,
micrisiemmens per cm),
pH (ph units), dissolved
oxygen (mg/l) ,
86-(ppm) or milligrams per
liter (mg/L
516- Microbial Diversity
Indices:
Typically measured in
indices such as Shannon or
Simpson index
(normalized).

2-4-2-WATER RESOURCE INDEX
D

9-km²

9
326
169
244

Changes in water
resources
D
D

Groundwater damage
Subsequent
groundwater
contamination

toxic elements

-mg/dm^3

mg/l

3-ENERGY USE AND CARBON GENERATION INDICATORS
307

D

on-site fuel combustion

308

D

310

I from on-site fuel
combustion

purchased electricity
climate change
Ozone depletion
Human toxicity
Acidification
Eutrophication
Resource depletion
metrics

-Specific activities that had a high contribution include demolition
and the chemical sector
-Specific activities that had a high contribution include demolition
and the chemical sector

4-SECONDARY IMPACTS FROM SOCIAL AND ECONOMIC IMPACTS
-(Fernandes
2016

environmental pollution
due to no maintenance

435

Physical conversion of
land due to population
displacement

-limited access to and maintenance of fisheries in the affected area

99

fossils: kg Sb eq
$

Long term (more than 5 years)
1-GEOLOGY
1-1-SOIL AND SEDIMENT
Indicator no

Direct or
indirect

Indicator or index name

D

Contamination of soils and
sediments

240
209
567
461
91,210

D

sediment quality

236
67,350

D
D

208
82

D
D

205
259
588
104

D
D

sediment toxicity
Metals and Arsenic Concentration
in sediment
Suspended Sediment Loads
Injuries to sediments, watercourse
opacity, and oxygenation
Alterations in bottom sediments
soil contamination levels

378

D

405

D

147,461
312

D
D

D

SEDIMENT Metal Concentrations:
Measured in dissolved,
Channel erosion and sediment
yield.
The toxicity level of mud impacting
local communities
Soil environment change
sediment contamination

428

D

Suppressed stocks in the mud
passage and deposition areas
(MPDA)
Magnitude of mud and debris
flows

765

D

121,338,477

D

Erosion and sedimentation rates

767

D

Volume of sediment mobilized
into nearby water bodies

697
57,61,265,492,
529,695,708,75
0,808

D
D

sedimentation,
Soil erosion impact

Subcategory (how the indicator can be calculated or due
to)
209-Iron,Arsenic,Mercury
567-Measurements of elements like Iron (Fe), Aluminum (Al), and
Manganese (Mn) in water, sediment, and soil
samples.Concentrations of heavy metals and minerals in sediment
and soil samples downstream of the dam failure.
91-(e.g., turbidity and sediment load).
210-rate of waste discharge,Fe, As, Hg

unit
240-(mg/kg)
209-mg/kg ; ug/l
567-water conc(micrograms per
liter),
sediment and soil conc. (%)
91-mg/L
210-mg/kg ; ug/l
mg/kg
350-mg/kg
mg/kg ; ug/l

$ per m3 of tailings released
µg/L or mg/L
(mg/kg)

Chromium (Cr), Nickel (Ni), Cadmium (Cd), and Mercury (Hg)
Flood Frequency: Events per specified time.

(mg-kg)
channel erosion (meters),
sediment yeild (percentage)

-Fine sand, coarse sand, very fine sand, manganese (Mn), and iron
(Fe) fractions.
-Distance from B1 dam: Used to understand the spread and
distribution of tailings.
-Redox potential: Influencing the release of metals like
manganese.
-Hydraulic features: Such as the presence of the Igarapé
-thermoelectric plant weir, which acts as a barrier to tailings
migration.
-Historical mining activities and agricultural practices

Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese: mg/L
alcium: mg/L
Fine sand, coarse sand, very fine
sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value (normalized)

Peak Outflow Rates: m³/s (cubic meters per second)
Flood Volume Released: m³ (cubic meters)
Erosion Rate: m/year (meters per year),Rainfall Intensity
338-Volume of dam outwash material released (M m³),
Sedimentation depth in impacted water bodies (m)
Peak Outflow Rates: m³/s (cubic meters per second)
Flood Volume Released: m³ (cubic meters)
Erosion Rate: m/year (meters per year),Rainfall Intensity
750-Peak discharge (m3/s)
Flow depths (m)
Flow velocities (m/s)
Depth-velocity product (m2/s)
Inundation extent (mapped)
Tailings deposition extent (m)

100

121-%
338-(M m³)

57,61-M2
265-meters/year
529-m/y,
695-meters per year

66,136,503,553
,751,379

D

sediment displacement

D

sediment dynamics in the
landscape

427

777

D

372

D

751-Peak discharge (m3/s)
Flow depths (m)
Flow velocities (m/s)
Depth-velocity product (m2/s)
Inundation extent (mapped)
Tailings deposition extent (m)
427-Hydraulic parameters including flow, stage, shear stress,
stream power.
Peak discharge value (13,000 m³/s) ,Historical context of sediment
connectivity before the dam failure, considering landform
changes and sediment deposition patterns.Morphometric analysis
of landforms, including gravel bars, sand beds, and plunge
pools.Sediment source and yield relationships, historical patterns
of sediment connectivity, and effects on underlying geological
structures.

changes in creek flow
characteristics, sediment
entrainment, and debris flow
dynamics.
changes in sediment dynamics in
coastal marine environments
Suspended Particulate Matter (SPM) Concentration,Changes in
sediment grain size,
Sediment Bulk Density,Local wind and wave conditions affecting
sediment dispersal mechanisms.Turbidity

213

62

83

145,176,459

206

385,479,752

768

I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion

LONG TERM potential for
enhanced erosion and
remobilization of contaminated
particles due to heavy rainfall
potential changes in river
geometry

M2

427-PEAK FLOW M^3/S, FLOW
DEPTH (METRES), flow velocity
(m/s), bedrock channel erosion
depth (m), plunge pool area
(m^2), sediment volume (m^30,
sediment deposit thickness (cm)

PEAK FLOW M^3/S, FLOW
DEPTH (METRES), flow velocity
(m/s), bedrock channel erosion
depth (m), plunge pool area
(m^2), sediment volume (m^30,
sediment deposit thickness (cm)

mg/kg ; ug/l

Changes in riparian morphology

River Morphology

176-meter
Changes in fluvial dynamics

geomorphological consequences

479- erosion potential (m)
Changes in watershed hydrology

101

102,211,373,70
0

456

86
516

515

570

I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
sediment
quality,
sediment
displaceme
nt, erosion
I from
contaminati
on of
sediment
and soil

hydrological and
geomorphological changes

I from
contaminati
on of
sediment
and soil
I from
changes in
sediment
dynamics,
sediment
displaceme
nt, erosion

Bacterial and Archaeal Taxa
Abundance

D

land use and land cover change

Morphological damage

microbial communities CHANGE

86-tailings contamination(Heavy Metal Concentrations)

86-(ppm) or milligrams per liter
(mg/L
516- Microbial Diversity Indices:
Typically measured in indices
such as Shannon or Simpson
index (normalized).

heavy metal contamination in sediments

%, mg/kg

Long-term Geochemical Changes

1-2-LAND USE/ LAND COVER
1
316
368
403
430
538
520

1-km^2
316-Area Affected: Hectares (ha)

316- river, forest, clear water, agricultural land, built-up areas,
grassland, and mine/tailings.
403-Normalized Difference Vegetation Index (NDVI)
-Emergy accounting

403-NDVI:
Dimensionless index (no explicit
units stated).
430-emergy accounting (solar
energy),
total emergy-based dollar value

430-Emergy accounting
- acres
705

D

329
678,701

D
D

578
699

D
I from land
use /land
cover index

Land destruction due to debris
flows
Land degradation
Negative impacts to wetland
systems
Impact on Agricultural Land
changes in land development
patterns

hectares or acres
ha

Flood Inundation Depth,Flood Inundation Area

2-ECOLOGY
2-1-BIOCHEMICAL
517
105
126
340

D

Effects on Biogeochemical Cycles
and biology

517-Changes in microbial communities that could impact organic
matter recycling and nutrient cycling
105- Assessed through cytotoxic, genotoxic,
340-Changes in fish populations (e.g., Rainbow Trout)
Habitat availability for spawning species, Benthic organism
community composition over time
Plant test species growth metrics post-incident

102

105-%
340-number

106

D

93

D

273,360,798,54
7,185,222
455

I from
ECOLOGY
I from
ECOLOGY

mutagenic effects in various
organisms and changes in the
mitotic index.
loss of genetic variability and
biomass reduction
ecological consequences

185-AREA

Trophic diversity change

2-2-FLORA
D

Vegetation loss/mortality

3
142
156
212
739
472

156-quantitative assessment of vegetation

148, 42, 462
481

D
D

Vegetation Cover change
Plant Biomass Loss (PB)

764,77
313

D
D

Changes to riparian vegetation
the vigor of riparian forests

M2
Plant Height, Timing of Flood, and Other Factors ,Flood depth,
velocity, and
duration,Flood timing

<Normalized Difference Vegetation Index (NDVI).
Distance from B1 dam: Used to understand the spread and
distribution of tailings.
Redox potential: Influencing the release of metals like manganese.
Hydraulic features: Such as the presence of the Igarapé
thermoelectric plant weir, which acts as a barrier to tailings
migration.
101

D

3-km^2 or %
142-ha
739-Number of species
472-number

plant biomass loss (kg/ha)
ha
Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese: mg/L
Total calcium: mg/L
Fine sand, coarse sand, very fine
sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value (normalized)

Impact on plantations

2-3-FAUNA
2-3-1-TERRESTRIAL FAUNA
D

Terrestrial fauna loss

28
286
271
116
694
583
643

D

Wildlife habitat loss

35
115
141
226
330
394
482
807
92
275
521
548
149
463
535
551

D

739

739- Immediate impact on wildlife,
such as crocodiles, hippos, and fish due to water changes.
Water flow and volume (cubic meters)

739- NUMBER OF species

Number
694-hectars
D

Loss or deterioration of habitats,
specifically for species listed as
blue-listed or red-listed
Biodiversity loss

number
141-<Assessments of species richness and population stability
482-Species Richness, Survival Time of Animals, and Other Factors
,Flood depth, velocity, and duration,Migration ability of animals
274-Changes in species composition and diversity due to
environmental disturbances
521- due to flooding
524-Measurements of diversity indices (e.g., Shannon-Wiener
diversity index).
Environmental Quality Parameters:
Evaluation of affected water and habitat characteristics impacting
fish health and populations.,Long-term changes in ecosystem
health

103

Number
482-species richness (count)

598
181
84
603

D
D

245

D

75

D

Changes in the wildlife food chain
Changes in local wildlife
populations
Disruption of ecosystems and
habitats
Long-term degradation of
ecosystems and loss of
biodiversity.
Terrestrial wildlife impact

754
127
186,
563
622
625
685
817
707
97

D
D
D

Changes in habitat quality
cumulative effects on species
habitat destruction

D

Genetic diversity indices
(number of alleles, allelic richness,
observed and expected
heterozygosity)

58
76

D
D

Terrestrial ecosystem impact index
Birds

490

D

Long-lasting effects on biodiversity
and ecosystem services

D

Aquatic fauna loss

127
90

D
D

337

D

cumulative effects on species
Histopathological damage in
aquatic organisms,
marine benthic macrofauna
affected by the tailings mud

452
453
14

D

514

COUNTS,Estimates for recovery
times
for injured species and
ecosystems
number

number
(number of alleles, allelic richness, observed and expected
heterozygosity)Inbreeding coefficient (Fis)
M-ratio (indicator of population bottlenecks)
Population differentiation (Jost's D index)
Genetic structure (STRUCTURE and DAPC analyses)
Pollution

COUNT AND RATIO
COUNTS,Estimates for recovery
times
for injured species and
ecosystems

2-3-2-AQUATIC FAUNA
COUNTS,Estimates for recovery
times
for injured species and
ecosystems
number

74

Ecotoxicological impacts on fish
health

metal bioaccumulation.((disease prevalence, tissue damage)
337-Iron Mineralogical Set (IMS) Index, Species Abundance,
Richness, and Diversity,Sediment Characteristics: Such as grain
size and composition, which influence macrofauna composition.
Concentration of Metal(oid)s: Observed with the presence of
contaminants (V, Al, Ba, etc.) from the tailings mud.
IMS Index: Used to trace the presence of Fundão dam tailings
mud.
Sediment Variables: Including granulometry (average grain size),
total organic matter (TOM), total organic carbon (TOC), total
nitrogen (TN), and calcium carbonate (CaCO₃).
Metal(oid)s Concentrations: Including iron (Fe), aluminum (Al),
manganese (Mn), arsenic (As), silver (Ag), barium (Ba), cadmium
(Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead
(Pb), and vanadium (V).
Organic Pollutants: Including DDTs, PCBs, PAHs, and sterols.
452-DNA damage
Lipid peroxidation (LPO)
Protein carbonyls (PCO)
Histological damage in gills and liver
Metallothioneins concentration

104

Heavy Metal Concentrations:
mg/kg
Lipid Peroxidation: nmol/mg
protein

Activity of superoxide dismutase, Seasonal variations in metal
concentrations in the water and sediments
Trophic levels of fish communities
Environmental conditions (rainy vs. dry seasons),Integrated
Biomarker Response (IBR) index
Integrated Metal Bioaccumulation (IMB) index,Changes in
community structure (e.g., microbial community)
Changes in zooplankton diversity

DNA damage: quantified through
comet assays or similar measures
(exact units not specified)
Other biochemical indicators:
often expressed as activity units
(e.g., U/mg protein)

453-heavy metal concentration
14-due to increased turbidity and contamination by heavy metals.

679, 652

D

Potential mortality of mussel and
fish populations

457,799

D

health status of fish communities

753

D

125,171,349,37
5,411,
464,680,624
357

D

loss or deterioration of critical fish
or wildlife habitat
Changes in fish populations

D
D

Impact on fisheries
macrobenthic crustacean diversity
and abundance

59
546
637
279
725

D

Aquatic ecosystem impact

652-Growth and survival rates
of aquatic organisms like freshwater scuds and mayfly larvae,
Changes in food web dynamics due to the decline in populations
of key species
457-physiological alterations due to metal exposure and
seasonal variations in bioaccumulation.
799-esions and other indicators of contamination.

number
Species Richness: Number of different species recorded.
Species Abundance: Total number of individuals observed within a
species.
Environmental Variables: Parameters such as
turbidity and pH levels that are directly correlated to species
composition.
species richness (number), abundance(no. of individuals),
shannon wiener index (no dimension), turbidity (NTU), ph ,
temperature, salinity

59-POLUTION
546-changes in fish populations:trophic structure of fish
communities,Habitat Alteration,Changes in water quality
and habitat structure
279-Measures such as fish populations and plant health
725-Flood wave height: Measurement of flood intensity
(e.g., maximum height of flood waves).
Inundation area: Geographic area inundated by flood
waters.,Flow progression over time,Water depth and extent of
flooding,Water depth and extent of flooding,Time of wave arrival
at key cities
65

D

CHANGE aquatic ecosystems
(freshwater, marine, and coastal)

546-Trophic Position
Units: Not explicitly stated in the
summary.
Inference: May need to be
inferred from ecological studies.
Species Diversity
Units: Count (number of species).
Stable Isotope Ratios:
Units: δ13C and δ15N (per mil,
‰).
725-Flood Wave Height: Meters
(m).
Inundation Area: Square
kilometers (km²).
Initial Lake Level: Meters (m)

HEAVY METAL AND arsenic INCREASE

2-4-WATER
2-4-1- WATER QUALITY
26
2
56
94
108
113
117
187
193

D

Water quality degradation

94- turbidity, -CONTAMINTATION
108-suspended particulate matter (SPM),
113A-lkalinity
-Conductivity
-Chloride content,
-Total phosphorus
-Total solids
-Biochemical oxygen demand (BOD)
-Escherichia coli

105

2,108-NTU
94- mg/L
113-conductivity (uS/cm,
micrisiemmens per cm), -pH (ph
units),
-dissolved oxygen (mg/l)
-mg/L
-us/cm

204
225
269
272
311
328
343
358
401
451
467
468
504
528
537
552
573
579
339
626
653
706
744
783
792
800
804
111

-Nitrate
-Ph
117-HEAVY METAL
187- pollutant levels and sedimentation
193- alterations in solids, metals, and metalloids’
concentration,water pH levels
,204- solids, metals, metalloids
225- pollutant concentration
272- due to increased pH, metal concentrations(As, Ba, Cd, Cr, Cu,
Fe, Mn, Ni, Pb, Zn) and turbidity,suspended solids, and electrical
conductivity,Coliform levels,Factors associated with
anthropogenic influences ,dissolved oxygen
311- Chlorophyll-a, flow rate, total arsenic, dissolved manganese,
and total calcium.
Distance from B1 dam: Used to understand the spread and
distribution of tailings.
Redox potential: Influencing the release of metals like manganese.
Hydraulic features: Such as the presence of the Igarapé
thermoelectric plant weir, which acts as a barrier to tailings
migration.
Historical mining activities and agricultural practices
343- Comparison of pre- and post-event turbidity levels (NTU),
Plankton community structure metrics,copper levels, turbidity)
Fish mortality rates
401- Suspended Particulate Matter (SPM): mg/L
451- Water turbidity (measured in NTU)
Iron levels (measured in µg/L or mg/L)
Mortality rates of zebrafish embryos (expressed as
percentages),Microbial colony-forming units (CFU)
Rates of potential pathogenic microbial growth
Dissolved Metals: Concentrations of metals like iron and mercury
were measured, with iron levels increasing significantly
downstream.
Microbial Abundance:
467- Flood Velocity: Calculated through numerical simulations.
Flood Depth: Obtained from simulations,COD(Chemical Oxygen
Demand), TP(Total Phosphorus), TN(Total Nitrogen), and pH levels
in monitoring sites,Pollutant Concentration
468- Flood Duration,Velocity Changes
528- pH, turbidity
537- turbidity, chemical pollutants
573- turbidity, pH, total dissolved solids, and dissolved oxygen,
metal concentration
653-Concentrations of heavy metals
,Toxicity levels of copper
339- Metal concentrations in various media (mg/L or µg/L)
Toxicity testing results on sediment and water (general toxicity
scale)
744&783- Extent of pollutant dispersion downstream
792- concentrations of metals like manganese,aluminum,
phosphorus,Chlorophyll-a, flow rate, total arsenic, and total
calcium.
Sediment Quality Indicators: Fine sand, coarse sand, very fine
sand, manganese (Mn), and iron (Fe) fractions. and

106

mg/l)
- µg/L or mg/L
-mg/kg ; ug/l
pH: no units
117,187- mg/l
193-204,- µg/L or mg/L
272- mg/L
pH: no units
Electrical conductivity: μS/cm
(inferred)
Turbidity: NTU
Total suspended solids: mg/L
Dissolved oxygen: mg/L
Nutrients: mg/L
Contamination factor: no units
311- Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese: mg/L
Total calcium: mg/L
Fine sand, coarse sand, very fine
sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value (normalized)
343- mg/L for metal
concentrations and NTU for
turbidity.
451- turbidity (ntu), dissolved
metals (mg/l),
microbial colony units (cfu/ml),
zebrafish mortality (%)
467- H: No unit; dimensionless.
COD: mg/L (milligrams per liter).
TP (Total Phosphorus): mg/L.
TN (Total Nitrogen): mg/L.
,468,537,552,706- mg/L
792- Chlorophyll-a: mg/L
Flow rate: m³/s
Total arsenic: mg/L
Dissolved manganese: mg/L
Total calcium: mg/L
Fine sand, coarse sand, very fine
sand: g/kg
Mn, Fe fractions: mg/kg
NDVI: Index value (normalized)
111-mg/l)

changes in the riparian vegetation index (NDVI)

111-Chloride Content,

87,112
107

D
D

109
110
217,353,480,60
1,693,150,114,
636,383,239,64
,599,388,214,1
55,11

D
D
D

Turbidity
suspended particulate matter
(SPM)
Alkalinity,
Conductivity,
water pollution

87- NTU

(mg/l)
us/cm
217-Gray Water Footprint (WF)
480-Point Source (PS) and Non-Point Source (NPS) pollution,Flood
depth, velocity, and duration
601-Concentration of heavy metals in water (mg/L)
114-(e.g., heavy metals in water and sediment), TAILING RELEASE
239-concentrations of toxic metals like As, Cu, Pb, Zn
64-Cd, Cu, Cr, Fe, Hg, Mn, Pb, Zn, and arsenic)
214-Concentration of Toxic Metals
11- km2
155-heavy metals, cyanide concentrations

587

D

476
351
460,146
246

D
D
D
D

34

D

408

Long-term water pollution
potential
Maximum Water Surface Elevation
Dissolved oxygen
Water Environment
Transboundary migration of
effluent in rivers
Contamination of water bodies
Biophysical impacts on the river
system

89

D

Change Water quality parameters
(temperature,
conductivity, pH, dissolved
oxygen)

613

D

Prolonged persistent
contamination of water sources

217-cubic mters
480-mg/L
693-ppm for contaminants
114-mg/l, m³ for volume
239&64&388-mg/l
214-mg/kg ; ug/l
155- mg/dm^3

milligrams per liter (mg/L)

Km2
water quality, freshwater and saltwater biota, fish community
analysis, Historical land use data (e.g., deforestation rates)
Climate change models ,habitat disruption
water temperatute (degree
celsius), electrical
conductivity (uS/cm,
micrisiemmens per cm), pH (ph
units), dissolved oxygen (mg/l) ,
(mg/L)

2-4-2-WATER RESOURCE
D
9
326

9-km²
Changes in water resources
Subsequent groundwater
contamination
Groundwater damage
Subsequent groundwater
contamination
microbial communities CHANGE

169
244

D
D

86
516

I from
contaminati
on of water

745
627
227
19
620
717

I from
ECOLOGY

Long term Ecological pattern
change

I from
ECOLOGY
I from
ECOLOGY

Long-term environmental
degradation
Long-term environmental changes
from

644

toxic elements

-mg/dm^3

86-tailings contamination(Heavy Metal Concentrations)

mg/l
86-(ppm) or milligrams per liter
(mg/L
516- Microbial Diversity Indices:
Typically measured in indices
such as Shannon or Simpson
index (normalized).

107

534,526,654,61
2,
602,763,27,393

I from
ECOLOGY

(Fernandes
2016
435

D

the loss of recreational and
aesthetic aspects of reservoirs.
Long-term degradation of local
ecosystems

SECONDARY IMPACTS FROM SOCIAL AND ECONOMIC IMPACTS
environmental pollution due to no
maintenance
Physical conversion of land due to
population displacement

-limited access to and maintenance of fisheries in the affected
area

108

APPENDIX B
The following table lists the references corresponding to the indicator numbers presented in Appendix A.

Indicator number
222, 226, 225, 227, 220, 230, 229, 231, 224, 232, 228
206, 205, 193, 204, 202, 201, 198, 199, 194, 207, 195, 196, 203, 196,
200
472, 477, 468, 476, 470, 474, 471, 475, 478
236, 238, 235
273, 274,
143,
113, 112, 109,110,111
60,61,62,63
304,305,306
514,506,507,509,510,512,508,511,513
521,535,529,528,520,534,526,534,527,524,532,525,533,519
523,531,530,522
368,366,370,369,371
185,176,186,187,181,173,188,174,175,184,191,192,190,183,182,18
9
42,28,35,26,34,27,20,24,21,22,31,45,37,30,36,29,39,25,32,40,41,46
,48,47,43,44,38,23,33
233
365
57,56,49,58,59,53,54,55,52,50
216
156,171,155,169,160,158,170,166,167,168,162,172,161,163,157,15
9,165,164
3,14,2,11,9,1,19,8,6,16,18,4,5,7,17,15,12,13
798,799,800,795,796,794,801,797,803,793,802
271,265,269,260,267,263,264,266,270,268,261,262
807,817,808,804,806,813,805,815,816,814,810,812,809,811
665,660,659,661,666,667,668,671,672,670,663,662,673,669,670,66
4
126,142,115,141,116,127,125,127,121,136,117,114,123,138,131,12
9,137,132,135,118,140,133,124,134,128,120,139,130,122
567,573,570,571,575,568,572,569,574

109

Reference
(Ji et al. 2021)
(Guimarães et al.
2023)
(Yi Xiong 2011)
(Jumani et al. 2023)
(Lines et al. 2023)
(Ge et al. 2019)
(Santos et al. 2023)
(EL Bilali et al.
2022b)
(Mahmoud, Wang,
and Jin 2020)
(Ge, Qin, et al. 2020)
(Pramono Yakti et al.
2019)
(Peng and Zhang
2012)
(Gu et al. 2020)

NO
1
2

(Aqilah et al. 2024)

14

(Jiao, Li, and Ma
2022)
(Cavalheiro Paulelli
et al. 2023)
(Azam and Li 2010)
(Huang et al. 2017)
(Glotov et al. 2018)

15

(Aires et al. 2018)
(Shandro et al. 2016)
(Seema Jagtap 2016)
(Limin Zhang 2016)
(US department of
Homeland security
2011)
(G. W. Fernandes et
al. 2016)
(L. S. C. da Silva et al.
2024)

20
21
22
23
24

3
4
5
6
7
8
9
10
11
12
13

16
17
18
19

25
26

643,646,644,640,647,645,649,641,650,651,648,642
652,653,654,656,658,657,655
330,328,326,329,336,320,332,331,335,324,333,334,321,323,322,32
5
456,452, 457,453, 458
479,481,482,480
83,77,75,76,85,84,74,82,79,80,78,81,152,154,153
718,722,720,721,719
678,679,680,682,683,685,686,687,688,689,690,691,692,693,694,69
5,696,697,698,699,700, 701
548,546, 550,547
636,637,639,638
364
427
384,385,394,388,383,393,390,386,382,392,397,400,399,395,391,38
7,396,389
217,218,219
677,676,675
588,587,584,589,592,586,591,585,590
728,729,730,735,734,732,733,731
752,764,754,753,750,751,763,757,760,759,758,761,756,755,762
105,106,106,104,108,107
579,578,583,582,576,581,580,577
725,723,726,727,724
739,739,744,745,742,743,748,749,736,740,741,737,746,747
272
492,503,504,494,501,505,495,496,502,498,497,499,500

110

(Ministry of Forests
2017)
(Winston Szeto 2022)
(Manoah Muchanga
and Bretha Mzyece
2023)
(C. E. D. Vieira et al.
2022)
(Zhang et al. 2022)
(Czajkowski et al.
2023)
(He et al. 2020)
(FEMA 2012)

27

(D. R. de Carvalho et
al. 2024)
(Bonnie
Gestring
2021)
(Cavalheiro Paulelli
et al. 2022)
(Santos-González et
al. 2021)
(Rana et al. 2022)

35

(Islam and Murakami
2021)
(Department
of
Natural
Resources
2018)
(Xu et al. 2022)
(ACAPS 2023)
(Piésold 2017)
(dos Santos Vergilio
et al. 2021)
(Winarta,
Juwono,
and Dermawan 2019)
(Andredakis, Probst,
and
Annunziato
2016)
(The Institute of Risk
Management South
Africa 2015)
(Kütter et al. 2023)
(Ghimire
and
Schulenberg 2022)

40

28
29
30
31
32
33
34

36
37
38
39

41
42
43
44
45
46
47
48
49
50

408,417,418,419,420,421,422,423,424,425,426,409,410,411,412,41
3,414,415,416
464,459,462,463,461,460
537,538,536,541,543,542,539,540,544
286,279,277,278,283,291,292,285,281,293,293,294,282,289,290,28
8,280,284
102,101,99,100,103
360,357,358,362,361,359
483,484,490,487,485,488,489,486
257,245,240,259,239,246,244,245,249,248,252,251,253,255,241,24
2,247,254,258
,250, 256
598,603,599,601,613,612,602,594,608,595,607,610,609,600,611,60
6,604,597,605
299,300,301,303,302,
783,781,786,785,790,791,789,787,788,784,782
war
340,349,338,350,339,343,351,341,348,342,347,345,344
768,767,777,765,774,773,779,776,775,780,766,771,770,769,778,77
2
295,298,296,297
276
353,352,356,354,355
622,624,625,635,626,627,621,630,632,631,633,634,628,623,629
145,151,148,149,147,146,150
450,438,446,437,447,441,443,444,440,445,442,449,98
792,313,312,311,315,314
451
620,617,616,618,614,619
467
66,67,64,65,69,70,71,72,73,68
707,708,706,705,717,714,711,716,713,712,703,702,709,704,710,71
5

111

(Sánchez et al. 2018)

51

(Wu et al. 2019)
(Ge 2021)
(McCartney 2009)

52
53
54

(de Oliveira et al.
2022)
(Oliveira-Filho et al.
2023)
(G. B. Carvalho and
Corteletti 2021)
(Kossoff et al. 2014)

55

(Garcia et al. 2024)

59

(Lumbroso et al.
2021)
(Ramirez et al. 2022)
(Wernick
WSP
Golder
Vancouver
and McMahen 2016)
(Tannant
and
Skermer 2013)
(IEEE Staff 2009)
(Liu 2011)
(Faiqa
Norkhairi,
Thiruchelvam,
and
Hasini 2018)
(David Morhart 2010)
(Ge, Li, et al. 2020)
(Shandro et al. 2017)
(Mendes et al. 2023)
(Thompson et al.
2020)
(Gao et al. 2024)
(Wang and Zhou
2010)
(Costa et al. 2022)
(“Testalinden Dam
(British
Columbia,
2010) _ Case Study _
ASDSO
Lessons
Learned,” n.d.)

60

56
57
58

61
62
63
64
65
66
67
68
69
70
71
72
73
74
75

402,405,401,403,404,406,407
93,92,97,90,91,94,96,95
373,375,372,377,376,374
307,308,309,310
211,212,212,213,208,210,209,214,215
337
563,551,553,552,562,560,558,565,559,561,566,564,555,554,557,55
6
517,515,516
86,88,89,87
316,317,319
431,428,430,435,436,433,434,429,432
98
152,153,154

112

(Silva Rotta et al.
2020)
(De Biasi et al. 2023)
(Quaresma et al.
2020)
(Martinez et al. 2018)
(Hatje et al. 2017)
(Nascimento et al.
2022)
(Stamou, Politis, and
Xanthopoulou 2005)
(L. Fernandes et al.
2022)
(P. I. N. de Almeida et
al. 2023)
(Moraga, Gurkan, and
Sebnem
Duzgun
2020)
(Scarpelin et al. 2022)
(Mahmoody Vanolya
and Rukundo 2017)
(Ge et al. 2022)

76
77
78
79
80
81
82
83
84
85
86
87
88

APPENDIX C
In this table, the framework features and the Fundão Dam failure indicators are presented. The
titles for short-term and long-term environmental, economic, and social categories are highlighted
in green rows. The class titles are displayed in blue, while the original dam failure impact
framework’s indices and indicators are shown in yellow. The Fundão indicators, which follow
these titles, are placed in white rows.

Table of all Fundão failure impact indicators adjusted into the framework
(references of this table is table 2 and the Lactec resources referred in methodology section of
chapter 3)
Indicator
economic short term
1-immediate economic
loss
disruption of local
businesses index
loss of agricultural
productivity
the bean, maize and
crotalaria productivity
property loss and
damage
property
destruction/damage
building damage due to
mud contact
building damage due to
reconstruction activity
infrastructure loss and
damage index
important facilities
affected
damages in mineral
extraction processes
3-secondary impacts
from
environment and social
impacts
heritage loss and
damage index
losses related to local
cultural assets
archaeological assets (
buried)
archaeological assets
(sedimentary layers
disturbed)
m2
archaeological assets (
began to suffer
accelerated degradation
of archaeological ) m2
environmental
restoration index
tecnosoil impact g m-2
digging birds(he
diagnosis of damage to
wildlife,terrestrial
fauna)
number
ichtyofauna(fish
mortality)
native vegetation
impact (native)

Baseline

Damage/change

% of change

Score

Normalized
value

Index
score

No change

0

1

0

0

43

5

0.444444444

0.5

57

6

0.555555556

146,226.01 ha

34,174.2 ha

23

3

0.222222222

1,035,410 m²

703,947 m² buried

68

7

0.666666667

925,494

363,774

39.30592743

4

0.333333333

77,698

67,727

87.16697985

9

0.888888889

Class score

Category
score

0.240740741

0.222222222

0.433333333

0.62962963

0.625925926
28.127,70 TON

28.127,70 TON

100

10

1

30,327

474 reduction

1.56

1

0

1,687,000

1603000 LOSS

95%

10

1

16

2

0.111111111

0.622222222

113

marine area>deposition
of
waste on the ocean
floor(fc)
economic long term
5-secondary impacts
from
environment and social
impacts
long-term
environmental damage
index
vegetation impact (25
years)
tecnosoil impact g m2(loss discounted c),
85 years
ichtyofauna (163 years)
marine area(loss of
environmental
suitability,) m2
digging birds(he
diagnosis of damage to
wildlife,terrestrial
fauna)(30 years)
long term heritage loss
and damage index
archaeological assets,
social short term
1-immediate loss

28.127,70 TON
1,687,000
2.051.816.010,93
Weighted :1.882.732.595,20m2=
188.273,26 ha
30,327 individuals.

2.719,23

1.035.410,00+925.494,00+77.698,00

25.783.612,50 m2

Deadths

1.4 milion people affected (baseline
report)

loss of cultural assets
index
Impacts on
archaeological/cultural
sites
archaeological assets (
buried)
archaeological assets
(sedimentary layers
disturbed)
m2
archaeological assets (
began to suffer
accelerated degradation
of archaeological ) m2
impacts on traditional
land use and resources
modification of the
landscape or
context of
implementation of
material cultural assets
interruption or transfer
of access to and/or use
of material cultural
property
alteration of parts or
sectors of historical
and/or traditional routes
and paths
shifts in cultural
practices
change in cultural
practices
changing spaces related
to cultural practices
change in the
circulation of cultural
practices and goods
changing the
community relations
network
changing memory
reference spaces
access to traditional
food sources: reflects
stress on dietary
practices
change in access to raw
materials and
associated

600

10

1

2

0.5

307.710,88 TON

3

1

10.105.769,16

3

1

3

1

3

1

no natural recovery

3

1

1

19 PEOPLE

0.0013

4

0.75

0.75

1,035,410 m²

703,947 m² buried

68

7

0.666666667

925,494

363,774

39.30592743

4

0.333333333

77,698

67,727

87.16697985

9

0.888888889

3645

171

4.691358025

1

0

3645

79

2.167352538

1

0

3645

132

3.621399177

1

0

0.95
23.534.157.440,00m2=2.353.415,74
ha

163 years

0.9

0.95

0.222222222
0.486111111
0.421296296
140

35

25

3

0.222222222

140

31

22.14285714

3

0.222222222

140

7

5

1

0

140

36

25.71428571

3

0.222222222

140

14

10

2

0.111111111

140

4

2.857142857

1

0

implements necessary
for the production of
cultural goods
2-social service
service supply index
disruption of water
supply

114

damage to the use of
water for public supply
access to clean water
and sanitation facility
counts
access to water(people
aaffected)
3-health impact and
mental issues
mental issues index
mental health
impacts/issues
mental disorders
hospitalizations
miners mental health
social unrest index
social unrest and
turmoil
(triggered by panic and
loss of life)
social suffering
health problems index
damage to
atmosphere(and effect
on people)
public health outcomes
linked to contamination
events
mental disorders
disorders due to
population exposed to
high levels of al, as, hg,
and n (people)
skin lesions disorders
due to population
exposed to
high levels of al, as, hg,
and n (people)
malaise disorders due
to population exposed
to
high levels of al, as, hg,
and n (people)
gastrointestinal
disorders due to
population exposed to
high levels of al, as, hg,
and n (people)
bone pain due to
population exposed to
high levels of al, as, hg,
and n (people)
social long term
5-secondary impacts
from env
and economic impacts
loss of cultural assets
index
archaeological assets,
env short term
1-geology
contamination of soils
and sediments index
contamination of soils
and sediments
aquatic>change in epts
concentration in
sediments inc1
aquatic>change in epts
concentration in
sediments in c2a
aquatic>change in epts
concentration in
sediments in c2b
marin> increase in epts
concentration in
sediment(estuary of the
doce river)
al dissolved,as total, fe,,
hg,, mn, ni, zn
marin> increase in epts
concentration in
sediment marine region
al dissolved,as total, fe,,
hg,, mn, ni, zn
soil contamination by
epts(ag, al, as, ba, cd,

39 municilalities

18 locations had their
supply systems directly rendered
temporarily unfeasible

60

6

0.555555556

0.388888889

1000000

300000

30

3

0.222222222

two fold

100

10

1

40.5

5

0.444444444

9

6.428571429

1

0

10times more than standard

900

10

1

60

6

0.555555556

38

4

0.333333333

0.388888889

0.722222222

0
140

0.388888889

0.444444444

1.035.410,00 + 925.494,00 +
77.698,00

25.783.612,50 m2

40

4

0.333333333

30

3

0.222222222

25

3

0.222222222

there is no natural
recovery of an
archaeological asset.

3

1

7.69

8

0.777777778

30.76

4

0.333333333

76.92

8

0.777777778

1
1

1

0.43
0.648148148
legislated limits

27.6 times more, 16.4,
1.5,2.2,10,3.3,1.5

>90% change

10

1

legislated limits

4.9, 5, 10.6,1.7,,36,,2.9

>90% change

10

1

0

1

0

115

co, cr, cu, ni,
pb, sb, hg, se, sn and
zn,)
injuries to sediments
and sedimetns index
injuries to sediments,
watercourse opacity,
and oxygenation
aquatic>silting of
hydroelectric
reservoirs
aquatic>(damage to
sediment
quality)change in the
benthonic
macroinvertebrate
community present in
the sediment in c1(sum
of approximate
proportion of minimum
values of indicators
analyzed
in river environment in
compartment 1)
aquatic>change in the
granulometric
composition of the
sediment in c1 (clay)
aquatic>change in the
granulometric
composition of the
sediment in c2a(clay)
aquatic>change in the
granulometric
composition of the
sediment in c2(clay)
aquatic>change in the
granulometric
composition of the
sediment in c3(clay)
marin> (damage to
sediment quality)
change in the structure
of benthic communities
of fish funds
unconsolidated
seabed clay content
soil environment
change index
changes in soil
permeability
and water flow
change in bearing
capacity and soil
deformability
kpa/preconsolidation
stress
tecnosoil
formation(waste)
changes in soil fertility
and production
potential (water ph)
erosion and
displacement imact
index
soil erosion impact
increase in erosion
processes(soil)
t.ha-1 year-1
sediment displacement
marin> increase in
sediment deposition
sediment dynamics in
the landscape
aquatic>changes in
sediment transport
dynamics along the
doce river
indirect impact from
soil and sediment index
damage to
underground features
land use/ land cover
index
land use and land cover
change
land use and land cover
change(tailing
area=33% increase
change)

41

90

28

3

0.222222222

125

10

1

3.4+

1

0
0.277777778

120

5.2%-

1

0

9.2%-

1

0

7.7%-

1

0

50-

5

0.444444444

60%

6

0.555555556

100 TIMES MORE

9900

10

1

95

20.83333333

3

0.222222222

75

8

0.777777778

5.9

6.3

7

7

0.666666667

44.1

54.8

20

2

0.111111111

6 times greater

>90% change

10

1

0.666666667

0.666666667

(AVERAGE)856.15%

10

1

60

63.6

6.955555556

33%

4

0.333333333

0.48
22

14 damaged

116

0.365079365

land use and land cover
change(water
resource=8.7%
decrease change)
land use and land cover
change(urban
area=4.52% decrease
change)
land use and land cover
change(disturbed
vegetation=81.62%
decrease vegetation in
the entire
rio doce river basin.)
land degradation
damage to protected
areas
marin>damage to
protected areas
solid waste generation
areas
2-ecology
biochemical
impact(index)
mutagenic effects in
various organisms and
changes in the mitotic
index.
changes in the
mitotic
index(water)(containing
100% of river water )
flora index
vegetation
loss/mortality
loss of wood forest
resources
vegetation cover
change
change in vegetation
cover
increasing edge effectin
c1 ( landscape metrics
(edge areas and number
of fragments)
terrestrial fauna index
changes in local
wildlife populations
change in bees
population
changes in wildlife
populations in (digging
bird)( 474 loss)
disruption of
ecosystems and habitats
loss of connectivity in
the
landscape(fauna)
terrestrial wildlife
impact
worsening physical
conditions of
the fauna
impacts on seabird
(brown booby) (as)
impacts on seabird
(red-billed
tropicbird) (as)
impacts on seabird
(trindade petrel) (as)
changes in habitat
quality
loss of habitat quality
( environmental
suitability loss)
aquatic fauna index
loss or deterioration of
critical fish or wildlife
habitat
changes in the
composition and
structure of the fish
community
increasing the richness
and abundance of
exotic fishes(number)
impacts of
environmental disasters

8.70%

1

0

4.52%

1

0

81.62%

9

0.888888889

36 areas

22damaged

61

7

0.666666667

23 areas

16 damged

69

6

0.555555556

total of APDL= 28,082.34 hectares

3,503 hm² = 3,503 hectares(to be
removed)

12.4

2

0.111111111

30% reduction

3

0.222222222

2.7

1

0

13.02% reduction

2

0.111111111

>90% change

10

1

0

1

0

1.562963696

1

0

15% decrease

2

0.111111111

24%

3

0.222222222

0

1

0

10 times MORE

>90% change

10

1

13 times

>90% change

10

1

50

5

0.444444444

50% reduced

5

0.444444444

90% increase

9

0.888888889

47%

5

0.444444444

0.222222222

an average of 154.24 cubic meters of
wood per hectare,* 28,082.34
hectares
total 4331420.1216m3

120,015.69 m³

0.37037037

233

30,327

565(number of fragments)

474

(24% fauna poulationshowed
ectoparasites)

137 spieces

0.347222222

0.62345679

47% suitable area impacted

117

0.427211934

on
shrimp species
change in the
phytoplankton
community number of
speices per station, c1
change in the
phytoplankton
community frequency
of occurrence of
species at the river
stations, c1
change in the
phytoplankton
community frequency
of occurrence of
species at the river
stations, c2a
change in the
phytoplankton
community frequency
of occurrence of
species at the river
stations, c2b
changes in zooplankton
communities in
c1(species richness)
changes in zooplankton
communities in
c1(abundance)
changes in zooplankton
communities in
c2a(number of species,)
changes in zooplankton
communities in
c2b(species richness)
changes in zooplankton
communities in
c2b((abundance)
increase in
bioaccumulation of
ichtyofauna-fish-(cr)
increase in
bioaccumulation of
ichtyofauna -fish-(cu)
increase in
bioaccumulation of
ichtyofauna -fish-(fe)
increase in
bioaccumulation of
ichtyofauna -fish-(mn)
increase in
bioaccumulation of
ichtyofauna -fish-(zn)
marin>reduction of the
richness and diversity
of the ichtyofauna in
the marine environment
adjacent to the mouth
of the doce
river(estuarine
ichthyofauna richness)
water quality index
water quality
degradation
marin>increasing solids
concentrations in
water(the doce river
estuary (turbidity)
increasing solids
concentrations in
water(turbidity)
increased epts
concentrations in water
ai, as, cd, pb, cr, hg,
mn, dissolved iron
marin>increased epts
concentration in
water(doce river
estuary)
al dissolved,as
total,cd,cr, hg, mn, zn
marin>increased epts
concentration in
water(marine region)
al dissolved,as
total,cd,cr, hg, mn, zn
marin spm
concentration(mg/l)
suspended particulate
matter
(spm)(maximum)

16

11

31%dropped

4

0.333333333

23%

3

0.222222222

45%

5

0.444444444

36%

4

0.333333333

6

0.555555556

98% reduction

10

14

2

0.111111111

56

6

0.555555556

50

5

0.444444444

an increase in concentrations up to
two times for Cr

100

10

1

an increase in concentrations up to
38 times for Cu

>90% change

10

1

an increase in concentrations up to
times, 25 times for Fe

>90% change

10

1

an increase in concentrations up to
10 times for Mn

>90% change

10

1

an increase in concentrations up to
10 times for Zn

>90% change

10

1

25

45.5% reduction

5

0.444444444

50 TIMES HIGHER

>90% change

10

1

historical maximum

2,000 times higher

>90% change

10

1

legislated limits

320,10,34,165,57,4.4,9360,107

>90% change

10

1

legislated limits

55,2,8,96,10,51,3

>90% change

10

1

legislated limits

2,130,202,5,4410,46,296

>90% change

10

1

100

9000

>90% change

10

1

23.0 g/m3

38.7 g/m3

68.26

7

0.666666667

60% reduction

45

1

0.833333333

118

reduction of dissolved
oxygen (do)
concentrations in water
water resource index
change in the drainage
area of watercourses(%
of subbasin affected)
changes in the
configuration of the
drainage network of
watercourses
(geometric
elements:width
exponent b,depth
exponent,velocity
exponent)
env long term
1-geology
soil and sediment index
tecnosoil impact g m2(loss discounted c),
2-ecology
flora index
vegetation impact
aqua fauna index
ichtyofauna
marine area(loss of
environmental
suitability,) m2
terrestrial fauna index
digging birds(he
diagnosis of damage to
wildlife,terrestrial
fauna)

legislated limits

HIGHEST % OF CHANGE IN
ELEMENTS(width exponent
changes)

28.127,70 TON

0

1

0

20%

2

0.111111111

25%

3

0.222222222

3

1

1

2

0.5

0.5

3

1

1

3

1

3

1

0.166666667

307.710,88 TON

1,687,000
2.051.816.010,93
Weighted :1.882.732.595,20m2=
188.273,26 ha

10.105.769,16

30,327 individuals.

2.719,23

23.534.157.440,00m2=2.353.415,74
ha

163

1

0.833333333
0.916666667
1

The productivity of bean, maize, and crotalaria, extracted from literature (Almeida et al., 2022),
was assessed using the Target Hazard Quotient (THQ), which measures the cumulative noncarcinogenic risk to the population. A THQ value greater than 1 indicates a potential risk,
suggesting that food consumption may lead to harmful effects. Conversely, a THQ value below 1
signifies an exposure level lower than the reference dose, implying that long-term consumption of
the analyzed foods is unlikely to cause adverse health effects (Almeida et al., 2022).
(L. A. DA Silva Junior and Santos 2023) calculated the damages to buildings affected in the area
According to their data, 152 buildings were impacted by the failure, some due to direct mud contact
and others as a result of reconstruction activities.
The damages in mineral extraction processes indicator, extracted from Lactec report (Lactec
2020b), revealed that 469 mining processes were affected. The majority of these processes were
in the research authorization phase (focused on the qualification and quantification of the mineral
asset) at the time of the disaster. Regarding the type of substances involved in the mining sectors,

119

most of the affected mining processes were related to the extraction of sand, followed by gold and
clay (Lactec 2020b).
Buried archaeological assets impacts are defined as the accumulation of mining waste and other
materials deposited on archaeological sites and buildings. In addition to mining waste, the damage
includes rocks, soil, vegetation of various sizes, construction debris, household equipment,
utensils, and transported movable archaeological artifacts, all of which can impact archaeological
buildings. The disturbance of sedimentary layers impact refers to sudden or gradual changes that
affect the morphology of archaeological soils and sediments, altering the archaeological matrix of
the area. Another major damage related to archaeological assets is the accelerated degradation of
archaeological materials. This damage is defined by the interaction of waste, soil, and other
mixtures with archaeological remains and structures, whether solidified in floodplains and slopes
or diluted in the waters of reservoirs, rivers, and the sea. Acting as a catalyst, these materials
expedite the deterioration of preserved evidence. This type of damage was particularly significant
in assessing the impact on underwater archaeological assets (Lactec 2020b).
Technosoil, is the waste soil when the waste from the Fundão dam failure stripped away the natural
soil layer, 10.75 hm³ of waste was deposited, forming a tailings layer with an average thickness of
1.04 m, replacing the natural alluvial soils. Regarding the impact of technosoil, the carbon stock
(C) in the soil was used as a key metric, measured in tons per hectare, considering a depth of 20
cm. Based on the calculation done by Lactec team, 85 years is needed for the natural recovery of
this damaged soil. However, this recovery could be earlier if the external recovery by human
positive recovery activities happens (Alkimin De Lacerda 2021). Another short-term economic
impact from the environmental damage is the impact on digging birds, which refers to the damage
to wildlife and its associated economic consequences. The Trogon surrucura, a bird species

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widespread throughout this region and endemic to the Atlantic Forest, is also found along the Doce
River basin. Lactec team selected this species as an indicator to assess the impacts of the dam
failure on local wildlife. The measurement was conducted by considering the population of the
species. For the long-term effect, 30 years needed to be considered as a natural recovery of the
birds to the baseline level. (Alkimin De Lacerda 2021).
To assess the impacts on the marine area and their economic effects, The Contamination Factor
(CF) was used as the metric to assess this impact. The observed increase was found to be up to 42
times greater when compared to the simulated natural deposition over the same period (CF = 42).
The ichthyofauna was impacted by 44 hm³ of mining waste that contained high concentrations of
metals, water, and other materials and fish in the affected area died due to asphyxiation caused by
an excess of suspended material and a drastic reduction in oxygen levels in the water. This was
further corroborated by the exposure, swelling, and collapse of the fish gills found dead. In addition
to the high mortality rate from asphyxiation, many fish were also buried. The number of fish
affected was selected as the metric for this damage by the Lactec research group. 163 years are
needed for natural recovery of fish in the affected area (Alkimin De Lacerda 2021; Lactec 2020a)
It is important to note that, although the Lactec reports include various classes of vegetation, only
the impact on native vegetation was extracted since this indicator percentage of change was found.
The metric in their study was the number of hectares were affected. The recovery time needed to
have the same amount of vegetation in the impacted area is calculated as 25 years by natural
recovery. However, Lactec team concluded that external recovery such as human intervention can
accelerate the recovery process. (Lactec 2020a; Alkimin De Lacerda 2021; Lactec 2018)

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Regarding the impacted people after the dam failure, it is reported that over one million people
across 35 cities were impacted by the spill of approximately 50 million m³ of mud waste, leading
to 19 fatalities(C. C. Pereira et al. 2024) .
The data used for the archaeological assets is the same as that used in the economic section which
is described previously.(Lactec 2020a; Alkimin De Lacerda 2021). Even if a particular structure
remains completely intact, the damage of its surrounding environment constitutes damage to
cultural property. This results in the loss of part of its communicative, symbolic, and significant
value, among other aspects. Therefore, this was considered as an indicator for the modification of
the landscape or context of implementation of material cultural assets indicator. The "Alteration
of Parts or Sectors of Historical and/or Traditional Routes and Paths" indicator refers to the loss
or change of key elements within large structures and infrastructures that connect various human
settlements such as towns, farms, sites, cities and other cultural assets. These routes and paths are
integral to the experience, understanding, and appreciation of these assets.
The "Interruption or Transfer of Access to and/or Use of Material Cultural Property" indicator
highlights when access to cultural assets by society has been disrupted or halted. This may happen
if an area needs to be isolated due to ongoing construction, restoration work, or to prevent further
damage to that area. A total of 35 assets suffered change in cultural practices, of which 37.1%
celebrations, 28.6% expression, 28.6% places and 5.7% crafts, knowledge and ways of doing
things. Also, the change in places of cultural practices is another indicator which shows 31
properties were damaged and the impact was considered in the framework.
The community relation network indicator shows the rupture of networks of transmission of
knowledge and solidarity between individuals have knowledge related to cultural assets. Changing
memory reference spaces focuses on the enjoyment of a given space, the historical continuity of

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sociocultural processes in those spaces across generations. A total of four assets damaged by the
failure and access to traditional food sources was changed, 50% of which were forms of expression
and 50% crafts, knowledge and ways of doing things. Water supply systems relying on Gualaxo
do Norte, Doce, and Carmo rivers for raw water were shut down, as the existing treatment
technologies were unable to produce safe drinking water from these sources and it damaged the
access of public to the water supply system and 300000 people were affected(Lactec 2020b; G.
W. Fernandes et al. 2016)
Regarding the scores and the impacts of mental health, score of 3.0 is used as the cutoff point for
the clinical threshold for identifying individuals who are likely suffering from a common mental
disorder based on studies conducted in primary care settings in Brazil. In addition, scores above
2.0 serve as a trigger for the implementation of preventive strategies. 40.5% of miners participated
in a survey done after the failure by (Motta and Borges 2021)exceeded the alert score of Brazil,
and 17 participants surpassed the cutoff point. Social suffering raised due to damage to the
relationships, dissatisfaction with the actions taken by the Renova Foundation, and district about
the usage of Doc River. Furthermore, the disorders caused from the failure of Fundão due to the
existence of heavy metals such as Al, As, Hg, and N increased. (Cavalheiro Paulelli et al. 2022).
Regarding the long-term social impact, the only available data pertains to the damage to
archaeological assets, which cannot naturally recover. Additionally, further deterioration and
increased damage are expected to occur over the years (Alkimin De Lacerda et al., 2021; Lactec,
2020).
For Potentially Toxic Elements (PTE) concentration in sediments, the available data showed the
changes above historical maximum in different regions near the dam and the study includes
changes in Al, AS, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn. The percentage of change was

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varied for different regions from 7.69% to 76.92%. PTE change in the marine area was calculated
in Doce River estuary and marine sector and the changes in Al, AS, Ba, Cd, Co, Cr, Cu, Fe, Hg,
Mn, Ni, Pb, Zn were considered. However, PTEs in the soil was not changed according to the
reports by Lactec. Several other indicators show the damages to the sediments after the dam failure.
The heist change was regarding the change in the benthonic macroinvertebrate community present
in the sediment in the aquatic environment. According to the observations by Lactec, significant
increase of sediment input was occurred in the studied stations and showing average change of
856.5% for sediment transport dynamics along the Doce river. Also, the data about the marine
environment sediment shows 50% change in sediment quality. The indicator used for this part was
change in the structure of benthic communities of fish funds unconsolidated. Furthermore, the clay
content in the seabed was changed by 60% showing changes in the sediment quality in the marine
area. Moreover, to calculate the sediment deposition in the marine area, calculation was performed
through mathematical simulations of a hypothetical scenarios, first where the dam failure did not
occur, to calculate the area and thickness of deposition naturally and second scenario included the
effects of the disaster. The dataset used in the modeling were oceanographic, hydrological and
meteorological data for before and after failure periods. The analysis of data showed more than 6
times increased disposition of sediment in the marin area(Lactec, 2020).
The mud wave removed soils, followed by waste deposition, resulting in changes to the soil
properties and the failure effected the soil slop stability, permeability and fertility. According to
Lactec the most change happened to soil permeability with 100 times change. Also, Inderbitzen,
pinhole test, and crumb test were done by Lactec to assess the soil erosion, and it showed 10.7
t.ha-1 year-1 increase of erosion process after dam failure. The formation of a new soil order
known as technosoil is another damage to the soil in the failure area. This technosoil contains a

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cemented layer or at least 20% artifacts or human-made materials such as the Fe mining waste
within the top 100 cm of the soil profile. In this context, technosoil has effectively replaced the
original natural topsoil, significantly altering the physical and chemical properties of the soil. For
the long-term effect, 85 years needed to be considered as a natural recovery of this effect. (Alkimin
De Lacerda, Bastos, and Graf De Miranda 2017; Lactec 2020b; 2020a; Alkimin De Lacerda 2021)
The underground features damages were considered as indirect damage caused from sediment and
soil damages in this study. To assess damage to underground features, from 22 sites 14 sites were
considered damaged by Lactec. This assessment included cavities and shelters near the Santarém
Dam, two former gold mining sites, and five cavities along the Doce River. 3 assets were buried
and were damaged significantly. (Alkimin De Lacerda, Bastos, and Graf De Miranda 2017; Lactec
2020b)
the Land Change Modeler (LCM) tool was used for the land use and land cover changes and
damages in the areas of municipalities of Mariana and Barra Longa, state of Minas Gerais, Brazil,
cover in total area of 1578 km2 by(Aires et al. 2018) showing about 81% change in vegetation
cover after dam failure. The protected areas considered for the damage assessment to protected
areas after dam failure include Conservation Units (UCs) and other Protected Natural Areas for
both terrestrial and marine environment. Indigenous Lands were also considered as protected
natural areas by Lactec. (Lactec 2020b)
the cytogenotoxic effects of the released mine waste in the water studied as changes in the mitotic
and samples with different amount of river water were tested. All impacted-site samples with more
than 40% of river water exhibited significant reductions in the mitotic index. The impacted site
samples containing 100% of river water had 25-35% reductions in mitotic index.(Quadra et al.
2019)

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An average of 154.24 cubic meters of wood per hectare, totaling 120,015.69 m³, with an error of
approximately 13% was calculated as damage to wood forest resources while the total volume of
wood resources in the area was 4331420.1216 m3. (Alkimin De Lacerda, Bastos, and Graf De
Miranda 2017; Lactec 2020b; 2020e). Also, by remote sensing techniques and analysis of digital
processing of images it was shown that 13.2% reduction of vegetation cover in the municipality
of Mariana-MG.(C. A. da Silva Junior et al., 2018). For the long-term effects 25 years of natural
recovery is needed for have the vegetation cover back to the baseline value at the time. The number
of fragments and the proportion of edge areas were considered for edge effect indicator as a change
in vegetation cover in the area. The number of fragments in the area defined by Lactec as
“compartment 1 “ (from Fundão dam to Barra Longa municipality) was increased after failure.
(Lactec 2020b; Alkimin De Lacerda 2021)
Digging bird(a trough surrucura, common species throughout its distribution area) was selected as
an indicator of damage to terrestrial fauna such as change in population, loss of connectivity in the
landscape, worsening physical conditions of the fauna, and loss of habitat quality by lactec and the
reduction of number of this species was shown after failure. For the long-term effect, 30 years
needed to be considered as a natural recovery of the birds to the baseline level. However, there
was not any change in bees’ populations based on another study. For the habitat quality assessment,
the environmental suitability across various fragments was considered. (Lactec 2020b; K. I. C.
Vieira et al. 2020; Alkimin De Lacerda 2021). Also, the effects of failure on two types of seabirds
were assessed by the impact of As on those seabirds and the As concentration in seabird blood was
more than ten times increased after failure (Bauer et al., 2024).
Fish, zooplankton and phytoplankton were the indicators selected by Lactec for aquatic damage in
the entire length of the Doce river and its main tributaries, extending from the site of the Fundão

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tailings dam to the boundary of the estuarine region, and lakes, lagoons in the fluvial-marine plain
near Colatina and Linhares, and hydropower plant reservoirs, where damming restricts water flow.
For the long-term effect, 163 years needed to be considered as a natural recovery of the fish as an
indicator for ichthyofauna to the baseline level. phytoplankton were assessed by analyzing shifts
in community structure, including species richness and density and the change in composition as
frequency of occurrence of species. Bioaccumulation, referring to the increase of potential exotic
elements in fish organs, was assessed by Lactec using 17 PTE, with data from 5 PTE presented in
the table of this study. Also, rise of exotic species in the environment was confirmed by Lactec
studies which it led to the decline or extinction of native populations. The exotic species were
increased by 90% after failure in compared to a study in 2007, and it was shown by 19 samples
with a focus on those originating from other Neotropical River basins (84.2%)(Lactec 2020b;
Alkimin De Lacerda 2021; Lactec, n.d.; Alkimin De Lacerda, Bastos, and Graf De Miranda 2017;
Lactec 2020a; 2020d).
Also, (W. G. Pereira et al. 2024) discussed the area of environmental suitability of shrimps
impacted by tailings plumes as an indicator showing the impacts on marine fauna, and the impacted
area ranged from 27 to 47 %.
In order to find the effects of dam failure on water quality, several indicators had been studied. An
increase in turbidity levels was observed between the Fundão dam and the Baguari HPP dam and
Doce River estuary and in the marine region by Lactec and showing 2000 times exceeding the
historical data between Fundão and HPP dams and 50 times more for the marine area. Also,
suspended particulate matter was increased significantly in the marine area. The concentration of
the PTE in the water near the dam and in the marine area, the measurements showed up to 9360
times more than legislated limit after dam failure. Fundão dam failure also, effected on the water

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courses of the region and it changed 20% of the subbasins between Fundão dam and the Risoleta
Neves Hydroelectric Plant and changes in the watercourse network configuration was showed by
assessing the geometric components and the width of the river from downstream of the Fundão
dam and upstream of the Risoleta Neves HPP was used in this study.(Lactec 2020b; Alkimin De
Lacerda, Bastos, and Graf De Miranda 2017)

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