the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jan 2023
Analysis of flood warning and evacuation efficiency by comparing damage and life-loss estimates with real consequences related to the São Francisco tailings dam failure in Brazil
Abstract. Using mathematical modelling and computer simulations, economic damages and life loss estimates are results that, when prospectively designed based on the comparison of different flood alert scenarios that can be implemented, provide important insights for the elaboration of more robust alerts and the most effective emergency planning. The purpose of this work is to evaluate the use of flood damage and life loss models in floods caused by tailings dams through the application of these models in the real case of the São Francisco dam failure, which occurred in January 2007 in the city of Miraí, in Brazil. The models applied showed agreement with the actual damage observed, and the impact of different lower efficient alert systems showed more catastrophic scenarios in terms of loss of life. The results of this work indicate the potential benefits of using these consequences in risk assessment and may help the brazilian and international legislation on dam safety in new regulations.
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RC1: 'Comment on egusphere-2022-1393', Darren Lumbroso, 01 Feb 2023
The paper is of general interest. Tailings dams pose significant risks to people and properties living downstream of them, as well as the environment. In the past decade there have been numerous tailings dams failures both in Brazil and other countries which have highlighted the need for improvements in risk assessments and emergency planning for these structures.
The papers states that the direct loss of life is related to three factors i.e. number of people at risk; effectiveness of the evacuation and the estimate of the fatality rate. However, the loss of life caused by floods and dam failures is related to many other factors including: the characteristics of the flood (e.g. its velocity, depth, amount of debris, temperature etc), characteristics of people (e.g. gender, age, health, mass, height, culture etc), when the flood occurred (e.g. day of the week, time of day etc), effectiveness of emergency planning, dissemination and effectiveness of warnings etc. The paper needs to bring out these and other factors otherwise it appears that there are simply three factors affecting the loss of life.
The paper states that the HEC-RAS modelling software was used to model the flood wave caused by the failure of the São Francisco tailings dam in Brazil. It has been stated that there are several papers which demonstrate that tailings flows can be modelled as Newtonian rather than a non-Newtonian flows. It is important to note that of the papers which have been cited by the author Travis et al. 2012 state that HEC-RAS may be appropriate for hyper-concentrated and debris flow modelling if key coefficients are modified. One of these parameters in the Manning’s n roughness coefficient. The Manning’s n values quoted in the paper do not appear to have been modified to take Travis et al.’s recommendations into account. In addition, Travis et al., indicate that their findings cannot be applied to unsteady HEC-RAS models. One of the other papers cited as justification for modelling the flow as Newtonian by Martin et al., 2015 found that that when comparing Newtonian to Non-Newtonian flow for tailings dam failures for a specific site the peak discharge only varied by 5% and that the depth of the flow by 10%. However, neither Martin et al. or Travis et al. used their models to estimate loss of life or damage to buildings. Debris flow resulting from tailings dams failures are likely to result in a higher loss of life and more damage to property than Newtonian (i.e. water) flow. The authors need to provide further justification as to why a model that represent non-Newtonian flow (e.g. MIKE21 or FLO-2D) was not used.
The paper could be improved by providing more details of how the evacuation was organised. For example, where did people evacuate to, how long did it take, were they well prepared?
The LifeSim software has been used to model the loss of life and evacuation. However, some of the limitations of LifeSim need to brought out more; for example, LifeSim uses functions based on failures and evacuations for large dams in the USA. It does not appear that it is possible to alter these functions to take into account the different nature of tailings compared to water. Both the HEC-RAS and LifeSim models have been set up using relatively coarse data (e.g. a DEM with a 30 m grid).
It is stated that the study is “a pioneer” in estimating economic damage and loss of life for tailings dams. There have been other papers that have done this and economic damages for dams have been estimated for over 20 years (e.g. see Lee and Noh, The impacts of uncertainty in the predicted dam breach floods on economic damage estimation, 2003). Hence it is not clear that this claim can be justified.
The paper could be made more accessible by avoiding terms that are not commonly used to in the fields of flood risk management and dam breaks. For example, it is unlikely to be clear to many readers what the terms “prospective analyses” and “prospective quantification” mean; in addition, sentences such as “The water started to overflow through the spillway and through the contact of the massif with one of the dam abutments” are unclear in what they are trying to convey. It would greatly improve the readability and clarity of the paper if unclear terms and ambiguous sentences could be replaced with simpler ones and terms that are widely used for flood risk management and dam break studies.
It would be helpful if the paper was proof read before it were resubmitted. For example, tailings is misspelt and Brazil is spelt as “brazil”. In Table 1 the location of the cross-sections downstream of the dam is given in metres (m); however, it appears that is actually in kilometres (km). Quoting velocities and depths to two decimal places gives a misleading representation of the accuracy of the hydraulic modelling results. The uncertainty of hydraulic modelling results as a result of the Digital Elevation Model (DEM), hydrology and hydraulics need to be better explained.
Citation: https://doi.org/10.5194/egusphere-2022-1393-RC1 -
AC1: 'Reply on RC1', André Felipe Rocha, 07 Feb 2023
Dear Darren Lumbroso,
Thank you for accepting to review our paper and for the initial comments and discussion. We also believe that the application of consequences models brings huge improvements in risk assessment and emergency planning for tailings dam safety. Within this context, we expect that this work may contribute to advances in practical applications based on the topic related state of the art. We agree and will proofread the paper before the resubmission, adjusting the text considering your initial recommendations.
We agree that there are many other factors related to life loss caused by floods and dam failures that should be included in the general discussion. In our paper, we presented the main categories of factors listed by Jonkman et al. (2008). These categories represent a broader view of the phenomena. The specific factors mentioned in the comments are certainly included in these main categories. For example, factors related to the characteristics of the flood and characteristics of people are encompassed by the estimate of the fatality rate factor; the number of people at risk includes factors related to when the flood occurred; and the effectiveness of the alert system and emergency planning is embedded by the effectiveness of the evacuation. But, indeed, we agree that more discussion about these and other factors would aggregate this description of state of art about life loss estimation in the paper.
We considered Newtonian flow in HEC-RAS because the tailings in the reservoir had a volumetric solid concentration of 12 %, which is considered the flow as aqueous according to O’Brien and Julien (1985). These authors stated that volumetric solid concentration in the waste stream is usually higher than 20 %. Under these considerations, using more robust models such as the ones mentioned, or the more recent HEC-RAS version incorporating non-Newtonian flow should not provide significant improvements to the simulation once the flow behavior is similar to Newtonian flow under these conditions. Furthermore, not enough data is available for more accurate simulations considering rheological parameters. According to O’Brien and Julien (1985), the consideration of flow resistance, like Manning adjustment technics proposed by Travis et al. (2012), would be necessary for higher volumetric solid concentration (>20%) which is not the case in this study. However, in the attempt of calibrating the flood map, some sensitivity tests were performed for Manning roughness in order to better represent the real event. This information may be included in the final version of the article.
For representing the evacuation that occurred in Miraí town during the dam failure event, we consulted local studies that qualitatively described what happened. We agree that the organization of the evacuation could be better understood with a better explanation and insertion of a table summarizing all aspects of the alert and evacuation processes, as done by Bilali et al. (2021, 2022). Therefore, for a deeper comprehension of this important aspect of the accident, we consider producing and including this table in the final article.
All the representation of alert and evacuation (warning issuance delay, warning diffusion delay, mobilization time) was based on Sorensen and Mileti's (2015) works. The authors analyzed many cases, not only about floods but mainly about chemical and fire accidents. They proposed many defaults according to the characteristics of the event and the population. Both evacuation and life loss rates used within LifeSim can be modified by the users. So, it is possible to adapt the process of alert and evacuation to incorporate specific characteristics for a specific case, and it is possible to change the life loss rates by incorporating the impact of the debris and other characteristics of tailings in the contact human-fluid. We will insert a brief discussion about this in the paper.
By “a pioneer” in estimating economic damage and loss of life for tailings dams, we meant this work is the first analyzing jointly these types of consequences in tailings dams failures. Some studies worked with economic damages valuation for water reservoirs dams (Lee and Noh (2003), as you remember) and tailings dams (Veizaga et al., 2017), and others considered life loss estimation for water dams (Bilali et al., 2021 and 2022; and Kalinina et al., 2021). Lumbroso and Davison (2021) were the first study to evaluate loss of life for tailings dam failure. However, this study is the first one to estimate and validate both consequences in the same study related to a tailings dam failure.
Finally, indeed the study had many hydrology and hydraulics uncertainties, including the coarse DEM data available, the physic breach register lacks and others. Some of them were reduced by improvements (e.g., the DEM was refined using terrestrial topographic survey data). Furthermore, we evaluated the impacts of these uncertainties on the impacted population estimates, using two different vulnerability scenarios designed according to the observed and simulated extent of the flood. Considering these two scenarios, we noticed that the life loss estimates were not significantly impacted by these uncertainties. Nevertheless, we will improve this explanation in the paper in order to give more comprehension related to the limits of the performed simulations.
Citation: https://doi.org/10.5194/egusphere-2022-1393-AC1
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AC1: 'Reply on RC1', André Felipe Rocha, 07 Feb 2023
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RC2: 'Comment on egusphere-2022-1393', Lukas Riedel, 17 Feb 2023
General commentsThe paper investigates the effects of evacuation scenarios on possible fatalities in the affected population by modeling the tailings dam failure of the São Francisco dam in a case study. Tailings dam failures are more common than conventional dam failures and pose a larger risk to the environment and downstream population. The paper is a welcome addition to the research on tailings dam failures and emphasizes the need for more sophisticated models to investigate the potential risks, especially with regard to life loss. The main conclusion of the paper is that an efficient warning and evacuation infrastructure is crucial to minimze fatalities, and that modelling tailings dam failures can support decision makers in that regard.The authors use the LifeSim agent-based model with a Monte-Carlo approach to simulate the evacuation of affected population under different warning and evacuation efficiencies. However, they only employ a single instance of the HEC-RAS model to simulate the flood. While the simulated total flood area is in reasonable agreement with the observed total flood area, there are significant differences in the spatial distribution, with several "over-" and "underestimated" regions, as the authors point out. They also discuss several issues with modelling floods from tailings dam failures, which are mostly due to the reservoirs typically containing a mixture of various fluids and solids. Among these issues are changes in viscosity and possibly non-Newtonian fluid dynamics. The authors acknowledge these sources of significant model error, but then rely on a best-effort estimate. Apart from the moderate agreement with the observed flood extent, there is little evidence on the accuracy of the flood model. The authors mention that the dam break occured "around 3:00 a.m." and that evacuation of Miraí took place "at dawn", which is too inaccurate to verify the flood timings of the model. The study would profit significantly from incorporating the aforementioned uncertainties into the flood model, possibly through a probabilistic approach similar to the one used in the agent-based model. A general need for probabilistic flood modeling is also identified by Gerl et al. (2016). The uncertainty in the flood model is crucial because the authors point out that the flood dynamics can change significantly under varying fluid properties, and that the flood timing is "one of the main parameters in loss of life".Specific CommentsThe authors claim that their study pioneers the exploration of models that estimate both economic damages and loss of life in floods from tailings dam failure. However, as they cite themselves, studies on economic damages from floods are abundant (Gerl et al., 2016), and Lumbroso et al. (2021) estimated loss of life in a similar tailings dam failure case study. Calling this study a "pioneer" is thus misleading. The authors could alternatively make clear that they combine established but disjoint approaches to flood impact modelling in a single study.The authors use LifeSim version 1.0.1, and justify its use by it being the most widely used version of LifeSim. However, LifeSim version 2.0 was released in August 2021. Judging from the cited articles it is expected that v2.0 receives less usage overall than v1.0 due to its much shorter lifetime. The authors should clarify why they chose not to use a more recent version of LifeSim that potentially fixed several issues present in older versions.I did not find a way to access the reference (O’Brien and Julien, 1985). If there is no way of retrieving it, it might be helpful to reference a different publication instead. According to the authors, O’Brien and Julien (1985) argue that volumetric solid concentration in waste streams is usually higher than 20%, and that therefore the fluid must be considered non-Newtonian. The authors state that an analysis from 2006 detected a solid concentration of 12% in the São Francisco reservoir, and that hence the fluid can be considered "aqueous" and Newtonian. Without knowledge of the exact arguments by O’Brien and Julien (1985), this conclusion does not appear justified. A solid concentration of 12% could very well warrant the use of non-Newtonian dynamics and a change in fluid viscosity. Additionally, it remains unclear what is meant by "waste stream", as different types of waste should result in different fluid properties. The authors should clarify this argumentation and they further need to cite the alleged analysis of the reservoir in 2006.The authors claim that their model correctly captures the economic damages resulting from the flood event. These results could be conveyed more concisely in a table, stating the distribution of damages (e.g., median, quantiles). It remains unclear how the Consumer Price Index correction factors were selected. The corresponding source should be clearly cited.The authors recapture the events of the São Francisco dam break and the subsequent flooding of Miraí in section 2. However, any statements regarding the reports of witnesses and reported economic damages are missing references. The authors should make clear where they draw this information from.The authors indicate that source code and data for reproducing their simulations are available on request. As there seems to be no problem with disclosing them, the authors should remove this obstacle and upload their code and data to a public repository, e.g. GitHub or Zenodo.Technical Corrections and Suggestions
- The first sentence of the abstract is hard to read. Please shorten it or split it in two sentences.
- L 392: "The best suitability of this model in tailings dam failure events can be achieved by changing the model standards." Please clarify what is meant by this sentence.
- L 181ff: The paragraph describing the flood propagation in the model can be significantly shortened. The propagation seems irrelevant for the further investigation and there is no data to verify these results.
- L 382: Remove "extremely"
- L 384: Remove "completely"
- Fig. 10 contains a typographic error "Fisrt Alert". Please also make clear that this is an adapted work from a figure in the LifeSim user manual.
- Fig. 6: The lines are hard to distinguish. Use solid lines and colors from a sequential color scheme.
- L 386: Incrompehensible sentence.
- Fig. 2, 5, 8 are missing labels on the x- and y-axes.
- Fig. 4: Please clarify the source "Landsat 5" and add a reference
- Fig. 2, 5, 7, 8, 9: Source listed as "(c) Google Earth". Please make sure to follow the attribution instructions by Google: https://about.google/brand-resource-center/products-and-services/geo-guidelines/#required-attribution
- L 585: (USACE 1895) is an unclear reference. I could not find the cited item.
- L 586: (USACE 2018): Please provide a link to the software.
- Tab. 5 has the same caption as Tab. 4.
- L 552: (Rocha 2015): Please provide a link, I found https://repositorio.ufmg.br/handle/1843/BUBD-A9VN49
Citation: https://doi.org/10.5194/egusphere-2022-1393-RC2 -
AC2: 'Reply on RC2', André Felipe Rocha, 01 Mar 2023
Dear Lukas Riedel,
Thank you for accepting to comment on our paper. Indeed, more robust studies about tailing dam failures with the application of consequences models and others are important and bring huge improvements in risk assessment and emergency planning for this type of structure.
We agree that uncertainty analysis in flood modeling is extremely necessary for the flood consequence model. By the way, this is a theme that we are developing in our flood research group at the Federal University of Minas Gerais. Specifically in this study, we tried to represent the real event that occurred in Mirai, in 2007. Our aim was to verify the application of consequence models by comparing real and estimated outcomes. It was possible to collect the real event data mainly based on local technical reports and information collected from local authorities and post-event local studies. The breach characteristics, arrival time at the city and the extension of the flood are examples of this data. Even though the flood extent uncertainty was not the main focus of the study, considering its relevance to the whole process of evaluation, this uncertainty was partially incorporated into the evaluation by considering different scenarios. We evaluated the impacts of the flood model uncertainties on the impacted population estimates using two different vulnerability scenarios designed according to the observed and simulated extent of the flood. It consisted in compensating the differences between simulated and real event flood extent in terms of the number of people exposed. We spatially increased and reduced the amount of population exposed in the buildings nearby areas where differences were observed between simulations and the real event. Considering these two scenarios, we noticed that the life loss estimates were not significantly impacted by these uncertainties. Nevertheless, we will improve this explanation in the paper to give more comprehension related to the limits of the performed simulations.
Concerning the software version used, we first applied the more recent LifeSim 2.0, version released in August 2021. We identified some bugs in the second version of the software which compromised the evacuation module analysis done in this article. LifeSim 2.0 does not run when it is considered a fraction of the population evacuate on foot. Moreover, when we use other parametrization in the alert and evacuation process instead of the defaults present in the model, LifeSim 2.0 also does not run. These bugs were reported to the Risk Management Center of the Hydrologic Engineering Center. We received an answer from the RMC team reporting to us that they will fix it for the next version. More information about the capabilities of the next version can be explored at this link: https://publibrary.planusace.us/#/document/6477d9a0-3e5e-4621-c7d4-de95a4dcc631. Therefore, we opted to use the LifeSim 1.0 version. In our paper, we highlight the main differences between these two versions, but we will reinforce this choice in the final version of the article. Furthermore, it is expected that no significant changes would be observed in the results of the simulations if the more recent version could be achieved without these bugs, once the differences between the versions do not affect the original method of loss evaluation. In general, the method of both versions is the same following the work and data used by Aboelata and Bowles (2005). The differences between them are some considerations about uncertainties sampling; validation efforts; availability of other features like agricultural losses, indirect income and job losses; and another classification (based on the same data) of limits for building submergence and fatality rates.
The O’Brien and Julien (1985) study is available at this link: https://www.engr.colostate.edu/~pierre/ce_old/Projects/Paperspdf/O%27Brien-Julien%20UtahPDF.pdf. The authors defined classes of flow type by several experiments performed at Colorado State University on mud flow samples. We will add more discussion about this study and others of the same authors to emphasize our option of considering a Newtonian flow for the propagation of dam failure flood. Despite this consideration, we achieved results quite similar related to the flooded area, taking into account that uncertainty is also present in local topography data.
The main characteristics of the real flood event were obtained from Rocha (2015), available at https://smarh.eng.ufmg.br/diss_defesas_detalhes.php?aluno=1135. The author brought all available details concerning the hydrodynamic flood wave propagation. This information was crucial for parameterizing our flood model and for the vulnerability and exposure analysis. We will make it clearer in the text and, as you recommend, we provide the link to this work.
For the preprint, we decided to use the option of code and data available on request. Even though, the assets, including the codes, the models, etc.. Within the final version of the paper, we will make it accessible in a public repository as suggested.
Furthermore, for resubmission, we will consider and analyze all technical corrections and suggestions. Thank you a lot for proofreading the paper and for noticing some mistakes and possible improvements in text writing.
Citation: https://doi.org/10.5194/egusphere-2022-1393-AC2
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1393', Darren Lumbroso, 01 Feb 2023
The paper is of general interest. Tailings dams pose significant risks to people and properties living downstream of them, as well as the environment. In the past decade there have been numerous tailings dams failures both in Brazil and other countries which have highlighted the need for improvements in risk assessments and emergency planning for these structures.
The papers states that the direct loss of life is related to three factors i.e. number of people at risk; effectiveness of the evacuation and the estimate of the fatality rate. However, the loss of life caused by floods and dam failures is related to many other factors including: the characteristics of the flood (e.g. its velocity, depth, amount of debris, temperature etc), characteristics of people (e.g. gender, age, health, mass, height, culture etc), when the flood occurred (e.g. day of the week, time of day etc), effectiveness of emergency planning, dissemination and effectiveness of warnings etc. The paper needs to bring out these and other factors otherwise it appears that there are simply three factors affecting the loss of life.
The paper states that the HEC-RAS modelling software was used to model the flood wave caused by the failure of the São Francisco tailings dam in Brazil. It has been stated that there are several papers which demonstrate that tailings flows can be modelled as Newtonian rather than a non-Newtonian flows. It is important to note that of the papers which have been cited by the author Travis et al. 2012 state that HEC-RAS may be appropriate for hyper-concentrated and debris flow modelling if key coefficients are modified. One of these parameters in the Manning’s n roughness coefficient. The Manning’s n values quoted in the paper do not appear to have been modified to take Travis et al.’s recommendations into account. In addition, Travis et al., indicate that their findings cannot be applied to unsteady HEC-RAS models. One of the other papers cited as justification for modelling the flow as Newtonian by Martin et al., 2015 found that that when comparing Newtonian to Non-Newtonian flow for tailings dam failures for a specific site the peak discharge only varied by 5% and that the depth of the flow by 10%. However, neither Martin et al. or Travis et al. used their models to estimate loss of life or damage to buildings. Debris flow resulting from tailings dams failures are likely to result in a higher loss of life and more damage to property than Newtonian (i.e. water) flow. The authors need to provide further justification as to why a model that represent non-Newtonian flow (e.g. MIKE21 or FLO-2D) was not used.
The paper could be improved by providing more details of how the evacuation was organised. For example, where did people evacuate to, how long did it take, were they well prepared?
The LifeSim software has been used to model the loss of life and evacuation. However, some of the limitations of LifeSim need to brought out more; for example, LifeSim uses functions based on failures and evacuations for large dams in the USA. It does not appear that it is possible to alter these functions to take into account the different nature of tailings compared to water. Both the HEC-RAS and LifeSim models have been set up using relatively coarse data (e.g. a DEM with a 30 m grid).
It is stated that the study is “a pioneer” in estimating economic damage and loss of life for tailings dams. There have been other papers that have done this and economic damages for dams have been estimated for over 20 years (e.g. see Lee and Noh, The impacts of uncertainty in the predicted dam breach floods on economic damage estimation, 2003). Hence it is not clear that this claim can be justified.
The paper could be made more accessible by avoiding terms that are not commonly used to in the fields of flood risk management and dam breaks. For example, it is unlikely to be clear to many readers what the terms “prospective analyses” and “prospective quantification” mean; in addition, sentences such as “The water started to overflow through the spillway and through the contact of the massif with one of the dam abutments” are unclear in what they are trying to convey. It would greatly improve the readability and clarity of the paper if unclear terms and ambiguous sentences could be replaced with simpler ones and terms that are widely used for flood risk management and dam break studies.
It would be helpful if the paper was proof read before it were resubmitted. For example, tailings is misspelt and Brazil is spelt as “brazil”. In Table 1 the location of the cross-sections downstream of the dam is given in metres (m); however, it appears that is actually in kilometres (km). Quoting velocities and depths to two decimal places gives a misleading representation of the accuracy of the hydraulic modelling results. The uncertainty of hydraulic modelling results as a result of the Digital Elevation Model (DEM), hydrology and hydraulics need to be better explained.
Citation: https://doi.org/10.5194/egusphere-2022-1393-RC1 -
AC1: 'Reply on RC1', André Felipe Rocha, 07 Feb 2023
Dear Darren Lumbroso,
Thank you for accepting to review our paper and for the initial comments and discussion. We also believe that the application of consequences models brings huge improvements in risk assessment and emergency planning for tailings dam safety. Within this context, we expect that this work may contribute to advances in practical applications based on the topic related state of the art. We agree and will proofread the paper before the resubmission, adjusting the text considering your initial recommendations.
We agree that there are many other factors related to life loss caused by floods and dam failures that should be included in the general discussion. In our paper, we presented the main categories of factors listed by Jonkman et al. (2008). These categories represent a broader view of the phenomena. The specific factors mentioned in the comments are certainly included in these main categories. For example, factors related to the characteristics of the flood and characteristics of people are encompassed by the estimate of the fatality rate factor; the number of people at risk includes factors related to when the flood occurred; and the effectiveness of the alert system and emergency planning is embedded by the effectiveness of the evacuation. But, indeed, we agree that more discussion about these and other factors would aggregate this description of state of art about life loss estimation in the paper.
We considered Newtonian flow in HEC-RAS because the tailings in the reservoir had a volumetric solid concentration of 12 %, which is considered the flow as aqueous according to O’Brien and Julien (1985). These authors stated that volumetric solid concentration in the waste stream is usually higher than 20 %. Under these considerations, using more robust models such as the ones mentioned, or the more recent HEC-RAS version incorporating non-Newtonian flow should not provide significant improvements to the simulation once the flow behavior is similar to Newtonian flow under these conditions. Furthermore, not enough data is available for more accurate simulations considering rheological parameters. According to O’Brien and Julien (1985), the consideration of flow resistance, like Manning adjustment technics proposed by Travis et al. (2012), would be necessary for higher volumetric solid concentration (>20%) which is not the case in this study. However, in the attempt of calibrating the flood map, some sensitivity tests were performed for Manning roughness in order to better represent the real event. This information may be included in the final version of the article.
For representing the evacuation that occurred in Miraí town during the dam failure event, we consulted local studies that qualitatively described what happened. We agree that the organization of the evacuation could be better understood with a better explanation and insertion of a table summarizing all aspects of the alert and evacuation processes, as done by Bilali et al. (2021, 2022). Therefore, for a deeper comprehension of this important aspect of the accident, we consider producing and including this table in the final article.
All the representation of alert and evacuation (warning issuance delay, warning diffusion delay, mobilization time) was based on Sorensen and Mileti's (2015) works. The authors analyzed many cases, not only about floods but mainly about chemical and fire accidents. They proposed many defaults according to the characteristics of the event and the population. Both evacuation and life loss rates used within LifeSim can be modified by the users. So, it is possible to adapt the process of alert and evacuation to incorporate specific characteristics for a specific case, and it is possible to change the life loss rates by incorporating the impact of the debris and other characteristics of tailings in the contact human-fluid. We will insert a brief discussion about this in the paper.
By “a pioneer” in estimating economic damage and loss of life for tailings dams, we meant this work is the first analyzing jointly these types of consequences in tailings dams failures. Some studies worked with economic damages valuation for water reservoirs dams (Lee and Noh (2003), as you remember) and tailings dams (Veizaga et al., 2017), and others considered life loss estimation for water dams (Bilali et al., 2021 and 2022; and Kalinina et al., 2021). Lumbroso and Davison (2021) were the first study to evaluate loss of life for tailings dam failure. However, this study is the first one to estimate and validate both consequences in the same study related to a tailings dam failure.
Finally, indeed the study had many hydrology and hydraulics uncertainties, including the coarse DEM data available, the physic breach register lacks and others. Some of them were reduced by improvements (e.g., the DEM was refined using terrestrial topographic survey data). Furthermore, we evaluated the impacts of these uncertainties on the impacted population estimates, using two different vulnerability scenarios designed according to the observed and simulated extent of the flood. Considering these two scenarios, we noticed that the life loss estimates were not significantly impacted by these uncertainties. Nevertheless, we will improve this explanation in the paper in order to give more comprehension related to the limits of the performed simulations.
Citation: https://doi.org/10.5194/egusphere-2022-1393-AC1
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AC1: 'Reply on RC1', André Felipe Rocha, 07 Feb 2023
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RC2: 'Comment on egusphere-2022-1393', Lukas Riedel, 17 Feb 2023
General commentsThe paper investigates the effects of evacuation scenarios on possible fatalities in the affected population by modeling the tailings dam failure of the São Francisco dam in a case study. Tailings dam failures are more common than conventional dam failures and pose a larger risk to the environment and downstream population. The paper is a welcome addition to the research on tailings dam failures and emphasizes the need for more sophisticated models to investigate the potential risks, especially with regard to life loss. The main conclusion of the paper is that an efficient warning and evacuation infrastructure is crucial to minimze fatalities, and that modelling tailings dam failures can support decision makers in that regard.The authors use the LifeSim agent-based model with a Monte-Carlo approach to simulate the evacuation of affected population under different warning and evacuation efficiencies. However, they only employ a single instance of the HEC-RAS model to simulate the flood. While the simulated total flood area is in reasonable agreement with the observed total flood area, there are significant differences in the spatial distribution, with several "over-" and "underestimated" regions, as the authors point out. They also discuss several issues with modelling floods from tailings dam failures, which are mostly due to the reservoirs typically containing a mixture of various fluids and solids. Among these issues are changes in viscosity and possibly non-Newtonian fluid dynamics. The authors acknowledge these sources of significant model error, but then rely on a best-effort estimate. Apart from the moderate agreement with the observed flood extent, there is little evidence on the accuracy of the flood model. The authors mention that the dam break occured "around 3:00 a.m." and that evacuation of Miraí took place "at dawn", which is too inaccurate to verify the flood timings of the model. The study would profit significantly from incorporating the aforementioned uncertainties into the flood model, possibly through a probabilistic approach similar to the one used in the agent-based model. A general need for probabilistic flood modeling is also identified by Gerl et al. (2016). The uncertainty in the flood model is crucial because the authors point out that the flood dynamics can change significantly under varying fluid properties, and that the flood timing is "one of the main parameters in loss of life".Specific CommentsThe authors claim that their study pioneers the exploration of models that estimate both economic damages and loss of life in floods from tailings dam failure. However, as they cite themselves, studies on economic damages from floods are abundant (Gerl et al., 2016), and Lumbroso et al. (2021) estimated loss of life in a similar tailings dam failure case study. Calling this study a "pioneer" is thus misleading. The authors could alternatively make clear that they combine established but disjoint approaches to flood impact modelling in a single study.The authors use LifeSim version 1.0.1, and justify its use by it being the most widely used version of LifeSim. However, LifeSim version 2.0 was released in August 2021. Judging from the cited articles it is expected that v2.0 receives less usage overall than v1.0 due to its much shorter lifetime. The authors should clarify why they chose not to use a more recent version of LifeSim that potentially fixed several issues present in older versions.I did not find a way to access the reference (O’Brien and Julien, 1985). If there is no way of retrieving it, it might be helpful to reference a different publication instead. According to the authors, O’Brien and Julien (1985) argue that volumetric solid concentration in waste streams is usually higher than 20%, and that therefore the fluid must be considered non-Newtonian. The authors state that an analysis from 2006 detected a solid concentration of 12% in the São Francisco reservoir, and that hence the fluid can be considered "aqueous" and Newtonian. Without knowledge of the exact arguments by O’Brien and Julien (1985), this conclusion does not appear justified. A solid concentration of 12% could very well warrant the use of non-Newtonian dynamics and a change in fluid viscosity. Additionally, it remains unclear what is meant by "waste stream", as different types of waste should result in different fluid properties. The authors should clarify this argumentation and they further need to cite the alleged analysis of the reservoir in 2006.The authors claim that their model correctly captures the economic damages resulting from the flood event. These results could be conveyed more concisely in a table, stating the distribution of damages (e.g., median, quantiles). It remains unclear how the Consumer Price Index correction factors were selected. The corresponding source should be clearly cited.The authors recapture the events of the São Francisco dam break and the subsequent flooding of Miraí in section 2. However, any statements regarding the reports of witnesses and reported economic damages are missing references. The authors should make clear where they draw this information from.The authors indicate that source code and data for reproducing their simulations are available on request. As there seems to be no problem with disclosing them, the authors should remove this obstacle and upload their code and data to a public repository, e.g. GitHub or Zenodo.Technical Corrections and Suggestions
- The first sentence of the abstract is hard to read. Please shorten it or split it in two sentences.
- L 392: "The best suitability of this model in tailings dam failure events can be achieved by changing the model standards." Please clarify what is meant by this sentence.
- L 181ff: The paragraph describing the flood propagation in the model can be significantly shortened. The propagation seems irrelevant for the further investigation and there is no data to verify these results.
- L 382: Remove "extremely"
- L 384: Remove "completely"
- Fig. 10 contains a typographic error "Fisrt Alert". Please also make clear that this is an adapted work from a figure in the LifeSim user manual.
- Fig. 6: The lines are hard to distinguish. Use solid lines and colors from a sequential color scheme.
- L 386: Incrompehensible sentence.
- Fig. 2, 5, 8 are missing labels on the x- and y-axes.
- Fig. 4: Please clarify the source "Landsat 5" and add a reference
- Fig. 2, 5, 7, 8, 9: Source listed as "(c) Google Earth". Please make sure to follow the attribution instructions by Google: https://about.google/brand-resource-center/products-and-services/geo-guidelines/#required-attribution
- L 585: (USACE 1895) is an unclear reference. I could not find the cited item.
- L 586: (USACE 2018): Please provide a link to the software.
- Tab. 5 has the same caption as Tab. 4.
- L 552: (Rocha 2015): Please provide a link, I found https://repositorio.ufmg.br/handle/1843/BUBD-A9VN49
Citation: https://doi.org/10.5194/egusphere-2022-1393-RC2 -
AC2: 'Reply on RC2', André Felipe Rocha, 01 Mar 2023
Dear Lukas Riedel,
Thank you for accepting to comment on our paper. Indeed, more robust studies about tailing dam failures with the application of consequences models and others are important and bring huge improvements in risk assessment and emergency planning for this type of structure.
We agree that uncertainty analysis in flood modeling is extremely necessary for the flood consequence model. By the way, this is a theme that we are developing in our flood research group at the Federal University of Minas Gerais. Specifically in this study, we tried to represent the real event that occurred in Mirai, in 2007. Our aim was to verify the application of consequence models by comparing real and estimated outcomes. It was possible to collect the real event data mainly based on local technical reports and information collected from local authorities and post-event local studies. The breach characteristics, arrival time at the city and the extension of the flood are examples of this data. Even though the flood extent uncertainty was not the main focus of the study, considering its relevance to the whole process of evaluation, this uncertainty was partially incorporated into the evaluation by considering different scenarios. We evaluated the impacts of the flood model uncertainties on the impacted population estimates using two different vulnerability scenarios designed according to the observed and simulated extent of the flood. It consisted in compensating the differences between simulated and real event flood extent in terms of the number of people exposed. We spatially increased and reduced the amount of population exposed in the buildings nearby areas where differences were observed between simulations and the real event. Considering these two scenarios, we noticed that the life loss estimates were not significantly impacted by these uncertainties. Nevertheless, we will improve this explanation in the paper to give more comprehension related to the limits of the performed simulations.
Concerning the software version used, we first applied the more recent LifeSim 2.0, version released in August 2021. We identified some bugs in the second version of the software which compromised the evacuation module analysis done in this article. LifeSim 2.0 does not run when it is considered a fraction of the population evacuate on foot. Moreover, when we use other parametrization in the alert and evacuation process instead of the defaults present in the model, LifeSim 2.0 also does not run. These bugs were reported to the Risk Management Center of the Hydrologic Engineering Center. We received an answer from the RMC team reporting to us that they will fix it for the next version. More information about the capabilities of the next version can be explored at this link: https://publibrary.planusace.us/#/document/6477d9a0-3e5e-4621-c7d4-de95a4dcc631. Therefore, we opted to use the LifeSim 1.0 version. In our paper, we highlight the main differences between these two versions, but we will reinforce this choice in the final version of the article. Furthermore, it is expected that no significant changes would be observed in the results of the simulations if the more recent version could be achieved without these bugs, once the differences between the versions do not affect the original method of loss evaluation. In general, the method of both versions is the same following the work and data used by Aboelata and Bowles (2005). The differences between them are some considerations about uncertainties sampling; validation efforts; availability of other features like agricultural losses, indirect income and job losses; and another classification (based on the same data) of limits for building submergence and fatality rates.
The O’Brien and Julien (1985) study is available at this link: https://www.engr.colostate.edu/~pierre/ce_old/Projects/Paperspdf/O%27Brien-Julien%20UtahPDF.pdf. The authors defined classes of flow type by several experiments performed at Colorado State University on mud flow samples. We will add more discussion about this study and others of the same authors to emphasize our option of considering a Newtonian flow for the propagation of dam failure flood. Despite this consideration, we achieved results quite similar related to the flooded area, taking into account that uncertainty is also present in local topography data.
The main characteristics of the real flood event were obtained from Rocha (2015), available at https://smarh.eng.ufmg.br/diss_defesas_detalhes.php?aluno=1135. The author brought all available details concerning the hydrodynamic flood wave propagation. This information was crucial for parameterizing our flood model and for the vulnerability and exposure analysis. We will make it clearer in the text and, as you recommend, we provide the link to this work.
For the preprint, we decided to use the option of code and data available on request. Even though, the assets, including the codes, the models, etc.. Within the final version of the paper, we will make it accessible in a public repository as suggested.
Furthermore, for resubmission, we will consider and analyze all technical corrections and suggestions. Thank you a lot for proofreading the paper and for noticing some mistakes and possible improvements in text writing.
Citation: https://doi.org/10.5194/egusphere-2022-1393-AC2
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HEC-RAS Model André Felipe Rocha da Silva https://1drv.ms/u/s!AtLNxPU-2kOTgdIDYT1Lf6BXWiTQIA?e=pSsSs5
HEC-LifeSim Model André Felipe Rocha da Silva https://1drv.ms/u/s!AtLNxPU-2kOTgdICw9C9GLm4vUfFEg?e=iMs2YC
Shapefiles André Felipe Rocha da Silva https://1drv.ms/u/s!AtLNxPU-2kOTgdIGTwCT6f2akuxQmw?e=5jJI4D
Scripts André Felipe Rocha da Silva https://1drv.ms/u/s!AtLNxPU-2kOTgdIEuFXmij6LNCsEqA?e=qojyLF
Excel André Felipe Rocha da Silva https://1drv.ms/u/s!AtLNxPU-2kOTgdIFwAn8Ap-2F-QCjA?e=28xU1L
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