the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Feb 2025
Review Article: Analysis of sediment disaster risk assessment surveys in Brazil: A critical review and recommendations
Abstract. Climate change-induced sediment-related disasters in Brazil are intensifying, posing substantial risks. Studies on Brazilian disaster risk reduction are abundant, but those on federal risk assessment surveys are scarce. To address this gap, we analyzed five surveys, including the Municipal Risk Reduction Plan (PMRR), Geological Risk Survey (GRS), Susceptibility Survey (SS), Geotechnical Aptitude for Urbanization (GAUC), and Geological Hazard Survey (GHS). We conducted a meta-analysis of 300 scholarly publications and public datasets to assess these surveys, evaluating input data, methods, outcomes, applicability, effectiveness, and cost–benefit, guided by global recommendations. Spearman’s rank correlation and McDonald’s Omega were employed to evaluate survey associations with initiatives. The results reveal each survey’s unique contributions and challenges, such as limited national coverage and underutilization of quantitative methods. GHS stands out for its versatility, including climate change adaptation countermeasures and decision-maker relevance, but it lacks legal support and limited coverage. GRS and SS are well established but need considerable methodological updates, while GAUC is underutilized due to complexity and high costs. Despite the reproducibility and cost-time efficiency challenges, PMRR exhibits substantial correlation with implementing disaster risk reduction activities. Recommendations include standardizing procedures, enhancing data collection and analysis, improving outputs, and a progressive multilevel approach.
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RC1: 'Comment on egusphere-2024-2255', Anonymous Referee #1, 06 Apr 2025
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AC1: 'Reply on RC1', Thiago Santos, 11 Apr 2025
Dear Referee
We sincerely thank you for your constructive feedback and insightful questions, which have significantly improved the clarity, transparency, and methodological rigor of our manuscript.
We have carefully considered and addressed each of your comments.
Below, we provide a point-by-point answer to your questions and suggestions.
We hope our replies meet your expectations and help clarify the issues.
Please do not hesitate to let us know if further clarification is required.
With kind regards,
Thiago
1) This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper. Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
Thank you for your valuable comments and suggestions. We agree that providing a brief explanation of each federal risk assessment method is essential for ensuring clarity and helping the reader follow the comparisons and discussions presented throughout the manuscript. To address this, we added a new section 2. This section outlines the main objectives, scope, responsible institutions, and typical applications of five federal surveys in Brazil. (L93 - L152 in the revised manuscript).
Other points I noticed are listed below.
2) Introduction: The past knowledge or information on natural disasters in general and those on landslides disasters are mixed together. So that some organization is necessary. For example, it would be possible to first state those on natural disasters in general, and then on specific landslides disasters.
Thank you for your valuable suggestion. We agree that the initial version of the introduction mixed general disaster concepts with specific information on sediment disasters. So, in the first four paragraphs of the revised manuscript, we argue about previous studies and reports on general disaster concepts (L24 - L61 in the revised manuscript). Then, we introduce previous studies and reports on sediment disasters in the fifth to seventh paragraphs (L62 - L92 in the revised manuscript).
3) Line 31: What is “sediment disaster”? Write the definition.
According to the reviewer’s comments, we added the definition of sediment disasters (L62 - L68 in the revised manuscript). As follows: In this study, sediment disasters refer to hazardous natural phenomena resulting from the movement, accumulation, or erosion of soil, rock, or debris materials, typically triggered by gravitational forces and/or hydrometeorological conditions (Uchida et al., 2009). Typical processes that cause sediment disasters include landslides, debris flows, mudslides, rockfalls, and severe soil erosion (Dai et al., 2002; Hungr et al., 2014). Sediment disasters are subject to the complex effects of two factors: natural factors, such as terrain morphology, hydrological regimes, and vegetation cover, and anthropogenic activities, such as road excavations, cut-and-fill operations, unregulated urban sprawl on unstable slopes, and the presence of informal settlements in high-risk zones.
4) Line 76: What is “the core elements”? What kind of elements did the previous studies deal with?
We revised the text to clearly express which core elements are lacking in the current literature (L87 - L90 in the revised manuscript). The final paragraph was rewritten as follows:
“In Brazil, a comparative evaluation of the five federal risk assessment methodologies initiated after 2004, including the PMRR, Geological Risk Survey (GRS), Susceptibility Survey (SS), Geotechnical Aptitude for Urbanization Charts (GAUC), and Geological Hazard Survey (GHS), was conducted. Information was collected and reviewed from official guidelines and their updates (Alheiros, 2006; Brasil, 2007; Bittar, 2014; Pimentel & Dutra, 2018; Lana et al., 2021). Recently, Mendonça et al. (2023) focused exclusively on evaluating the effectiveness of the PMRR. Dias et al. (2021) conducted technical comparisons of various landslide susceptibility mapping methods, including the official SS, and several academic approaches. Rocha et al. (2021) argued the effectiveness of SS, GAUC, and GHS based on the case studies in Nova Friburgo, Rio de Janeiro state. However, no previous studies have undertaken a systematic and comparative analysis encompassing all five federal risk assessment methodologies currently implemented in Brazil. Moreover, the existing literature has not thoroughly examined the methodological components, national coverage, their suitability to inform and support DRR initiatives, and cost per beneficiary. This study seeks to bridge these critical gaps by offering a comprehensive evaluation of each federal survey, identifying methodological deficiencies, and proposing evidence-based improvements to enhance the Brazilian DRR strategies for a more resilient society.”
5) Line 120: …disaster reduction… --> …disaster risk reduction… ?
Yes. Update to enhance clarity and standardization. Disaster Risk Reduction
6) Line 125: There are two “Local Scale”s.
Removed the repeated local scale. And included the corrected Site-specific scale (< 1: 5,000).
7) Line 126-127: This sentence seems to be very difficult to understand. I would like it to be written more clearly.
Thank you for your observation. We agree that the original sentence required greater clarity and have reformulated the passage to enhance its readability and explicability. The revised section now clearly outlines the categorization of the surveys and the rationale behind the assessment scales (L204–L210 in the revised manuscript). However, we are not entirely sure if we have fully captured the intent of your comment. If the updated version still does not address your concern adequately, we would be grateful if you could kindly provide further clarification so we can make the necessary improvements.
Specifically, the text now reads:
“This study adopted a structured approach to evaluate the applicability of federal surveys across DRR initiatives. The classification framework was based on guidelines from Fell et al. (2008) and Corominas et al. (2013), emphasizing scale-appropriate zoning and risk management practices. Surveys were first grouped into four operational categories—National Scale (< 1: 250,000), Regional Scale (1: 250,000 to 1: 25,000), Local Scale (1: 25,000 to 1: 1,000), and Site-specific scale (< 1: 5,000)—according to their spatial resolution, data availability, intended outputs, and use cases. In parallel, DRR initiatives were assessed based on their functional purpose, governance level, and spatial requirements, using relevance indicators adapted from Corominas et al. (2013) across four scales of analysis. Then, we applied a cross-referential matching process to align initiative demands with survey capabilities. Applicability scores were assigned on a 0–4 ordinal scale, representing levels of relevance from “not applicable” to “fully applicable.” These scores reflect thematic alignment and technical-spatial compatibility. Intermediate values were used when surveys could partially support initiatives beyond their initial scope, capturing contextual flexibility.”
8) Line 143-144: Is “structural measure (SC)" included in the “six non-structural initiatives”? It seems obviously contradictory.
Corrected the sentence. Five non-structural counter measurements (MP, LULOL, SL, EWS) and one structural counter measurement (SC). We revised it as follows:
“These comprise five nonstructural initiatives promoting appropriate land-use policies (MP, LULOL, and SL), management (EWS, ERP), and one structural measure (SC).” (L225 -L226 in the revised manuscript).
9) Line 177: “cost-benefit ratio” --> “cost per beneficiary” ?
Systematically adjusted the terminology related to cost per beneficiary to ensure consistency and standardization.
10) Figure 1.: At the top are figures comparing the number of disasters per state with total area, urban area, population and so on. I would like a clear explanation of how the number of disasters is counted. If 10 landslides occur in one heavy rainfall event, should each be counted as one, or should they be counted as ten? This would likely change the interpretation of the figures.
It is essential to clarify that the disaster counts in this database (Brasil, 2023) are based on officially recognized disaster declarations, not on individual occurrences of phenomena. In other words, a single event recorded in the Atlas may encompass multiple landslides triggered by the same meteorological episode. Therefore, the number of disasters reflects the number of declared emergencies rather than the total number of individual landslide occurrences.
The methods section includes the enhanced version (3.1 Data collection and analysis). (L163 – L166 in the revised manuscript).
“In this database, disasters are recorded based on the issuance of official emergency or disaster declarations, rather than on the count of individual physical phenomena. For example, a single entry may represent one or several landslides that occurred during the same rainfall event. Therefore, the disaster count in this study reflects the number of formally recognized events at the municipal level, not the total number of landslide occurrences.”
11) What is “critical municipalities”? It means the 286 municipalities? Now that the explanation appears in the latter part, you should add some explanation on it before readers see this figure.
We have clarified the definition of "critical municipalities" in Section 3.1 (Data Collection and Analysis), where we explain that this designation is based on federal risk classifications and specify that the total number of municipalities included in our study is 821, based on the most recent available data. (L166 - L171 in the revised manuscript).
“The municipalities highly susceptible to sediment disasters were retrieved from the Ministry of Regional Development—National Secretariat of Civil Protection and Defense (MDR, 2012), which designates these locations as 'critical municipalities' due to their elevated risk levels. Initially, 286 cities were identified under this classification. The number was later expanded to 821 based on updated federal reports. In this study, we utilized the most recent data, comprising 821 critical municipalities, for our analysis. These areas have been prioritized for the implementation of DRR assessment surveys. “
12) Figure 2: Why is this figure just for PMRR, GRS, and SS? Why are not GHS and GAUC shown?
Thank you for raising this important point. The decision to include only PMRR, GRS, and SS in Figure 2 was based on the extent of their municipal coverage across Brazil. While GAUC and GHS are indeed part of our assessment framework, they were excluded from this specific figure due to their limited representativeness. According to Table 1, GAUC and GHS assessments have been conducted in only 17 and 12 municipalities, respectively—a notably small sample size compared to the broader implementation of the other methods. So, we add this in the text: “GAUC and GHS were excluded from this figure due to the small number of municipalities implemented (17 and 12 respectively).” (L323 – L325 in the revised manuscript).
13) Line 195: “rho” should be written in Greek letter.
The term “rho” has been updated to the corresponding Greek symbol (ρ) in the revised manuscript to ensure proper formatting and consistency with academic standards. (L277 in the revised manuscript).
14) Line 292-293: “plot scale” and “partial plot” How large are they?
We agree that “plot scale” and “partial plot” require further clarification. We added the size of each scale as follows:
“The topographic units used to assess risk vary depending on the purpose of each survey. The SS, GAUC, and GHS conduct catchment (> 10 ha) analyses (Table 1; Fig. 3). In some instances, GHS also conducts plot scale (1 – 100 m2) analyses. On the other hand, the PMRR and GRS employ partial plot (100 – 500 m2) and hillslope (> 500 m2 – 10 ha) examinations.” (L355 – L357 in the revised manuscript).
15) Line 345-347: If it is written in the literature, the accuracy of the prediction should be evaluated not only in terms of the hit rate but also in terms of the miss rate. The GHS method may have determined in advance that 95% of the collapsed areas were dangerous, but it would be appropriate to also indicate how many slopes were determined to be dangerous but did not collapse.
According to the reviewer’s comment, we revised the original version about the missed rate, which was not directly shown in the previous study. On the other hand, we did find out what range was considered dangerous, so we have added that information as follows. “However, the authors also reported that 47% of areas were classified as hazardous by the GHS, suggesting that there were also many slopes that were deemed dangerous but did not collapse.” (L411 - L413 in the revised manuscript)
16) Line 376: Where is Figure 7?
Thank you for catching this error. The reference to Figure 7 was a mistake introduced during the final editing stage, and it should be referred to as Figure 4.
17) Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
Thank you for pointing out this incredible mistake. We have revised the text to ensure that all percentage values correspond directly to those in Figure 4. Additionally, the graphs have been improved to visually emphasize regional divisions through color coding, enhancing interpretability and regional comparison.” (L440 - L475 in the revised manuscript)
“Risk assessment surveys are vital resources for various risk-management initiatives. Therefore, the effectiveness of these surveys can be evaluated by examining the activities and initiatives developed from the basic information provided by them. Figure 4 illustrates the distribution of municipalities across the Brazilian states that have adopted various DRR initiatives, such as master plans (MP), landslide–specific laws (SL), land-use and land-occupation laws (LULOL), early warning systems (EWS), emergency response plans (ERP), and structural countermeasures (SC). The regional distribution of DRR initiatives across Brazilian states reveals notable implementation-level contrasts. First, excluding the Federal District, the implementation of landslide-specific laws (LSL) remains notably low across all states. Only Rio de Janeiro and Pará exceed the 5% threshold, standing out as the exceptions in this category. In the Northern region, most states exhibit relatively low adoption of disaster risk reduction measures. However, Amazonas, Pará, and Amapá present higher percentages in specific regional indicators. Amazonas, for instance, shows considerable efforts in implementing emergency response plans (23%) and early warning systems (11%). Pará demonstrates moderate values across all initiatives, particularly in master plans (13%) and LULOL (12%). Amapá also stands out with 31% of municipalities having ERP and 19% implementing LULOL. In contrast, Roraima and Tocantins register the lowest levels in the region, with most indicators below 5%, and complete absence of early warning systems, emergency plans, and structural countermeasures in Roraima. In the Northeastern region, the implementation pattern is more heterogeneous. States such as Pernambuco and Alagoas lead in most indicators. Pernambuco exhibits significant adoption of master plans (20%), emergency response plans (25%), and structural countermeasures (11%), while Alagoas shows high percentages in early warning systems (14%) and ERP (18%). Other states like Ceará and Bahia demonstrate moderate values across all initiatives. In contrast, Piauí and Paraíba appear among the least engaged in the region, with consistently low percentages for specific plans, early warning systems, and structural countermeasures. In the Midwestern region, results vary significantly. The Federal District represents a clear outlier, reporting 100% implementation for all DRR categories except for structural countermeasures. Mato Grosso do Sul follows with a comparatively high adoption of master plans (13%), LULOL (14%), and EWS (8%). Meanwhile, Mato Grosso and Goiás exhibit limited implementation, with most indicators—particularly EWS, ERP, and SC—remaining below 5%.
The Southeastern region stands out as the most advanced in DRR implementation. Rio de Janeiro and Espírito Santo lead the country, with exceptionally high percentages across nearly all indicators. Rio de Janeiro, for example, reports that 77% of municipalities have ERP, 41% have EWS, and 30% have SC. Espírito Santo shows similar results, including 63% ERP and 26% EWS. São Paulo and Minas Gerais also demonstrate widespread adoption, with São Paulo exceeding 10% in all indicators and Minas Gerais registering 20% for ERP and 18% for SC. In the Southern region, DRR measures are generally well adopted. Paraná shows the highest percentages for master plans (31%) and LULOL (31%) among all regional states. Santa Catarina also performs well, particularly in EWS (16%) and ERP (32%). While displaying lower values than its southern counterpart, Rio Grande do Sul still achieves notable implementation for ERP (30%). Overall, the Southeast and South regions exhibit the highest concentration of municipalities with DRR measures, while the North and Midwestern—excluding the Federal District—tend to lag, with considerable disparities within and between regions.”
18) Line 398: “Santa Catarina leads to… EWS implementations.” What is this sentence based on? I cannot find any evidences in Figure 4 or others.
Thank you for pointing this out. The sentence referring to Santa Catarina as a leader in EWS implementation has been removed from the revised manuscript. This adjustment was made to ensure consistency with the data presented in Figure 4. As explained in our response to Comment 17, the entire paragraph was updated to reflect only the values available in Figure 4 accurately.
19) Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear.
Based on the reviewer's comments, we have reconsidered Figure 6. Disaster prevention initiatives are highly diverse, so covering them all in one figure is difficult. In this study, we believe that following the reviewer's comments and organizing the six initiatives used in the previous section will improve the consistency of this manuscript and facilitate readers' understanding. We will, therefore, revise the figure and the contents of the main text accordingly.
20) Line 443: cost-benefit ratio --> cost per beneficiary?
We corrected it. Cost per beneficiary (L527in the revised manuscript)
21) Line 447: $0.0004 per beneficiary … $0.009 in Table.5 - Which is correct?
Thank you for pointing this out. The correct value is $0.009 per beneficiary, as indicated in Table 5. The discrepancy in the text has been corrected accordingly. (L531 in the revised manuscript)
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC1 -
RC2: 'Reply on AC1', Anonymous Referee #1, 29 Apr 2025
Thank you for your sincere response to my comments. However, it hasn't been uploaded the revised manuscript ,yet. I would like to see the revised version. Could you please let me see it?
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC2 -
AC2: 'Reply on RC2', Thiago Santos, 01 May 2025
Dear Referee,
Thank you again for your thoughtful comments and interest in reviewing our revised manuscript.
I sincerely appreciate your request to see the updated manuscript. To provide you with an appropriate response, I consulted the editorial office regarding the possibility of uploading a revised version at this stage. However, they clarified that, since the manuscript is still under open discussion, the journal's policy does not allow authors to upload revised versions until the end of the discussion period. As indicated in the peer-review process description, authors are expected to respond to all comments first, and only then may they be invited by the editor to submit a formal revision.
To address your request as much as possible within the current format, I have revised and included below the updated version of the specific section you previously commented on, particularly points 1, 17, and 19. I hope this partial revision clarifies how your suggestions are incorporated into the manuscript.
Additionally, I have uploaded a supplementary file in this discussion thread with a more suitable format (PDF) to make it easier for you to review the changes in context.
Thank you once again for your constructive feedback and understanding. I remain fully committed to incorporating all your suggestions in the final revised manuscript as soon as the editor officially requests it.
Please don’t hesitate to let me know if you have any further requests or suggestions.
Best regards,
Thiago Santos
Supplementary information1) This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper. Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
2. Overview of Federal Risk Assessment Surveys in Brazil
Since 2004, five different federal risk assessment surveys have been conducted in Brazil, each initiated at different times. The Municipal Risk Reduction Plan (PMRR) represents Brazil’s first nationwide initiative to establish a standardized framework for local-scale risk assessment and disaster mitigation planning. Developed in alignment with the National Policy for Civil Protection and Defense (PNPDEC; Law No. 12,608/2012), the PMRR promotes a paradigm shift from reactive post-disaster responses to proactive risk prevention (Mendonça et al., 2023). Its methodology involves a series of structured phases, including assessing geohydrological risk areas, designing structural countermeasures, cost estimation, and structural and non-structural action plans (Alheiros, 2006). Implementation is coordinated through the Union Resources Decentralized Execution Agreement, typically executed in partnership with universities, public agencies, or private entities (UFSC, 2007; Souza et al., 2008; IPPLAN, 2016), ensuring technical rigor and local contextualization.
While the PMRR offers a comprehensive and structured framework, the Geological Risk Survey (GRS) was developed as a more responsive diagnostic tool to rapidly assess geohydrological risks in urban environments. Grounded in the conceptual understanding of risk as the interaction among hazard, vulnerability, and potential damage (Tominaga, 2012), the GRS focuses on phenomena such as landslides, debris flows, rockfalls, floods, and flash floods (Lana et al., 2021). Supported by national legislation, it serves both as a strategic input for early warning systems at the federal level and as a technical resource for local land-use regulation, preparedness measures, and emergency response planning (Pozzobon et al., 2018). Its methodology comprises a desk-based analysis using geospatial and thematic data, followed by fieldwork to validate and classify risk areas based on terrain morphology and physical vulnerability of existing infrastructure (Pimentel et al., 2018). Although various state and municipal institutions—such as the Institute for Technological Research (IPT) in São Paulo and the Geotechnical Institute Foundation (GeoRio) in Rio de Janeiro—initially developed their own methodologies, the responsibility for standardizing and implementing the survey nationwide was later delegated to the Geological Survey of Brazil (GSB), a federal agency under the Ministry of Mines and Energy, by directive of the Civil House of the Presidency (e.g., Pascarelli et al., 2013; Lamberty & Binotto, 2022; DRM, 2023).
Whereas the GRS centers on the delineation of existing risk zones, the Susceptibility Survey (SS) seeks to anticipate where future hazards are likely to occur by evaluating the intrinsic predisposition of terrain to trigger geohydrological processes. Officially recognized in Brazil’s legal framework, the SS provides municipalities with technical input to inform land-use regulation and long-term urban planning (SGB, 2023b). This methodology encompasses a range of phenomena, including landslides, debris flows, floods, and flash floods (Antonelli et al., 2020). Its primary objective is to provide municipalities with technical support for territorial management and risk mitigation strategies. The approach is grounded in geospatial modeling techniques, which integrate historical inventory data with geological, hydrological, and geomorphological variables to produce susceptibility maps and classify terrain into distinct levels (Bittar, 2014). Fieldwork is carried out for validating the modeled results. While various academic institutions have proposed alternative approaches for conducting this type of assessment, the GSB is the officially designated authority responsible for implementing SS at the national level (e.g., Lorentz et al., 2016; Dias et al., 2021).
Building upon susceptibility assessments, the Geotechnical Aptitude for Urbanization Chart (GAUC) is a technical survey designed to evaluate the suitability of terrain for supporting various forms of land use, thereby guiding safe and sustainable urban development (Antonelli et al., 2021). Intended to inform municipal planning decisions, GAUC supports territorial management, land-use regulation, and disaster risk reduction policies by identifying geotechnical favorable zones for urban expansion (SGB, 2023c). The methodology involves integrating geological, geomorphological, pedological, and topographic data with historical records, complemented by detailed field and laboratory investigations to delineate homogeneous geotechnical units suitable for urbanization (Antonelli et al., 2021). Like the previous surveys, GAUC is endorsed by Law No. 12,608/2012 and serves as a planning instrument under the National Policy for Civil Protection and Defense. Although several academic institutions contribute to GAUC development, its systematic national implementation is carried out by the GSB (e.g., Ribeiro & Dias, 2020; Polivanov et al., 2024).
Completing the Brazilian risk assessment framework, the Geological Hazard Survey (GHS) was introduced to enhance the objectivity of hazard detection and provide predictive insight into potential runout distances of sediment-related events. Developed by the GSB, the GHS uses topographic thresholds derived from statistical analyses of historical events to identify susceptible areas and estimate the potential trajectory and runout extent (Pimentel et al., 2020). It is intended to support various stakeholders—including urban planners, civil defense authorities, and policymakers—by offering standardized and spatial outputs that inform land-use regulation, emergency preparedness, and risk mitigation strategies. Its methodological structure encompasses four key stages: compilation of spatial and thematic data, identification of strategic areas, desk-based hazard modeling using topographic conditioning factors, and final field validation to assign hazard classifications (Pimentel et al., 2018). The GHS has been applied in several municipal studies to support land-use planning and disaster risk management (e.g., Facuri & De Lima Picanço, 2021; Ribeiro et al., 2021; Rocha et al., 2021).
These five federal methodologies constitute the backbone of Brazil’s national strategy for assessing and managing geohydrological risks. Although they operate under a shared legal framework and pursue similar overarching goals, each survey differs substantially in scale, technical scope, practical applicability, and intended outcomes. These methodological distinctions raise critical questions regarding their complementarity, integration, and overall effectiveness in supporting disaster risk reduction (DRR) initiatives across multiple levels of governance. The following sections present a systematic comparative analysis to explore these issues, examining key dimensions such as survey design, territorial coverage, operational scale, suitability and alignment with DRR implementation, and relative cost per beneficiary.
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17) Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
5.2 Examining the correlation between risk assessment surveys and the implementation of disaster reduction countermeasures
Risk assessment surveys are vital resources for various risk-management initiatives. Therefore, the effectiveness of these surveys can be evaluated by examining the activities and initiatives developed from the basic information provided by them. Figure 4 illustrates the distribution of municipalities across the Brazilian states that have adopted various DRR initiatives, such as master plans (MP), landslide–specific laws (SL), land-use and land-occupation laws (LULOL), early warning systems (EWS), emergency response plans (ERP), and structural countermeasures (SC). The regional distribution of DRR initiatives across Brazilian states reveals notable contrasts in implementation levels. First, excluding the Federal District, the implementation of landslide-specific laws (LSL) remains notably low across all states. Only Rio de Janeiro and Pará exceed the 5% threshold, standing out as the exceptions in this category. In the Northern region, most states exhibit relatively low adoption of disaster risk reduction measures (Fig. 4). However, Amazonas, Pará, and Amapá present higher percentages in certain indicators in this region. Amazonas, for instance, shows considerable efforts in implementing emergency response plans (23%) and early warning systems (11%). Pará demonstrates moderate values across all initiatives, particularly in master plans (13%) and LULOL (12%). Amapá also stands out with 31% of municipalities having ERP and 19% implementing LULOL. In contrast, Roraima and Tocantins register the lowest levels in the region, with most indicators below 5%, and complete absence of early warning systems, emergency plans, and structural countermeasures in Roraima. In the Northeastern region, the implementation pattern is more heterogeneous (Fig. 4). States such as Pernambuco and Alagoas lead in most indicators. Pernambuco exhibits significant adoption of master plans (20%), emergency response plans (25%), and structural countermeasures (11%), while Alagoas shows high percentages in early warning systems (14%) and ERP (18%). Other states, such as Ceará and Bahia, demonstrate moderate values across all initiatives. In contrast, Piauí and Paraíba appear among the least engaged in the region, with consistently low percentages for specific plans, early warning systems, and structural countermeasures. In the Midwestern region, results vary significantly (Fig. 4). The Federal District represents a clear outlier, reporting 100% implementation for all DRR categories except for structural countermeasures. Mato Grosso do Sul follows with comparatively high adoption of master plans (13%), LULOL (14%), and EWS (8%). Meanwhile, Mato Grosso and Goiás exhibit limited implementation, with most indicators—particularly EWS, ERP, and SC—remaining below 5%.
The Southeastern region stands out as the most advanced in DRR implementation (Fig. 4). Rio de Janeiro and Espírito Santo lead the country, with exceptionally high percentages across nearly all indicators. Rio de Janeiro, for example, reports 77% of municipalities with ERP, 41% with EWS, and 30% with SC. Espírito Santo shows similar results, including 63% ERP and 26% EWS. São Paulo and Minas Gerais also demonstrate widespread adoption, with São Paulo exceeding 10% in all indicators and Minas Gerais registering 20% for ERP and 18% for SC. In the Southern region, DRR measures are generally well adopted (Fig. 4). Paraná shows the highest percentages for master plans (31%) and LULOL (31%) among all states in the region. Santa Catarina also performs well, particularly in EWS (16%) and ERP (32%). Rio Grande do Sul, while displaying lower values compared to its southern counterparts, still achieves notable implementation for ERP (30%). Overall, the Southeast and South regions exhibit the highest concentration of municipalities with DRR measures, while the North and Midwestern—excluding the Federal District—tend to lag behind, with considerable disparities within and between regions.
Figure 4: Distribution of municipalities in Brazilian states implementing sediment-related DRR initiatives (in percentages). Analysis based on IBGE (2020) DRR dataset. Bars are color-coded to represent Brazil’s five macro-regions.
…
19) Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear. 5.3 Applicability for disaster reduction initiatives
To complement the operational analysis presented in Section 5.2, a relevance matrix (Fig. 6) was developed to explore the applicability of federal risk assessment surveys across multiple dimensions of disaster risk reduction. While the previous section focused on the implementation status of key DRR initiatives based on official indicators from the municipal profiles (IBGE, 2020), the matrix presented here evaluates the suitability of these DRR derived from internationally recognized methodological frameworks (Hungr et al., 2005; Fell et al., 2008; Corominas et al., 2014). The matrix displays the degree to which each survey supports different DRR elements, using a gradient scale from dark blue (0—Not applicable) to dark red (4—Fully applicable). This classification reflects the functional alignment of each survey with best practices for its respective scale, taking into account its defined scope and the extent to which it is integrated into formal governance practices. The resulting overview highlights distinct differences in applicability among the methodologies.
The matrix reveals a clear differentiation in the breadth and depth of applicability among the five federal risk assessment methodologies. The PMRR and GRS exhibit consistently high applicability across a range of DRR initiatives, particularly in emergency response planning (ERP), early warning systems (EWS), and structural countermeasures (SC). Their operational versatility enables integration into a broad set of initiatives; however, their role in shaping legislative frameworks—particularly LSL and LULOL—remains limited. In contrast, the GAUC demonstrates strong alignment with legal instruments, though its contribution to ERP appears comparatively constrained. The SS similarly supports the legislative dimension, but its applicability is markedly lower in ERP and SC. Finally, the GHS stands out as the most applicable methodology, achieving either full (score 4) or substantial (score 3) relevance across all DRR categories. Its balanced integration emphasizes its utility as a comprehensive tool for multi-level risk governance.
Figure 6: Brazilian DRR assessment relevance matrix based on applicability recommendations from Hungr et al. (2005), Fell et al. (2008), and Corominas et al. (2014).
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RC3: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
I was not aware that the journal's policy does not allow authors to upload revised versions until the end of the discussion period. I apologize and thank you very much for your kind assistance.
Thanks to you, I was able to read a brief explanation of PMRR, GRS, GAUC, SS, and GHS, and was able to easily read through the Discussion chapter.
In addition, I have a few small comments as shown below.Figure 4 caption : “Distribution of municipalities in Brazilian state…” --> “The number of municipalities in Brazilian state...” ?
Figure 5 : What exactly do the vertical and horizontal axes in Figure 4 mean? Please add labels for them. For example, I imagined that the graph at the top left had the vertical axis representing the percentage of cities in the state that have MMP, and the horizontal axis representing the number of cities in the state that have implemented PMRR. Is this correct?
If yes,
Whether this understanding is correct or not, I think it would be better if you added an explanation to the main text so that readers can understand accurately.L515: Just for the GHS survey? Other surveys could be improved if frequency and magnitude analysis are incorporated or enhanced.
L553: 14 %? Does this mean the average percent of all the states shown in the Figure 4? If yes, some explanation is necessary in the text or in the figure.
L553-554: Is the strong correlation seen in Figure 5 just seen in the disaster-affected municipalities? In other words, did the parent-set of data shown in Figure 5 include only disaster-affected municipalities?
L603-604: As far as seeing the new Fig.6, PMRR and GRS are both better than GAUC.
I hope my comments are helpful to you.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC3 -
AC3: 'Reply on RC3', Thiago Santos, 03 May 2025
Thank you for your kind assistance, comments, and feedback on our manuscript. I also appreciate your understanding regarding the situation. Below, I respond to your additional comments and explain the adjustments we have made in the manuscript.
Figure 4 caption: “Distribution of municipalities in Brazilian state…” --> “The number of municipalities in Brazilian state...” ?
We revised the figure caption to clarify the values' meaning. This ensures that readers understand that the analysis reflects relative (not absolute) coverage of DRR initiatives across states.
Figure 4: Percentage of municipalities implementing sediment-related DRR initiatives in each Brazilian state. Values represent the proportion of municipalities (%) per state. Data source: IBGE (2020) DRR dataset. Bars are color-coded by Brazil’s five macro-regions.”
Figure 5 : What exactly do the vertical and horizontal axes in Figure 4 mean? Please add labels for them. For example, I imagined that the graph at the top left had the vertical axis representing the percentage of cities in the state with MP, and the horizontal axis representing the number of cities in the state that have implemented PMRR. Is this correct?
If yes,
Whether this understanding is correct or not, I think it would be better if you added an explanation to the main text so that readers can understand accurately.
Thank you for the valuable comment regarding Figure 5. You are correct: in each panel, the horizontal axis shows the number of municipalities per state implementing each risk assessment survey (PMRR, GRS, SS), and the vertical axis shows the number of municipalities per state adopting each specific DRR strategy (e.g., Municipal Master Plan, Local LULC, Local SL, EWS, ERP, SC). Both axes represent absolute counts of municipalities, not percentages.
To improve clarity, we did:
Add explicit axis labels to the figure.
Add a sentence in the results section to guide the reader:
As shown in Figure 5, the analysis considers absolute counts of municipalities per state for risk assessment surveys and DRR initiatives, providing a robust measure of their association. Because the GAUC and GHS have low implementation levels, they were removed from this analysis.
Modify the figure caption to explain that both axes show the number of municipalities per state.
Figure 5 caption: Spearman’s rank correlation between the number of municipalities per state implementing risk assessment surveys (PMRR, GRS, SS) and the number of municipalities per state adopting specific DRR strategies (Municipal Master Plan, Local LULOL, Local SL, EWS, ERP, SC). Analysis includes 5570 Brazilian municipalities based on IBGE (2020) and SGB (2023a, b).
L515: Just for the GHS survey? Other surveys could be improved if frequency and magnitude analysis are incorporated or enhanced.
Thank you for this valuable comment. We agree that incorporating frequency and magnitude analyses would also benefit the other surveys. We have revised the text to clarify that these improvements are not limited to the GHS survey, but could strengthen hazard assessment across multiple surveys. We appreciate this insight, which helped improve the generalizability of our recommendations.
Revised text: Incorporating frequency analyses and enhancing magnitude assessments are crucial for improving the GHS survey and advancing the overall effectiveness of quantitative risk assessment and prediction across the other surveys (Table 2).
L553: 14 %? Does this mean the average percent of all the states shown in the Figure 4? If yes, some explanation is necessary in the text or in the figure.
We confirm that the 14% refers to the overall percentage of Brazil’s 5,570 municipalities that have implemented Municipal Master Plans (MPs), as shown in Figure 4. This aggregate information is not explicitly labeled in the figure itself. Therefore, we included this clarification directly in the results section to ensure that readers understand the origin and meaning of this value, as you suggest.
Results section:
First, the Federal District was excluded because it contains only one municipality, which could distort the overall analysis. The results showed that among Brazil’s 5,570 municipalities, only about 15% on average have implemented master plans. This low implementation rate is consistent across most states. The Southeast and South regions demonstrate higher implementation levels (Fig. 4), while the North and Midwestern regions show considerably lower levels. Rio de Janeiro (33%), Espírito Santo (27%), and Santa Catarina (23%) lead in MP implementation, whereas states such as Tocantins (4%), Rondônia, Amazonas, Amapá, Piauí, and Paraíba (6%) fall below the national average.
Discussion section:
The results highlighted low adherence to master plan implementation, with approximately 15% of Brazil’s 5,570 municipalities reporting implementation (Fig. 4).
L553-554: Is the strong correlation seen in Figure 5 just seen in the disaster-affected municipalities? In other words, did the parent-set of data shown in Figure 5 include only disaster-affected municipalities?
Thank you for this important question. We clarify that the data presented in Figure 5 include all 5,570 Brazilian municipalities, not only those affected by disasters. Therefore, the strong correlations observed between legislative prevention mechanisms (MP, LULOL, SL) and surveys are based on the whole national dataset, regardless of whether municipalities have experienced past disaster events.
We recognize that the original discussion sentence could imply that the analysis was restricted to disaster-affected municipalities. To improve clarity, we revised the discussion sentence as follows:
The strong correlation between legislative prevention mechanisms (MP, LULOL, SL) and surveys (Fig. 5) indicates a general trend toward implementing legal measures across municipalities, which may be partly influenced by higher implementation rates in disaster-affected areas.
L603-604: As far as seeing the new Fig.6, PMRR and GRS are both better than GAUC.
Thank you for this important observation. We agree that, as shown in the updated Figure 6, PMRR and GRS rank higher in applicability than GAUC. We have revised the discussion section to clarify this point and to better reflect the relative performance of all surveys. Additionally, we recognize that GAUC has a distinct purpose compared to the other surveys analyzed, as it focuses primarily on urban planning in non-consolidated safety areas rather than exclusively on disaster prevention. This distinct scope may partially explain its more moderate performance in the DRR applicability ranking. We appreciate this comment, which allowed us to improve the precision and nuance of our interpretation.
Despite these strengths, the applicability of SS is considered moderate to low compared to other surveys (Fig. 6). Notably, PMRR and GRS rank high in applicability. In contrast, while demonstrating moderate potential, the GAUC, introduced in 2014, serves a distinct purpose focused on urban planning in non-consolidated safety areas, which may limit its performance in DRR-specific strategies. Finally, the GHS, launched in 2018, exhibits the highest applicability in DRR initiatives. However, GAUC and GHS present challenges, including moderate execution times, inherent costs, and require more detailed analyses (Antonelli et al., 2021; Pimentel and Dutra, 2018). Due to these complexities, they incur moderate to high costs per beneficiary and show relatively low adherence among practitioners.
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC3
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AC3: 'Reply on RC3', Thiago Santos, 03 May 2025
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RC4: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
Finally, I believe that no further peer review is necessary.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC4 -
AC4: 'Reply on RC4', Thiago Santos, 03 May 2025
Thank you for your message. We truly appreciate your constructive feedback and the time you dedicated to reviewing our manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC4
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AC4: 'Reply on RC4', Thiago Santos, 03 May 2025
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RC3: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
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AC2: 'Reply on RC2', Thiago Santos, 01 May 2025
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RC2: 'Reply on AC1', Anonymous Referee #1, 29 Apr 2025
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AC1: 'Reply on RC1', Thiago Santos, 11 Apr 2025
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RC5: 'Comment on egusphere-2024-2255', Anonymous Referee #2, 08 May 2025
Dear Authors,
I read your manuscript about various landslide risk assessment surveys conducted at the federal level in Brazil. The manuscript analyzes five main survey approaches (the Municipal Risk Reduction Plan (PMRR), the Geological Risk Survey (GRS), the Susceptibility Survey (SS), the Geotechnical Aptitude for Urbanization Charts (GAUC), and the Geological Hazard Survey (GHS)), for each of which shortcomings and range of applicability have been analyzed. I also read the comments of previous reviewer and your answers, along with the modifications you made based on their suggestions. You already addressed all the suggestions I could have made, hence, I believe that your manuscript can be processed for publication without further revisions.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC5 -
AC5: 'Reply on RC5', Thiago Santos, 10 May 2025
Dear Reviewer #2,
Thank you very much for your evaluation and for dedicating your time to review our manuscript.
We sincerely appreciate your kind comments and your recognition of the revisions we made in response to Referee #1’s suggestions. These changes have strengthened the manuscript, making it clearer and more robust.
We are grateful for your support and encouraged by your positive feedback.
Finally, we hope that this article encourages a critical and constructive dialogue within Brazil’s scientific and technical community, aiming to strengthen methodological approaches to quantitative risk assessment. By reflecting on existing federal frameworks, our intention is to support their refinement and enhance their effectiveness in disaster risk prevention strategies.
Best regards,
On behalf of all co-authorsThiago Dutra dos Santos
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC5
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AC5: 'Reply on RC5', Thiago Santos, 10 May 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2255', Anonymous Referee #1, 06 Apr 2025
This paper is based on an extensive review of landslide prevention measures and landslide risk assessment methods in the Federative Republic of Brazil, and is highly rated as a paper that can provide readers with broad knowledge and deep insights into landslide risk assessment methods in Brazil.
However, I would like to strongly point out that it is very difficult to understand the points of this paper for readers who are not familiar with the details of these methods used in Brazil.
This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper.
Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
Other points I noticed are listed below.
Introduction: The past knowledge or information on natural disasters in general and those on landslides disasters are mixed together. So that some organization is necessary. For example, it would be possible to first state those on natural disasters in general, and then on specific landslides disasters.
Line 31: What is “sediment disaster”? Write the definition.
Line 76: What is “the core elements”? What kind of elements did the previous studies deal with?
Line 120: …disaster reduction… --> …disaster risk reduction… ?
Line 125: There are two “Local Scale”s.
Line 126-127: This sentence seems to be very difficult to understand. I would like it to be written more clearly.
Line 143-144: Is “structural measure (SC)" included in the “six non-structural initiatives”? It seems obviously contradictory.
Line 177: “cost-benefit ratio” --> “cost per beneficiary” ?
Figure 1.: At the top are figures comparing the number of disasters per state with total area, urban area, population and so on. I would like a clear explanation of how the number of disasters is counted. If 10 landslides occur in one heavy rainfall event, should each be counted as one, or should they be counted as ten? This would likely change the interpretation of the figures.
What is “critical municipalities”? It means the 286 municipalities? Now that the explanation appears in the latter part, you should add some explanation on it before readers see this figure.
Figure 2: Why is this figure just for PMRR, GRS, and SS? Why are not GHS and GAUC shown?
Line 195: “rho” should be written in Greek letter.
Line 292-293: “plot scale” and “partial plot” How large are they?
Line 345-347: If it is written in the literature, the accuracy of the prediction should be evaluated not only in terms of the hit rate but also in terms of the miss rate. The GHS method may have determined in advance that 95% of the collapsed areas were dangerous, but it would be appropriate to also indicate how many slopes were determined to be dangerous but did not collapse.
Line 376: Where is Figure 7 ?
Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
Line 398: “Santa Catarina leads to… EWS implementations.” What is this sentence based on? I cannot find any evidences in Figure 4 or others.
Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear.
Line 443: cost-benefit ratio --> cost per beneficiary?
Line 447: $0.0004 per beneficiary … $0.009 in Table.5
Which is correct?Citation: https://doi.org/10.5194/egusphere-2024-2255-RC1 -
AC1: 'Reply on RC1', Thiago Santos, 11 Apr 2025
Dear Referee
We sincerely thank you for your constructive feedback and insightful questions, which have significantly improved the clarity, transparency, and methodological rigor of our manuscript.
We have carefully considered and addressed each of your comments.
Below, we provide a point-by-point answer to your questions and suggestions.
We hope our replies meet your expectations and help clarify the issues.
Please do not hesitate to let us know if further clarification is required.
With kind regards,
Thiago
1) This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper. Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
Thank you for your valuable comments and suggestions. We agree that providing a brief explanation of each federal risk assessment method is essential for ensuring clarity and helping the reader follow the comparisons and discussions presented throughout the manuscript. To address this, we added a new section 2. This section outlines the main objectives, scope, responsible institutions, and typical applications of five federal surveys in Brazil. (L93 - L152 in the revised manuscript).
Other points I noticed are listed below.
2) Introduction: The past knowledge or information on natural disasters in general and those on landslides disasters are mixed together. So that some organization is necessary. For example, it would be possible to first state those on natural disasters in general, and then on specific landslides disasters.
Thank you for your valuable suggestion. We agree that the initial version of the introduction mixed general disaster concepts with specific information on sediment disasters. So, in the first four paragraphs of the revised manuscript, we argue about previous studies and reports on general disaster concepts (L24 - L61 in the revised manuscript). Then, we introduce previous studies and reports on sediment disasters in the fifth to seventh paragraphs (L62 - L92 in the revised manuscript).
3) Line 31: What is “sediment disaster”? Write the definition.
According to the reviewer’s comments, we added the definition of sediment disasters (L62 - L68 in the revised manuscript). As follows: In this study, sediment disasters refer to hazardous natural phenomena resulting from the movement, accumulation, or erosion of soil, rock, or debris materials, typically triggered by gravitational forces and/or hydrometeorological conditions (Uchida et al., 2009). Typical processes that cause sediment disasters include landslides, debris flows, mudslides, rockfalls, and severe soil erosion (Dai et al., 2002; Hungr et al., 2014). Sediment disasters are subject to the complex effects of two factors: natural factors, such as terrain morphology, hydrological regimes, and vegetation cover, and anthropogenic activities, such as road excavations, cut-and-fill operations, unregulated urban sprawl on unstable slopes, and the presence of informal settlements in high-risk zones.
4) Line 76: What is “the core elements”? What kind of elements did the previous studies deal with?
We revised the text to clearly express which core elements are lacking in the current literature (L87 - L90 in the revised manuscript). The final paragraph was rewritten as follows:
“In Brazil, a comparative evaluation of the five federal risk assessment methodologies initiated after 2004, including the PMRR, Geological Risk Survey (GRS), Susceptibility Survey (SS), Geotechnical Aptitude for Urbanization Charts (GAUC), and Geological Hazard Survey (GHS), was conducted. Information was collected and reviewed from official guidelines and their updates (Alheiros, 2006; Brasil, 2007; Bittar, 2014; Pimentel & Dutra, 2018; Lana et al., 2021). Recently, Mendonça et al. (2023) focused exclusively on evaluating the effectiveness of the PMRR. Dias et al. (2021) conducted technical comparisons of various landslide susceptibility mapping methods, including the official SS, and several academic approaches. Rocha et al. (2021) argued the effectiveness of SS, GAUC, and GHS based on the case studies in Nova Friburgo, Rio de Janeiro state. However, no previous studies have undertaken a systematic and comparative analysis encompassing all five federal risk assessment methodologies currently implemented in Brazil. Moreover, the existing literature has not thoroughly examined the methodological components, national coverage, their suitability to inform and support DRR initiatives, and cost per beneficiary. This study seeks to bridge these critical gaps by offering a comprehensive evaluation of each federal survey, identifying methodological deficiencies, and proposing evidence-based improvements to enhance the Brazilian DRR strategies for a more resilient society.”
5) Line 120: …disaster reduction… --> …disaster risk reduction… ?
Yes. Update to enhance clarity and standardization. Disaster Risk Reduction
6) Line 125: There are two “Local Scale”s.
Removed the repeated local scale. And included the corrected Site-specific scale (< 1: 5,000).
7) Line 126-127: This sentence seems to be very difficult to understand. I would like it to be written more clearly.
Thank you for your observation. We agree that the original sentence required greater clarity and have reformulated the passage to enhance its readability and explicability. The revised section now clearly outlines the categorization of the surveys and the rationale behind the assessment scales (L204–L210 in the revised manuscript). However, we are not entirely sure if we have fully captured the intent of your comment. If the updated version still does not address your concern adequately, we would be grateful if you could kindly provide further clarification so we can make the necessary improvements.
Specifically, the text now reads:
“This study adopted a structured approach to evaluate the applicability of federal surveys across DRR initiatives. The classification framework was based on guidelines from Fell et al. (2008) and Corominas et al. (2013), emphasizing scale-appropriate zoning and risk management practices. Surveys were first grouped into four operational categories—National Scale (< 1: 250,000), Regional Scale (1: 250,000 to 1: 25,000), Local Scale (1: 25,000 to 1: 1,000), and Site-specific scale (< 1: 5,000)—according to their spatial resolution, data availability, intended outputs, and use cases. In parallel, DRR initiatives were assessed based on their functional purpose, governance level, and spatial requirements, using relevance indicators adapted from Corominas et al. (2013) across four scales of analysis. Then, we applied a cross-referential matching process to align initiative demands with survey capabilities. Applicability scores were assigned on a 0–4 ordinal scale, representing levels of relevance from “not applicable” to “fully applicable.” These scores reflect thematic alignment and technical-spatial compatibility. Intermediate values were used when surveys could partially support initiatives beyond their initial scope, capturing contextual flexibility.”
8) Line 143-144: Is “structural measure (SC)" included in the “six non-structural initiatives”? It seems obviously contradictory.
Corrected the sentence. Five non-structural counter measurements (MP, LULOL, SL, EWS) and one structural counter measurement (SC). We revised it as follows:
“These comprise five nonstructural initiatives promoting appropriate land-use policies (MP, LULOL, and SL), management (EWS, ERP), and one structural measure (SC).” (L225 -L226 in the revised manuscript).
9) Line 177: “cost-benefit ratio” --> “cost per beneficiary” ?
Systematically adjusted the terminology related to cost per beneficiary to ensure consistency and standardization.
10) Figure 1.: At the top are figures comparing the number of disasters per state with total area, urban area, population and so on. I would like a clear explanation of how the number of disasters is counted. If 10 landslides occur in one heavy rainfall event, should each be counted as one, or should they be counted as ten? This would likely change the interpretation of the figures.
It is essential to clarify that the disaster counts in this database (Brasil, 2023) are based on officially recognized disaster declarations, not on individual occurrences of phenomena. In other words, a single event recorded in the Atlas may encompass multiple landslides triggered by the same meteorological episode. Therefore, the number of disasters reflects the number of declared emergencies rather than the total number of individual landslide occurrences.
The methods section includes the enhanced version (3.1 Data collection and analysis). (L163 – L166 in the revised manuscript).
“In this database, disasters are recorded based on the issuance of official emergency or disaster declarations, rather than on the count of individual physical phenomena. For example, a single entry may represent one or several landslides that occurred during the same rainfall event. Therefore, the disaster count in this study reflects the number of formally recognized events at the municipal level, not the total number of landslide occurrences.”
11) What is “critical municipalities”? It means the 286 municipalities? Now that the explanation appears in the latter part, you should add some explanation on it before readers see this figure.
We have clarified the definition of "critical municipalities" in Section 3.1 (Data Collection and Analysis), where we explain that this designation is based on federal risk classifications and specify that the total number of municipalities included in our study is 821, based on the most recent available data. (L166 - L171 in the revised manuscript).
“The municipalities highly susceptible to sediment disasters were retrieved from the Ministry of Regional Development—National Secretariat of Civil Protection and Defense (MDR, 2012), which designates these locations as 'critical municipalities' due to their elevated risk levels. Initially, 286 cities were identified under this classification. The number was later expanded to 821 based on updated federal reports. In this study, we utilized the most recent data, comprising 821 critical municipalities, for our analysis. These areas have been prioritized for the implementation of DRR assessment surveys. “
12) Figure 2: Why is this figure just for PMRR, GRS, and SS? Why are not GHS and GAUC shown?
Thank you for raising this important point. The decision to include only PMRR, GRS, and SS in Figure 2 was based on the extent of their municipal coverage across Brazil. While GAUC and GHS are indeed part of our assessment framework, they were excluded from this specific figure due to their limited representativeness. According to Table 1, GAUC and GHS assessments have been conducted in only 17 and 12 municipalities, respectively—a notably small sample size compared to the broader implementation of the other methods. So, we add this in the text: “GAUC and GHS were excluded from this figure due to the small number of municipalities implemented (17 and 12 respectively).” (L323 – L325 in the revised manuscript).
13) Line 195: “rho” should be written in Greek letter.
The term “rho” has been updated to the corresponding Greek symbol (ρ) in the revised manuscript to ensure proper formatting and consistency with academic standards. (L277 in the revised manuscript).
14) Line 292-293: “plot scale” and “partial plot” How large are they?
We agree that “plot scale” and “partial plot” require further clarification. We added the size of each scale as follows:
“The topographic units used to assess risk vary depending on the purpose of each survey. The SS, GAUC, and GHS conduct catchment (> 10 ha) analyses (Table 1; Fig. 3). In some instances, GHS also conducts plot scale (1 – 100 m2) analyses. On the other hand, the PMRR and GRS employ partial plot (100 – 500 m2) and hillslope (> 500 m2 – 10 ha) examinations.” (L355 – L357 in the revised manuscript).
15) Line 345-347: If it is written in the literature, the accuracy of the prediction should be evaluated not only in terms of the hit rate but also in terms of the miss rate. The GHS method may have determined in advance that 95% of the collapsed areas were dangerous, but it would be appropriate to also indicate how many slopes were determined to be dangerous but did not collapse.
According to the reviewer’s comment, we revised the original version about the missed rate, which was not directly shown in the previous study. On the other hand, we did find out what range was considered dangerous, so we have added that information as follows. “However, the authors also reported that 47% of areas were classified as hazardous by the GHS, suggesting that there were also many slopes that were deemed dangerous but did not collapse.” (L411 - L413 in the revised manuscript)
16) Line 376: Where is Figure 7?
Thank you for catching this error. The reference to Figure 7 was a mistake introduced during the final editing stage, and it should be referred to as Figure 4.
17) Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
Thank you for pointing out this incredible mistake. We have revised the text to ensure that all percentage values correspond directly to those in Figure 4. Additionally, the graphs have been improved to visually emphasize regional divisions through color coding, enhancing interpretability and regional comparison.” (L440 - L475 in the revised manuscript)
“Risk assessment surveys are vital resources for various risk-management initiatives. Therefore, the effectiveness of these surveys can be evaluated by examining the activities and initiatives developed from the basic information provided by them. Figure 4 illustrates the distribution of municipalities across the Brazilian states that have adopted various DRR initiatives, such as master plans (MP), landslide–specific laws (SL), land-use and land-occupation laws (LULOL), early warning systems (EWS), emergency response plans (ERP), and structural countermeasures (SC). The regional distribution of DRR initiatives across Brazilian states reveals notable implementation-level contrasts. First, excluding the Federal District, the implementation of landslide-specific laws (LSL) remains notably low across all states. Only Rio de Janeiro and Pará exceed the 5% threshold, standing out as the exceptions in this category. In the Northern region, most states exhibit relatively low adoption of disaster risk reduction measures. However, Amazonas, Pará, and Amapá present higher percentages in specific regional indicators. Amazonas, for instance, shows considerable efforts in implementing emergency response plans (23%) and early warning systems (11%). Pará demonstrates moderate values across all initiatives, particularly in master plans (13%) and LULOL (12%). Amapá also stands out with 31% of municipalities having ERP and 19% implementing LULOL. In contrast, Roraima and Tocantins register the lowest levels in the region, with most indicators below 5%, and complete absence of early warning systems, emergency plans, and structural countermeasures in Roraima. In the Northeastern region, the implementation pattern is more heterogeneous. States such as Pernambuco and Alagoas lead in most indicators. Pernambuco exhibits significant adoption of master plans (20%), emergency response plans (25%), and structural countermeasures (11%), while Alagoas shows high percentages in early warning systems (14%) and ERP (18%). Other states like Ceará and Bahia demonstrate moderate values across all initiatives. In contrast, Piauí and Paraíba appear among the least engaged in the region, with consistently low percentages for specific plans, early warning systems, and structural countermeasures. In the Midwestern region, results vary significantly. The Federal District represents a clear outlier, reporting 100% implementation for all DRR categories except for structural countermeasures. Mato Grosso do Sul follows with a comparatively high adoption of master plans (13%), LULOL (14%), and EWS (8%). Meanwhile, Mato Grosso and Goiás exhibit limited implementation, with most indicators—particularly EWS, ERP, and SC—remaining below 5%.
The Southeastern region stands out as the most advanced in DRR implementation. Rio de Janeiro and Espírito Santo lead the country, with exceptionally high percentages across nearly all indicators. Rio de Janeiro, for example, reports that 77% of municipalities have ERP, 41% have EWS, and 30% have SC. Espírito Santo shows similar results, including 63% ERP and 26% EWS. São Paulo and Minas Gerais also demonstrate widespread adoption, with São Paulo exceeding 10% in all indicators and Minas Gerais registering 20% for ERP and 18% for SC. In the Southern region, DRR measures are generally well adopted. Paraná shows the highest percentages for master plans (31%) and LULOL (31%) among all regional states. Santa Catarina also performs well, particularly in EWS (16%) and ERP (32%). While displaying lower values than its southern counterpart, Rio Grande do Sul still achieves notable implementation for ERP (30%). Overall, the Southeast and South regions exhibit the highest concentration of municipalities with DRR measures, while the North and Midwestern—excluding the Federal District—tend to lag, with considerable disparities within and between regions.”
18) Line 398: “Santa Catarina leads to… EWS implementations.” What is this sentence based on? I cannot find any evidences in Figure 4 or others.
Thank you for pointing this out. The sentence referring to Santa Catarina as a leader in EWS implementation has been removed from the revised manuscript. This adjustment was made to ensure consistency with the data presented in Figure 4. As explained in our response to Comment 17, the entire paragraph was updated to reflect only the values available in Figure 4 accurately.
19) Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear.
Based on the reviewer's comments, we have reconsidered Figure 6. Disaster prevention initiatives are highly diverse, so covering them all in one figure is difficult. In this study, we believe that following the reviewer's comments and organizing the six initiatives used in the previous section will improve the consistency of this manuscript and facilitate readers' understanding. We will, therefore, revise the figure and the contents of the main text accordingly.
20) Line 443: cost-benefit ratio --> cost per beneficiary?
We corrected it. Cost per beneficiary (L527in the revised manuscript)
21) Line 447: $0.0004 per beneficiary … $0.009 in Table.5 - Which is correct?
Thank you for pointing this out. The correct value is $0.009 per beneficiary, as indicated in Table 5. The discrepancy in the text has been corrected accordingly. (L531 in the revised manuscript)
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC1 -
RC2: 'Reply on AC1', Anonymous Referee #1, 29 Apr 2025
Thank you for your sincere response to my comments. However, it hasn't been uploaded the revised manuscript ,yet. I would like to see the revised version. Could you please let me see it?
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC2 -
AC2: 'Reply on RC2', Thiago Santos, 01 May 2025
Dear Referee,
Thank you again for your thoughtful comments and interest in reviewing our revised manuscript.
I sincerely appreciate your request to see the updated manuscript. To provide you with an appropriate response, I consulted the editorial office regarding the possibility of uploading a revised version at this stage. However, they clarified that, since the manuscript is still under open discussion, the journal's policy does not allow authors to upload revised versions until the end of the discussion period. As indicated in the peer-review process description, authors are expected to respond to all comments first, and only then may they be invited by the editor to submit a formal revision.
To address your request as much as possible within the current format, I have revised and included below the updated version of the specific section you previously commented on, particularly points 1, 17, and 19. I hope this partial revision clarifies how your suggestions are incorporated into the manuscript.
Additionally, I have uploaded a supplementary file in this discussion thread with a more suitable format (PDF) to make it easier for you to review the changes in context.
Thank you once again for your constructive feedback and understanding. I remain fully committed to incorporating all your suggestions in the final revised manuscript as soon as the editor officially requests it.
Please don’t hesitate to let me know if you have any further requests or suggestions.
Best regards,
Thiago Santos
Supplementary information1) This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper. Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
2. Overview of Federal Risk Assessment Surveys in Brazil
Since 2004, five different federal risk assessment surveys have been conducted in Brazil, each initiated at different times. The Municipal Risk Reduction Plan (PMRR) represents Brazil’s first nationwide initiative to establish a standardized framework for local-scale risk assessment and disaster mitigation planning. Developed in alignment with the National Policy for Civil Protection and Defense (PNPDEC; Law No. 12,608/2012), the PMRR promotes a paradigm shift from reactive post-disaster responses to proactive risk prevention (Mendonça et al., 2023). Its methodology involves a series of structured phases, including assessing geohydrological risk areas, designing structural countermeasures, cost estimation, and structural and non-structural action plans (Alheiros, 2006). Implementation is coordinated through the Union Resources Decentralized Execution Agreement, typically executed in partnership with universities, public agencies, or private entities (UFSC, 2007; Souza et al., 2008; IPPLAN, 2016), ensuring technical rigor and local contextualization.
While the PMRR offers a comprehensive and structured framework, the Geological Risk Survey (GRS) was developed as a more responsive diagnostic tool to rapidly assess geohydrological risks in urban environments. Grounded in the conceptual understanding of risk as the interaction among hazard, vulnerability, and potential damage (Tominaga, 2012), the GRS focuses on phenomena such as landslides, debris flows, rockfalls, floods, and flash floods (Lana et al., 2021). Supported by national legislation, it serves both as a strategic input for early warning systems at the federal level and as a technical resource for local land-use regulation, preparedness measures, and emergency response planning (Pozzobon et al., 2018). Its methodology comprises a desk-based analysis using geospatial and thematic data, followed by fieldwork to validate and classify risk areas based on terrain morphology and physical vulnerability of existing infrastructure (Pimentel et al., 2018). Although various state and municipal institutions—such as the Institute for Technological Research (IPT) in São Paulo and the Geotechnical Institute Foundation (GeoRio) in Rio de Janeiro—initially developed their own methodologies, the responsibility for standardizing and implementing the survey nationwide was later delegated to the Geological Survey of Brazil (GSB), a federal agency under the Ministry of Mines and Energy, by directive of the Civil House of the Presidency (e.g., Pascarelli et al., 2013; Lamberty & Binotto, 2022; DRM, 2023).
Whereas the GRS centers on the delineation of existing risk zones, the Susceptibility Survey (SS) seeks to anticipate where future hazards are likely to occur by evaluating the intrinsic predisposition of terrain to trigger geohydrological processes. Officially recognized in Brazil’s legal framework, the SS provides municipalities with technical input to inform land-use regulation and long-term urban planning (SGB, 2023b). This methodology encompasses a range of phenomena, including landslides, debris flows, floods, and flash floods (Antonelli et al., 2020). Its primary objective is to provide municipalities with technical support for territorial management and risk mitigation strategies. The approach is grounded in geospatial modeling techniques, which integrate historical inventory data with geological, hydrological, and geomorphological variables to produce susceptibility maps and classify terrain into distinct levels (Bittar, 2014). Fieldwork is carried out for validating the modeled results. While various academic institutions have proposed alternative approaches for conducting this type of assessment, the GSB is the officially designated authority responsible for implementing SS at the national level (e.g., Lorentz et al., 2016; Dias et al., 2021).
Building upon susceptibility assessments, the Geotechnical Aptitude for Urbanization Chart (GAUC) is a technical survey designed to evaluate the suitability of terrain for supporting various forms of land use, thereby guiding safe and sustainable urban development (Antonelli et al., 2021). Intended to inform municipal planning decisions, GAUC supports territorial management, land-use regulation, and disaster risk reduction policies by identifying geotechnical favorable zones for urban expansion (SGB, 2023c). The methodology involves integrating geological, geomorphological, pedological, and topographic data with historical records, complemented by detailed field and laboratory investigations to delineate homogeneous geotechnical units suitable for urbanization (Antonelli et al., 2021). Like the previous surveys, GAUC is endorsed by Law No. 12,608/2012 and serves as a planning instrument under the National Policy for Civil Protection and Defense. Although several academic institutions contribute to GAUC development, its systematic national implementation is carried out by the GSB (e.g., Ribeiro & Dias, 2020; Polivanov et al., 2024).
Completing the Brazilian risk assessment framework, the Geological Hazard Survey (GHS) was introduced to enhance the objectivity of hazard detection and provide predictive insight into potential runout distances of sediment-related events. Developed by the GSB, the GHS uses topographic thresholds derived from statistical analyses of historical events to identify susceptible areas and estimate the potential trajectory and runout extent (Pimentel et al., 2020). It is intended to support various stakeholders—including urban planners, civil defense authorities, and policymakers—by offering standardized and spatial outputs that inform land-use regulation, emergency preparedness, and risk mitigation strategies. Its methodological structure encompasses four key stages: compilation of spatial and thematic data, identification of strategic areas, desk-based hazard modeling using topographic conditioning factors, and final field validation to assign hazard classifications (Pimentel et al., 2018). The GHS has been applied in several municipal studies to support land-use planning and disaster risk management (e.g., Facuri & De Lima Picanço, 2021; Ribeiro et al., 2021; Rocha et al., 2021).
These five federal methodologies constitute the backbone of Brazil’s national strategy for assessing and managing geohydrological risks. Although they operate under a shared legal framework and pursue similar overarching goals, each survey differs substantially in scale, technical scope, practical applicability, and intended outcomes. These methodological distinctions raise critical questions regarding their complementarity, integration, and overall effectiveness in supporting disaster risk reduction (DRR) initiatives across multiple levels of governance. The following sections present a systematic comparative analysis to explore these issues, examining key dimensions such as survey design, territorial coverage, operational scale, suitability and alignment with DRR implementation, and relative cost per beneficiary.
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17) Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
5.2 Examining the correlation between risk assessment surveys and the implementation of disaster reduction countermeasures
Risk assessment surveys are vital resources for various risk-management initiatives. Therefore, the effectiveness of these surveys can be evaluated by examining the activities and initiatives developed from the basic information provided by them. Figure 4 illustrates the distribution of municipalities across the Brazilian states that have adopted various DRR initiatives, such as master plans (MP), landslide–specific laws (SL), land-use and land-occupation laws (LULOL), early warning systems (EWS), emergency response plans (ERP), and structural countermeasures (SC). The regional distribution of DRR initiatives across Brazilian states reveals notable contrasts in implementation levels. First, excluding the Federal District, the implementation of landslide-specific laws (LSL) remains notably low across all states. Only Rio de Janeiro and Pará exceed the 5% threshold, standing out as the exceptions in this category. In the Northern region, most states exhibit relatively low adoption of disaster risk reduction measures (Fig. 4). However, Amazonas, Pará, and Amapá present higher percentages in certain indicators in this region. Amazonas, for instance, shows considerable efforts in implementing emergency response plans (23%) and early warning systems (11%). Pará demonstrates moderate values across all initiatives, particularly in master plans (13%) and LULOL (12%). Amapá also stands out with 31% of municipalities having ERP and 19% implementing LULOL. In contrast, Roraima and Tocantins register the lowest levels in the region, with most indicators below 5%, and complete absence of early warning systems, emergency plans, and structural countermeasures in Roraima. In the Northeastern region, the implementation pattern is more heterogeneous (Fig. 4). States such as Pernambuco and Alagoas lead in most indicators. Pernambuco exhibits significant adoption of master plans (20%), emergency response plans (25%), and structural countermeasures (11%), while Alagoas shows high percentages in early warning systems (14%) and ERP (18%). Other states, such as Ceará and Bahia, demonstrate moderate values across all initiatives. In contrast, Piauí and Paraíba appear among the least engaged in the region, with consistently low percentages for specific plans, early warning systems, and structural countermeasures. In the Midwestern region, results vary significantly (Fig. 4). The Federal District represents a clear outlier, reporting 100% implementation for all DRR categories except for structural countermeasures. Mato Grosso do Sul follows with comparatively high adoption of master plans (13%), LULOL (14%), and EWS (8%). Meanwhile, Mato Grosso and Goiás exhibit limited implementation, with most indicators—particularly EWS, ERP, and SC—remaining below 5%.
The Southeastern region stands out as the most advanced in DRR implementation (Fig. 4). Rio de Janeiro and Espírito Santo lead the country, with exceptionally high percentages across nearly all indicators. Rio de Janeiro, for example, reports 77% of municipalities with ERP, 41% with EWS, and 30% with SC. Espírito Santo shows similar results, including 63% ERP and 26% EWS. São Paulo and Minas Gerais also demonstrate widespread adoption, with São Paulo exceeding 10% in all indicators and Minas Gerais registering 20% for ERP and 18% for SC. In the Southern region, DRR measures are generally well adopted (Fig. 4). Paraná shows the highest percentages for master plans (31%) and LULOL (31%) among all states in the region. Santa Catarina also performs well, particularly in EWS (16%) and ERP (32%). Rio Grande do Sul, while displaying lower values compared to its southern counterparts, still achieves notable implementation for ERP (30%). Overall, the Southeast and South regions exhibit the highest concentration of municipalities with DRR measures, while the North and Midwestern—excluding the Federal District—tend to lag behind, with considerable disparities within and between regions.
Figure 4: Distribution of municipalities in Brazilian states implementing sediment-related DRR initiatives (in percentages). Analysis based on IBGE (2020) DRR dataset. Bars are color-coded to represent Brazil’s five macro-regions.
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19) Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear. 5.3 Applicability for disaster reduction initiatives
To complement the operational analysis presented in Section 5.2, a relevance matrix (Fig. 6) was developed to explore the applicability of federal risk assessment surveys across multiple dimensions of disaster risk reduction. While the previous section focused on the implementation status of key DRR initiatives based on official indicators from the municipal profiles (IBGE, 2020), the matrix presented here evaluates the suitability of these DRR derived from internationally recognized methodological frameworks (Hungr et al., 2005; Fell et al., 2008; Corominas et al., 2014). The matrix displays the degree to which each survey supports different DRR elements, using a gradient scale from dark blue (0—Not applicable) to dark red (4—Fully applicable). This classification reflects the functional alignment of each survey with best practices for its respective scale, taking into account its defined scope and the extent to which it is integrated into formal governance practices. The resulting overview highlights distinct differences in applicability among the methodologies.
The matrix reveals a clear differentiation in the breadth and depth of applicability among the five federal risk assessment methodologies. The PMRR and GRS exhibit consistently high applicability across a range of DRR initiatives, particularly in emergency response planning (ERP), early warning systems (EWS), and structural countermeasures (SC). Their operational versatility enables integration into a broad set of initiatives; however, their role in shaping legislative frameworks—particularly LSL and LULOL—remains limited. In contrast, the GAUC demonstrates strong alignment with legal instruments, though its contribution to ERP appears comparatively constrained. The SS similarly supports the legislative dimension, but its applicability is markedly lower in ERP and SC. Finally, the GHS stands out as the most applicable methodology, achieving either full (score 4) or substantial (score 3) relevance across all DRR categories. Its balanced integration emphasizes its utility as a comprehensive tool for multi-level risk governance.
Figure 6: Brazilian DRR assessment relevance matrix based on applicability recommendations from Hungr et al. (2005), Fell et al. (2008), and Corominas et al. (2014).
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RC3: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
I was not aware that the journal's policy does not allow authors to upload revised versions until the end of the discussion period. I apologize and thank you very much for your kind assistance.
Thanks to you, I was able to read a brief explanation of PMRR, GRS, GAUC, SS, and GHS, and was able to easily read through the Discussion chapter.
In addition, I have a few small comments as shown below.Figure 4 caption : “Distribution of municipalities in Brazilian state…” --> “The number of municipalities in Brazilian state...” ?
Figure 5 : What exactly do the vertical and horizontal axes in Figure 4 mean? Please add labels for them. For example, I imagined that the graph at the top left had the vertical axis representing the percentage of cities in the state that have MMP, and the horizontal axis representing the number of cities in the state that have implemented PMRR. Is this correct?
If yes,
Whether this understanding is correct or not, I think it would be better if you added an explanation to the main text so that readers can understand accurately.L515: Just for the GHS survey? Other surveys could be improved if frequency and magnitude analysis are incorporated or enhanced.
L553: 14 %? Does this mean the average percent of all the states shown in the Figure 4? If yes, some explanation is necessary in the text or in the figure.
L553-554: Is the strong correlation seen in Figure 5 just seen in the disaster-affected municipalities? In other words, did the parent-set of data shown in Figure 5 include only disaster-affected municipalities?
L603-604: As far as seeing the new Fig.6, PMRR and GRS are both better than GAUC.
I hope my comments are helpful to you.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC3 -
AC3: 'Reply on RC3', Thiago Santos, 03 May 2025
Thank you for your kind assistance, comments, and feedback on our manuscript. I also appreciate your understanding regarding the situation. Below, I respond to your additional comments and explain the adjustments we have made in the manuscript.
Figure 4 caption: “Distribution of municipalities in Brazilian state…” --> “The number of municipalities in Brazilian state...” ?
We revised the figure caption to clarify the values' meaning. This ensures that readers understand that the analysis reflects relative (not absolute) coverage of DRR initiatives across states.
Figure 4: Percentage of municipalities implementing sediment-related DRR initiatives in each Brazilian state. Values represent the proportion of municipalities (%) per state. Data source: IBGE (2020) DRR dataset. Bars are color-coded by Brazil’s five macro-regions.”
Figure 5 : What exactly do the vertical and horizontal axes in Figure 4 mean? Please add labels for them. For example, I imagined that the graph at the top left had the vertical axis representing the percentage of cities in the state with MP, and the horizontal axis representing the number of cities in the state that have implemented PMRR. Is this correct?
If yes,
Whether this understanding is correct or not, I think it would be better if you added an explanation to the main text so that readers can understand accurately.
Thank you for the valuable comment regarding Figure 5. You are correct: in each panel, the horizontal axis shows the number of municipalities per state implementing each risk assessment survey (PMRR, GRS, SS), and the vertical axis shows the number of municipalities per state adopting each specific DRR strategy (e.g., Municipal Master Plan, Local LULC, Local SL, EWS, ERP, SC). Both axes represent absolute counts of municipalities, not percentages.
To improve clarity, we did:
Add explicit axis labels to the figure.
Add a sentence in the results section to guide the reader:
As shown in Figure 5, the analysis considers absolute counts of municipalities per state for risk assessment surveys and DRR initiatives, providing a robust measure of their association. Because the GAUC and GHS have low implementation levels, they were removed from this analysis.
Modify the figure caption to explain that both axes show the number of municipalities per state.
Figure 5 caption: Spearman’s rank correlation between the number of municipalities per state implementing risk assessment surveys (PMRR, GRS, SS) and the number of municipalities per state adopting specific DRR strategies (Municipal Master Plan, Local LULOL, Local SL, EWS, ERP, SC). Analysis includes 5570 Brazilian municipalities based on IBGE (2020) and SGB (2023a, b).
L515: Just for the GHS survey? Other surveys could be improved if frequency and magnitude analysis are incorporated or enhanced.
Thank you for this valuable comment. We agree that incorporating frequency and magnitude analyses would also benefit the other surveys. We have revised the text to clarify that these improvements are not limited to the GHS survey, but could strengthen hazard assessment across multiple surveys. We appreciate this insight, which helped improve the generalizability of our recommendations.
Revised text: Incorporating frequency analyses and enhancing magnitude assessments are crucial for improving the GHS survey and advancing the overall effectiveness of quantitative risk assessment and prediction across the other surveys (Table 2).
L553: 14 %? Does this mean the average percent of all the states shown in the Figure 4? If yes, some explanation is necessary in the text or in the figure.
We confirm that the 14% refers to the overall percentage of Brazil’s 5,570 municipalities that have implemented Municipal Master Plans (MPs), as shown in Figure 4. This aggregate information is not explicitly labeled in the figure itself. Therefore, we included this clarification directly in the results section to ensure that readers understand the origin and meaning of this value, as you suggest.
Results section:
First, the Federal District was excluded because it contains only one municipality, which could distort the overall analysis. The results showed that among Brazil’s 5,570 municipalities, only about 15% on average have implemented master plans. This low implementation rate is consistent across most states. The Southeast and South regions demonstrate higher implementation levels (Fig. 4), while the North and Midwestern regions show considerably lower levels. Rio de Janeiro (33%), Espírito Santo (27%), and Santa Catarina (23%) lead in MP implementation, whereas states such as Tocantins (4%), Rondônia, Amazonas, Amapá, Piauí, and Paraíba (6%) fall below the national average.
Discussion section:
The results highlighted low adherence to master plan implementation, with approximately 15% of Brazil’s 5,570 municipalities reporting implementation (Fig. 4).
L553-554: Is the strong correlation seen in Figure 5 just seen in the disaster-affected municipalities? In other words, did the parent-set of data shown in Figure 5 include only disaster-affected municipalities?
Thank you for this important question. We clarify that the data presented in Figure 5 include all 5,570 Brazilian municipalities, not only those affected by disasters. Therefore, the strong correlations observed between legislative prevention mechanisms (MP, LULOL, SL) and surveys are based on the whole national dataset, regardless of whether municipalities have experienced past disaster events.
We recognize that the original discussion sentence could imply that the analysis was restricted to disaster-affected municipalities. To improve clarity, we revised the discussion sentence as follows:
The strong correlation between legislative prevention mechanisms (MP, LULOL, SL) and surveys (Fig. 5) indicates a general trend toward implementing legal measures across municipalities, which may be partly influenced by higher implementation rates in disaster-affected areas.
L603-604: As far as seeing the new Fig.6, PMRR and GRS are both better than GAUC.
Thank you for this important observation. We agree that, as shown in the updated Figure 6, PMRR and GRS rank higher in applicability than GAUC. We have revised the discussion section to clarify this point and to better reflect the relative performance of all surveys. Additionally, we recognize that GAUC has a distinct purpose compared to the other surveys analyzed, as it focuses primarily on urban planning in non-consolidated safety areas rather than exclusively on disaster prevention. This distinct scope may partially explain its more moderate performance in the DRR applicability ranking. We appreciate this comment, which allowed us to improve the precision and nuance of our interpretation.
Despite these strengths, the applicability of SS is considered moderate to low compared to other surveys (Fig. 6). Notably, PMRR and GRS rank high in applicability. In contrast, while demonstrating moderate potential, the GAUC, introduced in 2014, serves a distinct purpose focused on urban planning in non-consolidated safety areas, which may limit its performance in DRR-specific strategies. Finally, the GHS, launched in 2018, exhibits the highest applicability in DRR initiatives. However, GAUC and GHS present challenges, including moderate execution times, inherent costs, and require more detailed analyses (Antonelli et al., 2021; Pimentel and Dutra, 2018). Due to these complexities, they incur moderate to high costs per beneficiary and show relatively low adherence among practitioners.
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC3
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AC3: 'Reply on RC3', Thiago Santos, 03 May 2025
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RC4: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
Finally, I believe that no further peer review is necessary.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC4 -
AC4: 'Reply on RC4', Thiago Santos, 03 May 2025
Thank you for your message. We truly appreciate your constructive feedback and the time you dedicated to reviewing our manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC4
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AC4: 'Reply on RC4', Thiago Santos, 03 May 2025
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RC3: 'Reply on AC2', Anonymous Referee #1, 02 May 2025
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AC2: 'Reply on RC2', Thiago Santos, 01 May 2025
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RC2: 'Reply on AC1', Anonymous Referee #1, 29 Apr 2025
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AC1: 'Reply on RC1', Thiago Santos, 11 Apr 2025
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RC5: 'Comment on egusphere-2024-2255', Anonymous Referee #2, 08 May 2025
Dear Authors,
I read your manuscript about various landslide risk assessment surveys conducted at the federal level in Brazil. The manuscript analyzes five main survey approaches (the Municipal Risk Reduction Plan (PMRR), the Geological Risk Survey (GRS), the Susceptibility Survey (SS), the Geotechnical Aptitude for Urbanization Charts (GAUC), and the Geological Hazard Survey (GHS)), for each of which shortcomings and range of applicability have been analyzed. I also read the comments of previous reviewer and your answers, along with the modifications you made based on their suggestions. You already addressed all the suggestions I could have made, hence, I believe that your manuscript can be processed for publication without further revisions.
Citation: https://doi.org/10.5194/egusphere-2024-2255-RC5 -
AC5: 'Reply on RC5', Thiago Santos, 10 May 2025
Dear Reviewer #2,
Thank you very much for your evaluation and for dedicating your time to review our manuscript.
We sincerely appreciate your kind comments and your recognition of the revisions we made in response to Referee #1’s suggestions. These changes have strengthened the manuscript, making it clearer and more robust.
We are grateful for your support and encouraged by your positive feedback.
Finally, we hope that this article encourages a critical and constructive dialogue within Brazil’s scientific and technical community, aiming to strengthen methodological approaches to quantitative risk assessment. By reflecting on existing federal frameworks, our intention is to support their refinement and enhance their effectiveness in disaster risk prevention strategies.
Best regards,
On behalf of all co-authorsThiago Dutra dos Santos
Citation: https://doi.org/10.5194/egusphere-2024-2255-AC5
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AC5: 'Reply on RC5', Thiago Santos, 10 May 2025
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Thiago Dutra dos Santos
Taro Uchida
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This paper is based on an extensive review of landslide prevention measures and landslide risk assessment methods in the Federative Republic of Brazil, and is highly rated as a paper that can provide readers with broad knowledge and deep insights into landslide risk assessment methods in Brazil.
However, I would like to strongly point out that it is very difficult to understand the points of this paper for readers who are not familiar with the details of these methods used in Brazil.
This paper compares and discusses five methods: PMRR, GRS, GAUC, SS, and GHS. At the very least, a brief explanation of each method should be written in order to allow the reader to follow the arguments of this paper.
Unless this point is clearly stated, it is difficult to properly review and comment on the results and discussion of this paper. So that I believe that a re-review is necessary.
Other points I noticed are listed below.
Introduction: The past knowledge or information on natural disasters in general and those on landslides disasters are mixed together. So that some organization is necessary. For example, it would be possible to first state those on natural disasters in general, and then on specific landslides disasters.
Line 31: What is “sediment disaster”? Write the definition.
Line 76: What is “the core elements”? What kind of elements did the previous studies deal with?
Line 120: …disaster reduction… --> …disaster risk reduction… ?
Line 125: There are two “Local Scale”s.
Line 126-127: This sentence seems to be very difficult to understand. I would like it to be written more clearly.
Line 143-144: Is “structural measure (SC)" included in the “six non-structural initiatives”? It seems obviously contradictory.
Line 177: “cost-benefit ratio” --> “cost per beneficiary” ?
Figure 1.: At the top are figures comparing the number of disasters per state with total area, urban area, population and so on. I would like a clear explanation of how the number of disasters is counted. If 10 landslides occur in one heavy rainfall event, should each be counted as one, or should they be counted as ten? This would likely change the interpretation of the figures.
What is “critical municipalities”? It means the 286 municipalities? Now that the explanation appears in the latter part, you should add some explanation on it before readers see this figure.
Figure 2: Why is this figure just for PMRR, GRS, and SS? Why are not GHS and GAUC shown?
Line 195: “rho” should be written in Greek letter.
Line 292-293: “plot scale” and “partial plot” How large are they?
Line 345-347: If it is written in the literature, the accuracy of the prediction should be evaluated not only in terms of the hit rate but also in terms of the miss rate. The GHS method may have determined in advance that 95% of the collapsed areas were dangerous, but it would be appropriate to also indicate how many slopes were determined to be dangerous but did not collapse.
Line 376: Where is Figure 7 ?
Line 384 – 402: The percentage values in the text cannot be found in Figure 4. Please either add a figure or discuss only the values that can be found in the figure.
Line 398: “Santa Catarina leads to… EWS implementations.” What is this sentence based on? I cannot find any evidences in Figure 4 or others.
Figure 6: The correspondence with the six initiatives written in section 2.3.3 is unclear.
Line 443: cost-benefit ratio --> cost per beneficiary?
Line 447: $0.0004 per beneficiary … $0.009 in Table.5
Which is correct?