the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deadly disasters in Southeastern South America: Flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
- 1Graduate Program in Natural Disasters, Unesp/Cemaden, São José dos Campos, Brazil
- 2National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos, Brazil
- 3National Institute of Education, Earth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University (NTU), Singapore
- 4Institute of Earth Sciences, Academia Sinica, Taiwan
- 5Institute of Science and Technology, São Paulo State University (Unesp), Department of Environmental Engineering, São José dos Campos, Brazil
- 6University of Campinas, Institute of Geosciences (IG/Unicamp), Brazil
- 7Institute of Advanced Studies, University of São Paulo (IEA/USP), São Paulo, Brazil
- 1Graduate Program in Natural Disasters, Unesp/Cemaden, São José dos Campos, Brazil
- 2National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos, Brazil
- 3National Institute of Education, Earth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University (NTU), Singapore
- 4Institute of Earth Sciences, Academia Sinica, Taiwan
- 5Institute of Science and Technology, São Paulo State University (Unesp), Department of Environmental Engineering, São José dos Campos, Brazil
- 6University of Campinas, Institute of Geosciences (IG/Unicamp), Brazil
- 7Institute of Advanced Studies, University of São Paulo (IEA/USP), São Paulo, Brazil
Abstract. On February 15, 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within three hours (258 mm). It resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. In this paper, we analyzed the root cause and the key triggering factors of this landslide disaster by assessing the spatial relationship of landslide occurrence with various environmental factors. Rainfall data were retrieved from 1977 to 2022, while other remote sensing data from 1985 to 2020, were utilized to map the landslide scars, soil moisture, terrain attributes, line-of-sight displacement (land surface deformation), and urban sprawling .. to. The results showed that the average rainfall for February 2022 was 200 mm, the heaviest recorded in Petrópolis since 1932. From the rainfall spatial distribution, heavy rainfall was also recorded mostly in regions where the landslide occurred. As for terrain, 23 % of slopes between 45–60° had landslide occurrences and east-facing slopes appeared to be the most conducive for landslides as they recorded landslide occurrences of about 9 to 11 %. Regarding the soil moisture, higher variability was found in the lower altitude (842 m) where the residential area is concentrated. From our land deformation assessment, the area is geologically stable, and the landslide occurred only in the thin layer at the surface of the 1,700 buildings found in the region of interest, 1,021 are on the slope between 20 to 45° and about 60 houses were directly affected by the landslides. As such, we conclude that the heavy rainfall was not the only cause responsible for the catastrophic event of February 15, 2022; a combination of unplanned urban growth on slopes between 45–60°, removal of vegetation, and the absence of inspection were also significant elements of this natural disaster.
Enner Alcântara et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2022-163', Anonymous Referee #1, 14 Jul 2022
Summary
This is a very interesting and well-written article that thoroughly describes the causality, process, and consequence of the catastrophic landslide events over Petrópolis, Rio de Janeiro recently. Explanations for the different driving factors including meteorological conditions, topographic layout, land use changes etc. are well presented. I think this paper is a significant contribution to the community and worthy of prompt publication after a minor revision.
Major comments
- The introduction of the meteorological background is a little weak. Some in-depth description like synoptic pattern, mesoscale discussion, and most importantly, the mesoscale convective cell that brought the torrential rainfall should be added. In this way, the weather extreme could become more tangible to the readers than just a number of precipitation amount. In addition to the spatial distribution, the vertical extent of the mesoscale convective system could also be indicative of the severity of the event. Therefore the authors may look into the weather radar data if possible.
- The inhomogeneous distribution of landslide occurrences of west-facing vs. east-facing slope is interesting. In addition to the listed multiple reasons, could the orographic barrier effect also play a role in the difference of landslide susceptibility on the lee side of hill vs. the windward side, given the prevalence of easterly wind? (Kumar et al., 2017)
Reference:
Kumar, A., A. K. L. Asthana, R. Singh, R. Jayangondaperumal, A. K. Gupta, and S. S. Bhakuni, 2017: Geomorphology assessment of landslide hazards induced by extreme rainfall event in Jammu and Kashmir Himalaya, northwest India. Geomorphology, 284, 72–87, https://doi.org/10.1016/j.geomorph.2017.01.003.
Minor comments
- Line 26, this sentence is awkward, consider rewriting it.
- Line 27, the average ‘monthly’ rainfall.
- Line 42, missing space between ‘city’ and ‘is’.
- Line 44, missing space between ‘on’ and ‘February’.
- Line 45, by rain gauges?
- Line 46, change ‘combined’ to ‘accumulation’.
- Line 53, missing space between ‘away’ and ‘cars’.
- Line 58, change ‘some’ to ‘more than’.
- Line 60, change ‘1.161’ to ‘1,161’.
- Lien 61, change ‘heavy rain’ to ‘torrential precipitation’.
- Line 101, change ‘worse’ to ‘worst’.
- Line 137, it is difficult to locate the rain gauges from Figure 1d.
- Line 165, what resampling method was used?
- Line 192, ‘the beginning of 2022’.
- Line 256, ‘Figure 2 shows the January-February daily rainfall distribution from 1977 to 2022’.
- Line 265, it’s better to include the description of what the box and whisker stands for.
- Line 271, change ‘accumulated’ to ‘accumulation’. Change ‘biggest’ to ‘largest’.
- Line 279, ‘Figure 4a depicts the station-based 24-h rainfall accumulation over Petrópolis’.
- Line 285, no need to show so much blank before the events. May consider shrinking the temporal duration to 2/15/2022 12:00.
- Line 289, it’s two 3-h intervals from 16:00 to 21:00.
- Line 350, panels in Figure 7 are not aligned well.
- Line 427, the description of the mesoscale convective cell needs to improve.
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AC1: 'Reply on RC1', Enner Alcântara, 16 Aug 2022
Dear Reviewer,
Thank you for your time and effort in reviewing our manuscript and providing us with constructive feedback and comments that will help improve the quality of the manuscript. Please find attached our response to each of your comments in pdf format.
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RC2: 'Comment on nhess-2022-163', Anonymous Referee #2, 09 Aug 2022
Dear Editor and authors,
My comments about the manuscript can be found in the attached file.
Best regards.
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AC2: 'Reply on RC2', Enner Alcântara, 13 Sep 2022
Dear Reviewer,
Thank you for your time and effort in reviewing our manuscript and providing us with constructive feedback and comments that will help improve the quality of the manuscript. Please find attached our response to each of your comments in pdf format.
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AC2: 'Reply on RC2', Enner Alcântara, 13 Sep 2022
Enner Alcântara et al.
Enner Alcântara et al.
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