the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Oct 2024
RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event
Abstract. Landslide inventories are crucial for evaluating susceptibility, hazards, and risks, and for devising resilience strategies in mountainous regions. This importance is amplified in the context of climate change, as existing inventories might not adequately reflect changing stability conditions. In May 2023, the Emilia-Romagna region of Italy was hit by two major rainfall events, leading to widespread flooding and the triggering of thousands of landslides. Predominantly, these were shallow debris slides and debris flows, occurring on slopes previously deemed stable based on historical data with no prior landslides recorded. Our team supported the Civil Protection Agency through field surveys and mapping efforts to pinpoint and record these landslides, prioritizing areas critical to immediate public safety and focusing on thorough mapping for future recovery planning. The outcome is a detailed map of all landslides induced by these events, manually identified using high-resolution aerial photography (0.2 m pixel resolution, RGB+NIR four bands) and categorized with the help of a 3D viewer. This comprehensive landslide inventory, comprising 80997 polygons, has been made openly accessible to the scientific community.
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RC1: 'Comment on essd-2024-407', Olimpiu Pop, 06 Nov 2024
The study entitled "RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event”, by Matteo Berti et al. is a laborious study presenting the process and results of the landslide inventory conducted following an extreme rainfall event occurred in May 2023 in Emilia-Romangna region in Italy.
Overall, I consider that the paper is well structured, with a large development of the section presenting the research context, with a clear and detailed emphasis on the methodological working steps followed in landslide mapping and with valuable results of landslide typology and their spatial distribution mapped.
Although I don’t have any doubts regarding the correctness of the methodology applied to map the landslides in the study area, I have a concern regarding the way in which the authors mapped the landslides. During the mapping process, how the authors distinguished between the landslides (re)activated during previous extreme rainfall events, and those landslides resulted during and/or after the 2023 event? If an inventory landslide map does exist in the study area with landslide types and their distribution before the 2023 event, can the authors confirm that the same criteria were previously applied to distinguish between various type of landslides?
I have few specific comments, which are listed below:
Lines 109 – 110: It is stated here that the existing geological map was used to delineate geological formations. You should indicate the scale of the geological map used for this purpose, in order to allow the reader to appreciate how detailed is the spatial distribution of the lithological units on the map.
Lines 196 – 198: You stated here that “…fine-grained soils have lower permeability and are more likely to fail during extended periods of rainfall rather than during brief events, where surface runoff and flooding are more prevalent”. How do you know that, is this from your previous observations, or this is coming from other sources (in this case, you should indicate the references)? Please explain this.
Figure 13 (a) and (b): You should add in the figure caption the meaning of the symbol letters attributed to each of the coloured polygons on the map.
Citation: https://doi.org/10.5194/essd-2024-407-RC1 -
RC2: 'Comment on essd-2024-407', Anonymous Referee #2, 07 Nov 2024
This is a review of the manuscript titled as “RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event” by Berti et al. The main objective of the work is to present the inventory the authors have produced post-2023 rainfall-induced landslide event in the Emilia-Romagna region of Italy.
The article is well-written and organized. The overall quality of figures, tables, and equations are good. The use of English language is generally good. The methodology is well described and the outcome, i.e., the landslide inventory, is a valuable resource for further research and application. This reviewer also appreciates the authors’ effort to clearly outline the limitations of the work.
Even though the paper goes into great details about the background of the research and different steps followed to develop this inventory, a couple of segments could be improved.
For example, the segment “Landslide identification and mapping” lacks in specific example of how the identification was done. It could be helpful if the authors could include a case showing the satellite image and how the landslide was identified and the boundary was marked. Adding a validation example will also help the reader on the effectiveness of the method and the uncertainties around the drawn boundary. The authors could also include an explanation on how they distinguish the landslides triggered by the 2023 event from the existing/previous ones.
In the segment “Landslides classification”, the authors cite several existing methods that have been deployed in the current study. It presumes that readers may already know different landslide classifications. It would be helpful if the authors include a small background on different classification schemes of the cited articles and the one adopted by the current study.
Editorial comments:
Line 39: “Therefore it is important ……” could be simplified
Line 202: “1 km x 1 km” instead of “1 x 1 km”
Line 324: 𝐴𝑇𝑜𝑡 in equation 2 is probably not defined.
Citation: https://doi.org/10.5194/essd-2024-407-RC2 - RC3: 'Comment on essd-2024-407', Anonymous Referee #3, 07 Nov 2024
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AC1: 'Reply to Reviewers RC1-RC2-RC3', Matteo Berti, 04 Dec 2024
First and foremost, we would like to express our sincere gratitude to the reviewers for dedicating time to review our manuscript and for providing insightful comments that have significantly enhanced our work.
Please find our detailed responses in the attached document.
Data sets
RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event Marco Pizziolo et al. https://doi.org/10.5281/zenodo.13742643
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