| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Site | mtc-m12e.sid.inpe.br (namespace prefix: upn:4CUH36S) | |||
| Identificador | 8JMKD2USNRW34T/4DD5TUL | |||
| Repositório | sid.inpe.br/mtc-m12e/2025/04.28.11.00 | |||
| Última Atualização | 2025:04.28.11.00.26 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m12e/2025/04.28.11.00.26 | |||
| Última Atualização dos Metadados | 2025:05.03.09.26.48 (UTC) administrator | |||
| Chave Secundária | INPE--PRE/ | |||
| DOI | 10.5194/egusphere-egu25-15304 | |||
| Chave de Citação | QuevedoMAMOHAG:2025:CoSuLa | |||
| Título | Could such a large landslide event be expected in Rio Grande do Sul, southern Brazil? Using past events to predict the area impacted by the 2024 Mega Disaster  | |||
| Ano | 2025 | |||
| Data de Acesso | 25 out. 2026 | |||
| Tipo Secundário | PRE CI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 288 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Quevedo, Renata P. 2 Maciel, Daniel Andrade 3 Andrades Filho, Clódis O. 4 Mexias, Lorenzo F. S. 5 Oliveira, Guilherme G. 6 Herrmann, Pâmela B. 7 Alves, Fábio C. 8 Glade, Thomas | |||
| ORCID | 1 0000-0002-7528-9166 2 3 4 5 6 7 0000-0002-2941-8393 8 0000-0001-9676-4416 | |||
| Grupo | 1 2 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 University of Vienna 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade Federal do Rio Grande do Sul (UFRGS) 4 Universidade Federal do Rio Grande do Sul (UFRGS) 5 Universidade Federal do Rio Grande do Sul (UFRGS) 6 Universidade Federal do Rio Grande do Sul (UFRGS) 7 Universidade Federal do Oeste da Bahia (UFOB) 8 University of Vienna | |||
| Endereço de e-Mail do Autor | 1 renata.quevedo@univie.ac.at 2 daniel.maciel@inpe.br 3 clodis.filho@ufrgs.br 4 lorenzo.mexias@ufrgs.br 5 g.g.oliveira10@gmail.com 6 pamelaboelter@gmail.com 7 fabio.alves@ufob.edu.br 8 thomas.glade@univie.ac.at | |||
| Nome do Evento | EGU General Assembly | |||
| Localização do Evento | Vienna, Austria | |||
| Data | 27 apr. - 02 may 2025 | |||
| Editora (Publisher) | EGU | |||
| Páginas | e15304 | |||
| Título do Livro | Proceedings | |||
| Tipo Terciário | Oral Session | |||
| Acervo Hospedeiro | urlib.net/www/2025/02.16.04.07 upn:4CUH36S | |||
| Histórico (UTC) | 2025-04-28 11:02:39 :: simone -> administrator :: 2025 2025-05-03 09:26:48 :: administrator -> simone :: 2025 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Resumo | n May 2024, a Mega Disaster hit 96% of the municipalities in Rio Grande do Sul (RS) state, southern Brazil, causing 182 casualties and impacting approximately 2.4 million people. In addition to the floods that hit the capital Porto Alegre, more than 15,000 landslides were recorded due to the extreme rainfall event (> 600 mm in some regions), severely impacting an area of nearly 18,000 km². Although other landslide events have been recorded in RS in the past, none of them have matched the magnitude of this one. In this sense, we aimed to generate a landslide susceptibility model, based on historical data and evaluate its capacity to forecast the areas affected by landslides in 2024. This retrospective assessment was performed using an inventory of four past events between 1995 and 2017, totalling 1,211 landslides, represented by 15,580 points. We randomly selected the same number of points (15,580) over the RS to represent non-landslide areas and split the entire sample set into training (70%) and validation (30%). A Random Forest model, leveraging seven morphometric parameters, was employed to generate the map, which was evaluated with the validation sample set. A second validation was carried out considering the landslides in 2024, represented by 324,500 points. This validation was based on the relationship closeness between 2024 landslides and each susceptibility class using frequency ratio. The last evaluation consisted of analysing landslide areas (rupture, propagation, and deposition) and their distribution in each susceptibility class. To achieve this, we automatically divided the 2024 landslide points into three sets, according to the altitude difference found in each polygon. Our landslide susceptibility map presented a high performance, with an overall accuracy of 0.9, being capable of correctly classifying 64% of 2024 landslides into susceptible areas (very high, high, and moderate susceptibility classes). The very high susceptibility class accounted for 31% of the 2024 landslides and had a frequency ratio of 13.04, showing a high correlation between landslide locations and the analysed class. Further analysis revealed that the model successfully predicted 79% of rupture zones, highlighting its robustness in identifying key prone areas. While the model performed well in identifying rupture and propagation areas as susceptible, its predictions for deposition zones were less accurate, likely due to limitations in the historical inventory, which was carried out after 2017, when most landslide deposition areas were no longer visible in remote sensing imagery. Furthermore, even though the 2024 Mega Disaster was responsible for 12.5 times more landslides than all the previous inventory, our model based on 1,211 landslides correctly classified around 9,600 landslides (64%) in susceptible areas. Therefore, although the 2024 extreme rainfall event was much larger than any previously recorded in the region, many areas could have already been identified as susceptible. Finally, the existence of a more complete landslide inventory (including rupture, propagation, and deposition areas) provides more accurate susceptibility maps, which can support territorial planning, contributing to disaster risk management, mitigation strategies, and land use policie. | |||
| Área | SRE | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > LabISA > Could such a... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Could such a... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| URI dos dados | upn:4CUH36S:8JMKD2USNRW34T/4DD5TUL | |||
| URL dos dados | http://urlib.net/ibi/8JMKD2USNRW34T/4DD5TUL | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD2USNRW34T/4DD5TUL | |||
| Idioma | en | |||
| Arquivo Alvo | EGU25-15304-print.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | urlib.net/www/2025/02.16.04.07.51 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE | |||
| Lista de Itens Citando | sid.inpe.br/bibdigital/2022/04.03.22.23 - 108 sid.inpe.br/bibdigital/2020/09.18.00.06 - 58 sid.inpe.br/bibdigital@80/2006/04.07.15.50.13 - 6 urlib.net/www/2025/02.16.04.07.51 - 1 | |||
| 6. Notas | ||||
| Campos Vazios | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress edition editor format isbn issn keywords label lineage mark nextedition notes numberofvolumes organization parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||