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Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds

1
Department of Earth Sciences, University of Florence, Via Giorgio La Pira, 4, 50121 Florence, Italy
2
National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), Estrada Doutor Altino Bondensan, 500-Distrito de Eugênio de Melo, São José dos Campos 12209, SP, Brazil
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(5), 203; https://doi.org/10.3390/geosciences9050203
Received: 21 March 2019 / Revised: 29 April 2019 / Accepted: 5 May 2019 / Published: 8 May 2019
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Abstract

In 2011 Brazil experienced the worst disaster in the country’s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of rain and episodes of landslides. Due to these experiences, we used the MaCumBa (Massive CUMulative Brisk Analyser) software to identify rainfall intensity–duration thresholds capable of triggering landslides in the most affected municipalities of this region. More than 3000 landslides and rain data from a 10-year long dataset were used to define the thresholds and one year was used to validate the results. In this work, a set of three thresholds capable of defining increasing alert levels (moderate, high and very high) has been defined for each municipality. Results show that such thresholds may be used for early alerts. In the future, the same methodology can be replicated to other Brazilian municipalities with different datasets, leading to more accurate warning systems. View Full-Text
Keywords: landslide; EWS; rainfall threshold; forecasting; hazard landslide; EWS; rainfall threshold; forecasting; hazard
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Rosi, A.; Canavesi, V.; Segoni, S.; Dias Nery, T.; Catani, F.; Casagli, N. Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds. Geosciences 2019, 9, 203.

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