TXT-tool 2.039-1.5 An Algorithm for the Objective Reconstruction of Rainfall Events Responsible for Landslides

Massimo Melillo, Maria Teresa Brunetti, Silvia Peruccacci, Stefano Luigi Gariano and Fausto Guzzetti, 2018, TXT-tool 2.039-1.5 An Algorithm for the Objective Reconstruction of Rainfall Events Responsible for Landslides, Fundamentals, Mapping and Monitoring, edited by Kyoji Sassa, Fausto Guzzetti, Hiromitsu Yamagishi, ?eljko Arbanas, Nicola Casagli, Mauri McSaveney, Khang Dang, pp. 433–447. Kyoto_ Kyoji Sassa editor, 2018,
URL: http://www.cnr.it/prodotto/i/384092

The primary trigger of damaging landslides in Italy is intense or prolonged rainfall. Definition of the rainfall conditions responsible for landslides is a crucial issue and may contribute to reducing landslide risk. Criteria for identifying the rainfall conditions that could initiate slope failures are still lacking or uncertain. Expert investigators usually reconstruct rainfall events manually. In this paper, we propose an algorithm for the objective and reproducible definition of rainfall conditions responsible for landslides, from a series of hourly rainfall data. The algorithm, which is implemented in R (http://www.r-project.org), performs a series of actions_ (i) removes isolated events with negligible amount of rainfall and random noise generated by the rain gauge; (ii) aggregates rainfall measurements in order to obtain a sequence of distinct rainfall events; (iii) identifies single or multiple rainfall conditions responsible for the slope failures. The result is the objective reconstruction of the duration, D, and the cumulated rainfall, E, for rainfall events, and for rainfall conditions that have resulted in landslides. We tested the algorithm using rainfall and landslide information for the period between January 2002 and December 2012 in Sicily, Southern Italy. The algorithm reconstructed 13,537 rainfall events and 343 rainfall conditions as possible triggers using the information on 163 documented landslides. The comparison between automatic and manually method highlights that most (87.7%) of the rainfall conditions obtained manually were reconstructed accurately. Use of the algorithm is the objective reconstruction of the duration, D, and the cumulated rainfall, E, for rainfall events, and for rainfall conditions that have resulted in landslides. We tested the algorithm using rainfall and landslide should contribute to reducing the current subjectivity inherent in the manual treatment of the rainfall and landslide data.

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