Maria Elena Martinotti1, Luca Pisano2,6, Ivan Marchesini1, Mauro Rossi1, Silvia Peruccacci1, Maria Teresa Brunetti1, Massimo Melillo1, Giuseppe Amoruso3, Pierluigi Loiacono3, Carmela Vennari2,4, Giovanna Vessia2,5, Maria Trabace3, Mario Parise2,a, and Fausto Guzzetti1, 2017, Landslides, floods and sinkholes in a karst environment_ the 1-6 September 2014 Gargano event, southern Italy, Natural hazards and earth system sciences (Online) 17 (2017): 467–480. doi_10.5194/nhess-17-467-2017,
URL: http://www.cnr.it/prodotto/i/368589

In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was avail- able. Our analysis revealed that in the promontory, rainfall- driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble-non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geo- hydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.

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