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 available. 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 geohydrological 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.

Over the last 40 years, many contributions have identified empirical rainfall thresholds (e.g. rainfall intensity (I) vs. rainfall duration (D), cumulated rainfall vs. rainfall duration (ED), cumulated rainfall vs. rainfall intensity (EI)) for the possible initiation of shallow landslides, based on local and global inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has only rarely been addressed. Objective criteria for estimating the rainfall responsible for the landslide occurrence play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented. The first criterion is based on the analysis of the time series of rainfall mean intensity values over 1 month preceding the landslide occurrence. The second criterion is based on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure that is written in the R language. A sample of 100 shallow landslides collected in Italy from 2002 to 2012 was used to calibrate the procedure. The cumulated event rainfall (E) and duration (D) of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the D, E diagram. The results are discussed by comparing the D, E pairs calculated by the automated procedure and the ones by the expert method.