Abstract

Abstract. 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.

Highlights

  • Rainfall is a known trigger of landslides, and its role in initiating slope instability depends on the local geological, morphological and hydrological conditions, vegetation cover, and their complex interactions (De Vita and Reichenbach, 1998; Guzzetti, 1998; Wieczorek and Guzzetti, 2000)

  • The first criterion is based on the analysis of the time series of rainfall mean intensity values over 1 month preceding the landslide occurrence

  • Information on rainfall measurements, including rainfall duration (D) and cumulated event rainfall (E), considered responsible for the slope failures is obtained typically from single rain gauges or from networks of rain gauges (Aleotti, 2004; Guzzetti et al, 2007), examples exist of the use of satellite-derived rainfall estimates (Hong et al, 2006; Bach Kirschbaum et al, 2009)

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Summary

Introduction

Rainfall is a known trigger of landslides, and its role in initiating slope instability depends on the local geological, morphological and hydrological conditions, vegetation cover, and their complex interactions (De Vita and Reichenbach, 1998; Guzzetti, 1998; Wieczorek and Guzzetti, 2000). Information on (i) the geographical location, (ii) the occurrence time of the landslide, and (iii) the amount of rainfall that presumably caused the slope instability is necessary. Information on rainfall measurements, including rainfall duration (D) and cumulated event rainfall (E), considered responsible for the slope failures is obtained typically from single rain gauges or from networks of rain gauges (Aleotti, 2004; Guzzetti et al, 2007), examples exist of the use of satellite-derived rainfall estimates (Hong et al, 2006; Bach Kirschbaum et al, 2009). The procedure explores a record of rainfall measurements and determines the rainfall conditions (i.e. the D, in hours, and the E, in millimetres), considered responsible for the failure, using information on the landslide occurrence time. We tested the procedure using a sample of 100 rainfall events that initiated 100 single landslides in Italy in the 11-year period from 2002 to 2012

Background
The expert method
The automated procedure
Data input and preliminary processing
Calculation of cumulated mean rainfall intensities
Examination of the cumulated rainfall mean intensity series
Searching for the actual TS
Software
Discussion on possible applications of the automated procedure
Final remarks
Full Text
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