Abstract
In many areas of the world, the prediction of rainfall-induced landslides is usually carried out by means of empirical rainfall thresholds. Their definition is complicated by several issues, among which are the evaluation and quantification of diverse uncertainties resulting from data and methods. Threshold effectiveness and reliability strongly depend on the quality and quantity of rainfall measurements and landslide information used as input. In this work, the influence of the temporal resolution of rainfall measurements on the calculation of landslide-triggering rainfall thresholds is evaluated and discussed. For the purpose, hourly rainfall measurements collected by 172 rain gauges and geographical and temporal information on the occurrence of 561 rainfall-induced landslides in Liguria region (northern Italy) in the period 2004–2014 are used. To assess the impact of different temporal resolutions on the thresholds, rainfall measurements are clustered in increasing bins of 1, 3, 6, 12 and 24 h. A comprehensive tool is applied to each dataset to automatically reconstruct the rainfall conditions responsible for the failures and to calculate frequentist cumulated event rainfall–rainfall duration (ED) thresholds. Then, using a quantitative procedure, the calculated ED thresholds are validated. The main finding of the work is that the use of rainfall measurements with different temporal resolutions results in considerable variations of the shape and the validity range of the thresholds. Decreasing the rainfall temporal resolution, thresholds with smaller intercepts, higher slopes, shorter ranges of validity and higher uncertainties are obtained. On the other hand, it seems that the rainfall temporal resolution does not influence the validation procedure and the threshold performance indicators. Overall, the use of rainfall data with coarse temporal resolution causes a systematic underestimation of thresholds at short durations, resulting in relevant drawbacks (e.g. false alarms) if the thresholds are implemented in operational systems for landslide prediction.
Highlights
Rainfall-induced landslides are among the most widespread and commonly occurring natural hazards in many areas of the world, with an increasing frequency in the last years (Gariano and Guzzetti 2016; Froude and Petley 2018; Haque et al 2019)
Empirical rainfall thresholds are determined by analysing past rainfall conditions that have presumably resulted in landslides
Landslide information and rainfall measurements clustered at five increasing temporal intervals provide 440 MPRC per each temporal resolution
Summary
Rainfall-induced landslides are among the most widespread and commonly occurring natural hazards in many areas of the world, with an increasing frequency in the last years (Gariano and Guzzetti 2016; Froude and Petley 2018; Haque et al 2019). Despite some criticisms (Bogaard and Greco 2018), they are extensively adopted at various spatial scales for operational landslide prediction and early warning, for shallow phenomena in wide areas (Intrieri et al 2013; Chae et al 2017; Greco and Pagano 2017; Piciullo et al 2018; Segoni et al 2018a, b; Guzzetti et al 2020). From a theoretical point of view, in the case of ED thresholds, the two variables are not dependent on each other; contrarily, in the case of ID thresholds, the rainfall mean intensity depends on the rainfall duration For this reason, it is preferable to define ED thresholds, in which the two variables measure independent quantities
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