Probabilistic rainfall thresholds for triggering debris flows in a human-modified landscape

Giannecchini, Roberto; Galanti, Yuri; Avanzi, G. D’Amato; Barsanti, Michele

doi:10.1016/j.geomorph.2015.12.012

Cited by 46 publications

(27 citation statements)

References 68 publications

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“…When constructing thresholds, some studies plot not only rainfall events that triggered landslides, but include rainfall events that did not trigger any landslides (Zêzere et al, 2015;Gariano et al, 2015;Guo et al, 2016;Giannecchini et al, 2016). The threshold is then constructed in a way to maximise the number of triggering events and minimise the number of non-triggering events above the threshold.…”

Section: Methodsmentioning

confidence: 99%

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

et al. 2018

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Rainfall thresholds express the minimum levels of rainfall that need to be reached or exceeded in order for landslides to occur in a particular area. They are a common tool in expressing the temporal portion of landslide hazard analysis. Numerous rainfall thresholds have been developed for different areas worldwide, however none of these are focused on landslides occurring on the engineered slopes on transport infrastructure networks. This paper uses empirical method to develop the rainfall thresholds for landslides on the Irish Rail network earthworks. For comparison, rainfall thresholds are also developed for natural terrain in Ireland. The results show that particular thresholds involving relatively low rainfall intensities are applicable for Ireland, owing to the specific climate. Furthermore, the comparison shows that rainfall thresholds for engineered slopes are lower than those for landslides occurring on the natural terrain. This has severe implications as it indicates that there is a significant risk involved when using generic weather alerts (developed largely for natural terrain) for infrastructure management, and showcases the need for developing railway and road specific rainfall thresholds for landslides.

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Section: Methodsmentioning

confidence: 99%

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

et al. 2018

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“…The determination of thresholds can be approached as a binary classification problem, where rainfall conditions coinciding with landslides are separated from rain conditions that do not coincide with landslides. As illustrated in Figure , binary classification yields four mutually exclusive contingencies and ROC analysis is used to assess a classifier's performance, including for landslide thresholds (Wilks, ; Gariano et al, ; Abancó et al, ; Giannecchini et al, ; Piciullo et al, ). For each rain variable, the contingencies are calculated and an ROC curve is formed by plotting the false and true positive rate, where each point on the curve represents a different threshold value.…”

Section: Methodsmentioning

confidence: 99%

“…However, establishing these is non‐trivial. Landscapes containing slopes that are susceptible to generating landslides can be regarded as being in equilibrium with the long‐term environmental conditions, such as rainfall and land use, to which these have been exposed (Giannecchini et al, ). It therefore follows that, when these conditions deviate from the long‐term trend, a response in the landscape will be manifested.…”

Section: Introductionmentioning

confidence: 99%

“…Other techniques explicitly consider the rain events that do not initiate landslides (NRE) as well. Thresholds with the greatest predictive accuracy are selected using receiver operating characteristic analysis (ROC) to maximize the number of correctly identified LRE and minimize the number of NRE and incorrect results (Jakob and Weatherly, ; Staley et al, ; Segoni et al, , ; Giannecchini et al, ; Piciullo et al, ). Alternatively, thresholds are selected that provide the greatest conditional probability of landslide occurrence and using analysis of daily rainfall time series rather than events (Chleborad, ; Chleborad et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Comparing threshold definition techniques for rainfall‐induced landslides: A national assessment using radar rainfall

Postance

Hillier

Dijkstra

et al. 2017

Earth Surf Processes Landf

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Translational landslides and debris flows are often initiated during intense or prolonged rainfall. Empirical thresholds aim to classify the rain conditions that are commonly associated with landslide occurrence and therefore improve understating of these hazards and predictive ability. Objective techniques that are used to determine these thresholds are likely to be affected by the length of the rain record used, yet this is not routinely considered. Moreover, remotely sensed spatially continuous rainfall observations are under-exploited. This study compares and evaluates the effect of rain record length on two objective threshold selection techniques in a national assessment of Scotland using weather radar data. Thresholds selected by 'threat score' are sensitive to rain record length whereas, in a first application to landslides, 'optimal point' (OP) thresholds prove relatively consistent. OP thresholds increase landslide detection and may therefore be applicable in early-warning systems. Thresholds combining 1-and 12-day antecedence variables best distinguish landslide initiation conditions and indicate that Scottish landslides may be initiated by lower rain accumulation and intensities than previously thought.

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“…For these types of landslides, one of the measures for risk mitigation is the adoption of early warning systems depends upon rainfall thresholds that identify the critical amount of precipitation for landslide triggering [12]. The empirical rainfall thresholds are used in an early warning system of landslides for forecasting the possible occurrence of rainfall-induced landslides [13]. Cellular Automata (CA) model as a self-organizing approach provides a simulation for predicting the rainfall through simple local interaction rules [14].…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Large Scale Rainfall Data Using Regression Automata Models

Manepalli¹,

Subramanian²

2018

IJIES

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Rainfall is considered as the most important phenomena of the climate system. Due to the lack of adequate irrigation facilities, agriculture becomes vulnerable, which is the backbone of a country's economy. The rainfall can be able to predict by using selective appropriate predictors. Though several models have been developed for forecasting and predicting in Time Series (TS), there is no ideal model to predict the rainfall. In recent years, Automata is useful for forecasting and prediction of hydrological TS because automata help to predict the rainfall from the uncertainty data. The motivation of this work is to design a reliable tool for predicting daily rainfall in advance using Regression Automata (RA) models. The proposed method uses three different RA models for predicting rainfall from the collected data for four stations in Queensland State. The results clearly show that the all the three RA models can predict the rainfall very efficiently in various terms such as error rate, coefficients and mean square error.

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Probabilistic rainfall thresholds for triggering debris flows in a human-modified landscape

Cited by 46 publications

References 68 publications

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

Comparing threshold definition techniques for rainfall‐induced landslides: A national assessment using radar rainfall

Time Series Analysis of Large Scale Rainfall Data Using Regression Automata Models

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