scholarly journals Derivation and evaluation of landslide triggering thresholds by a Monte Carlo approach

2014 ◽  
Vol 11 (3) ◽  
pp. 2759-2794 ◽  
Author(s):  
D. J. Peres ◽  
A. Cancelliere
Keyword(s):  
Monte Carlo ◽  
Power Law ◽  
Rainfall Intensity ◽  
Historical Data ◽  
Soil Depth ◽  
Early Warning Systems ◽  
Data Availability ◽  
Antecedent Rainfall ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Rainfall thresholds are the basis of early warning systems able to promptly warn about the potential triggering of landslides in an area. Following a common empirical methodology, thresholds may be derived through the analysis of historical rainfall and landslide data, by drawing an envelope curve of triggering rainfall events, represented by their intensity and duration. Nonetheless, reliability of empirical thresholds is generally affected by the historical data quality and availability. Moreover, rainfall intensity and duration alone may not be able to capture most of the uncertainty related to landslide triggering. In this work Monte Carlo simulations are carried out to generate a synthetic rainfall series by a stochastic model and the corresponding landslide response by means of an hydrological and geotechnical model. The series are of virtually unlimited length and present no interruption in data availability, and the triggering instants can be precisely identified, overcoming some of the most important quality and availability drawbacks of using historical data. Receiver Operating Characteristic (ROC) analysis is carried out to derive and evaluate landslide triggering thresholds, considering both triggering and non-triggering rainfall. The effect of variability of both rainfall intensity within events and of initial conditions as determined by antecedent rainfall is analysed as well. The proposed methodology is applied to the landslide-prone area of Peloritani Mountains, Northeastern Sicily, Italy. Results show that power-law ID equations can adequately represent the triggering conditions due to transient infiltration response to temporally-variable rainfall and hence may be of good performance for a hillslope with small specific contributing area. On the other hand, as specific contributing areas become larger, past rainfall has an increasing importance, and an antecedent rainfall variable should be used in addition to ID power-laws to achieve adequate reliability. Results also indicate that for short rainfall durations uniform hyetographs may have a stronger destabilizing effect than the stochastically-variable ones, while the opposite may occur for greater durations. Thus a power-law ID threshold may perform better than a model deterministic one that is derived considering uniform hyetographs and a prefixed initial condition. Further analyses show that predictability of landslides decreases with soil depth and geomechanical strength.

scholarly journals Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach

2014 ◽  
Vol 18 (12) ◽  
pp. 4913-4931 ◽  
Author(s):  
D. J. Peres ◽  
A. Cancelliere
Keyword(s):  
Monte Carlo ◽  
Slope Stability ◽  
Power Law ◽  
Rainfall Intensity ◽  
Antecedent Rainfall ◽  
Rainfall Events ◽  
Physically Based ◽  
Power Law Equation ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Assessment of landslide-triggering rainfall thresholds is useful for early warning in prone areas. In this paper, it is shown how stochastic rainfall models and hydrological and slope stability physically based models can be advantageously combined in a Monte Carlo simulation framework to generate virtually unlimited-length synthetic rainfall and related slope stability factor of safety data, exploiting the information contained in observed rainfall records and field-measurements of soil hydraulic and geotechnical parameters. The synthetic data set, dichotomized in triggering and non-triggering rainfall events, is analyzed by receiver operating characteristics (ROC) analysis to derive stochastic-input physically based thresholds that optimize the trade-off between correct and wrong predictions. Moreover, the specific modeling framework implemented in this work, based on hourly analysis, enables one to analyze the uncertainty related to variability of rainfall intensity within events and to past rainfall (antecedent rainfall). A specific focus is dedicated to the widely used power-law rainfall intensity–duration (I–D) thresholds. Results indicate that variability of intensity during rainfall events influences significantly rainfall intensity and duration associated with landslide triggering. Remarkably, when a time-variable rainfall-rate event is considered, the simulated triggering points may be separated with a very good approximation from the non-triggering ones by a I–D power-law equation, while a representation of rainfall as constant–intensity hyetographs globally leads to non-conservative results. This indicates that the I–D power-law equation is adequate to represent the triggering part due to transient infiltration produced by rainfall events of variable intensity and thus gives a physically based justification for this widely used threshold form, which provides results that are valid when landslide occurrence is mostly due to that part. These conditions are more likely to occur in hillslopes of low specific upslope contributing area, relatively high hydraulic conductivity and high critical wetness ratio. Otherwise, rainfall time history occurring before single rainfall events influences landslide triggering, determining whether a threshold based only on rainfall intensity and duration may be sufficient or it needs to be improved by the introduction of antecedent rainfall variables. Further analyses show that predictability of landslides decreases with soil depth, critical wetness ratio and the increase of vertical basal drainage (leakage) that occurs in the presence of a fractured bedrock.

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

scholarly journals Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability

Landslides ◽  
2021 ◽  
Author(s):  
David J. Peres ◽  
Antonino Cancelliere
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Statistical Properties ◽  
Data Availability ◽  
Practical Advantage ◽  
Rainfall Events ◽  
Landslide Early Warning ◽  
Sampling Variation ◽  
Triggering Events ◽  
Triggering Rainfall

AbstractRainfall intensity-duration landslide-triggering thresholds have become widespread for the development of landslide early warning systems. Thresholds can be in principle determined using rainfall event datasets of three types: (a) rainfall events associated with landslides (triggering rainfall) only, (b) rainfall events not associated with landslides (non-triggering rainfall) only, (c) both triggering and non-triggering rainfall. In this paper, through Monte Carlo simulation, we compare these three possible approaches based on the following statistical properties: robustness, sampling variation, and performance. It is found that methods based only on triggering rainfall can be the worst with respect to those three investigated properties. Methods based on both triggering and non-triggering rainfall perform the best, as they could be built to provide the best trade-off between correct and wrong predictions; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked in the literature, imply good robustness and low sampling variation, and performances that can often be acceptable and better than thresholds derived from only triggering events. To use solely triggering rainfall—which is the most common practice in the literature—yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events. Based on these results, it can be stated that methods based only on non-triggering rainfall deserve wider attention. Methods for threshold identification based on only non-triggering rainfall may have the practical advantage that can be in principle used where limited information on landslide occurrence is available (newly instrumented areas). The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall suggests that threshold determination in future applications may start from identifying thresholds from non-triggering events only, and then move to methods considering also the triggering events as landslide information starts to become more available.

scholarly journals Rainfall events with shallow landslides in the Entella catchment, Liguria, northern Italy

2018 ◽  
Vol 18 (9) ◽  
pp. 2367-2386 ◽  
Author(s):  
Anna Roccati ◽  
Francesco Faccini ◽  
Fabio Luino ◽  
Laura Turconi ◽  
Fausto Guzzetti
Keyword(s):  
River Basin ◽  
Rainfall Intensity ◽  
Geographical Location ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Shallow Landslides ◽  
Landslide Risk ◽  
Antecedent Rainfall ◽  
Rainfall Events ◽  
Earth Flows

Abstract. In recent decades, the Entella River basin, in the Liguria Apennines, northern Italy, was hit by numerous intense rainfall events that triggered shallow landslides and earth flows, causing casualties and extensive damage. We analyzed landslide information obtained from different sources and rainfall data recorded in the period 2002–2016 by rain gauges scattered throughout the catchment, to identify the event rainfall duration, D (in h), and rainfall intensity, I (in mm h−1), that presumably caused the landslide events. Rainfall-induced landslides affected the whole catchment area, but were most frequent and abundant in the central part, where the three most severe events hit on 23–24 November 2002, 21–22 October 2013 and 10–11 November 2014. Examining the timing and location of the slope failures, we found that the rainfall-induced landslides occurred primarily at the same time or within 6 h from the maximum peak rainfall intensity, and at or near the geographical location where the rainfall intensity was largest. Failures involved mainly forested and natural surfaces, and secondarily cultivated and terraced slopes, with different levels of maintenance. Man-made structures frequently characterize the landslide source areas. Adopting a frequentist approach, we define the event rainfall intensity–event duration (ID) threshold for the possible initiation of shallow landslides and hyper-concentrated flows in the Entella River basin. The threshold is lower than most of the curves proposed in the literature for similar mountain catchments, local areas and single regions in Italy. The result suggests a high susceptibility to rainfall-induced shallow landslides of the Entella catchment due to its high-relief topography, geological and geomorphological settings, meteorological and rainfall conditions, and human interference. Analysis of the antecedent rainfall conditions for different periods, from 3 to 15 days, revealed that the antecedent rainfall did not play a significant role in the initiation of landslides in the Entella catchment. We expect that our findings will be useful in regional to local landslides early warning systems, and for land planning aimed at reducing landslide risk in the study area.

scholarly journals Rainfall events with shallow landslides in the Entella catchment (Liguria, Northern Italy)

2018 ◽  
Author(s):  
Anna Roccati ◽  
Francesco Faccini ◽  
Fabio Luino ◽  
Laura Turconi ◽  
Fausto Guzzetti
Keyword(s):  
River Basin ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Geographical Location ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Shallow Landslides ◽  
Antecedent Rainfall ◽  
Rainfall Events ◽  
Landslides And Debris Flows

Abstract. In the recent decades, the Entella River basin, in the Liguria Apennines, Northern Italy, was hit by numerous intense rainfall events that triggered shallow landslides, soil slips and debris flows, causing casualties and extensive damage. We analysed landslides information obtained from different sources and rainfall data recorded in the period 2002–2016 by rain gauges scattered in the catchment, to identify the event rainfall duration, D (in h), and rainfall intensity, I (in mm h−1), that presumably caused the landslide events. Rainfall-induced landslides affected all the catchment area, but were most frequent and abundant in the central part, where the three most severe events hit on 24 November 2002, 21–22 October 2013, and 10 November 2014. Examining the timing and location of the failures, we found that the rainfall-induced landslides occurred primarily at the same time or within six hours from the maximum peak rainfall intensity, and at or near the geographical location where the rainfall intensity was largest. Adopting a Frequentist approach, we define the event rainfall intensity–event duration ID, threshold for the possible initiation of shallow landslides and debris flows in the Entella River basin. The threshold is lower than most of the thresholds proposed in the literature for similar mountain catchments, local areas and single regions in Italy. Analysis of the antecedent rainfall conditions for different periods, from 3 to 15 days, revealed that the antecedent rainfall did not play a significant role in the initiation of landslides in the Entella catchment. We expect that our findings will be useful in regional to local landslides early warning systems, and for land-planning aimed at reducing landslides risk in the study area.

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

scholarly journals Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

2014 ◽  
Vol 7 (3) ◽  
pp. 1211-1224 ◽  
Author(s):  
W. Zhang ◽  
Q. Zhang ◽  
Y. Huang ◽  
T. T. Li ◽  
J. Y. Bian ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Rice Variety ◽  
Scale Up ◽  
Methane Emissions ◽  
Data Availability ◽  
Scale Model ◽  
Anthropogenic Source ◽  
Rice Paddies ◽  
Data Scarcity

Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5–89.3% of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44–7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3% of the total emissions, ranging from 18.3–28.0% for early, late and middle rice ecosystems. The 95% confidence interval of the estimation was 4.5–8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

scholarly journals Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

2017 ◽  
Author(s):  
David J. Peres ◽  
Antonino Cancelliere ◽  
Roberto Greco ◽  
Thom A. Bogaard
Keyword(s):  
Early Warning ◽  
Continuous Series ◽  
Uncertainty Sources ◽  
Uncertain Knowledge ◽  
Physically Based ◽  
Negative Impacts ◽  
Set Up ◽  
The Impact ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide–triggering thresholds. In this paper, we perform a quantitative analysis of the impacts that the uncertain knowledge of landslide initiation instants have on the assessment of landslide intensity–duration early warning thresholds. The analysis is based on an ideal synthetic database of rainfall and landslide data, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model. This dataset is then perturbed according to hypothetical reporting scenarios, that allow to simulate possible errors in landslide triggering instants, as derived from historical archives. The impact of these errors is analysed by combining different criteria to single-out rainfall events from a continuous series and different temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant. Errors influence thresholds in a way that they are generally underestimated. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall, limits the possibility to set up links between thresholds and physio-geographical factors.

scholarly journals Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bappaditya Koley ◽  
Anindita Nath ◽  
Subhajit Saraswati ◽  
Kaushik Bandyopadhyay ◽  
Bidhan Chandra Ray
Keyword(s):  
Rainfall Intensity ◽  
Lower Boundary ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Sikkim Himalaya ◽  
North East ◽  
The North

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

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