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 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 Sensitivity analysis and calibration of a dynamic physically-based slope stability model

2017 ◽  
Author(s):  
Thomas Zieher ◽  
Martin Rutzinger ◽  
Barbara Schneider-Muntau ◽  
Frank Perzl ◽  
David Leidinger ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Slope Stability ◽  
Laboratory Tests ◽  
Rainfall Event ◽  
Model Ensemble ◽  
Rainfall Events ◽  
Stability Model ◽  
Physically Based ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Physically-based modelling of slope stability at catchment scale is still a challenging task. Applying a physically-based model at such scale (1 : 10,000 to 1 : 50,000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) if parameters cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically-based slope stability models. In the present study a dynamic physically-based coupled hydrological/geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multi-temporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, effective angle of internal friction, effective cohesion) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.5 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak runoff increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

scholarly journals Sensitivity analysis and calibration of a dynamic physically based slope stability model

2017 ◽  
Vol 17 (6) ◽  
pp. 971-992 ◽  
Author(s):  
Thomas Zieher ◽  
Martin Rutzinger ◽  
Barbara Schneider-Muntau ◽  
Frank Perzl ◽  
David Leidinger ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Slope Stability ◽  
Laboratory Tests ◽  
Rainfall Event ◽  
Model Ensemble ◽  
Rainfall Events ◽  
Stability Model ◽  
Physically Based ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological–geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

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 HIRESSS: a physically based slope stability simulator for HPC applications

2013 ◽  
Vol 13 (1) ◽  
pp. 151-166 ◽  
Author(s):  
G. Rossi ◽  
F. Catani ◽  
L. Leoni ◽  
S. Segoni ◽  
V. Tofani
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Slope Stability ◽  
Real Time ◽  
Unsaturated Soil ◽  
Hydrological Model ◽  
Pressure Head ◽  
Stability Model ◽  
Physically Based ◽  
Geotechnical Stability

Abstract. HIRESSS (HIgh REsolution Slope Stability Simulator) is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. The hydrological model receives the rainfall data as dynamical input and provides the pressure head as perturbation to the geotechnical stability model that computes the factor of safety (FS) in probabilistic terms. The hydrological model is based on an analytical solution of an approximated form of the Richards equation under the wet condition hypothesis and it is introduced as a modeled form of hydraulic diffusivity to improve the hydrological response. The geotechnical stability model is based on an infinite slope model that takes into account the unsaturated soil condition. During the slope stability analysis the proposed model takes into account the increase in strength and cohesion due to matric suction in unsaturated soil, where the pressure head is negative. Moreover, the soil mass variation on partially saturated soil caused by water infiltration is modeled. The model is then inserted into a Monte Carlo simulation, to manage the typical uncertainty in the values of the input geotechnical and hydrological parameters, which is a common weak point of deterministic models. The Monte Carlo simulation manages a probability distribution of input parameters providing results in terms of slope failure probability. The developed software uses the computational power offered by multicore and multiprocessor hardware, from modern workstations to supercomputing facilities (HPC), to achieve the simulation in reasonable runtimes, compatible with civil protection real time monitoring. A first test of HIRESSS in three different areas is presented to evaluate the reliability of the results and the runtime performance on large areas.

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

2018 ◽  
Vol 18 (2) ◽  
pp. 633-646 ◽  
Author(s):  
David J. Peres ◽  
Antonino Cancelliere ◽  
Roberto Greco ◽  
Thom A. Bogaard
Keyword(s):  
Early Warning ◽  
Continuous Series ◽  
Uncertainty Sources ◽  
Uncertain Knowledge ◽  
Physically Based ◽  
Landslide Early Warning ◽  
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 of uncertain knowledge of landslide initiation instants on the assessment of rainfall intensity–duration landslide early warning thresholds. The analysis is based on a synthetic database of rainfall and landslide information, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model, and is therefore error-free in terms of knowledge of triggering instants. This dataset is then perturbed according to hypothetical reporting scenarios that allow simulation of possible errors in landslide-triggering instants as retrieved from historical archives. The impact of these errors is analysed jointly using different criteria to single out rainfall events from a continuous series and two typical temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant, especially when errors exceed 1 day or the actual instants follow the erroneous ones. Errors generally lead to underestimated thresholds, i.e. lower than those that would be obtained from an error-free dataset. 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 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.

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