scholarly journals Comparison of the return period for landslide‐triggering rainfall events in Japan based on standardization of the rainfall period

2021 ◽  
Author(s):  
Haruka Tsunetaka
Keyword(s):  
Return Period ◽  
Rainfall Events ◽  
Triggering Rainfall ◽  
Landslide Triggering

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 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.

Modeling impacts of climate change on return period of landslide triggering

2018 ◽  
Vol 567 ◽  
pp. 420-434 ◽  
Author(s):  
D.J. Peres ◽  
A. Cancelliere
Keyword(s):  
Climate Change ◽  
Return Period ◽  
Landslide Triggering ◽  
Impacts Of Climate Change

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 Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

Landslides ◽  
2020 ◽  
Author(s):  
Suk Woo Kim ◽  
Kun Woo Chun ◽  
Minseok Kim ◽  
Filippo Catani ◽  
Byoungkoo Choi ◽  
...  
Keyword(s):  
South Korea ◽  
Shallow Landslide ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Triggering Rainfall ◽  
Landslide Triggering

scholarly journals Estimation of daily runoff coefficient of the pervious surfaces for the climate conditions of the city of Lviv

2020 ◽  
Vol 5 (3) ◽  
pp. 136-142
Author(s):  
Volodymyr Zhuk ◽  
◽  
Lesya Vovk ◽  
Pavlo Mysak ◽  
◽  
...  
Keyword(s):  
Return Period ◽  
Daily Rainfall ◽  
Curve Number ◽  
Runoff Coefficient ◽  
Rainfall Events ◽  
Climate Conditions ◽  
Method Of Calculation ◽  
Geological Conditions ◽  
The City ◽  
Maximum Daily Rainfall

The method of calculation of daily runoff coefficients based on the SCS USDA curve number method is presented in this paper. The calculated values ​​of daily runoff coefficients for climatic and geological conditions of the city of Lviv for maximum daily rainfall events with a return period of 0.1 – 5 years are obtained.

Advancing rainfall-induced landslide detection using homogeneous slope units and distributed rainfall thresholds

2021 ◽  
Author(s):  
Guoqiang Jia ◽  
Stefano Luigi Gariano ◽  
Qiuhong Tang
Keyword(s):  
Correlation Coefficient ◽  
Environmental Conditions ◽  
Rainfall Threshold ◽  
Mean Annual Precipitation ◽  
Support Vector ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Independent Variables ◽  
Triggering Rainfall ◽  
Landslide Triggering

<p>A better detection of landslide occurrence is critical for disaster prevention and mitigation, and a standing pursuit owing to increasing and widespread impact of slope failures on human activities and natural environment in a changing world. However, the detection of rainfall-induced landslide is limited in some areas by data scarcity and method applicability. In this study, we proposed distributed rainfall thresholds within homogeneous slope units, by considering the interaction of landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. Homogeneous slope units are extracted based on detailed terrain analysis. Various landforms are identified and used to obtain slope units with homogeneous slope traits. The concept behind the distributed rainfall threshold models is that rainfall threshold for landslide occurrence varies with geo-environmental conditions such as slope gradient. Thus, a link can be established between landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. We used elevation, slope, plan and profile curvature, mean annual precipitation and temperature, soil texture and land cover as independent variables. Rainfall duration and cumulated rainfall of landslide-triggering rainfall events are automatically calculated and used, the former as one of independent variables, and the latter as the dependent variable. A support vector regression (SVR) and a multiple linear regression (MLR) method are used. The error and correlation coefficient measurement indicate a better performance of SVR method. Compared with grid units, the model scores high accuracy for slope units. The models are implemented at a regional scale (Guangdong, China). The SVR model in slope units ran with error of 0.16 mm and correlation coefficient of 0.93.</p>

Re-Estimation Method of Landslide-Triggering Rainfall Thresholds After an Earthquake with the Two Conceptual Models

2012 ◽  
pp. 455-463
Author(s):  
Yasuhiro Shuin ◽  
Norifumi Hotta ◽  
Masakazu Suzuki ◽  
Keigo Matsue ◽  
Kazuhiro Aruga ◽  
...  
Keyword(s):  
Conceptual Models ◽  
Estimation Method ◽  
Rainfall Thresholds ◽  
Triggering Rainfall ◽  
Landslide Triggering
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