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 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.

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

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 Landslide-triggering Rainfall Thresholds after Major Earthquakes for Early Warning

2014 ◽  
Vol 7 (2) ◽  
pp. 56-62 ◽  
Author(s):  
Yasuhiro NOMURA ◽  
Atsushi OKAMOTO ◽  
Kazumasa KURAMOTO ◽  
Hiroshi IKEDA
Keyword(s):  
Early Warning ◽  
Rainfall Thresholds ◽  
Triggering Rainfall ◽  
Landslide Triggering

Landslide-triggering rainfall thresholds: a conceptual framework

2010 ◽  
Vol 43 (1) ◽  
pp. 69-84 ◽  
Author(s):  
J. Cepeda ◽  
K. Höeg ◽  
F. Nadim
Keyword(s):  
Conceptual Framework ◽  
Rainfall Thresholds ◽  
Triggering Rainfall ◽  
Landslide Triggering

Definition of 3D rainfall thresholds for operative LEWS

2021 ◽  
Author(s):  
Ascanio Rosi ◽  
Antonio Monni ◽  
Angela Gallucci ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
False Positives ◽  
Time Interval ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Antecedent Soil Moisture ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Definition Of ◽  
Landslide Triggering

<p>Rainfall induced landslide is one of the most common hazards worldwide and it is responsible every year of huge losses, both economic and social. <br>Because of the high impact of this kind of natural hazard, the forecasting of the meteorological condition associated with the initiation of landslide has become paramount in the recent years and several papers addressing this issue have been published.<br>When working over large areas, the definition of rainfall thresholds is the most used approach, since it requires few data that can be easily retrieved: landslide triggering date and location and rainfall recording associated to landslide events.<br>The intensity-duration threshold is the most used approach and it showed over the time its potential to be implemented in an operative landslide early warning system (LEWS), but literature papers showed that this approach is affected by a main drawback, i.e., the high number of false positives (events that are not capable of triggering landslides are classified as landslide triggering events).<br>To overcome this problem several authors tried to combine these thresholds with other parameters and recently one of the most promising approach is the use of the antecedent soil moisture condition, but this parameter is note very easily available for large areas and it is difficult to retrieve it in real time, so as it can be used in a LEWS.<br>In our work we used antecedent rainfall to simulate the progressive saturation of the soil and then the soil moisture condition associated with the initiation of landslides.<br>In a given area the total rainfall recorded by each rain gauge over a defined period of time prior the landslide is considered and used to define a parameter named MeAR (Mean Antecedent Rainfall), which represent the mean rainfall of the area over a given time interval, as recorded by all the active rain gauges.<br>The MeAR parameter has been coupled with classical I-D thresholds to define 3D thresholds, where the conditions associated with the initiation of a landslide are defined by a portion of a 3D space, instead of a portion of a 2D plane. This approach has been tested in Emilia-Romagna region (Italy) and it resulted the possibility of reducing false positives from 30% up to 80% on different areas.</p>

scholarly journals Can we get more out of rainfall thresholds? The temporal resolution tradeoff and the role of antecedent wetness and rainfall spatial variability

2020 ◽  
Author(s):  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Temporal Resolution ◽  
Daily Rainfall ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Total Rainfall ◽  
Hourly Rainfall ◽  
Landslide Triggering

<p>Rainfall event properties like maximum intensity, total rainfall depth, or their representation in the form of intensity-duration (ID) or total rainfall-duration (ED) curves, are commonly used to determine the triggering rainfall (event) conditions required for landslide initiation. This rainfall data-driven prediction of landsliding can be extended by the inclusion of antecedent wetness conditions. Although useful for first order assessment of landslide triggering conditions in warning systems, this approach relies heavily on data quality and temporal resolution, which may affect the overall predictive model performance as well as its reliability.</p><p>In this work, we address three key aspects of rainfall thresholds when applied at large spatial scales: (a) the tradeoffs between higher and lower temporal resolution (hourly or daily) (b) the spatial variability associated with long term rainfall, and (c) the added value of antecedent rainfall as predictor. We explore all of these by utilizing a long-term landslide inventory, containing more than 2500 records in Switzerland and 3 gridded rainfall records: a long daily rainfall dataset and two derived hourly products, disaggregated using stations or radar hourly measurements.</p><p>We observe that while predictive performances improve slightly when utilizing high quality hourly record (using radar information), the length of the record decreases, as well as the number of landslides in the inventory, which affects the reliability of the thresholds. A tradeoff has to be found between using long records of less accurate daily rainfall data and landslide timing, and shorter records with highly accurate hourly rainfall data and landslide timing. Even daily rainfall data may give reasonable predictive performance if thresholds are estimated with a long landslide inventory. Good quality hourly rainfall data significantly improve performance, but historical records tend to be shorter or less accurate (e.g. fewer stations available) and landslides with known timing are fewer. Considering antecedent rainfall, we observe that it is generally higher prior to landslide-triggering events and this can partially explain the false alarms and misses of an intensity-duration threshold. Nevertheless, in our study antecedent rainfall shows less predictive power by itself than the rainfall event characteristics. Finally, we show that we can improve the performances of the rainfall thresholds by accounting for local climatology in which we define new thresholds by normalizing the event characteristics with a chosen quantile of the local rainfall distribution or using the mean annual precipitation.</p>

A deterministic approach for shallow landslide triggering scenarios in the southern Messina area (north-eastern Sicily, Italy)

2015 ◽  
Vol 35 ◽  
pp. 272-275
Author(s):  
Luca Schilirò ◽  
Carlo Esposito ◽  
Gabriele Scarascia Mugnozza
Keyword(s):  
Shallow Landslide ◽  
Deterministic Approach ◽  
North Eastern ◽  
Area North ◽  
Landslide Triggering
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