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

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>

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

Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds

2021 ◽  
Author(s):  
Nunziarita Palazzolo ◽  
David J. Peres ◽  
Enrico Creaco ◽  
Antonino Cancelliere
Keyword(s):  
Soil Moisture ◽  
Rainfall Event ◽  
Rainfall Thresholds ◽  
Rainfall Duration ◽  
Soil Moisture Data ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
Different Depths ◽  
Moisture Conditions ◽  
Landslide Triggering

<p>Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.</p>

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 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.

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