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.

scholarly journals Heavy Rainfall Triggering Shallow Landslides: A Susceptibility Assessment by a GIS-Approach in a Ligurian Apennine Catchment (Italy)

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 605 ◽  
Author(s):  
Anna Roccati ◽  
Francesco Faccini ◽  
Fabio Luino ◽  
Andrea Ciampalini ◽  
Laura Turconi
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslides ◽  
Decision Makers ◽  
Slope Instability ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
Rainfall Events ◽  
Earth Flows ◽  
Weighted Values

In recent decades, the Entella River basin (eastern Liguria) has been affected by several rainfall events that induced widespread shallow landslides and earth flows on the slopes; roads, buildings, structures and infrastructure suffered extensive damage due to the instability processes. In this paper, a GIS-based approach for analyzing and assessing a simplified landslide susceptibility in the Entella River catchment is presented. Starting from landslide information mainly provided from newspaper articles and unpublished reports from municipal archives, we performed a series of comparative analyses using a set of thematic maps to assess the influence of predisposing natural and anthropic factors. By evaluating the statistical distribution of landslides in different categories, we assigned weighted values to each parameter, according to their influence on the instability processes. A simplified, reproducible, but effective approach to assess landslide susceptibility in the study area was performed by combining all predisposing factors. The resulting scores in proneness to slope instability classes may be used to generate a simplified landslides susceptibility map of the catchment area which would be easy to regularly update every time a rainfall event that is able to trigger shallow landslides occurs; this would provide a useful tool for local authorities and decision makers for identifying areas which could potentially be affected by instability processes, and would help in determining the most suitable measures in land-planning and landslide risk management.

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.

How to account for misclassification costs in shallow-landslide rainfall thresholds?

2021 ◽  
Author(s):  
Paolo Frattini ◽  
Gianluca Sala ◽  
Camilla Lanfranconi ◽  
Giulia Rusconi ◽  
Giovanni Crosta
Keyword(s):  
False Alarm ◽  
Rainfall Intensity ◽  
Rainfall Threshold ◽  
Shallow Landslides ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Misclassification Costs ◽  
Triggering Events ◽  
The Cost

<p>Rainfall is one of the most significant triggering factors for shallow landslides. The early warning for such phenomena requires the definition of a threshold based on a critical rainfall condition that may lead to diffuse landsliding. The developing of these thresholds is frequently done through empirical or statistical approaches that aim at identifying thresholds between rainfall events that triggered or non-triggered landslides. Such approaches present several problems related to the identification of the exact amount of rainfall that triggered landslides, the local geo-environmental conditions at the landslide site, and the minimum rainfall amount used to define the non-triggering events. Furthermore, these thresholds lead to misclassifications (false negative or false positive) that always induce costs for the society. The aim of this research is to address these limitations, accounting for classification costs in order to select the optimal thresholds for landslide risk management.</p><p>Starting from a database of shallow landslides occurred during five regional-scale rainfall events in the Italian Central Alps, we extracted the triggering rainfall intensities by adjusting rain gouge data with weather radar data. This adjustment significantly improved the information regarding the rainfall intensity at the landslide site and, although an uncertainty related to the exact timing of occurrence has still remained. Therefore, we identified the rainfall thresholds through the Receiver Operating Characteristic (ROC) approach, by identifying the optimal rainfall intensity that separates triggering and non-triggering events. To evaluate the effect related to the application of different minimum rainfall for non-triggering events, we have adopted three different values obtaining similar results, thus demonstrating that the ROC approach is not sensitive to the choice of the minimum rainfall threshold. In order to include the effect of misclassification costs we have developed cost-sensitive rainfall threshold curves by using cost-curve approach (Drummond and Holte 2000). As far as we know, this is the first attempt to build a cost-sensitive rainfall threshold for landslides that allows to explicitly account for misclassification costs. For the development of the cost-sensitive threshold curve, we had to define a reference cost scenario in which we have quantified several cost items for both missed alarms and false alarms. By using this scenario, the cost-sensitive rainfall threshold results to be lower than the ROC threshold to minimize the missed alarms, the costs of which are seven times greater than the false alarm costs. Since the misclassification costs could vary according to different socio-economic contexts and emergency organization, we developed different extreme scenarios to evaluate the sensitivity of misclassification costs on the rainfall thresholds. In the scenario with maximum false-alarm cost and minimum missed-alarm cost, the rainfall threshold increases in order to minimize the false alarms. Conversely, the rainfall thresholds decreases in the scenario with minimum false-alarm cost and maximum missed-alarm costs. We found that the range of variation between the curves of these extreme scenarios is as much as half an order of magnitude.</p>

scholarly journals Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

2011 ◽  
Vol 11 (7) ◽  
pp. 1927-1947 ◽  
Author(s):  
L. Montrasio ◽  
R. Valentino ◽  
G. L. Losi
Keyword(s):  
Real Time ◽  
Regional Scale ◽  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Landslide Risk ◽  
Time Interval ◽  
Slip Model ◽  
Temporal Prediction ◽  
Physically Based

Abstract. In the framework of landslide risk management, it appears relevant to assess, both in space and in time, the triggering of rainfall-induced shallow landslides, in order to prevent damages due to these kind of disasters. In this context, the use of real-time landslide early warning systems has been attracting more and more attention from the scientific community. This paper deals with the application, on a regional scale, of two physically-based stability models: SLIP (Shallow Landslides Instability Prediction) and TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis). A back analysis of some recent case-histories of soil slips which occurred in the territory of the central Emilian Apennine, Emilia Romagna Region (Northern Italy) is carried out and the main results are shown. The study area is described from geological and climatic viewpoints. The acquisition of geospatial information regarding the topography, the soil properties and the local landslide inventory is also explained. The paper outlines the main features of the SLIP model and the basic assumptions of TRIGRS. Particular attention is devoted to the discussion of the input data, which have been stored and managed through a Geographic Information System (GIS) platform. Results of the SLIP model on a regional scale, over a one year time interval, are finally presented. The results predicted by the SLIP model are analysed both in terms of safety factor (Fs) maps, corresponding to particular rainfall events, and in terms of time-varying percentage of unstable areas over the considered time interval. The paper compares observed landslide localizations with those predicted by the SLIP model. A further quantitative comparison between SLIP and TRIGRS, both applied to the most important event occurred during the analysed period, is presented. The limits of the SLIP model, mainly due to some restrictions of simplifying the physically based relationships, are analysed in detail. Although an improvement, in terms of spatial accuracy, is needed, thanks to the fast calculation and the satisfactory temporal prediction of landslides, the SLIP model applied on the study area shows certain potential as a landslides forecasting tool on a regional scale.

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 Automated reconstruction of rainfall events responsible for shallow landslides

2014 ◽  
Vol 14 (9) ◽  
pp. 2399-2408 ◽  
Author(s):  
G. Vessia ◽  
M. Parise ◽  
M. T. Brunetti ◽  
S. Peruccacci ◽  
M. Rossi ◽  
...  
Keyword(s):  
Rainfall Intensity ◽  
Shallow Landslides ◽  
Threshold Values ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Mean Intensity ◽  
Landslide Occurrence ◽  
R Language ◽  
Rain Gauges ◽  
Rainfall Duration

Abstract. Over the last 40 years, many contributions have identified empirical rainfall thresholds (e.g. rainfall intensity (I) vs. rainfall duration (D), cumulated rainfall vs. rainfall duration (ED), cumulated rainfall vs. rainfall intensity (EI)) for the possible initiation of shallow landslides, based on local and global inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has only rarely been addressed. Objective criteria for estimating the rainfall responsible for the landslide occurrence play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented. The first criterion is based on the analysis of the time series of rainfall mean intensity values over 1 month preceding the landslide occurrence. The second criterion is based on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure that is written in the R language. A sample of 100 shallow landslides collected in Italy from 2002 to 2012 was used to calibrate the procedure. The cumulated event rainfall (E) and duration (D) of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the D, E diagram. The results are discussed by comparing the D, E pairs calculated by the automated procedure and the ones by the expert method.

Towards an early-warning system for rainfall-induced landslides in forested catchments: a case study in Valsassina (Northern Italy)

2020 ◽  
Author(s):  
Michele Placido Antonio Gatto ◽  
Gian Battista Bischetti ◽  
Chiara Miodini ◽  
Lorella Montrasio
Keyword(s):  
Real Time ◽  
Debris Flows ◽  
Early Warning System ◽  
Warning System ◽  
Root Systems ◽  
Northern Italy ◽  
Shallow Landslides ◽  
Landslide Risk ◽  
Physically Based ◽  
Soil Movements

<p>Rainfall-induced soil slips are one of the most common and critical natural phenomena affecting the steep slopes in mountainous regions. These soil processes cause - directly and indirectly - huge damages to human-life, infrastructures and properties, especially when evolve into rapid soil movements such as debris avalanches, debris flows, flow slides, and rockslides. In this context, a landslide risk management that includes an accurate and robust real-time landslide early warning system at large scale (catchment or regional) for assessing the triggering soil slips both in space and in time, is necessary. This purpose appears more complicated where the forest covers most of the territory of a region and landslide-triggering thresholds cannot catch the exact process. In addition, most of physically-based models for real-time landslide warning neglect the role of vegetation, which is well-recognised to be fundamental in preventing shallow soil movements. In fact, forests influence hydrological and mechanical properties of the shallower soil layers through the beneficial effects of root systems and the canopy cover (reducing soil moisture, intercepting precipitation, reinforcing the soil resistance, etc.).</p><p>The present study proposes a modified version of the physically-based stability model, SLIP (Shallow Landslides Instability Prediction), based on the limit equilibrium method applied to an infinite slope and on a simplified modelling of the water down-flow. SLIP was integrated with a quantification of the rainfall interception by the forest canopy, and of the soil reinforcement provided by root systems as a function of tree species and tree density (which are data available from the forest management plans). The adapted model was applied to two mountainous catchments located in Valsassina (Northern Italy) and almost completely covered by forests (conifers, broadleaves and mixed). The study area was affected by shallow landslides and debris flows occurred after extreme meteorological events during autumn 2018. The model accuracy was tested through a back-analysis on the recent soil slips, mapped into a landslide inventory that was produced comparing high-resolution multi-temporal satellite images. The results provide an accurate risk map, identifying the areas of sediments source that can evolve into more threating soil movements.</p><p>The specific development of more accurate physically-based model can reasonably be an important tool for landslide risk management. Combined with a radar rainfall forecasting method, SLIP can be useful for addressing the real-time civil protection response to the emergencies. Moreover, the proposed method can play a key role in identifying the priorities inside the catchment management strategy, e.g. removing accumulated sediments in reservoirs, designing additional geotechnical or soil-bioengineering countermeasures, or evaluating the protection function of the forests.</p>

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 The role of antecedent soil moisture conditions on rainfall-triggered shallow landslides

2018 ◽  
Author(s):  
Maurizio Lazzari ◽  
Marco Piccarreta ◽  
Salvatore Manfreda
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Human Life ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Antecedent Soil Moisture ◽  
Antecedent Moisture ◽  
Moisture Conditions ◽  
Antecedent Moisture Conditions

Abstract. Rainfall-triggered shallow landslides have caused losses of human life and millions of euros in damage to property in all parts of the world. The need to prevent such phenomena combined with the difficulty to describe the geo-physical processes over large scales led to the adoption of empirical rainfall thresholds derived from the observed relationship between rainfall intensity/duration and landslide occurrence. These thresholds are generally obtained neglecting the role of the antecedent moisture conditions that should be taken into consideration. In the present manuscript, we explored the role of antecedent soil moisture on the critical rainfall intensity–duration thresholds highlighting its critical impact. Therefore, traditional approaches that neglect such parameter may have a limited value in the early-warning systems. This study was carried out using a record of 326 landslides occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e. rainstorm intensity and duration), we also derived the antecedent moisture conditions using a parsimonious hydrological model.

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