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 Regional physically based landslide early warning modelling: soil parameterisation and validation of the results

2018 ◽  
Author(s):  
Teresa Salvatici ◽  
Veronica Tofani ◽  
Guglielmo Rossi ◽  
Michele D'Ambrosio ◽  
Carlo Tacconi Stefanelli ◽  
...  
Keyword(s):  
Slope Stability ◽  
Real Time ◽  
Early Warning ◽  
Regional Scale ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Mean Annual Precipitation ◽  
Good Prediction Accuracy ◽  
Physically Based ◽  
Landslide Early Warning

Abstract. In this work, we apply a physically-based model, namely the HIRESSS (High REsolution Stability Simulator) model, to forecast the occurrence of shallow landslides at regional scale. The final aim is the set-up of an early warning system at regional scale for shallow landslides. HIRESSS is a physically based distributed slope stability simulator for analysing shallow landslide triggering conditions in real time and in large areas using parallel computational techniques. The software can run in real-time by assimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrological input parameters. The test area is a portion of the Valle d'Aosta region, located in North-West Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. of Dora Baltea's river floodplain to 4810 m a.s.l. of Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features lead to a high hydrogeological hazard in the whole territory, as mass movements interest the 70 % of the municipality areas (mainly shallow rapid landslides and rock falls). In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed with 12 survey points. The data collected contributes to generate input map of parameters for HIRESSS model. In order to take into account the effect of vegetation on slope stability, the contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. The model was applied in back analysis on two past events that have affected Valle d'Aosta region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, has provided good results and a good prediction accuracy of the HIRESSS model both from temporal and spatial point of view. A statistical analysis of the HIRESSS outputs in terms of failure probability has been carried out in order to define reliable alert levels for regional landslide early warning systems.

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 Evaluation of shallow landslide-triggering scenarios through a physically based approach: an example of application in the southern Messina area (northeastern Sicily, Italy)

2015 ◽  
Vol 15 (9) ◽  
pp. 2091-2109 ◽  
Author(s):  
L. Schilirò ◽  
C. Esposito ◽  
G. Scarascia Mugnozza
Keyword(s):  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Landslide Event ◽  
Wide Range ◽  
Physically Based ◽  
Triggering Process ◽  
Landslide Triggering ◽  
Northeastern Sicily

Abstract. Rainfall-induced shallow landslides are a widespread phenomenon that frequently causes substantial damage to property, as well as numerous casualties. In recent~years a wide range of physically based models have been developed to analyze the triggering process of these events. Specifically, in this paper we propose an approach for the evaluation of different shallow landslide-triggering scenarios by means of the TRIGRS (transient rainfall infiltration and grid-based slope stability) numerical model. For the validation of the model, a back analysis of the landslide event that occurred in the study area (located SW of Messina, northeastern Sicily, Italy) on 1 October 2009 was performed, by using different methods and techniques for the definition of the input parameters. After evaluating the reliability of the model through comparison with the 2009 landslide inventory, different triggering scenarios were defined using rainfall values derived from the rainfall probability curves, reconstructed on the basis of daily and hourly historical rainfall data. The results emphasize how these phenomena are likely to occur in the area, given that even short-duration (1–3 h) rainfall events with a relatively low return period (e.g., 10–20~years) can trigger numerous slope failures. Furthermore, for the same rainfall amount, the daily simulations underestimate the instability conditions. The high susceptibility of this area to shallow landslides is testified by the high number of landslide/flood events that have occurred in the past and are summarized in this paper by means of archival research. Considering the main features of the proposed approach, the authors suggest that this methodology could be applied to different areas, even for the development of landslide early warning systems.

scholarly journals Evaluation of shallow landslide triggering scenarios through a physically-based approach: an example of application in the southern Messina area (north-eastern Sicily, Italy)

2015 ◽  
Vol 3 (5) ◽  
pp. 2975-3022 ◽  
Author(s):  
L. Schilirò ◽  
C. Esposito ◽  
G. Scarascia Mugnozza
Keyword(s):  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Landslide Event ◽  
Wide Range ◽  
North Eastern ◽  
Physically Based ◽  
Triggering Process ◽  
Landslide Triggering

Abstract. Rainfall-induced shallow landslides are a widespread phenomenon that frequently causes substantial damage to property, as well as numerous casualties. In recent years a wide range of physically-based models has been developed to analyze the triggering process of these events. Specifically, in this paper we propose an approach for the evaluation of different shallow landslide triggering scenarios by means of TRIGRS numerical model. For the calibration of the model, a back-analysis of the landslide event occurred in the study area (located SW of Messina, north-eastern Sicily, Italy) on 1 October 2009 was performed, by using different methods and techniques for the definition of the input parameters. After evaluating the reliability of the model through the comparison with the 2009 landslide inventory, different triggering scenarios were defined using rainfall values derived from the rainfall probability curves, reconstructed on the basis of daily and hourly historical rainfall data. The results emphasize how these phenomena are likely to occur in the area, given that even short-duration (3–6 h) rainfall events having a relatively low return period (e.g. 10 years) can trigger numerous slope failures. On the contrary, for the same rainfall amount, the daily simulations overestimate the instability conditions. The tendency of shallow landslides to trigger in this area agrees with the high number of landslide/flood events occurred in the past and summarized in this paper by means of archival researches. Considering the main features of the proposed approach, the authors suggest that this methodology could be applied to different areas, even for the development of landslide early warning systems.

scholarly journals Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Luca Schilirò ◽  
José Cepeda ◽  
Graziella Devoli ◽  
Luca Piciullo
Keyword(s):  
Numerical Simulations ◽  
Regional Scale ◽  
Shallow Landslides ◽  
South Eastern ◽  
Physically Based ◽  
Triggering Conditions ◽  
Cover Thickness ◽  
Regional Analyses ◽  
Factor Maps ◽  
Eastern Norway

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

scholarly journals floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2255
Author(s):  
Julian Hofmann ◽  
Holger Schüttrumpf
Keyword(s):  
Real Time ◽  
Hydrodynamic Model ◽  
Early Warning Systems ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Flood Prediction ◽  
Adversarial Network ◽  
Pluvial Flooding ◽  
Physically Based ◽  
Fully Connected

Using machine learning for pluvial flood prediction tasks has gained growing attention in the past years. In particular, data-driven models using artificial neuronal networks show promising results, shortening the computation times of physically based simulations. However, recent approaches have used mainly conventional fully connected neural networks which were (a) restricted to spatially uniform precipitation events and (b) limited to a small amount of input data. In this work, a deep convolutional generative adversarial network has been developed to predict pluvial flooding caused by nonlinear spatial heterogeny rainfall events. The model developed, floodGAN, is based on an image-to-image translation approach whereby the model learns to generate 2D inundation predictions conditioned by heterogenous rainfall distributions—through the minimax game of two adversarial networks. The training data for the floodGAN model was generated using a physically based hydrodynamic model. To evaluate the performance and accuracy of the floodGAN, model multiple tests were conducted using both synthetic events and a historic rainfall event. The results demonstrate that the proposed floodGAN model is up to 106 times faster than the hydrodynamic model and promising in terms of accuracy and generalizability. Therefore, it bridges the gap between detailed flood modelling and real-time applications such as end-to-end early warning systems.

scholarly journals Development of A Methodology For Predicting Landslide Hazards At A Regional Scale

2021 ◽  
Author(s):  
Blanche Richer ◽  
Ali Saeidi ◽  
Maxime Boivin ◽  
Alain Rouleau
Keyword(s):  
Stability Analysis ◽  
3D Model ◽  
Regional Scale ◽  
Back Analysis ◽  
Initial Step ◽  
Landslide Risk ◽  
Target Area ◽  
Eastern Canada ◽  
Study Region ◽  
Electric Induction

Abstract Landslide risk analysis is a common geotechnical evaluation and aims to protect life and infrastructure. In the case of sensitive clay zones, landslides can affect large areas and are difficult to predict. Here we propose a methodology to determine the landslide hazard across a large territory, and we apply our approach to the Saint-Jean-Vianney area, Quebec, Canada. The initial step consists of creating a 3D model of the surficial deposits of the target area. After creating a chart of the material electrical resistivity adapted for eastern Canada, we applied electric induction to interpret the regional soil. We collected samples from the main lithologies and estimated selected soil geotechnical parameters in laboratory tests. We transposed parameter values obtained from the samples to a larger scale that of a slope using the results of a back analysis undertaken on an earlier, smaller slide within the same area. The regional 3D model of deposits is then used to develop a zonation map of at-risk slopes and their respective constraint areas with the study region. This approach allowed us to target specific areas where a more precise stability analysis would be required. Our methodology offers an effective tool for stability analysis in territories characterized by the presence of sensitive clays.

scholarly journals Integrating Satellite Soil Moisture and Rainfall Data on a Data-Driven Model for the Assessment of Shallow Landslides Hazard

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 42
Author(s):  
Meisina ◽  
Bordoni ◽  
Lucchelli ◽  
Brocca ◽  
Ciabatta ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Methodological Approach ◽  
Regional Scale ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Triggering Factors ◽  
Physically Based ◽  
Physically Based Models ◽  
Triggering Events

Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the events, according to the return time of the triggering events, which generally correspond to intense and concentrated rainfalls. Susceptibility and hazard of a territory are usually assessed by means of physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall amounts. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Moreover, physically-based models require, sometimes, significant computation power, which limit their implementations at regional scale. Data-driven models could overcome both of these limitations, even if they are generally built up taking into only the predisposing factors of shallow instabilities. Thus, they allow usually to estimate the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the hazard. This work presents the preliminary results of the development and the implementation of data-driven model able to estimate the hazard of a territory towards shallow landslides. The model is based on a Genetic Algorithm Model (GAM), which links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to the soil moisture content and to the rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in different catchments of 30–40 km2 located in Oltrepò Pavese area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

scholarly journals THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 105-107 ◽  
Author(s):  
W. Y. Li ◽  
C. Liu ◽  
J. Gao
Keyword(s):  
Hot Spot ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Physical Models ◽  
Slope Instability ◽  
Hazard Evaluation ◽  
Landslide Risk ◽  
Physically Based Model ◽  
Multi Scale ◽  
Physically Based

Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

scholarly journals Forecasting flood hazards in real time: a surrogate model for hydrometeorological events in an Andean watershed

2020 ◽  
Vol 20 (12) ◽  
pp. 3261-3277
Author(s):  
María Teresa Contreras ◽  
Jorge Gironás ◽  
Cristián Escauriaza
Keyword(s):  
Real Time ◽  
Surrogate Model ◽  
Numerical Models ◽  
Weather Forecast ◽  
Early Warning Systems ◽  
High Fidelity ◽  
Flood Hazards ◽  
Mountain Regions ◽  
Flood Prediction ◽  
Physically Based

Abstract. Growing urban development, combined with the influence of El Niño and climate change, has increased the threat of large unprecedented floods induced by extreme precipitation in populated areas near mountain regions of South America. High-fidelity numerical models with physically based formulations can now predict inundations with a substantial level of detail for these regions, incorporating the complex morphology, and copying with insufficient data and the uncertainty posed by the variability of sediment concentrations. These simulations, however, typically have large computational costs, especially if there are multiple scenarios to deal with the uncertainty associated with weather forecast and unknown conditions. In this investigation we develop a surrogate model or meta-model to provide a rapid response flood prediction to extreme hydrometeorological events. Storms are characterized with a small set of parameters, and a high-fidelity model is used to create a database of flood propagation under different conditions. We use kriging to perform an interpolation and regression on the parameter space that characterize real events, efficiently approximating the flow depths in the urban area. This is the first application of a surrogate model in the Andes region. It represents a powerful tool to improve the prediction of flood hazards in real time, employing low computational resources. Thus, future advancements can focus on using and improving these models to develop early warning systems that help decision makers, managers, and city planners in mountain regions.

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