scholarly journals Shallow landslides involving weathered and fractured bedrock: a comparative susceptibility analysis between deterministic and statistical models

2020 ◽  
Author(s):  
Enrico D'Addario ◽  
Leonardo Disperati ◽  
José Luís Zêzere ◽  
Raquel De Melo ◽  
Sérgio Oliveira
Keyword(s):  
Statistical Models ◽  
Regional Scale ◽  
Central Italy ◽  
Shallow Landslide ◽  
Geological Strength Index ◽  
Predictive Capacity ◽  
Predisposing Factor ◽  
Fractured Bedrock ◽  
Susceptibility Analysis ◽  
Physically Based

<p>Shallow landslide susceptibility modelling at regional scale may be performed using both a physically based and statistical approach. For the same area, these two approaches can have inconsistent results, mainly because the two methods are conceptually different. Physically based models are based on the infinite slope model and consists on the computation cell by cell of a safety factor comparing between driving and resisting forces. The assumption that landslides occur in slopes that are characterized by predisposing factors similar to those in which landslides have occurred in the past, is the concept behind the statistical models. The aim of this work is to compare the two approach and investigate the differences between the two models. The study area is located in northern Tuscany, central Italy, in which an extensive field survey highlighted that about 60% of landslides involve bedrock. For this reason, we developed a physically based susceptibility analysis taking into account both the surficial layer (slope deposit, SD) and the underlying layer (BR), characterized by weathered and fractured bedrock. This model is compared to the statistically based one, which take into account topographic and geologic predisposing factor as well as bedrock geo-mechanical properties, such Geological Strength Index (GSI), Schmidt hammer rebound values (Rv) and Joint density (Jv). The accuracy of the models is evaluated using a multi-temporal landslide inventory, in which involving bedrock landslides are distinct from slope deposits landslides. Within this general framework results are discussed regarding the model’s predictive capacity and spatial agreement.</p>

scholarly journals The Role of Initial Soil Conditions in Shallow Landslide Triggering: Insights from Physically Based Approaches

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
L. Schilirò ◽  
G. Poueme Djueyep ◽  
C. Esposito ◽  
G. Scarascia Mugnozza
Keyword(s):  
Central Italy ◽  
Shallow Landslide ◽  
Rainfall Event ◽  
Soil Conditions ◽  
Initial Water Content ◽  
Extreme Rainfall Event ◽  
Wetting Front ◽  
Clayey Sand ◽  
Physically Based

In the last years, the shallow landslide phenomenon has increasingly been investigated through physically based models, which try to extend over large-area simplified slope stability analyses using physical and mechanical parameters of the involved material. However, the parameterization of such models is usually challenging even at the slope scale, due to the numerous parameters involved in the failure mechanism. In particular, considering the scale of the phenomenon, the role of transient hydrology is essential. For this reason, in this work we present the outcome of different experimental tests conducted on a soil slope model with a sloping flume. The tested material was sampled on Monte Mario Hill (Rome, Central Italy), an area which has been frequently affected by rainfall-induced landslide events in the past. In this respect, we also performed a physically based numerical analysis at the field conditions, in order to evaluate the response of the terrain to a recent extreme rainfall event. The results of the flume tests show that, for the same material, two different triggering mechanisms (i.e., uprise of a temporary water table and advance of the wetting front) occur by varying the initial water content only. At the same time, the results of the numerical simulations indicate that clayey sand and lean clay are the soil types mostly influenced by the abovementioned rainfall event, since the initial moisture conditions enhance the formation of a wide wetting front within the soil profile.

scholarly journals Regional-scale susceptibility modelling of shallow landslides involving the weathered and fractured sub-surface bedrock

2021 ◽  
Author(s):  
Enrico D'Addario ◽  
Leonardo Disperati ◽  
Josè Luis Zezerè ◽  
Raquel Melo ◽  
Sergio Cruz Oliveira
Keyword(s):  
Slope Failure ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Data Driven ◽  
Field Observations ◽  
Rupture Surface ◽  
Slope Deposits ◽  
Physically Based ◽  
Engineering Geological Properties

<p>Landsliding is a complex phenomenon and its modelling aimed at predicting where the processes are most likely to occur is a tricky issue to be performed. Apart the chosen modelling approach, for both data-driven and physically-based models, paying adequate attention to the predisposing and triggering factors, as well as the input parameters is no less important. Generally, shallow landslides mobilize relatively small volumes of material sliding along a nearly planar rupture surface which is assumed to be roughly parallel to the ground surface. In the literature it is also widely accepted that shallow landslides involve only unconsolidated slope deposits (i.e., the colluvium), then the rupture surface corresponds to the discontinuity between the bedrock and the overlying loose soil. In this work, based on systematic field observations, we highlight that shallow landslides often involve also portions of the sub-surface bedrock showing different levels of weathering and fracturing. Then, we show that the engineering geological properties of slope deposits, as well as those related to the underlying bedrock, must be considered to obtain more reliable shallow landslides susceptibility assessment. As a first task, a multi-temporal shallow landslide inventory was built by photointerpretation of aerial orthoimages. Then, a new fieldwork-based method is proposed and implemented to acquire, process and spatialize the engineering geological properties of both slope deposits and bedrock. To support the regional scale approach, field observations were collected within, in the neighbour and far from the shallow landslide areas. Finally, both physically-based and data-driven methods were implemented to assess and compare shallow landslide susceptibility at regional scale, as well as to analyse the role of spatial distribution of rock mass quality for shallow slope failure development. The results highlight that, according to geology, structural setting and morphometric conditions, bedrock properties spatially change, defining clusters influencing both the distribution and characters of shallow landslides. As a consequence, the physically-based modelling provides better prediction accuracy when two possible rupture surfaces are analysed, the shallower one located at the slope deposit / bedrock discontinuity, and the deeper one located at the bottom of the fractured and weathered bedrock horizon. Even though the physically-based and data-driven models provide similar results in terms of ROC curves, the resulting susceptibility maps highlight quite substantial differences.</p>

scholarly journals Application of a physically based model to forecast shallow landslides at a regional scale

2018 ◽  
Vol 18 (7) ◽  
pp. 1919-1935 ◽  
Author(s):  
Teresa Salvatici ◽  
Veronica Tofani ◽  
Guglielmo Rossi ◽  
Michele D'Ambrosio ◽  
Carlo Tacconi Stefanelli ◽  
...  
Keyword(s):  
Slope Stability ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Richards Equation ◽  
Mean Annual Precipitation ◽  
Good Prediction ◽  
Physically Based Model ◽  
Good Prediction Accuracy ◽  
Physically Based

Abstract. In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. 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 landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.

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 Semi-Automatic Identification and Pre-Screening of Geological–Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets

2019 ◽  
Vol 11 (14) ◽  
pp. 1675 ◽  
Author(s):  
Tomás ◽  
Pagán ◽  
Navarro ◽  
Cano ◽  
Pastor ◽  
...  
Keyword(s):  
Regional Scale ◽  
Central Italy ◽  
Geographical Information ◽  
External Information ◽  
Automatic Identification ◽  
Persistent Scatterer Interferometry ◽  
Active Deformation ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
Map Scale

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima–Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5,000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale.

scholarly journals A Novel Physically Based Distributed Model for Irrigation Districts’ Water Movement

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 692
Author(s):  
Boyu Mi ◽  
Haorui Chen ◽  
Shaoli Wang ◽  
Yinlong Jin ◽  
Jiangdong Jia ◽  
...  
Keyword(s):  
Soil Water ◽  
Water Movement ◽  
Regional Scale ◽  
Irrigation District ◽  
Distributed Model ◽  
Efficiency Coefficient ◽  
Good Correspondence ◽  
Groundwater Movement ◽  
Irrigation Districts ◽  
Physically Based

The water movement research in irrigation districts is important for food production. Many hydrological models have been proposed to simulate the water movement on the regional scale, yet few of them have comprehensively considered processes in the irrigation districts. A novel physically based distributed model, the Irrigation Districts Model (IDM), was constructed in this study to address this problem. The model combined the 1D canal and ditch flow, the 1D soil water movement, the 2D groundwater movement, and the water interactions among these processes. It was calibrated and verified with two-year experimental data from Shahaoqu Sub-Irrigation Area in Hetao Irrigation District. The overall water balance error is 2.9% and 1.6% for the two years, respectively. The Nash–Sutcliffe efficiency coefficient (NSE) of water table depth and soil water content is 0.72 and 0.64 in the calibration year and 0.68 and 0.64 in the verification year. The results show good correspondence between the simulation and observation. It is practicable to apply the model in water movement research of irrigation districts.

scholarly journals Evaluating performances of simplified physically based models for landslide susceptibility

2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace
Keyword(s):  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Test Case ◽  
Susceptibility Analysis ◽  
Physically Based ◽  
Private And Public ◽  
Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2021 ◽  
Vol 14 (2) ◽  
pp. 905-921
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.

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