Parameterizing a physically based shallow landslide model in a data poor region

2009 ◽  
Vol 34 (6) ◽  
pp. 867-881 ◽  
Author(s):  
Sekhar L. Kuriakose ◽  
L. P. H. van Beek ◽  
C. J. van Westen
2018 ◽  
Vol 9 (1) ◽  
pp. 295-324 ◽  
Author(s):  
Luca Schilirò ◽  
Andrea Cevasco ◽  
Carlo Esposito ◽  
Gabriele Scarascia Mugnozza

2013 ◽  
Vol 13 (3) ◽  
pp. 559-573 ◽  
Author(s):  
D. Zizioli ◽  
C. Meisina ◽  
R. Valentino ◽  
L. Montrasio

Abstract. On the 27 and 28 April 2009, the area of Oltrepo Pavese in northern Italy was affected by a very intense rainfall event that caused a great number of shallow landslides. These instabilities occurred on slopes covered by vineyards or recently formed woodlands and caused damage to many roads and one human loss. Based on aerial photographs taken immediately after the event and field surveys, more than 1600 landslides were detected. After acquiring topographical data, geotechnical properties of the soils and land use, susceptibility analysis on a territorial scale was carried out. In particular, different physically based models were applied to two contiguous sites with the same geological context but different typologies and sizes of shallow landslides. This paper presents the comparison between the ex-post results obtained from the different approaches. On the basis of the observed landslide localizations, the accuracy of the different models was evaluated, and the significant results are highlighted.


Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
L. Schilirò ◽  
G. Poueme Djueyep ◽  
C. Esposito ◽  
G. Scarascia Mugnozza

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.


2020 ◽  
Author(s):  
Enrico D'Addario ◽  
Leonardo Disperati ◽  
José Luís Zêzere ◽  
Raquel De Melo ◽  
Sérgio Oliveira

<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>


2018 ◽  
Vol 6 (1) ◽  
pp. 49-75 ◽  
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Sai Siddhartha Nudurupati ◽  
Christina Bandaragoda ◽  
Nicole M. Gasparini ◽  
...  

Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.


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