Distributed modelling of shallow landslides triggered by intense rainfall

Crosta, Giovanni B.; Frattini, Paolo

doi:10.5194/nhess-3-81-2003

Cited by 318 publications

(201 citation statements)

References 21 publications

Supporting

Mentioning

175

Contrasting

Unclassified

Order By: Relevance

“…Dietrich and Montgomery, 1998;Montgomery and Dietrich, 1994) and (ii) dynamic models (e.g. Baum et al, 2010;Crosta and Frattini, 2003). In contrast to steady-state models, dynamic models allow for the spatio-temporal assessment of hillslope hydrology and stability.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Zieher

Rutzinger

Schneider‐Muntau

et al. 2017

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 "behavioural model runs" with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

show abstract

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Zieher

Rutzinger

Schneider‐Muntau

et al. 2017

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…To study storm event-induced landslides on a regional scale, a deterministic physical-based method is commonly used which requires the employment of an infinite-slope model and a hydrological model (Okimura and Ichikawa, 1985;Dietrich et al, 1986Dietrich et al, , 1995Keefer et al, 1987; Correspondence to: C.-T. Lee (ct@ncu.edu.tw) and Dietrich, 1994;Wu and Sidle, 1995;Montgomery et al, 1998;Terlien, 1998;Crozer, 1999;Polemic and Sdao, 1999;Iverson, 2000;Borga et al, 2002;Wilkinson et al, 2002;Crosta and Frattini, 2003;Malet et al, 2005;Baum et al, 2005;Salciarini et al, 2006;Claessens et al, 2007aClaessens et al, , 2007bSchmidt et al, 2008). This approach requires the strength parameters, failure depth and soil conductibility for every point in the limit-equilibrium slope stability calculation, a requirement which can cause serious problems in terms of acquisition and control of spatial variability of the variables (Hutchinson, 1995;Guzzetti et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Statistical approach to storm event-induced landslides susceptibility

Lee

Huang

Lee

et al. 2008

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. For the interpretation of the storm event-induced landslide distribution for an area, deterministic methods are frequently used, while a region's landslide susceptibility is commonly predicted via a statistical approach based upon multi-temporal landslide inventories and environmental factors. In this study we try to use an event-based landslide inventory, a set of environmental variables and a triggering factor to build a susceptibility model for a region which is solved using a multivariate statistical method. Data for shallow landslides triggered by the 2002 typhoon, Toraji, in central western Taiwan, are selected for training the susceptibility model. The maximum rainfall intensity of the storm event is found to be an effective triggering factor affecting the landslide distribution and this is used in the model. The model is built for the Kuohsing region and validated using data from the neighboring Tungshih area and a subsequent storm event -the 2004 typhoon, Mindulle, which affected both the Kuohsing and the Tungshih areas. The results show that we can accurately interpret the landslide distribution in the study area and predict the occurrence of landslides in the neighboring region in a subsequent typhoon event. The advantage of this statistical method is that neither hydrological data, strength data, failure depth, nor a long-period landslide inventory is needed as input.

show abstract

“…Quantitative information on soil moisture before and during a rainfall event is an important information to forecast the time and location of shallow landslides (Campbell, 1975;Crosta and Frattini, 2003) and floods (Goodrich et al, 1994). This information is difficult to obtain, because the classical point measurements techniques (e.g., Time Domain Reflectometry, gravimetric, geoelectric) are unable to cover large areas.…”

Section: Introductionmentioning

confidence: 99%

Preliminary analysis of distributed in situ soil moisture measurements

2005

View full text Add to dashboard Cite

Abstract. Surface soil moisture content is highly variable in both space and time. Remote sensing can provide an effective methodology for mapping surface moisture content over large areas but ground based measurements are required to test its reliability and to calibrate retrieval algorithms. Recently, we had the opportunity to design and perform an experiment aimed at jointly acquiring measurements of surface soil water content at various locations and remotely sensed hyperspectral data. The area selected for the experiment is located in central Umbria and it extends for 90 km 2 . For the area, detailed lithological and multi-temporal landslide inventory maps were available. We identified eight plots where measurements of soil water content were made using a Time Domain Reflectometer (TDR). The plots range in size from 100 m 2 to 600 m 2 , and cover a variety of topographic and morphological settings. The TDR measurements were conducted during four days, on 5 April, 15 April, 2 May and 3 May 2004. On 3 May the NERC airborne CASI 2 acquired the hyperspectral data. Preliminary analysis concerning the matching between the landslides and the soil moisture were reported. Statistical and geostatistical analysis investigating the spatial-temporal soil moisture distribution were performed. These results will be compared with the data of surface temperature obtained from the remotely sensed hyperspectral sensor.

show abstract

Distributed modelling of shallow landslides triggered by intense rainfall

Cited by 318 publications

References 21 publications

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Statistical approach to storm event-induced landslides susceptibility

Preliminary analysis of distributed in situ soil moisture measurements

Contact Info

Product

Resources

About