Area-scale landslide hazard and risk assessment

Romeo, Roberto; Floris, Mario; Veneri, Francesco

doi:10.1007/s00254-006-0294-1

Cited by 25 publications

(8 citation statements)

References 12 publications

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“…surface erosion and episodic processes like the small landslides on the upper slope of the LW in 2003) are considered, the return interval of the Kumanodaira landslide would be shorter. This return interval is longer than the reported value for Pliocene deposits in Italy (<50 yr; Romeo et al, ) and pyroclastic flow deposits in Japan (10 2 –10 3 yr; Shimokawa et al, ), and similar to or shorter than recurrence periods at sites in western North America (10 3 –10 4 yr; Dietrich and Dunne, ; Reneau et al, ). Another important factor affecting the soil‐depth recovery is the evacuation of stored soil in the landslide scar during heavy rainfall events (Reneau et al, ).…”

Section: Soil‐depth Recovery In the Landslide Scarmentioning

confidence: 50%

Temporal and spatial variation of infilling processes in a landslide scar in a steep mountainous region, Japan

Imaizumi

Sidle

Togari-Ohta

et al. 2014

Earth Surf Processes Landf

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The duration of the soil-depth recovery needed for reoccurrence of shallow colluvial landslides at a given site in humid regions is much longer than the return period of rainfall needed to generate sufficient pore water pressure to initiate a landslide. Knowledge of the rate of change in soil depth in landslide scars is therefore necessary to evaluate return intervals of landslides. Spatial variation in sediment transport at the Kumanodaira landslide scar in central Japan was investigated by field observations. Spatial distribution of the rate of change in soil depth was estimated using sediment transport data and geographic information system (GIS) analysis. Observations revealed that the timing of sediment transport differed for shallow and deep soil layers. Near-surface sediment transport (mostly dry ravel and some shallow soil creep at depths ≤0·05 m) measured in sediment traps was active in winter and early spring and was affected by freezing-thawing; soil creep of subsoil (i.e. >0·05 m), monitored by strain probes, was active in summer and autumn when precipitation was abundant. Near-surface sediment flux was estimated by a power law function of slope gradient. Deeper soil creep was more affected by relative location to the landslide scar, which influences soil depth, than by slope gradient. Our study indicated that the rate of soil-depth recovery is high just below the head scarp of the landslide. Abrupt changes in the longitudinal slope topography immediately above, within and just below the head scarp became smoother with time due to degradation proximate to the landslide head scarp and flanks, as well as aggradation just below the head scarp.

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Section: Soil‐depth Recovery In the Landslide Scarmentioning

confidence: 50%

Temporal and spatial variation of infilling processes in a landslide scar in a steep mountainous region, Japan

Imaizumi

Sidle

Togari-Ohta

et al. 2014

Earth Surf Processes Landf

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“…The calculation of landslide hazard in terms of spatial location of landslides, size, and frequency is the first step in risk assessment and management of SES [ Guzzetti , ]. Exposure and vulnerability of SES are assessed after the detection of the landslide hazard and the risk is determined by the product of hazard, vulnerability, and exposure [ Ardizzone et al , ; Guzzetti et al , ; Romeo et al , ; Ardizzone et al , ; Fell et al , ; de Luiz Rosito Listo and Vieira , ]. The determination of the hazard of landslides has been traditionally performed using heuristic, statistical, or correlative models [ Catani et al , ; Caniani et al , ; Goetz et al , ] and deterministic or stochastic physical‐based models [ Ermini et al , ].…”

Section: Introductionmentioning

confidence: 99%

Detecting fingerprints of landslide drivers: A MaxEnt model

2013

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[1] Landslides are important geomorphic events that sculpt river basins by eroding hillslopes and providing sediments to coastal areas. However, landslides are also hazardous events for socio-ecological systems in river basins causing enormous biodiversity, economic, and social impacts. We propose a probabilistic spatially explicit model for the prediction of landslide patterns based on a maximum entropy principle model (MAXENT). The model inputs are the centers of mass of historical landslides and environmental variables at the basin scale. The model has only three parameters requiring calibration: the threshold for the network extraction, the trade-off factor between model complexity and accuracy, and the threshold of landslide susceptibility. The calibration on a subset of observations detects the environmental drivers and their relative importance for landslides. We employ the model in the Arno basin, Italy, selected because of its widespread landslide dynamics and the large availability of landslide observations. The model reproduces the size distribution and location of over 27,500 historical landslides for the Arno basin with an accuracy of 86% obtained from the variable-landslide inference on about 37% of observed landslides. Future landslide patterns are predicted for 17 A1B and A2 rainfall scenarios and for a multimodel ensemble from 2000 to 2100. We show that potential landslide hazard is strongly correlated with variation in the 12 and 48 h rainfall with a return time of 10 years. As the climate gets wetter, the average probability of landslides gets higher which is shown by the landslide size distribution. Hence, the landslide size distribution is a fingerprint of the geomorphic effectiveness of rainfall as a function of climate change. MAXENT is proposed as a parsimonious model for the prediction of landslide patterns with respect to more complex models. The need for very accurately sampled and delineated landslides is lower than for other prediction models. Moreover, the model informs about the drivers of landslides and their relative importance without assumptions on the main triggering factors. This is important to inform monitoring of environmental variables. Our modeling approach can enhance the planning of socio-ecological systems in river basins by improving the accuracy of landslide prediction in space and time.

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“…Roads, bridges, oil-gas pipelines, and other infrastructures in mountain regions, as well as the people’s lives and property, are seriously threatened by landslides [1]. …”

Section: Introductionmentioning

confidence: 99%

A High Performance Piezoelectric Sensor for Dynamic Force Monitoring of Landslide

Cheng

Chen

et al. 2017

Sensors

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Due to the increasing influence of human engineering activities, it is important to monitor the transient disturbance during the evolution process of landslide. For this purpose, a high-performance piezoelectric sensor is presented in this paper. To adapt the high static and dynamic stress environment in slope engineering, two key techniques, namely, the self-structure pressure distribution method (SSPDM) and the capacitive circuit voltage distribution method (CCVDM) are employed in the design of the sensor. The SSPDM can greatly improve the compressive capacity and the CCVDM can quantitatively decrease the high direct response voltage. Then, the calibration experiments are conducted via the independently invented static and transient mechanism since the conventional testing machines cannot match the calibration requirements. The sensitivity coefficient is obtained and the results reveal that the sensor has the characteristics of high compressive capacity, stable sensitivities under different static preload levels and wide-range dynamic measuring linearity. Finally, to reduce the measuring error caused by charge leakage of the piezoelectric element, a low-frequency correction method is proposed and experimental verified. Therefore, with the satisfactory static and dynamic properties and the improving low-frequency measuring reliability, the sensor can complement dynamic monitoring capability of the existing landslide monitoring and forecasting system.

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Area-scale landslide hazard and risk assessment

Cited by 25 publications

References 12 publications

Temporal and spatial variation of infilling processes in a landslide scar in a steep mountainous region, Japan

Temporal and spatial variation of infilling processes in a landslide scar in a steep mountainous region, Japan

Detecting fingerprints of landslide drivers: A MaxEnt model

A High Performance Piezoelectric Sensor for Dynamic Force Monitoring of Landslide

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