Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

Lepore, Chiara; Arnone, Elisa; Noto, Leonardo; Sivandran, G.; Bras, Rafael L.

doi:10.5194/hess-17-3371-2013

Cited by 77 publications

(83 citation statements)

References 61 publications

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“…Lanni et al (2012) utilized a dynamic topographic hydrological model to describe the subsurface processes and linked it with a simple hillslope slope-stability model for modeling the initiation of shallow landslides. Arnone et al (2011), Lepore et al (2013), Camera et al (2013), and Tao and Barros (2014) have also conducted similar studies among others. However, there is still much room for conducting studies on coupling hydrological models with landslide models for large-scale flood and landslide prediction (Bogaard and Greco, 2014).…”

Section: Introductionmentioning

confidence: 72%

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Zhang

Xue

Hong

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Severe storm-triggered floods and landslides are two major natural hazards in the US, causing property losses of USD 6 billion and approximately 110-160 fatalities per year nationwide. Moreover, floods and landslides often occur in a cascading manner, posing significant risk and leading to losses that are significantly greater than the sum of the losses from the hazards when acting separately. It is pertinent to couple hydrological and geotechnical modeling processes to an integrated flood-landslide cascading disaster modeling system for improved disaster preparedness and hazard management. In this study, we developed the iCRESTRIGRS model, a coupled flash flood and landslide initiation modeling system, by integrating the Coupled Routing and Excess STorage (CREST) model with the physically based Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) landslide model. The iCRESTRIGRS system is evaluated in four river basins in western North Carolina that experienced a large number of floods, landslides and debris flows triggered by heavy rainfall from Hurricane Ivan during 16-18 September 2004. The modeled hourly hydrographs at four USGS gauge stations show generally good agreement with the observations during the entire storm period. In terms of landslide prediction in this case study, the coupled model has a global accuracy of 98.9 % and a true positive rate of 56.4 %. More importantly, it shows an improved predictive capability for landslides relative to the stand-alone TRIGRS model. This study highlights the important physical connection between rainfall, hydrological processes and slope stability, and provides a useful prototype model system for operational forecasting of flood and landslide.

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Section: Introductionmentioning

confidence: 72%

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Zhang

Xue

Hong

et al. 2016

Hydrol. Earth Syst. Sci.

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“…(1a) were assigned a constant value of 2000 and 1000 kg m −3 , respectively, similar to Pack et al (2005). The factor of safety has been found to be insensitive to soil density (Hammond et al, 1992;Lepore et al, 2013).…”

Section: Vegetation and Soil Parametersmentioning

confidence: 99%

“…We neglect groundwater leakage to the bedrock in recharge estimation and apparent soil cohesion through the effect of surface tension in unsaturated zones (e.g., Lepore et al, 2013), both of which could be added to future updates to the component. Tree and snow surcharge is also disregarded, although it may have some stabilizing effect where soils are shallower than 1 m (Hammond et al, 1992).…”

Section: Model Limitationsmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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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 km 2 . 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 midelevations (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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“…The above‐mentioned hydrologic models are usually coupled with a slope stability method allowing a stability analysis related to a certain rainfall event. The most adopted approach uses the infinite slope assumption (Montgomery and Dietrich, ; Iverson, ; Borga et al , ; Morissey et al , ; Talebi et al , ; Tarolli et al , ; Lepore et al , ), wherein the failure surface is postulated parallel to the basement underlying the slipping mantle. Therefore, the failure mechanism is predetermined, and it is analytically parameterized with a stability index commonly known as the safety factor.…”

Section: Introductionmentioning

confidence: 83%

Rainfall‐triggered shallow landslides: infiltration dynamics in a physical hillslope model

Lora

Camporese

Troch

et al. 2016

Hydrological Processes

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In this study, we investigate the triggering of shallow landslides through the analysis of physical experiments performed in an artificial hillslope. The physical model consists of a reinforced concrete box containing a soil prism with maximum height of 3.5 m, length of 6 m, and width of 2 m. In order to analyze and examine the factors leading to the failure and the triggering modes, the hillslope is equipped with sensors to monitor the pore water pressure and moisture content response to rainfall in a 60 cm thick sand layer overlying a sandy clay soil. Two experiments were performed with different degrees of the sand initial compaction, to investigate the role of porosity on the hydrologic response and the subsequent failure. The experimental results showed that, with initially loose sand, the failure occurred suddenly, without premonitory signs, the soil behaving like a viscous fluid. The collected data showed a rapid increase of the water pressure contextual to the failure of the sand layer. In the second experiment, with initially dense sand, three levels of instability were observed: i) abundant runoff with limited erosion of the ground surface; ii) local slip detachments involving a soil thickness of few centimeters; and iii) a slow advancement of the entire sand layer volume. The hydrologic dynamics observed in the landslide experiments were simulated with numerical solutions of the Richards equation. The results of the simulations agree well with the experiments for the loose sand, while for the dense sand the comparison between experimental and numerical results shows some limitations related to the assumptions of single phase and rigid soil matrix implicit in the Richards equation. This article is protected by copyright. All rights reserved

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Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

Cited by 77 publications

References 61 publications

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

A hydroclimatological approach to predicting regional landslide probability using Landlab

Rainfall‐triggered shallow landslides: infiltration dynamics in a physical hillslope model

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