Validation of the Shallow Landslide Model, SHALSTAB, for forest management

Dietrich, W. E.; Bellugi, D. G.; Asua, Raphael Real De

doi:10.1029/ws002p0195

Cited by 137 publications

(138 citation statements)

References 31 publications

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“…The model used is that proposed by Mongomery and Dietrich (1994) and named as SHALSTAB later on by Dietrich et al (1998). SHALSTAB shares similar governing equation with SINMAP (stability index mapping) proposed by Pack et al (1998).…”

Section: Shalstab Model and Operationmentioning

confidence: 99%

Optimal estimator for assessing landslide model performance

Huang

Kao

2006

Hydrol. Earth Syst. Sci.

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Abstract. The commonly used success rate (SR) in evaluating cell-based landslide model performance is based on the ratio of successfully predicted landslide sites over total actual landslide sites without considering the performance in predicting stable cells. We proposed a modified SR (MSR), in which the performance of stable cell prediction is included. The advantage of MSR is to avoid over-and under-prediction while upholding the stable sensitivity throughout all simulated cases. Stochastic analyses are conducted by using artificial landslide maps and simulations with a full range of performances (from worst to perfect) in both stable and unstable cell predictions. Stochastic analyses reveal mathematical responses of estimators to various model results in calculating performance. The Kappa method, which is commonly used for satellite image analysis, is improper for landslide modeling giving inconsistent performance when landslide coverage changes. To examine differences among SR and MSR in real model application, we applied the SHALSTAB model onto a mountainous watershed in Taiwan. Case study shows that stable and unstable cell predictions are inter-exclusive in SHALSTAB model. The optimal estimator should compromise landslide over-and under-prediction. According to our 4000 simulations, the best simulation generated by MSR projects 83 hits over 131 actual landslide sites while the unstable cells cover only 16% of the studied watershed. By contrast, despite the fact that the best simulation deduced from SR projects 120 hits over 131 actual landslide sites, this high performance is only obtained when unstable cells cover an incredibly high landslide cover (∼75%) of the entire watershed exhibiting a significant landslide over-prediction.

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Section: Shalstab Model and Operationmentioning

confidence: 99%

Optimal estimator for assessing landslide model performance

Huang

Kao

2006

Hydrol. Earth Syst. Sci.

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“…Slope failure probability was calculated using SHALSTAB (Montgomery and Dietrich, 1994), a distributed, process based slope stability model that has been widely used to predict relative failure probabilities (Montgomery et al, 1998;Dietrich et al, 2001;Borga et al, 2002;Fernandes et al, 2004). Relative failure probability is expressed as the ratio of rainfall (Q) to transmissivity (T) required for a cell to fail, it can be calculated using the equation: (2) where: ρ s is the soil density, ρ w is the density of water and ϕ is the angle of internal friction of the soil.…”

Section: Assessment Of Filter Impactsmentioning

confidence: 99%

The potential of digital filtering of generic topographic data for geomorphological research

Milledge

Lane

Warburton

2008

Earth Surf Processes Landf

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High resolution terrain models generated from widely available Interferometric Synthetic Aperture Radar (IfSAR) and digital photogrammetry are an exciting resource for geomorphological research. However, these data contain error, necessitating pre-processing to improve their quality. We evaluate the ability of digital filters to improve topographic representation, using: (1) a Gaussian noise removal filter; (2) the proprietary filters commonly applied to these datasets; and (3) a terrain sensitive filter, similar to those applied to laser altimetry data. Topographic representation is assessed in terms of both absolute accuracy measured with reference to independent check data and derived geomorphological variables (slope, upslope contributing area, topographic index and landslide failure probability) from a steepland catchment in northern England. Results suggest that proprietary filters often degrade or fail to improve precision. A combination of terrain sensitive and Gaussian filters performs best for both IfSAR and digital photogrammetry datasets, improving the precision of photogrammetry digital elevation models (DEMs) by more than 50 per cent relative to the unfiltered data. High-frequency noise and high-magnitude gross errors corrupt geomorphological variables derived from unfiltered photogrammetry DEMs. However, a terrain sensitive filter effectively removes gross errors and noise is minimized using a Gaussian filter. These improvements propagate through derived variables in a landslide prediction model, to reduce the area of predicted instability by up to 29 per cent of the study area. Interferometric Synthetic Aperture Radar is susceptible to removal of topographic detail by oversmoothing and its errors are less sensitive to filtering (maximum improvement in precision of 5 per cent relative to the raw data).

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“…However, one should note that overprediction of landsliding susceptibility has been observed in other applications of topographic index-based shallow landsliding models (e.g. Dietrich et al, 2001) and may be due to a number of reasons, including: (i) inaccurate topographic data, (ii) legacy effects of previous landslides, and (iii) limitation of the landslide surveys.…”

Section: Results and Comparison With Qdslammentioning

confidence: 99%

Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution

Lanni

Borga

Rigon

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Topographic index-based hydrological models have gained wide use to describe the hydrological control on the triggering of rainfall-induced shallow landslides at the catchment scale. A common assumption in these models is that a spatially continuous water table occurs simultaneously across the catchment. However, during a rainfall event isolated patches of subsurface saturation form above an impeding layer and their hydrological connectivity is a necessary condition for lateral flow initiation at a point on the hillslope.Here, a new hydrological model is presented, which allows us to account for the concept of hydrological connectivity while keeping the simplicity of the topographic index approach. A dynamic topographic index is used to describe the transient lateral flow that is established at a hillslope element when the rainfall amount exceeds a threshold value allowing for (a) development of a perched water table above an impeding layer, and (b) hydrological connectivity between the hillslope element and its own upslope contributing area. A spatially variable soil depth is the main control of hydrological connectivity in the model. The hydrological model is coupled with the infinite slope stability model and with a scaling model for the rainfall frequency-duration relationship to determine the return period of the critical rainfall needed to cause instability on three catchments located in the Italian Alps, where a survey of soil depth spatial distribution is available. The model is compared with a quasi-dynamic model in which the dynamic nature of the hydrological connectivity is neglected. The results show a better performance of the new model in predicting observed shallow landslides, implying that soil depth spatial variability and connectivity bear a significant control on shallow landsliding.

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Validation of the Shallow Landslide Model, SHALSTAB, for forest management

Cited by 137 publications

References 31 publications

Optimal estimator for assessing landslide model performance

Optimal estimator for assessing landslide model performance

The potential of digital filtering of generic topographic data for geomorphological research

Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution

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