Calibration and resolution effects on model performance for predicting shallow landslide locations in Taiwan

In this paper we optimise the spatially explicit prediction of landslide hazard, landslide triggering and subsequent movement downslope of materials for a mountainous catchment in Taiwan. The location prediction is optimised by subsequently adding three location parameters: rainfall distribution, la...

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Bibliographic Details
Published in:Geomorphology (Amsterdam) 2011-10, Vol.133 (3), p.168-177
Main Authors: Keijsers, J.G.S., Schoorl, J.M., Chang, K.-T., Chiang, S.-H., Claessens, L., Veldkamp, A.
Format: Article
Language:English
Subjects:
Calibration
Digital elevation model
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Exact sciences and technology
Geomorphology
Geomorphology, landform evolution
inventories
Land use
Landslide modelling
Landslides
Marine and continental quaternary
Mathematical models
Model calibration
model validation
Natural hazards: prediction, damages, etc
prediction
rain
Rainfall
Rainfall distribution
regolith
Slopes
soil depth
Surficial geology
Topography
watersheds
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Summary:In this paper we optimise the spatially explicit prediction of landslide hazard, landslide triggering and subsequent movement downslope of materials for a mountainous catchment in Taiwan. The location prediction is optimised by subsequently adding three location parameters: rainfall distribution, land-use classes and DEM derived slopes. Then the three most important model parameters are calibrated to find the best prediction for both stable and unstable areas. The landslides predicted by the LAPSUS-LS model are compared with a landslide inventory to validate the output. The optimal model settings for the calibration area are then applied to a validation area. Results show that model performance can be improved by adding the spatial distribution of rainfall and by stratifying according to land-use classes. Landslide prediction is better with fine resolution DEMs, mainly because the local topography is smoothed in coarser resolutions. Although in general the amount of landslides is over-predicted, the overall performance indicates that the model is able to capture the important factors determining landslide location. Additional spatially distributed data such as regolith or soil depth and regeneration rates of the legacy effect can further enhance the model's prediction.
ISSN:0169-555X
1872-695X
DOI:10.1016/j.geomorph.2011.03.020