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| Titel |
Influence of Specific Contributing Area algorithms on slope failure prediction in landslide modeling |
| VerfasserIn |
J.-C. Huang, S.-J. Kao, M.-L. Hsu, Y.-A. Liu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 7, no. 6 ; Nr. 7, no. 6 (2007-12-06), S.781-792 |
| Datensatznummer |
250004823
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| Publikation (Nr.) |
 copernicus.org/nhess-7-781-2007.pdf |
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| Zusammenfassung |
This study anatomized algorithm effects of specific contributing area (SCA)
on soil wetness estimation, consequently landslide prediction, in SHALSTAB.
A subtropical mountainous catchment during three typhoon invasions is
targeted. The peak 2-day rainfall intensity of the three typhoons: Haitang,
Mindulle and Herb are 144, 248 and 327 mm/day, respectively. We use modified
success rate (MSR) to retrieve the most satisfying mean condition for model
parameters in SHALSTAB at three rainfall intensities and respective
pre-typhoon NDVI themes. Simulation indicates that algorithm affects the
prediction of landslide susceptibility (i.e. FS, Factor of Safety)
significantly.
Based on fixed NDVI and the mean condition, we simulate by using full scale
rainfall intensity from 0 to 1200 mm/day. Simulations show that predicted
unstable area coverage increases non-linearly as rainfall intensity
increases for all algorithms yet with different increasing trends. Compared
to Dinf, D8 always gives lower coverage of predicted unstable area during
three typhoons. By contrast, FD8 gives higher coverage areas. The absolute
difference (compared to Dinf) in predicted unstable area ranges from ~−3% to +4% (percent watershed area). The relative difference
(compared to Dinf) ranges from −15% to as high as +40%. The maximum
absolute and relative differences in unstable area prediction occur around
the condition of 100–300 mm/day, which is common in subtropical mountainous
region.
Theoretical relationship among slope, rainfall intensity, SCA and FS value
was derived in which FS values are very sensitive to algorithms in the field
of slope from 37 to 52degree. Results imply any comparison among SCA-related
landslide models or engineering application of rainfall return period
analysis must base on the same algorithm to obtain comparable results. This
study clarifies the SCA algorithm effect on FS prediction and deepens our
understanding on landslide modeling. |
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