Advanced Search
DSpace@MIT

The use of a distributed hydrologic model to predict dynamic landslide susceptibility for a humid basin in Puerto Rico

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Rafael L. Bras. en_US
dc.contributor.author Kamal, Sameer A. (Sameer Ahmed) en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.coverage.spatial nwpr--- en_US
dc.date.accessioned 2010-05-25T20:54:50Z
dc.date.available 2010-05-25T20:54:50Z
dc.date.issued 2009 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/55154
dc.description Thesis (Env. E.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009. en_US
dc.description "September 2009." Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 86-91). en_US
dc.description.abstract This thesis describes the use of a distributed hydrology model in conjunction with a Factor of Safety (FS) algorithm to predict dynamic landslide susceptibility for a humid basin in Puerto Rico. The Mameyes basin, located in the Luquillo Experimental Forest in Puerto Rico, was selected for modeling based on the rich ensemble of soil, vegetation, topographical, meteorological and historic landslide data available. The basin was parameterized into the TIN-based Real-time Integrated Basin Simulator (tRIBS) with particular emphasis on vegetation parameters for broadleaf evergreen trees in tropical climates. The basin was forced with precipitation data that included a synthesized rainfall event likely to result in a landslide based on rainfall intensity-duration thresholds. The basin's response was assessed mainly in terms of soil moisture and values of selected vegetation parameters, which served as the dynamic inputs into the FS algorithm. en_US
dc.description.abstract (cont.) An off-line FS algorithm was developed and tested using typical values for parameters encountered in the Mameyes basin. Sensitivity analyses indicated that slope angle, soil cohesion and soil moisture were the most sensitive parameters in this FS algorithm. When the tRIBS / FS Algorithm combination was employed over the entire basin, landslides were indicated in 48 out of 13,169 modeled locations. The spatial distribution of landslides compared favorably to a static landslide susceptibility map developed in previous work by Lepore et al. (2008b) while the temporal distribution of landslides was correlated with rainfall events. Landslides were predicted over a range of slope angle values, including on relatively gentle slopes where the modeled soil moisture drove the instability. The results demonstrate that the tRIBS/FS algorithm combination developed in this work is able to capture the key dynamics associated with slope stability, specifically the interactions between the slope angle and the soil moisture state. en_US
dc.description.statementofresponsibility by Sameer A. Kamal. en_US
dc.format.extent 100 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Civil and Environmental Engineering. en_US
dc.title The use of a distributed hydrologic model to predict dynamic landslide susceptibility for a humid basin in Puerto Rico en_US
dc.type Thesis en_US
dc.description.degree Env.E. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.identifier.oclc 607534867 en_US


Files in this item

Name Size Format Description
607534867.pdf 15.71Mb PDF Preview, non-printable (open to all)
607534867-MIT.pdf 16.06Mb PDF Full printable version (MIT only)

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage