Abstract
GIS and distributed hydrological models are important tools for shallow landslide prediction, particularly as such disasters are exacerbated by global change driven changes in precipitation regimes. The main objective of this chapter is to outline a detailed methodology for shallow landslide risk assessment using GIS and a hydrological model. We have developed a method to assess shallow landslide risk using GIS tools and a distributed hydrological model and further used this method to analyze the probability of shallow landslides in a case study. The physically based distributed landslide model was developed by integrating a grid-based distributed kinematic wave rainfall-runoff model combined with an infinite slope stability module. Application of the model to assess shallow landslide risk using rainfall data for Kyushu Island shows that the model can successfully predict the effect of rainfall distribution and intensity on the driving variables that trigger shallow landslides. The modeling system has broad applicability for shallow landslide prediction and warning.
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Acknowledgments
The authors thanks the supports from the Japan Institute of Country-ology and Engineering (JICE) Grant Number 13003, Water and Urban Initiative Project at The United Nations University Institute for the Advanced Study of Sustainability (UNU-IAS), the Kyoto University Inter-Graduate School Program for Sustainable Development and Survivable Societies (GSS), MEXT Program for Leading Graduate Schools 2011–2018, Designing Local Frameworks for Integrated Water Resources Management at the Research Institute for Humanity and Nature (RIHN), Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (A) Grant Number 24248041.
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Luo, P. et al. (2015). Modelling Shallow Landslide Risk Using GIS and a Distributed Hydro-geotechnical Model. In: Li, J., Yang, X. (eds) Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sensing/Photogrammetry. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9813-6_11
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DOI: https://doi.org/10.1007/978-94-017-9813-6_11
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