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.
Keywords GIS • Distributed hydro-geotechnical model • Shallow landslide risk •
Probability • Kyushu Island
11.1 Introduction
As climate change intensifies during the twenty-first century, extreme events such
as typhoons, extreme rainfall, droughts, etc. are expected to become more common.
Recent decades have seen more frequent shallow landslides, driven by typhoons
and extreme rainfall events (Duan et al. 2014). Shallow landslide risk mapping is a
necessary tool for the risk management community. Recent developments in GIS
tools and hydrological/geotechnical modeling enable researchers and resource
managers to analyze land surfaces for shallow landslide potential.
As GIS tools have become more commonplace, they have been widely used in
hydrological modeling. The Soil and Water Assessment Tool (SWAT) combined
with ArcGIS (called ArcSWAT) is becoming a popular modeling tool applied for
studying water resources in the USA (CEAP 2008; Gassman et al. 2007), China
(Zhang et al. 2008), Japan (Luo et al. 2012), and West Africa (Schuol et al. 2008).
Hydrological models such as grid-Cell Distributed Rainfall Runoff Model Version
3 (CDRMV3) take input hydrological data including flow accumulation, flow
direction and so on from ArcGIS (Luo et al. 2014a). The Geospatial Hydrologic
Modeling Extension (HEC-GeoHMS) is a public-domain software package also
linked with ArcGIS. TOPMODEL, originally developed at the University of Leeds
(United Kingdom) in the mid-1970s has recently been coupled with the Geographic
Resources Analysis Support System (GRASS) GIS software. A GIS-based framework for systematic landslide hazard analysis was developed and applied in Hong
Kong with geologic, climatic, historical landslide data and rainfall data (Chau
et al. 2004). Safety maps for slope stability in the northern part of the Rasuwa
district in Nepal were generated through an analysis of physical processes using
GIS tools (Acharya et al. 2006). Spatial analysis and prediction of landslide hazards
have also used GIS techniques in the Xiaojiang watershed in Southwest China (Lan
et al. 2004). Using GIS, the dynamic characteristics of shallow landslides can be
analyzed in response to rainfall events (Lan et al. 2005). A grid-based GIS framework is required for susceptibility and hazard assessment of shallow landslides
(Godt et al. 2008). The proliferation of GIS tools and extensions has vastly
expanded the potential for hydrological modeling and shallow landslide analysis.
222
P. Luo et al.
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.
Keywords GIS • Distributed hydro-geotechnical model • Shallow landslide risk •
Probability • Kyushu Island
11.1 Introduction
As climate change intensifies during the twenty-first century, extreme events such
as typhoons, extreme rainfall, droughts, etc. are expected to become more common.
Recent decades have seen more frequent shallow landslides, driven by typhoons
and extreme rainfall events (Duan et al. 2014). Shallow landslide risk mapping is a
necessary tool for the risk management community. Recent developments in GIS
tools and hydrological/geotechnical modeling enable researchers and resource
managers to analyze land surfaces for shallow landslide potential.
As GIS tools have become more commonplace, they have been widely used in
hydrological modeling. The Soil and Water Assessment Tool (SWAT) combined
with ArcGIS (called ArcSWAT) is becoming a popular modeling tool applied for
studying water resources in the USA (CEAP 2008; Gassman et al. 2007), China
(Zhang et al. 2008), Japan (Luo et al. 2012), and West Africa (Schuol et al. 2008).
Hydrological models such as grid-Cell Distributed Rainfall Runoff Model Version
3 (CDRMV3) take input hydrological data including flow accumulation, flow
direction and so on from ArcGIS (Luo et al. 2014a). The Geospatial Hydrologic
Modeling Extension (HEC-GeoHMS) is a public-domain software package also
linked with ArcGIS. TOPMODEL, originally developed at the University of Leeds
(United Kingdom) in the mid-1970s has recently been coupled with the Geographic
Resources Analysis Support System (GRASS) GIS software. A GIS-based framework for systematic landslide hazard analysis was developed and applied in Hong
Kong with geologic, climatic, historical landslide data and rainfall data (Chau
et al. 2004). Safety maps for slope stability in the northern part of the Rasuwa
district in Nepal were generated through an analysis of physical processes using
GIS tools (Acharya et al. 2006). Spatial analysis and prediction of landslide hazards
have also used GIS techniques in the Xiaojiang watershed in Southwest China (Lan
et al. 2004). Using GIS, the dynamic characteristics of shallow landslides can be
analyzed in response to rainfall events (Lan et al. 2005). A grid-based GIS framework is required for susceptibility and hazard assessment of shallow landslides
(Godt et al. 2008). The proliferation of GIS tools and extensions has vastly
expanded the potential for hydrological modeling and shallow landslide analysis.
222
P. Luo et al.