landslide susceptibility assessments -...
TRANSCRIPT
Landslides: Some terminology
Landslide inventory: Where do which landslides occur?Landslide susceptibility: Where could landslides occur?Landslide hazard: When do what landslides occur?Landslide risk: What consequences do landslides have?
“Risk = Hazard X Vulnerability”
Landslide definition: Movement of a mass of rock, debris or earth down a slope (Cruden, 1991).
Landslide susceptibility assessment:Overview of methods
Direct Indirect Qualitative Quantitative
Geomorphologic mapping √ √Heuristic analysis (Index-based) √ √Analysis of inventories √ √Statistical modeling √ √Process based (Conceptual) √ √
(Guzzetti, 2005)
Qualitative susceptibility zoning without landslide data
Categorical DataSoil Map Vegetation Map
Continuous DataSlope Angle Distance to Streams
Conceptual example: Heuristic weighted matrix overlay
(25% each map)
Strengths:- rough assessments can be made without landslide inventories- high benefit/cost ratio- suitable for first-order reconnaissance
Weaknesses:- requires a-priori knowledge on factors controlling landslides- ranking/weighting rules difficult to establish- high subjectivity, uncertainty
Conceptual example: Heuristic weighted matrix overlay
Qualitative susceptibility zoning without landslide data
Susceptibility zoning with landslide data
Conceptual example: Landslide density mapping
Landslide inventory map Landslide density mapof terrain units (watersheds)
Landslide density: area covered by landslides / area of terrain unit
Conceptual example: Landslide density mapping
Strengths:- Provides quantitative measure on landslide distribution- Provides straightforward comparison of different regions- Easily upscalable
Weaknesses:- Requires universal mapping unit definition- Assumes continuous landslide density in space- Cannot provide estimates on future landslides
Susceptibility zoning with landslide data
Inventory-based statistical susceptibility zoning
Conceptual Example: Bivariate statistical mapping
Categorical DataSoil Map Vegetation Map
Continuous DataSlope Angle Distance to Streams
⎟⎟⎠
⎞⎜⎜⎝
⎛∑=
landslides
lassparameterclandslidesLSIρ
ρ /ln
Conceptual Example: Bivariate statistical mapping
Strengths:- Provides a good combination between expert-derivedparameter choices and quantitative spatial analysis
- Renders quantitative and objective measure on landslide susceptibility
Weaknesses:- Assumes independence of input parameters- Requires complete landslide inventory maps- Possible drawbacks from upscaling of training areas
Inventory-based statistical susceptibility zoning
Physically-based susceptibility mapping
Conceptual Example: Deterministic Factor of Safety mapping
Soil parameter map
Silt:φ=30°, T=0,4m2/h
Clay:φ=25°,T=0,04m2/h
Sand:φ=35°, T=8m2/h
Slope angle (°)
0
67
θθγφθγγ
cossintancos)( 2
DDwFS
s
ws −=
γs : material unit weight, γw : water unit weight,w: relative wetness index, D: vertical material depth,φ : material friction angle, θ: topographic slope dip
Conceptual Example: Deterministic Factor of Safety mapping
Strengths:- Based on sound physical models- Capable for predictive landslide analysis- Renders information on landslide hazard
Weaknesses:- Requires high accuracy of input parameters- Predictive models difficult to evaluate- Complex modeling hard to perform at smaller scales
Physically-based susceptibility mapping
Required data
Heuristic analysis: Spatially distributed information on controlling factors, expert knowledge on susceptibility criteria
Inventory Analysis: Landslide inventory maps
Statistical modeling: Thematic factor maps, inventory maps
Physically based modeling: Spatially distributed data on material parameters and thickness; Physical data on triggering conditions
Conclusive remarks
Landslides are local phenomena controlled by a large variety of internal and external factors
Ground condition factors do include both soil and bedrock properties
A wide array of landslide susceptibility assessment approaches do exist and is used on national/regional levels
The choice of appropriate techniques depends on the nature of the problem, the observation scale and data availability
Without landslide inventory data, only speculative and conceptual susceptibility zoning approaches can be conductedBut, at least, these cannot be evaluated
Mapping Units
Grid Cells: Problems to represent continuous datain discrete form
Terrain Units: Subjective delineation of homogeneousterrain areas
Unique condition units: Based on reclassified conditioningfactor maps
Slope Units: Delineation of hydrological boundariesAdministrative Units: Do not reflect environmental changes