spatial classification data are of minimal value to support model representation because the...
Post on 28-Mar-2015
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Spatial classification data are of minimal value to support model representation
because the uncertainty associated with parameterization is too high.
Spatial classification data are of minimal value to support model representation
because the uncertainty associated with parameterization is too high.
What are these data? Purpose: give
insight to processes; informs us of organization• Overlay analysis
Mapable features• Soil, veg, geology,
snowcover• Geophysics• Remote sensing
Streams
Soils
Hydrography
Channels
Terrain Surfaces
Rainfall Response
Digital Orthophotos
Dominant Runoff Processes
Hortonrarely saturatedsometimes saturatedFrequently saturatedOften saturatedAlways saturatedSubsurface flowDrained areasNo runoff
Weiler
Useful when…
Uncertainty is realized: translation from qualitative to quantitative is not abused • Uncertainty in spatial delineation• Interpretation (subjective or expert-based)• Do not rely on absolute numbers (statistical
distributions, classes)
Describes storage & response Ground truth (validate); evaluate density
needed for characterization
Key considerations
Topology is important Provides insight & guides
conceptualization Mapping is scale dependent and lumps
or splits units. May lose information (could be good or bad)
Index changes: datasets to quantify & assess land-use changes
Final points
Should be a first step in any study of catchment
Tool for classification Modeling (development / validation) Perhaps field mapping skills are lost in
the recent generation of hydrologists Should be thinking about training
students to recognize features, realize uncertainty, and guide on proper usage
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