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