Presentation on theme: "Spatial classification data are of minimal value to support model representation because the uncertainty associated with parameterization is too high."— Presentation transcript:
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 Horton rarely saturated sometimes saturated Frequently saturated Often saturated Always saturated Subsurface flow Drained areas No 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