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Abstract
The detailed, spatially explicit mapping of fluvial landscapes is fundamental to river science. However, data models capable of incorporating system variability are largely lacking from scientific literature. This research examines a reach of the Naches River, a wandering gravel-bed river located in central Washington State. Stream gauge records are used to calculate flow probabilities, and River2D, a two-dimensional hydrodynamic model, is combined with a LiDAR data set to model flows and compute spatially distributed process variables (e.g. shear stress and stream power). In a geographic information system, fuzzy logic is used to model flow transition zones. The resulting data model, through its incorporation of system spatial and temporal variability, provides an alternative to the static classifications traditionally employed in fluvial research.
Acknowledgements
The project described in this paper was supported by the Cooperative State Research, Education, and Extension Service (CSREES), an agency within the US Department of Agriculture CSREES of the USDA (http://www.csrees.usda.gov/), who fund the National Consortium for Rural Geospatial Innovations in America (RGIS) (http://www.ruralgis.org/). Additionally, this research was partially supported by the Research Fund of Central Washington University, Ellensburg, Washington.