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A Spatially Explicit Model for Mapping Headwater Streams
Authors:Periann P Russell  Susan M Gale  Breda Muñoz  John R Dorney  Matthew J Rubino
Institution:1. Division of Water Resources, North Carolina Department of Environment and Natural Resources, Raleigh, North Carolina;2. Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina;3. RTI International, Raleigh, North Carolina;4. Atkins North America, Raleigh, North Carolina;5. North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology, North Carolina State University, Raleigh, North Carolina
Abstract:Headwater streams are the primary sources of water in a drainage network and serve as a critical hydrologic link between the surrounding landscape and larger, downstream surface waters. Many states, including North Carolina, regulate activity in and near headwater streams for the protection of water quality and aquatic resources. A fundamental tool for regulatory management is an accurate representation of streams on a map. Limited resources preclude field mapping every headwater stream and its origin across a large region. It is more practical to develop a model for headwater streams based on a sample of field data that can then be extrapolated to a larger area of interest. The North Carolina Division of Water Quality has developed a cost‐effective method for modeling and mapping the location, length, and flow classification (intermittent and perennial) of headwater streams. We used a multiple logistic regression approach that combined field data and terrain derivatives for watersheds located in the Triassic Basins ecoregion. Field data were collected using a standard methodology for identifying headwater streams and origins. Terrain derivatives were generated from digital elevation models interpolated from bare‐earth Light Detection and Range data. Model accuracies greater than 80% were achieved in classifying stream presence and absence, stream length and perennial stream length, but were not as consistent in predicting intermittent stream length.
Keywords:hydrology  hydrologic model  headwater streams  stream mapping  logistic regression model  LiDAR     GIS     stream regulation
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