2014•JAWRA Journal of the American Water Resources AssociationRequires access

A Spatially Explicit Model for Mapping Headwater Streams

Periann Russell, Susan Marie Gale, Breda Muñoz, John R. Dorney, Matthew J. Rubino

Open publisher page 30 citations

Abstract

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.

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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.

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Available abstract

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.

Key concepts: STREAMS, Perennial stream, Ecoregion, Hydrology (agriculture), Terrain, Environmental science, Digital elevation model, Field (mathematics)

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