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Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management

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Abstract

Watershed management and policies affecting downstream ecosystems benefit from identifying relationships between land cover and water quality. However, different data sources can create dissimilarities in land cover estimates and models that characterize ecosystem responses. We used a spatially balanced stream study (1) to effectively sample development and urban stressor gradients while representing the extent of a large coastal watershed (>4400 km2), (2) to document differences between estimates of watershed land cover using 30-m resolution national land cover database (NLCD) and <1-m resolution land cover data, and (3) to determine if predictive models and relationships between water quality and land cover differed when using these two land cover datasets. Increased concentrations of nutrients, anions, and cations had similarly significant correlations with increased watershed percent impervious cover (IC), regardless of data resolution. The NLCD underestimated percent forest for 71/76 sites by a mean of 11 % and overestimated percent wetlands for 71/76 sites by a mean of 8 %. The NLCD almost always underestimated IC at low development intensities and overestimated IC at high development intensities. As a result of underestimated IC, regression models using NLCD data predicted mean background concentrations of NO3 − and Cl− that were 475 and 177 %, respectively, of those predicted when using finer resolution land cover data. Our sampling design could help states and other agencies seeking to create monitoring programs and indicators responsive to anthropogenic impacts. Differences between land cover datasets could affect resource protection due to misguided management targets, watershed development and conservation practices, or water quality criteria.

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Acknowledgments

Emily Seelen and Joe Bishop assisted with field sampling and laboratory processing of samples. Comments on an earlier draft by Jeff Hollister, Taylor Jarnagin, Betty Kreakie, James Lake, Anthony Olsen, and Glen Thursby are greatly appreciated. NJS research (1) was supported in part by an appointment to the Oak Ridge Institute for Science and Education participant research program supported by an interagency agreement between the United States Environmental Protection Agency (EPA) and Department of Energy and (2) may not necessarily reflect the views of EPA, and no official endorsement should be inferred. Research described in this article has been funded by the EPA, and this manuscript, tracking number ORD-10047, has been reviewed by the Atlantic Ecology Division and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

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Authors and Affiliations

  1. Atlantic Ecology Division, Oak Ridge Institute for Science and Education Fellow c/o Environmental Protection Agency, Narragansett, RI, USA

    Nathan J. Smucker

  2. Atlantic Ecology Division, Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Narragansett, RI, USA

    Anne Kuhn & Jonathan R. Serbst

  3. Raytheon Company, Narragansett, RI, USA

    Michael A. Charpentier

  4. Greater Research Opportunities for Undergraduates Program, University of Puerto Rico c/o Environmental Protection Agency, San Juan, Puerto Rico

    Carlos J. Cruz-Quinones

  5. Mid-continent Ecology Division, Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Duluth, MN, USA

    Colleen M. Elonen, Terri M. Jicha & Brian H. Hill

  6. Louis Calder Center—Biological Field Station and Department of Biological Sciences, Fordham University, Armonk, NY, USA

    Sarah B. Whorley & John D. Wehr

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  1. Nathan J. Smucker
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  2. Anne Kuhn
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Correspondence to Nathan J. Smucker.

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Smucker, N.J., Kuhn, A., Charpentier, M.A. et al. Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management. Environmental Management 57, 683–695 (2016). https://doi.org/10.1007/s00267-015-0629-3

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