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27.11.2015 | Ausgabe 3/2016

Environmental Management 3/2016

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

Zeitschrift:
Environmental Management > Ausgabe 3/2016
Autoren:
Nathan J. Smucker, Anne Kuhn, Michael A. Charpentier, Carlos J. Cruz-Quinones, Colleen M. Elonen, Sarah B. Whorley, Terri M. Jicha, Jonathan R. Serbst, Brian H. Hill, John D. Wehr

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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