Characterizing and classifying urban watersheds with compositional and structural attributes |
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| Authors: | Joseph M Delesantro Jonathan M Duncan Diego Riveros-Iregui Joanna R Blaszczak Emily S Bernhardt Dean L Urban Lawrence E Band |
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| Institution: | 1. Environment, Ecology and Energy Program, University of North Carolina, Chapel Hill, North Carolina, USA;2. Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, Pennsylvania, USA;3. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA;4. Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, USA;5. Department of Biology, Duke University, Durham, North Carolina, USA;6. Nicholas School of the Environment, Duke University, Durham, North Carolina, USA;7. Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA
Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA |
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| Abstract: | Current land-use classifications used to assess urbanization effects on stream water quality date back to the 1980s when limited information was available to characterize watershed attributes that mediate non-point source pollution. With high resolution remote sensing and widely used GIS tools, there has been a vast increase in the availability and precision of geospatial data of built environments. In this study, we leverage geospatial data to expand the characterization of developed landscapes and create a typology that allows us to better understand the impact of complex developed landscapes across the rural to urban gradient. We assess the ability of the developed landscape typology to reveal patterns in stream water chemistry previously undetected by traditional land-cover based classification. We examine the distribution of land-cover, infrastructure, topography and geology across 3876 National Hydrography Dataset Plus catchments in the Piedmont region of North Carolina, USA. From this dataset, we generate metrics to evaluate the abundance, density and position of landscape features relative to streams, catchment outlets and topographic wetness metrics. While impervious surfaces are a key distinguishing feature of the urban landscape, sanitary infrastructure, population density and geology are better predictors of baseflow stream water chemistry. Unsupervised clustering was used to generate a distinct developed landscape typology based on the expanded, high-resolution landscape feature information. Using stream chemistry data from 37 developed headwater catchments, we compared the baseflow water chemistry grouped by traditional land-cover based classes of urbanization (rural, low, medium and high density) to our composition and structure-based classification (a nine-class typology). The typology based on 22 metrics of developed landscape composition and structure explained over 50% of the variation in NO3?-N, TDN, DOC, Cl?, and Br? concentration, while the ISC-based classification only significantly explained 23% of the variation in TDN. These results demonstrate the importance of infrastructure, population and geology in defining developed landscapes and improving discrete classes for water management. |
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| Keywords: | cluster analysis land use landcover non-point source urban water quality management |
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