Natural and anthropogenic landscape factors including climate and human land uses operate over large spatial extents to affect aquatic systems in a given location. Based in part on this understanding, freshwater ecologists incorporate a holistic view of freshwater systems that includes landscapes drained by waterbodies (Blanchet et al.2009; Brown et al.1996; Crosbie et al.2012; Gudmundsson et al.2012; Haddeland et al.2011). This view is acknowledged as a "landscape approach," and numerous studies have shown how hydrologic, thermal, chemical, and biological properties of freshwater systems are influenced by landscape characteristics of their catchments (Allan2004). Hydrologists and engineers also acknowledge the influence of catchment characteristics as shown by the prevalence of basin-scale initiatives focused on freshwater systems, with examples including storm water management efforts, floodplain delineation, and development of nonpoint source pollution control strategies (e.g., Sprague and Gronberg2012). Similarly, natural resource managers charged with conserving and protecting freshwaters increasingly incorporate a landscape perspective into management activities, expanding a historically site-focused view to address basin- or regional-scale influences on freshwater habitats (Palmer et al.2008; Poiani et al.2000).
Accounting for landscape-scale influences on aquatic systems has been facilitated through data and approaches developed with Geographic Information Systems (GIS). With GIS, measured (i.e., by satellites, by census) or modelled estimates of various landscape information can be attributed within spatially-explicit units such as catchments of freshwater systems. For instance, high-resolution coverages of landscape features like vegetation and/or soil allow for understanding spatially-explicit controls on catchment hydrology. Future and current climate data may also be mapped or modelled to differentially characterize influences across catchments. Also, mapped locations of human land uses and anthropogenic disturbances allow managers and decision makers to evaluate and prioritize management actions across large regions to improve and protect aquatic habitats. Such work is being conducted by multiple local, state, and federal organizations and initiatives throughout the United States, with examples of federal agencies working over large extents including the U.S. Fish and Wildlife Service (e.g., Landscape Conservation Cooperativeshttp://www.fws.gov/landscape-conservation/lcc.html) and US Geological Survey (e.g., Aquatic GAP Programhttp://gapanalysis.usgs.gov/, Climate Science Centershttp://www.doi.gov/csc/index.cfm).
Despite the importance of landscape-scale studies to management efforts for freshwater systems, such studies are challenged by the need for summarizing and synthesizing information over large areas. One contributing factor stems from the historical lack in consistency in describing discrete river reaches and their catchments over large areas. This challenge, however, is being addressed in part by development of extensive coverages of river networks (e.g. NHD,http://nhd.usgs.gov/), as well as by descriptions of spatial frameworks that incorporate standard definitions of rivers and catchments for analysis (i.e., Wang et al.2011, Sowa and Annis2007). Another contributing factor is the dendritic nature of river networks. Morphologically analogous to a tree, river systems accumulate water and substances from upstream tributaries and their respective subcatchments, yet the dendritic form of rivers network may lead to difficulties in summarizing and accounting for these influences. Examples of such upstream influences include numbers of point source pollutant sites occurring along a river network as well as nonpoint source pollutants (e.g., excess nutrients) drained from agricultural lands within river catchments. With GIS, such landscape information can be represented as point locations along river networks, or polygons or grid coverages over catchments. While the upstream information for one given stream location can be attributed and summarized easily, iteratively generating upstream summaries of such information for every stream location in a network throughout a large region can result in processing challenges, which can render the process unwieldy, exceeding typically-accessible computer processing capabilities. A further complication exists for braided river channels. Braided channels often occur near river mouths of large river basins, where stream power may increase, width to depth ratios may increase, and/or the amount and type of bedload may increase. In braided streams, stream channels become divided by multiple small bars or islands, and upstream summary of information requires explicit characterization of all upstream fluvial pathways. When river networks incorporate waterways that are braided, accounting for multiple pathways complicates the upstream summarization process, leading to various inaccuracies in aggregation of upstream information (insert within Figure 1). These challenges are exacerbated when landscape-scale studies for freshwater systems attempt to incorporate multiple landscape information layers. Summarizing information from multiple layers for every stream throughout a large region becomes a tremendous workload requiring substantial processing time.
To address the challenges of summarizing landscape information within river systems throughout large regions and to accurately summarize the information throughout braided river networks, we developed an approach to acquire summaries of upstream landscape information for every stream in a river network, including networks with braided channels. In applying this approach, we have confirmed accurate and consistent summaries of information over very large regions, including the conterminous Untied States. This approach can be applied to any river coverage with network topology defined and can include summary of landscape information from within catchments or from the river network itself. This paper presents detailed information on this approach and offers suggestions for applying it to river networks of interest.
Requirements for the stream network layer
Three requirements are necessary to apply our approach to acquire upstream summaries of landscape information from throughout river networks and their catchments. First, the stream network must be available in a digital geospatial format, referred to here as a digital stream networks. For our approach, digital stream networks can be represented in one of two types of vector mapping layers (Figure 2). One type includes a polyline layer that delineates the stream network including headwaters, tributaries, mainstems, and line junctions characterising points at which these fluvial bodies intersect. An example is the National Hydrography Dataset (NHD,http://nhd.usgs.gov/) for the United States. The second option is a polygon layer representing areas within a stream network that drain to specific sections of the streams. An example is the layer of functional elementary catchments (FECs) within European catchments and Rivers network system (Ecrins) (http://www.eea.europa.eu/data-and-maps/data/european-catchments-and-rivers-network). Some digital stream networks include both polyline and polygon layers, and one example is the National Hydrography Dataset Plus Version 1 (NHDPlusV1,http://www.horizon-systems.com/NHDPlus/NHDPlusV1_home.php) for the conterminous United States. These digital stream networks provide a spatial framework in linking geospatial and landscape information (e.g. climate, soil, landuse) to catchments and reaches of the represented river network. The second requirement for our approach is that these polylines or polygons are broken into discrete units, such as stream segments or drainage areas of digital stream networks (Figure 2). Each unit in the network must be assigned a unique identifier. These unique and discrete units are referred to as stream units hereafter, and spatial information can be attributed and associated to these units. Finally, the third requirement is a key piece of information that describes network topology. Each stream unit needs an attribute that indicates the identifiers of the immediately upstream units. Figure 2a shows two stream units S2 and S3 that are the immediate upstream of the stream unit S1, and Figure 2b shows two stream units D2 and D3 that are the immediate upstream of stream unit D1. Both of the upstream units (i.e. S2 and S3, or D2 and D3) need to be indicated as occurring above unit S1 or D1, respectively. Identifying upstream units of a given stream unit requires knowledge of the flow direction within a stream network, and such information can be generated using elevation maps and GIS processing steps if it is not already incorporated into an existing digital stream network dataset. This information on network topology is essential for describing where a stream unit is located in a network. It stores the spatial relationship among stream units, ultimately allowing us to rebuild the context of the stream network to incorporate into the process of acquiring upstream summaries.