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Abstract Wetland hydrologic connections to downstream waters influence stream water quality. However, no systematic approach for characterizing this connectivity exists. Here using physical principles, we categorized conterminous US freshwater wetlands into four hydrologic connectivity classes based on stream contact and flowpath depth to the nearest stream: riparian, non-riparian shallow, non-riparian mid-depth and non-riparian deep. These classes were heterogeneously distributed over the conterminous United States; for example, riparian dominated the south-eastern and Gulf coasts, while non-riparian deep dominated the Upper Midwest and High Plains. Analysis of a national stream dataset indicated acidification and organic matter brownification increased with connectivity. Eutrophication and sedimentation decreased with wetland area but did not respond to connectivity. This classification advances our mechanistic understanding of wetland influences on water quality nationally and could be applied globally.
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This content will become publicly available on May 11, 2027
Network Connectivity-based stream classification for the Conterminous United States
Stream classification plays an important role in the study and management of freshwater ecosystems. Many classification schemes exist that focus predominantly on physical habitat, hydrology, and thermal regimes, but few frameworks explicitly include river network connectivity. Because river network connectivity directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed, including connectivity metrics in river classification provides opportunities for advancing riverine research at large spatial extents. We developed a robust framework and dataset that incorporates a network connectivity-based stream classification system for the conterminous United States (NetConUS), utilizing the National Hydrography Dataset Plus version 2 (NHD). Connectivity classes are differentiated by degree centrality, eigen-vector centrality, clustering co-efficient, closeness centrality and betweenness centrality and comprise five types: central streams, peripheral streams, mainstem streams, cluster stream and convergent streams. The streams classification was validated using Bayesian Neural Networks (BNNs) to account for uncertainty in the assignment of network connectivity classes. The dataset captures the structural roles of stream segments within river networks and provides opportunities to include network connectivity metrics alongside geophysical descriptors in analyses of freshwater fauna at the extent of the conterminous US.
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- PAR ID:
- 10683059
- Publisher / Repository:
- Scientific Data
- Date Published:
- Journal Name:
- Scientific Data
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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