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Mapping headwater streams and inland wetlands in the US: a review of geospatial datasets and emerging approaches
Citation:
Christensen, J., H. Golden, L. Alexander, K. Fritz, C. Lane, M. Weber, B. Pickard, R. Kwok, AND M. Keefer. Mapping headwater streams and inland wetlands in the US: a review of geospatial datasets and emerging approaches. 2022 Joint Aquatic Sciences Meeting, Grand Rapids, MI, May 14 - 20, 2022.
Impact/Purpose:
Headwater streams and wetlands support healthy watersheds, yet it is unknown what datasets in the US are used for stream-related federal and state policies and what methods can be used to help improve those datasets. We reviewed current federal and state geospatial stream datasets. From this review, we highlight gaps in current datasets to assist policy decisions and encourage the promotion of high resolution stream and wetland mapping at broader scales so these systems can more accurately be incorporated in water resources research and policy decisions.
Description:
Headwater streams and inland wetlands provide essential functions supporting healthy watersheds and downstream waters. However, scientists and aquatic resource managers lack a comprehensive synthesis of national and state stream and wetland geospatial datasets and emerging technologies that can further improve these data. This potentially leads to limited protection and management of these important aquatic resources. We conducted a review of existing federal and state stream and wetland geospatial datasets, focusing on their spatial extent, permanence classifications, and current limitations. We also examined recent peer-reviewed literature (2005-2020) for emerging methods that can potentially improve the estimation, representation, and integration of stream and wetland datasets. We found that federal and state datasets rely heavily on the US Geological Survey’s National Hydrography Dataset (NHD) for stream information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service’s National Wetlands Inventory (NWI), with only two states using non-NWI datasets. Our synthesis revealed that LiDAR-based technologies hold promise for advancing stream and wetland mapping at limited spatial extents, and machine learning techniques may help to scale-up these LiDAR-derived estimates. High-resolution commercial imagery, supported by public imagery and cloud computing, may further aid characterization of the spatial and temporal dynamics of streams and wetlands. Models integrating both stream and wetland dynamics are limited, and field-based efforts must remain a key component in developing improved headwater stream and wetland datasets. Continued financial and partnership support of existing databases is also needed to enhance mapping and inform water resources research and policy decisions.
