Citation:

Christensen, J., H. Golden, L. Alexander, B. Pickard, K. Fritz, C. Lane, M. Weber, R. Kwok, AND M. Keefer. Headwater streams and inland wetlands: Status and advancements of geospatial datasets and maps across the United States. Earth-Science Reviews. Elsevier B.V., Amsterdam, Netherlands, 235:104230, (2022). https://doi.org/10.1016/j.earscirev.2022.104230

Impact/Purpose:

Headwater streams and freshwater wetlands are important to healthy watersheds but aquatic resource managers need to know what datasets and tools are available to them to manage streams and wetlands. We did a review of national and state stream and wetlands geospatial datasets (including spatial extent, resolution and any streamflow calssifications), identified gaps in our existing datasets and reviewed emerging technologies and tools that can help close those gaps. We found a heavy reliance by states on the National Hydrography Dataset and the National Wetlands Inventory. We reviewed LiDAR approaches, remotely sensed imagery, field collection techniques, and modeling techniques to better map stream and wetland extents and streamflow permanence. We concluded with future directions to enhance the science of stream and wetland mapping. The fields of remote sensing, cloud computing, and machine learning are all rapidly advancing and this article illustrates how with focused support, existing national databases can be supported and enhanced by these emerging technologies. The article will be of interest to OW as it seeks for additional geodatasets and emerging tools to support Clean Water Act policy and implementation.

Description:

Headwater streams and inland wetlands provide essential functions that support 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. We conducted a review of existing United States (US) 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 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 for stream extent and duration information. Only eleven states (22%) had additional stream extent information and seven states (14%) provided additional duration information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service's National Wetlands Inventory (NWI) Geospatial Dataset, 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. While machine learning techniques may help to scale-up these LiDAR-derived estimates, challenges related to preprocessing and data workflows remain. 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, especially using multi-platform and multi-temporal machine learning approaches. 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.

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