4.7 Article

Headwater streams and inland wetlands: Status and advancements of geospatial datasets and maps across the United States

Journal

EARTH-SCIENCE REVIEWS
Volume 235, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.earscirev.2022.104230

Keywords

Headwaters; Inland wetlands; Mapping; Streamflow permanence; CONUS; NHD; NWI; Remote sensing; Field assessments; LiDAR; Dynamic modeling; Machine learning

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This study reviews the geospatial datasets of stream and wetland in the United States and identifies their limitations. It also explores emerging technologies that can potentially improve the estimation, representation, and integration of these datasets.
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 na-tional 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, representa-tion, 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 Na-tional 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, chal-lenges 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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