Evidence map›Paper›PMID 36970305›Full record

ArticleEarth-science reviews2022

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

Jay R Christensen, Heather E Golden, Laurie C Alexander, Brian R Pickard, Ken M Fritz, Charles R Lane, Marc H Weber, Rose M Kwok, Madeline N Keefer

Open access · greenAbstract read
In one paragraph

Article in Earth-science reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.0field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Non-perennial segments in river networks.Nature reviews. Earth & environment · 2023
    Article
  5. Mapping global non-floodplain wetlands.Earth system science data · 2023
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Jay R ChristensenCenter for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
Heather E GoldenCenter for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
Laurie C AlexanderCenter for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Washington DC 20460 USA Region 10, US Environmental Protection Agency, Portland, OR 97205, USA.
Brian R PickardTetraTech Inc, Research Triangle Park, NC 27709 USA.
Ken M FritzCenter for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
Charles R LaneCenter for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Athens, GA, 30605 USA.
Marc H WeberCenter for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Corvallis, OR 97333 USA.
Rose M KwokOffice of Wetlands, Oceans, and Watersheds, Office of Water, US Environmental Protection Agency, Washington, DC 20460, USA.
Madeline N KeeferTetraTech Inc, Research Triangle Park, NC 27709 USA.
Environmental Protection Agency · USTriangle · US

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

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.

Indexed as

CONUSdynamic modelingfield assessmentsheadwatersinland wetlandsLiDARmachine learningmappingNHDNWIremote sensingstreamflow permanence

Identifiers

PMID36970305
PMCPMC10031651
OpenAlexW4307819177

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.