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Title: Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)

Abstract

This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland.The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).

Authors:
ORCiD logo ; ORCiD logo ; ORCiD logo ; ORCiD logo
  1. University of Toledo; University of Toledo
  2. Ohio State University
  3. University of Toledo
Publication Date:
Research Org.:
Environmental System Science Data Infrastructure for a Virtual Ecosystem; COMPASS-FME
Sponsoring Org.:
U.S. DOE > Office of Science > Biological and Environmental Research (BER)
Subject:
54 ENVIRONMENTAL SCIENCES; Coastal freshwater wetland; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > CARBON DIOXIDE; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > METHANE; EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > BIOGEOCHEMICAL CYCLES; EARTH SCIENCE > BIOSPHERE > VEGETATION > MACROPHYTES; ESS-DIVE CSV File Formatting Guidelines Reporting Format; ESS-DIVE File Level Metadata Reporting Format; ESS-DIVE Location Metadata Reporting Format; Eddy covariance; Terrestrial-aquatic interface; Water elevation change
OSTI Identifier:
2500238
DOI:
https://doi.org/10.15485/2500238

Citation Formats

Tang, Angela Che Ing, Missik, Justine, Bohrer, Gil, and Forbrich, Inke. Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022). United States: N. p., 2025. Web. doi:10.15485/2500238.
Tang, Angela Che Ing, Missik, Justine, Bohrer, Gil, & Forbrich, Inke. Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022). United States. doi:https://doi.org/10.15485/2500238
Tang, Angela Che Ing, Missik, Justine, Bohrer, Gil, and Forbrich, Inke. 2025. "Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)". United States. doi:https://doi.org/10.15485/2500238. https://www.osti.gov/servlets/purl/2500238. Pub date:Thu Jan 16 04:00:00 UTC 2025
@article{osti_2500238,
title = {Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)},
author = {Tang, Angela Che Ing and Missik, Justine and Bohrer, Gil and Forbrich, Inke},
abstractNote = {This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland.The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).},
doi = {10.15485/2500238},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 16 04:00:00 UTC 2025},
month = {Thu Jan 16 04:00:00 UTC 2025}
}