Constraining Carbon Dioxide and Methane Fluxes from Diverse Tidal Wetlands: Standardizing Measurements and Analysis Across a Network of Eddy Covariance Sites in North America and Canada
Authors
Patty Oikawa1* (patty.oikawa@csueastbay.edu), Jessica Silberman1, Eduardo Gamez Jr.1, Maiyah Matsumura1, Christopher Gough2, Scott Neubauer2, Lisa Haber2, Sara Tenda2, Karina Schäfer3, Suman Dhakal3, Sara Knox4, Katrina Poppe4, Sarah Russell4, Rodrigo Vargas5, Himal Archarya5, Ellen Stuart- Haëntjens6, Lisamarie Windham-Myers6
Institutions
1California State University–East Bay, Hayward, CA; 2Virginia Commonwealth University, Richmond, VA; 3Rutgers University–Newark, NJ; 4The University of British Columbia, Vancouver, Canada; 5University of Delaware, Newark, DE; 6California Water Science Center, U.S. Geological Survey, Sacramento, CA
Abstract
Tidal wetlands and other blue carbon (C) systems are the strongest long-term C sinks per unit area, yet these ecosystems are not well represented in Earth System Models. A Network of North American Tidal Wetlands: Understanding Through Coordinated Research Activities (NATURA) is a project that unites seven eddy covariance flux sites and aims to improve process-based modeling of these critical ecosystems. Researchers are coupling field measurements, statistical analyses, and experimental mesocosms in a model experiment (ModEx) approach to (1) improve net ecosystem exchange (NEE) partitioning into gross primary production (GPP) and ecosystem respiration (Reco); (2) quantify the influence of nitrate and salinity on GPP and Methane (CH4) fluxes; (3) derive thresholds and responses of C fluxes to non-periodic pulses of salinity and nitrogen (N); and (4) improve biogeochemical models. An analysis comparing NEE partitioning approaches revealed that daytime partitioning deviated significantly from nighttime, artificial neural network (ANN) and stable isotope partitioning approaches. Researchers are currently testing all partitioning approaches across three sites within the network. Researchers have coupled the MEM model with an adapted version of the PEPRMT model (called PEPRMT-Tidal) with increased performance after incorporating nitrate and salinity data to help predict GPP, Reco, and CH4 exchange. The GPP and Reco modules explained on average 59% of the variation in carbon dioxide (CO2) exchange with consistently low model error (normalized RMSE <1). The CH4 module also explained the majority of variance in CH4 emissions (54%), with an average normalized RMSE of 1.15. In Spring 2023, researchers ran mesocosm experiments demonstrating strong decreases in NEE in response to salinity and sulfate pulses followed by rapid resilience. On the other hand, CH4 emissions significantly decreased in response to salinity and sulfate pulses with no quick recovery. Researchers are using these mesocosm data in a ModEx approach to improve modeling of the effects of nitrogen and salinity on GPP and CH4 fluxes.