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High-frequency Data Integration for Landscape Model Calibration of Carbon Fluxes Across Diverse Tidal Marshes

Active Dates 9/1/2021-8/31/2024
Program Area Environmental Systems Science
Project Description
Terrestrial Aquatic Interfaces (TAIs), and tidal wetlands in particular, store large amounts of carbon yet are not well represented in Earth System Models (ESMs). Predictions of carbon cycling and greenhouse gas (GHG) emissions in tidal wetlands are highly uncertain. Eddy covariance (EC) towers provide ecosystem-scale GHG flux data at a temporal resolution (every 30min) that is helpful for parameterizing and improving mechanistic realism in ESMs. We propose to use a network of eddy covariance towers and standardized ancillary data streams, along with mesocosm experiments and statistical analyses, across diverse tidal wetlands of North America to develop and improve biogeochemical modeling at the TAI. Our overarching objective is to improve understanding and process-based modeling of gross primary productivity (GPP) and CH4 emission responses, both non-linear and asynchronous, to stressors including plant inundation, disturbance, salinity and nitrogen loading.

Observational and Experimental Measurements and Analyses

Our network of 7 eddy covariance towers will measure net ecosystem exchange of CO2 and CH4 along with critical ancillary variables including water table height, plant inundation, porewater and tidal channel salinity and nitrate, and soil respiration measurements. First, we will develop new algorithms for partitioning net ecosystem exchange measurements of CO2 (NEE) into GPP and ecosystem respiration (Reco) and validate the new algorithms using stable isotope partitioning. Using the flux and ancillary data streams across the tower network, we will use statistical analyses (i.e. piecewise regressions, wavelets and information theory) to identify thresholds and lag responses between GPP and CH4 emissions and changes in plant inundation, salinity and nitrate across multiple temporal scales (e.g. diel-interannual). Thresholds and non-linear or asynchronous responses detected in statistical analyses will be targeted using mesocosm experiments where inundation, salinity and nitrate pulses will be studied under controlled conditions.

Biogeochemical Model Development

The observational and experimental data and statistical analyses will be used to inform model structural improvements of a tidal wetland biogeochemical model MEM-PEPRMT. MEM-PEPRMT will be parameterized using a Model-Data Fusion approach. The CH4 submodule of MEM-PEPRMT will be compared with a machine learning model built using CH4 emission data. MEM-PEPRMT model performance will be evaluated against the validation dataset (a subset of sites not included in model parameterization) and the machine learning model. Finally, the improved MEM-PEPRMT model will be compared with PFLOTRAN-E3SM at an independent site in the Chesapeake Bay, an ESM that has recently been adapted for improved performance in tidal wetlands. This comparison will help inform how model structure and structural error in ESMs can be improved and quantified, respectively. Our research will improve predictive modeling of GPP and CH4 by ESMs in tidal marshes, a critical but poorly constrained TAI that plays a disproportionately large role in soil carbon storage.
Award Recipient(s)
  • California State University East Bay Foundation (PI: Oikawa, Patricia)
  • U.S. Geological Survey (PI: Windham-Myers, Lisamarie)