A Modeling Approach for Addressing Sensitivity and Uncertainty of Estuarine Greenhouse Gas (CO2 and CH4) Dynamics. Issue 6 (1st June 2022)

• Record Type: Journal Article
• Authors: Huang, Peisheng; De Sousa, Eduardo R.; Wells, Naomi S.; Eyre, Bradley D.; Gibbes, Badin; Hipsey, Matthew R.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-01
• Subjects: greenhouse gas emissions -- estuary -- biogeochemical models -- methane -- carbon dioxide -- Brisbane River; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006722
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Estuaries make an important contribution to the global greenhouse gas budget. Yet modeling predictions of carbon dioxide (CO2 ) and methane (CH4 ) emissions from estuaries remain highly uncertain due to both simplified assumptions about the underpinning hydrologic and biologic processes and inadequate data availability to uniquely define parameters related to CO2 and CH4 processes. This study presents a modeling framework to quantify the sensitivity and uncertainty of predicted CO2 and CH4 concentrations and emissions, which is demonstrated through application to a subtropical urban estuary (Brisbane River, Australia). A 3D hydrodynamic‐biogeochemical model was constructed, and calibrated using the model‐independent Parameter ESTimation software (PEST) with field data sets that captured strong gradients of CO2 and CH4 concentrations and emissions along the estuary. The approach refined uncertainty in the estimation of whole‐estuary annual emissions, and enabled us to assess the sensitivity and uncertainty of CO2 and CH4 dynamics. Estuarine CO2 concentrations were most sensitive to uncertainty in riverine inputs, whereas estuarine CH4 concentrations were most sensitive to sediment production and pelagic oxidation. Over the modeled year, variance in the daily fluctuations in carbon emissions from this case‐study spanned the full range of emission rates reported for estuaries around the world, highlighting that spatially or temporally limited sampling regimes could significantly bias estuarine greenhouse gas emission estimates. The combination of targeted field campaigns with the modeling approach presented in this study can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions. Plain Language Summary: Global estuaries are a major source of CO2 emission and play a disproportionate role in global carbon cycling relative to their area. Estuaries are also a source of CH4 that has approximately 34 times the global‐warming potential of CO2 over a 100‐year time period. However, large uncertainties remain in estimating the CO2 and CH4 emissions from estuaries due to limited observations to cover their large spatiotemporal variations, and lack of knowledge of their dynamics in response to the external inputs and internal biogeochemical reactions. In this study, we developed a modeling framework to address the sensitivity and uncertainty of CO2 and CH4 dynamics in the Brisbane River Estuary to both the riverine and wastewater inputs and biogeochemical reaction parameters. We showed that CO2 concentrations were most sensitive to catchment inputs and CH4 concentrations were most sensitive to the rate of sediment production and oxidation in the water. The estimated annual‐total CO2 equivalent emission from Brisbane River Estuary in 2016 was 3, 514 ± 445 tonnes. The combination of targeted field campaigns with the modeling approach can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions. Key Points: Estuarine CO2 is most sensitive to external riverine CO2 inputs Estuarine CH4 is most sensitive to sediment production and oxidation in the water The modeling approach constrained the uncertainty in the estimation of CO2 and CH4 outgassing