A Modeling Approach for Addressing Sensitivity and Uncertainty of Estuarine Greenhouse Gas (CO2 and CH4) Dynamics

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.