Environmental Controls, Emergent Scaling, and Predictions of Greenhouse Gas (GHG) Fluxes in Coastal Salt Marshes. Issue 7 (28th July 2018) - Abdul‐Aziz, Omar I.; Ishtiaq, Khandker S.; Tang, Jianwu; Moseman‐Valtierra, Serena; Kroeger, Kevin D.; Gonneea, Meagan Eagle; Mora, Jordan; Morkeski, Kate
Abstract: Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO2 ) and methane (CH4 ) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO2 and CH4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May–October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO2 uptake and low CH4 emission, which did not significantly vary with the nitrogen loading gradient (5–126 kg · ha −1  · year −1 ) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4–5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law‐based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash‐Sutcliffe Efficiency = 0.80–0.91). The scaling models are available as a user‐friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment. Key Points: Coastal salt marshes in Cape Cod, Massachusetts, USA, exhibited high net CO2 uptake and low CH4 emission Soil temperature was the strongest driver of the GHG fluxes, which had weak linkages with well water level, soil moisture, and porewater pH Emergent power law‐based scaling models successfully predicted the GHG fluxes from light (PAR), soil temperature, and porewater salinity