#Q&A: Global methane emissions soaring, but how much was due to wetlands?

Q. Are methane emissions hard to estimate?
There are many methane sources. In order to draw up a budget you need to add them all up. We can reasonably estimate the contributions of human-caused methane emissions. However, it’s hard to estimate methane emissions from biogenic sources like wetlands, which are estimated to make up 20% to 30% of the global methane emissions budget.
In wetlands, methane is produced from microbial activity. Once it’s produced, there are multiple pathways in which methane is consumed and transported from the soil to the atmosphere: plants, bubbling, and diffusion. All of these processes are uncertain in their own right and putting them together makes it hard to make predictions. Plants, for example, can draw methane out of the soil and directly release it into the atmosphere, bypassing the oxidation step that’s otherwise active at the soil-air interface when land isn’t submerged. It’s a more complicated set of physical and biological processes compared to modeling and predicting carbon dioxide emissions.
It’s also challenging to identify how much land area is under wetlands from satellite images. The coverage of transient wetlands, for instance, can change over a season or over multiple years because of drainage. Also, wetlands often have emergent vegetation, which can complicate remote sensing estimates.
Q. What is your team’s contribution toward making better emission estimates from wetlands?
As part of the Global Carbon Project, there are 13 major modeling centers using 13 independent models to estimate wetland methane emissions, and we are one of those groups. Our model, which is integrated in Department of Energy’s Earth system model, E3SM (Energy Exascale Earth System Model), represents widely distributed wetlands and includes many processes that are relevant to these landscapes. As with other models, variables like temperature, precipitation, and methane emissions data collected continuously from 80 wetland sites that are part of the global FLUXNET network are used to evaluate and improve the model. Within those site-level comparisons, we also include information on wetland type: ferns, swamps, bogs, etc.; vegetation, which is the carbon input into the system; microbial activity; along with estimates of water table depth, which is a strong controller of methane emissions.
This information allows us to evaluate a wide range of processes and interactions that ultimately influence our emission estimates. But these complex biological processes also introduce a large uncertainty range in methane emission predictions. Our aim was to build a model that represents these important processes in a relatively mechanistic way that can be tested directly against observations from the field.
Q. Do we know if some models perform better than others?
It is still not clear which approach is best. But I think there is value in using the full spectrum of models, from the simplest to the most nuanced. Eventually, we are all hoping to improve the predictability of methane emissions from wetlands.
The final emissions reported in the paper is an average of estimates from each of the 13 models.
Q. Are wetlands in certain regions emitting more methane than in others?
There is a large latitudinal gradient in wetland methane emissions. The fluxes are larger in the tropics than in the high latitudes and temperate zones. It’s a lot warmer in the tropics, so you get a lot of biological activity and more production of methane than from the high latitudes where it’s really cold. We’ve estimated annual emissions totaling over 110 million tons from tropical wetlands versus about 10 million tons from the high latitudes.
That pattern is not surprising and has been recognized for a long time. Also, those emissions are natural, so they will continue, as long as we don’t drain the wetlands, which does happen.
Q. Do you expect emissions from wetlands to increase in the future?
Our simulations suggest that methane emissions will continue to increase as the world warms and atmospheric carbon dioxide concentrations increase. Our group is participating in ongoing GCP efforts to synthesize these types of future estimates from several of the global modeling groups.
Q. What are your next steps in improving the estimation capabilities of your current model?
We’re thinking about using machine learning tools to help build relationships between wetland methane emissions and all the factors that we think control these emissions. The input will be emissions data collected at the FLUXNET wetland sites along with other relevant variables—wetland characteristics, vegetation, climate—pertaining to these regions. Once you know the relationship strengths between these variables and methane emissions, you can extrapolate them to other wetland sites for which we don’t have emissions data. Of course, this type of approach will require testing at a subset of sites where benchmarking observations are available to ensure the appropriateness of regional to global extrapolations.
We also are interested in integrating these types of observationally constrained machine-learning models with the more mechanistic models, with the hope of improving the overall predictability of the global representations.