Joseph S. Rabaey, Pascal Bodmer, Abigail Bar, Sophia Pushlar, Meredith A. Holgerson
Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
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Introduction
Freshwater ecosystems are one of the largest global sources of methane (CH4), a potent greenhouse gas. Ponds are key contributors to CH4 emissions despite their small size, releasing large yet highly variable amounts of CH4 to the atmosphere. Constructed stormwater ponds represent an often-overlooked source of anthropogenic CH4 emissions, which can greatly increase the greenhouse gas footprint of urban areas.
While the different pathways of CH4 in ponds are well established (Fig. A), studies have largely focused on broad scale correlates of emissions rather than a process-based approach. As a result, the mechanisms influencing variability in pond emissions remain poorly understood, complicating efforts to upscale within local and global carbon budgets.
Research Questions
- How do the different CH4 pathways (e.g. storage, oxidation) shape emission patterns?
- What are drivers of each individual CH4 pathway?
- What are the sources of uncertainty in pond CH4 emissions?
An infographic of the major pathways of methane in ponds. The caption reads: “Figure A. Conceptual diagram showing the major pathways of methane (CH4) in ponds. Numbers within the arrows are the range that each pathway contributed as a percentage of total production for the 15 ponds in this study.”
Methods
In this study, we measured CH4 dynamics in 15 ponds across New York State (NYS). We used an isotopic mass-balance approach to quantify CH4 oxidation rates and partition total CH4 production into its major pathways: storage, oxidation, and emissions (both diffusive and ebullitive).
We calculated total CH4 production as the sum of all pathways and used multiple regression models to look at drivers of each pathway. We specifically included total production in the models for each pathway, to see how drivers impact rates outside of simply increasing CH4 production.
Results
We found that metrics of ecosystem productivity strongly regulated CH4 production (R2 = 0.77), with the best predictors being submerged macrophyte coverage, total phosphorus (TP) concentration, and agricultural area in the catchment (Fig. C). Macrophytes and TP explained much of the variance but were not correlated to each other.
Pond morphology, stratification, and sheltering influenced CH4 storage and eventual emission (Fig. D). Oxidation greatly influenced emissions, but remained difficult to predict and was only correlated with CH4 production.
Six scatterplots display total CH4 production. The 6 variables on the x-axes are sub. macrophyte cover, agricultural area, bottom pH, DOC, total nitrogen, and total phosphorus. The caption reads “Figure C. Drivers of total CH4 production. Regression lines are only shown for correlations with a P-value < 0.1. Dotted lines represent the slope ± SE”
Twelve scatterplots display CH4 diffusion, CH4 ebullition, CH4 oxidation, and CH4 storage. The caption reads: “Figure D. Relationships between total CH4 production (total prod.) and diffusion (Diff.; dark blue), ebullition (Ebu.; light blue), oxidation (Oxi.; orange), and storage (Stor.; violet), as well as the next two most important model variables. All model variable relationships are shown vs. the residuals of a model with total CH4 production.
Discussion
Many studies have used measures of ecosystem productivity (i.e. TP, chla, land cover) to relate to pond CH4 emissions. Here, we show that these productivity measure directly influence CH4 production, while storage and oxidation played a large role in shaping CH4 emissions (Fig. C). The percent contribution of each pathway to total CH4 production varied widely, including oxidation accounting for up to 71% of produced CH4 (Fig. B).
Ultimately, nutrients and system productivity drive CH4 production, while morphology, oxygen regimes, and sheltering influence the fate of produced CH4. CH4 oxidation and storage remain relatively understudied in ponds, and expanding this approach across broader scales could reduce uncertainty in emission estimates. Notably, oxidation was the most difficult pathway to predict, and should be a key priority to further understand variability in pond emissions.
A bar chart displays diffusion, ebullition, oxidation, and storage of CH4 for 15 ponds. The caption reads: Figure B. The rate of total CH4 production in the study ponds, including diffusion (dark blue), ebullition (light blue), oxidation (orange), and storage (violet).”
Acknowledgements
This work was made possible by the NYSDEC Office of Climate Change (NSF DEB Grant 2143449). We would like to thank all the graduate and undergraduate researchers who helped with the fieldwork for this project, including Kathryn Gannon, Caroline Darling, and Mira Lamble.
References
J. A. Rosentreter et al., Nat. Geosci. 14, 225–230 (2021).
K. Calvin et al., “IPCC, 2023: Climate Change 2023: Synthesis Report.” (IPCC, 2023).
M. A. Holgerson, P. A. Raymond, Nature Geosci. 9, 222–226 (2016).
S. Herrero Ortega et al., Glob Change Biol. 25, 4234–4243 (2019).
T. DelSontro et al., Limnol Oceanogr Lett. 3, 64–75 (2018).
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