Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling
| Autor: | Will Barnett, Michelle C. Platz, Alexander Hall, Jake J. Beaulieu, David A. Balz, Karen M. White, Sarah Waldo |
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| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
geography Watershed geography.geographical_feature_category 010504 meteorology & atmospheric sciences Ecology Land use Paleontology Soil Science Sampling (statistics) Forestry Aquatic Science Atmospheric sciences 01 natural sciences Methane Sink (geography) Article chemistry.chemical_compound chemistry Agricultural land Greenhouse gas Carbon dioxide Environmental science 0105 earth and related environmental sciences Water Science and Technology |
| Zdroj: | J Geophys Res Biogeosci |
| ISSN: | 2169-8953 |
| Popis: | Estimating carbon dioxide (CO(2)) and methane (CH(4)) emission rates from reservoirs is important for regional and national greenhouse gas inventories. A lack of methodologically consistent data sets for many parts of the world, including agriculturally intensive areas of the United States, poses a major challenge to the development of models for predicting emission rates. In this study, we used a systematic approach to measure CO(2) and CH(4) diffusive and ebullitive emission rates from 32 reservoirs distributed across an agricultural to forested land use gradient in the United States. We found that all reservoirs were a source of CH(4) to the atmosphere, with ebullition being the dominant emission pathway in 75% of the systems. Ebullition was a negligible emission pathway for CO(2), and 65% of sampled reservoirs were a net CO(2) sink. Boosted regression trees (BRTs), a type of machine learning algorithm, identified reservoir morphology and watershed agricultural land use as important predictors of emission rates. We used the BRT to predict CH(4) emission rates for reservoirs in the U.S. state of Ohio and estimate they are the fourth largest anthropogenic CH(4) source in the state. Our work demonstrates that CH(4) emission rates for reservoirs in our study region can be predicted from information in readily available national geodatabases. Expanded sampling campaigns could generate the data needed to train models for upscaling in other U.S. regions or nationally. |
| Databáze: | OpenAIRE |
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