Files and links (1)
CC BY V4.0, Open Access
Journal article
Estimating Drivers and Pathways for Hydroelectric Reservoir Methane Emissions Using a New Mechanistic Model
Journal of geophysical research. Biogeosciences, Vol.127(8)
08/01/2022
Abstract
Methane is an important greenhouse gas that is naturally produced in lake and reservoir sediment, among other sources. Hydroelectric power reservoirs produce renewable energy, yet also emit methane at their surfaces, and from turbines and downstream reaches. To better understand drivers and pathways of methane emissions, we have developed a new mechanistic model for methane emissions as a function of carbon inputs, chemical decomposition, and physical processes. Results also show that downstream methane emissions have the potential to exceed surface emissions if turbines pull from stratified, anoxic waters. Large uncertainties remain in model inputs, and future work should focus on improved understanding of carbon loading to reservoirs, as well as decomposition rates and turbine intake depths.
Metrics
1 File views/ downloads
14 Record Views
Details
- Title
- Estimating Drivers and Pathways for Hydroelectric Reservoir Methane Emissions Using a New Mechanistic Model
- Creators
- Kyle B. Delwiche - Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley CA USAJohn HarrisonJoannes D Maasakkers - SRON Netherlands Institute for Space Research Leiden The NetherlandsMelissa P. Sulprizio - School of Engineering and Applied Science Harvard University Cambridge MA USAJohn Worden - Jet Propulsion Laboratory California Institute for Technology Pasadena CA USADaniel J. Jacob - School of Engineering and Applied Science Harvard University Cambridge MA USAElsie M. Sunderland - School of Engineering and Applied Science Harvard University Cambridge MA USA
- Publication Details
- Journal of geophysical research. Biogeosciences, Vol.127(8)
- Academic Unit
- Environment, School of the (CAS)
- Grants
- DISES, 2109259, National Science Foundation (United States, Alexandria) - NSF
- Identifiers
- 99900922133601842
- Language
- English
- Resource Type
- Journal article