Abstract. Natural wetlands constitute the largest and most uncertain sourceof methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance datafrom 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available athttps://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).
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Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst
Abstract. Waters impounded behind dams (i.e., reservoirs) areimportant sources of greenhouses gases (GHGs), especially methane (CH4), butemission estimates are not well constrained due to high spatial and temporalvariability, limitations in monitoring methods to characterize hot spot andhot moment emissions, and the limited number of studies that investigatediurnal, seasonal, and interannual patterns in emissions. In this study, weinvestigate the temporal patterns and biophysical drivers of CH4emissions from Acton Lake, a small eutrophic reservoir, using a combinationof methods: eddy covariance monitoring, continuous warm-season ebullitionmeasurements, spatial emission surveys, and measurements of key drivers ofCH4 production and emission. We used an artificial neural network togap fill the eddy covariance time series and to explore the relativeimportance of biophysical drivers on the interannual timescale. We combinedspatial and temporal monitoring information to estimate annualwhole-reservoir emissions. Acton Lake had cumulative areal emission rates of45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m−2 in 2017 and 2018,respectively, or 109 ± 14 and 123 ± 10 Mg CH4 in 2017 and2018 across the whole 2.4 km2 area of the lake. The main differencebetween years was a period of elevated emissions lasting less than 2 weeksin the spring of 2018, which contributed 17 % of the annual emissions inthe shallow region of the reservoir. The spring burst coincided with aphytoplankton bloom, which was likely driven by favorable precipitation andtemperature conditions in 2018 compared to 2017. Combining spatiallyextensive measurements with temporally continuous monitoring enabled us toquantify aspects of the spatial and temporal variability in CH4emission. We found that the relationships between CH4 emissions andsediment temperature depended on location within the reservoir, and we observed a clearspatiotemporal offset in maximum CH4 emissions as a function ofreservoir depth. These findings suggest a strong spatial pattern in CH4biogeochemistry within this relatively small (2.4 km2) reservoir. Inaddressing the need for a better understanding of GHG emissions fromreservoirs, there is a trade-off in intensive measurements of one water bodyvs. short-term and/or spatially limited measurements in many waterbodies. The insights from multi-year, continuous, spatially extensivestudies like this one can be used to inform both the study design andemission upscaling from spatially or temporally limited results,specifically the importance of trophic status and intra-reservoirvariability in assumptions about upscaling CH4 emissions.
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- PAR ID:
- 10399019
- Date Published:
- Journal Name:
- Biogeosciences
- Volume:
- 18
- Issue:
- 19
- ISSN:
- 1726-4189
- Page Range / eLocation ID:
- 5291 to 5311
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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