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Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.
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Science to Commerce: A Commercial-Scale Protocol for Carbon Trading Applied to a 28-Year Record of Forest Carbon Monitoring at the Harvard Forest
Forest carbon sequestration offset protocols have been employed for more than 20 years with limited success in slowing deforestation and increasing forest carbon trading volume. Direct measurement of forest carbon flux improves quantification for trading but has not been applied to forest carbon research projects with more than 600 site installations worldwide. In this study, we apply carbon accounting methods, scaling hours to decades to 28-years of scientific CO2 eddy covariance data for the Harvard Forest (US-Ha1), located in central Massachusetts, USA, establishing commercial carbon trading protocols and applications for similar sites. We illustrate and explain transactions of high-frequency direct measurement for CO2 net ecosystem exchange (NEE, gC m−2 year−1) that track and monetize ecosystem carbon dynamics in contrast to approaches that rely on forest mensuration and growth models. NEE, based on eddy covariance methodology, quantifies loss of CO2 by ecosystem respiration accounted for as an unavoidable debit to net carbon sequestration. Retrospective analysis of the US-Ha1 NEE times series including carbon pricing, interval analysis, and ton-year exit accounting and revenue scenarios inform entrepreneur, investor, and landowner forest carbon commercialization strategies. CO2 efflux accounts for ~45% of US-Ha1 NEE, or an error of ~466% if excluded; however, the decades-old coupled human and natural system remains a financially viable net carbon sink. We introduce isoflux NEE for t13C16O2 and t12C18O16O to directly partition and quantify daytime ecosystem respiration and photosynthesis, creating new soil carbon commerce applications and derivative products in contrast to undifferentiated bulk soil carbon pool approaches. Eddy covariance NEE methods harmonize and standardize carbon commerce across diverse forest applications including, a New England, USA regional eddy covariance network, the Paris Agreement, and related climate mitigation platforms.
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- Award ID(s):
- 1832210
- PAR ID:
- 10213481
- Date Published:
- Journal Name:
- Land
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2073-445X
- Page Range / eLocation ID:
- 163
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/land10020163
