Using machine learning to identify and map controls of growing-season carbon dioxide and methane fluxes in the Mackenzie Delta region ...
With climate change, this permafrost carbon pool is at risk of destabilization and emission to the atmosphere. Two field campaigns were conducted in the Mackenzie Delta Region to measure carbon dioxide and methane fluxes with eddy covariance and chamber methods. Illisarvik, a drained thermokarst lak...
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| Format: | Text |
| Language: | English |
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University of British Columbia
2022
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| Online Access: | https://dx.doi.org/10.14288/1.0421939 https://doi.library.ubc.ca/10.14288/1.0421939 |
| Summary: | With climate change, this permafrost carbon pool is at risk of destabilization and emission to the atmosphere. Two field campaigns were conducted in the Mackenzie Delta Region to measure carbon dioxide and methane fluxes with eddy covariance and chamber methods. Illisarvik, a drained thermokarst lake, was studied during the peak growing season in 2016. Fish Island, a low center polygonal peatland, was studied over the 2017 growing season. Half-hourly fluxes were calculated and filtered with quality control tests. Flux footprints were calculated and overlaid on landscape classification maps to estimate the relative flux contributions from different vegetation types and microtopographic features. For both data sets, neural networks (NN) were trained to map flux responses to large sets of potential soil and weather drivers and flux contributions by landscape classification. The NN were pruned to identify the strongest drivers, which is a novel approach that has not been applied to eddy covariance data ... |
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