Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems
Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domain...
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Online Access: | https://doi.org/10.3389/fenvs.2022.939238 https://doaj.org/article/47d53d2e74c4450b8614d64f250cd11c |
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ftdoajarticles:oai:doaj.org/article:47d53d2e74c4450b8614d64f250cd11c 2023-05-15T13:09:13+02:00 Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems Yihui Wang Fengming Yuan Kyle A. Arndt Jianzhao Liu Liyuan He Yunjiang Zuo Donatella Zona David A. Lipson Walter C. Oechel Daniel M. Ricciuto Stan D. Wullschleger Peter E. Thornton Xiaofeng Xu 2022-07-01T00:00:00Z https://doi.org/10.3389/fenvs.2022.939238 https://doaj.org/article/47d53d2e74c4450b8614d64f250cd11c EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fenvs.2022.939238/full https://doaj.org/toc/2296-665X 2296-665X doi:10.3389/fenvs.2022.939238 https://doaj.org/article/47d53d2e74c4450b8614d64f250cd11c Frontiers in Environmental Science, Vol 10 (2022) methane footprint upscaling landscape scale CLM-microbe Environmental sciences GE1-350 article 2022 ftdoajarticles https://doi.org/10.3389/fenvs.2022.939238 2022-12-30T22:54:24Z Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, ... Article in Journal/Newspaper Alaska North Slope Arctic north slope Tundra Alaska Directory of Open Access Journals: DOAJ Articles Arctic Frontiers in Environmental Science 10 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
methane footprint upscaling landscape scale CLM-microbe Environmental sciences GE1-350 |
spellingShingle |
methane footprint upscaling landscape scale CLM-microbe Environmental sciences GE1-350 Yihui Wang Fengming Yuan Kyle A. Arndt Jianzhao Liu Liyuan He Yunjiang Zuo Donatella Zona David A. Lipson Walter C. Oechel Daniel M. Ricciuto Stan D. Wullschleger Peter E. Thornton Xiaofeng Xu Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
topic_facet |
methane footprint upscaling landscape scale CLM-microbe Environmental sciences GE1-350 |
description |
Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, ... |
format |
Article in Journal/Newspaper |
author |
Yihui Wang Fengming Yuan Kyle A. Arndt Jianzhao Liu Liyuan He Yunjiang Zuo Donatella Zona David A. Lipson Walter C. Oechel Daniel M. Ricciuto Stan D. Wullschleger Peter E. Thornton Xiaofeng Xu |
author_facet |
Yihui Wang Fengming Yuan Kyle A. Arndt Jianzhao Liu Liyuan He Yunjiang Zuo Donatella Zona David A. Lipson Walter C. Oechel Daniel M. Ricciuto Stan D. Wullschleger Peter E. Thornton Xiaofeng Xu |
author_sort |
Yihui Wang |
title |
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
title_short |
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
title_full |
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
title_fullStr |
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
title_full_unstemmed |
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems |
title_sort |
upscaling methane flux from plot level to eddy covariance tower domains in five alaskan tundra ecosystems |
publisher |
Frontiers Media S.A. |
publishDate |
2022 |
url |
https://doi.org/10.3389/fenvs.2022.939238 https://doaj.org/article/47d53d2e74c4450b8614d64f250cd11c |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Alaska North Slope Arctic north slope Tundra Alaska |
genre_facet |
Alaska North Slope Arctic north slope Tundra Alaska |
op_source |
Frontiers in Environmental Science, Vol 10 (2022) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fenvs.2022.939238/full https://doaj.org/toc/2296-665X 2296-665X doi:10.3389/fenvs.2022.939238 https://doaj.org/article/47d53d2e74c4450b8614d64f250cd11c |
op_doi |
https://doi.org/10.3389/fenvs.2022.939238 |
container_title |
Frontiers in Environmental Science |
container_volume |
10 |
_version_ |
1766167249464328192 |