Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks

Reed, David E.; Poe, Jeralyn; Abraha, Michael; Dahlin, Kyla M.; Chen, Jiquan

doi:10.1029/2021jg006363

Title: Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks

Journal Article · 04 August 2021 · Journal of Geophysical Research. Biogeosciences

DOI: https://doi.org/10.1029/2021jg006363 · OSTI ID:1849061

^[1]; Poe, Jeralyn ^[2]; Abraha, Michael ^[3];

^[3];

^[3]

Michigan State Univ., East Lansing, MI (United States). Dept. of Geography, Environment, and Spatial Sciences; Univ. of Science and Arts of Oklahoma, Chickasha, OK (United States). Environmental Science; Michigan State Univ., East Lansing, MI (United States)
Michigan State Univ., East Lansing, MI (United States). Dept. of Geography, Environment, and Spatial Sciences; Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing, and Cyber Systems
Michigan State Univ., East Lansing, MI (United States). Dept. of Geography, Environment, and Spatial Sciences

The eddy covariance (EC) method is one of the most widely used approaches to quantify surface-atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land-cover type sites of corn, restored-prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land-cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (R_eco), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE, R_eco, and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums of R_eco and GPP fluxes without needing the constraint of similar land-cover-type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land-cover types across the landscape.

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Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: FC02-07ER64494; SC0018409

OSTI ID:: 1849061

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Journal Issue: 8 Vol. 126; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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