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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Livestock, Forage and Pasture Management Research Unit » Research » Publications at this Location » Publication #411960
Research Project: Integrated Research to Enhance Forage and Food Production from Southern Great Plains Agroecosystems

Location: Livestock, Forage and Pasture Management Research Unit

Title: Exponential or unimodal relationships between nighttime ecosystem respiration and temperature at the eddy covariance flux tower sites

Author
item MENG, CHENG - University Of Oklahoma
item XIAO, XIANGMING - University Of Oklahoma
item NEWMAN, GREGORY - University Of Oklahoma
item Wagle, Pradeep
item ZHANG, CHENCHEN - University Of Oklahoma
item PAN, LI - University Of Oklahoma
item PAN, BAIHONG - University Of Oklahoma
item QIN, YUANWEI - University Of Oklahoma

Submitted to: Ecology Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/8/2024
Publication Date: 10/10/2024
Citation: Meng, C., Xiao, X., Newman, G., Wagle, P., Zhang, C., Pan, L., Pan, B., Qin, Y. 2024. Exponential or unimodal relationships between nighttime ecosystem respiration and temperature at the eddy covariance flux tower sites. Ecology Letters. 27(10). Article 14532. https://doi.org/10.1111/ele.14532.
DOI: https://doi.org/10.1111/ele.14532

Interpretive Summary: Ecosystem respiration (Reco - release of CO2 by the ecosystem) is a key determinant of terrestrial carbon balance. Currently, most biogeochemical models calculate Reco by using exponential-type temperature equations, based on the assumption Reco increases with rising temperatures. This study analyzed nighttime net ecosystem CO2 exchange (NEEnight or nighttime Reco) data over years from 195 FLUXNET2015 sites and re-evaluated the responses of NEEnight to nighttime air (Tair) and soil (Tsoil) temperature. The study found unimodal relationships (higher rates of NEE at moderate temperatures compared to low or high temperatures) between NEEnight and Tair at 77 sites, between NEEnight and Tsoil at 82 sites, and between NEEnight and both Tair and Tsoil at 66 sites. Using the exponential-type instead of unimodal equations significantly overestimated NEEnight (by around 200%) for the highest temperatures measured. The exponential models predict NEEnight increases with temperature, even beyond the optimum temperature range. The results suggest that using unimodal equations instead of exponential-type equations in biogeochemical models could significantly improve accuracy and reduce uncertainty of ecosystem respiration estimates in terrestrial carbon budgets.

Technical Abstract: Ecosystem respiration (Reco) is a key determinant of terrestrial carbon balance (sink or source) and is affected substantially by air (Tair) and soil (Tsoil) temperatures. Most, if not all, biogeochemical models currently calculate Reco using exponential-type temperature equations, predicting an increase in Reco with rising temperatures across the entire range of temperatures. We analyzed nighttime net ecosystem CO2 exchange (NEEnight or nighttime Reco) data from 195 FLUXNET2015 sites and re-evaluated the responses of NEEnight to nighttime Tair and Tsoil. Site-specific optimum temperature (Topt-site) parameters were estimated at these sites. We observed unimodal relationships (i.e., higher rates of NEE at moderate temperatures than at low or high temperatures) between NEEnight and Tair at 77 sites, between NEEnight and Tsoil at 82 sites, and between NEEnight and both Tair and Tsoil at 66 sites. We further assessed the impact of using exponential-type or unimodal equations on NEEnight. The results showed that the exponential-type equations significantly overestimated (by around 200%) NEEnight at the sites highest measured temperatures. The overestimation is due to these models predicting a continuous rise of NEEnight with increasing temperature even when the temperature exceeds the optimum temperature range. Therefore, adopting unimodal equations instead of exponential-type equations in biogeochemical models may significantly improve accuracy and reduce uncertainty of ecosystem respiration estimates.