Skip to Main Content
Comparing methods for partitioning a decade of carbon dioxide and water vapor fluxes in a temperate forestAuthor(s): Benjamin N. Sulman; Daniel Tyler Roman; Todd M. Scanlon; Lixin Wang; Kimberly A. Novick
Source: Agricultural and Forest Meteorology
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
Download Publication (2.0 MB)
DescriptionThe eddy covariance (EC) method is routinely used to measure net ecosystem fluxes of carbon dioxide (CO2) and evapotranspiration (ET) in terrestrial ecosystems. It is often desirable to partition CO2 flux into gross primary production (GPP) and ecosystem respiration (RE), and to partition ET into evaporation and transpiration. We applied multiple partitioning methods, including the recently-developed flux variance similarity (FVS) partitioning method, to a ten-year record of ET and CO2 fluxes measured using EC at Morgan Monroe State Forest, a temperate, deciduous forest located in south-central Indiana, USA. While the FVS method has previously been demonstrated in croplands and grasslands, this is the first evaluation of the method in a forest. CO2 fluxes were partitioned using nonlinear regressions, FVS, and sub-canopy EC measurements. ET was partitioned using FVS and sub-canopy EC measurements, and sub-canopy potential evapotranspiration was calculated as an additional constraint on forest floor evaporation. Leaf gas exchange measurements were used to parameterize a model of water use efficiency (WUE) necessary for the FVS method. Scaled leaf gas exchange measurements also provided additional independent estimates of GPP and transpiration. There was good agreement among partitioning methods for transpiration and GPP, which also agreed well with scaled leaf gas exchange measurements. There was higher variability among methods for RE and evaporation. The sub-canopy flux method yielded lower estimates of evaporation and RE than FVS and lower estimates of RE than the nonlinear regression method, likely due to the exclusion of flux sources within the canopy but above the top of the sub-canopy tower for the sub-canopy flux method. Based on a sensitivity test, FVS flux partitioning was moderately sensitive to errors in WUE values, and underestimates of WUE significantly reduced the rate at which the algorithm was able to produce a physically valid solution. FVS partitioning has unique potential for retroactive ET partitioningat EC sites, because it relies on the same continuous measurements as EC and does not require additionalspecialized equipment. FVS also has advantages for partitioning CO2 fluxes, since it does not rely on the mechanistic assumptions necessary for the commonly used nonlinear regression technique.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationSulman, Benjamin N.; Roman, D. Tyler; Scanlon, Todd M.; Wang, Lixin; Novick, Kimberly A. 2016. Comparing methods for partitioning a decade of carbon dioxide and water vapor fluxes in a temperate forest. Agricultural and Forest Meteorology. 226-227: 229-245.
KeywordsCO2 flux, Ecohydrology, Eddy covariance, Evapotranspiration, Flux partitioning, Water use efficiency
- Short-term impacts of nutrient manipulations on leaf gas exchange and biomass partitioning in contrasting 2-year-old Pinus taeda clones during seedling establishment
- Effects of urbanization on watershed evapotranspiration and Its components in southern China
- Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China
XML: View XML