High temporal frequency measurements of greenhouse gas emissions from soils

Abstract

Abstract. Carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) are the most important anthropogenic greenhouse gases (GHGs). Variation in soil moisture can be very dynamic, and it is one of the dominant factors controlling the net exchange of these three GHGs. Although technologies for high-frequency, precise measurements of CO 2 have been available for years, methods for measuring soil fluxes of CH 4 and N 2 O at high temporal frequency have been hampered by lack of appropriate technology for in situ real-time measurements. A previously developed automated chamber system for measuring CO 2 flux from soils was configured to run in line with a new quantum cascade laser (QCLAS) instrument that measures N 2 O and CH 4 . Here we present data from a forested wetland in Maine and an agricultural field in North Dakota, which provided examples of both net uptake and production for N 2 O and CH 4 . The objective was to provide a range of conditions in which to run the new system and to compare results to a traditional manual static-chamber method.The high-precision and more-than-10-times-lower minimum detectable flux of the QCLAS system, compared to the manual system, provided confidence in measurements of small N 2 O uptake in the forested wetland. At the agricultural field, the greatest difference between the automated and manual sampling systems came from the effect of the relatively infrequent manual sampling of the high spatial variation, or "hot spots", in GHG fluxes. Hot spots greatly influenced the seasonal estimates, particularly for N 2 O, over one 74-day alfalfa crop cycle. The high temporal frequency of the automated system clearly characterized the transient response of all three GHGs to precipitation and demonstrated a clear diel pattern related to temperature for GHGs. A combination of high-frequency automated and spatially distributed chambers would be ideal for characterizing hot spots and "hot moments" of GHG fluxes.

A simple calculation algorithm to separate high-resolution CH<sub>4</sub> flux measurements into ebullition- and diffusion-derived components

“…Improvements in measurement techniques, particularly by using high-resolution gas analysers (e.g. eddy covariance (EC) measurements), allow for high-temporal-resolution records of CH 4 emissions (Schrier-Uijl et al, 2011;Wille et al, 2008). Recently, a growing number of experimental GHG studies have employed automatic chambers (ACs) (Koskinen et al, 2014;Lai et al, 2014;Ramos et al, 2006), which can provide flux data with an enhanced temporal resolution and capture short-term temporal (e.g.…”

“…In addition, AC measurements can also represent small-scale spatial variability, and thus identify potential hot spots of CH 4 emissions (Koskinen et al, 2014;Lai et al, 2014). AC systems therefore combine the advantages of chamber measurements and micrometeorological methods with respect to the quantification of spatial as well as temporal dynamics of CH 4 emissions (Savage et al, 2014;Lai et al, 2012).…”

Evaluation of a lower-powered analyzer and sampling system for eddy-covariance measurements of nitrous oxide fluxes

“…Both N 2 O analyzers were well suited for the long-term and continuous measurement of F N 2 O that are required to fully capture the temporal dynamics of N 2 O emissions (Savage et al, 2014;Shurpali et al, 2016). Both operated continuously for over 1.5 years through the various environmental conditions encountered in Southern Ontario.…”

Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

“…Soil emissions of CO 2 and N 2 O can be subject to significant diurnal patterns, with peak values observed in the early afternoon (Savage et al, 2014), impeding the up-scaling of hourly measured emissions (usually obtained at midday) to daily values. We performed multiple linear regression (ordinary least squares regression including air temperature, relative humidity and water-filled pores space) to account for the difference between, for example, daytime (Wohlfahrt et al, 2005a) and night-time respiration (Wohlfahrt et al, 2005b).…”