PERIODIC EMISSIONS WITH EVENTS
The discussion so far has focused on how measurement scheduling can influence the observation of a periodic and (relatively) predictable flux time-series, driven almost entirely by diurnal temperature variation. We will now look at how these measurement effects can change with the addition of short-term, high output events. Soil emissions of CO2 exhibit a strong response to sudden moisture change, whether through proportional microbial response or through more dramatic processes such as the “Birch effect” (Birch, 1964). This response can create a short-lived peak in emissions, often several times larger than the daily mean.
Consider now the same three researchers at the same field site mentioned previously, one day in the future when a significant rain event occurs, as shown in Figure 3. This episodic event results in a dramatic but short-term release of CO2 from the soil that quickly decays back to the previous periodic baseline. The three apparent trends disagree substantially as to what this response looks like, as do the new total emission estimates of 63.4, 67.1 and 52.0 g CO2/m2 for researchers A, B and C respectively. If we assume once again that researcher C’s estimates accurately reflect the cumulative total, measurement schedule A resulted in an overestimation of 22 % while B’s resulted in a overestimation of 29.1 %.
The majority of the CO2 response to this rain event took place over a six hour period. We can see that if the daily measurements had been taken only a few hours earlier or later in the day, this event would have been missed entirely. As the frequency of flux measurements decreases from daily to weekly or even coarser, a periodic sampling approach is likely to produce increasingly biased estimates.
Some gas species such as N2O typically follow low emission seasonal baselines with occasional dramatic pulses due to strong rain or treatment effects (Millar and Robertson, 2014; Butterbach-Bahl et al., 2004). Often the non-event sections of the time-series can be closely approximated by a baseline trend, meaning that the vast majority of emissions variability occurs within short-lived response periods. For sparse sampling, excluding or only partially resolving these events can significantly bias the estimates of cumulative emissions. Figure 4 shows how these discrete pulses can be several orders of magnitude larger than the typical background rate for N2O. In their discussion, Cavigelli et al. (2014) lay out three common approaches for temporal sampling of N2O emissions: Periodic, Episodic and Combination. Each approach strikes a different balance between convenience, cost and ability to capture short-term events, while also striving to minimize the error in total emission estimates.
Many of the potential biases discussed herein are a product of sparse data and low temporal resolution. Depending on the specific area of study and field site, researchers have attempted to address this lack of data through event-focused sampling routines or through the use of automated chamber systems. Automated systems offer several advantages, including more standardized sampling, lower labour requirements and complete and highly-resolved temporal data not readily obtainable through manual sampling methods. However, these instruments have non-trivial power requirements and often lack the flexibility and spatial coverage of sampling methods, and so the specific needs and resources of the study will determine the ideal approach. Given the importance of characterizing a field site to establishing an effective sampling routine, a combination of automated deployment and targeted manual sampling may be an effective alternative, as suggested by Cavigelli et al. (2014).
To hear more on the subject, here is a talk given by Cavigelli (2014) at the Global Research Alliance’s (GRA) Croplands Research Group for a nitrous oxide emissions methodology workshop.
Birch, H.F., 1964. Mineralisation of plant nitrogen following alternate wet and dry conditions. Plant and Soil 20: 43-49, https://doi.org/10.1007/BF01378096.
Butterbach-Bahl, K., Kock, M., Willibald, G., Hewett, B., Buhagiar, S., Papen, H., Kiese, R., 2004. Temporal variations of fluxes of NO, NO2, N2O, CO₂ and CH₄ in a tropical rain forest ecosystem. Global Biogeochem. Cy., 18 (3): GB3012, https://doi.org/10.1029/2004GB002243.
Cavigelli, M., Davis, B., Mirsky, S., Needleman, B., 2014. Novel approaches to interpolating N2O flux between episodic sampling points and improving sampling vial storage times. ASA N2O Workshop, November 6, 2014, Long Beach, CA
Davidson, E.A., Belk, E., Boone, R.D., 1998. Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest. Glob. Change Biol. 4(2): 217-227, https://doi.org/10.1046/j.1365-2486.1998.00128.x.
Lloyd, J., Taylor, J.A., 1994. On the temperature dependence of soil respiration. Funct. Ecol. 8(3): 315- 323, https://doi.org/10.2307/2389824.
Millar, N., Robertson, G.P., 2014. Manual Chamber Sampling Strategies to Help Account for Temporal Variability of Nitrous Oxide Emissions from Agricultural Cropping Systems. ASA N2O Workshop, November 2014
Parkin, T.B., Kaspar, T.C., 2003. Temperature Controls on Diurnal Carbon Dioxide Flux: Implications for Estimating Soil Carbon Loss. Soil Sci. Soc. Am. J. 67(6): 1763-1722, https://doi.org/10.2136/sssaj2003.1763.
Parkin, T.B., Kaspar, T.C., 2004. Temporal Variability of Soil Carbon Dioxide Flux: Effect of Sampling Frequency on Cumulative Carbon Loss Estimation. Soil Sci. Soc. Am. J. 68(4): 1234-1241, https://doi.org/10.2136/sssaj2004.1234.
Smith, K.A., Dobbie, K.E., 2001. The impact of sampling frequency and sampling times on chamber-based measurements of N2O emissions from fertilized soils. Glob. Change Biol. 7(8): 933-945, https://doi.org/10.1046/j.1354-1013.2001.00450.x.
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