Biases of Temporally Sparse Data and Measurement Scheduling on Flux Estimates: Part 3

Part three of the three part series. Here are Part 1 and Part 2.

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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.


Figure 4: N2O emissions showing high magnitude pulses during several rain events, as modified from Cavigelli et al., 2014.


As shown by several researchers (Cavigelli et al., 2014; Smith and Dobbie, 2001), simply using a regularly scheduled sparse sampling routine will at best provide a reasonable estimate that relies on offsetting errors (“getting the right answer for the wrong reason”). This method may suffice for approximating total emissions but is unlikely to provide sufficient temporal data for treatment or process analysis studies and may be difficult to design for sites where N2O is strongly correlated with varying temperature and moisture (Butterbach-Bahl et al., 2004). Where automated sampling is not possible, the Combination approach (Flessa et al., 2002) is a particularly useful method of extending the utility of manual sampling. Using this principle, periodic measurements are augmented by dense data collection during and after episodic events such as rainfall or land management treatments. By capturing regular estimates of the baseline trend as well as episodic events, this technique improves the ability of manual sampling to estimate total emissions and can also provide process level insight for event-driven studies.


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.




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