Biases of Temporally Sparse Data and Measurement Scheduling on Flux Estimates: Part 2

Part two of the three part series. Here is Part 1.


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 %.

rain-event-soil-flux-chambers

Figure 4. Observed flux estimates from researchers A, B, and C during a significant rain event on the fourth day of measurements.

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.

Part 3: Event-driven Emission…