Part one of a three part series.
While collecting samples/measurements in the same order, at the same time every day is convenient, it can seriously bias your findings. This article offers a look at how to avoid some schedule-induced issues when measuring ecosystem variables.
Estimates of baseline and event-driven greenhouse-gas (GHG) emissions from soils play an increasingly important role in numerous areas, including improving our understanding of ecosystem processes, climate change mitigation and agricultural yields. These emissions vary substantially in their temporal variability, spatial variability and response to periodic and episodic events, not only between different gas species but also across field sites and seasons for a single gas. Despite considerable research on the techniques and best practices for obtaining accurate gas emission estimates, comparatively few studies have examined the effects and possible biases that result from when and how often these measurements are collected.
The most common method of measuring these soil gas fluxes is through the use of accumulation chambers, which deploy a chamber on the soil surface and estimate the flux by measuring the rate of concentration change inside. This method is particularly useful for gas species with low soil-atmosphere gradients (such as CH4 or N2O) as the elevated chamber concentrations improve measurement quality for lower precision instruments. Often these measurements are achieved through manual sampling, where several gas samples are extracted from the chamber during the measurement period and later analyzed using a gas chromatograph or similar equipment.
Depending on the concentration measuring technique, chamber sampling can be expensive and/or labour and travel intensive, often resulting in measurement schedules that are based on convenience, weather conditions, cost or habit. The important influences of sampling order, density and frequency on emission estimates are often overlooked. The common practice for most manual sampling involves collecting field measurements at the same time each visit, with the time of the measurements being implicitly determined by lab schedule and travel distance. Many of these studies also collect sparse or infrequent measurements, due to the complexity and expense of obtaining and analyzing gas samples. Depending on the measurement schedule, field site and gas species of interest, this low-data approach can suffice for seasonal totals, but may lead to critical episodic events being missed or misinterpreted.
Figure 1. Examples of an automated soil flux chamber (left, photo: OzFlux), and a static soil flux chamber (right, photo: USDA).
Researchers have adopted automated chamber techniques in order to collect more frequent emission measurements or to ensure that all significant emission events are observed. These systems deploy chambers and sample gases automatically, often using a linked gas analyzer to measure concentrations in situ. This automation produces a much higher temporal data density, both for the concentration time series used to estimate flux and the number of flux measurements over all. Despite the increased complexity of field deployment and power requirement, these systems offer improved accuracy for total emissions estimates and the means to examine diurnal ecological processes.
In this white paper we will look at three different types of flux data: periodic emissions, periodic emissions with events and event-driven emissions. These represent a spectrum of typical gas emission behaviour: from being primarily cyclical (controlled by temperature or photosynthesis) to primarily episodic (controlled by rainfall or treatment effects). Using examples of both automated and manual sampling, we will examine possible sources of bias in emission estimates stemming from measurement scheduling and sparse temporal data.
Many of the primary controlling factors of soil gas flux vary on diurnal or sub-diurnal scales, such as temperature, moisture, photosynthesis and wind. These environmental variables often exhibit periodic behaviour, such as the quasi-sinusoidal temperatures encountered at most field sites, leading to strong diurnal patterns in soil gas emissions. This poses several significant obstacles for measurement scheduling, particularly in studies where these variables are not observed or recorded.
Soil CO2 flux is an example of a gas that is relatively straight-forward to measure as its (biological) flux is primarily driven by temperature (Lloyd and Taylor, 1994; Davidson et al., 1998). Consider the example of two different researchers A and B, who are both using daily manual sampling to measure CO2 fluxes and estimate total emissions. Researcher A collects their samples each day at 9:00 AM while researcher B collects samples at 12:00 PM. An example of this measurement scheme applied to field data is shown in Figure 1 for a three day period in late May 2011, with dashed lines showing interpolated trends.
Based on this data, we can see that each researcher will observe substantially different apparent trends in CO2 flux. For the same field site, now add a third researcher C. Researcher C collects automated measurements at a one-hour interval for the entire three day period, with this data shown in Figure 2 along with air temperature measurements. This additional data reveals the underlying cause for the differences in A and B’s results: the emissions of CO2 were being strongly driven by temperature, resulting in a pronounced diurnal cycle. Parkin and Kaspar (2003) offer a detailed study of this phenomenon, demonstrating that using a scheduled daily measurement for CO2 flux estimates can result in a deviation of up to 30 % from the daily mean. The net impact this bias has on estimated fluxes depends on the daily emission range, meaning that the estimation error will change with environmental and seasonal trends.
The dashed-lines of Figures 1 and 2 show what the apparent temporal trends look like using simple linear interpolation. Using these approximations to predict the total emission of CO2 yields estimates of 30.9, 45.4 and 35.1 g CO2/m2 over the three day period for researchers A, B and C respectively. Using researcher C’s estimate as an accurate measure of total emissions, we can see that researcher A underestimates total emissions by 12.1 % while researcher B overestimates total emissions by 29.4 %, showing that both temporal trends and cumulative emission estimates can be significantly influenced by time-of-day scheduling and the temporal resolution of measurements. Parkin and Kaspar (2004) applied a similar re-sampling technique to automated CO2 flux measurements over a three month deployment, concluding that manually sampling as frequently as every two days biases the total emissions estimate by over 10 %, while weekly sampling increases the deviation by up to 30 %.
While beyond the scope of this discussion, it is worth noting that this problem also affects sequentially measured spatial data (including automated sampling), as the sequence and length of chamber measurements can lead to a similar time-of-day influence. This temporal-spatial convolution can be extremely difficult to detect and correct for, due to the high degree of spatial variability most gas species exhibit. As a result, attempting to correct for this bias would become even more difficult when comparing emissions estimates between different treatment sites or field experiments, as the line between true experimental differences and schedule-induced biases would blur.