Continuous soil respiration measurements and data quality control using the FD chamber technique

Abstract:

Continuous soil respiration data sets have become increasingly common with the availability of automated soil respiration measurement systems. These continuous data have revealed a great deal about short time-scale temporal responses to environmental drivers such as soil temperature and moisture content, as well as linkages between above- and below-ground processes.

Forced Diffusion (FD) is a novel method for continuous measurement of soil respiration (Risk et al., 2011). The FD technique is functionally similar to dynamic steady-state chamber systems but uses a diffusive membrane to regulate the flow of gases rather than a pump. Measurement of soil respiration using this diffusive regulation approach has several benefits including reduced power consumption and the ability to function in harsh environments including under snow pack.

Here we present a continuous multi-month forest soil respiration data set collected using the FD technique in Nova Scotia, Canada. Data spanning the autumn (August-December) will be presented, which includes both autotrophic senescence as well as the Atlantic hurricane season. Temporal dynamics associated with long-term and short-term temperature variability are evident in the data set, as well as multiple respiration pulse events associated with heavy rainfalls during autumnal storms. We will also demonstrate the application of a straightforward algorithm used for quality control (QC) of continuous soil respiration data. The QC technique uses a combination of predictive modeling and comparison of probability density functions (Lavoie et al., 2015) that result in robust identification of outliers in continuous soil respiration data sets.