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Global climate change, driven by increasing atmospheric concentrations of carbon dioxide (CO2), is a foremost environmental concern, and considerable research has been focused on quantifying the components of the global carbon (C) cycle. Soil respiration (SR), representing an aggregation of below-ground processes by both heterotrophs and autotrophs, contributes 30–80% of the total respiratory efflux in most ecosystems (Davidson et al. 2002) and is therefore a major component of the C cycle. Understanding the mechanisms of, and potential changes to, the soil–atmosphere exchange of CO2 through SR is a critical aspect of understanding ecosystem responses to climate change. Thus, measurements of SR have become a primary tool for terrestrial carbon cycling research.
SR is influenced by several factors, primarily temperature, soil moisture, root growth and substrate supply (Davidson, Janssens & Luo 2006; Liu et al. 2006). Two primary goals of measuring SR are to determine an annual SR budget and to develop improved models of SR which move beyond simple temperature functions. Additionally, these measurements have been used to inform and constrain models of decadal scale changes in soil C stocks (Gaudinski et al. 2000), and to characterize seasonal and interannual patterns of below-ground C allocation (Davidson et al. 2002, 2006).
Since SR is influenced by factors which cause variation at hourly, diel, seasonal and interannual time scales, measurements must be made at the appropriate temporal frequency in order to address research questions at each of these temporal scales. Current SR measurement techniques include manual and automated chamber systems (Savage & Davidson 2003; Liu et al. 2006; Carbone & Vargas 2008). Manual measurements of SR are made by a researcher at discreet points in time (typically weekly or less frequently) but with sampling across the landscape, whereas autochambers function without supervision and more or less continuously but at a limited number of fixed sampling points.
Due to equipment constraints, manual sampling usually occurs only on days without precipitation. Thus, the immediate and potentially large effects of soil moisture changes on SR (Lee et al. 2004; Xu, Baldocchi & Tang 2004) are missed, which may bias estimates of annual SR. The limited sampling frequency requires interpolation or modelling to calculate annual fluxes, but spatially extensive sampling is possible, which provides good characterization of site heterogeneity (Stoyan et al. 2000).
With automated SR systems, each chamber is typically sampled every half hour, 24 h a day, 7 days a week, providing an abundance of data – potentially tens of thousands of measurements per year. The continuity of measurements permits modelling and statistical analysis of SR at time scales from minutes to months. Disadvantages of an automated system include more complicated maintenance, higher initial cost and more restricted spatial sampling imposed by power constraints, infrastructure and semi-permanent installation.
Several publications have compared different designs of SR chambers and addressed their sources of error and bias (Hutchinson & Livingston 2001; Davidson et al. 2002). Protocols for evaluating data quality have not been explicitly addressed, probably because manual measurements are usually very few (e.g. a few hundred per year) that most researchers can visually inspect their entire data sets. By comparison, within the community of researchers using the eddy covariance method to measure whole-ecosystem CO2 fluxes, standardized methods for processing and quality control have been developed and are being widely adopted (Papale et al. 2006; Moffat et al. 2007). Now that large quantities of data are being generated by automated SR systems at many research sites, there is a need for similar protocols to be developed for SR data.
Information about data uncertainties is needed for basic quality control, to make statistical comparisons between models and data, and to provide confidence intervals (CIs) on C budgets. As eddy covariance and SR data are increasingly being used in a data–model fusion context by C cycle researchers, there is a need for data errors and uncertainties to be characterized so that these can be accounted for in the assimilation process (Raupach et al. 2005). While this information is available for eddy covariance measurements (Richardson et al. 2006a, 2008), there are few comparable data for SR data (Richardson et al. 2006b).
The objectives of this paper are to (i) derive a data quality protocol for automated SR data, (ii) evaluate random measurement error and systematic sampling uncertainties in automated SR data, (iii) compare manual and automated SR data, and (iv) use high frequency data to provide guidelines regarding an appropriate sampling strategy for manual SR measurements.