Interpreting diel hysteresis between soil respiration and temperature

Abstract

Increasing use of automated soil respiration chambers in recent years has demonstrated complex diel relationships between soil respiration and temperature that are not apparent from less frequent measurements. Soil surface flux is often lagged from soil temperature by several hours, which results in semielliptical hysteresis loops when surface flux is plotted as a function of soil temperature. Both biological and physical explanations have been suggested for hysteresis patterns, and there is currently no consensus on their causes or how such data should be analyzed to interpret the sensitivity of respiration to temperature. We used a one-dimensional soil CO 2 and heat transport model based on physical first principles to demonstrate a theoretical basis for lags between surface flux and soil temperatures. Using numerical simulations, we demonstrated that diel phase lags between surface flux and soil temperature can result from heat and CO 2 transport processes alone. While factors other than temperature that vary on a diel basis, such as carbon substrate supply and atmospheric CO 2 concentration, can additionally alter lag times and hysteresis patterns to varying degrees, physical transport processes alone are sufficient to create hysteresis. Therefore, the existence of hysteresis does not necessarily indicate soil respiration is influenced by photosynthetic carbon supply. We also demonstrated how lags can cause errors in Q 10 values calculated from regressions of surface flux and soil temperature measured at a single depth. Furthermore, synchronizing surface flux and soil temperature to account for transport-related lags generally does not improve Q 10 estimation. In order to calculate the sensitivity of soil respiration to temperature, we suggest using approaches that account for the gradients in temperature and production existing within the soil. We conclude that consideration of heat and CO 2 transport processes is a requirement to correctly interpret diel soil respiration patterns.

Hydration status and diurnal trophic interactions shape microbial community function in desert biocrusts

“…We consider ambient temperature as a sinusoidal function for the surface boundary condition (Phillips et al, 2011):…”

An inversion approach for determining distribution of production and temperature sensitivity of soil respiration

“…This study uses a 1-D CO 2 and heat transport model described by Phillips et al (2011), originally developed by Nickerson and Risk (2009). This model, with existing versions in Perl and R (R Core Team, 2015), was recoded in C to increase computational efficiency for the parameter solving routine.…”

“…These thermal and gas diffusion processes, and the resulting lags, can be captured in a simple one-dimensional (1-D) physical heat and gas transport soil model (Nickerson and Risk, 2009;Phillips et al, 2011). Though not done to date for the soil respiration system, it is possible to use such a model in inverse fashion for estimating the value of parameters like Q 10 and Z p by looping the forward model iteratively through possible parameter combinations, with observed measurements as a constraint.…”

“…A known source of error in this approach originates in the physics of soil heat and gas transport, which might separate a change in surface soil temperature (normally a 5 or 10 cm temperature is used for deriving Q 10 ) from the resultant change in CO 2 flux measured at the surface. The lags depend most heavily on soil heat transport (Phillips et al, 2011), because changes in surface temperature are shifted and dampened significantly as a function of depth, with each successive soil layer experiencing a reduced temperature change in amplitude. Gas diffusion also plays an important role, and even if soil microbes and roots produced CO 2 instantaneously upon receipt of thermal energy at the characteristic production depths, gases still take time to diffuse upward.…”

“…1). Phillips et al (2011) demonstrated that such lags can lead to severe misinterpretation of data when attempting to extract true Q 10 values through regression of surface flux and a temperature measurement at a single depth.…”