The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling

Abstract

Background An acceleration of model-data synthesis activities has leveraged many terrestrial carbon datasets, but utilization of soil respiration (R S) data has not kept pace. Scope We identify three major challenges in interpreting R S data, and opportunities to utilize it more extensively and creatively: (1) When R S is compared to ecosystem respiration (R ECO) measured from EC towers, it is not uncommon to find R S > R ECO. We argue this is most likely due to difficulties in calculating R ECO , which provides an opportunity to utilize R S for EC quality control. (2) R S integrates belowground heterotrophic and autotrophic activity, but many models include only an explicit heterotrophic output. Opportunities exist to use the total R S flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. (3) R S is generally measured at a very different resolution than that needed for comparison to EC or ecosystem-to global-scale models. Downscaling EC fluxes to match the scale of R S , and improvement of R S upscaling techniques will improve resolution challenges. Conclusions R S data can bring a range of benefits to model development, particularly with larger databases and improved data sharing protocols to make R S data more robust and broadly available to the research community.

Soil Respiration Components and their Temperature Sensitivity Under Chemical Fertilizer and Compost Application: The Role of Nitrogen Supply and Compost Substrate Quality

“…The root exclusion method is a widely accepted method to partition Rs components (Phillips et al, ; Subke et al, ). In the present study, this method was achieved by not sowing the maize crop, which might generate fewer biases in comparison to root trenching in forests due to less disturbance and especially the absence of biases induced by severed roots decomposition (Suleau et al, ).…”

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

“…The bias term is included to account for the scaling from the discrete soil collars to the stand as a whole. This term was also introduced because observed soil chamber fluxes were typically over twice the ecosystem respiration estimated from the eddy-covariance tower (Phillips et al, 2017). While the introduction of the bias term makes it impossible for this data to constrain the magnitude of soil carbon fluxes, it does provide information on the shape of the functional response (e.g.…”

Carbon fluxes and interannual drivers in a temperate forest ecosystem assessed through comparison of top-down and bottom-up approaches

“…Including soil respiration and litterfall measurements at flux sites provides valuable information on both above and belowground ecosystem C fluxes allowing for not only cross validation of ecosystem C fluxes but the ability to more rigorously test ecosystem models (McFarlane et al, 2014;Phillips et al, 2017 Interannual variations in GPP, Re, NEP, and parameters describing light response functions are determined by both direct and indirect drivers, and have the potential to provide insight into how ecosystems might respond under future climate. A complication in understanding the drivers of interannual C variation from eddy covariance is the abundance of gap-filled data.…”