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

• Published in: Plant and soil Vol. 413; no. 1/2; pp. 1 - 27
• Main Authors: Phillips, Claire L., Bond-Lamberty, Ben, Desai, Ankur R., Lavoie, Martin, Risk, Dave, Tang, Jianwu, Todd-Brown, Katherine, Vargas, Rodrigo
• Format: Journal Article
• Language: English
• Published: Cham Springer 01-04-2017; Springer International Publishing; Springer Nature B.V
• Subjects: Analysis; Biomedical and Life Sciences; Carbon cycle; Data collection; Datasets; Ecology; Life Sciences; MARSCHNER REVIEW; Measurement; Plant Physiology; Plant Sciences; Quality control; Respiration; Scale models; Soil carbon; soil respiration; Soil Science & Conservation; Soils; Soil respiration; CO; Carbon; Data-model fusion
• ISSN: 0032-079X; 1573-5036
• DOI: 10.1007/s11104-016-3084-x

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