Field measurements of soil respiration: principles and constraints, potentials and limitations of different methods

Pumpanen, Jukka; Longdoz, Bernard; Kutsch, Werner L.

doi:10.1017/cbo9780511711794.003

Cited by 30 publications

(27 citation statements)

References 66 publications

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“…At the minute-to-minute time scale, the relatively high R S variability (CV of 17%) observed here is likely driven primarily by measurement error, for example, chamber pressure excursions due to the Venturi effect (Davidson et al, 2002;L. Xu, 2006) or CO 2 saturation effects (Pumpanen et al, 2010). Rapid changes in light-driven photosynthesis feeding root respiration (Bader et al, 2016) could potentially contribute as well.…”

Section: Discussionmentioning

confidence: 78%

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

et al. 2019

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The high temporal variability of the soil-to-atmosphere CO 2 flux (soil respiration, R S ) has been studied at hourly to multiannual time scales but remains less well understood than R S spatial variability. How R S fluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of R S over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global R S database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11-12%) time scales. Successive R S values measured at a given collar exhibited a strong hour-to-hour correlation (r = 0.931) and a moderate correlation at a 2-hr lag (0.289); day-to-day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily R S means were well correlated at a 1-day lag (r = 0.856) but not at any further time lag. In a linear mixed-effects model predicting R S , soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of R S fluxes, quantify sampling requirements, and improve error propagation.Plain Language Summary Soils give off carbon dioxide, generated by microbes and plant roots, to the atmosphere. How this "soil respiration" (R S ) varies in time, as one measures at minute, hourly, daily, or longer time scales, is related to the processes driving it and has implications for how we estimate this flow of carbon across space and time. We measured R S in a coastal deciduous forest in Maryland, USA, and found that the variability of R S -how much it changed between successive measurementsvaried at the different time scales. R S values quickly became increasingly random (i.e., uncorrelated with each other) as one measured repeatedly over time. These results help us understand the factors driving this large flow of carbon to the atmosphere and improve our ability to estimate R S at times and places not directly measured.

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Section: Discussionmentioning

confidence: 78%

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

et al. 2019

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“…Generally, in closed chambers the CO 2 flux is estimated by measuring the increase of CO 2 in the chamber's head space during a known period of time (Pumpanen et al, 2004(Pumpanen et al, , 2009). The soil CO 2 -C efflux can then be determined from the increase in the CO 2 concentration c t .…”

Section: Soil Respiration Measurementsmentioning

confidence: 99%

“…The soil CO 2 -C efflux can then be determined from the increase in the CO 2 concentration c t . In closed static chambers the CO 2 concentration increase c t is determined from air sampled with syringes, which are then analyzed for CO 2 with a CO 2 analyzer (Borken et al, 1999;Pumpanen et al, 2009). In closed dynamic systems, c t is determined with portable infrared gas analyzers with the air circulating between the chamber and the analyzer (Pumpanen et al, 2009).…”

Section: Soil Respiration Measurementsmentioning

confidence: 99%

Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints

et al. 2014

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Abstract. Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO 2 . Soil organic carbon (SOC) turnover has been studied using the 14 C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the 14 C values of CO 2 that evolved during the incubation of soil and roots have been widely used together with 14 C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observational constraints to determine SOC turnover times. Thus, we focus on (1) how different combinations of observational constraints help to narrow estimates of turnover times and other parameters of a simple two-pool model, the Introductory Carbon Balance Model (ICBM); (2) whether relaxing the steady-state assumption in a multiple constraints approach allows the source/sink strength of the soil to be determined while estimating turnover times at the same time. To this end ICBM was adapted to model SOC and SO 14 C in parallel with litterfall and the 14 C of litterfall as driving variables. The 14 C of the atmosphere with its prominent bomb peak was used as a proxy for the 14 C of litterfall. Data from three spruce-dominated temperate forests in Germany and the USA (Coulissenhieb II, Solling D0 and Howland Tower site) were used to estimate the parameters of ICBM via Bayesian calibration. Key findings are as follows: (1) the joint use of all four observational constraints (SOC stock and its 14 C, HR flux and its 14 C) helped to considerably narrow turnover times of the young pool (primarily by 14 C of HR) and the old pool (primarily by 14 C of SOC). Furthermore, the joint use of all observational constraints made it possible to constrain the humification factor in ICBM, which describes the fraction of the annual outflux from the young pool that enters the old pool. The Bayesian parameter estimation yielded the following turnover times (mean ± standard deviation) for SOC in the young pool: Coulissenhieb II 1.1 ± 0.5 years, Solling D0 5.7 ± 0.8 years and Howland Tower 0.8 ± 0.4 years. Turnover times for the old pool were 377 ± 61 years (Coulissenhieb II), 313 ± 66 years (Solling D0) and 184 ± 42 years (Howland Tower), respectively. (2) At all three sites the multiple constraints approach was not able to determine if the soil has been losing or storing carbon. Nevertheless, the relaxed steady-state assumption hardly introduced any additional uncertainty for the other parameter estimates. Overall the results suggest that using 14 C data from more than one carbon pool or flux helps to better constrain SOC models.

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“…4 demonstrate that the smaller soil CO2 efflux measured with the DOC system was mainly due to the lower soil temperature inside the chamber. The smaller efflux may also have been due to a pressure difference between the inside of the chamber and the atmosphere caused by air circulation and air mixing inside the chamber (Ngao et al, 2006;Pumpanen et al, 2009). However, this study did not provide enough information to allow us to discuss the effect of such pressure differences on the chamber CO2 efflux.…”

Section: Comparison Of Soil Co2 Effluxes Measuredmentioning

confidence: 99%

Evaluation of a portable chamber system for soil CO2 efflux measurement and the potential errors caused by internal compensation and water vapor dilution

Matsuura

Mori

Hojito

et al. 2011

J. Agric. Meteorol.

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A portable, easy-to-install chamber system for rapid measurement of soil CO2 efflux is needed for the immediate analysis of CO2 flushes after events such as tillage and grassland renovation. We conducted field measurements and simple simulations to validate the effectiveness of a portable chamber system with an infrared CO2 gas analyzer (IRGA) and a temperature/humidity sensor (the SIR system). We confirmed that a measurement period of 60-180 s after the closure of the chamber was sufficient for the calculation of soil CO2 efflux. We compared the SIR system with a closed static chamber system and an automated open/closed chamber system. The efflux data obtained with the SIR system were linearly related to the data obtained with the other systems, and the relationships between the soil temperature and efflux were similar in the three systems. These results show that the SIR system provided soil CO2 efflux values with a reasonable accuracy relative to the other systems. We also examined how the calculated efflux values were affected by the internal compensation of the IRGA for environmental parameters and water vapor dilution in the chamber. The potential error caused by the default internal compensation of the IRGA was within 3.1 under general environmental conditions. The effect of water vapor dilution was large ( 20 ) for small CO2 effluxes. The combined effects of the default internal compensation and water vapor dilution were larger for small CO2 effluxes than for large CO2 effluxes, and the effects became larger under low air pressure conditions. We recommend that accurate environmental compensation and water vapor correction be applied for analysis of small CO2 effluxes when there is a rapid increase in water vapor, especially under low air pressure conditions.

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Field measurements of soil respiration: principles and constraints, potentials and limitations of different methods

Cited by 30 publications

References 66 publications

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Evaluation of a portable chamber system for soil CO2 efflux measurement and the potential errors caused by internal compensation and water vapor dilution

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Field measurements of soil respiration: principles and constraints, potentials and limitations of different methods

Cited by 30 publications

References 66 publications

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

Bayesian calibration of a soil organic carbon model using Δ&lt;sup&gt;14&lt;/sup&gt;C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Evaluation of a portable chamber system for soil CO2 efflux measurement and the potential errors caused by internal compensation and water vapor dilution

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Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints