Future Global Soil Respiration Rates Will Swell Despite Regional Decreases in Temperature Sensitivity Caused by Rising Temperature

Steele, Meredith K.; Day, Susan D.; Thomas, R. Quinn

doi:10.1029/2018ef000937

Cited by 33 publications

(39 citation statements)

References 73 publications

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“…We suspect that the common decision not to measure during the dormant season and to instead extrapolate nonwinter measurements to an annual flux) results in significant “missed” variability. Significant numbers of studies estimate annual R S from growing‐season only fluxes (Jian et al, ), and the errors on these estimates are almost certainly larger than ones based on full‐year sampling.…”

Section: Discussionmentioning

confidence: 99%

“…We also suggest that it enables better standards for reporting R S , specifically with respect to errors associated with temporally sparse data sets (Jian, Steele, Day, Quinn Thomas, & Hodges, ). This should allow us to better distinguish natural variability from measurement error, provide benchmarks for confidently ascribing observed changes in R S to climatic drivers (Bond‐Lamberty & Thomson, ), and enable more accurate estimation of spatially and temporally upscaled fluxes (Jian, Steele, Day, & Thomas, ). For example, studies with limited data might apply some standard amount of uncertainty based on the CV distributions shown in Figure (although rigorously defining and applying data reporting standards for such an approach would require a consensus among the soil research community).…”

Section: Discussionmentioning

confidence: 99%

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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: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…Daily fluxes were calculated by multiplying the measured hourly flux by 24, given that fluxes measured between 08:30 and 11:20 were found to be representative of the mean daily flux (Yang et al 2017). Sampling in the morning hours has been shown to minimise the effect of soil temperature in soil respiration (Jian et al 2018). Research in forests and in rangelands in Kenya showed a negligible effect of soil temperature on CH 4 fluxes (Werner et al 2007;Zhu et al 2018).…”

Section: Gas Sampling and Analysismentioning

confidence: 99%

Soil carbon dioxide and methane fluxes from forests and other land use types in an African tropical montane region

Wanyama¹,

Pelster

Butterbach‐Bahl

et al. 2019

Biogeochemistry

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In the last 40 years, large areas of the Mau forest, the largest contiguous tropical montane forest in East Africa, have been cleared for agriculture. To date, there are no empirical data on how this land use change affects carbon dioxide (CO 2 ) fluxes from soil respiration and soil methane (CH 4 ) fluxes. This study reports measured annual soil CO 2 and CH 4 fluxes from the native Mau forest and previously forested lands converted to smallholder grazing land, smallholder and commercial tea plantations and eucalyptus plantations. Fluxes were measured weekly from August 2015 to August 2016 using the static chamber method. Grazing lands had the highest (p = 0.028) cumulative respiratory CO 2 fluxes (25.6 ± 2.9 Mg CO 2 -C ha -1 year -1 ), whereas lowest fluxes were observed in commercial tea plantations (5.6 ± 0.5 Mg CO 2 -C ha -1 year -1 ). Soil respiratory CO 2 fluxes were ().,-volV) ( 01234567 89().,-volV) concentrations (p = 0.03). Annual soil CH 4 can be explained by mainly soil water content and bulk density and these factors are related to gas diffusion. Our study shows that converting tropical montane forests to managed land use types affects soil CO 2 and CH 4 fluxes. Specifically, the CH 4 sink strength in managed land use types of these montane tropical soils was reduced to less than half of the sink strength in the native forest. Soil respiratory CO 2 fluxes were also altered by land use with grazing lands emitting 3-4 times more CO 2 than the other land use types.

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“…This simulation performance is better than that found in previous studies. For example, 11 soil respiration model studies at national, regional, and global scales (presenting predictable proportions of 26-68%) were reviewed by Chen et al [41], and previous work has been conducted on soil respiration simulation in China (RMSE = 799 g CO 2 m −2 yr −1 [42]) and on global soil respiration [3,43,44]. The simulation error of soil CH 4 fluxes using the present method also demonstrates a better simulation accuracy, yet it has rarely been discussed on an annual scale (RMSE = 76 g CH 4 m −2 yr −1 [45]).…”

Section: Feasibility Of Estimating Af Using Low-frequency Flux Data Omentioning

confidence: 99%

A Method for Estimating Annual Cumulative Soil/Ecosystem Respiration and CH4 Flux from Sporadic Data Collected Using the Chamber Method

Yang

et al. 2019

Atmosphere

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Measurements of greenhouse gas fluxes over many ecosystems have been made as part of the attempt to quantify global carbon and nitrogen cycles. In particular, annual flux observations are of great value for regional flux assessments, as well as model development and optimization. The chamber method is a popular approach for soil/ecosystem respiration and CH4 flux observations of terrestrial ecosystems. However, in situ flux chamber measurements are usually made with non-continuous sampling. To date, efficient methods for the application of such sporadic data to upscale temporally and obtain annual cumulative fluxes have not yet been determined. To address this issue, we tested the adequacy of non-continuous sampling using multi-source data aggregation. We collected 330 site-years monthly soil/ecosystem respiration and 154 site-years monthly CH4 flux data in China, all obtained using the chamber method. The data were randomly divided into a training group and verification group. Fluxes of all possible sampling months of a year, i.e., 4094 different month combinations were used to obtain the annual cumulative flux. The results showed a good linear relationship between the monthly flux and the annual cumulative flux. The flux obtained during the warm season from May to October generally played a more important role in annual flux estimations, as compared to other months. An independent verification analysis showed that the monthly flux of 1 to 4 months explained up to 67%, 89%, 94%, and 97% of the variability of the annual cumulative soil/ecosystem respiration and 92%, 99%, 99%, and 99% of the variability of the annual cumulative CH4 flux. This study supports the use of chamber-observed sporadic flux data, which remains the most commonly-used method for annual flux estimating. The flux estimation method used in this study can be used as a guide for designing sampling programs with the intention of estimating the annual cumulative flux.

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Future Global Soil Respiration Rates Will Swell Despite Regional Decreases in Temperature Sensitivity Caused by Rising Temperature

Cited by 33 publications

References 73 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

Soil carbon dioxide and methane fluxes from forests and other land use types in an African tropical montane region

A Method for Estimating Annual Cumulative Soil/Ecosystem Respiration and CH4 Flux from Sporadic Data Collected Using the Chamber Method

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