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

Bond‐Lamberty, Ben; Pennington, Stephanie C.; Jian, Jinshi; Megonigal, J. Patrick; Sengupta, Aditi; Ward, Nicholas

doi:10.1029/2019JG005265

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

Journal Article · Fri Nov 29 00:00:00 EST 2019 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1029/2019JG005265· OSTI ID:1576657

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Joint Global Change Research Institute Pacific Northwest National Laboratory College Park MD USA
Smithsonian Environmental Research Center Edgewater MD USA
Biological Sciences Division Pacific Northwest National Laboratory Richland WA USA
Marine Sciences Laboratory Pacific Northwest National Laboratory Sequim WA USA, School of Oceanography University of Washington Seattle WA USA

Abstract 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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: DE‐AC05‐76RL01830

OSTI ID:: 1576657

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Vol. 124 Journal Issue: 11; ISSN 2169-8953

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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