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

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.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.

Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estimates in a Global Database

“…Jian et al (2018b) determined that this annual sampling frequency resulted in annual R S values that were within ±30% of the true R S mean 80% of the time. A similar conclusion was found in three temperate forests (Bond-Lamberty et al, 2019). This study thus demonstrated that while large uncertainty may exist with a 21-day measurement frequency, it may still reasonably capture the average annual mean of R S .…”

“…However, in a shrubland located at semiarid Mediterranean, California, measuring from 9:00 to 11:00 caused a bias of −29% to +40% compared with the diurnal mean R S flux (Cueva et al, 2017). The results from three mid-Atlantic deciduous forests showed that R S rate from mid-morning and late afternoon (15:00-16:00) were most close to the daily mean R S (Bond-Lamberty et al, 2019). At the global scale, Jian, et al (2018b) found that diurnal variation contributed less than 6% of bias to scale daily R S estimates; moreover, Jian, et al (2018b) showed that midmorning is a representative time window to capture diurnal mean R S because soil and air temperature reach diurnal mean between 9:00 to 12:00.…”

Soil Respiration Response to Simulated Precipitation Change Depends on Ecosystem Type and Study Duration

“…While previous work has demonstrated the need for manipulative experiments with frequent sampling intervals (Vicca et al., 2014), the mechanism underlying this is not well understood. We strongly encourage new data syntheses explicitly focused on this phenomenon, and field experiments that prioritize measurements at shorter timescales (Bond‐Lamberty et al., 2019). Additionally, our results are sensitive to the limited amount of data from long‐term experiments, especially in desert ecosystems where only one study (Li et al., 2020) contributed data for more than 1 year.…”

Controls of Seasonal and Interannual Variations on Soil Respiration in a Meadow Steppe in Eastern Inner Mongolia

“…In the interannual scale, previous studies showed that soil respiration variation is more substantially dominated by soil moisture or is rainfall-dependent [14,16,17]. Recent reports highlighted that the longterm interannual variability of soil respiration cannot be fully explained by climatic factors alone, but eco-physiological factors such as GPP, phenomenon, and fine root turnover should also be taken into account [18][19][20][21]. All this evidence indicated that the patterns of soil respiration may be dominated by different driving factors at different timescales.…”