How many measurements are needed to estimate accurate daily and annual soil respiration fluxes? Analysis using data from a temperate rainforest

Abstract

<p><strong>Abstract.</strong> Making accurate estimations of daily and annual <i>R</i><sub>s</sub> fluxes is key for understanding the carbon cycle process and projecting effects of climate change. In this study we used high-frequency sampling (24-measurements per day) of <i>R</i><sub>s</sub> in a temperate rainforest during one year, with the objective of answering the questions of when and how often measurements should be made to obtain accurate estimations of daily and annual <i>R</i><sub>s</sub>. In this aim, we randomly selected data to simulate samplings of 1, 2, 4 or 6 measurements per day (distributed either during the whole day or only during daytime) combined with 4, 6, 12, 26 or 52 measurements per year. Based on the comparison of partial-data series with the full-data series, we estimated the performance of different partial sampling strategies based on bias, precision and accuracy. In the case of annual <i>R</i><sub>s</sub> estimation, we compared the performance of interpolation vs. using non-linear modelling based on soil temperature. The results show that, under our study conditions, sampling twice a day was enough to accurately estimate daily <i>R</i><sub>s</sub> (RMSE < 10 % of average daily flux), even if both measurements were done during daytime. The highest reduction in RMSE for the estimation of annual <i>R</i><sub>s</sub> was achieved when increasing from 4 to 6 measurements per year, but reductions were still relevant when further increasing the frequency of sampling. In conclusion, we found that increasing the number of field campaigns was more effective than increasing the number of measurements per day, provided a minimum of two measurements per day was used. Including nighttime measurements significantly reduced the bias and was relevant in reducing the number of field campaigns when a lower level of acceptable error (RMSE < 5 %) was established. Using non-linear modelling instead of linear interpolation did improve the estimation of annual <i>R</i><sub>s</sub> but not as expected. Given that most of the studies of <i>R</i><sub>s</sub> use manual sampling techniques and apply only one measurement per day, we suggest making an intensive sampling at the beginning of the study to determine minimum daily and annual frequencies of sampling.</p>

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

“…In an agricultural field, Parkin and Kaspar () concluded that sampling every 3 days was required to be within 20% of the true flux, while sampling every 20 days would result in errors from −40% to +60%. Conversely, tropical forests studied by Perez‐Quezada et al () tended to require relatively low measurement frequency (Jian, Steele, Day, Quinn Thomas, & Hodges, ), probably due to their low monthly climatic variability. In a comprehensive analysis of temporal variations in R S globally, Jian, Steele, Thomas, Day, and Hodges () concluded that R S should be measured at least once per day to achieve ±10% of the true mean with 95% confidence; once per month resulted in 80% confidence of being within ±30% of the mean.…”

“…In general it will be more important in systems with larger diel flux variability (Jian et al, ), driven by strong day‐to‐night contrasts; conversely, it might be expected to be weaker in the long summer daylight at high latitudes, for example. At a longer time scale, Perez‐Quezada et al () used hourly measurements in a temperate rainforest over 1 year to determine what sampling frequency was required to accurately estimate daily and annual R S fluxes. In addition, effective gap filling is strongly dependent on understanding temporal dynamics (Gomez‐Casanovas et al, ) and the degree to which successive measurements in time are correlated with each other (Ramsey, ).…”