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

Abstract

Abstract. Making accurate estimations of daily and annual Rs 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 Rs in a temperate rainforest during 1 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 Rs. 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 Rs 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 Rs (RMSE < 10 % of average daily flux), even if both measurements were done during daytime. The highest reduction in RMSE for the estimation of annual Rs was achieved when increasing from four to six measurements per year, but reductions were still relevant when further increasing the frequency of sampling. 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 night-time 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 Rs, but not as expected. In conclusion, given that most of the studies of Rs use manual sampling techniques and apply only one measurement per day, we suggest performing an intensive sampling at the beginning of the study to determine minimum daily and annual frequencies of sampling.

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

“…In an agricultural field, Parkin and Kaspar (2004) 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 (2016) tended to require relatively low measurement frequency (Jian, Steele, Day, Quinn Thomas, & Hodges, 2018), probably due to their low monthly climatic variability. In a comprehensive analysis of temporal variations in R S globally, 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.…”

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

“…Compared to the annual soil respiration determined from high frequency data (8592 observations per year, whose sum was denoted as obsAF in Figure 12), the simulated annual soil respiration (4 observations per year) showed a difference ranging from −30% to 54% of the obsAF (Figure 12), among which 276 simulated annual emissions exhibited a difference of −20% to 20% of the obsAF, while 193 simulated annual emissions exhibited a difference of −10% to 10%. Compared to Perez-Quezada's [56] simulation of annual respiration (mean: 4288 g CO 2 m −2 yr −1 ; RMSE: 538 g CO 2 m −2 yr −1 , 13% of the obsAF of 4150 g CO 2 m −2 yr −1 ) using 4 day-time observations per year and their linear and non-linear models, the simulative accuracy of our simulation was slightly better (mean: 4212 g CO 2 m −2 yr −1 ; RMSE: 507 g CO 2 m −2 yr −1 , 12% of the obsAF), which might benefit from month selection of the present study. Perez-Quezada's study pointed out that no less than 2 observations per day (repeat 4 times per year) or no less than 1 observation per month is necessary to decrease RMSE to be lower than 10% of the obsAF.…”

“…The flux difference between a low and high sampling frequency has been quantified at site scale. For instance, a study in a temperate rainforest carried out 1-year high-frequency (24 measurements per day) sampling of soil respiration [56], and randomly selected low-frequency data to calculate annual emission. This was compared to the full-data annual emission.…”

“…Considering that both the hypothesis and the statistical method introduced uncertainties into the simulation accuracy of the present equations, one critical question arises: Compared to the annual flux derived by high frequency measurement, how large is the error of the simulated annual flux that was calculated using the present method? Perez-Quezada et al [56] published their full dataset of three automatic chambers. We therefore used their data to explore the question.…”

Soil CO2 Efflux Measurements by Alkali Absorption and Infrared Gas Analyzer in the Brazilian Semiarid Region

“…To extend these periods, it would be necessary to perform a large number of measurements (Panosso et al, 2012). According to Perez-Quezada et al (2016), this is possible, and despite the high cost of the equipment, the IRGA methodology has a better cost/benefit ratio than others.…”