Gap filling strategies and error in estimating annual soil respiration

Soil respiration ( R soil ) is one of the largest CO 2 fluxes in the global carbon (C) cycle. Estimation of annual R soil requires extrapolation of survey measurements or gap filling of automated records to produce a complete time series. Although many gap filling methodologies have been employed, there is no standardized procedure for producing defensible estimates of annual R soil . Here, we test the reliability of nine different gap filling techniques by inserting artificial gaps into 20 automated R soil records and comparing gap filling R soil estimates of each technique to measured values. We show that although the most commonly used techniques do not, on average, produce large systematic biases, gap filling accuracy may be significantly improved through application of the most reliable methods. All methods performed best at lower gap fractions and had relatively high, systematic errors for simulated survey measurements. Overall, the most accurate technique estimated R soil based on the soil temperature dependence of R soil by assuming constant temperature sensitivity and linearly interpolating reference respiration ( R soil at 10 °C) across gaps. The linear interpolation method was the second best‐performing method. In contrast, estimating R soil based on a single annual R soil – T soil relationship, which is currently the most commonly used technique, was among the most poorly‐performing methods. Thus, our analysis demonstrates that gap filling accuracy may be improved substantially without sacrificing computational simplicity. Improved and standardized techniques for estimation of annual R soil will be valuable for understanding the role of R soil in the global C cycle.