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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #288864
Title: Gap filling strategies and error in estimating annual soil respiration

Author
item GOMEZ-CASANOVAS, NURIA - University Of Illinois
item ANDERSON-TEIXEIRA, KRISTINA - University Of Illinois
item ZERI, MARCELO - University Of Illinois
item Bernacchi, Carl
item DELUCIA, EVAN - University Of Illinois

Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2012
Publication Date: 5/2/2013
Citation: Gomez-Casanovas, N., Anderson-Teixeira, K., Zeri, M., Bernacchi, C.J., DeLucia, E.H. 2013. Gap filling strategies and error in estimating annual soil respiration. Global Change Biology. 19(6):1941-1952.

Interpretive Summary: Soil respiration (Rsoil) is one of the largest CO2 fluxes in the global carbon (C) cycle. Quantifying the contribution of Rsoil to the global C cycle requires estimates of total annual Rsoil. However, the large temporal variability of Rsoil rates presents a methodological challenge to obtaining reliable annual sums. Measurements of Rsoil may be made using either manual or automated systems. Measurements using manual systems typically are made at weekly to monthly intervals for a season (e.g., the growing season) or an entire year, and over a specific period of the day believed to be representative of the daily Rsoil flux. In contrast, Rsoil measurements made using automated systems are designed to sample continuously over long periods of time. Regardless of the methodology used, continuous year-long data records appropriate for constructing annual Rsoil sums are generally not obtained as Rsoil records usually contain gaps (i.e. half hour or longer periods of time with Rsoil missing values).While many gap-filling methodologies have been employed, there is of yet no standardized procedure for producing defensible estimates of annual Rsoil. Here, we test the reliability of nine different gap-filling techniques by inserting artificial gaps into 20 automated Rsoil records and comparing gap-filling Rsoil estimates of each technique to measured values. Data gaps in a Rsoil records were filled using simple algorithms (e.g., linear interpolation) or by prediction of missing values based on the relationship of Rsoil to known variables (e.g., soil temperature and soil moisture). Introduced gaps ranged from none to >99% gap. 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 manual measurements. Overall, the most accurate technique estimated Rsoil based on soil temperature dependence of Rsoil by assuming constant temperature sensitivity over the entire year and linearly interpolating reference respiration (Rsoil at 10°C) across gaps. The linear interpolation method was the second best-performing method. In contrast, estimating Rsoil based on a single annual Rsoil - Tsoil 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 Rsoil will be valuable for understanding the role of Rsoil in the global C cycle.

Technical Abstract: Soil respiration (Rsoil) is one of the largest CO2 fluxes in the global carbon (C) cycle. Estimation of annual Rsoil requires extrapolation of survey measurements or gap-filling of automated records to produce a complete time series. While many gap-filling methodologies have been employed, there is of yet no standardized procedure for producing defensible estimates of annual Rsoil. Here, we test the reliability of nine different gap-filling techniques by inserting artificial gaps into 20 automated Rsoil records and comparing gap-filling Rsoil 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 Rsoil based on soil temperature dependence of Rsoil by assuming constant temperature sensitivity and linearly interpolating reference respiration (Rsoil at 10°C) across gaps. The linear interpolation method was the second best-performing method. In contrast, estimating Rsoil based on a single annual Rsoil - Tsoil 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 Rsoil will be valuable for understanding the role of Rsoil in the global C cycle.