Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data‐model integration. Issue 12 (3rd December 2016)

• Record Type: Journal Article
• Authors: Ogle, Kiona; Ryan, Edmund; Dijkstra, Feike A.; Pendall, Elise
• Is Part Of: Journal of geophysical research. Volume 121:Issue 12(2016:Dec.)
• Page Start: 2935
• Page End: 2948
• Publication Date: 2016-12-03
• Subjects: Bayesian modeling -- borrowing of strength -- diffusion equation -- Fick's law -- global change experiment -- soil respiration; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1002/2016JG003385
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Nonsteady state chambers are often employed to measure soil CO2 fluxes. CO2 concentrations ( C ) in the headspace are sampled at different times ( t ), and fluxes ( f ) are calculated from regressions of C versus t based on a limited number of observations. Variability in the data can lead to poor fits and unreliable f estimates; groups with too few observations or poor fits are often discarded, resulting in "missing" f values. We solve these problems by fitting linear (steady state) and nonlinear (nonsteady state, diffusion based) models of C versus t, within a hierarchical Bayesian framework. Data are from the Prairie Heating and CO2 Enrichment study that manipulated atmospheric CO2, temperature, soil moisture, and vegetation. CO2 was collected from static chambers biweekly during five growing seasons, resulting in >12, 000 samples and >3100 groups and associated fluxes. We compare f estimates based on nonhierarchical and hierarchical Bayesian (B versus HB) versions of the linear and diffusion‐based (L versus D) models, resulting in four different models (BL, BD, HBL, and HBD). Three models fit the data exceptionally well ( R 2 ≥ 0.98), but the BD model was inferior ( R 2 = 0.87). The nonhierarchical models (BL and BD) produced highly uncertain f estimates (wide 95% credible intervals), whereas the hierarchical models (HBL and HBD) produced very precise estimates. Of the hierarchical versions, the linear model (HBL) underestimated f by ~33% relative to the nonsteady state model (HBD). The hierarchical models offer improvements upon traditional nonhierarchical approaches to estimating f, and we provide example code for the models. Key Points: A hierarchical modeling approach facilitates accurate estimates of soil CO2 fluxes The approach is applied to data obtained from nonsteady state soil chambers A hierarchical, nonsteady state diffusion model of CO2 fluxes performed best