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Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration

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Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration. / Ogle, Kiona; Ryan, Edmund; Dijkstra, Feike; Pendall, Elise.

In: Journal of Geophysical Research: Biogeosciences, Vol. 121, No. 12, 12.2016, p. 2935-2948.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Ogle, K, Ryan, E, Dijkstra, F & Pendall, E 2016, 'Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration', Journal of Geophysical Research: Biogeosciences, vol. 121, no. 12, pp. 2935-2948. https://doi.org/10.1002/2016JG003385

APA

Ogle, K., Ryan, E., Dijkstra, F., & Pendall, E. (2016). Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration. Journal of Geophysical Research: Biogeosciences, 121(12), 2935-2948. https://doi.org/10.1002/2016JG003385

Vancouver

Ogle K, Ryan E, Dijkstra F, Pendall E. Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration. Journal of Geophysical Research: Biogeosciences. 2016 Dec;121(12):2935-2948. https://doi.org/10.1002/2016JG003385

Author

Ogle, Kiona ; Ryan, Edmund ; Dijkstra, Feike ; Pendall, Elise. / Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration. In: Journal of Geophysical Research: Biogeosciences. 2016 ; Vol. 121, No. 12. pp. 2935-2948.

Bibtex

@article{1093d0d4072048979b08b663d0c9a4ba,
title = "Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration",
abstract = "Non-steady 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 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 non-linear (non-steady state, diffusion based) models of C versus t, within in a hierarchical Bayesian framework. Data are from the Prairie Heating and CO2 Enrichment (PHACE) study that manipulated atmospheric CO2, temperature, soil moisture, and vegetation. CO2 was collected from static chambers bi-weekly during five growing seasons, resulting in >12,000 samples and >3100 groups and associated fluxes. We compare f estimates based on non-hierarchical and hierarchical Bayesian (B vs HB) versions of the linear and diffusion-based (L vs D) models, resulting in four different models (BL, BD, HBL, HBD). Three models fit the data exceptionally well (R2 ≥ 0.98), but the BD model was inferior (R2 = 0.87). The non-hierarchical models (BL, BD) produced highly uncertain f estimates f (wide 95% CIs), whereas the hierarchical models (HBL, HBD) produced very precise estimates. Of the hierarchical versions, the linear model (HBL) underestimated f by ~33% relative to the non-steady state model (HBD). The hierarchical models offer improvements upon traditional non-hierarchical approaches to estimating f, and we provide example code for the models. ",
keywords = "Bayesian modeling, borrowing of strength, diffusion equation, Fick's law, global change experiment, soil respiration",
author = "Kiona Ogle and Edmund Ryan and Feike Dijkstra and Elise Pendall",
year = "2016",
month = dec,
doi = "10.1002/2016JG003385",
language = "English",
volume = "121",
pages = "2935--2948",
journal = "Journal of Geophysical Research: Biogeosciences",
issn = "2169-8953",
publisher = "AMER GEOPHYSICAL UNION",
number = "12",

}

RIS

TY - JOUR

T1 - Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration

AU - Ogle, Kiona

AU - Ryan, Edmund

AU - Dijkstra, Feike

AU - Pendall, Elise

PY - 2016/12

Y1 - 2016/12

N2 - Non-steady 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 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 non-linear (non-steady state, diffusion based) models of C versus t, within in a hierarchical Bayesian framework. Data are from the Prairie Heating and CO2 Enrichment (PHACE) study that manipulated atmospheric CO2, temperature, soil moisture, and vegetation. CO2 was collected from static chambers bi-weekly during five growing seasons, resulting in >12,000 samples and >3100 groups and associated fluxes. We compare f estimates based on non-hierarchical and hierarchical Bayesian (B vs HB) versions of the linear and diffusion-based (L vs D) models, resulting in four different models (BL, BD, HBL, HBD). Three models fit the data exceptionally well (R2 ≥ 0.98), but the BD model was inferior (R2 = 0.87). The non-hierarchical models (BL, BD) produced highly uncertain f estimates f (wide 95% CIs), whereas the hierarchical models (HBL, HBD) produced very precise estimates. Of the hierarchical versions, the linear model (HBL) underestimated f by ~33% relative to the non-steady state model (HBD). The hierarchical models offer improvements upon traditional non-hierarchical approaches to estimating f, and we provide example code for the models.

AB - Non-steady 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 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 non-linear (non-steady state, diffusion based) models of C versus t, within in a hierarchical Bayesian framework. Data are from the Prairie Heating and CO2 Enrichment (PHACE) study that manipulated atmospheric CO2, temperature, soil moisture, and vegetation. CO2 was collected from static chambers bi-weekly during five growing seasons, resulting in >12,000 samples and >3100 groups and associated fluxes. We compare f estimates based on non-hierarchical and hierarchical Bayesian (B vs HB) versions of the linear and diffusion-based (L vs D) models, resulting in four different models (BL, BD, HBL, HBD). Three models fit the data exceptionally well (R2 ≥ 0.98), but the BD model was inferior (R2 = 0.87). The non-hierarchical models (BL, BD) produced highly uncertain f estimates f (wide 95% CIs), whereas the hierarchical models (HBL, HBD) produced very precise estimates. Of the hierarchical versions, the linear model (HBL) underestimated f by ~33% relative to the non-steady state model (HBD). The hierarchical models offer improvements upon traditional non-hierarchical approaches to estimating f, and we provide example code for the models.

KW - Bayesian modeling

KW - borrowing of strength

KW - diffusion equation

KW - Fick's law

KW - global change experiment

KW - soil respiration

U2 - 10.1002/2016JG003385

DO - 10.1002/2016JG003385

M3 - Journal article

VL - 121

SP - 2935

EP - 2948

JO - Journal of Geophysical Research: Biogeosciences

JF - Journal of Geophysical Research: Biogeosciences

SN - 2169-8953

IS - 12

ER -