Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis
Abstract
Inferences about subsurface CO2 fluxes often rely on surface soil respiration (Rsoil) estimates because directly measuring subsurface microbial and root respiration (collectively, CO2 production, STotal), is difficult. To evaluate how well Rsoil serves as a proxy for STotal, we applied the nonsteady state DEconvolution of Temporally varying Ecosystem Carbon componenTs model (0.01-m vertical resolution), using 6-hourly data from a Wyoming grassland, in six simulations that cross three soil types (clay, sandy loam, and sandy) with two depth distributions of subsurface biota. We used cross-wavelet coherence analysis to examine temporal coherence (localized linear correlation) and offsets (lags) between STotal and Rsoil and fluxes and drivers (e.g., soil temperature and moisture). Cross-wavelet coherence revealed higher coherence between fluxes and drivers than linear regressions between concurrent variables. Soil texture and moisture exerted the strongest controls over coherence between CO2 fluxes. Coherence between CO2 fluxes in all soil types was strong at short (~1 day) and long periods (>8 days), but soil type controlled lags, and rainfall events decoupled the fluxes at periods of 1–8 days for several days in sandy soil, up to 1 week in sandy loam, and for a month or more in clay soil. Concentrating root and microbial biomass nearermore »
- Authors:
-
[1];
[2];
[3];
- Northern Arizona Univ., Flagstaff, AZ (United States)
- Lancaster Univ. (United Kingdom)
- Univ. of Western Sydney, NSW (Australia). Hawkesbury Inst. for the Environment
- Publication Date:
- Research Org.:
- Univ. of Wyoming, Laramie, WY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1537330
- Grant/Contract Number:
- SC0006973
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research. Biogeosciences
- Additional Journal Information:
- Journal Volume: 123; Journal Issue: 4; Journal ID: ISSN 2169-8953
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Environmental Sciences & Ecology; Geology
Citation Formats
Samuels-Crow, Kimberly E., Ryan, Edmund, Pendall, Elise, and Ogle, Kiona. Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis. United States: N. p., 2018.
Web. doi:10.1002/2017jg004207.
Samuels-Crow, Kimberly E., Ryan, Edmund, Pendall, Elise, & Ogle, Kiona. Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis. United States. https://doi.org/10.1002/2017jg004207
Samuels-Crow, Kimberly E., Ryan, Edmund, Pendall, Elise, and Ogle, Kiona. Fri .
"Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis". United States. https://doi.org/10.1002/2017jg004207. https://www.osti.gov/servlets/purl/1537330.
@article{osti_1537330,
title = {Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis},
author = {Samuels-Crow, Kimberly E. and Ryan, Edmund and Pendall, Elise and Ogle, Kiona},
abstractNote = {Inferences about subsurface CO2 fluxes often rely on surface soil respiration (Rsoil) estimates because directly measuring subsurface microbial and root respiration (collectively, CO2 production, STotal), is difficult. To evaluate how well Rsoil serves as a proxy for STotal, we applied the nonsteady state DEconvolution of Temporally varying Ecosystem Carbon componenTs model (0.01-m vertical resolution), using 6-hourly data from a Wyoming grassland, in six simulations that cross three soil types (clay, sandy loam, and sandy) with two depth distributions of subsurface biota. We used cross-wavelet coherence analysis to examine temporal coherence (localized linear correlation) and offsets (lags) between STotal and Rsoil and fluxes and drivers (e.g., soil temperature and moisture). Cross-wavelet coherence revealed higher coherence between fluxes and drivers than linear regressions between concurrent variables. Soil texture and moisture exerted the strongest controls over coherence between CO2 fluxes. Coherence between CO2 fluxes in all soil types was strong at short (~1 day) and long periods (>8 days), but soil type controlled lags, and rainfall events decoupled the fluxes at periods of 1–8 days for several days in sandy soil, up to 1 week in sandy loam, and for a month or more in clay soil. Concentrating root and microbial biomass nearer the surface decreased lags in all soil types and increased coherence up to 10% in clay soil. The assumption of high temporal coherence between Rsoil and STotal is likely valid in dry, sandy soil, but may lead to underestimates of short-term STotal in semiarid grasslands with fine-grained and/or wet soil.},
doi = {10.1002/2017jg004207},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 4,
volume = 123,
place = {United States},
year = {Fri Mar 30 00:00:00 EDT 2018},
month = {Fri Mar 30 00:00:00 EDT 2018}
}
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