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Title: Prediction of annual soil respiration from its flux at mean annual temperature

Abstract

Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO2) across time and space is important to constrain and understand ecosystem to global scale SR, a major terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SRannual) from the flux measured at mean soil temperature (SRMAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SRMAST to predict SRannual. SRMAST estimated using a variety of methods all showed clear relationships with annual SRannual. Two single-rate methods (i.e., using the single SR rate most close to mean annual soil temperature, or the single SR rate most close to mean annual air temperature) showed the most pronounced divergence from the true SRannual, but errors significantly decreased when using multiple SR rates within 1 ? of the mean annual soil temperature to estimate SRMAST. SRannual was most closely correlated with SRMAST estimated via a Q10 relationship, but this method has a potential autocorrelation issue that we exploremore » and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SRMAAT) can be used to predict SRannual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SRannual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. Universitat Innsbruck
  3. Academia
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1606232
Report Number(s):
PNNL-SA-147713
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Agricultural and Forest Meteorology
Additional Journal Information:
Journal Volume: 287
Country of Publication:
United States
Language:
English
Subject:
Soil respiration, temperature, modeling

Citation Formats

Jian, Jinshi, Bahn, Michael, Wang, Chuankuan, Bailey, Vanessa L., and Bond-Lamberty, Benjamin. Prediction of annual soil respiration from its flux at mean annual temperature. United States: N. p., 2020. Web. doi:10.1016/j.agrformet.2020.107961.
Jian, Jinshi, Bahn, Michael, Wang, Chuankuan, Bailey, Vanessa L., & Bond-Lamberty, Benjamin. Prediction of annual soil respiration from its flux at mean annual temperature. United States. doi:10.1016/j.agrformet.2020.107961.
Jian, Jinshi, Bahn, Michael, Wang, Chuankuan, Bailey, Vanessa L., and Bond-Lamberty, Benjamin. Mon . "Prediction of annual soil respiration from its flux at mean annual temperature". United States. doi:10.1016/j.agrformet.2020.107961.
@article{osti_1606232,
title = {Prediction of annual soil respiration from its flux at mean annual temperature},
author = {Jian, Jinshi and Bahn, Michael and Wang, Chuankuan and Bailey, Vanessa L. and Bond-Lamberty, Benjamin},
abstractNote = {Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO2) across time and space is important to constrain and understand ecosystem to global scale SR, a major terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SRannual) from the flux measured at mean soil temperature (SRMAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SRMAST to predict SRannual. SRMAST estimated using a variety of methods all showed clear relationships with annual SRannual. Two single-rate methods (i.e., using the single SR rate most close to mean annual soil temperature, or the single SR rate most close to mean annual air temperature) showed the most pronounced divergence from the true SRannual, but errors significantly decreased when using multiple SR rates within 1 ? of the mean annual soil temperature to estimate SRMAST. SRannual was most closely correlated with SRMAST estimated via a Q10 relationship, but this method has a potential autocorrelation issue that we explore and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SRMAAT) can be used to predict SRannual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SRannual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions.},
doi = {10.1016/j.agrformet.2020.107961},
journal = {Agricultural and Forest Meteorology},
number = ,
volume = 287,
place = {United States},
year = {2020},
month = {6}
}