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United States Department of Agriculture

Agricultural Research Service

Research Project: FARMING PRACTICES FOR THE NORTHERN CORN BELT TO PROTECT SOIL RESOURCES, SUPPORT BIOFUEL PRODUCTION AND REDUCE GLOBAL WARMING POTENTIAL

Location: Soil and Water Management Research

Title: Effects of soil physical nonuniformity on chamber-based gas flux estimates

Authors
item Venterea, Rodney
item Baker, John

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 9, 2008
Publication Date: September 1, 2008
Repository URL: http://hdl.handle.net/10113/19357
Citation: Venterea, R.T., Baker, J.M. 2008. Effects of soil physical nonuniformity on chamber-based gas flux estimates. Soil Science Society of America Journal. 72:1410-1417.

Interpretive Summary: Measurement of soil-to-atmosphere exchange of biogenic trace gases using chamber-based techniques is commonly applied to a wide range of research topics in agronomy, soil science, biogeochemistry, and ecology. While chamber methods are less expensive and easier to use than other methods, they have important limitations. In particular, there is wide understanding that deploying static chambers inevitably alters soil-atmosphere gas exchange, at least to some degree, by disturbing the interfacial concentration gradient. This effect may have important implications related to greenhouse has impacts and ecosystem nutrient budgets. In general, for trace gases that tend to be emitted from soil to the atmosphere (e.g., CO2 and N2O), this effect would cause systematic underestimation of mass fluxes. There is currently no broad consensus regarding the most appropriate mathematical method for calculating fluxes based on chamber concentration time series data. Recently, a dramatic change in chamber methodology has been suggested designed to enhance the non-linearity in time series data based on application of a fully theoretically-based non-linear model, the non-steady state diffusive flux estimator (NDFE). The objective of this study was to further evaluate the generality of the NDFE model. Numerical simulations were employed to simulate the response of chamber headspace time series data to alterations in soil physical properties and biochemical kinetics using actual and hypothetical scenarios. The results indicate that vertical variations in near-surface bulk density and porosity commonly found in agricultural and forest soils can result in chamber versus time data that are substantially different from that predicted by the NDFE model. Because these differences tend to increase with increased deployment time, potential errors associated with NDFE-based predictions would also be expected to increase with deployment time. The results also place limitations on the validity of the NDFE model as a function of the strength of the source term for reactive trace gases such as N2O and CH4. Finally, the results indicate that for a broad range of conditions, more conventional techniques whereby linearity in data are encouraged are more favorable than the NDFE approach. These findings should be useful to scientists engaged in examining the role of role of terrestrial ecosystems in regulating atmospheric greenhouse gases.

Technical Abstract: Measurement of soil-to-atmosphere exchange of biogenic trace gases using chamber-based techniques is commonly applied to a wide range of research topics in agronomy, soil science, biogeochemistry, and ecology, including studies addressing the role of terrestrial ecosystems in regulating atmospheric greenhouse gases (GHG). Although chamber methods can be highly labor intensive, they are inexpensive and technically simple relative to alternatives such as micrometeorological methods. There is wide understanding that deploying static chambers inevitably alters soil-atmosphere gas exchange, at least to some degree, by disturbing the interfacial concentration gradient. This effect can result in non-linear patterns of chamber trace gas concentration during the deployment period which complicates estimation of actual pre-deployment flux. There is currently no broad consensus regarding the most appropriate mathematical method for calculating fluxes based on chamber concentration time series data. Most researchers to date have attempted to minimize disturbance of the interfacial gradient by adjusting deployment times, measurement intervals, and chamber volumes so that chamber concentration time series data approach linearity. Another option has been to fit empirical or semi-empirical non-linear models to data sets in order to estimate pre-deployment flux. More recently, a fully theoretically-based non-linear model, the non-steady state diffusive flux estimator (NDFE) has been proposed by Livingstone et al. (2006). The authors conclude that a dramatic change in chamber methodology designed to enhance the non-linearity in time series data will produce the most valid flux data. However, two important assumptions of the NDFE model have not been examined: (i) uniform physical conditions in the soil profile with respect to the vertical dimension, and (ii) the lack of gas consumption processes within the soil. Additionally, there has yet to be an examination of the usefulness of the NDFE model as compared to more conventional methods as a function of the magnitude of the fluxes. The objective of this study was to further evaluate the generality of the NDFE model by examining these issues. Numerical simulations were employed to simulate the response of chamber headspace time series data to alterations in soil physical properties and biochemical kinetics using actual and hypothetical scenarios. The results indicate that vertical variations in near-surface bulk density and porosity commonly found in agricultural and forest soils can result in chamber versus time data that are substantially different from that predicted by the NDFE model. Because these differences tend to increase with increased deployment time, potential errors associated with NDFE-based predictions would also be expected to increase with deployment time. The results also place limitations on the validity of the NDFE model as a function of the strength of the source term for reactive trace gases such as N2O and CH4. Finally, the results indicate that for a broad range of conditions, more conventional techniques whereby linearity in data are encouraged are more favorable than the NDFE approach.

Last Modified: 9/10/2014