Numerical Evaluation of Static-Chamber Measurements of Soil—Atmosphere Gas Exchange: Identification of Physical Processes
- Richard W. Healy ,
- Robert G. Striegl,
- Thomas F. Russell,
- Gordon L. Hutchinson and
- Gerald P. Livingston
Water Resources Division, U.S. Geological Survey, MS 413, Box 25046, Denver Federal Center, Lakewood, CO 80215-0046
Dep. of Mathematics, Univ. of Colorado, P.O. Box 173364, Campus Box 170, Denver, CO 80217-3364
USDA-ARS-NPA, Natural Resources Research Center, Soil-Plant-Nutrient Research Unit, P.O. Box E, Ft. Collins, CO 80522-0470
NASA Ames Research Center, Earth Science Division, Johnson Controls World Services, SGE:239-20, Moffett Field, CA 94035-1000
The exchange of gases between soil and atmosphere is an important process that affects atmospheric chemistry and therefore climate. The static-chamber method is the most commonly used technique for estimating the rate of that exchange. We examined the method under hypothetical field conditions where diffusion was the only mechanism for gas transport and the atmosphere outside the chamber was maintained at a fixed concentration. Analytical and numerical solutions to the soil gas diffusion equation in one and three dimensions demonstrated that gas flux density to a static chamber deployed on the soil surface was less in magnitude than the ambient exchange rate in the absence of the chamber. This discrepancy, which increased with chamber deployment time and air-filled porosity of soil, is attributed to two physical factors: distortion of the soil gas concentration gradient (the magnitude was decreased in the vertical component and increased in the radial component) and the slow transport rate of diffusion relative to mixing within the chamber. Instantaneous flux density to a chamber decreased continuously with time; steepest decreases occurred so quickly following deployment and in response to such slight changes in mean chamber headspace concentration that they would likely go undetected by most field procedures. Adverse influences of these factors were reduced by mixing the chamber headspace, minimizing deployment time, maximizing the height and radius of the chamber, and pushing the rim of the chamber into the soil. Nonlinear models were superior to a linear regression model for estimating flux densities from mean headspace concentrations, suggesting that linearity of headspace concentration with time was not necessarily a good indicator of measurement accuracy.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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