Upscaling plot-scale methane flux to a eddy covariance tower domain in Barrow, AK: integrating in-situ data with a microbial functional group-based model

Monday, 15 December 2014
Xiaofeng Xu1, Melanie S Hahn2, Jitendra Kumar3, Fengming Yuan4, Guoping Tang3, Peter E Thornton5, Margaret S Torn6 and Stan D Wullschleger5, (1)University of Texas at El Paso, El Paso, TX, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)ORNL, Environmental Science Division, Oak Ridge, TN, United States, (4)ORNL, Oak Ridge, TN, United States, (5)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (6)Berkeley Lab/UC Berkeley, Berkeley, CA, United States
Substantial spatial heterogeneity of methane flux has been recognized as a key uncertainty for estimating land-atmosphere exchange and further predicting the behavior of the climate system. Static-chamber method has been widely used to measure methane flux at plot-scale of tens of centimeter while it is unable for the scale beyond, and the eddy covariance technique has been used for in-situ measuring methane flux at a scale of tens of meters while it lacks of mechanistic representation of biogeochemical processes at smaller scale. How to link methane flux at these two scales while keeping primary spatial variations is critically important for accurate quantification of methane flux. A data-model integration approach is a valuable tool to scale-up methane flux while sustaining spatial heterogeneity.

In this study, we take advantage of a set of in-situ measurements of methane flux at plot-scale and a flux tower domain scale, and a high-resolution dataset of vegetation distribution and meteorology data, as well as a newly-developed microbial functional group-based methane module (incorporated in CLM4.5). A footprint model is used to characterize the domain of the eddy covariance tower in which high-resolution model simulations are carried out. The ecosystem model is first parameterized with plot-scale measurements of methane flux and ecosystem properties before it is used for regional simulations across the flux tower domain. The simulated regional methane flux will be weighted by spatial contribution of land surface methane flux estimated by the footprint model, and then compared with eddy covariance measurement. The low and high boundaries of the methane flux in the domain will be estimated for its potential uncertainties during this upscaling processes by comparing with empirical modeling method. The upscaling approach with data-model integration adopted in this study is valuable as it considers spatial heterogeneity of ecosystem properties and the dynamic representativeness of tower over the season and across the spatial domain.