![]() ![]() We then used a parameter prediction approach to refine the non-linear gap-filling equations based on micrometeorological conditions. We assessed the potential physiological drivers of these parameters with linear models using micrometeorological predictors. We used as our base the Michaelis-Menten and Van't Hoff functions. We used a non-linear regression approach with moving windows of different lengths (15, 30, and 60-days) to estimate non-linear regression parameters for one year of flux data from a long-leaf pine site at the Joseph Jones Ecological Research Center. Preserving the variance of these data will provide unbiased and precise estimates of NEE over time, which mimic natural fluctuations. While the methods used so far have provided robust estimates of the mean value of NEE, little attention has been paid to preserving the variance structures embodied by the flux data. In order to have estimates of net ecosystem exchange of carbon dioxide (NEE) with high precision and accuracy, robust gap-filling methods to impute missing data are required. However, measurements from EC data are missing for various reasons: precipitation, routine maintenance, or lack of vertical turbulence. To represent carbon dynamics, in terms of exchange of CO2 between the terrestrial ecosystem and the atmosphere, eddy covariance (EC) data has been collected using eddy flux towers from various sites across globe for more than two decades. Gap-filling methods to impute eddy covariance flux data by preserving variance. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |