Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help
 

Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

View PDF (5.9 MB)

Title: Statistical uncertainty of eddy flux-based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

Author: Hagen, S.C.; Braswell, B.H.; Linder, E.; Frolking, S.; Richardson, A.D.; Hollinger. D.Y, David; Hollinger. D.Y,;

Date: 2006

Source: Journal of Geophysical Research. (111): D08S03, doi:10.1029/2005JD006154

Publication Series: Scientific Journal (JRNL)

Description: We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest atmosphere CO2 fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ~100%) but is much less at annual timescales (~10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data.

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
  • This publication may be available in hard copy. Check the Northern Research Station web site to request a printed copy of this publication.
  • Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.

XML: View XML

Citation:


Hagen, S.C.; Braswell, B.H.; Linder, E.; Frolking, S.; Richardson, A.D.; Hollinger. D.Y 2006. Statistical uncertainty of eddy flux-based estimates of gross ecosystem carbon exchange at Howland Forest, Maine. Journal of Geophysical Research. (111): D08S03, doi:10.1029/2005JD006154

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.