Towards a robust, generalizable non-linear regression gap filling algorithm (NLR_EM)

Ankur R Desai – National Center for Atmospheric Research (NCAR) Boulder, Colorado, USA University of Wisconsin, Atmospheric & Oceanic Sciences, Madison, Wisconsin, USA Pennsylvania State University, Meteorology, University Park, Pennsylvania, USA Bruce D Cook – University of Minnesota, Forest Resources St. Paul, Minnesota, USA Kenneth J Davis – Pennsylvania State University, Meteorology University Park, Pennsylvania, USA Gap Filling Workshop 18 Sept 2006 Max-Planck BGC, Jena, Germany

Goals

 Simple, fast, general GPP/RE and gap-filling estimation for eddy flux NEE  Theoretically meaningful parameters  Statistically valid regression  Flexible moving window regression  Temperature / PAR forcing only  Used at ChEAS Ameriflux sites  Code written in IDL, available to all

Primary references

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Desai, A. R., P. Bolstad, B. D. Cook, K. J. Davis, and E. V. Carey, 2005: Comparing net ecosystem exchange of carbon dioxide between an old-growth and mature forest in the upper Midwest, USA. Agric.For.Meteorol., 10.1016/j.agrformet.2004.09.005). 128, 33-55 (doi: Cook, B. D., K. J. Davis, W. Wang, A. R. Desai, B. W. Berger, R. M. Teclaw, J. M. Martin, P. Bolstad, P. Bakwin, C. Yi, and W. Heilman, 2004: Carbon exchange and venting anomalies in an upland deciduous forest in northern Wisconsin, USA. Agric.For.Meteorol., (doi:10.1016/j.agrformet.2004.06.008). 126, 271-295 Eyring, H., 1935: The activated complex in chemical reactions. J.Chem.Phys., 3, 107-115.

Sites that use NLR_EM

 http://cheas.psu.edu

 Sites that use it: Sylvania Old-growth, Lost Creek wetland, WLEF 447-m tall tower (3 levels), Willow Creek upland. Others only site-to-site comp.

Algorithm highlights

 1 parameter set per day for ER and GPP  30-60 day moving window: size increases until 200 good half-hourly points – all user definable  One-tailed t-test for parameter fit  if confidence < 0.90, replace ER/GPP with daily mean ER/GPP over window  mostly occurs in winter  Monte-Carlo random gap generator to compute sensitivity of filling to gaps - reported for sites

Respiration

 Use nighttime u*-screened NEE and 5 cm soil temperature (can use air temp instead)  Regress against Eyring equation:  similar to Arrhenius but more accurate description of reaction activation energies by including entropy  For regression, total carbon content not needed

Respiration

 Gibbs free energy:  Regress with linear form of equation:

Example of ΔG++

 From Cook et al (2004)

GPP

 Simple 2 or 3 parameter equation:  Can relate b1/b2 to Amax and quantum yield  b3 can be included as an intercept  T-test failure replaces b1 with mean GPP  Levenberg-Marquardt non-linear regression

Example of b1/b2

 From Cook et al (2004)

Error estimation

 Simple Monte Carlo estimate of error induced by gap-filling  100 sets of random 10-40 artificial gaps of lengths 30 minutes – 5 days  Error reported as standard deviation and range of NEE across 100 sets (e.g., Desai et al., 2005; Desai et al., in press, Ag. For Met.)

Other notes

 Can use filled or non-filled met data  ChEAS filled met relies on cluster of met sites  Without filled met data, mean diurnal values and interpolation used to fill either met or flux  Can use other ER/GPP equations  Day/night determined by sunrise/set at lat/lon and by a low PAR criterion  Algorithm has been used across Ameriflux in a Modis GPP – flux tower evaluation project  Filling NEE = ER - GPP

Next steps

 Harder to use well in non-temperate sites  Exploration of phenologically controlled windows  Not good for moisture-limited sites  Cross-site Gibbs free energy comparisons require total carbon content, but has promising use for examining ER parameter spatial var.

 Interested in understanding model bias in gap filling and exploring energy activation across sites used in this study

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 GPP/ER intercomparison  Tuesday, 13:00  Most gap-filling methods can produce GPP/ER  How variable are they?

  Across methods / sites Due to gaps in NEE  Can we infer ecosystem parameters?

 I have most of these data, but not all  Send them in to me  or else