1 / 11

NLR_AM (AQRTa) model

NLR_AM (AQRTa) model. Asko Noormets University of Toledo Toledo, Ohio, USA. Model. R 10 ( μ mol CO 2 m -2 s -1 ) – reference respiration Ea (kJ mol -1 K -1 ) – activation energy R (8.3134 J mol -1 K -1 ) – universal gas constant T ref ( K ) – reference air temperature

tam
Télécharger la présentation

NLR_AM (AQRTa) model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NLR_AM (AQRTa) model Asko Noormets University of Toledo Toledo, Ohio, USA

  2. Model R10 (μmol CO2 m-2 s-1) – reference respiration Ea (kJ mol-1 K-1) – activation energy R (8.3134 J mol-1 K-1) – universal gas constant Tref (K) – reference air temperature Ta (K) – ambient air temperature a (mol CO2 mol-1 PAR) – apparent quantum yield f (μmol quanta m-2 s-1) – PAR Pmax (μmol CO2 m-2 s-1 ) – light-saturated assimilation rate Noormets et al., In press, Ecosystems

  3. Features • Monthly time-window • Co-fitting respiration and assimilation models Reflect functional changes Provide robust parameter estimates

  4. 1. Monthly time window • Information content • diurnal • synoptic • phenological Baldocchi et al., 2001, GCB Katul & Parlange, 1995, Water Resour. Res. Stoy et al., 2005, Tree Physiol.; 2006, GCB

  5. Seasonal changes Noormets et al., 2006. Ch. 4 in Ecology of Hierarchical Landscapes DeForest et al., 2006. Int. J. Biomet.

  6. Time window • Weekly vs. monthly integration • domain of functional change • diurnal – pH, Rubisco, CHO • seasonal – SLA, xylem conductance, SWC, VPD

  7. Time window • Weekly vs. monthly integration • domain of functional change • diurnal – pH, Rubisco, CHO • seasonal – SLA, xylem conductance, VPD, SWC • Overlapping vs. non-overlapping windows

  8. 2. Co-fitting R & A • Constrains parameter estimates • broader data spread = better constrained model • Compromises nighttime accuracy

  9. be1_2001, fi1_2002 fi1_2001 Other models Cofilled: AQRT Also done: RT+AQ

  10. Tair vs. Tsoil & confounding factors • Tair and Tsoil are out of phase • SR peaks at ~4-7 PM • ER higher after dusk than before dawn • CHO transport highest after dusk • ? Tsoil- vs. CHO-dependence ?

  11. Conclusions - ways to improve Need: • Formal criteria for selecting time window • More sophisticated ER function • Likelihood-based estimates

More Related