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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

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NLR_AM (AQRTa) model

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  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

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