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Carbon balance of a maize canopy: different gap filling strategies

INTERREG IIIa Project Nr. 3c.10 Impacts of climate change on vegetation in the Upper Rhine Valley. Gap Filling Comparison Workshop Jena, Germany, September 18-20, 2006. Carbon balance of a maize canopy: different gap filling strategies.

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Carbon balance of a maize canopy: different gap filling strategies

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  1. INTERREG IIIa Project Nr. 3c.10 Impacts of climate change on vegetation in the Upper Rhine Valley Gap Filling Comparison WorkshopJena, Germany, September 18-20, 2006 Carbon balance of a maize canopy: different gap filling strategies Irene Lehner, Eva van Gorsel, Vanessa Haverd, Roland Vogt

  2. Outline Site & Set-up Gap filling methods Results Conclusions

  3. Site Air photograph July 16th 2004

  4. Sonic (Campbell CSAT3) IRGA (Li-Cor Li7500) Radiation (Kipp&Zonen CNR1) PPFD (Li-Cor Li190SB) Ventilated Psychrometer Soil Heat Flux Plate (Rimco HP3) Soil Temperature (Campbell CS107B) Soil Moisture (Campbell CS616) Set-up

  5. Method I - Parameterisation periods of assimilation * a‘ ecosystem quantum yield PPFD photosynthetic photon flux density NEEsat net ecosystem exchange at „optimum“ light Rdayecosystem respiration during daytime Ts soil temperature *Michaelis & Menten (1913) in Falge et al. (2001), Agric.For.Meteorol. 107, 43-69

  6. Method I - Parameterisation periods of respiration * Rnightecosystem respiration at night a,b parameter Ts soil temperature * van‘t Hoff (1898) in Lloyd & Taylor (1994), Funct. Ecol. 8, 315-323

  7. Method II – Neural Network INPUT NEURAL NETWORK OUTPUT PAR vegetation height air temperature CO2-flux soil temperature absolute humidity

  8. Method II – Neural Network y = 0.8092x + 0.0074 R2 = 0.80

  9. Method II – Neural Network

  10. Method II – Neural Network

  11. Method III - SVAT Multi-layered canopy model (Leuning et al., 1995; Wang&Leuning, 1998) Leaf-level model Stomatal conductance photosynthesis (Collatz et al., 1992) energy partitioning Radiation sub-model Rates of absorption by sunlit/shaded leaves and soil (Goudrian&van Laar, 1994) Soil sub-model moisture temperature evaporation Optimisation by Levenberg-Marquard algorithm

  12. Method III - SVAT y = 0.987x R2 = 0.76

  13. Results I – comparison

  14. Results II – fallow

  15. Conclusions • parameterisation fails during fallow • NN based on hourly values doesn‘t capture medium size gaps • to aggregate monthly and yearly NEE values • NN can be driven on a daily basis • statistical and process orientated models lead • to comparable results

  16. Thanks Thank you for your attention! This study would not be feasible without the assistance of many people in the field and in the office as well as without the financing by the seco and the EU. Thanks!

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