1 / 66

Nicolas Delpierre (dir. Eric Dufrêne)

Etude du déterminisme des variations interannuelles des échanges carbonés des écosystèmes forestiers européens: une approche basée sur la modélisation des processus. Nicolas Delpierre (dir. Eric Dufrêne). Saclay, 14 décembre 2009. Terrestrial vegetation modulates atmospheric [CO 2 ].

noe
Download Presentation

Nicolas Delpierre (dir. Eric Dufrêne)

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. Etude du déterminisme des variations interannuelles des échanges carbonés des écosystèmes forestiers européens: une approche basée sur la modélisation des processus Nicolas Delpierre (dir. Eric Dufrêne) Saclay, 14 décembre 2009

  2. Terrestrial vegetation modulates atmospheric [CO2] Fossil fuels + Land Use Change 2 Le Quéré et al., 2009

  3. Terrestrial vegetation modulates atmospheric [CO2] Fossil fuels + Land Use Change Atm increase Atmosph. 40% 2 Le Quéré et al., 2009

  4. Terrestrial vegetation modulates atmospheric [CO2] Fossil fuels + Land Use Change Atm increase Ocean uptake Ocean 30% Atmosph. 40% 2 Le Quéré et al., 2009

  5. Terrestrial vegetation modulates atmospheric [CO2] Fossil fuels + Land Use Change Atm increase Ocean uptake Vegetation uptake Ocean 30% Atmosph. 40% Vegetation 30% • Vegetation C uptake accounts for most of the IAV • Forests ~60% of vegetation uptake 2 Le Quéré et al., 2009

  6. FLUXNET Monitoring the vegetation / atmosphere C exchanges Forest sites Non-forest sites CARBOEUROPE network 8

  7. CARBOEUROPE Ecological gradient Coniferous forests Pinus spp. Picea spp. Deciduous forests Fagus sylvatica Quercus spp. Evergreen Broadleaves Quercus ilex Mixed forests 9

  8. CARBOEUROPE Annual NEP sums 2001 2001 2001 2001 2003 2003 2003 2003 2005 2005 2005 2005 2007 2007 2007 2007 Boreal (Pinus) Temperate (Picea) Temperate (Fagus) Mediterranean (Q.ilex) 11

  9. CARBOEUROPE Explaining Intersite variations of the C balance Southern <52°N Northern >52°N One color = One site GPP (gC / m² / y) R²=0.40 R²=0.80 Temperature Water balance adapted from Reichstein et al., 2007 12

  10. CARBOEUROPE Explaining Intersite variations of the C balance Southern <52°N Northern >52°N GPP (gC / m² / y) R²=0.40 R²=0.80 Reco (gC / m² / y) R²=0.30 R²=0.70 Temperature Water balance adapted from Reichstein et al., 2007 12

  11. CARBOEUROPE Explaining Intersite variations of the C balance Southern <52°N Northern >52°N What about interannual variations ??? GPP (gC / m² / y) R²=0.40 R²=0.80 Reco (gC / m² / y) R²=0.30 R²=0.70 NEP (gC / m² / y) R²<0.10 R²=0.20 adapted from Reichstein et al., 2007 Temperature Water balance 12

  12. CARBOEUROPE Explaining Interannual variations of the C balance Southern <52°N Northern >52°N Significant Relationships 5 sites over 25 GPP (gC / m² / y) Significant Relationships 3 sites over 25 Reco (gC / m² / y) Significant Relationships 4 sites over 25 NEP (gC / m² / y) Temperature Water balance 13

  13. Empirical vs. Process-based models Statistical models 14

  14. Empirical vs. Process-based models Statistical models Process Based model 14

  15. Empirical vs. Process-based models Statistical models Process Based model Quantify the influences ofClimateandBiological drivers operating at several timescales to determine the interannual variations of GPP, Reco and NEP 14

  16. Criteria for using CASTANEA as a deconvolution tool 1) Biological realism of the simulated processes • Seasonality of photosynthesis in conifers • Seasonality of photosynthesis in deciduous species - Spring phase - Autumn phase 2) Accuracy of flux simulations • Evaluation of data quality • Model validation at multiple time scales • Availability of Statistical tools • for signal deconvolution • SA technique revealing seasonal influences • SA technique revealing influences at multiple time scales 15

  17. OUTLINE 1. Materials & methods An overview of the CASTANEA model 2. Modelling canopy senescence in deciduous forests 3. Model Validation 4. Influence of climate and biological drivers across time scales 16

  18. 1.Materials & methods An overview of the CASTANEA model

  19. CASTANEA model CO2 Water vapour Solar radiation temperature GPP Transpiration Radiation interception Global PAR Photosynthesis Stomatal Cond. Dufrêne et al., 2005 17

  20. CASTANEA model CO2 Water vapour Solar radiation Precipitations temperature GPP Canopy evaporation Transpiration Radiation interception Global PAR Canopy interception Photosynthesis Stomatal Cond. Throughfall Soil evaporation Stem flow Litter Surface Root zone drainage Dufrêne et al., 2005 17

  21. CASTANEA model CO2 Water vapour Solar radiation Precipitations temperature GPP Reco Canopy evaporation Transpiration Radiation interception Global PAR Canopy interception Photosynthesis Stomatal Cond. Carbon Allocation C leaves Throughfall Soil evaporation Growth Respiration Heterotrophic Respiration C aerial wood Stem flow Maintenance Respiration C reserves C litter Litter C surface Surface C coarse roots C deep Root zone C fine roots drainage Dufrêne et al., 2005 17

  22. CASTANEA Modelling the C balance of European forests Deciduous forests Soroe Hainich (Temperate Beech) Hesse Coniferous forests Hyytiälä (Boreal Pine) Tharandt (Temperate Spruce) Evergreen Bleaves Puéchabon (Mediterranean Q. ilex) 18

  23. 2. Modelling canopy senescence in deciduous forests

  24. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall N resorption Davi et al. (2005) Modelled NEP 19

  25. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall N resorption Davi et al. (2005) Modelled NEP 19

  26. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall N resorption Davi et al. (2005) Modelled NEP 19

  27. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall N resorption Davi et al. (2005) Modelled NEP 19

  28. Autumn phenology The RENECOFOR dataset (1997-2006) Oak Beech RENECOFOR observations Sen90 = 90% x 36 trees 20

  29. Designing a bioclimatic model Literature review • Other potential drivers • Water balance • Mineral deficits • atmospheric pollution • parasites… 21

  30. Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec Designing a bioclimatic model Model formulation Tbase Temperature Model parameters • Senescence initiation date • Base temperature • Critical T sum Daylength Temperature Model formulation • non-linear T x DayLength effects Relative senescence Relative senescence 22

  31. Senescence model assessment (1) Beech Simulations Fitting subset Validation subset Observations Prediction error =13 days (Observation resolution = 7 days) 23 Delpierre et al., 2009

  32. Senescence model assessment (2) Beech observations Validation statistics simulations Yellowing date (DoY) 1997 1999 2001 2003 2005 Important reduction of the prediction error • Observation uncertainty averaging • Reduced contribution of extreme dates 24

  33. Canopy senescence Original modelling scheme Sep Sep Oct Oct Nov Nov Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall • Improved N resorption Davi et al. (2005) 25

  34. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall • Improved N resorption Davi et al. (2005) Modelled NEP Over estimation 25

  35. Canopy senescence Original modelling scheme Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Hesse forest Fagus sylvatica 49°N Leaf fall • Improved N resorption x N investment ø Davi et al. (2005) Modelled NEP • Improved 25

  36. 3. Model Validation

  37. Model validation across time scales DAILY 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 Hyytiälä (Pinus) R²=0.92 bias= +0.11 Tharandt (Picea) R²=0.91 bias= +0.10 Hainich (Fagus) R²=0.95 bias= -0.08 Puéchabon (Q. ilex) R²=0.74 bias= +0.21 26

  38. Model validation across time scales ANNUAL üModel validated ûModel challenged FIHyy RMSE=13, r²=0.82 DETha RMSE=66, r²=0.51 FRPue RMSE=59, r²=0.82 • CASTANEA reproduces 36% - 82% of C flux interannual variance 27

  39. 4. Influence of climate & biological drivers across time scales

  40. Defining Flux IAV across time scales GPP Tharandt (Picea abies) 2000-2007 28

  41. Defining Flux IAV across time scales Jan Dec Jul Apr Oct Mean annual pattern GPP Tharandt (Picea abies) 2000-2007 29

  42. DefiningFlux IAV across time scales GPP Tharandt (Picea abies) 2000-2007 30

  43. DefiningFlux IAV across time scales GPP Tharandt (Picea abies) 2000-2007 30

  44. Defining Flux IAV across time scales GPP Tharandt (Picea abies) 2000-2007 31

  45. Defining Flux IAV across time scales integration GPP Tharandt (Picea abies) 2000-2007 31

  46. No effect No effect No effect No effect No effect Conifers 32

  47. Climate and biological drivers interact at different time scales hour day month year hour day month year Climate drivers Biological drivers Global radiation Temperature VPD Leaf Area Index Variance index Variance index annual cycle annual cycle daily cycle 33

  48. Climate and biological drivers interact at different time scales hour day month year hour day month year Climate drivers Biological drivers Global radiation Temperature VPD GPP Leaf Area Index GPP Variance index Variance index annual cycle annual cycle daily cycle • Climate modulates short-term flux variability • Climate + Biological drivers modulate flux IAV 33

  49. Constrained simulations Single driver contribution to flux modulation Hyytiälä, Boreal Pine blue = « mean Rg » reference grey = original flux (year 2000) Day of Year Proper Rg effect on GPP Day of Year 34

  50. Constrained simulations Hyytiälä, Boreal Pine 2000 2002 2004 2006 Hyytiälä, Boreal Pine 2000 2002 2004 2006 8 years of daily GPP anomalies due to radiation 8 years of daily GPP anomalies due to Water Stress 35

More Related