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Eco-hydrology of trees during Drought Evapotranspiration of Forests By: Emma Daniels (0448257) Supervisors: Prof. dr. ir. M.F.P. (Marc) Bierkens	(Physical Geography) Dr. S.C. (Stefan) Dekker 		(CopernicusInstitute)
Research objectives To create an eco-hydrological model to investigate the hypothesis that trees create deep fine root mass to prevent carbon loss under severe drought To calibrate, validate and simulate water and carbon fluxes for deciduous and evergreen tree species in Germany and France respectively 2 Introduction - Model - Results - Discussion - Conclusion
Observations Evaporation observations of forests in the Netherlands during periods of regular drought show that trees are able to evaporate almost at full potential, while surrounding crops and grasses show an evaporation reduction (Schuurmans, 2008). Leuzingeret al. (2005) found that during the extreme drought in the summer of 2003 daily peak values of sap flow decreased to only about half of the early summer maxima in FagussylvaticaL.  3 Introduction- Model - Results - Discussion- Conclusion
Hypothesis Every daya tree will maximize its Net Carbon Profit by the most optimal choice of the following  strategies:  (1) keeping assimilation going by replacing fine root mass and extracting water from deeper down the profile or  (2) decreasing maintenance respiration by decreasing leave area.  4 Introduction - Model - Results - Discussion - Conclusion
Climate change Changes in hydrological cycle Magnitude and frequency of droughts in Southern Europe will increase Reduction in carbon storage by forests Shift towards drought tolerant species? CO2 fertilization effect? 5 Multi-model mean changes in evaporation (mm /day). Changes are annual means for the SRES A1B scenario for the period 2080 to 2099 relative to 1980 to 1999. Introduction - Model - Results - Discussion - Conclusion
6 Introduction - Model - Results - Discussion - Conclusion
Vegetation Optimality Modelling Assumption: Natural species have co-evolved with their environment over a long period of time and natural selection has led to a vegetation composition that is most suited for the given environmental conditions Objective function: ‘Net Carbon Profit’ Adjustable variables:  Electron transport capacity (Jmax) Root distribution  7 Introduction - Model - Results - Discussion - Conclusion
DE-Hai: Hainich National Park in Thuringia region   FR-Pue: 35 km NW of Montpellier  8 Introduction - Model - Results - Discussion - Conclusion
Fluxnet sites Hainich (DE-Hai)  Central Germany  Mean 6.8 °C and 775 mm  Temperate climate Tree age 1-250 years Deciduous trees FagussylvaticaL., 65% Soil: loamy clay Puechabon (FR-Pue)  Herault region in France Mean 13.5 °C and 872 mm  Subtropical-Mediterranean climate  Evergreen trees Quercusilex L. Soil: silty clay loam 9 Introduction - Model - Results - Discussion - Conclusion
Matlab Model Multilayer canopy Diffuse and direct sunlight Leaf stomata  Stomatal conductance Gs (gas exchange)  Root mass distribution Hypothetical root mechanism 10 Introduction - Model - Results - Discussion - Conclusion
Main equations Relationship between Et and Ag for a fixed electron transport rate (JA) and atmospheric  vapor deficit (Dv), but variable stomatal conductivity (Gs). The upper limit for Ag is determined by JA, while the initial slope of the relationship is determined by Dv. 11 Introduction - Model - Results - Discussion - Conclusion
Hypothetical root mechanism CO2 = Ag – Rs – Rw NCP = Ag – Rft – Rv – Rr Cumulative NCP over  	the evaluated time period  Up to 5% of the total root mass is replaced from the soil layer with the lowest water content  Cost of creating new fine roots 12 Introduction - Model - Results - Discussion - Conclusion
Parameter calibration DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm Parameters optimised off-line with this stochastic optimization algorithm are: ce, me, CRland Jmaxtop Phenology, which was modelled with a degree-day method, was optimized with an unconstrained nonlinear optimization function 13 Introduction - Model - Results - Discussion - Conclusion
Parameter setting Results for the last 5000 runs of the DREAM optimisation of the parameters me and ce (empirical parameters defining water use efficiency) and CRl (leaf respiration coefficient) and Jmaxtop (electron transport capacity at the top of the canopy) for the Hainich site, showing the initial and final range and outcome.  14 Introduction - Model - Results - Discussion - Conclusion
Validation summer 2005  15 Introduction - Model - Results - Discussion - Conclusion
Soil moisture 2005 16 Introduction - Model - Results - Discussion - Conclusion
CO2 and H2O fluxes 2005 17 Introduction - Model - Results - Discussion - Conclusion
Simulations 2003 18 Introduction - Model - Results - Discussion - Conclusion
Discussion  Nighttime fluxes Numerical instability Water stress: Mq<0.9*Mqx Degree-day method 19 Introduction - Model - Results - Discussion - Conclusion
Hypothesis Every day a tree will maximize its Net Carbon Profit by the most optimal choice of the following  strategies:  (1) keeping assimilation going by replacing fine root mass and extracting water from deeper down the profile or  (2) decreasing maintenance respiration by decreasing leave area.  20 Introduction - Model - Results - Discussion - Conclusion
Root adaptation 2003 Net Carbon Profit evaluation period of 1 to 7 days 21 Introduction - Model - Results - Discussion - Conclusion
Conclusions Observed and modelled CO2 and H2O fluxes are similar With hypothetical root mechanism in place: Higher Net Carbon Profit More carbon assimilation and higher H2O fluxes during drought Decrease of evaporation only simulated under more severe drought 22 Introduction - Model - Results - Discussion - Conclusion
Questions? 23
Input parameters 24
Dendrology Puechabon 25
Soil texture triangle 26

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MSc Presentation Emma

  • 1. Eco-hydrology of trees during Drought Evapotranspiration of Forests By: Emma Daniels (0448257) Supervisors: Prof. dr. ir. M.F.P. (Marc) Bierkens (Physical Geography) Dr. S.C. (Stefan) Dekker (CopernicusInstitute)
  • 2. Research objectives To create an eco-hydrological model to investigate the hypothesis that trees create deep fine root mass to prevent carbon loss under severe drought To calibrate, validate and simulate water and carbon fluxes for deciduous and evergreen tree species in Germany and France respectively 2 Introduction - Model - Results - Discussion - Conclusion
  • 3. Observations Evaporation observations of forests in the Netherlands during periods of regular drought show that trees are able to evaporate almost at full potential, while surrounding crops and grasses show an evaporation reduction (Schuurmans, 2008). Leuzingeret al. (2005) found that during the extreme drought in the summer of 2003 daily peak values of sap flow decreased to only about half of the early summer maxima in FagussylvaticaL. 3 Introduction- Model - Results - Discussion- Conclusion
  • 4. Hypothesis Every daya tree will maximize its Net Carbon Profit by the most optimal choice of the following strategies: (1) keeping assimilation going by replacing fine root mass and extracting water from deeper down the profile or (2) decreasing maintenance respiration by decreasing leave area. 4 Introduction - Model - Results - Discussion - Conclusion
  • 5. Climate change Changes in hydrological cycle Magnitude and frequency of droughts in Southern Europe will increase Reduction in carbon storage by forests Shift towards drought tolerant species? CO2 fertilization effect? 5 Multi-model mean changes in evaporation (mm /day). Changes are annual means for the SRES A1B scenario for the period 2080 to 2099 relative to 1980 to 1999. Introduction - Model - Results - Discussion - Conclusion
  • 6. 6 Introduction - Model - Results - Discussion - Conclusion
  • 7. Vegetation Optimality Modelling Assumption: Natural species have co-evolved with their environment over a long period of time and natural selection has led to a vegetation composition that is most suited for the given environmental conditions Objective function: ‘Net Carbon Profit’ Adjustable variables: Electron transport capacity (Jmax) Root distribution 7 Introduction - Model - Results - Discussion - Conclusion
  • 8. DE-Hai: Hainich National Park in Thuringia region FR-Pue: 35 km NW of Montpellier 8 Introduction - Model - Results - Discussion - Conclusion
  • 9. Fluxnet sites Hainich (DE-Hai) Central Germany Mean 6.8 °C and 775 mm Temperate climate Tree age 1-250 years Deciduous trees FagussylvaticaL., 65% Soil: loamy clay Puechabon (FR-Pue) Herault region in France Mean 13.5 °C and 872 mm Subtropical-Mediterranean climate Evergreen trees Quercusilex L. Soil: silty clay loam 9 Introduction - Model - Results - Discussion - Conclusion
  • 10. Matlab Model Multilayer canopy Diffuse and direct sunlight Leaf stomata Stomatal conductance Gs (gas exchange) Root mass distribution Hypothetical root mechanism 10 Introduction - Model - Results - Discussion - Conclusion
  • 11. Main equations Relationship between Et and Ag for a fixed electron transport rate (JA) and atmospheric vapor deficit (Dv), but variable stomatal conductivity (Gs). The upper limit for Ag is determined by JA, while the initial slope of the relationship is determined by Dv. 11 Introduction - Model - Results - Discussion - Conclusion
  • 12. Hypothetical root mechanism CO2 = Ag – Rs – Rw NCP = Ag – Rft – Rv – Rr Cumulative NCP over the evaluated time period Up to 5% of the total root mass is replaced from the soil layer with the lowest water content Cost of creating new fine roots 12 Introduction - Model - Results - Discussion - Conclusion
  • 13. Parameter calibration DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm Parameters optimised off-line with this stochastic optimization algorithm are: ce, me, CRland Jmaxtop Phenology, which was modelled with a degree-day method, was optimized with an unconstrained nonlinear optimization function 13 Introduction - Model - Results - Discussion - Conclusion
  • 14. Parameter setting Results for the last 5000 runs of the DREAM optimisation of the parameters me and ce (empirical parameters defining water use efficiency) and CRl (leaf respiration coefficient) and Jmaxtop (electron transport capacity at the top of the canopy) for the Hainich site, showing the initial and final range and outcome. 14 Introduction - Model - Results - Discussion - Conclusion
  • 15. Validation summer 2005 15 Introduction - Model - Results - Discussion - Conclusion
  • 16. Soil moisture 2005 16 Introduction - Model - Results - Discussion - Conclusion
  • 17. CO2 and H2O fluxes 2005 17 Introduction - Model - Results - Discussion - Conclusion
  • 18. Simulations 2003 18 Introduction - Model - Results - Discussion - Conclusion
  • 19. Discussion Nighttime fluxes Numerical instability Water stress: Mq<0.9*Mqx Degree-day method 19 Introduction - Model - Results - Discussion - Conclusion
  • 20. Hypothesis Every day a tree will maximize its Net Carbon Profit by the most optimal choice of the following strategies: (1) keeping assimilation going by replacing fine root mass and extracting water from deeper down the profile or (2) decreasing maintenance respiration by decreasing leave area. 20 Introduction - Model - Results - Discussion - Conclusion
  • 21. Root adaptation 2003 Net Carbon Profit evaluation period of 1 to 7 days 21 Introduction - Model - Results - Discussion - Conclusion
  • 22. Conclusions Observed and modelled CO2 and H2O fluxes are similar With hypothetical root mechanism in place: Higher Net Carbon Profit More carbon assimilation and higher H2O fluxes during drought Decrease of evaporation only simulated under more severe drought 22 Introduction - Model - Results - Discussion - Conclusion