MSc Presentation Emma
- 1. Eco-hydrology of trees during DroughtEvapotranspiration of ForestsBy: Emma Daniels (0448257)Supervisors:Prof. dr. ir. M.F.P. (Marc) Bierkens (Physical Geography)Dr. S.C. (Stefan) Dekker (CopernicusInstitute)
- 2. Research objectivesTo create an eco-hydrological model to investigate the hypothesis that trees create deep fine root mass to prevent carbon loss under severe droughtTo calibrate, validate and simulate water and carbon fluxes for deciduous and evergreen tree species in Germany and France respectively2Introduction - Model - Results - Discussion - Conclusion
- 3. ObservationsEvaporation 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. 3Introduction- Model - Results - Discussion- Conclusion
- 4. HypothesisEvery 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. 4Introduction - Model - Results - Discussion - Conclusion
- 5. Climate changeChanges in hydrological cycleMagnitude and frequency of droughts in Southern Europe will increaseReduction in carbon storage by forestsShift towards drought tolerant species?CO2 fertilization effect?5Multi-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. 6Introduction - Model - Results - Discussion - Conclusion
- 7. Vegetation Optimality ModellingAssumption: 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 conditionsObjective function: ‘Net Carbon Profit’Adjustable variables: Electron transport capacity (Jmax)Root distribution 7Introduction - Model - Results - Discussion - Conclusion
- 8. DE-Hai: Hainich National Park in Thuringia region FR-Pue: 35 km NW of Montpellier 8Introduction - Model - Results - Discussion - Conclusion
- 9. Fluxnet sitesHainich (DE-Hai) Central Germany Mean 6.8 °C and 775 mm Temperate climateTree age 1-250 yearsDeciduous treesFagussylvaticaL., 65%Soil: loamy clayPuechabon (FR-Pue) Herault region in FranceMean 13.5 °C and 872 mm Subtropical-Mediterranean climate Evergreen treesQuercusilex L.Soil: silty clay loam9Introduction - Model - Results - Discussion - Conclusion
- 10. Matlab ModelMultilayer canopyDiffuse and direct sunlightLeaf stomata Stomatal conductance Gs (gas exchange) Root mass distributionHypothetical root mechanism10Introduction - Model - Results - Discussion - Conclusion
- 11. Main equationsRelationship 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.11Introduction - Model - Results - Discussion - Conclusion
- 12. Hypothetical root mechanismCO2 = Ag – Rs – RwNCP = Ag – Rft – Rv – RrCumulative 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 roots12Introduction - Model - Results - Discussion - Conclusion
- 13. Parameter calibrationDiffeRential Evolution Adaptive Metropolis (DREAM) algorithmParameters optimised off-line with this stochastic optimization algorithm are: ce, me, CRland JmaxtopPhenology, which was modelled with a degree-day method, was optimized with an unconstrained nonlinear optimization function13Introduction - Model - Results - Discussion - Conclusion
- 14. Parameter settingResults 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. 14Introduction - Model - Results - Discussion - Conclusion
- 15. Validation summer 2005 15Introduction - Model - Results - Discussion - Conclusion
- 16. Soil moisture 200516Introduction - Model - Results - Discussion - Conclusion
- 17. CO2 and H2O fluxes 200517Introduction - Model - Results - Discussion - Conclusion
- 18. Simulations 200318Introduction - Model - Results - Discussion - Conclusion
- 19. Discussion Nighttime fluxesNumerical instabilityWater stress:Mq<0.9*MqxDegree-day method19Introduction - Model - Results - Discussion - Conclusion
- 20. HypothesisEvery 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. 20Introduction - Model - Results - Discussion - Conclusion
- 21. Root adaptation 2003Net Carbon Profit evaluation period of 1 to 7 days21Introduction - Model - Results - Discussion - Conclusion
- 22. ConclusionsObserved and modelled CO2 and H2O fluxes are similarWith hypothetical root mechanism in place:Higher Net Carbon ProfitMore carbon assimilation and higher H2O fluxes during droughtDecrease of evaporation only simulated under more severe drought22Introduction - Model - Results - Discussion - Conclusion
- 23. Questions?23