1. Physical and Biological Processes that Controls Water Vapor Exchange between Vegetation and the Atmosphere Dennis Baldocchi Department of Environmental Science, Policy and Management University of California, Berkeley Shortcourse on ADAPTIVE MANAGEMENT OF MEDITERRANEAN FOREST ECOSYSTEMS TO CLIMATE CHANGE Zaragosa, Spain May, 2010

2. Outline Processes, Supply vs Demand, Short and Long Time scales –Short Energy Meteorology –Long Leaf area index Nutrition Plant Functional type –Short to Long Surface Conductance Soil Moisture Time –Day/Night –Seasonal –Interannual Space –Land Use –PBL/Landscape –Globe

3. Water and the Environment: Biogeophysical-Ecohydrological View

4. Processes and Linkages: Roles of Time and Space Scales

5. The Effects of Forests and Crops on Surface Energy Balance

6. Temperate vs Boreal Forests

7. Role of Vegetation Type (grass vs woodland) on California Energy Balance

8. Chapin et al 2008, Frontiers in Ecology and Environment Global Change and Microclimates

9. Evaporation Evaporation is the “physical process by which a liquid or solid is converted to a gaseous state” (Glossary of Meteorology). Plant canopies introduce water vapor into the atmosphere via transpiration and the evaporation of water from the soil and free water on the leaves and stems.

10. Potential Evaporation “the evaporation from an extended surface of a short grass that is supplied with water and the canopy covers the ground completely.”

11. Can Potential ET > Pan ET?

12. Yes! The surface area of transpiring leaf area exceeds the surface area of a water body, as an evaporation pan. Water is partially transparent to sunlight and stores heat energy. –the energy available to evaporate water will be different than that used to evaporate water from vegetation Evaporation pans are also subject to error due to the oasis effect and from animals drinking from it.

13. Potential Evaporation: Priestley-Taylor Equation s, slope of saturation vapor pressure-Temperature curve , psychrometric constant Rn, net radiation (W m -2 ) S, Soil heat flux (W m -2 )

14. Actual Evaporation Aerodynamic Approach Energy Balance (Bowen Ratio) Approach Eddy Covariance Lysimeter Evaporation Pan Soil Water Budget Combination Method –Penman Equation –Penman-Monteith Equation –Modified Priestly-Taylor Method Climatological Methods –Thornthwaite Equation

15. Aerodynamic Method E is f() of humidity Deficits and Turbulent Mixing

16. Eddy Exchange of Kwater ~ K momentum K w ~ K m Evaluate E by Measuring Vertical Gradients of vapor pressure (e) and Wind Speed (u)

17. Energy Balance Method E, latent heat flux density H, sensible heat flux density S, soil heat flux density Rn, net Radiation flux density

18. Bowen Ratio Method, measured with temperature and humidity gradients

19. Eddy Covariance, Flux Density: mol m -2 s -1 or J m -2 s -1

20. Thornthwaite Equ L is daylength in hours N is number of days in a month T is mean monthly air temperature I is a heat index, computed as a function of the sum of 12 monthly temperature indices, i

21. Why Should the Thornthwaite Equation be used with Caution? Evaporation and temperature are out of phase with one another It has no physiological feedback Can not be applied to short term studies temperature is not a suitable proxy for radiation on short time scales.

22. Penman Monteith Equation Function of: Available Energy (Rn-S) Vapor Pressure Deficit (D) Aerodynamic Conductance (Gh) Surface Conductance (Gs)

23. Results and Discussion

24. Effects of Functional Types and R sfc on Normalized Evaporation R c is a f(LAI, N, soil moisture, Ps Pathway)

25. Stomatal Conductance Scales with Photosynthesis Wilson et al. 2001, Tree Physiology Schulze et al 1994. Annual Rev Ecology Photosynthetic Capacity Scales with Nitrogen Stomatal Conductance scales with Nitrogen Stomatal Conductance Scales with N, via Photosynthesis

26. Canopy Surface Conductance does not equal the Canopy Stomatal Conductance Be Careful about using G can to compute isotopic discrimination

27. Linking Water and Carbon: Potential to assess G c with Remote Sensing Xu + DDB, 2003 AgForMet

28. Processed by M. Falk Gc Exhibits Scale ‘Invariance’

29. Effects of Leaf Area and Photosynthetic Capacity on Normalized Evaporation: Well-Watered Conditions Canveg Model, Baldocchi and Meyers, 1998 AgForMet Priestley-Taylor = 1.26

30. Effects of Leaf Area and Photosynthetic Capacity on Normalized Evaporation: Watered-Deficits

31. Eco-hydrology: ET, Functional Type, Physiological Capacity and Drought

32. Use Appropriate and Root-Weighted Soil Moisture Soil Moisture, arthimetic average Chen, Baldocchi et al, WWR 2009. Soil Moisture, root-weighted

33. ET and Soil Water Deficits: Root-Weighted Soil Moisture Baldocchi et al., 2004 AgForMet

34. ET and Soil Water Deficits: Water Potential Root-Weighted Soil Moisture Matches Pre-Dawn Water Potential ET of Annual Grass responds to water deficits differently than Trees

35. Leaf Area Index scales with Water Balance Deficits

37. LAI and Ps Capacity also affects Soil vs Total Evaporation

38. Seasonal and Annual Time Scales Potential and Actual Evaporation are Decoupled in Semi-Arid System

40. Evergreen and Deciduous Mediterranean Oaks Annual Grassland

41. Interannual Variation

43. Stand Age also affects differences between ET of forest vs grassland

44. Case Study: Energetics of a Grassland and Oak Savanna Measurements and Model

45. Case Study: Savanna Woodland adjacent to Grassland 1.Savanna absorbs much more Radiation (3.18 GJ m -2 y -1 ) than the Grassland (2.28 GJ m -2 y -1 ) ;  Rn: 28.4 W m -2

46. Landscape Differences On Short Time Scales, Grass ET > Forest ET Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, 2008

47. Role of Land Use on ET: On Annual Time Scale, Forest ET > Grass ET Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, 2008

48. Tapping Groundwater Increases Ecosystem Resilience, And Reduces Inter-annual Variability in ET

49. 5. Mean Potential Temperature differences are relatively small (0.84 C; grass: 290.72 vs savanna: 291.56 K); despite large differences in Energy Fluxes--albeit the Darker vegetation is Warmer Compare to Greenhouse Sensitivity ~2-4 K/(4 W m -2 )

50. Baldocchi and Ryu, 2010, in press Fluxnet Published Eddy Covariance

51. Fisher et al., 2008 Global ET pdf, 1989, ISLSCP

52. FluxNET WUE Carbon Assimilation Scales with Water Use

53. About 46% of Annual Precipitation is Lost Via Forest Evaporation, Globally Baldocchi and Ryu, 2010, in press

54. Small Inter-Annual Variability in ET compared to PPT In Semi-Arid Regions, Most ET is lost as Precipitation

55. Maximum ET is Capped (< 500 mm/y) Near Lower Limit of Mediterranean PPT Baldocchi et al Ecol Applications, in press

56. Test of the Budyko Curve with Evaporation Flux Measurements Evap Demand >> Precipitation Precipitation >> Evaporation, which Is energy limited

57. Fluxnet Analysis of Evaporation limited by Energy or Rain

58. Forest Biodiversity is Negatively Correlated with Normalized Evaporation Baldocchi, 2005 In: Forest Diversity and Function: Temperate and Boreal Systems.

59. ET: Spatial Scale

60. Assessing Spatial Averages with Subgrid Variability

61. Sub-Grid Variability: MODIS and IKONOS Baldocchi et al 2005 Tellus Use Power Law scaling to Estimate small scale Variance

62. Errors in ET Scaling Baldocchi et al 2005 Tellus

63. Total Latent heat flux Canopy Transpiration Soil Evaporation Intercepted Evaporation Fisher et al Global ET Model f wet fgfg f APAR f IPAR fcfc fTfT f SM T opt

64. Fisher et al, 2008 Global ET, 1989, ISLSCP ET (mm/y)reference 613Fisher et al 286Mu et al. 2007 467Van den Hurk et al 2003 649Boslilovich 2006 560Jackson et al 2003 410Yuan et al 2010 550Jung et al, in press 272-441Dirmeyer et al 2006

65. Conclusions Biophysical data and theory help explain powerful ideas of Budyko and Monteith that provide framework for upscaling and global synthesis of ET –ET scales with canopy conductance, which scales with LAI and Ps capacity, which scales with precipitation and N

66. Be Careful Short-Term Differences in Potential and Actual ET may not hold at Annual Time Scales –Grass has greater potential for ET than Savanna Sub-Grid Variability in surface properties can produce huge errors in upscaled ET at the 1 km Modis Pixel Scale