1. Lessons Learned about Ecosystem Evaporation from Long-term, Global Flux Networks Dennis Baldocchi and Youngryel Ryu University of California, Berkeley EPFL LATSIS Symposium Lausanne, Switzerland October, 2010

2. Motivation, Part 1 Most Annual Water Budgets are Indirect, Inferred from Water Budgets (ET ~ Precipitation – Runoff) Global Network of Direct, Continuous and Multi-year Carbon and Water Eddy Covariance Flux Measurements Exists that has been Under-Utilized with regards to the Annual Water Budget of Terrestrial Ecosystems

3. …it is becoming possible to routinely measure evaporation and soil moisture, based on surface and satellite-mounted observation. We can therefore move away from merely closing a water budget, towards considering all the components and dynamics of the hydrological cycle based on observational evidence of all fluxes and states. – A.J. Dolman and de Jeu, 2010 Nature Geosciences Motivation, Part 2

4. Big Picture Question Regarding Predicting and Quantifying Global Evaporation: How can We Be ‘Everywhere All the Time?’

5. Over Arching Questions What is Annual ET, as measured directly by Eddy Covariance? How does Annual ET respond to Precipitation and Available Energy, & Drought? What is Annual ET at Regional and Global Scales using New Generation of Ecohydrological Information, Flux Networks and Satellite-based Remote Sensing?

6. Eddy Covariance Technique Direct In situ Quasi-Continuous

7. Restrictions and Conditions for Producing Annual Water Budgets from Eddy Covariance Flux Measurements Steady-State Conditions, dC/dt ~ 0 Extensive Fetch, 100m - 1km Level Terrain, < 0-10 o slope Gaps-Filled Accurately, with Minimum Bias

8. FLUXNET: From Sea to Shining Sea 500+ Sites, circa 2009 www.fluxdata.org

9. Global distribution of Flux Towers Covers Climate Space Well

10. Is There an Energy Balance Closure Problem?: Evidence from FLUXNET Wilson et al, 2002 AgForMet Timing/SeasonInstrument/Canopy Roughness

11. Is the Energy Balance Closure Problem a Red-Herring? Forest Energy Balance is Prone to Close when Storage is Considered Lindroth et al. 2010, Biogeoscience

12. Evidence for Energy Balance Closure: Other Examples from Crops, Grasslands and Forests with Careful Attention to Soil and Bole Heat Storage

13. Reasonable Agreement Observed between Eddy Flux measurements of ET + Catchment Studies Scott, 2010, AgForMet Arizona Grassland

14. Part 1, Reading the Data of Annual Flux Measurements

15. Flux Measurements Reveal Diverse Information on Seasonal Cycles

16. TakeHome Points: ET > 200 mm/y Median = 402 mm/y Skewed Distribution, Max ~ 2300 mm/y

17. Classic View, The Budyko Curve with Evaporation Flux Measurements Evap Demand >> Precipitation Precipitation >> Evaporation, which Is energy limited Defines Bounds, But Many Sources of Variance Remain X and Y are AutoCorrelated, through ppt

18. Annual Sums of Latent Energy Scales with Equilibrium Energy, in a Saturating Fashion

19. Annual Precipitation explains 75% of the Variation in Water Lost Via Forest Evaporation, Globally About 46% of Annual Precipitation to Forests, Globally, is Evaporated to the Atmosphere

20. A linear additive model has the following statistics: ET = -141 + 116*Rn + 0.378 * ppt, r 2 = 0.819. The color bar refers to annual ET Statistical Model between Annual Forest ET, Net Radiation and Precipitation

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

22. Maximum ET is Capped (< 500 mm/y) Near Lower Limit of Mediterranean PPT Baldocchi et al. 2010 Ecological Applications

23. Tapping Groundwater Increases Ecosystem Resilience, And Reduces Inter-annual Variability in ET Consistent with Findings of Stoy et al, Later-Succession Ecosystems invest to Reduce Risk

24. Pre-Dawn Water Potential Represents Mix of Dry Soil and Water Table Miller et al WRR, 2010 During Summer MidDay Water Potential is Less Negative than Shallow Soil Water Potential

25. Don’t Forget Ecology Stand Age also affects differences between ET of forest vs grassland

26. Part 2, Global Integration of ET ‘Space: The final frontier … To boldly go where no man has gone before’ Captain James Kirk, Starship Enterprise

27. Motivation – Global Estimates of ET range from 5.8-8.5 10 13 m 3 /y – Current Class of Remote Sensing-based Estimates of Global ET models rely on Empirical approach (machine learning technique) Form of the Penman-Monteith Equation, with poor constaint on surface Conductance Form of the Priestley-Taylor Equation, with empirical tuning of alpha, with soil moisture deficits Many forcings come from coarse reanalysis data (several tens of km resolution) At most, LAI, NDVI, LST are used from satellite – We need a Biophysically-based Model, Driven with High- Resolution Spatio-Temporal Drivers for Diagnosis and Prediction and No Tuning Global ET with a Hybrid Remote-Sensing/Flux Measurement Approach

28. remote sensing of CO 2 Temporal scale Spatial scale [km] hour day week month year decade century local 0.1 1 10 100 1000 10 000 global forest inventory plot Countries EU plot/site tall tower obser- vatories Forest/soil inventories Eddy covariance towers Landsurface remote sensing From point to globe via integration with remote sensing (and gridded metorology) From: Markus Reichstein, MPI

29. Challenge for Landscape to Global Upscaling Converting Virtual ‘Cubism’ back to Virtual ‘Reality’ Realistic Spatialization of Flux Data Requires the Merging Numerous Data Layers with varying Time Stamps (hourly, daily, weekly), Spatial Resolution (1 km to 0.5 degree) and Data Sources (Satellites, Flux Networks, Climate Stations) and Using these Data to Force Mechanistic Biophysical Model

30. Lessons Learned from the CanOak Model 25+ years of Developing and Testing a Hierarchy of Scaling Models with Flux Measurements at Contrasting Oak Woodland Sites in Tennessee and California We Must: Couple Carbon and Water Fluxes Assess Non-Linear Biophysical Functions with Leaf-Level Microclimate Conditions Consider Sun and Shade fractions separately Consider effects of Clumped Vegetation on Light Transfer Consider Seasonal Variations in Physiological Capacity of Leaves and Structure of the Canopy

31. Necessary Attributes of Global Biophysical ET Model: Applying Lessons from the Berkeley Biomet Class and CANOAK Treat Canopy as Dual Source (Sun/Shade), Two-Layer (Vegetation/Soil) system – Treat Non-Linear Processes with Statistical Rigor (Norman, 1980s) Requires Information on Direct and Diffuse Portions of Sunlight – Monte Carlo Atmospheric Radiative Transfer model (Kobayashi + Iwabuchi,, 2008) Light transfer through canopies MUST consider Leaf Clumping – Apply New Global Clumping Maps of Chen et al./Pisek et al. Couple Carbon-Water Fluxes for Constrained Stomatal Conductance Simulations – Photosynthesis and Transpiration on Sun/Shade Leaf Fractions (dePury and Farquhar, 1996) – Compute Leaf Energy Balance to compute Leaf Saturation Vapor Pressure, IR emission and Respiration Correctly – Photosynthesis of C 3 and C 4 vegetation Must be considered Separately Use Emerging Ecosystem Scaling Rules to parameterize models, based on remote sensing spatio-temporal inputs – Vcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al;Schulze et al.; Wright et al.) – Seasonality in Vcmax is considered (Wang et al.)

32. Atmospheric radiative transfer Canopy photosynthesis, Evaporation, Radiative transfer Soil evaporation Beam PAR NIR Diffuse PAR NIR Albdeo->Nitrogen -> Vcmax, Jmax LAI, Clumping-> canopy radiative transfer dePury & Farquhar two leaf Photosynthesis model Rnet Surface conductance Penman-Monteith evaporation model Radiation at understory Soil evaporation shadesunlit BESS, Berkeley Evaporation Science Simulator

33. Help from ModisAzure -Azure Service for Remote Sensing Geoscience Scientific Results Download Reduction #1 Queue Source Metadata AzureMODIS Service Web Role Portal Request Queue Analysis Reduction Stage Data Collection Stage Source Imagery Download Sites... Reprojection Queue Derivation Reduction StageReprojection Stage Reduction #2 Queue Download Queue Scientists Science results Puts the Small Biomet Lab into the Global Ecology, Computationally-Intensive Ball Park

34. Automation – Downloads thousands of files of MODIS data from NASA ftp Reprojection – Converts one geo-spatial representation to another. – Example: latitude-longitude swaths converted to sinusoidal cells to merge MODIS Land and Atmosphere Products Spatial resampling – Converts one spatial resolution to another. – Example is converting from 1 km to 5 km pixels. Temporal resampling – Converts one temporal resolution to another. – Converts daily observation to 8 day averages. Gap filling – Assigns values to pixels without data either due to inherent data issues such as clouds or missing pixels. Masking – Eliminates uninteresting or unneeded pixels. – Examples are eliminating pixels over the ocean when computing a land product or outside a spatial feature such as a watershed. Tasked Performed with MODIS-AZURE h12v04h13v04h11v04h10v04h09v04h08v04 h12v05h11v05h10v05h09v05h08v05 h11v06h10v06h09v06h08v06

35. Photosynthetic CapacityLeaf Area Index Solar Radiation Humidity Deficits

36. Leaf Clumping Map, Chen et al. 2005C 4 Vegetation Map, Still et al. 2003

37. Validate Model Across FLUXNET

38. = 503 mm/y == 7.2 10 13 m 3 /y Ryu et al. in preparation

39. Global land evaporation: 503 mm yr-1

40. Ryu et al. unpublished MODIS-Driven Product Using Biophysics via Cloud Computing

41. Ryu et al. unpublished Down-Scale to Regions for Policy and Management Decisions

42. Scaling and Window Size

43. Water Management Issues: How Much Water is Lost from the Delta?

44. Global ET What is the Right Answer? (mm/y) referenceET (m 3 /y) 613Fisher et al 550Jung et al 2010, Nature6.5 10 13 286Mu et al. 2007 539 +/- 9Zhang et al. 2010, WRR 467Van den Hurk et al 2003 649Boslilovich 2006 560Jackson et al 2003 410Yuan et al 2010 Dirmeyer et al 20065.8-8.5 10 13 Alton et al., 2009~6.5 10 13 503Ryu et al7.2 10 13 Why range? Errors in ET? Differences in Land area? Cartesian vs Area-Weighted Averaging? Grid Resolution?

45. Conclusions A new Global Database of Directly Measured values of Annual Evaporation has Emerged – Many Semi-Arid Ecosystems Tap Ground-Water Resources to Minimize Risk and Vulnerability to Seasonal Drought – Expand Duration of Database to Study Interannual Variation with Climate Fluctuations and Trends Several New Evaporation Systems are producing new estimates of Global, Continental and Local Evaporation at Weekly to Annual Scales at high spatial Resolution, 1-5 km – Mechanistic Biophysical Models enable us to Predict and Diagnose Cause and Effect into the Future and Past – Working with Jim Hunt to Tests the BESS system at Catchment scale – Products can be used for policy and management and set Priors for large scale inversion modelling. – Future Work involves Considering Terrain on Radiation Fields, surface wetness and soil water budgets

47. Evapo- transpiration (mm/yr) Jung et al. 2010 Nature Global average: 550 mm/yr ~ 6% 65 Eg/yr (±10-15%) Up-scaling evapotranspiration

48. Mean ET 539 mm/y

49. Fisher et al ET maps, 1995: 580 +/- 400 mm/y; Cartesian 665 mm/y; area-weighted Global ET 0.5 Deg Resolution; ISLSCP Met Drivers

50. Using Flux Data to produce Global ET maps, V2 ET (mm H 2 O y -1 ) Fig.9 Global Evapotranspiration (ET) driven by interpolated MERRA meteorological data and 0.5º×0.6º MODIS data averaged from 2000 to 2003. Wenping Yuan et al 2010 RSE 417±38 mm year−1

51. Martin Jung/Markus Reichstein Using Flux data to produce Global ET maps, v3

52. Mean Global ET: 613 mm/y Fisher et al, Remote Sensing Environment, 2008 Global ET, 1989, ISLSCP, V1

54. Canopy Conductance scales with LAI, which scales with Water Budget and Nutrition Explaining Budyko, part I

55. ESPM 129 Biometeorology

56. Optimizing Seasonality of Vcmax improves Prediction of Fluxes Wang et al, 2007 GCB

57. Critical Partnership with Microsoft Azure Cloud Computing System: Puts the Small Biomet Lab into the Global Ecology Computationally-Intensive Ball Park