Lessons Learned on Ecosystem Evaporation from Long-term, Global Flux Networks

Lessons Learned about Ecosystem Evaporation from Long-term, Global Flux NetworksDennis Baldocchi and Youngryel RyuUniversity of California, Berkeley

EPFL LATSIS SymposiumLausanne, SwitzerlandOctober, 2010

30 minutes1Motivation, Part 1Most 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

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 2Big Picture Question Regarding Predicting and Quantifying Global Evaporation:How can We Be Everywhere All the Time?

Wedding at Cana, Veronese, the Louvre, Paris4Over Arching QuestionsWhat 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?

Eddy Covariance Technique

DirectIn situQuasi-Continuous6Restrictions and Conditions for Producing Annual Water Budgets from Eddy Covariance Flux MeasurementsSteady-State Conditions, dC/dt ~ 0Extensive Fetch, 100m - 1kmLevel Terrain, < 0-10o slopeGaps-Filled Accurately, with Minimum Bias

FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009

www.fluxdata.org8Global distribution of Flux Towers Covers Climate Space Well

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Is There an Energy Balance Closure Problem?:Evidence from FLUXNETWilson et al, 2002 AgForMet

Timing/SeasonInstrument/Canopy Roughness

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

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

Reasonable Agreement Observed between Eddy Flux measurements of ET + Catchment Studies

Scott, 2010, AgForMetArizona GrasslandPart 1, Reading the Data of Annual Flux Measurements

Flux Measurements Reveal Diverse Information on Seasonal Cycles

TakeHome Points: ET > 200 mm/yMedian = 402 mm/ySkewed Distribution, Max ~ 2300 mm/y

Classic View, The Budyko Curve with Evaporation Flux MeasurementsEvap Demand >>PrecipitationPrecipitation >>Evaporation, whichIs energy limitedDefines Bounds, But Many Sources of Variance RemainX and Y are AutoCorrelated, through pptPpt/Rn17

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

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

A linear additive model has the following statistics: ET = -141 + 116*Rn + 0.378 * ppt, r2 = 0.819. The color bar refers to annual ETStatistical Model between Annual Forest ET, Net Radiation and Precipitation

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

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

Tapping Groundwater Increases Ecosystem Resilience,And Reduces Inter-annual Variability in ETConsistent with Findings of Stoy et al, Later-Succession Ecosystems invest to Reduce RiskPre-Dawn Water Potential Represents Mix of Dry Soil and Water Table

Miller et al WRR, 2010During Summer MidDay Water Potential is Less Negative than Shallow Soil Water Potential

Dont Forget EcologyStand Age also affects differences between ET of forest vs grassland25

Part 2, Global Integration of ETSpace: The final frontier To boldly go where no man has gone before

Captain James Kirk, Starship EnterpriseMotivationGlobal Estimates of ET range from 5.8-8.5 1013 m3/yCurrent Class of Remote Sensing-based Estimates of Global ET models rely onEmpirical approach (machine learning technique)Form of the Penman-Monteith Equation, with poor constaint on surface ConductanceForm of the Priestley-Taylor Equation, with empirical tuning of alpha, with soil moisture deficitsMany forcings come from coarse reanalysis data (several tens of km resolution)At most, LAI, NDVI, LST are used from satelliteWe 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 Approachremote sensingof CO2Temporal scaleSpatial scale [km]hourdayweekmonthyeardecadecenturylocal 0.1 1 10 100 1000 10 000 globalforestinventoryplotCountriesEUplot/sitetalltowerobser-vatoriesForest/soil inventoriesEddycovariancetowersLandsurface remote sensing

From point to globe via integration with remote sensing (and gridded metorology)From: Markus Reichstein, MPI28Look at what we have: observations. Only they provide the door to the real world. They should provide the backbone of the research.To paraphrase the Philosopher Imanuel Kant, models without data are empty, and data without models are blind.Eddy + forestRemote sensing of vegRemote stat + tallRemote sensing CO2 (dnn, label ausserhalb)

Challenge for Landscape to Global Upscaling

Converting Virtual Cubism back to Virtual RealityRealistic Spatialization of Flux DataRequires 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

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 CaliforniaWe Must:Couple Carbon and Water FluxesAssess Non-Linear Biophysical Functions with Leaf-Level Microclimate ConditionsConsider Sun and Shade fractions separatelyConsider effects of Clumped Vegetation on Light TransferConsider Seasonal Variations in Physiological Capacity of Leaves and Structure of the CanopyNecessary Attributes of Global Biophysical ET Model: Applying Lessons from the Berkeley Biomet Class and CANOAKTreat Canopy as Dual Source (Sun/Shade), Two-Layer (Vegetation/Soil) systemTreat Non-Linear Processes with Statistical Rigor (Norman, 1980s)Requires Information on Direct and Diffuse Portions of SunlightMonte Carlo Atmospheric Radiative Transfer model (Kobayashi + Iwabuchi,, 2008)Light transfer through canopies MUST consider Leaf ClumpingApply New Global Clumping Maps of Chen et al./Pisek et al.Couple Carbon-Water Fluxes for Constrained Stomatal Conductance SimulationsPhotosynthesis 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 CorrectlyPhotosynthesis of C3 and C4 vegetation Must be considered SeparatelyUse Emerging Ecosystem Scaling Rules to parameterize models, based on remote sensing spatio-temporal inputsVcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al;Schulze et al.; Wright et al.)Seasonality in Vcmax is considered (Wang et al.)AtmosphericradiativetransferCanopy photosynthesis,Evaporation, Radiative transferSoil evaporationBeam PAR NIRDiffuse PAR NIRAlbdeo->Nitrogen -> Vcmax, JmaxLAI, Clumping-> canopy radiative transferdePury & Farquhar two leaf Photosynthesis modelRnetSurface conductancePenman-Monteithevaporation modelRadiation at understorySoil evaporationshadesunlitBESS, Berkeley Evaporation Science SimulatorHelp from ModisAzure -Azure Service for Remote Sensing GeoscienceScientific Results Download

Reduction #1 QueueSource Metadata AzureMODIS Service Web Role Portal

Request QueueAnalysis Reduction Stage

Data Collection StageSource Imagery Download Sites

. . .Reprojection QueueDerivation Reduction Stage

Reprojection StageReduction #2 QueueDownloadQueue

ScientistsScience results

Puts the Small Biomet Lab into the Global Ecology, Computationally-Intensive Ball Park

AutomationDownloads thousands of files of MODIS data from NASA ftpReprojectionConverts 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.MaskingEliminates 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

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Photosynthetic CapacityLeaf Area IndexSolar RadiationHumidity Deficits

Leaf Clumping Map, Chen et al. 2005

C4 Vegetation Map, Still et al. 2003Validate Model Across FLUXNET

= 503 mm/y == 7.2 1013 m3/yRyu et al. in preparation

Global land evaporation: 503 mm yr-1

Ryu et al. unpublishedMODIS-Driven Product Using Biophysics via Cloud Computing

Ryu et al. unpublishedDown-Scale to Regions for Policy and Management Decisions

Scaling and Window Size

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

Global ET What is the Right Answer? (mm/y)referenceET (m3/y)613Fisher et al550Jung et al 2010, Nature6.5 1013286Mu et al. 2007539 +/- 9Zhang et al. 2010, WRR467Van den Hurk et al 2003649Boslilovich 2006560Jackson et al 2003410Yuan et al 2010Dirmeyer et al 20065.8-8.5 1013Alton et al., 2009~6.5 1013503Ryu et al7.2 1013Why range?Errors in ET?Differences in Land area?Cartesian vs Area-Weighted Averaging?Grid Resolution?44Van den Hurk et al 2003 JGR annual average land ET 1.28 kg m-2 d-1467.2 mm/y; 649 mm/y Boslilovich; 560 mm from Jackson, land ET is 70,000 km3 and assuming 125 10^6 km2 land area; Mu et al 286 +/- 237 mm/yConclusionsA new Global Database of Directly Measured values of Annual Evaporation has EmergedMany Semi-Arid Ecosystems Tap Ground-Water Resources to Minimize Risk and Vulnerability to Seasonal DroughtExpand Duration of Database to Study Interannual Variation with Climate Fluctuations and TrendsSeveral New Evaporation Systems are producing new estimates of Global, Continental and Local Evaporation at Weekly to Annual Scales at high spatial Resolution, 1-5 kmMechanistic Biophysical Models enable us to Predict and Diagnose Cause and Effect into the Future and PastWorking with Jim Hunt to Tests the BESS system at Catchment scaleProducts 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

Evapo-transpiration (mm/yr)

Jung et al. 2010 NatureGlobal average: 550 mm/yr ~ 6% 65 Eg/yr (10-15%)Up-scaling evapotranspiration

Mean ET 539 mm/yFisher et al ET maps, 1995: 580 +/- 400 mm/y; Cartesian665 mm/y; area-weighted

Global ET 0.5 Deg Resolution; ISLSCP Met DriversUsing Flux Data to produce Global ET maps, V2

ET (mm H2O y-1)

Fig.9 Global Evapotranspiration (ET) driven by interpolated MERRA meteorological data and 0.50.6 MODIS data averaged from 2000 to 2003.Wenping Yuan et al 2010 RSE41738 mm year1

Martin Jung/Markus ReichsteinUsing Flux data to produce Global ET maps, v3

Mean Global ET: 613 mm/yFisher et al, Remote Sensing Environment, 2008Global ET, 1989, ISLSCP, V152Van den Hurk et al 2003 JGR annual average land ET 1.28 kg m-2 d-1467.2 mm/y; 649 mm/y Boslilovich; 560 mm from Jackson, land ET is 70,000 km3 and assuming 125 10^6 km2 land area; Yuan et al 2010 41738 mm year1 across veg

Canopy Conductance scales with LAI, which scales with Water Budget and NutritionExplaining Budyko, part IESPM 129 Biometeorology

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Optimizing Seasonality of Vcmax improves Prediction of FluxesWang et al, 2007 GCB56Critical Partnership with Microsoft Azure Cloud Computing System:Puts the Small Biomet Lab into the Global Ecology Computationally-Intensive Ball Park