Canopy Modeling: Lessons from Models

Transcription

1 Canopy Modeling: Lessons from Models Dennis Baldocchi ESPM 228 University of California, Berkeley Spring, /4/2016 ESPM 228 Advanced Topics in Biometeorology

2 CANOAK MODEL Physiology Photosynthesis Stomatal Conductance Transpiration F CO2 H LE Albedo Micrometeorology Leaf/Soil Energy Balance Radiative Transfer Lagrangian Turbulent Transfer G soil

3 CANOAK Schematic Meteorological and Plant inputs R g,l in, T a, q a, [CO 2 ], u, P, ppt, LAI, h, d,l, z o Radiative Transfer: Q par,r nir f( ) LongwaveRadiative Transfer: f(t l,ir up,ir dn, Stomatal Conductace= f(a,ci,tl, Boundary Layer Conductace= f(u,l Leaf Energy Balance: H, E, T l Leaf Photosynthesis and Respiration: f(g s, T l,c i, g b, Q par ) Source/Sinks: S T,S q,s C Scalar Profiles: T,q,C

4 Model Parameters Leaf Area Index Photosynthetic Capacity, J max, V cmax Kinetics Basal Respiration, leaf/soil

5 Seasonality in LAI, Deciduous Forests 7 6 Deciduous forest Leaf Area Index Day of year

6 J max and V cmax scale with one another Wullschleger, 1993 J Expt Bot

7 Practical Assessment for Vcmax in sites with many species and spatial variability Data of KB Wilson Wohlfarht 1999 mountain grassland species Vcmax= Pmx r 2 =0.87 Vcmax b[0] b[1] r ² Vcmax ( mol m -2 s -1 ) Amax Pml ( mol m -2 s -1 )

8 Seasonality in V cmax White oak 50 V cmax ( mol m -2 s -1 ) Day of year Wilson et al Tree Physiol

9 High V cmax must be Achieved in Seasonally Droughted Ecosystems to attain Positive Carbon Balance Quercus alba (Wilson et al) Quercus douglasii (Xu and Baldocchi) V cmax DOY Area under the Curves are Similar

10 Today, We Know Parameters have Uncertainty Hollinger and Richardson 2005 Tree Physiol ESPM 228 Adv Topics Micromet & Biomet

11 Monte Carlo Model parameterization Verbeek et al 2006 Tree Physiol ESPM 228 Adv Topics Micromet & Biomet Medlyn et al 2005 Tree Physiol

12 Results and Discussion P1. Validation and Testing

13 Model Test: Hourly to Annual Time Scale W alker Branch W atershed 10 m easured calculated NEE ( mol m -2 s -1 ) NEE computed ( mol m -2 s -1 ) b[0] b[1] r ² NEE measured ( mol m -2 s -1 )

14 Model Test: Hourly Data Tem perate Deciduous Forest, M easured Calculated 300 LE (W m -2 ) LE calculated (W m -2 ) Coefficients: b [0 ]: b [1 ]: r ²: W eek LE m easured (W m -2 )

15 Time Scales of Interannual Variability are recreated forcing model withi weather and seasonal changes in LAI and Vcmax ns wc (n)/w'c' canoak data n, cycles per hour

16 ESPM 228 Adv Topics Micromet & Biomet

17 P2. Sensitivity and Science Questions f(time, space, Parameters & Processes)

18 What is Interannual Variability, beyond the measurement record? Temperate Deciduous Forest: Canoak -400 Net Ecosystem C Exchange (g C m -2 yr -1 ) CANOAK Measured and Gap-Filled Year

19 Decadal Scales of Variability, Information exists at Long time scales Walker Branch Watershed, TN: CANOAK 1 year 130 days 0.1 ns nee / nee years Frequency (1/day)

20 NEE and Growing Season Length 0 Temperate Deciduous Forests NEE (g C m -2 year -1 ) CANOAK, Oak Ridge, TN Published Measurements, r 2 = D ays w ith N E E < 0

21 Importance of Vcmax 4 2 NEE (gc m -2 d -1 ) V cmax (73 mol m -2 s -1 ) V cmax (50 mol m -2 s -1 ) Day V cmax (73) V cmax (50) % difference NEE (gc m-2 a-1) E (MJ m -2 a -1 ) H (MJ m -2 a -1 )

22 Vertical Variations in Vcmax, are they needed? Canoak, 1998 vcmax =f(z): 1521 gc m -2 y -1 vcmax =const: 1571 gc m -2 y Canopy Ps, gc m-2 d Day

23 Light Use Efficiency and Net Primary Productivity NPP= f Q p Tree Tree Tree Tree

24 Emergent Processes: Impact of Leaf Clumping on Canopy Light Response Curves Deciduous forest F c ( mol m -2 s -1 ) (a ) m o d e l: s p h e ric a l le a v e s F c ( mol m -2 s -1 ) (b ) m e a s u r e d m o d e l: c lu m p e d le a v e s PPFD ( m o l m -2 s -1 )

25 LUE and Leaf Area crop canopy V cm ax = 100 m ol m -2 s LAI=5 LAI=3 LAI=1 P c ( mol m -1 s -1 ) P A R ( m o l m -2 s -1 )

26 LUE and Ps Capacity crop canopy LA I = V cmax = 100 m ol m -2 s -1 V cmax = 50 V cmax = 25 P c ( mol m -1 s -1 ) PA R ( m ol m -2 s -1 )

27 Developing Simple Model from Complex One CANVEG 1800 EUROFLUX Walker Branch Watershed Duke: Ellsworth/Katul Metolius Young: Law et al Metolius old: Law et al Harvard: Barford et al GPP (gc m -2 yr -1 ) V cmax LAI/fpar

28 Role of Leaf Angle Inclination and Clumping on Fluxes WBW F c (gc m -2 d -1 ) erect leaves clumped plane leaves Day clumped random spherical erectophile planophile NEE (gc m -2 a -1 ) E (MJ m -2 a -1 ) H (MJ m -2 a -1 )

29 Interaction between Clumping and Leaf Area Temperate Deciduous Forest Flux sph /Flux clp canopy photosynthesis E NEE LAI

30 How Sky Conditions Affect NEE? Temperate Broad-leaved Forest Spring 1995 (days 130 to 170) Sunny days diffuse/total <= 0.3 Cloudy days diffuse/total >= 0.7 NEE ( mol m -2 s -1 ) PPFD ( mol m -2 s -1 )

31 Conversion of direct to diffuse light increases light capture P diffuse 2 / 2 0 P cos sin d 0 LAI 3 Diffuse Radiation Beam Radiation, = pi/2 Beam Radiation, =pi/ P 0

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33 0.5 Diffuse light effect (slope) [ - ] Leaf area index [m 2 m -2 ] Knohl and Baldocchi, 2008 JGR Biogeosci

34 30 A 25 CO 2 Flux [µmol m -2 s -1 ] Canopy photosynthesis Net ecosystem exchange 08 B Transpiration [mmol m -2 s -1 ] Water use efficiency [µmol CO 2 mmol -1 H 2 O] R d /R s [ - ] Transpiration Water use efficiency 3.2 Knohl and Baldocchi, 2008 JGR Biogeosci

35 Do We Need to Consider Canopy Microclimate [C] Feedbacks on Fluxes? 200 Ave Daily LE, q(z)= q ; T(z) = Ta a b [0]: b [1]: r ²: Ave Daily LE, f(z, w ) (W m -2 ) 200 Ave Daily H, q(z)= q ; T(z) = Ta a b[0] 2.72 b[1] r ² Ave Daily H, f(z, w ) (W m -2 ) Ave Daily F c, q(z)= q a ; T(z) = T a ; C(z)=C a b[0] b [1] r ² Ave Daily F c, f(z, w ) (W m -2 )

36 Water Use Efficiency A Ca Ci r r r s m b T ( es ea ) P r r b s A T M ( p p )( r r ) c c, a c, i a s M ( e ( T ) e )( r r ) e k e v s l a a, c s, c s a

37 Complex Leaf Response to vpd A/T es(tk)-ea

38 Isotope Models of WUE A E p p( 1 i ) p 16. vpd a b a p i ( ) p a p p i a

39 Definitions of Isotopic Discrimination C/ C reactants C/ C products Rair ( 1) 1000 R plant a 1 p p C / C sample C / C s tan dard

40 Interpreting Stable Isotopes A E pi p( 1 ) p 16. vpd CANOAK A/E=f(C i /C a, D) A/T (mmol mol -1 ) Conventional theory C i /C a

41 Don t Forget Feedbacks A/T (mmol mol -1 ) C i /C a A E pi p(1 ) p pi 1.6 vpd( f ( )) p a A T D (kpa) Ca( 1 Ci / Ca) C / C i a

42 DeConstructing WUE C i /C a 1/D:(f(C i /C a )) A/T Factor C i /C a A T Ca( 1 Ci / Ca) C / C i a

43 Long Term Changes in WUE: Has CO2 Changed Enough to Matter? Keenan et al Nature

44 Water use Efficiency and CO2 CANOAK, 1982 Meteorology, Oak Ridge, TN WUE (gc m -2 y -1 /kg H2O m -2 y -1 ) Coefficients: b[0] b[1] e-3 r ² [CO2] ppm

45 Isotopes Infer Leaf Temperatures of Tree Leaves are Constrained, ~ 21 C Leaf Temperature Growing Season Temperature Helliker and Richter 2008 Nature

46 Leaf Temperature, Modeled with CANOAK, as a Central Tendency near 20 C 0.12 Temperate Broadleaved Forest Days 100 to 273 pdf Canoak Model T leaf

47 Isotopes Evaluate a Flux-Weighted Temperature 0.14 Ponderosa Pine, Metolius, OR T leaf T air 0.08 pdf Flux-wted Tleaf = 23.6 C T leaf (C) Transpiration (E) Weighted Leaf Temperature ET leaf Tleaf Edt dt

48 Transpiration Weighted Leaf Temperature for Oak Savanna Canopy Temperature, Weighted by Transpiration Oak Savanna, Ione, CA; CANOAK-3D <Tleaf> = 25.2 C 0.08 pdf Tleaf

49 Leaf size, CO 2 and Temperature: why oak leaves are small in CA and large in TN pdf tsun ambient CO2 =1500 ppm, 100 mm leaf pdf tsun sm all leaves 0.08 sunlit leaves, daytime Oak Ridge, TN Probability Tleaf

50 Temperate Deciduous Forest: 1997 Role of Leaf length 4 2 NEE (gc m -2 d -1 ) mm 10 mm Day 0.1 m 0.01m m NEE (gc m-2 a -1 ) E (MJ m -2 a -1 ) H (MJ m -2 a -1 )

51 Physiological Capacity and Leaf Temperature: Why Low Capacity Leaves Can t Be Sunlit::or don t leave the potted Laurel Tree in the Sun Temperate Deciduous Forest Sunlit leaves, probability density V cmax = 73 mol m -2 s -1 V cmax = 10 mol m -2 s T leaf ( o C)

52 Study area for test of CANOAK 3d g f e d c b a Traverse radiometer system 1 2 3

53 Simulated understory (1m above the ground) radiations near the tram site Downward PAR Upward PAR Net radiation (W m 2) (W m 2) (W m 2) Kobayashi et al AgForMet

54 Simulated images (RGB composite) Simulation AVIRIS 5/12/2006 Simulation AVIRIS 8/5/2007 Kobayashi et al AgForMet

55 Importance of Model Hierarchy Testing Kobayashi et al

56 Comparison of simulated and tram measured PAR and net radiation DOY 124 DOY 194 DOY 215 PAR (obs.) PAR (Sim.) Hour Hour Hour Hour Hour Hour Kobayashi et al AgForMet

57 Comparison of top of the tower net radiation, sensible heat and latent heat Net radiation Sensible heat Latent heat Kobayashi et al AgForMet

58 Below Canopy Fluxes 300 Rnet (W m-2) Ponderosa Pine Forest Floor D , 1996 m easured calculated 75 E (W m-2) H (W m -2 ) G (W m-2) F ig u r e 1 5 e n b m o d.s p w 12/8/99: lai eff = 1.8, z litte r= Tim e (hours)

59 Below Canopy Fluxes and Canopy Structure and Function Q E,soil /Q E LAI * V cm ax

60 Impact of Thermal Stratification P o n d e r o s P in e F o r e s t F lo o r R a = f(s ta b ility ) R a : n e u t r a l Rn (W m -2 ) E (W m -2 ) H (W m -2 ) G (W m -2 ) T im e ( h o u r s ) F i g u r e 1 6 e n m o d s t b. s p w 12/8/99

61 Impact of Litter Rn (W m -2 ) E (W m -2 ) P o n d e ro s P in e F o re s t F lo o r Litter depth, 0.01 m litter depth, 0.02 m litter depth, 0.05 m H (W m -2 ) G (W m -2 ) Figure 17 e n m o d lit.s p w 1 2 /8 / Tim e (hours)

62 Part 2, Upscaling from Landscapes to the Globe Space: The final frontier To boldly go where no man has gone before Captain James Kirk, Starship Enterprise

63 Big Picture Question Regarding Predicting and Quantifying the Breathing of the Biosphere : How can We Be Everywhere All the Time?

64 Big Leaf Model Ohm s Law Analog for Fluxes V F R ESPM 111 Ecosystem Ecology

65 Motivation Current Global-Scale Remote Sensing Products tend to rely on Highly-Tuned Light Use Efficiency Approach GPP=PAR*fPAR*LUE (since Monteith 1960 s) Empirical, Data-Driven Approach (machine learning technique) Some Forcings come from Satellite Remote Sensing Snap Shots, at fine Spatial scale ( < 1 km) Other Forcings come from coarse reanalysis data (several tens of km resolution) Hypothesis, We can do Better by: Applying the Principles taught in Biometeorology 129 and Ecosystem Ecology 111 which Reflect Intellectual Advances in these Fields over the past Decade Merging Vast Environmental Databases Utilizing Microsoft Cloud Computational Resources

66 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

67 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 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.)

68 BESS, Breathing Earth Science Simulator Atmospheric radiative transfer Beam PAR NIR Diffuse PAR NIR Rnet Canopy photosynthesis, Evaporation, Radiative transfer shade sunlit Albdeo >Nitrogen > Vcmax, Jmax LAI, Clumping > canopy radiative transfer Surface conductance depury & Farquhar two leaf Photosynthesis model Penman Monteith evaporation model Radiation at understory Soil evaporation Soil evaporation

69 Challenge for a Computationally Challenged Biometeorology Lab: Extracting Data Drivers from Global Remote Sensing to Run the Model MOD04 MOD05 MOD06 MOD07 aerosol Precipitable water cloud Temperature, ozone Atmospheric radiative transfer Net radiation Youngryel was lonely with 1 PC MCD43 albedo MOD11 Skin temperature MOD15 POLDER LAI Foliage clumping Canopy radiative transfer

70 Size and Number of Candidate Data Sets is Enormous US: 15 tiles FluxTower: 32 tiles Global: 193 tiles 1. Global 1 year source data: 2.4 TB (10 yr: 24 TB) 2. How to know which source files are missed among >0.1 million files

71 Barriers to Global Remote Sensing by the Berkeley Biometeorology Lab Data processing Global 1 year calculation: 9000 CPU hours That is, 375 days. 1 year calculation takes 1 year!

72 Photosynthetic Capacity Leaf Area Index Solar Radiation Humidity Deficits

73 Test of BESS Model with Flux Towers

74 Test of BESS model with Data Driven Model (Jung et al.) and Basin Water Balance Ryu et al 2012 GBC

75 What is Globally Integrated GPP?

76 UpScaling GPP Regionally with Sun Shade Coupled Energy Balance Photosynthesis Model Ryu et al. unpublished

77 Global Evaporation at 1 to 5 km scale <ET> = 503 mm/y == m 3 /y An Independent, Bottom Up Alternative to Residuals based on the Global Water Balance, ET = Precipitation Runoff

78 Issues How Good is Good Enough? How Much Detail Is Enough? Where and When can we Simplify? Assessing Errors and Variability in Model Parameters Constraining Model Parameters Assessing Errors in Driving Meteorological Conditions Biases in Test Data used to validate Models

79 Hanson et al Ecol Mono ESPM 228 Adv Topics Micromet & Biomet

80 Model Validation: Who is Right and Wrong, and Why? How Good is Good Enough? Hansen et al, 2004 Ecol Monograph ESPM 228 Adv Topics Biomet and Micromet

81 Our Ability to Model Global Gross Primary Productivity Remains Poor None of the models in this study match estimated GPP within the range of uncertainty of the observed fluxes Schaeffer et al JGR Biogeosciences

82 Many C Cycles Model Don t Simulate GPP Light Response, Well Schaeffer et al 2012, JGR Biogeosciences

83 Conclusions Biophysical Models that Couple Aspects of Micrometeorology, Ecophysiology and Biogeochemistry Produce Accurate and Constrained Fluxes of C and Energy, across Multiple Time Scales Models can be used to Interpret Field Data LUE is affected by LAI, Clumping, direct/diffuse radiation, Ps capacity NEE is affected by length of growing season Interactions between leaf size, Ps capacity and position help leaves avoid lethal temperatures Below canopy fluxes are affected by T stratification and litter

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90 CO 2 Microclimate 120 day 120 night layer 60 layer CO 2 ppm CO 2 ppm

91 Temperature Microclimate 120 day layer T air C

92 Lesson/Exercise Vary CO2 and compute fluxes, ambient, +100, +300, +500 ppm Vary LAI and compute Fluxes, 1,2,4,8