全球碳循环_Modeling Carbon Cycle _lecture5（英文）

1、Lecture 5: Modeling Carbon Cycle I. Model OverviewWhat is a model?Why do we need the model?The roles of models II. Introduction to Carbon Models Empirical modelsProcess modelsHybrid models Strength and weaknessIII. Model Applications Challenge: Model validation TRIPLEX modelWhat is a Model ? A model

2、 is an abstraction of a real system We use models in two ways:- conceptual model- formal model Real systemModelModel Catalog Conceptual (Word or Flowcharts) Models: used to represent ourconcepts or knowledge and describe the interactions between the components of a system Mathematical (Statistical)

3、Models: used to present the a conceptualModel or other types by using mathematical notation. Computer Simulation Models: Mathematical models cab be translated Into computer languages and implemented on a computer Why Do We Need Models?Three methods to assess the effects of a changing environment on

4、ecosystems (Botkin 1993): (a) our knowledge of the past (b) present measurements(c) our ability to project into the future Our knowledge of the past and present measurements have been of limited use Long-term monitoring of the forest has proven difficult due to cost and long-term commitmentCurrent e

5、xperimental techniques are not directly applicable to complicated environmental changeThe Roles of Models Models as research tools to increase our knowledge Models as management tool to help to make decisions Models as education tools to help to understand the Earth system Empirical vs. Process Mode

6、ls Empirical (statistical) models: are derived from large amounts of field data, and describe carbon cycle (or tree growth) as a regressionfunction of variables (biotic: age, basal area, density; abiotic: temp. prec., radiation, light etc.)First global NPP model (MIAMI mode: Lieth, 1975)Helmut Lieth

7、 (1975) developed this first global NPP model to quantify the relationship between NPP and the mean annual temperature and total precipitation using 52 field sitesRef. Lieth, H. 1975. In: Lieth, H and R.H. Whittaker (eds.). Primary Productivityof Biosphere, Springer-Verlag, New York, USA, pp. 237-26

8、3. MIAMI Model (Lieth, 1975) NPPT = 3000 / (1+e (1.315-0.119T)orNPPp=3000 (1-e-0.000664P)Where NPP: (g/m2/yr) T: mean annual temperature (C)P: total annual precipitation (mm) Global NPP was estimated about 63 x1015 g C yr-1 (assuming nature vegetation), which is surprisingly similar to the satellite

9、 picture of “greenness” (NDVI for June 1982 by NASA) (kg C/m2/yr)(kg C/m2/yr)(kg C/m2/yr)Empirical vs. Process Models Process (Mechanistic) Models: are developed to represent keyecosystem processes or simulate the dependence of carbon cycle on a number of interacting processes (such as photosynthesi

10、s, respiration, decomposition, and nutrient cycling etc)Major Processesof a Comprehensive Process Model Energy balance Carbon balance Nutrient balance Water balanceCurrent Process-Based ModelsSpatial ScalesOrgan (Leaf or Canopy) modelse.g. Farquhars Models (Farquhar et al , 1980); FOEST-BGC (Running

11、 and Coughlan, 1988); MAESTRO (Wang and Jarvis, 1990 ); BIOMASS (McMurtrie et al. 1990);B. Individual tree ecophysiological models e.g. ECOPHYS (Rauscher et al. 1990); TREGRO (Winstein and Yanai, 1994); TREE-BGC (Korol et al., 1994)C. Community models (gap or succession models)e.g. JABOWA (Botkin et

12、 al. 1972); FORET (Shugart and West, 1977); ZELIG (Smith and Urban, 1988); LINKAGE (Pastor and Post, 1985)D. Stand or Ecosystem modelse.g. PnET (Aber and Federer, 1992); CENTURY (Parton et al. (1987); TRIPLEX (Peng et al. 2002)E. Landscape models e.g. FIRE-BGC (Keane et al., 1996 ); LANDIS (He et al

13、. 1996)F. Global modelse.g. BIOME3 (Haxeltine and Prentice, 1996); MAPSS (Neilson, 1993); IBIS (Foley et al., 1996)Process (Mechanistic) Models Farquhars Models (Farquhar et al , 1980): is one of most physiologically sound presently available model to quantify the leaf Photosynthesis of C3 plants.Th

14、e model simulates the photosynthetic rate of C3 plants as a functionof leaf irradiance, intercellular CO2 concentration and leaf temperature. Net photosynthesis (mm-2s-1) is expressed as:A = Vc0.5V0 Rd(1)Where Vc and V0 are rates of carboxylation and oxygenation, respectively Rd is dark respirationF

15、arquhars ModelsThe term: Vc 0.5V0 is expressed by Farquhar et al (1980) as:Vc 0.5V0 = min Wc, Wj (1- I/Ci )(2)Where denotes the minimum of, Wc: the Rubisco limited rate of carboxylation, Wj: the ribulose bisphosphate (RuBP)-limited rate of carboxylation when RuBP regereneration is limited by electro

16、n transport. I: the CO2 compensation point in the absent of dark respirationCi: the intercellular CO2 concentration So, A = min Wc, Wj (1- I/Ci ) Rd(3)Farquhars ModelsThe rate Wc is expressed as:Wc = Vcmax Ci / Ci + Kc (1+O/Ko)(4)Where Vcmax is the maximum carboxylation rate Kc and Ko are the Michae

17、lis-Menten kinetic coefficients for CO2 and O2 The rate Wj can be expressed as:Wj = J (Ci- I)/ (4Ci 8I) (5) Where J is the potential rate of electron transport and dependent on lightAccording to Collatz et al (1991),Rd = 0.015 Vcmax (6)References:1. Farquhar, G.D., S. von Caemmerer and J.A. Berry. 1

18、980. A biochemical model of phtosynthetic CO2 assimilation in Leave of C3 Species. Planta, 149: 78-90.2. Collatz, G.J., J.T. Ball, C. Grivet and J.A. Berry. 1991. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar bou

19、ndary layer. Agric. For. Meteorol. 54: 107-136Farquhars ModelsWhat is the CENTURY ?CENTURY, as developed by Parton et al. (1987, 1993), is a process-based ecosystem model of plant-soil, which simulates the long-term biogeochemical cycles of C, N, P, and S for various ecosystems, including grassland,

20、 agricultural land, savannas and forests. Website: /projects/century/nrel.htmCENTURY 4.0 Submodelsforest productionsoil organic matter/decompositionwater budgetgrassland/cropmanagement and events scheduling functionsTime step: monthlyCarbon pools (boxes) and fluxes (arrows) i

21、n CENTURY 4.0ATMOSPHERELeafFine Branches Large WoodCoarse RootFine Root VEGETATIONSurface LitterMetab.Struc.Dead Large WoodDead Fine BranchesDead Coarse RootSurfaceMicrobeActiveSOMSlow SOMPassiveSOMLITTERSOILNPPASSIMILATIONLeached CPlantRespirationMicrobialRespirationSoil RespirationCO2 CO2 CO2CO2CO

22、2Root LitterMetab.Struc.(Peng et al. 1998)Major strength and weakness of empirical models:Describe the best relationship between the measured data and the variablesrequire only simple inputs and can be easily constructedare not robust, for example, for analyzing the consequences of environmental str

23、ess and climatic changesMajor strength and weakness of process models:the inclusion of the ecophysiological principles and their long-term forecasting ability within changing environmentsMost process-based models are research tools and are not appropriate for management applicationsThey are too comp

24、lex and require a large amount of information and inputsStrength and Weakness of Process Models “ Empirical and process models can be married into hybrid models in which the shortcomings of both approaches can be overcome to some extent. Specifically, incorporating the key elements of empirical and

25、process approaches into a hybrid ecosystem modeling approach can result in a model that predicts forest growth, production and carbon dynamics in both the short and long term. ”Rationale for Hybrid Models (Peng, For. Ecol. Manag., 2000)Hybrid modeling is a promising approach that bridges the gaps be

26、tween empirical and process models For more detailed discussions, please try to read :1) Peng, 2000. Growth and yield models for uneven-aged stands:past, present and future. Forest Ecology and Management. 132: 259-2792) Peng, 2000. From static biogeographical model to dynamic globalvegetation model:

27、 a global perspective on modelling vegetation dynamics.135: 33-54 ( can be downloaded from website: www.crc.uqam.ca - under “Publication” )Challenge: ValidationCalibration is the estimation and adjustment of model parameters and constants to improve the agreement between model output and a data set.

28、Validation is testing a model to see how well it predicts. (How well does the model capture the structure, controls, and dynamics of a real forest ecosystem). First questions is: what variable do we want to validate (test)? The second question is finding adequate data.Waring and Running (1998) recom

29、mend a group of variables that can be accurately measured in the field and reflect a range of forest interaction linked carbon, nitrogen and water cycles. These include: Leaf area index (LAI) Net primary productivity (NPP) Stem biomass Leaf litterfall Leaf nitrogen content Total Height Diameter at b

30、reast height (DBH) Basal Area (BA) Total VolumeVariables for Validating Process Model Greenhouse or experimental dataTree growth plots (PSP, TSP)Forest inventory (NPP, Biomass)Flux tower (CO2, NPP, NEP etc.)Remote Sensing (LAI, NDVI-NPP)Paleoecological data (tree-ring, pollen) Data for Validating Pr

31、ocess ModelTRIPLEX: A generic hybrid model for predicting forest growth and carbon and nitrogen dynamics Developed based on well-established models: 3-PG (Landsberg and Waring, 1997) TREEDYN3.0 (Bossel, 1996) CENTURY4.0 (Parton et al., 1987, 1993)Bridges the gap between forest growth and yield and p

32、rocess-based C balance modelsCan be used for: 1) Making forest management decisions (e.g., G&Y prediction) 2) Quantifying forest carbon budgets 3) Assessing the effects of climate change on forest ecosystems(Peng et al. 2002, Ecol. Model, 153: 109-130)Real Biological System: Jack Pine Stands(Ontario

33、, Canada)Key Features of TRIPLEX:Driving variables (main inputs): Monthly climate data; LAI; N deposition and fixation rate; geo-location Mass balances: C, N, and water pools and fluxes fully balanced Time step: Monthly C flux and allocation calculation; annual tree growth, C , N, and water budgetOu

34、tputs: H, DBH, BA, volume, NPP, biomass, soil C, N, and water dynamicsModelling strategy: OOP (objective-oriented programming - C+) and model reuse approachesTREEDYN3 (Bossel,1996); 3-PG (Landsberg and Waring, 1997); CENTURY4.0 (Parton et al., 1993)TemperatureN mineralizationNStoreLeachingN limitati

35、on PrecipitationSoilwaterRunoffMoisturePool: Process: IncrementHeightDiameterVolumeBasal AreaMortalityDisturbanceThinningTree numberHarvestingWoodproductionAtmospheric CO2GPPC AllocationLeafsCNRootsWoodCNCLitter fallCNStructureMetabolicCNCN Active (C, N)Slow (C, N)Passive (C, N)NDecomposition(C, N)T

36、RIPLEX Model FrameworkSolar radiation7/25/2022TRIPLEX 1.0 User InterfaceTRIPLEX: Computer Simulation Model (Peng et al. 2002)12 PSP(0.08ha each)Forest type: Jack pine ( Pinus banksiana Lamb.)SABOCTMTOne Case Study BO: Boreal; CT: Cool Temperate; MT: Moderate Temperate; SA: SubarticOntarioLocation: L

37、onglac (Kimberly Clark Ltd.)Longlac We have 6 consecutive measurements (very 5 yr) for DBH, H, tree density (1952-1982) Use first measurements (1952) to calibrate the TRIPLEX model Use the other 5 measurements to validate (1957 - 1982) Calibration and Validation for TRIPLEX ModelComparison of Simula

38、tions and Observations (solid diagonal is the 1:1 line; N=60)Simulated Relative Errors for Stand Age (=simulation - observation/observation)10%15%20%25%Comparison of Averaged Simulations and Observations - HeightComparison of Averaged Simulations and Observations - Tree VolumeComparison of Averaged

39、Simulations and Observations - Stem DensityComparison of Averaged Simulations and Observations - Aboveground Biomass (Hegyi, 1972) CanadaOntarioLongitude and Latitude : -80.7, 48.8 Temperature: 1.2oC Precipitation: 814.8 mm Study area: Lake Abitibi Model ForestModeling Forest Growth and Carbon Dynam

40、ics at Landscape level in Lake Abitibi Model Forest(May 12, 2002) MethodTRIPLEX modelNCDynamicsBiomassNPPSoil C & NDBHHeightVolume. ArcGIS ArcViewSpatialDistributionSimulation ModelOutputsModel inputsForestLAMF Local data (stands and spatial data)SoilOntario Land Inventory Prime land Information Sys

41、tem (OLIPIS)A soil profile and organic carbon data base for Canadian forestClimateDatabase from Environment CanadaCanadian Centre for Climate Modeling (CCCMa database)Model validation32 black spruce, 9 jack pine, 8 trembling aspen plots (measured in 1995)TRIPLEX vs. Forest InventoryTRIPLEX vs. PSPFi

42、g. 4 The comparison between NPP (t C ha-1 yr-1) simulations at landscape (a) and remote sensing (b) levels for the LAMF in 1994. (a) was based on the TRIPLEX model simulation for 1994 (averaged 3.28 tC ha-1 yr-1, SD=0.79), and (b) was converted using spatial data from Liu et al. (2002) for 1994 (ave

43、raged 3.08 tC ha-1 yr-1, SD=1.15). The grid size is 3x3 km. TRIPLEX(Zhou et al, 2005) (b) Remote Sensing (Liu et al, 2002)Comparing NPP Spatial Distribution at Landscape LevelKappa Statistic (k) = 0.55Good agreement if 0.55K0.7Simulated Biomass (t ha-1)in 2000Simulated NPP (tC ha-1yr-1)in 2000Biomas

44、s C pool: 55.5Aboveground: 42.2Belowground: 13.3Harvesting CAbout 0.1C release: 1.0C update: 3.0Net carbon balance (NCB) = 2.0 Mt C C budget of LAMF forest ecosystem in 2000:Litter and Soil C pool: 83.7Unit: Mt CTRIPLEX-Flux Model Development NCCO2WaterLightreactionsO2CalvincycleSugarH2OCO2StomaCell

45、Shaded leafEnergyTRIPLEX-Flux (two leaves, daily)TRIPLEX1.0 (big leaf, monthly)Sunlit leafModel Testing for 2 Flux tower sites(Fluxnet-Canada)110 yrs black spruce75 yrs mixedwoodModel Validation OBS Flux Tower Daily Simulation using TRIPLEX-fluxNEPModel Validation Using OBS Flux Tower Daily Simulati

46、on using TRIPLEX-fluxBoreal Mixedwood Site (Ontario) Ontario station in 2004Sensitivity NEP to T increaseNEP Temperature increase (C)Ca (CO2 concentration increase)NEPSensitivity NEP to CO2 increaseSensitivity of NEP to variation in PPFDSimulation result comparison Uncertainties and LimitationsModel

47、 structure, process, mechanism and inputs Uncertainty in observations (eddy-flux measurements): underestimation of night-time respiratory fluxes;Lack of energy balance closureGap-filling for missing data (about 12% error)Disturbances: fire, insects, air pollution (ozone, UVB), DOC Scaling up site heterogeneity (species composition and soil texture)UncertaintyChallenges for TRIPLEX DevelopmentContinued testing of the models ability to simulate NPP, belowground biomass, soil C, N and water (BOREAS sites as well as Canada-Fluxnet)Developi