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Process-based modelling of vegetations and uncertainty quantification

Process-based modelling of vegetations and uncertainty quantification. Marcel van Oijen (CEH-Edinburgh). Course ‘Statistics for Environmental Evaluation’ Glasgow, 2012-08-29. Contents. Process-based modelling of vegetations The Bayesian approach

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Process-based modelling of vegetations and uncertainty quantification

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  1. Process-based modelling of vegetations and uncertainty quantification Marcel van Oijen (CEH-Edinburgh) Course ‘Statistics for Environmental Evaluation’ Glasgow, 2012-08-29

  2. Contents • Process-based modelling of vegetations • The Bayesian approach • Bayesian Calibration (BC) of process-based models • Bayesian Model Comparison (BMC) • Limitations of BC & BMC • On the usage of BC & BMC, now and in the future • Ongoing work: Bayes & European forests • References, Summary, Discussion

  3. 1. Process-based modelling of vegetations

  4. 1.1 Ecosystem PBMs simulate biogeochemistry Atmosphere N C H2O Tree N H2O H2O C N Soil N H2O C Subsoil

  5. 1.2 I/O of PBMs Atmospheric drivers Parameters & initial constants vegetation Parameters & initial constants soil Simulation of time series of plant and soil variables Management & land use Output Input Model

  6. 1.3 I/O of empirical models Y = P1 + P2 * t Two parameters: P1 = slope P2 = intercept Output Input Model

  7. 1.4 Applications of forest PBMs Assessing how much of soil C is in ‘fast’ resp. ‘slow’ pools Estimating C-sequestration across the UK (1920-2000) Explaining time series of greenhouse gas emissions [Yeluripati et al., 2009] [Van Oijen et al., 2011] [Van Oijen & Thomson, 2010]

  8. 1.5 Coupled vegetation-climate modelling White et al (1998)

  9. 1.5 Coupled vegetation-climate modelling Death of Amazonian rain forest ?! White et al (1998)

  10. 1.6 Forest models and uncertainty Model [Levy et al, 2004]

  11. 1.6 Forest models and uncertainty bgc century hybrid NdepUE (kg C kg-1 N) [Levy et al, 2004]

  12. 1.7 There are many models! Status: 680 models (28.08.2012) http://ecobas.org/www-server/index.html Search models (by subject) Result of query : Subject : Forestry 78 models found ACRU; Agricultural Catchments Research Unit Model AMORPHYS: A forest model based on tree morphology and physiology AREFS: The Automated Regional Ecological Forecast System BIOMASS: Forest canopy carbon and water balance model BROOK: BROOK, BROOK2 and BROOK90 BWIN: Program for forest stand analysis and prognosis CACTOS: California Conifer Timber Output Simulator CALPRO: The growth model for uneven-aged mixed conifer stands in California CARDYN: CARbonDYNamics CARRY: CARRY - contaminant transport model CRYPTOS: CRYPTOS CUPID: A comprehensive model of plant-environment interaction DENIT: DenNit DRYADES: Dryades DYNLAYER: Dynamic forest simulator EFIMOD: Dynamic Model of the "Mixed Stand/Soil" System in European Boreal Forests EFISCEN: European Forest Information Model (…)

  13. 1.8 Coupled vegetation-climate modelling Death of Amazonian rain forest ?! White et al (1998)

  14. 1.9 Amazonia revisited: model uncertainties 1. Change in precipitation and temperature over Amazonia predicted by 20 GCMs 3. Resulting change in rainforest biomass predicted by 3 vegetation models x 20 GCMs x 4 scenarios 2. Change in rainforest biomass predicted by 3 vegetation models for most extreme scenario (HadCM3 climate, A1FI) Galbraith et al. (2010). %

  15. 1.10 Reality check ! In every study using systems analysis and simulation: Model parameters, inputs and structure are uncertain How reliable are these model studies: • Sufficient data for model parameterization? • Sufficient data for model input? • How plausible are the different models? How to deal with uncertainties optimally?

  16. 2. The Bayesian approach

  17. 2.1 Probability Theory Uncertainties are everywhere: Models (environmental inputs, parameters, structure), Data Uncertainties can be expressed as probability distributions (pdf’s) We need methods that: • Quantify all uncertainties • Show how to reduce them • Efficiently transfer information: data  models  model application Calculating with uncertainties (pdf’s) = Probability Theory

  18. 2.2 Bayesian Calibration of process-based models DATA Prior pdf for the parameters Likelihood of the data Bayes’ Theorem `P(|D)=P() P(D|) / P(D) Scaling constant ( = ∫ P() P(D|) d  ) Posterior pdf for the parameters

  19. 2.3 Proof of Bayes’ Theorem B A P(A&B) = P(B) P(A|B)  P(A|B) = P(A) P(B|A) / P(B) Product Rule Bayes’ Theorem

  20. 2.3 Proof of Bayes’ Theorem P(A&B) = P(B) P(A|B) = P(A) P(B|A)  P(A|B) = P(A) P(B|A) / P(B) Product Rule Bayes’ Theorem  / = 1/3 3/7 5/7 1/5 B P(B) = 3/7 A P(A) = 5/7 P(B|A) = 1/5

  21. 2.4 An example of Bayesian calibration Bayes’ Theorem `P(|D)=P() P(D|) / P(D) Prior pdf for the parameters Likelihood of the data Scaling constant ( = ∫ P() P(D|) d  ) Posterior pdf for the parameters

  22. 2.5 Bayes & Human decision making “ ”

  23. 2.6 Bayes & the Egyptian pharao’s

  24. 2.7 Bayes & Extrasensory perception

  25. 2.8 Bayes & The climate

  26. 2.9 Bayes & The spread of languages

  27. 2.11 Bayes: What and why? • We want to use data and models to explain and predict ecosystem behaviour • Data as well as model inputs, parameters and outputs are uncertain • No prediction is complete without quantifying the uncertainty. No explanation is complete without analysing the uncertainty • Uncertainties can be expressed as probability density functions (pdf’s) • Probability theory tells us how to work with pdf’s: Bayes Theorem (BT) tells us how a pdf changes when new information arrives • BT: Prior pdf  Posterior pdf • BT: Posterior = Prior x Likelihood / Evidence • BT: P(θ|D) = P(θ) P(D|θ) / P(D) • BT: P(θ|D)  P(θ) P(D|θ)

  28. 3. Bayesian Calibration (BC)of process-based models

  29. 3.1 Bayesian updating of probabilities for process-based models Bayes’ Theorem: Prior probability → Posterior prob. Model parameterization: P(params) → P(params|data) Model selection: P(models) → P(model|data)

  30. 3.2 Bayesian Calibration of process-based models DATA Prior pdf for the parameters Likelihood of the data Bayes’ Theorem P(|D)=P() P(D|) / P(D) Scaling constant ( = ∫ P() P(D|) d  ) Posterior pdf for the parameters

  31. 3.3 Process-based forest models Height Environmental scenarios NPP Initial values Soil C Parameters Model

  32. 3.4 Process-based forest model BASFOR 40+ parameters 12+ output variables BASFOR

  33. 3.5 BASFOR: outputs Carbon in trees (standing + thinned) Volume (standing) Carbon in soil

  34. 3.6 BASFOR: parameter uncertainty

  35. 3.7 BASFOR: prior output uncertainty Carbon in trees (standing + thinned) Volume (standing) Carbon in soil

  36. 3.8 Data Dodd Wood (R. Matthews, Forest Research) Carbon in trees (standing + thinned) Volume (standing) Carbon in soil

  37. 3.9 Using data in Bayesian calibration of BASFOR Prior pdf Data Bayesian calibration Posterior pdf

  38. 3.10 Bayesian calibration: posterior uncertainty Carbon in trees (standing + thinned) Volume (standing) Carbon in soil

  39. 3.11 Calculating the posterior using MCMC MCMC trace plots P(|D)P() P(D|f()) • Start anywhere in parameter-space: p1..39(i=0) • Randomly choose p(i+1) = p(i) + δ • IF: [ P(p(i+1)) P(D|f(p(i+1))) ] / [ P(p(i)) P(D|f(p(i))) ] > Random[0,1] • THEN: accept p(i+1) • ELSE: reject p(i+1) • i=i+1 • 4. IF i < 104 GOTO 2 Metropolis et al (1953) Sample of 104 -105 parameter vectors from the posterior distribution P(|D) for the parameters

  40. 3.12 BC using MCMC: an example in EXCEL Click here for BC_MCMC1.xls

  41. install.packages("mvtnorm") require(mvtnorm) chainLength = 10000 data <- matrix(c(10,6.09,1.83, 20,8.81,2.64, 30,10.66,3.27), nrow=3, ncol=3, byrow=T) param <- matrix(c(0,5,10, 0,0.5,1) , nrow=2, ncol=3, byrow=T) pMinima <- c(param[1,1], param[2,1]) pMaxima <- c(param[1,3], param[2,3]) logli <- matrix(, nrow=3, ncol=1) vcovProposal = diag( (0.05*(pMaxima-pMinima)) ^2 ) pValues <- c(param[1,2], param[2,2]) pChain <- matrix(0, nrow=chainLength, ncol = length(pValues)+1) logPrior0 <- sum(log(dunif(pValues, min=pMinima, max=pMaxima))) model <- function (times,intercept,slope) {y <- intercept+slope*times return(y)} for (i in 1:3) {logli[i] <- -0.5*((model(data[i,1],pValues[1],pValues[2])- data[i,2])/data[i,3])^2 - log(data[i,3])} logL0 <- sum(logli) pChain[1,] <- c(pValues, logL0) # Keep first values for (c in (2 : chainLength)){ candidatepValues <- rmvnorm(n=1, mean=pValues, sigma=vcovProposal) if (all(candidatepValues>pMinima) && all(candidatepValues<pMaxima)) {Prior1 <- prod(dunif(candidatepValues, pMinima, pMaxima))} else {Prior1 <- 0} if (Prior1 > 0) { for (i in 1:3){logli[i] <- -0.5*((model(data[i,1],candidatepValues[1],candidatepValues[2])- data[i,2])/data[i,3])^2 - log(data[i,3])} logL1 <- sum(logli) logalpha <- (log(Prior1)+logL1) - (logPrior0+logL0) if ( log(runif(1, min = 0, max =1)) < logalpha ) { pValues <- candidatepValues logPrior0 <- log(Prior1) logL0 <- logL1}} pChain[c,1:2] <- pValues pChain[c,3] <- logL0 } nAccepted = length(unique(pChain[,1])) acceptance = (paste(nAccepted, "out of ", chainLength, "candidates accepted ( = ", round(100*nAccepted/chainLength), "%)")) print(acceptance) mp <- apply(pChain, 2, mean) print(mp) pCovMatrix <- cov(pChain) print(pCovMatrix) MCMC in R

  42. 3.14 Using data in Bayesian calibration of BASFOR Prior pdf Data Bayesian calibration Posterior pdf

  43. 3.15 Parameter correlations 39 parameters 39 parameters

  44. 3.16 Continued calibration when new data become available Prior pdf New data Bayesian calibration Prior pdf Posterior pdf

  45. 3.17 Continued calibration when new data become available Prior pdf Posterior pdf Prior pdf New data Bayesian calibration

  46. 3.18 Bayesian projects at CEH-Edinburgh [CO2] Uncertainty in earth system resilience (Clare Britton & David Cameron) Time Parameterization and uncertainty quantification of 3-PG model of forest growth & C-stock (Genevieve Patenaude, Ronnie Milne, M. v.Oijen) • Selection of forest models (NitroEurope team) • Data Assimilation forest EC data(David Cameron, Mat Williams) • Risk of frost damage in grassland(Stig Morten Thorsen, Anne-Grete Roer, MvO) • Uncertainty in agricultural soil models(Lehuger, Reinds, MvO) • Uncertainty in UK C-sequestration (MvO, Jonty Rougier, Ron Smith, Tommy Brown, Amanda Thomson)

  47. 3.19 Maps of (1) predictions, (2) uncertainties C-sequestration (model output for 1920-2000) Uncertainty of C-sequestration [Van Oijen & Thomson, 2010]

  48. 3.20 What kind of measurements would have reduced uncertainty the most ?

  49. 3.21 Prior predictive uncertainty & height-data Prior pred. uncertainty Biomass Height Height data Skogaby

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