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VSD+ training session, Indianapolis 2014 VSD+ PROPS Gert Jan Reinds.

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Presentation on theme: "VSD+ training session, Indianapolis 2014 VSD+ PROPS Gert Jan Reinds."— Presentation transcript:

1 VSD+ training session, Indianapolis 2014 VSD+ PROPS Gert Jan Reinds

2 VSD+ tool set VSD o dynamic modeling of soil acidification o soil eutrophication (N availability) o carbon sequestration

3 VSD+ tool set VSD+ (VSD + explicit C and N modeling) o dynamic modeling of soil acidification o soil eutrophication (N availability) o carbon sequestration VSD+

4 VSD+ tool set VSD+ abiotic conditions for vegetation input of fresh organic material temperature, moisture MetHyd (hydrology, modifying factors) MetHyd (hydrology, modifying factors) vegetation model (PROPS) GrowUP (growth, litterfall and uptake)

5 VSD+ tool set VSD+ MetHyd (hydrology, modifying factors) MetHyd (hydrology, modifying factors) GrowUP (growth, litterfall and uptake) vegetation model (PROPS)

6 How to prepare input for VSD+

7 VSD+ input essential o hydrology o uptake of N and BC, and input of fresh organics optional maintain as default need calibration

8 period thick bulkdens CEC pCO2fac cRCOO deposition X_we (non calcareous soils) parentCa (calcareous soils, default = -1) Essential thick should be depth of rooting zone: m for forest approx m for grasslands thick should be depth of rooting zone: m for forest approx m for grasslands start before first obs. (> 10 yrs) if bsat_0 = -1 start at low deposition period start before first obs. (> 10 yrs) if bsat_0 = -1 start at low deposition period total deposition (as in EMEP), not throughfall (as in measurements) In VSD+ Help: How to calculate total deposition from throughfall and bulk deposition. total deposition (as in EMEP), not throughfall (as in measurements) In VSD+ Help: How to calculate total deposition from throughfall and bulk deposition.

9 Hydrology temperature (TempC) average moisture content (theta) precipitation surplus (percol) modifying factors for mineralisation, nitrification and denitrification (rfmiR, rfnit, rfdenit) alternative: use MetHyd tool

10 Uptake and input of organic material net uptake of Ca, Mg, K (Ca_upt, Mg_upt, K_upt) total uptake of N (N_gupt) input of organic C and N (Clf, Nlf) for forests you can use the GrowUp tool

11 Optional bsat_0 (ECa_0/EMg_0/EK_0) Nfix if not given (default = -1): bsat_0 in steady state with initial deposition if not given (default = -1): bsat_0 in steady state with initial deposition only necessary for areas with very low N inputs (e.g. north Scandinavia)

12 Defaults kmin_x frhu_x CN_x expAl RCOOpars organic C and N turnover parameters for protonation of organic acids (default if ‘RCOOmod’ = Oliver) parameters for protonation of organic acids (default if ‘RCOOmod’ = Oliver) exponent for H + in Al (hydr)oxide equilibrium (default = 3) exponent for H + in Al (hydr)oxide equilibrium (default = 3)

13 exchange constants means and st.dev. in Mapping Manual (soil types) exchange constants means and st.dev. in Mapping Manual (soil types) Calibrate ■ lgKAlBC ■ lgKHBC ■ lgKAlox ■ Cpool_0 ■ CNrat_0 equilibrium constant for Al (hydr)oxides mean = 9, st.dev. = 1 equilibrium constant for Al (hydr)oxides mean = 9, st.dev. = 1 initial Cpool size and C/N ratio -give values if observation during large period -calibrate if few observations initial Cpool size and C/N ratio -give values if observation during large period -calibrate if few observations

14 Methyd

15 GrowUp tool to calculate: - uptake of N, Ca, Mg and K - input of C and N from litterfall and root turnover  for forests only  includes management actions (planting, thinning, clear-cut)  two forest types: - uniform age - mixed uneven aged (natural rejuvenation)

16 Demo VSD+ straightforward runs

17 PROPS; model for computing species occurrence probabilities  Based on a data base with 3400 sites from NL, AT, IR, (UK, DK, ICP Forest) with observed plant species composition and measured abiotic conditions (pH, C/N) etc.  Temperature and precipitation: climate database  From this set we compute optimal values for each abiotic conditions  Use this to assign abiotic conditions to sites in Europe with observed plant species composition (if possible)  Derive response functions for each species in the large data set

18 PROPS model versions Relationship between abiotic conditions and plant species occurrence.

19 Possible plant species diversity indices Diversity indices General indices Compare to a reference state Desired species Simpson index Shannon index Simpson index Shannon index Czekanowski (Bray- Curtis) index Buckland occurrence index Czekanowski (Bray- Curtis) index Buckland occurrence index Red List Index Habitat Suitability index Red List Index Habitat Suitability index

20 Habitat Suitability (HS) Index p j = probability/suitability/possibility of plant j p opt,j = optima (maximum) prob. of plant j n = number of plants Which species? Suggestion: n = number of desired (typical) species

21 Probability isolines: single species

22 Assigning species to EUNIS classes  E10 - Frisian-Danish coastal heaths on leached dune-sands  Dominant and most frequent species in different layers  Herb layer Calluna vulgaris, Empetrum nigrum, Genista anglica, Genista pilosa, Carex arenaria, Carex pilulifera, Erica tetralix, Salix repens subsp. dunensis, Deschampsia flexuosa, Danthonia decumbens, Festuca ovina, Nardus stricta, Molinia caerulea, Polypodium vulgare, Genista tinctoria, Lotus corniculatus, Orchis morio, Potentilla erecta, Ammophila arenaria  Moss layer (incl. lichens) Dicranum scoparium, Pleurozium schreberi, Scleropodium purum, Hypnum cupressiforme, Platismatia glauca, Cladina portentosa, Cladina arbuscula, Cladonia pyxidata, Cetraria aculeata  Diagnostically important species  Calluna vulgaris, Empetrum nigrum, Erica tetralix, Genista anglica, Genista pilosa, Salix repens subsp. dunensis, Carex arenaria, Pyrola rotundifolia, Pyrola minor, Scleropodium purum, Pleurozium schreberi Map of the natural vegetation of Europe

23 Combined probability isolines (British lowland blanket bogs, 15 species); climate dependency T=12°C T=3°C

24 PROPS: results pH 1:1

25 Robustness...

26 PROPS demo

27 Bayesian Calibration of the model VSD+ Gert Jan Reinds

28 Contents  Introduction  Theory  Method  What to calibrate  Examples for VSDplus  Conclusions

29 Introduction  For application of models at sites we need to calibrate the model because there is an uncertainty and variability in input parameters  In VSD we can calibrate by fitting to the observations:

30 How to deal with uncertainty in observations and multi signal calibration Often there is uncertainty in the measurements We have output parameters that are influenced by more than one input parameter

31 Pr(A|B) is the posterior probability of A given B Pr(A) is the prior probability of A not taking into account information about B. L(B|A) is the standardized likelihood of B given A In the calibration of VSD, a prior distribution (A) of each VSD input parameter is defined based on available knowledge; for candidate points from normal distributions close to the mean the probability will be large, for points in the ‘tail’ of the distribution the probability will be low. Then the posterior distribution of input parameters (Pr (A|B)) is computed based on the prior probability in combination with comparison of the model outcome with a set of uncertain measurements giving the likelihood L(B|A): the better the model is able to reproduce the measurements, the higher the likelihood Bayes Theorem

32 Procedure  Determine for each model parameter suited for calibration its prior distribution (normal, uniform,..)  Run the model with samples from these distributions and compare the results from each run with measurements of output parameters (concentrations in soil solution and their standard deviation)  Accept the run if the goodness of fit is sufficient and store the associated input parameters  The vectors of stored input parameters provide the posterior distribution of the model parameters

33 How to sample  The method relies on a large number of runs, so we have to take many samples from the input data distributions (10 4 – 10 5 )  We use a Markov Chain Monte Carlo (MCMC) approach (known as Metropolis-Hastings Random Walk)  Each point is accepted or rejected; accepted points are stored and so is the point with the highest posterior probability (i.e. the point with a combination of high prior probability and good model fit); this is what you see in the VSDp calibration output

34 Metropolis Hastings Random Walk

35 What to calibrate  lgKAlox: requires observations of pH and Al  lgKAlBc, lgKHBc; requires observation(s) of base saturation (EBc). Note: we start the calibation assuming EBc to be in equilibrium with deposition (inputs): start the calibration run preferably in pre-industrial time (<=1900)  Cpool_0: requires observation(s) of the Cpool  CNrat_0: requires observation(s) of C/N

36 DEMO  Standard calibration

37 Support Support for you: For support on VSD+ modeling you can contact CCE Support for us: To further develop, test, calibrate and validate VSD+ we like your input!  Forest not in NW-Europe  Non-forest vegetation

38 Questions? latest version of VSD+ GrowUp MetHyd can be downloaded soon from: we will distribute USB sticks for now


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