VSD training session Indianapolis 2014 VSD PROPS Gert

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

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

VSD+ tool set VSD o dynamic modeling of soil acidification o soil eutrophication (N

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

VSD+ tool set VSD+ (VSD + explicit C and N modeling) o dynamic modeling

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

VSD+ tool set Grow. UP input of fresh (growth, litterfall organic material and uptake)

VSD+ tool set Grow. UP input of fresh (growth, litterfall organic material and uptake) VSD+ Met. Hyd temperature, (hydrology, modifying moisture factors) vegetation abiotic conditions modelfor vegetation (PROPS)

VSD+ tool set Grow. UP (growth, litterfall and uptake) VSD+ Met. Hyd (hydrology, modifying

VSD+ tool set Grow. UP (growth, litterfall and uptake) VSD+ Met. Hyd (hydrology, modifying factors) vegetation model (PROPS)

How to prepare input for VSD+

How to prepare input for VSD+

VSD+ input • essential o hydrology o uptake of N and BC, and input

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

Essential start before first obs. (> 10 yrs) • period thick should be depth

Essential start before first obs. (> 10 yrs) • period thick should be depth of if bsat_0 = zone: -1 start at low rooting • thick deposition period • bulkdens 0. 5 - 1 m forest approx. 0. 25 m for grasslands • CEC • p. CO 2 fac • c. RCOO total deposition (as in EMEP), not throughfall (as in measurements) • deposition In VSD+ Help: How to calculate total • X_we (non calcareous soils) deposition from throughfall and bulk • parent. Ca (calcareous soils, default = -1) deposition.

Hydrology • temperature (Temp. C) • average moisture content (theta) • precipitation surplus (percol)

Hydrology • temperature (Temp. C) • average moisture content (theta) • precipitation surplus (percol) • modifying factors for mineralisation, nitrification and denitrification (rfmi. R, rfnit, rfdenit) alternative: use Met. Hyd tool

Uptake and input of organic material • net uptake of Ca, Mg, K (Ca_upt,

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) forests you can use the Grow. Up tool

Optional if not given (default = -1): • • bsat_0 (ECa_0/EMg_0/EK_0) Nfix only necessary

Optional if not given (default = -1): • • bsat_0 (ECa_0/EMg_0/EK_0) Nfix only necessary for areasbsat_0 with in steady state with initial deposition very low N inputs (e. g. north Scandinavia)

Defaults • kmin_x • frhu_x • CN_x • exp. Al • RCOOpars organic C

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

Calibrate ■ lg. KAl. BC ■ lg. KHBC ■ lg. KAlox ■ Cpool_0 ■

Calibrate ■ lg. KAl. BC ■ lg. KHBC ■ lg. KAlox ■ Cpool_0 ■ CNrat_0 exchange constants means and st. dev. in Mapping Manual (soil types) 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

Methyd

Methyd

Grow. Up tool to calculate: - uptake of N, Ca, Mg and K §

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

Demo VSD+ straightforward runs

Demo VSD+ straightforward runs

PROPS; model for computing species occurrence probabilities § Based on a data base with

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 (p. H, 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 800000 sites in Europe with observed plant species composition (if possible) § Derive response functions for each species in the large data set

PROPS model versions Relationship between abiotic conditions and plant species occurrence. pi trans o

PROPS model versions Relationship between abiotic conditions and plant species occurrence. pi trans o p a Ev n ratio Hydrology Precipitatio n Tem C/N p. H 3 c. NO Np oo l pera ture

Possible plant species diversity indices Diversity indices General indices Simpson index Shannon index Compare

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

Habitat Suitability (HS) Index pj = probability/suitability/possibility of plant j popt, j = optima

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

Probability isolines: single species

Probability isolines: single species

Assigning species to EUNIS classes § E 10 - Frisian-Danish coastal heaths on leached

Assigning species to EUNIS classes § E 10 - 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 tetralix, Genista anglica, Genista pilosa, Salix repens subsp. dunensis, Carex Map nigrum, of the Erica natural vegetation of Europe arenaria, Pyrola rotundifolia, Pyrola minor, Scleropodium purum, Pleurozium schreberi

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

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

PROPS: results p. H 1: 1

PROPS: results p. H 1: 1

Robustness. . .

Robustness. . .

PROPS demo

PROPS demo

Bayesian Calibration of the model VSD+ Gert Jan Reinds

Bayesian Calibration of the model VSD+ Gert Jan Reinds

Contents § Introduction § Theory § Method § What to calibrate § Examples for

Contents § Introduction § Theory § Method § What to calibrate § Examples for VSDplus § Conclusions

Introduction § For application of models at sites we need to calibrate the model

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:

n n How to deal with uncertainty in observations and multi signal calibration Often

n n 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

Bayes Theorem Pr(A|B) is the posterior probability of A given B Pr(A) is the

Bayes Theorem 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 base 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

Procedure § Determine for each model parameter suited for calibration its prior distribution (normal,

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

How to sample § The method relies on a large number of runs, so

How to sample § The method relies on a large number of runs, so we have to take many samples from the input data distributions (104 – 105) § 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

Metropolis Hastings Random Walk

Metropolis Hastings Random Walk

What to calibrate § lg. KAlox: requires observations of p. H and Al §

What to calibrate § lg. KAlox: requires observations of p. H and Al § lg. KAl. Bc, lg. KHBc; 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

DEMO § Standard calibration

DEMO § Standard calibration

Support for you: For support on VSD+ modeling you can contact CCE Support for

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

Questions? latest version of • VSD+ • Grow. Up • Met. Hyd can be

Questions? latest version of • VSD+ • Grow. Up • Met. Hyd can be downloaded soon from: www. wge-cce. org we will distribute USB sticks for now