VSD+ training session, Indianapolis 2014

VSD+ PROPS

Gert Jan Reinds

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 of soil acidification

o soil eutrophication (N availability)

o carbon sequestration

VSD+

VSD+ tool set

VSD+abiotic conditions for vegetation

input of fresh organic material

temperature, moisture

MetHyd(hydrology, modifying factors)

vegetation model

(PROPS)

GrowUP (growth, litterfall

and uptake)

VSD+ tool set

VSD+

MetHyd(hydrology, modifying factors)

GrowUP (growth, litterfall

and uptake)

vegetation model

(PROPS)

How to prepare input for VSD+

VSD+ input

• essentialo hydrologyo uptake of N and BC, and input of fresh organics

• optional

• maintain as default

• need calibration

• 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:

0.5 - 1 m for forestapprox. 0.25 m for grasslands

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.

Hydrology

• temperature (TempC)

• average moisture content (theta)

• precipitation surplus (percol)

• modifying factors for mineralisation, nitrification and denitrification (rfmiR, rfnit, rfdenit)

alternative: use MetHyd tool

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

Optional

• bsat_0 (ECa_0/EMg_0/EK_0)

• Nfix

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)

Defaults

• kmin_x

• frhu_x

• CN_x

• expAl

• RCOOpars

organic C and N turnover

parameters for protonation of organic acids

(default if ‘RCOOmod’ = Oliver)

exponent for H+ in Al (hydr)oxide equilibrium(default = 3)

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. = 1initial Cpool size and C/N ratio- give values if observation during

large period- calibrate if few observations

Methyd

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)

Demo VSD+ straightforward runs

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 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.

Evapotranspi

ration

Hydrology

Precipitation

Temperature

Npoo

l

cNO

3

pH C

/N

Possible plant species diversity indices

Diversity indices

General indicesCompare to a

reference stateDesired species

Simpson index

Shannon index

Czekanowski (Bray- Curtis) indexBuckland occurrence index

Red List Index

Habitat Suitability index

Habitat Suitability (HS) Index

pj = probability/suitability/possibility of plant jpopt,j = optima (maximum) prob. of plant jn = number of plants

Which species?Suggestion: n = number of desired (typical) species

𝐻𝑆= 1𝑛 ( 𝑝1

𝑝𝑜𝑝𝑡 ,1

+𝑝2

𝑝𝑜𝑝𝑡 ,2

+…+𝑝𝑛

𝑝𝑜𝑝𝑡 ,𝑛)

Probability isolines: single species

0.0010000.0100000.0500000.1000000.200000

Calluna_vulgaris in 1996

NO3 concentration (mg NO3/kg)28272625242322212019181716151413121110987654321

pH

7.2

7

6.8

6.6

6.4

6.2

6

5.8

5.6

5.4

5.2

5

4.8

4.6

4.4

4.2

4

3.8

3.6

3.4

3.2

3

2.8

2.6

2.4

2.2

2

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

Combined probability isolines (British lowland blanket

bogs, 15 species); climate dependency

T=12°CT=3°C

PROPS: results

y = 0.47x + 3.0202R² = 0.496

3

4

5

6

7

8

9

3 4 5 6 7 8 9

Calc

ulat

ed

Measured

pH curves GJpH

1:1

Robustness...

0.0010000.0010000.0100000.0100000.0500000.0500000.1000000.1000000.2000000.2000000.3000000.3000000.5000000.500000

All selected species in 1996

NO3 concentration (mg NO3/kg)5048464442403836343230282624222018161412108642

pH

9

8.5

8

7.5

7

6.5

6

5.5

5

4.5

4

3.5

3

2.5

2

0.0010000.0010000.0010000.0100000.0100000.0500000.0500000.1000000.2000000.3000000.500000

All selected species in 1996

NO3 concentration (mg NO3/kg)5048464442403836343230282624222018161412108642

pH

9

8.5

8

7.5

7

6.5

6

5.5

5

4.5

4

3.5

3

2.5

2

PROPS demo

Bayesian Calibration of the model VSD+

Gert Jan Reinds

Contents

Introduction

Theory

Method

What to calibrate

Examples for VSDplus

Conclusions

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:

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

Pr(A|B) is the posterior probability of A given BPr(A) is the prior probability of A not taking into account information about B. L(B|A) is the standardized likelihood of B given AIn 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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.5 1 1.5 2 2.5 3 3.5

parameter value

pro

bab

ilit

y

prior

low prior probility

high prior probility

)Pr()|()|Pr( AABLBA

3

3.2

3.4

3.6

3.8

4

4.2

4.4

4.6

4.8

1985 1990 1995 2000 2005 2010

year

sim

ula

ted

pH

simulation with lowlikelihood

simulation with highlikelihood

observed values

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 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

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

DEMO

Standard calibration

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

Questions?

latest version of• VSD+• GrowUp• MetHydcan be downloaded soonfrom: www.wge-cce.org

we will distribute USB sticks for now