Transcript VSD+ training session (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 availability)
o carbon sequestration
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
GrowUP
input
of fresh
(growth, litterfall
organic
material
and uptake)
VSD+
MetHyd
temperature,
(hydrology,
modifying
moisture
factors)
vegetation
abiotic
conditions
modelfor
vegetation
(PROPS)
VSD+ tool set
GrowUP
(growth, litterfall
and uptake)
VSD+
MetHyd
(hydrology,
modifying
factors)
vegetation
model
(PROPS)
How to prepare input for VSD+
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 of
if bsat_0
= zone:
-1 start at low
rooting
• thick
deposition period
• bulkdens
0.5 - 1 m for forest
approx. 0.25 m for grasslands
• CEC
• pCO2fac
• cRCOO
total deposition (as in EMEP),
• deposition
not throughfall (as in measurements)
• X_we (non calcareous soils)
In VSD+ Help: How to calculate total
deposition from throughfall and bulk
• parentCa (calcareous soils,
default = -1)
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
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
• expAl
• 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
■ lgKAlBC
■ lgKHBC
■ lgKAlox
■ 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
GrowUp
tool to calculate:
-
uptake of N, Ca, Mg and K



for 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
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.
Possible plant species diversity indices
Diversity indices
General indices
Simpson index
Shannon index
Compare to a
reference state
Czekanowski (BrayCurtis) index
Buckland occurrence
index
Desired species
Red List Index
Habitat Suitability
index
Habitat Suitability (HS) Index
1 𝑝1
𝑝2
𝑝𝑛
𝐻𝑆 =
+
+ ⋯+
𝑛 𝑝𝑜𝑝𝑡,1 𝑝𝑜𝑝𝑡,2
𝑝𝑜𝑝𝑡,𝑛
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
Calluna_vulgaris in 1996
7.2
7
0.001000
0.010000
0.050000
0.100000
0.200000
6.8
6.6
6.4
6.2
6
5.8
5.6
5.4
5.2
5
pH
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
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
NO3 concentration (mg NO3/kg)
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
Erica tetralix,
Genista anglica,
Genista pilosa, Salix repens subsp. dunensis, Carex
Map nigrum,
of the 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
PROPS: results
pH curves GJ
pH
9
1:1
8
Calculated
7
6
y = 0.47x + 3.0202
R² = 0.496
5
4
3
3
4
5
6
Measured
7
8
9
Robustness...
All selected species in 1996
All selected species in 1996
9
9
0.001000
0.001000
0.010000
0.010000
0.050000
0.050000
0.100000
0.100000
0.200000
0.200000
0.300000
0.300000
0.500000
0.500000
8.5
8
7.5
7
6.5
8
7.5
7
6.5
6
pH
6
pH
0.001000
0.001000
0.001000
0.010000
0.010000
0.050000
0.050000
0.100000
0.200000
0.300000
0.500000
8.5
5.5
5.5
5
5
4.5
4.5
4
4
3.5
3.5
3
3
2.5
2.5
2
2
4
6
8
10
12
14
16
18
20 22 24 26 28 30 32
NO3 concentration (mg NO3/kg)
34
36
38
40
42
44
46
48
50
2
2
4
6
8
10
12
14
16
18
20 22 24 26 28 30 32
NO3 concentration (mg NO3/kg)
34
36
38
40
42
44
46
48
50
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
Bayes Theorem
Pr(A | B)  L( B | A) Pr(A)
0.9
4.8
0.8
4.6
0.7
4.4
0.6
prior
0.5
low prior probility
0.4
high prior probility
0.3
simulated pH
probability
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 wi
be low.
4
3.4
3.2
0.5
1
1.5
2
parameter value
2.5
3
3.5
observed values
3.6
0.1
0
simulation with high
likelihood
3.8
0.2
0
simulation with low
likelihood
4.2
3
1985
1990
1995
2000
2005
2010
year
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, 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
• MetHyd
can be downloaded soon
from: www.wge-cce.org
we will distribute USB sticks for now