Showcase of a Biome-BGC
workflow presentation
Zoltán BARCZA
Training Workshop for Ecosystem Modelling studies
Budapest, 29-30 May 2014
Biome-BGC
Typical process-based biogeochemical model to
simulate plant growth with full accounting on carbon,
nitrogen and water flows.
Due to the complex nature of plant growth and mortality
and their drivers large uncertainties exist in
• model structure – caused by many simplifications and
assumptions
• parameterization – plant traits change from point to
point
• driving environmental variables – meteorology, soil,
topography…
• magnitude and nature of human intervention
Role of measurement data
Measurements: we can ‘train’ the model to perform
better [=calibration]
PROBLEM: state-of-the-art models are highly complex
and non-linear so common sense [tuning model
parameters manually] does not work anymore
SOLUTION: statistical calibration; all parameters are
changed simultaneously, and we use mathematical
statistics to evaluate the model against data [GLUE,
Bayesian calibration, etc.]
PROBLEM AGAIN: if model structure has errors, can we
trust the calibrated parameters? Not really….
Biome-BGC within BioVeL
- development of Biome-BGC, current BioVeL-supported
version is Biome-BGC MuSo v2.2.1
- major improvements: implementation of human
intervention, major improvement in soil hydrology and
herbaceous vegetation phenology [+ other small details
(“Devil lives in details”)]
- continuous development = continuous optimization! 
Can we use the model without calibration/
optimization?
Biome-BGC: plant function type logic
PFT: classification of plants based on basic traits like
leaf longevity and woody/non-woody characteristics
But what about the PFT logic?
Support for the PFT concept in grasslands!!!!
Model parameter estimation (calibration)
- GLUE method (based on Monte-Carlo method)
- Bayesian calibration (Monte-Carlo method with
Metropolis algorithm)
- Levenberg-Marquardt
- Kalman filter
- genetic algorithm… + many other
The result is optimized parameters + uncertainty
intervals for parameters (a posteriori distribution).
Additionally, confidence interval can be estimated for the
prognostic run
Basics of calibration
As we have both reference data (measurement) and
simulated data (Biome-BGC) for the same variable (e.g.
GPP, Reco), we can compare them and judge the quality
of the simulation.
Question: how can we say which simulation is
better than another?
6
MODEL1
MODEL2
MEASURED
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
2
6
6
5
5
MEASURED
MEASURED
1
4
3
2
y = 1.23x + 1.74
R2 = 0.94
1
4
3
2
y = 1.03x - 0.15
R2 = 0.18
1
0
0
0
1
2
3
MODEL
4
5
6
0
1
2
3
MODEL
4
5
6
mean of model and measurement:
MODEL1
MODEL2
MEASURED
1.4
3.5
3.5
BIAS of MODEL1: -2.1
BIAS of MODEL2: 0!!!
+ measurement uncertainty
12
MODEL1
MODEL2
MEASURED
10
8
6
4
2
0
1
-2
-4
2
3
4
5
6
7
8
9
10
11
12
13
14
Message
There are different metrics to quantify measurementmodel agreement/mismatch.
We should choose one objective function that fits our
needs.
Example: use RMSE to quantify the bias
RMSE
MODEL1
MODEL2
2.09
0.94
6
MODEL1
MODEL2
MEASURED
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
If misfit is higher, simulation is worse. So we
need to minimize misfit to get good simulation.
The usual statistical expression of model
goodness is likelihood, e.g.:
There are many likelihood definitions in the
literature. Hidy et al., 2012:
Further problems: multi-objective calibration
Monitoring of carbon balance components usually
involve measurement of different components [e.g. stem
and root biomass, litter, NEE, H2O flux…].
It would be nice to optimize the model taking into
account more than one data stream.
This is possible – the question is once again: how
should we construct model-measurement misfit
(objective function).
BioVeL approach
Example for using eddy-covariance data to optimize
Biome-BGC MuSo
1  GPP
  
n i 1 
n
J GPP
1  Reco
  
n i 1 
n
J Reco
1  LE
  
n i 1 
n
J LE
i
simulated
 GPP

i
simulated
i
simulated
i
observed
 Reco

 LE

i
observed



2
i
observed



2
individual cost
functions



2
BioVeL approach
J total
J GPP  J Reco  J LE

3
 1

L
exp

J

total 
2
 2

2
1
The datastreams are equally important!
aggregate multiobjective cost function
[Keenan et al. 2011
Oecologia]
Aggregate likelihood
MACSUR is a knowledge hub within FACCE-JPI (Joint
Programming Initiative for Agriculture, Climate Change,
and Food Security).
MACSUR gathers the excellence of existing research in
livestock, crop, and trade science to describe how climate
variability and change will affect regional farming
systems and food production in Europe in the near and the
far future and the associated risks and opportunities for
European food security.
MACSUR
Biome-BGC MuSo participates in Grassland model
intercomparison [part of LiveM theme, grassland and
livestock modelling]
TASKS:
• blind tests [previously calibrated models are run using
driving data and management]
• calibrated runs [participants has to re-calibrate their
models using information from data-rich sites (eddycovariance sites)]
GRASSLAND INTERCOMPARISON
Sites
Climatic relations on the study sites
Annual precipitation (mm)
1400
Sassari
Rothamsted
1200
Matta
Lelystad
Kempten
1000
Laqueuille
Monte Bodone
800
Grillenburg
Oensingen
600
400
0
5
10
15
Mean annual temperature (°C)
20
25
CALIBRATION
It would not have been possible without BioVeL
infrastructure!
• Monte-Carlo experiment was used
• post-processing was performed using IDL
• post-processing was the testbed for the workflow
representation [GLUE]
CALIBRATION
MCE settings
EPC
MuSo 2.2
#row number
min
13
0.01
15
0.5
20
0.1
21
14.0
39
0.7
41
30.0
43
0.1
45
0.001
EPC_END
INI
34
0.3
42
0.001
INI_END
max
0.2
2.5
0.9
44.0
1.
80.0
0.3
0.006
1.5
0.003
description [optional]
(1/yr)
annual whole-plant mortality fraction
(ratio) (ALLOCATION) new fine root C : new leaf
(prop.) (ALLOCATION) current growth proportion
(kgC/kgN) C:N of leaves
(DIM)
canopy light extinction coefficient
(m2/kgC) canopy average specific leaf area
(DIM)
fraction of leaf N in Rubisco
(m/s)
maximum stomatal conductance
(m)
maximum root depth
(kgN/m2/yr) symbiotic+asymbiotic fixation of N
Guidance: White et al. 2000 (typically)
CALIBRATION
GLUE was used to visualize and post-process the
results. What is GLUE? General Likelihood Uncertainty
Estimation
Oensingen
0. maximum root depth
1. symbiotic+asymbiotic fixation of N
2. annual whole-plant mortality fraction
3. new fine root C : new leaf
4. current growth proportion
5. C:N of leaves
6. canopy light extinction coeff
7. canopy average specific leaf area
8. fraction of leaf N in Rubisco
9. maximum stomatal conductance
Laqueuille-intensive
0. maximum root depth
1. symbiotic+asymbiotic fixation of N
2. annual whole-plant mortality fraction
3. new fine root C : new leaf
4. current growth proportion
5. C:N of leaves
6. canopy light extinction coeff
7. canopy average specific leaf area
8. fraction of leaf N in Rubisco
9. maximum stomatal conductance
Monte-Bodone
0. maximum root depth
1. symbiotic+asymbiotic fixation of N
2. annual whole-plant mortality fraction
3. new fine root C : new leaf
4. current growth proportion
5. C:N of leaves
6. canopy light extinction coeff
7. canopy average specific leaf area
8. fraction of leaf N in Rubisco
9. maximum stomatal conductance
1
2
3
4
5
a posteriori parameter
uncertainty
Oensingen
Grillenburg
Laqu-ext
Laqu-int
Monte Bodone
6
top 5%
5
max LH
uncalibrated
site number
4
3
2
1
0
0
0.2
0.4
0.6
0.8
1
Canopy LIGHT EXTINCTION Coeff. [Dim]
1.2
1
2
3
4
5
Oensingen
Grillenburg
Laqu-ext
Laqu-int
Monte Bodone
6
top 5%
5
max LH
uncalibrated
site number
4
3
2
1
0
0
0.002
0.004
0.006
0.008
Max. STOMATAL CONDUCTANCE [m/s]
0.01
1
2
3
4
5
Oensingen
Grillenburg
Laqu-ext
Laqu-int
Monte Bodone
indication of structural
problems!!!
6
top 5%
max LH
5
uncalibrated
site number
4
3
2
1
0
0
0.2
0.4
0.6
0.8
Current GROWTH PROPORTION [ratio]
1
Grillenburg
18
measGPP
blindGPP
calGPP
16
14
12
10
8
6
4
2
0
-2
-4
1
46
91
136 181
226 271
316 361
406 451 496
541 586
631 676
721 766
811 856
901 946 991 1036 1081
Grillenburg – GPP-blind and GPP-calibrated
16
18
y = 0.74x + 0.30
R2 = 0.59
16
y = 0.79x + 0.88
R2 = 0.63
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
0
0
-2
2
4
6
8
10
12
14
16
0
18
-2
2
4
6
8
10
12
14
16
18
Thank you
for your
attention