Showcase of a Biome-BGC workflow presentation

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?

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MODEL1

MODEL2

MEASURED

y = 1.23x + 1.74

R2 = 0.94

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MODEL

ME

AS

UR

ED

1 2

y = 1.03x - 0.15

R2 = 0.180

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MODELM

EA

SU

RE

D

MODEL1 MODEL2 MEASURED

1.4 3.5 3.5

mean of model and measurement:

BIAS of MODEL1: -2.1BIAS of MODEL2: 0!!!

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MODEL1

MODEL2

MEASURED

+ measurement uncertainty

Message

There are different metrics to quantify measurement-model 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

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MODEL1

MODEL2

MEASURED

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

n

i

iobserved

isimulated

n 1

2

GPP

GPPGPP1J

n

i

iobserved

isimulated

n 1

2

Reco

RecoReco1J

n

i

iobserved

isimulated

n 1

2

LE

LELE1J

individual cost functions

BioVeL approach

3JJJ

J LERecoGPPtotal

totalJ

21

exp2

1L

2

The datastreams are equally important!

aggregate multi-objective 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 (eddy-covariance sites)]

GRASSLAND INTERCOMPARISON

Sites

400

600

800

1000

1200

1400

0 5 10 15 20 25

Ann

ual p

reci

pita

tion

(mm

)

Mean annual temperature (°C)

Climatic relations on the study sites

Sassari

Rothamsted

Matta

Lelystad

Kempten

Laqueuille

Monte Bodone

Grillenburg

Oensingen

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 max description [optional]13 0.01 0.2 (1/yr) annual whole-plant mortality fraction 15 0.5 2.5 (ratio) (ALLOCATION) new fine root C : new leaf20 0.1 0.9 (prop.) (ALLOCATION) current growth proportion21 14.0 44.0 (kgC/kgN) C:N of leaves 39 0.7 1. (DIM) canopy light extinction coefficient41 30.0 80.0 (m2/kgC) canopy average specific leaf area43 0.1 0.3 (DIM) fraction of leaf N in Rubisco45 0.001 0.006 (m/s) maximum stomatal conductanceEPC_ENDINI34 0.3 1.5 (m) maximum root depth42 0.001 0.003 (kgN/m2/yr) symbiotic+asymbiotic fixation of NINI_END

Guidance: White et al. 2000 (typically)

CALIBRATION

GLUE was used to visualize and post-process the results. What is GLUE? General Likelihood Uncertainty Estimation

0. maximum root depth1. symbiotic+asymbiotic fixation of N2. annual whole-plant mortality fraction3. new fine root C : new leaf4. current growth proportion5. C:N of leaves6. canopy light extinction coeff7. canopy average specific leaf area 8. fraction of leaf N in Rubisco9. maximum stomatal conductance

Oensingen


Laqueuille-intensive


Monte-Bodone

1 Oensingen2 Grillenburg3 Laqu-ext4 Laqu-int5 Monte Bodone

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Canopy LIGHT EXTINCTION Coeff. [Dim]

site

nu

mb

er

top 5%

max LH

uncalibrated

a posteriori parameter uncertainty


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0 0.002 0.004 0.006 0.008 0.01

Max. STOMATAL CONDUCTANCE [m/s]

site

nu

mb

er

top 5%

max LH

uncalibrated


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Current GROWTH PROPORTION [ratio]

site

nu

mb

er

top 5%

max LH

uncalibrated

indication of structural problems!!!

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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

measGPP

blindGPP

calGPP

Grillenburg

y = 0.74x + 0.30

R2 = 0.59

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y = 0.79x + 0.88

R2 = 0.63

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Grillenburg – GPP-blind and GPP-calibrated

Thank you Thank you for your for your attentionattention

Documents

Showcase of a Biome-BGC workflow presentation