Primary Productivity and Water Balance of Grassland Vegetation on ...
Model inter-comparison on climate change in relation to grassland productivity
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Transcript of Model inter-comparison on climate change in relation to grassland productivity
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Model inter-comparison on climate change in relation to grassland
productivityShaoxiu Ma, Gianni Bellocchi
Romain Lardy, Haythem Ben-Touhami, Katja Klumpp and modelling teams
lNRA Clermont-Theix-LyonUR 874 - Grassland Ecosystem (UREP)
Ecosystem functioning & valuation web services and workflowsJune 6-7, 2013
ELTE - Eötvös Loránd University, Budapest, Hungary
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Outline
Overview the concepts and objectives of MACSUR
Methodology
Preliminary results
Outlook
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Networking
Modelling
Integration
Coordination of Knowledge Hub
Capacity building
CropM
TradeM
LiveM Met
hodo
logi
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Case
stud
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Regi
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http://www.macsur.eu
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WP2Model inter-comparison on climate change in relation to livestock and grassland
Task 2.4
Modelinter-comparison
Task 2.1
Identification ofavailable models
Task 2.2
Development of methodsfor model evaluation
Task 2.3
Definition of a protocolfor model inter-comparison
WP3Improving the assessmentof climate change impact
on livestock and grasslandat farm level
WP4Contribution to cross-cutting activities with integrated studies at
regional level
WP1Building and exploring datasets and models on climate change in relation
to livestock and grassland
MACSUR perspective (Grassland)
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To quantify uncertainties due to model structure
To discover strengths and weaknesses in grassland models
MACSUR perspective (Grassland)
The focus of grassland model inter-comparison in MACSUR project
http://www.macsur.eu
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Data supplier Modelling team
(datasets) (model runs)
Coordinator(data segregation; output evaluation, uncertainty
analysis)
Questionaire for modelling teams Guideline and minimum dataset requirement for model evaluation A common protocol for model inter-comparison Model inter-comparison at selected sites in Europe
The pathway for model inter-comparison
Methodology
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Identification of available grassland models
Documentation of core algorithms
Sensitivity tests to changes of CO2, temperature and precipitation
Evaluation of model performances
Uncertainty analyses
Run of un-calibrated and calibrated models
Modelling frameworkMethodology
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Grassland-specific
Biome models
Selected models:
Crop models (adapted to grasslands)
Methodology
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Methodology
Climate manipulation
experiments
FACE, Warming and
precipitation
Field experiments
Cutting, grazing, fertilization
Eddy flux measurements
Interested datasets:
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Location of observational sites
Eddy flux measurements( e.g. NEE, GPP, RECO, ET, SWC)
Methodology
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InputClimate scenarios
scen1 scen2 scen3 scen4 scen5 scen6 baseline
Temperature(°C)
Standard deviation -25% -10% -5% 5% 10% 25% current
Precipitation(mm)
Standard deviation -25% -10% -5% 5% 10% 25% current
CO2 (ppm) 380pm 5% 10% 15% 25% 50% 100% 380
Sensitivity tests
Methodology
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use of multiple evaluation metrics
use of fuzzy-logic to aggregate metrics into synthetic
indicators
Enlarged concept of model performance:
agreement with data + model structure
Model evaluation
Methodology
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Fuzzy-logic based integrated indicators / 1
Methodology
Rivington et al., 2005, Agr. Forest Meteorol.
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MethodologyFuzzy-logic based integrated indicators / 2
Confalonieri et al., 2009, Ecol. Modell.
agreement with data
model structure
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single VS multiple site evaluation
Methodology
Robustness: variability of model performance with the variability of conditions
(-, worst; 1, best)
(0, best; +, worst)
Confalonieri et al., 2010, Ecol. Modell.
(-1, +1)
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Ratio of relevance parameters (Rp)F Partial U
≥ 0.10 ↔ ≤ 0.50
AIC relative weight (wk)F Partial U
≥ 0.70 ↔ ≤ 0.30
0.000.500.501.00
F FF UU FU U
ComplexityF Partial U0 ↔ 1
AgreementF Partial U0 ↔ 1
0.000.250.500.750.250.500.751.00
MCIm
0.000.200.600.800.200.400.801.00
F F FF F UF U FF U UU F FU F UU U FU U U
membership functionS[x; a = min (F, U); b = max (F, U)]
membershipfunction
S[x; a = 0; b = 1
Index of agreement (d)F Partial U
≥ 0.90 ↔ ≤ 0.70
Probability of equal means (P(t))F Partial U
≥ 0.10 ↔ ≤ 0.05
Correlation coefficient (R)F Partial U
≥ 0.90 ↔ ≤ 0.70expertweight
Index of robustness (IR)F Partial U
1 ↔ 10
0.001.00
FU
RobustnessF Partial U0 ↔ 1
F F FF F UF U FF U UU F FU F UU U FU U U
membership functionS[x; a = min (F, U); b = max (F, U)]
membership functionS[x; a = min (F, U); b =max (F, U)]
Methodology
agreement with data
model structure
Robustness
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Preliminary resultsObserved GPP vs Estimated GPP (g C/ Monthly) for Oensingen
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Uncertainty of the simulated GPP from different grassland models
for Oensingen
Preliminary results
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Model range
Uncertainty of the simulated GPP from different grassland models
for Oensingen
Preliminary results
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Sensitivity of GPP (Oensingen from PaSim model)Temperature
Preliminary results
Precipitation
CO2
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OutlookSensitivity of GPP of different grassland models on the
same site(virtual results)Temperature
Precipitation
CO2
Observed
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Uncertainty of the simulated yield from different grassland models and sites (virtual results)
Outlook
adapted from Palosuo, 2011
Mod
el1
Mod
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Mod
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Mod
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Mod
el5
Mod
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Mod
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Mod
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obse
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Crop
?
Biome
?
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Uncertainty of the simulated yield from different grassland models for each sites (virtual results)
Outlook
adapted from Palosuo, 2011
Site
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Site
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Site
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Site
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Site
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Site
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Site
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Site
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Semi-arid
?Humid
?
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Document all models in the inter-comparison
Run sensitivity tests and evaluate models at a variety of sites
Expand the number of models (process-based) and datasets (representative of European grassland regions) …
Future actions
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Thanks a lot for your attention!