Nicolas Delpierre (dir. Eric Dufrêne)

Etude du déterminisme des variations interannuelles des échanges carbonés des

écosystèmes forestiers européens: une approche basée sur la modélisation des

processusNicolas DelpierreNicolas Delpierre (dir. Eric Dufrêne)

Saclay, 14 décembre 2009

Terrestrial vegetation modulates atmospheric [CO2]

1960 1970 1980 1990 2000 2010

Fossil fuels + Land Use Change

Le Quéré et al., 2009

1960 1970 1980 1990 2000 2010

Atmosph.40%

Fossil fuels + Land Use ChangeAtm increase

1960 1970 1980 1990 2000 2010

Atmosph.40%

Ocean30%

Fossil fuels + Land Use ChangeAtm increaseOcean uptake

1960 1970 1980 1990 2000 2010

Atmosph.40%

Ocean30%

Vegetation30%

Vegetation C uptakeVegetation C uptake accounts for most of the IAV

Forests ~60% of vegetation uptake

Fossil fuels + Land Use ChangeAtm increaseOcean uptakeVegetation uptake

FLUXNETMonitoring the vegetation / atmosphere C exchanges

Forest sitesNon-forest sites

8CARBOEUROPE CARBOEUROPE

networknetwork

CARBOEUROPEEcological gradient

Coniferous forests

Pinus spp.Picea spp.

Deciduous forests

Fagus sylvaticaQuercus spp.

Evergreen Broadleaves

Quercus ilex

Mixed forests

CARBOEUROPEAnnual NEP sums

Boreal (Pinus)

Temperate (Picea)

Temperate (Fagus)

Mediterranean (Q.ilex)

2001 2003 2005 2007

2001 2003 2005 200711

R²=0.40R²=0.40 R²=0.80R²=0.80

CARBOEUROPEExplaining Intersite variations of the C balance

Water balance Temperature

GPP(gC / m² / y)

One color =

One site

Southern <52°N Northern >52°N

adapted from Reichstein et al., 2007

R²=0.40R²=0.40 R²=0.80R²=0.80

R²=0.30R²=0.30 R²=0.70R²=0.70

GPP(gC / m² / y)

Reco(gC / m² / y)

adapted from Reichstein et al., 2007

R²=0.40R²=0.40 R²=0.80R²=0.80

GPP(gC / m² / y)

R²=0.30R²=0.30 R²=0.70R²=0.70

Reco(gC / m² / y)

R²<0.10R²<0.10 R²=0.20R²=0.20

NEP(gC / m² / y)

What about interannual variations

Water balance Temperatureadapted from Reichstein et al., 2007

CARBOEUROPEExplaining Interannual variations of the C balance

GPP(gC / m² / y)

Reco(gC / m² / y)

NEP(gC / m² / y)

SignificantRelationships

5 sites over 25

3 sites over 25

4 sites over 25

Info used Logical linkInterannual

Climate indexes

Annual climate correlative

Empirical vs. Process-based models

Statisticalmodels

Climate indexes

CASTANEACASTANEAClimateClimate

Biological Biological driversdrivers

?? ?? To be To be

tested ??tested ??

Statisticalmodels

Process

Basedmodel

Climate indexes

CASTANEACASTANEAClimateClimate

Biological Biological driversdrivers

explanatory Proces

sBasedmodel

Quantify the influences of ClimateClimate and Biological driversBiological driversoperating at several timescalesseveral timescales to determine the interannual variations of GPP, Reco and NEP

Statisticalmodels

3)Availability of Statistical tools for signal deconvolution

Criteria for using CASTANEA as a deconvolution tool

1) Biological realism of the simulated processes

2) Accuracy of flux simulations

Seasonality of photosynthesis in conifers Seasonality of photosynthesis in deciduous species

- Spring phase- Autumn phase

Evaluation of data quality Model validation at multiple time scales

SA technique revealing seasonal influences SA technique revealing influences at multiple time scales

4. Influence of climate and biological drivers

across time scales

OUTLINEOUTLINE

2. Modelling canopy senescence in deciduous

forests

1. Materials & methodsAn overview of the CASTANEA model

3. Model Validation

1. Materials & methodsAn overview of the CASTANEA model

Solar radiation

temperature

Radiation interceptionGlobal PAR

PhotosynthesisStomatal Cond.

CASTANEAmodel

Dufrêne et al., 2005

Transpiration

Water vapour

Solar radiation

temperature

Water vapour

Photosynthesis

Precipitations

Canopy interception

Throughfall

Stem flow

Litter

Surface

Root zone

drainage

Soil evaporation

TranspirationCanopy

evaporation

Stomatal Cond.

CASTANEAmodel

Dufrêne et al., 2005 17

Solar radiation

temperature

Water vapour

Photosynthesis

Precipitations

Canopy interception

Throughfall

Stem flow

Litter

Surface

Root zone

Soil evaporation

Transpiration

Carbon AllocationC leaves

C coarse roots

C fine roots

Growth Respiration

C litter

C surface

C deep

HeterotrophicRespiration

Canopy evaporation

drainage

Stomatal Cond.

C aerial wood

C reserves Maintenance Respiration

CASTANEAmodel

Dufrêne et al., 2005 17

CASTANEA Modelling the C balance of European forests

Coniferous forests

Hyytiälä(Boreal Pine)

Tharandt(Temperate Spruce)

Evergreen Bleaves

Puéchabon(Mediterranean Q. ilex)

Deciduous forests

SoroeHainich (Temperate Beech)Hesse

2. Modelling canopy

senescencein deciduous

forests

Canopy senescence Original modelling scheme

Hesse forestFagus sylvatica

Leaf fall

N resorption

Sep Oct Nov Dec

Modelled NEP

Davi et al. (2005)

Leaf fall

N resorption

Sep Oct Nov Dec

Modelled NEP

Davi et al. (2005)Sep Oct Nov Dec

Leaf fall

N resorption

Sep Oct Nov Dec

Modelled NEP

Leaf fall

N resorption

Sep Oct Nov Dec

Modelled NEP

OakOakBeechBeech

RENECOFOR observationsSenSen9090 = 90% x 36 trees = 90% x 36 trees

Sites n MAT (°C) Alt (m) Sen90

Beech 18 159 9.8 400 20 October

Autumn phenology The RENECOFOR dataset (1997-2006)

Driver Effect References

TemperaturesTemperatures

Addicott, 1968

Low temperatures ++ Schnelle, 1952

Schulze, 1970

PhotoperiodPhotoperiod

Long days ++ / / --Addicott, 1968Seyfert, 1970Chuine, 2001

Estrella & Menzel, 2006

Short days ++ / / --Other potential driversOther potential drivers

•Water balance•Mineral deficits

•atmospheric pollution•parasites…

Designing a bioclimatic modelLiterature review

reDesigning a bioclimatic model

Model formulation

Model parameters

Senescence initiation date Base temperature Critical T sum

Model formulation

non-linear T x DayLength effects

1.0Jul Aug Sep Oct Nov Dec

Jul Aug Sep Oct Nov Dec

RMSE ( days )

ME (%)

Null model 16a 0

White et al. 15a 10

Jolly et al. 15a 7

This study 13b 33

BeechBeech

Fitting subsetValidation subset

Senescence model assessment (1)

Prediction error =13 days(Observation resolution = 7 days)

Observations

Delpierre et al., 2009

RMSE (days)

ME (%)

Beech 2 46

Senescence model assessment (2)

BeechBeech

Validation statistics

Important reduction of the prediction error

Observation uncertainty averaging Reduced contribution of extreme dates

1997 1999 2001 2003 2005

observations

simulations

Leaf fall

N resorption

Sep Oct Nov Dec

Leaf fall

N resorption

Sep Oct Nov Dec

Modelled NEP

Leaf fall

Sep Oct Nov Dec

Modelled NEP

N investment

xN resorption

3. Model Validation

Model validation across time scalesDAILYDAILY

Hyytiälä(Pinus)

R²=0.92bias= +0.11

Tharandt(Picea)

R²=0.91bias= +0.10

Puéchabon(Q. ilex)

R²=0.74bias= +0.21

Hainich(Fagus)

R²=0.95bias= -0.08

2000 2001 2002 2003 2004 2005 2006 2007

Model validation across time scalesANNUALANNUAL

FIHyy RMSE=13, r²=0.82DETha RMSE=66, r²=0.51FRPue RMSE=59, r²=0.82

CASTANEA reproduces 36% - 82% of C flux interannual variance36% - 82% of C flux interannual variance

Model validated Model challenged

4. Influence ofclimate & biological

driversacross time scales

Defining Flux IAV across time scales

GPP Tharandt (Picea abies) 2000-2007 28

Jan Jul DecApr Oct

Mean annualpattern

GPP Tharandt (Picea abies) 2000-2007

Influence on Influence on GPPGPP

Influence on Influence on RecoReco

Climate drivers

Incident Radiation

Temperature

Relative Humidity

Soil water content

Biological drivers

Thermal acclimation

Canopy dynamics (LAI)

Woody biomass

Soil C stock

No effect

Conifers

dailycycle

annualcycle

Climate and biological drivers interact at different time scales

Climate drivers Biological drivers

Global radiationTemperatureVPD

Leaf Area Index

annualcycle

hour day month year hour day month year

dailycycle

annualcycle

Climate and biological drivers interact at different time scales

Climate drivers Biological drivers

Global radiationTemperatureVPD

Leaf Area Index

annualcycle

Climate modulates short-term flux variability Climate + Biological drivers modulate flux IAV 33

hour day month year hour day month year

Constrained simulations

blue = « mean Rg » referencegrey = original flux (year 2000)

Single driver contribution to flux modulationSingle driver contribution to flux modulation

Day of Year

Hyytiälä, Boreal Pine

Proper Rg effect on GPP

Constrained simulations

8 years of dailyGPP anomalies due to radiation

8 years of dailyGPP anomalies

due to Water Stress

2000 2002 2004 2006

OWT variance decomposition

Orthonormal wavelet transform

(Haar basis)

Localize and compare residual signal variances

across time scales

Residual signals variance spectra

d w m s y >y

2000 2002 2004 2006

OWT variance decomposition

Residual signals relative influences

Orthonormal wavelet transform

(Haar basis)

calculate relative influencesof both drivers

d w m s y >y

2000 2002 2004 2006

decreasing influence of climate drivers at higher timescales

Deconvolution across time scales

Hyytiälä (Boreal Pine)GPP

d w m s y >y

alAccP

RglobalRglobal + LAILAI + droughtdrought control GPP annual IAV

decreasing influence of climate drivers at higher timescales

alAccP

RglobalRglobal + LAILAI + droughtdrought control GPP annual IAV

RglobLAI

REWAccP

Significant contribution of biological driversbiological drivers to GPP-IAV modulation

alAccP

Climatedrivers

Biologicaldrivers

AccP45%

AccP9%

GPP-IAV controls in conifers(2000-2007)

Tharandt (Temperate Spruce)GPP

Stronger influence of thermal acclimationthermal acclimation at the warmer site !!!

+9°C+9°C +4°C+4°C

RglobTair

VPDVPD

AccPLAIBwood

CsoilCsoilClim

Thermal acclimation AccPThermal acclimation AccP

Jan Jul Nov

+9°C+9°C +4°C+4°C

AccP45%

AccP9%

AccP constraint ++++++AccP constraint ++

1.0Thermal acclimation AccPThermal acclimation AccP

Jan Jul Nov

AccP constraint ++++++AccP variations ++

AccP constraint ++AccP variations ++++++

+9°C+9°C +4°C+4°C

AccP45%

AccP9%

HyyHyyBoreal Boreal PinusPinus

ThaThaTemperateTemperate

PiceaPicea

HaiHaiTemperateTemperate

FagusFagus

PuePueMedit.Medit.Q. ilexQ. ilex

Contrast of thermal acclimation influence in conifers

Strong influence of REW• Recurrent in Puéchabon

• 2003 drought in Hainich

RglobTair

VPDVPD

AccPLAIBwood

CsoilCsoilClim

Flux-IAV controls in European forests (2000-2007)

GPPGPP

LAI Rg

RecoReco

PiceaPicea

FagusFagus

Temperature vs Soil Water control

Low influence of Biomass

RglobTair

VPDVPD

AccPLAIBwood

CsoilCsoilClim

GPPGPP

LAI Rg

REW REW

RecoReco

PiceaPicea

FagusFagus

RglobTair

VPDVPD

AccPLAIBwood

CsoilCsoilClim

GPPGPP

LAI Rg

REW REW

NEPNEP

AccP Temp

REWTemp

NEP control unpredictable from

elementary flux responses

Compensation effects

noticeable

CONCLUSION

Process-based modelsallow to address the determinism of C fluxes

from detailed processes to ecosystem scale

from hourly to decadal time scale

CONCLUSION

Increased contribution of Biological Drivers

at higher timescales

Constraint vs. Modulation

Unpredictability of NEP controls from GPP/Reco controls

CONCLUSION

Limits of the approach

Poor quality of Eddy Covariance nighttime fluxes Are our models reliable ?

(van Gorsel et al., 2009)

Model challenged at some sites (Hesse, Soroe)Are we missing something ?

PERSPECTIVES

Deconvolution methodology on longer time series

for an increased number of sites

Model developmentsSupra-decadal simulations

age effects (C allocation) acclimation (e.g. respiration, phenology)

Carbon-Water-Nitrogen coupling

Genericity

Nicolas Delpierre (dir. Eric Dufrêne)

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