Data Assimilation Experience in Carbon Cycle and Agriculture Models

Transcription

1 Data Assimilation Experience in Carbon Cycle and Agriculture Models F. Baret 1, R. casa 2, M. Weiss 1, R. Lopez-Lozano 1, P. Peylin 3, C. bacour 3, S. Buis 1 1 INRA-EMMAH UMR 1114, Avignon, France 2 University of Viterbo, Italy 3 LSCE, Orsay, France

2 Why assimilating? Ancillary Information/data Radiance observations Radiative Transfer Model Diagnostic variables Process model (dynamic) Variables of interest Model Parameters Assimilation allows to: input additional information in the system: Knowledge on some processes Exploitation of ancillary data (climate, soil, ) exploit the temporal dimension: process model as a link between dates access specific processes Run process models in prognostic mode : simulations for other conditions

3 Large scale data assimilation: case of current carbon cycle data assimilation systems (CCDAS) Little knowledge on soil, vegetation composition, Need for prognostic simulations (application to climate scenarios, or extension of local experiments to global scales ) Assimilation of fapar (little sensitive to scaling) and fluxes in ORCHIDEE model

4 The assimilation system PFT composition ecosystem parameters initial conditions climate parameters (x) M (X ) J(X ) Y flux flux measurements NEE, LE ORCHIDEE optimizer Y fapar satellite fapar TARANTOLA L-BFGS-B J( X ) / X B' errors G M ( X ) / X at last iteration Observations Bayesian term Cost function J( X ) ( Y M( X )) t R 1 ( Y M( X )) ( X X p ) t B 1 ( X X p ) Posterior error B' t 1 1 G R G B 1

5 Parameters considered flux + Parameter Governing processes flux fapar fapar Kpheno_crit Phenology x x x Senescence_temp_c Phenology x x x LAI_MAX Photosynthesis, Phenology, Energy balance x x x Leafagecrit Phenology x x x Tau_leafinit Phenology x x x Clumping Photosynthesis x x x LAI_init Photosynthesis, Phenology x x x Vcmax_opt Photosynthesis x x Humcste Photosynthesis, Water stress x x Gsslope Photosynthesis x x Tphoto_opt_c Photosynthesis x x Tphoto_min_c Photosynthesis x x SLA Photosynthesis, Respiration X X Kalbedo_veg Energy balance x x Q10 Heterotrophic respiration x x KsoilC Heterotrophic respiration x x Frac_growthresp Growth respiration x x Dpu_cste Hydrology, Energy balance x x

6 Differences between fapar products

7 Diagnostic variable description consistency Sud-Ouest: 100% C3G Sud-Ouest: 100% C3A sunflower PRIOR POSTERIOR MERIS Sud-Ouest: 100% C3A wheat Sud-Ouest: 100% C4A maize

8 Impact on diagnostic variables Assimilation of fapar data Strong artifacts could be induced on flux simulations when assimilating inconsistent products and/or observation error matrix

9 Impact on parameter estimates Selection of parameters and associated prior values and error matrix not straightforward

10 Conclusion for CCDAS Need better consistency between fapar products and quantified associated uncertainties (and structure) Sensitivity of ORCHIDEE model output to fapar Need to improve the consistency of diagnostic variables description (fapar here) between model and remote sensing products: derive an adequate observation operator Temporal frequency mandatory for a significant fraction of vegetation types and locations

11 Regional to field scale Much more ancillary information available Species (even cultivars) Cultural practices Soils Need for prognostic simulations Environmental scenarios, landscape management Cultural practices Assimilation on LAI (reduced scaling issues) Work on wheat and vineyard

12 Assimilation of POLDER observations in the STICS model over Alpilles-ReSeDA experiment

13 The airborne POLDER data Number of directions flights 3 bands 14 to 55 directions 20 m resolution

14 Ground measurements NPP (g/m²) LAI (m²/m²) Detailed measurements of canopy characteristics along the growth cycle

15 simulations simulations Performances of STICS over wheat canopies NPP (kg/ha) 250 Final soil nitrogen (kg/ha) crop inter-crop measurements measurements Final canopy nitrogen (kg/ha) 500 Final soil water (mm) version 4 crop inter-crop measurements measurements

16 Coupling STICS with RT model assimilation of radiances phenology organs partitioning factors organs relative water content total dry matter organs dry matter organs water content STICS surface soil moisture specific area factors INN INN -> Cab SAIL PROSPECT BRDF LAI Previous analysis showed that the following parameters were the most sensitive: iplt: sowing date adens: density effect on LAI They all are directly stlevamf: temperature for stem elongation linked to the stamflax: temperature for LAI max LAI dynamics stlaxsen: temperature for senescence

17 STICS default STICS forced POLDER Assim Nadir Assim 0-45 Assim 0-60 Assim Direct Forced LAI-5p obs00-5p obs45-5p obs60-5p Conclusions of this study Feasability of radiance assimilation LAI Selection of the variables to be tuned (here 5)?? Representation of the link between LAI and DMP: LAI good for DMP but not reflecting appropriately the effects of stresses Problems with realism of RT simulations due to DMP- Assumptions on architecture - Description of LAI dynamics by STICS NPP Difficulties in radiance assimilation - Errors on RT model poorly known - difficult with computer demanding models Some variables of interest little sensitive (DMP) but interest for prognostic simulations Assimilation system: STICS adjoint available now

18 The way forward Improved integration of canopy architecture description in canopy modeling Development of functional-structural plant models (FSPM) Illustration over vineyard

19 Nb. phytomers NII II 4D model of vineyard architecture Phytomer Axis II Axis I Axis II Phytomer I Phytomer Phytomer Phytomer Phytomer NII P0 NII P Position rangi on Axis I NII P2 Louarn, 2005

20 Realistic 4D vineyard modeling 100 C Day 300 C Day 550 C Day 1000 C Day (véraison)

21 Inclusion of stress functions: Functional-Structural Plant Models Effect of water stress on the development of axes FTSW 1-(Fraction of Transpirable Soil Water) FTSW From Pellegrino et al. (2004, 2006) and Lebon et al. (2006)

22 simulated LAI LAI performances measured LAI

23 Irrigation (250 mm) Irrigation (90mm) Without irrigation Simulation of water stress on architecture dynamics 1200 C.Day

24 simulated LAI simulated NDVI Simulation of the dynamics of LAI & reflectances 7.0 Pruning 0.7 Pruning 6.0 Full Irrigation Intermediate Irrigation Non Irrigated day of year day of year

25 Conclusion With Medium Spatial Resolution observations fapar probably better than LAI to reduce impacts of heterogeneity at Local scale (clumping) and Larger scale (landscape variability) Need improved satellite products and associated uncertainties: importance of validation activities!!! Will then allow radiance assimilation??? Need improved realism of LAI (fapar) description in vegetation models: definition, flexibility in magnitude and seasonality With High Spatial Resolution observations LAI (and chlorophyll) probably better suited Need improved realism of canopy architecture description: coupling between structural and functional models Better quantification of errors on LAI and its structure High revisit systems at high spatial resolution mandatory (S2)