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Calibrating SDGVM phenology with EO data: effects on Siberian C flux estimates

Calibrating SDGVM phenology with EO data: effects on Siberian C flux estimates. Ghislain Picard, Shaun Quegan, Mark Lomas, Ian Woodward (CTCD) Nicolas Delbart, Thuy Le Toan (CESBIO). Objectives. To use satellite data from central Siberia to calibrate the phenology module in SDGVM

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Calibrating SDGVM phenology with EO data: effects on Siberian C flux estimates

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  1. Calibrating SDGVM phenology with EO data: effects on Siberian C flux estimates Ghislain Picard, Shaun Quegan, Mark Lomas, Ian Woodward (CTCD) Nicolas Delbart, Thuy Le Toan (CESBIO)

  2. Objectives • To use satellite data from central Siberia to calibrate the phenology module in SDGVM • To assess the accuracy of the calibration • Bias • Noise • To assess the effect on calculations of NPP and NBP

  3. The SDGVM carbon cycle ATMOSPHERIC CO2 Photosynthesis GPP BIOPHYSICS NPP Soil Litter Fire Mortality GROWTH Thinning Disturbance NBP Biomass LEACHED

  4. SIBERIA-II: Multi-Sensor Concepts for Greenhouse Gas Accounting of Northern Eurasia5th Framework Project , 2002-2005

  5. Data • SPOT-VEG budburst 1998, 2000-02: 0.1o • Ground data; Komarov RAS, dates of bud-burst at 9 sites in the region. • Temperature data: ERA-40, 1.125o • GTOPO-30 DEM • Land cover: GLC2000

  6. The CESBIO budburst algorithm • Data set: SPOT-VEG 1998, 2000-2002 • Based on minimum in time-series of NDWI data • Uncertainties in recovered budburst date ~ 7 days

  7. The Date of budburst derived from minimum NDWI (VGT sensor, 2000) Day of year

  8. The Central Siberia dataset: ~ 2 M km2 120E 80E 80E 120E 75N 50N Land cover (IIASA) 1o x 1o forest map Lake Baikal

  9. The spring warming budburst algorithm min(0, T – T0) > Threshold, budburst occurs. When The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature). T0 Start of budburst

  10. Calibration parameters

  11. Calibration parameters (all years)

  12. Variability in optimising coefficients

  13. Spatial variation of model-data fit

  14. Comparison of ground data withn calibrated model

  15. Green-up relation to N Pacific Index

  16. More complex models RMSD (day) • Spring warming model 6.4 • Sequential model 6.5 • Parallel model 6.5 • Alternating model 6.4

  17. Effects on error in budburst on NPP1. ‘Random’ noise • Simulation 1. The phenology model is used from 1958 to 2000. • • Simulation 2. The phenology model is used from 1958 to 1999 and the VGT bud-burst is used in 2000. RMSD = 41.2 gC m-2y-1 = 8% of average NPP (572 gC m-2y-1)

  18. Effects on error in budburst on NPP2. Bias • Mismatch between vegetation (model) budburst and satellite derived parameter. • Bias in the temperature datasets driving the phenology model. Changing the base temperature T0 shifts the budburst day linearly by ~3.3 days per oC

  19. Effects of bias on NPP 1 day earlier BB => NPP increases by 10.1 gC m-2 y-1 (~2.2%) Growing season ~100 days Without adaptation, 5o C increase =>BB occurs 16 days earlier => 34% increase in NPP. Biases in NDVI can be up to 15 days due to snow effects => errors in NPP of 32%

  20. Effect of uncertainty in green-up day

  21. Conclusions • A simple 2-parameter spring warming model gives the best fit to EO and ground data • Differences between model and VGT and ground are ~6.5 days. • Noise errors in NPP estimates are ~8%. Bias effects are ~2.2% per day of bias. There are possible large associated errors if using simple parameters derived from NDVI (Picard et al. Global Change Biology, 11, 1-13, 2005)

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