1. Improving carbon cycle models with radar retrievals of forest biomass data Mathew Williams, Tim Hill and Casey Ryan School of GeoSciences, University of Edinburgh NERC CarbonFusion

2. Modelling the terrestrial C cycle

3. Biomass information affects NEP estimates Source: P Peylin Orchidee-FM Assume stand are 40-50 yrs Estimate age from biomass

4. Biomass dynamics (AGB)   C w = a w NPP – t w C w – P F C w – C w = wood C – a w = allocation of NPP to wood – t w = turnover rate of wood (lifespan) – P = probability of disturbance – F = fraction of wood lost in disturbance (intensity) – Disturbance magnitude M = PF, – spans degradation-deforestation

5. Tropical woodlands  the only biome determined by demography rather than by climate (Bond, 2008)

6. Stem biomass (tC/ha) Frequency Mozambican woodland biomass

7. Biomass-Backscatter relationship - PALSAR 96 ground calibration and validation plots (0.2-3 ha) Forest, woodland and cropland 10 x images from 2007-2010 Regression ~stable Mean R 2 = 0.50 Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha Ryan et al, in press (GCB)

11. Spatial distributions and land use Heavily deforested VillageFire protected undisturbed Village Newly colonised Town and hinterland Ryan et al, in press (GCB)

12. C mass balance model with disturbance

13. Definition of test scenarios  Synthetic experiment: Disturbance intensity (M = PF, vary all)  Mozambican experiment – Disturbed area (Mbalawa) – Protected area (Gorongosa Park) ALOS-PALSAR data

14. Synthetic experiment: Disturbance P and F

15. Mozambican experiment

16. Variability in disturbance characteristics is linked to variability in disturbance fluxes Mean disturbance flux

17. Summary  ALOS-PALSAR can produce biomass maps with confidence intervals  PDFs contain information on forest disturbance processes  Data assimilation has potential to provide novel information on biomass loss, with improved flux constraint in models  Next steps: evaluate global biomass products, explore spatial pattern information, transient disturbance, link to fire products

18. Thank you Acknowledgements: John Grace, Emily Woollen, Ed Mitchard, Iain Woodhouse Funding: NERC, ESA, EU

19. A-DALEC

20. Assimilation Approach  Generate PDF of differences in biomass from sequential SAR images  Generate simulated PDF of differences for a range of P, F (ensemble runs) with noise added  Compare similarity of observed and modelled difference PDFs  Most similar modelled difference PDFs were deemed most likely, and used to infer the driving disturbance regime

21. Results

22. Synthetic experiment 1: Disturbance intensity

24. Synthetic experiment 2: Observation bias

25. Synthetic experiment 3: Analysis area