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Enhancing Carbon Cycle Models with Radar Retrievals of Forest Biomass Data

Explore how radar retrievals of forest biomass data can improve carbon cycle models, affecting carbon sequestration estimates. This study uses Biomass-Backscatter relationships, ALOS-PALSAR data, and disturbance models to enhance our understanding. Results show potential for improved flux constraints in models. Next steps involve evaluating global biomass products and exploring spatial patterns to advance research.

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Enhancing Carbon Cycle Models with Radar Retrievals of Forest Biomass Data

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

  2. Modelling the terrestrial C cycle

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

  4. DCw = awNPP – tw Cw – P FCw Cw = wood C aw = allocation of NPP to wood tw = turnover rate of wood (lifespan) P = probability of disturbance F = fraction of wood lost in disturbance (intensity) Disturbance magnitude M = PF, spans degradation-deforestation Biomass dynamics (AGB)

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

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

  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 R2 = 0.50 Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha Ryan et al, in press (GCB)

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

  9. C mass balance model with disturbance

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

  11. Synthetic experiment: Disturbance P and F

  12. Mozambican experiment

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

  14. 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

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

  16. A-DALEC

  17. 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

  18. Results

  19. Synthetic experiment 1: Disturbance intensity

  20. Synthetic experiment 1: Disturbance intensity

  21. Synthetic experiment 2: Observation bias

  22. Synthetic experiment 3: Analysis area

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