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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
Modelling the terrestrial C cycle
Biomass information affects NEP estimates
Source: P Peylin
Orchidee-FM
Assume standare 40-50 yrs
Estimate age frombiomass
Biomass dynamics (AGB)
Cw = aw NPP – tw Cw – P F Cw
– 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
Tropical woodlands
the only biome determined by demography rather than by climate (Bond, 2008)
Stem biomass (tC/ha)
Fre
qu
en
cy
Mozambican woodland biomass
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 = 0.50Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha
Ryan et al, in press (GCB)
Spatial distributions and land use
Heavily deforested
Village Fire protected undisturbed
VillageNewly
colonised
Town and hinterland
Ryan et al, in press (GCB)
C mass balance model with disturbance
Definition of test scenarios
Synthetic experiment: Disturbance intensity (M = PF, vary all)
Mozambican experiment– Disturbed area (Mbalawa)– Protected area (Gorongosa Park)
ALOS-PALSAR data
Synthetic experiment: Disturbance P and F
Mozambican experiment
Variability in disturbance characteristics is linked to variability in disturbance fluxes
Mean disturbance flux
Mea
n di
stur
banc
e flu
x
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
Thank you
Acknowledgements:
John Grace, Emily Woollen, Ed Mitchard, Iain Woodhouse
Funding:NERC, ESA, EU
A-DALEC
Cf(foliage)
Cr(fine roots)
Cwbg(wood below
ground)
Cwag(wood above
ground)
Cs(Soil organic
matter)
Cl(litter)
Cdag(Above ground
wood debris)
Respiration Flux
GPP
Af
Ar
Awbg
Awag
Twag_dag
Twbg_dbg
Tdag_s
Tl_s
Ra
Rhs
nb: All foliage turns to litter each year
Case dependent disturbance loss
Previous year’s Cf(with LMA) determine GPP
Case dependent disturbance loss
Case dependent disturbance loss
Case dependent disturbance loss
Cdbg(Below groundwood debris)
Tbdg_s
Rloss
Rloss
Rloss
Rloss
nb: All fine roots die each year
Annual –DALEC (Process structure)
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
Results
Synthetic experiment 1: Disturbance intensity
Synthetic experiment 1: Disturbance intensity
Synthetic experiment 2: Observation bias
Synthetic experiment 3: Analysis area