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