On the Methodological Foundations of Understanding Regional Carbon Cycling of

Forest EcosystemsA. Shvidenko, E. Vaganov, D. Schepaschenko, F. Kraxner

International Institute for Applied Systems Analysis, Laxenburg, AustriaSiberian Federal University, Krasnoyarsk, Russia

V.N.Sukachev Institute of Forest, Siberian Branch RAS, Krasnoyarsk, Russia

XVIII IBFRA Science Conference “Cool Forests at Risks?”, Laxenburg, 17-20 September 2018

Two major system requirements to carbon account of terrestrial ecosystems: completeness and verifiability

• Full carbon account: ALL ecosystems, ALL processes, ALL carbon contained substances in a spatially and temporally explicit way (account ≥ 98% fluxes?)

Proxy: Net Ecosystem Carbon Account, Net Biome Production, …

• Verified: (1) reliable and comprehensive assessment of uncertainties; (2) possibility to manage uncertainties

Consequences: unbiasedness, accessibility for policymakers

• Uncertainty is an aggregation of insufficiencies of outputs of the accounting system, regardless of whether those insufficiencies result from a lack of knowledge, intricacy of the system, or other causes

Problem: uncertainty of uncertainties

Backgrounds of the methodology of FCA

The FCA is presented as a relevant combination of a pool-based approach

dC/dt = dPh/dt + dD/dt + dSOC/dt,where Ph, D and SOC are pools of phytomass, dead organic matter and

soil organic matter,

and a flux-based approach

NECB = NPP – HR- ANT – FHYD - FLIT,where NBP and NPP are net biome and net primary production, HR –

heterotrophic respiration, ANT – flux caused by disturbances and consumption, FHYD and FLIT- fluxes to hydrosphere and lithosphere, respectively

Diversity of regional estimates of carbon cycling are high: examples for Russia

Stock of Live biomass + detritus Mg C ha-1 43.8-91.4Soil Kg C -2 9.6-20.3NPP g C m-2 yr-1 204-614C sink (inventories) Tg C yr-1 90-1000

FCA as a system

Landscape-ecosystem approachProcess-based models

Flux measurementsMulti-sensor remote sensing concept

Inverse modelling

Terrestrial Biota Full Carbon Account (FCA) is a dynamic complicated open stochastic underspecified fuzzy system, with some features of a full complexity and wicked problemsAny individually used method of FCA is not able recognize structural uncertainty in a comprehensive way Major principles: integration, harmonizing and multiple constraints of independent resultsand uncertainties

Structure of FCA of forest ecosystems

Terrestrial Ecosystem FullVerified Carbon Account

proxy: NECB

Methods

Landscape-ecosystem approachNECB

Process-based models(DGVM, LDSM)

NBP

Inverse modellingCO2, CH4

Eddy covarianceNEE

Remote sensing assessment ofparameters

AGB, NPP, D

Intermediate and final results &“within methods” uncertainties

Harmonizing and mutual constraintsof results

Assessment of system’s resultsand uncertainties

Assessment of uncertainties: mutual constraints

• For LEA at each stage - standard error of functional Y = f (xi) where variables xi are known with standard errors mxi

• For ensembles of models (inverse modeling, DGVMs) – standard deviation between models is used

• For multiple constraints – the Bayesian approach, i.e.

NBPBayes =

where NBPi is assumed to be unbiased and Gaussian-distributed with variance Vi, i =1, …, n

ij xjxi

ji

ij

i

xi

i

y mmx

y

x

yrm

x

ym ))((2)( 2

i i ii

i

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NBP 1/

Information problems – common for the boreal forests

• Dynamic character and incompleteness of knowledge• Substantial territories of rapid change• Obsolete and incomplete information: many ecological and physiological

indicators of major global greenhouse gas cycles have never been a subject of forest inventories

• Dynamic character of all indicators of GHG cycles• Lack of operative integrated monitoring in a changing world• Lack of systems compatibility of definitions and classification schemes,

especially between different scientific disciplines• Inevitable numerous gaps in statistical data use of which are inevitable• …

Conclusion: A need a systems approach and integrated information systems

Landscape-ecosystem approach: a “semi-empirical”

background of FCA

• Unified structuring and information background of the FCA: compatibility with requirements to different methods

• Relevant combination of flux- and pool-based approaches• Strict mono-semantic definitions and proper classification schemes;

harmonization of these with other approaches• Explicit intra- and intersystem structuring: comprehensive and consistent

information background; explicit algorithmic form of accounting schemes, models and assumptions

• Spatially and temporally distribution of pools and fluxes• Correction of many year average estimates for environmental and climatic

indicators of individual years• Assessment of uncertainties at all stages and for all modules of the account

– intra-approach uncertainty• Comparative analysis with independent sources, harmonizing and multiple

constraints of the intermediate and final results• … As comprehensive as possible following the requirements of the applied

systems analysis

Integrated Land Information System - major principles

• Comprehensive aggregation of all knowledge on land cover, ecosystems and landscapes

• Hybrid land cover: A multi-layer and multi-scale GIS• Appropriate ecological regionalization and corresponding

resolution, finer resolution for regions of rapid changes• As comprehensive as possible attributive databases• Complimentary use of different relevant sources• Particular role of “multi-RS” concept• Certainty of data that are included in the ILIS should be known• Possibility of relevant updating of information

Hybrid Land Cover – an information basis of Integrated

Land Information System

Major attributive databases

• Forest live biomass by components (~ 11000 sample plots)

• NPP of ecosystems (~2500 sample plots)• Soil respiration (~810 studies, 2254 records)• State forest account (~aggregated data by

~1700 forest enterprises), state land account

• Forest pathological surveys• Disturbances• …Resolution of hybrid land cover of Russia: 2009 - 1 km, 2012 - ~250m, 2015 –150m

Example: Database of in situ biomass measurements (over 11100 records for Eurasia)

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Area of Russian forests due to different sources (million ha)

Source: Schepaschenko et al. Forest Science, 2015

Hybrid land cover: forest mask

The input RS products include land covers of 12 RS products: GLC2000, 1km, GlobCover 2009, 300m, MODIS land cover 2010, 500m; Landsat based forest masks: by Sexton 2000, 30m and by Hansen 2010, 30m; MODIS Vegetation Continuous Fields 2010, 230m; FAO World’s forest 2010, 250m; Radar based datasets: PALSAR forest mask 2010, 50m, ASAR growing stock 2010, 1km. All datasets were converted to 230m resolution.

Source: Schepaschenko et al. 2015

Forest area of Russia in 2015 is estimated at (M ha)Total 757.0Incl. on abond. agr. land 18.2

Satellite estimate of forested areas managed by SFA is at 45 М ha less than data of the State Forest Registry

European part +8%Asian part -7%

System of updating forest inventory data

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Official data of forest inventory underestimates the total growing stock of the

country’s forests at ~13% (about 10 billion m3) and average growing stock at 18%

Net growth of modal stands (examples for cedar and larch of southern taiga)

Biomass expansion factors as functions of tree species, age, stocking and productivity

Maximal use on unbiased information, models and methods: NPP as example

ТРFА = ТРFАst +ТРFA

br + ТРFAfol + ТРFA

root + ТРFAunder + ТРFA

gff

NPP = ТРFА – ТРFА-1

where ТРFА – total production, kg C m-2 or Mg C ha-1;A – forest stand age ; st – stem; br– branches; fol – foliage; root – roots; under – shrubs and undergrowth; gff – green forest floor.

Total production for stem wood

Total production for foliage

Ref. Shvidenko, Schepachenko et al. 2007

• Total production for stem wood

• Total production for foliage

Examples of the models of total forest production by fractions

HTC _5 in 2010 and 2012 and its impact on forest productivity

Correction by HTC

NPPcor = NPPbas·(2003)

1010

55

T TavrG

QGavr

- >>

Heterotrophic soil respiration (g С m-2)

20NPP < 100 101 - 150 151 - 200 201 - 300 301 - 400 401 - 600 > 600

Net Primary Production of Russian Forests (g С m-2)

Soil organic matter, on-ground organic layer + top 1 m (kg С m-2)

Components of carbon cycling (examples)

Ref. Schepaschenko et.al. 2013, Shvidenko & Schepaschenko 2013, Mukhortova et al. 2015

NECB for terrestrial vegetation of Russia by LEA (average for 2000-2015)

Land classes and

components

Flux, Tg C yr-1

Forest -547±148Open woodland -28±21Shrubs -22±12Natural grassland -44±26Agriculture land -12±28Wetland (undisturbed) -47±26Disturbed wetland +28±14Harvest and wood products +34±17Food products (import-export) +18±16

Flux to hydro- and lithosphere +42±36

NECB -576±164

NECB of terrestrial ecosystems of Russia in 2015 (g C /m2)

All ecosystems of Russia in 2000-2015 served as a net carbon sink at 0.5-0.7 Pg per year

Of this sink, ~90% was provided by forests

Source: Shvidenko et al. 2015

Full carbon account of Russian forests for 1990-2007 – pool based approach (Pan et al. 2011)

Inverse modeling, Pg C year-1

• Estimates for boreal Asia, Pg C year-1

Maksyutov et al., 2003 (1992-1996) -0.63±0.36Gurney et al.,2003 (1992-1996) -0.58±0.56Baker et al. (1988-2003) -0.37±0.24 Patra et al., 2006 (1999-2001) -0.33±0.78

• Estimates for Russia, Pg C year-1

Ciais et al., 2010 (2000-2005), 4 dif. Inv. -0.65±0.12Dolman et al., 2012 (1988-2008), 12 dif. Inv. -0.69±0.25 IIASA, 2015 (2000-2015, unpubl.), LEA -0.57±0.26

Average DGVM results for Russia (Tg C yr−1)

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Average of 8 DGVMs (CLM4, ORCIDEE, HYLAND, LPJGuess, LPJ, OCN, SDGVM, TRIFFID)Source: Sitch et al. 2008, Dolman et al. 2012

Forest NPP: 19 DGVMs (Cramer et al. 1999) 2690±530Forest NPP: LEA (this study) 2720±87

Mean annual net uptake and release of carbon for a set of eddy-covariance sites (sink ~1000 Tg C Dolman et al. 2013)

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Fluxes C-C02 and CH4 in 2012 (satellite GOSAT)

±0 3,100 6,2001,550 Kilometers

-454.98 - -165.53

-165.52 - -65.34

-65.33 - +23.73

+23.74 - +146.19

+146.2 - +391.11

+391.12 - +1,047.94

+1,047.95 - +2,383.88

±0 3,100 6,2001,550 Kilometers

-39.44 - -17.33

-17.32 - 0.2

0.21 - 2.08

2.09 - 5.05

5.06 - 9.36

9.37 - 29.31

year Emissions, Tg C yr-1

C-CO2 Fire CH4 CH4 Fire

2010 530 89 10.5 1.46

2011 689 87 10.4 1.11

2012 780 187 11.9 1.27

2013 885 85 9.4 0.80

2014 840 120 - -

Thank you

More information:http://www.iiasa.ac.at/Research/ESM

NPP by forest enterprises of Russia: LEA vsMODIS

Empirical NPP vs. MODIS NPPMODIS NPP = -105.5381+2.6561*x-0.0048*x̂ 2+2.9488E-6*x̂ 3 (R2 = 0.46)

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