Impact of Terrestrial Ecosystems of Russia on the Global Carbon - - PowerPoint PPT Presentation

Impact of Terrestrial Ecosystems of Russia on the Global Carbon Cycle for 2003-2008: An Attempt of Synthesis

• A. Shvidenko, D. Schepaschenko, S. Maksyutov,

IIASA (Laxenburg, Austria), Institute of Forest SB RAS (Krasnoyarsk, Russia), Moscow State University of Forest (Russia), NIES (Tsukuba, Japan)

ENVIROMIS-2010, 5-11 July 2010, Tomsk, Russia

Prerequisites

• Post Kyoto developments versus

Terrestrial Ecosystems Full Carbon Account (FCA)

• High variability of reported results of

estimation

• High and mostly unknown uncertainty
• Could the uncertainty of the FCA be made

acceptable for policy makers?

Need of Terrestrial Biota Verified Full Greenhouse Gas Account

• Key words: Full, Verified, Uncertainty
• Full: ALL ecosystems, ALL land classes and ALL

processes – spatially explicit and continuously in time

• Verified: (1) reliable and comprehensive

assessment of uncertainties; (2) possibility to manage uncertainties up to an acceptable level

• Uncertainty is an aggregation of insufficiencies of
• utputs of the accounting system, regardless of

whether those insufficiencies result from a lack of knowledge, intricacy of the system, or other causes

• Need of synthesis: what is current state of

knowledge of terrestrial ecosystems carbon accounting?

Major principles of the FCA: Integration, harmonization and multiple constraints

Landscape-ecosystem approach Process-based models Flux measurements Multi-sensor remote sensing concept Inverse modelling Terrestrial Biota Full Carbon Account is a dynamic very complicated

• pen stochastic fuzzy system (... full

complexity problem) The direct verification of results of FCA is not possible Structural uncertainty cannot be reliably recognized within any individually used method

IIASA ¡landscape-­‑ecosystem ¡approach: ¡a ¡semi-­‑ empirical ¡background ¡of ¡FCA ¡

• As ¡comprehensive ¡as ¡possible ¡following ¡the ¡requirements ¡of ¡

the ¡applied ¡systems ¡analysis ¡ ¡

• Relevant ¡combina;on ¡of ¡flux-­‑ ¡and ¡pool-­‑based ¡approaches ¡
• Strict ¡mono-­‑seman;c ¡defini;ons ¡and ¡proper ¡classifica;on ¡

schemes; ¡harmoniza;on ¡of ¡these ¡with ¡other ¡approaches ¡

• Explicit ¡intra-­‑ ¡and ¡intersystem ¡structuring: ¡op;miza;on ¡of ¡

input ¡data; ¡explicit ¡algorithmic ¡form ¡of ¡accoun;ng ¡schemes, ¡ models ¡and ¡assump;ons ¡

• Spa;ally ¡and ¡temporally ¡explicit ¡distribu;on ¡of ¡pools ¡and ¡

fluxes ¡

• Correc;on ¡of ¡many ¡year ¡average ¡es;mates ¡for ¡environmental ¡

and ¡clima;c ¡indicators ¡of ¡individual ¡years ¡

• Assessment ¡of ¡uncertain;es ¡at ¡all ¡stages ¡and ¡for ¡all ¡modules ¡
• f ¡the ¡account ¡– ¡intra-­‑approach ¡uncertainty ¡
• Compara;ve ¡analysis ¡with ¡independent ¡sources, ¡harmonizing ¡

and ¡mul;ple ¡constraints ¡of ¡the ¡intermediate ¡and ¡final ¡results ¡

Structure of the Integrated Land Information System

Multi-sensor remote sensing concept

• NOAA AVHRR
• MODIS
• GLC-2000
• MODIS-VCF
• LANDSAT TM
• ENVISAT MERIS
• ENVISAT ASAR
• JERS
• ERS-1 and ERS-2
• ALOS PALSAR

Fire Scar Detail

• n Test Area

Biomass by radars

Last results (Santoro et al. 2010) report possibility for assessing the growing stock up to 300-350 m3 with uncertainty of 10-15%

Courtesy by C.Schmullius

Hybrid land cover – a background of the Integrated Land Information System

(1 km resolution) Method: Schepaschenko et al. 2010

Results: carbon pools of terrestrial ecosystem (an example for 2005)

Area, mln ha Forests 794.7 Open woodland 82.6 Agricultural land 218.6 incl arable land 109.2 Wetland 146.9 Burnt area 27.5 G & Sh 300.8 Productive land 1571.4

Carbon stock, Pg C Soil 324.0 Incl surface organic layer 14.2 Live biomass 42.1 Incl forest LB 34.5 Dead wood in forest 8.6 Soil / Biomass C in forest 3.5 : 1

Reanalysis: Net primary production, Tg C yr-1 by vegetation classes and vegetation zone

Land class Polar Tundra Sparse taiga Middle taiga Southern taiga Temperate forest Steppe Desert Total Forest 0.0 48.4 337.2 1,363.5 636.1 133.4 66.4 9.8 2,594.7 Arable 0.0 0.0 0.0 2.0 44.5 70.6 294.0 1.8 412.8 Hayfield 0.0 0.0 0.3 11.5 25.1 9.4 33.5 15.0 94.8 Pasture 0.0 0.2 0.6 20.1 29.7 22.7 128.2 86.1 287.6 Fallow 0.0 0.0 0.1 4.3 7.1 4.2 5.5 0.1 21.2 Abandoned arable 0.0 0.1 0.5 11.0 59.1 24.1 51.4 5.3 151.6 Wetland 0.0 53.4 76.7 113.4 63.1 7.6 68.2 12.6 395.0 Open woodland 0.0 15.2 34.9 44.0 26.9 4.8 2.7 0.5 129.1 Burnt area 0.0 2.7 4.4 40.0 3.8 0.4 0.8 0.1 52.2 Grass & shrubland 0.3 181.4 42.9 590.9 48.5 42.8 77.0 15.6 999.3 Total 0.3 301.4 497.6 2,200.7 944.0 320.0 727.7 146.7 5,138.3

Net primary production, g C m-2 yr-1 by vegetation classes and vegetation zone

Land class Polar Tundra Sparse taiga Middle taiga Southern taiga Temperate forest Steppe Desert Total Forest

• 231

241 291 431 508 445 442 318

Arable

• 250

269 377 452 591 533 534 530

Hayfield

98

• 381

366 409 414 363 473 395

Pasture

• 313

304 316 382 374 383 605 422

Fallow

• 403

362 491 465 383 307 424

Abandoned arable

• 344

421 520 516 542 485 459 507

Wetland

121 213 260 403 652 2031 1380 273

Open woodland

• 246

240 334 486 457 470 619 314

Burnt area

69 139 126 113 448 511 519 517 151

Grass & shrubland

126 141 228 571 428 507 322 316

Total

60 126 214 322 444 537 521 572 323

An example of reanalysis: NPP of Russian forests (2009) based on a new empirical method

Components 14.7% 5.5% 27.9% 29.0% 6.3% 16.6%

Stem Branches Foliage Roots Understory GFF

Age groups 10.6% 30.4% 12.4% 26.8% 19.8%

Young Middleaged Immature Mature Overmature

Dominant species 14.3% 12.1% 2.0% 32.1% 6.9% 3.6% 17.9% 3.7% 7.4%

Pine Spruce Fir Larch Cedar HWD Birch Aspen Ohters

NPP 2.59 Pg C yr-1, or 318 g C ha-1 yr-1 Uncertainty 7% (CI 0.9) Difference with a previous inventory ~1/3

Method: Shvidenko et al.,

• Ecol. Model. 2007

Heterotrophic respiration, Tg C yr-1 by vegetation classes and vegetation zone

Land class Polar Tundra Sparse taiga Middle taiga Southern taiga Temperate forest Steppe Desert Total Forest

• 24.9

185.9 870.2 404.3 95.0 49.7 6.9 1,637.0 Arable

• 0.0

0.0 1.7 34.4 34.2 210.1 0.8 281.2 Hayfield 0.0

• 0.1

9.8 23.5 8.3 30.7 7.1 79.5 Pasture

• 0.1

0.6 19.3 28.1 21.8 110.5 31.6 212.0 Fallow

• 0.1

3.5 5.5 3.1 4.5 0.1 16.7 Abandoned arable

• 0.0

0.3 8.0 39.3 16.5 37.6 2.8 104.5 Bare fellow

• 0.0

0.3 4.2 6.2 37.8 0.6 49.2 Wetland 0.0 44.5 67.4 112.6 62.3 5.7 21.5 3.5 317.5 Open woodland

• 10.9

31.8 48.5 19.0 3.2 2.3 0.4 116.0 Burnt area

• 2.0

3.3 30.0 2.6 0.3 0.6 0.1 38.9 Grass & shrubland 0.2 175.5 40.0 272.1 33.0 23.3 58.1 9.1 611.4 Total 0.2 258.0 329.5 1,376.1 656.0 217.7 563.4 63.0 3,463.8

Heterotrophic respiration, g C m-2 by vegetation classes and vegetation zone

Land class Polar Tundra Sparse taiga Middle taiga Southern taiga Temperate forest Steppe Desert Total Forest

• 121

132 185 274 359 333 318 199

Arable

• 128

191 322 349 286 380 242 361

Hayfield

42

• 190

311 381 366 333 224 331

Pasture

• 186

277 304 361 359 330 222 311

Fallow

• 276

300 378 352 311 236 334

Abandoned arable

• 112

236 376 343 370 355 243 349

Bare fellow

• 157

328 358 338 358 220 352

Wetland

100 187 259 397 493 640 389 219

Open woodland

• 114

146 232 310 369 357 387 193

Burnt area

32 99 115 124 316 336 428 382 129

Grass & shrubland

95 106 171 390 296 379 240 147

Total

35 99 137 188 303 340 364 240 215

Disturbances

Several facts ▲ the total area of wild vegetation fires in Russia in 2003

enveloped 23 million ha including 17 million ha of forests (4.4 times all Austrian forests); ▲these fires produced direct carbon emissions at ~270 million ton of carbon– more than overall target of the Kyoto Protocol; the average flux for 2003-2008 is 160 mln t ▲an outbreak of Siberian moth in Russia in 2001 covered ~10 million ha ▲during the recent years insects damaged Canadian forests at the area of above 20 million ha

Way to estimate uncertainty

• Assessment of precision
• Standard sensitivity analysis (Monte Carlo,

error propagation)

• Transformation precision into uncertainty
• Harmonizing and multiple constraints of

results obtained by independent methodologies

Fire 2009

Emissions, g C per m2

< 10 11 - 25 26 - 50 51 - 100 101 - 250 251 - 500 501 - 1 000 1 001 - 1 500

Source: Global Fire Database GFED3, Giglio et al. 2010, van der Werf et al. 2010

Fire 1997-2009: Average annual area 8.8 mln ha, carbon emissions ~130 Tg C yr-1

Emissions, g C per m2 and year

< 1 1 1

• 2

5 2 6

• 5

5 1

• 7

5 7 6

• 1

1 1

• 2

2 1

• 3

> 3

Source: Global Fire Database GFED3, van der Werf et al. 2010

Net Ecosystem Carbon Balance for Russia

(average fluxes for 2003-2008, Tg C yr-1, sign “-“ means sink) Land classes and components Flux, Tg C yr-1 Forest

• 563±250

Open woodland

• 28±21

Shrubs

• 22±12

Natural grassland

• 58±26

Agriculture land

• 32±28

Wetland (undisturbed)

• 47±26

Disturbed wetland +36±20 Wood products +48±20 Food products (import-export) +18±16 Flux to hydro- and lithosphere +81±36 NECB (NBP)

• 567±259

NPP: comparison of independent estimates

50 100 150 200 250 50 100 150 200 250 Phytomass by [Shvidenko et al., 2002, 2007] Phytomass by [Usoltsev, 1998, 2007] 1 2 5 10 15 20 25 30 35 40 45 50 2 4 6 8 10 12 14 NPP by [Shvidenko et al., 2004] Methods 1, 2 [Usoltsev, 2007] 1 2 3

NPP calculated for Russia by 17 DGVM (Gusti 2009) gave the result +11 % to this study; variability of results by different models 22%

Integration: consistency of information, check of temporal trends, model-data fusion and model- data synthesis, etc.

Empirical NPP vs. MODIS NPP MODIS NPP = -105.5381+2.6561*x-0.0048*x^2+2.9488E-6*x^3 (R2 = 0.46) 100 200 300 400 500 600 700 800 900 1000 1100 Empirical NPP 100 200 300 400 500 600 700 800 900 1000 MODIS NPP NPPE NPPM

Inverse modeling

Bousquet et al., 1999, JGR

• 1.8±1.0
• Inverse modeling – Estimates for boreal Asia, Pg C

year-1

Maksyutov et al., 2003 (1992-1996)

• 0.63±0.36

Gurney et al.,2003 (1992-1996)

• 0.58±0.56

Baker et al. (1988-2003)

• 0.37±0.24

Patra et al., 2006 (1999-2001)

• 0.33±0.78
• Inverse modeling – Results for Russia, Pg C year-1

Ciais et al (2010),4 inversions for 2000-2004

• Inverse modeling – Results for Russia, Pg C year-1

Ciais et al (2010),4 inversions for 2000-2004 -0.65±0.12 This study (2003-2008), LEA

• 0.57±0.26

individual seasons: Temperature impact on forest NPP individual seasons: Temperature impact on forest NPP

Examination of different regression models ΔNPP = F(ΔDD>5oC, ΔP>5oC, Δ[CO2]) ΔHR = Φ(N>0oC, P>0oC, ΔT>0oC, W) ΔHR = φ (11 seasonal climatic indicators) Inter-seasonal variability of NPP and HR can reach 15-20%

Diverse published results

• NPP (Pg C yr-1, for all ecosystems): 2.75 (Filipchuk,

Moiseev 2003), 4.35 (Nilsson et al. 2003), 4.41 (Voronin et al. 2005), 4.73 (Zavarzin 2007), 5.14 (this study)

• NPP for forest ecosystems (g C m-2 yr-1): 204 (Filipchuk,

Moiseev 2003), 275 (Zamolodchikov, Utkin 2000), 318 (this study), 614 (Gower et al. 2001)

• HSR (Pg C yr-1, for all ecosystems): from 2.78

(Kurganova 2002) to 3.46 (this study)

• Disturbances (forest ecosystems, Tg C yr-1): from about

50 (“managed forests”, Zavarzin 2007) to 200-400 (different studies, including this one)

Reasons for diversity

• Incompleteness of the account
• Oversimplification of accounting schemes

and models used

• Different system boundaries
• Obsolete and uncertain information
• Lack of system design of the account

During 2003-2008

In 2003-2008 land of Russia provided the carbon sink between 0.6-0.7 Pg C per year

Carbon balance of selected NH regions from compiled land-based C accounting data

• 750
• 250

250 750 1250 1750

Canada USA Mexico EU-25 Russia China NH

Carbon flux into land ecosystems (Tg C yr-1)

Food products trade Wood products (incl. Trade) Peatlands degraded + peat use Rivers to ocean and lakes Peatlands & wetlands undisturbed Grassland & steppe Cropland Shrubland & desert Forest

Source: Ciais et al. (2010, in press)

Global forest carbon fluxes (Pg C yr-1) of 1990s and changes (%) over 2000s (vs. 1990s)

Source: Birdsey et al. (2010) in preparation

Warm but not very optimistic future: the world should be ready to increasing the global temperature by 4oC

Global average surface temperature scenarios for peak emissions at three different dates with 3%-per-year reductions in greenhouse gas emissions. Source: Parry et al. Nature 458, 30 April 2009

There ¡are ¡many ¡unresolved ¡ques@ons ¡and ¡ uncertain@es ¡

• How ¡are ¡terrestrial ¡ecosystems ¡func;oning ¡under ¡dynamic ¡

condi;ons ¡of ¡mul;ple ¡limita;ons ¡for ¡life ¡resources? ¡

• How ¡much ¡stable ¡is ¡the ¡direct ¡s;mula;on ¡of ¡photosynthesis ¡

and ¡NPP ¡by ¡the ¡environmental ¡change? ¡

• To ¡what ¡extent ¡do ¡the ¡limita;ons ¡bound ¡CO2 ¡fer;liza;on ¡

effect ¡and ¡how ¡long? ¡

• How ¡much ¡nitrogen ¡deposi;on ¡is ¡able ¡to ¡eliminate ¡lack ¡of ¡

available ¡nitrogen ¡in ¡high ¡la;tudes? ¡

• How ¡do ¡all ¡these ¡changes ¡interact ¡with ¡the ¡hydrological ¡cycle, ¡

par;cularly ¡with ¡water ¡stress? ¡

• How ¡will ¡destruc;on ¡of ¡permafrost ¡impact ¡forest ¡ecosystems ¡
• f ¡high ¡la;tudes? ¡ ¡