Soil carbon modelling applied for nation-wide forest carbon inventory

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Soil carbon modelling applied for nation-wide forest carbon inventory Raisa Mäkipää 1), Mikko Peltoniemi 1), Aleksi Lehtonen 1), Petteri Muukkonen 1), Taru Palosuo 2), Jari Liski 3) 1) Finnish Forest Research Institute 2) EFI (European Forest Institute) 3) SYKE (Finnish Environment Institute)

Background Outline Method to calculate C budget of forest vegetation and soil Carbon budget 1922-2004 in Finland s forests Eligibility of the method for national GHG reporting (IPCC guidance) Summary and discussion

Background GHG reporting under the Climate Convention and Kyoto Protocol (KP) Forests included in the KP (Art. 3.3 and 3.4) Sinks can compensate emission reductions GHG inventories of LULUCF sector need to be improved IPCC guidance for GHG reporting 1996 GL, 2003 GPG, and 2006 GL

Current methods Background Trees: national C budget of trees can be derived from NFI data and drain (harvests) statistics with biomass equations or BEFs Other vegetation: in general excluded Soil, litter and dead wood: repeated nation-wide soil surveys are not available (except in Sweden and UK?), modelling can provide first estimates? Need for applicable and consistent methods to assess changes in the all carbon pools at national scale

Dynamic soil C model integrated to NFI data on forest resources We integrated: forest inventory data (aggregated) on growing stock, area (forest land, no peat), growth indices, harvests, natural mortality biomass models / BEF(t) biomass turnover i.e. modelled litter input to soil soil decomposition model YASSO

Input data from forest inventory 2000 Growing stock Drain Increment Forest land 100 Growing stock (Mm³) Forest land (10 000 ha) 1500 1000 500 75 50 25 Increment, Drain (Mm³) 0 0 1922 1932 1942 1952 1962 1972 1982 1992 2002 Year

Methods - Biomass Biomass of trees tree species specific (Scots pine, Norway spruce, birch) agedependent BEFs for different biomass compartments Biomass of understorey vegetation biomass equations according to stand variables for groups of plant species Change in the biomass and carbon stock rate of stock change of consecutive inventories annual variation in growth accounted with the help of growth indices annual variation in harvests accounted

Tree species specific BEFs according to stand age for foliage,branches, etc. 0,7 0,65 Pine Spruce Birch BEF 0,6 0,55 0,5 15 25 35 50 70 90 110 130 150 Stand age, years Lehtonen et al. For Ecol Managem 188:211-224

Understorey Biomass Aboveground biomass (kg ha 1 ) 2000 1500 1000 500 Upland pine forests Dwarf shrubs Herbs & grasses Mosses Lichens 0 0 50 100 150 200 Stand age (years) Muukkonen et al. Silva Fennica, accepted Muukkonen & Mäkipää, submittted ms

Methods - Input to the soil Biomass turnover rates (year -1 ) used to estimate the litter production of trees and understorey vegetation Litter input of harvest residues based on harvest statistics and BEFs Litter input resulting from natural mortality based on NFI estimate and BEFs Lehtonen et al. 2004; Muukkonen & Lehtonen 2004; Muukkonen 2005; and other sources as reported in Liski et al. 2006

Methods - Input to the soil Turnover rates of understorey vegetation Bryophytes Lichens Dwarf shrubs, aboveground Herbs & grasses, aboveground Belowground (dwarf shrubs, herbs & grasses) 0.33 0.1 0.25 1.0 0.33

Methods - Input to the soil Turnover rates of Trees Spruce forests Pine forests Broadleaved forests S 1 N 2 S N S N Foliage 0.10 3 0.05 3 0.22 4 0.10 4 0.78 5 Branches & roots 0.0125 3 f(t) 6 0.0135 7 Stump bark 0.0 8 0.0030 9 0.0001 10 Reproductive origins & stem 0.0027 8 0.0052 9 0.0029 10 bark Fine roots 0.811 11 0.868 12 1.0 13 Lehtonen et al. 2004; Muukkonen & Lehtonen 2004; Muukkonen 2005; and other sources as reported in Liski et al. 2006

Litter production of standing trees Relative tree biomass Foliage Branches Stem Stump Fine roots Roots <5 cm Roots >5 cm Pine turnover rates 0.0052 0.22 0.007-0.06 0.0027 0.007-0.06 for pine in Southern Finland 0.868 Relative tree litter Foliage Branches Other Stump bark Fine roots Roots <5 cm Roots >5 cm Pine

Soil carbon method Decomposition, soil carbon (mineral soils) Litter dynamic soil carbon model YASSO (Liski et al 2005) input information litter production climate (temperature) Decomposition Liski J, Palosuo, T., Peltoniemi, M. and Sievänen, R. 2005. Ecological Modelling 189: 168-182

Biomass C stock & stock changes 750 Ground vegetation C stock Tree C stock 15 C stock (Tg) 500 250 Biomass C stock change 10 5 0 Biomass C stock change (Tg / year) 0-5 1922 1932 1942 1952 1962 1972 1982 1992 2002 Year Liski et al. 2006. C accumulation in Finland s forests..ann. For Sci. accepted.

Soil C stock and stock changes 1000 Soil C stock change Soil C stock with area changes Soil C stock 10 Soil C stock (Tg) 800 600 400 200 5 0-5 Soil C stock change (Tg / year) 0-10 1922 1932 1942 1952 1962 1972 1982 1992 2002 Year Liski et al. 2006. C accumulation in Finland s forests..ann. For Sci. accepted.

Uncertainty of soil carbon sink Upland soils Carbon sink (Tg) -5 0 5 10 1990 1992 1994 1996 1998 2000 2002 Year 2.5% C.l. 25% 50% 75% 97.5% Monni et al. 2006. Climatic Change, accepted.

Validity of this integrated method 12 11 k s = 0.0058 ± 0.0010 kga -1 m -2 Carbon (kgm -2 ) 10 9 8 7 6 5 4 3 2 1 0 Mesic site, Norway spruce Mesic site, Scots pine Sub-xeric site, Scots pine Measured organic layer k m = 0.0047 ± 0.0014 kga -1 m -2 (p = 0.0011) 0 10 20 30 40 50 60 70 80 90 100 110 120 Simulated rate of change in soil C agreed with measured change in humus layer of a chronosequency Stand age (a) Peltoniemi et al. 2004. GCB 10:2078-2091.

Summary Dynamic soil C model Yasso with litter input derived from NFI forest resource data -> Changes in the soil C stock can be estimated Soil model is applicable and NFI input data is available for the majority of the EU-countries Validity of the models tested independently as well as validity of the method as a whole (see references) This methodology is consistent with the IPCC 2006 draft - Model-Based Tier 3 Inventories (AFOLU 2.53)

Steps to develop a Tier 3 model-based inventory estimation system IPCC 2006 GL

Steps to develop a Tier 3 model-based inventory estimation system cont. IPCC 2006 GL

Discussion Forests may sequestrate 1/3 of annual CO 2 emissions of forested country (such as Finland) and that C sink can be partly credited Changes in the soil C can be reported as requested by the Climate Convention, but uncertainties are large Only few countries will use C sinks under the Art 3.4. of KP

Election of activities under Article 3.4 Forest management Cropland management Grazing land management Revegetation Austria - not yet decided - Belgium NO NO NO NO Denmark YES YES YES NO Finland NO NO NO NO France YES NO NO NO Greece Ireland YES - not yet decided - - not yet decided - Netherlands NO NO NO NO Portugal YES YES YES NO Sweden YES NO NO NO UK YES NO NO NO EU Climate Change Committee WG1, March 2006

Discussion The current interest TO USE OR NOT TO USE forest C sinks influences on negotiations of the subsequent commitment periods Future challenges Reliable forest GHG inventories All habitat types and all GHGs to be included Effects of LUC (especially transition period) Direct human influence (management practices vs.climate change and other indirect factors)

References Lehtonen, A., Mäkipää, R., Heikkinen, J., Sievänen, R. & Liski, J. 2004. Biomass expansion factors (BEF) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management 188: 211-224. Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K. T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling. 180: 305-315. Liski, J., Palosuo, T., Peltoniemi, M. & Sievänen, R. 2005. Carbon and decomposition model Yasso for forest soils. Ecological Modelling 189: 168-182. Liski, J., Lehtonen, A., Palosuo, T., Peltoniemi, M., Eggers, T., Muukkonen, P. & Mäkipää, R. 2006. Carbon accumulation in Finland's forests 1922-2002 - an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil. Annals of Forest Science, accepted. Monni, S., Peltoniemi, M., Palosuo, T., Lehtonen, A., Mäkipää, R. & Savolainen, I. 2006. Uncertainty of forest carbon stock changes - implications to the total uncertainty of GHG inventory of Finland. Climatic Change, accepted. Muukkonen, P. 2005. Needle biomass turnover rates of Scots Pine (Pinus sylvestris L.) derived from the needle-shed dynamics. Trees - Structure and Function 19: 273-279. Muukkonen, P. & Lehtonen, A. 2004. Needle and branch biomass turnover rates of Norway spruce (Picea abies (L.) Karst.). Canadian Journal of Forest Research, 34: 12, 2517 2527. Muukkonen, P. & Mäkipää, R. Biomass models of understorey vegetation according to site attributes in boreal forests. Submitted manuscript. Muukkonen, P., Mäkipää, R., Laiho, R., Minkkinen, K., Vasander, H. & Finér, L. 2006. The relationship between biomass and percentage cover in understorey vegetation of boreal coniferous forests. Silva Fennica, accepted for publication. Peltoniemi, M., Mäkipää, R., Liski, J. & Tamminen, P. 2004. Changes in soil carbon with stand age an evaluation of a modeling method with empirical data. Global Change Biology 10: 2078-2091. Peltoniemi, M., Palosuo, T., Monni, S. & Mäkipää, R. The factors affecting the uncertainty of sinks and stocks of carbon in Finnish forest soil and vegetation. Submitted manuscript.

Thank you for your attention Further information www.metla.fi/hanke/843002/ Project on Monitoring changes in the carbon stocks of forest soils and earlier projects of this research team www.metla.fi/hanke/3306/ www.efi.fi/projects/integrated www.efi.fi/projects/uncertainty