Monitoring soil carbon and nation wide carbon inventories Mikko Peltoniemi Finnish Forest Research Institute (Metla) Taru Palosuo European Forest Institute (EFI) with Raisa Mäkipää, Margareeta Häkkinen, Petteri Muukkonen and Aleksi Lehtonen at Metla, and collaborators Jari Liski and Kristiina Karhu at SYKE, Marcus Lindner at EFI COST 639, Vienna, 13.4.2007
Project presentation Part of the EU-funded Forest Focus Programme Duration 2005 Mar 2007 The objective of this study is to develop methods to monitor changes in the carbon stocks of forest soils. Modules: Model-based stratification Repeated organic layer measurements and plot-level sampling design Model evaluations
Model-based stratification & soil survey Motivation: Soil sampling is laborious Can we stratify sampling? Models can be used to provide correlates with expected changes of soil carbon Can we use models for stratification? How much sampling efficiency can be expected to improve?
Stratification method NFI permanent plots on forested mineral soil (N = 1719) Aim is to select a sub-sample of plots for repeated soil sampling based on model (MOTTI-YASSO) predicted ΔC = f(age, fert, loc, T, P, sp, manag. scen) Inclusion of uncertainties to simulations: Scenario Simulation Measurements of ΔC (no. of soil samples, m) Source: Peltoniemi et al.
Optimal strata with two assumptions of measurement uncertainties f(y) 0.0 0.4 0.8 1 soil samples Probability density of simulated ΔC in all plots Imprecise Precise 0.0 0.4 0.8 Inf soil samples 6 Cumulative5 function is 4 F(y) 0 1 2 3 the basis for 3 strata 2 boundaries 1 6 0 4 y (kgm 2 ) 0.0 1.0 2.0 6 5 4 3 2 1 6 0 4 y (kgm 2 )
Expected stratification gain in simulated sampling m = 1; proj. uncert = 5 m = 30; proj. uncert = 5 SE / SE srs 0.4 0.6 0.8 1.0 1.2 Equal Proportional Neyman 1 2 3 4 5 6 1 2 3 4 5 6 Peltoniemi et al. Preliminary results Number of strata
Model-based stratification Usefulness of stratification depends on Precision of measurements: Select paired repeated samples; or take enough samples and use spatial analysis Precision of simulations Increase precision of soil ΔC simulations Works best in predictable environment Uncertainties can be accounted for before stratification is made
Repeated org. layer sampling Fastest changes expected in org. layer Questions: Can we detect changes in organic layer C - and what are the rates of change? How many plots required for managed boreal forest soils?
Repeated soil sampling - Material 38 stands (24 pine, 14 spruce) measured 1985-89 (composite sample) stands now 40-80 years New measurements (2005) with spatial information (n=40 per plot) -> kriging based estimates of mean and variance of soil C Fig. Example sample plot; location of old and new sample points
Organic layer C in old and new measurements Significant mean ΔC = 404 g C m-2 (+23 g C m-2a-1) (Häkkinen et al. in prep) Plot
Required plot number for significant change detection (current sampling design and material) Häkkinen, M. et al. In prep.
Sampling distance Question: What is the optimal sampling distance between the sample points in boreal forest soils (to avoid autocorrelated samples)?
Sampling distance - Material 5 young Scots pine stands (measured 2004) 4 young Norway spruce stands (measured 2005) 1 old pine stand (Liski 1995) 1 old spruce stand appr. 100 soil samples / plot Fig. Example of sampling design within one plot Y Coord -1000-500 0 500 1000-1000 -500 0 500 1000 X Coord
Sampling distance - results To avoid correlated samples distance between sampling points should be > 7 m γ 0.5 0.4 0.3 Y Coord -1000-500 0 500 1000 2.5 kg m 2 2 1.5 1 0.2-1000 -500 0 500 1000 X Coord 0.1 0.0 0 250 500 750 1000 r (cm) Range 637.06 cm Nugget 0.183 Sill 0.319 REF: Muukkonen, Häkkinen Mäkipää, in prep. Fig. Spatial autocorrelation in one sample plot
Model evaluation Comparison: Motti-Yasso vs. Efimod-ROMUL Site: mesic Scots pine stand in middle boreal zone REF: Palosuo et al. manuscript in preparation
Model evaluation - a review Offspring of Workshop in Koli Models in country scale C accounting of forest soils Models: Yasso, ROMUL, SOILN, RothC, Forest- DNDC, CENTURY, FORCARB Implementations of temperature, moisture, soil texture and nitrogen effects in models Evaluation of availability of input data for models Compilation of references to relevant soil datasets REF: Mikko Peltoniemi, Esther Thürig, Stephen Ogle, Taru Palosuo, Marion Schrumpf, Thomas Wutzler, Klaus Butterbach-Bahl, Oleg Chertov, Alexander Komarov, Alexey Mikhailov, Annemieke Gärdenäs, Charles Perry, Jari Liski, Pete Smith, Raisa Mäkipää. Models in country scale carbon accounting of forest soils, In revision, Silva Fennica
This was a short presentation of our recent and ongoing studies. Thank you for your attention Further info and references: www.metla.fi/hanke/843002/ Project on Monitoring changes in the carbon stocks of forest soils and earlier projects www.metla.fi/hanke/3306/ www.efi.int/projects/integrated www.efi.int/projects/uncertainty