Monitoring changes in the carbon stocks of forest soils

Transcription

1 Monitoring changes in the carbon stocks of forest soils Raisa Mäkipää, Mikko Peltoniemi, Margareeta Häkkinen, Petteri Muukkonen, Aleksi Lehtonen at Metla with colaborators Jari Liski and Kristiina Karhu at SYKE Taru Palosuo and Marcus Lindner at EFI EU conference on Forest Focus C-studies Bryssels 22 Oct, 2007

2 Outline Introduction Research Questions and Results Conclusions

3 Introduction The EU have to report the changes in forest carbon stocks including soil (UNFCCC 1992, Kyoto Protocol 1997) Current soil surveys in Europe are NOT designed for monitoring of soil C changes Demanding to monitor small changes of a large soil C stock, since Spatial variation is large Soil sampling is laborious

4 Objective To develop methods to monitor changes in the carbon stocks of forest soils Modules: 1. Model evaluations 2. Model-based stratification 3. Analyses of repeated soil measurements 4. Plot-level sampling design 5. Cost estimation

5 1. Evaluation of soil C models We evaluated soil models that may be used for A country scale C accounting of forest soils (GHG inventory) Predicting soil responses to changed management practices Improving efficiency of soil sampling (by modelbased stratification) Peltoniemi et al Models in country scale carbon accounting of forest soils. Silva Fenn. 41:

6 1. Evaluation of soil C models Evaluated models: Yasso, ROMUL, SOILN, RothC, Forest-DNDC, CENTURY, FORCARB We conclude that Model selection is strongly guided by availability of representative input data In a country scale inventory simple models may be the only reasonable option to estimate soil C changes Process-based models are needed when soil responses to e.g. management practices are assessed An example: removal of harvest residues for bioenergy Peltoniemi et al Models in country scale carbon accounting of forest soils. Silva Fenn. 41:

7 1. Responses to management practices assessed with soil models (Yasso, ROMUL) Palosuo et al. Submitted manuscript

8 2. Does model bases stratification improve sampling efficiency in large scale inventories? Aim is to select a sub-sample of plots for repeated soil sampling based on model (MOTTI-YASSO) predicted Simulations for the NFI permanent plots on forested mineral soil (N = 1719) y= C = f(age, fert, loc, T, P, sp, manag. scen) Source: Peltoniemi, Heikkinen, Mäkipää Silva Fenn. 41:

9 2. Stratification gain in simulated sampling m = 1; proj. uncert = 5 m = 30; proj. uncert = 5 SE / SE srs Equal Proportional Neyman Number of strata Source: Peltoniemi, Heikkinen, Mäkipää Silva Fenn. 41:

10 2. Model-based stratification With model based stratification a number of sampled plots can be reduced by 25% without reducing precision 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 C simulations Works best in predictable environment (without successful prediction of stand future, improvement in sampling efficiency will be smaller) Predictions of future are difficult (harvests, thinnings)

11 3. Analyses of repeated soil sampling Question Can we detect changes in organic layer C - and what are the rates of change? How many plots required for managed boreal forest soils? Method Soil sampling repeated on 38 stands (now yr) Measured (one composite sample per plot) New measurements from organic layer, n=40 per plot, kriging based estimates of mean and variance of soil C

12 3. Measured change in C stock of organic layer Average annual change of 23 g C m -2 was significant Source: Häkkinen, Heikkinen, Mäkipää, submitted ms

13 3. Required plot number for change detection Number of plots need to sample >83 plots from this cohort (assuming measured between site variation and 10 yr interval) Change in the amount of C,gm 2

14 4. Sampling desing at plot-level Questions What should be spatial location of sample points to avoid correlated samples? How many samples per plot/stand are needed to obtain reliable plot level estimates of soil C stock? Method 10 coniferous stands sampled for organic layer >100 samples per plot Spatial auto-correlation of carbon stock analysed

15 4. Distance between sampling points To avoid correlated samples distance between sampling points should be > 7 m γ r (cm) Range cm Nugget Sill Fig. Spatial autocorrelation in one sample plot Source: Muukkonen, Häkkinen Mäkipää, manuscript

16 4. Effect of sample size (n) on precision 95% confidence interval P.sylv 1134 P.sylv1205 P.sylv1118 P. sylv 1025 P.syl 1004 P.abi 128 P.abi 157 P.abi 176 P.abi 194 P.abi Number of samples per plot n>20 gives precise estimates for soil C stock of organic layer

17 5. Estimation of monitoring costs at plot scale Costs of carbon measuring of soil organic layer (m cost ) are estimated as m cost = (k cost +n*w tot ) where k cost is fixed costs, w tot is variable costs, and n is number of soil samples per plot. Variable costs (w tot ) are estimated as w tot = w fld + w pre + w plw + w mst + w C where w fld = costs of sample boring in the field, w pre = preparation and drying of a sample, w plw = powdering, w mst = measuring of moisture content, and w C = carbon analysis of a soil sample.

18 5. Time and costs per sampled plot 70 Laboratory analysis 1200 Laboratory analysis 60 Sample preparation 1000 Sample preparation 50 Soil sampling Soil sampling Time (hours) Access to a sample plot and preparations Euros Access to a sample plot and preparations composite n=10 n=20 n=40 Number of samples (n) per plot 0 Composite n=10 n=20 n=40 Number of samples (n) per plot

19 5. Precision by costs (at plot scale) 95% confidence interval P.sylv 1134 P.sylv1205 P.sylv1118 P. sylv 1025 P.syl 1004 P.abi 128 P.abi 157 P.abi 176 P.abi 194 P.abi Euros

20 5. Estimation of monitoring costs Total monitoring costs of a network of sample plots are estimated with following sampling strategies All plots of a network are resampled every 5 years, 75% of the plots are resampled every 5 year (selection plots guided by model based stratification) 50% of the plots of a network are resampled every 10 years 37.5% of the plots are resampled every 10 years (selection plots guided by model based stratification) The monitoring costs of the network of sample plots (M) was estimated as M = p * N * m cost * F where p is proportion of plots to be sampled, N is total number of plots in a monitoring network, m cost is cost of carbon measuring of a plot, and F is sampling frequency (F=1 for annual sampling, F =1/10 for sampling of 10-year interval).

21 5.Monitoring costs of a network of 2000 sample plots 2.5 Monitoring costs, million euros Total Annual 0 All plots every 5 years Selected 75% every 5 years All plots every 10 yr Selected 75% every 10 years Sampling strategy

22 Conclusions Currently available models can be used in national GHG inventory for estimation of soil C changes Soil monitoring with repeated measurement is laborous Minimum number of sample plots for repeated soil measurements is >80 in a cohort of high rate of change >20 soil samples per plot are needed for reliable mean estimate of the C stock of organic layer Sampling efficiency can be improved and monitoring costs reduced using existing networks of measures plots increasing sampling interval stratification according to predicted changes of soil C Results and methods can be applied in other countries

23 Thank you for your attention Further information

24 Research articles resulting from this study Häkkinen, M., Heikkinen, J. & Mäkipää, R. Soil carbon changes detected with repeated soil sampling spatial within-site variation accounted in statistical analysis. Manuscript submitted in June Mäkipää, R., Lehtonen, A. & Peltoniemi, M State-of-the-art carbon inventories and ways to use them for carbon cycle research. Springer, Ecological Studies, manuscript accepted for publication. Mäkipää, R. et al. Monitoring changes in the carbon stocks of forest soils - efficiency of different sampling methods and costs of the monitoring. Manuscript in preparation. Muukkonen, P., Häkkinen, M. & Mäkipää, R. Spatial variability of soil organic carbon in humus layer of boreal forest soil. Manuscript in preparation. Palosuo, T., Peltoniemi, M., Komarov, A., Mikhailov, A. et al. Model based assessment of the effect of the intensified biomass collection on forest carbon balance. Manuscript, submitted in May Peltoniemi, M., Thürig, E., Ogle, S., Palosuo, T., Shrumpf, M., Wützler, T., Butterbach-Bahl, K., Chertov, O., Komarov, A., Mikhailov, A., Gärdenäs, A., Perry, C., Liski, J., Smith, P. & Mäkipää, R Models in country scale carbon accounting of forest soils. Silva Fennica 41: Peltoniemi, M., Heikkinen, J. & Mäkipää, R. Stratification of regional soil sampling by model-predicted change in soil carbon in forested mineral soils. Silva Fennica, 41:

25 Costs per soil sample Euros per a soil sample Laboratory analysis Sample preparation Soil sampling Access to a sample plot and preparations 2 0 Salaries Cost structure Other

26 2. Optimal strata for stratification f(y) soil samples Probability density of simulated C in all plots Imprecise Precise Inf soil samples 6 Cumulative5 function is 4 F(y) the basis for 3 strata 2 boundaries y (kgm 2 ) y (kgm 2 )

27 2. Stratification No stratification vs. approx. 6 optimal strata Optimal sampling (Neyman) Proportional (to size of strata) Equal

28 3. Repeated soil sampling - Material 38 stands (24 pine, 14 spruce), now years measured (one composite sample per plot) New meassurements with good spatial information (n=40 per plot) in > kriging based estimates of mean and variance of soil C Soil samples from organic layer CHN-analysis with LECO Fig. Example of sample plot; location of old and new sample points

29 Repeated soil sampling sample plots

30 Number of sample plots needed for detection of change with different rate of change (sd 174 g C/m2)

31 4. Material Spatial auto-correlation of carbon stock in humus layer (within site variation) studied 5 young Scots pine stands (measured 2004) 4 young Norway spruce stands (measured 2005) 1 old pine stand (Liski 1995) 1 old spruce stands appr. 100 soil samples / plot Fig. Example of sampling design within one plot Y Coord X Coord

32 Spatial variation of C stock of organic layer Y Coord kg m X Coord

33 Research questions and results of this project 1.Evaluation of soil C models as tools of large scale inventories 2.Does model bases stratification improve sampling efficiency in large scale inventories 3.What is sampling effort needed in soil carbon monitoring to be able to detect change 4.What is number of samples needed to obtain reliable plot level estimates of soil C stock