A multi-model comparison of soil carbon assessment of a coniferous forest stand

Environmental Modelling & Software 35 (2012) 38e49

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A multi-model comparison of soil carbon assessment of a coniferous forest stand Taru Palosuo a, *, Bente Foereid b,1, Magnus Svensson c, Narasinha Shurpali d, Aleksi Lehtonen e, Michael Herbst f, Tapio Linkosalo e, Carina Ortiz c, Gorana Rampazzo Todorovic g, Saulius Marcinkonis h, Changsheng Li i, Robert Jandl j a

MTT Agrifood Research Finland, Luutnantintie 13, 00410 Helsinki, Finland National Soil Research Institute, Cranfield University, Cranfield, Bedford MK43 0AL, UK c Swedish University of Agricultural Sciences, Department of Soil and Environment, P.O. Box 7014, SE 750 07 Uppsala, Sweden d University of Eastern Finland, Department of Environmental Science, Yliopistonranta 1 E, 70210 Kuopio, Finland e Finnish Forest Research Institute, Vantaa Research Centre, PO Box 18, 01301 Vantaa, Finland f Agrosphere, IBG-3, Research Centre Jülich GmbH, 52425 Jülich, Germany g University of Natural Resources and Applied Life Sciences, Institute of Soil Research, Department of Forest and Soil Sciences, Peter Jordan Strasse 82, 1190 Vienna, Austria h Voke Branch of the Lithuanian Institute of Agriculture, Zalioji a.2, Traku Voke, LT-02232 Vilnius, Lithuania i Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA j Forest Research and Training Center for Forest, Natural Hazards and Landscape (BFW), Seckendorff Gudent Weg 8, A-1131 Vienna, Austria b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 February 2011 Received in revised form 7 February 2012 Accepted 9 February 2012 Available online 8 March 2012

We simulated soil carbon stock dynamics of an Austrian coniferous forest stand with five soil-only models (Q, ROMUL, RothC, SoilCO2/RothC and Yasso07) and three plantesoil models (CENTURY, CoupModel and Forest-DNDC) for an 18-year period and the decomposition of a litter pulse over a 100-year period. The objectives of the study were to assess the consistency in soil carbon estimates applying a multi-model comparison and to present and discuss the sources of uncertainties that create the differences in model results. Additionally, we discuss the applicability of different modelling approaches from the view point of large-scale carbon assessments. Our simulation results showed a wide range in soil carbon stocks and stock change estimates reflecting substantial uncertainties in model estimates. The measured stock change estimate decreased much more than the model predictions. Model results varied not only due to the model structure and applied parameters, but also due to different input information and assumptions applied during the modelling processes. Initialization procedures applied with the models induced large differences among the modelled soil carbon stocks and stock change estimates. Decomposition estimates of the litter pulse driven by model structures and parameters also varied considerably. Our results support the use of relatively simple soil-only models with low data requirements in inventory type of large-scale carbon assessments. It is important that the modelling processes within the national inventories are transparently reported and special emphasis is put on how the models are used, which assumptions are applied and what is the quality of data used both as input and to calibrate the models. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Carbon balance Forest Model comparison Soil carbon Simulation model Uncertainty

1. Introduction Soils hold the largest stock of terrestrial carbon (C) in the biosphere with forest soils estimated to contain about half of that stock (Jobbágy and Jackson, 2000). In recognition of the size of the soil organic carbon (SOC) pool, countries are required to report changes in SOC and litter as well as the corresponding uncertainty in * Corresponding author. Tel.: þ358 40 186 9114; fax: þ358 20 772 040. E-mail address: taru.palosuo@mtt.fi (T. Palosuo). 1 Present address: University of Abertay, Kydd Building, Dundee DD1 1HG, UK. 1364-8152/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2012.02.004

their national greenhouse gas inventory in compliance with the UN Framework Convention on Climate Change (UNFCCC, 1992) and the Kyoto Protocol (UNFCCC, 1997). The Intergovernmental Panel on Climate Change (IPCC) guidelines for greenhouse gas inventories provide a default methodology (Tier 1) for C accounting. The Tier 1 assumption is that there would be no changes in the net SOC stock on “forest land remaining forest land” (IPCC, 2003). To apply this assumption, countries need to prove that their forest soil is not a source of C. The results from many studies (e.g. Bellamy et al., 2005) however, make it difficult to pursue the no-emission argument. In addition to that, countries should apply methods of higher Tier levels

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

(measurements or models) if SOC stock change of forests is a keycategory; this is relevant for countries with substantial forest areas. Due to the high costs of soil sampling and chemical analysis needed to report the SOC stock changes based on measurements (Conen et al., 2003; Mäkipää et al., 2008) models are widely used to estimate stock changes. Some countries, e.g. Canada, UK and Finland, use models in their SOC stock estimation for UNFCCC (UNFCCC, 2010). The Good Practice Guidance of the IPCC (2006) recognizes simulation models combined with inventory data as the most advanced way of reporting (Tier 3). Models are also commonly used in forest C assessment studies involving soil at stand (e.g. Svensson et al., 2008), regional (e.g. Schaldach and Alcamo, 2006), national (e.g. Liski et al., 2006; Ågren et al., 2007) and supra-national scales (e.g. Janssens et al., 2003). In addition to reporting and C assessment studies, models are also increasingly being used as decision support tools particularly on issues related to climate change (e.g. Smith et al., 2006). For the scenario purposes, mechanistic, process-based approaches that can cover the effects of changing environmental factors and management are seen more suitable than empirical, regression-type of modelling approaches. Large-scale model applications, where the input data are scarce and highly uncertain, are becoming more common. Therefore, robust models, which can apply basic stand and soil data as their input information, are required. Understanding the uncertainties related to model predictions is essential and those applying models within greenhouse gas inventories have an obligation to estimate and report the uncertainties related to their estimates using error propagation or Monte Carlo simulations (IPCC, 2003). Comparing outputs from different models using a common input dataset is another effective way of highlighting the uncertainties associated with modelling approaches. This idea is made use of, for example, in assessing the reliability of climate and general circulation models (Murphy et al., 2004). Range of model results in model comparison involves also the uncertainty related to model structure, which is the most difficult uncertainty source to quantify (Chatfield, 1995). Models describing SOC dynamics have been reviewed in recent years. For example, Peltoniemi et al. (2007) reviewed seven SOC models from the point of view of preparing country-scale SOC change estimates of forest soils for national greenhouse gas inventories. Smith et al. (1997) compared performance of nine soil organic matter models with long-term experiments with differing land management, mainly grassland, arable cropping and woodland. After this major model evaluation by Smith et al. (1997) for more than a decade ago, there has not been any model comparison involving the major SOC models. In addition, the increased use of models also by others than original model developers has made the usability of modelling tools and their proper use in applications important. In this study, we applied eight process-based soil simulation models on an intensively monitored Austrian forest stand. We assessed the consistency of the models’ estimates for forest C dynamics and compared model output with measured data. The objectives of the study were: 1) to assess the uncertainties related to SOC estimates applying a multi-model comparison, 2) to present and discuss the sources of uncertainties that create the differences in model results, and 3) to compare and discuss the applicability of different modelling approaches on large-scale C assessments.

39

the organic layer (F/H) and the uppermost 30 cm mineral soil layer. Modelestimated tree biomass C development, litter amounts, SOC stocks and stock changes were compared to each other and to measurements. We also simulated the decomposition of a litter pulse under the same site conditions over a 100-year period to compare the simulated decomposition dynamics of the models. The range of simulation results was taken as an indication of the uncertainties related to simulations and the sources of uncertainties are discussed based on experiences gathered during the modelling process. 2.1. Study site The Murau site (47 030 4300 N, 14 060 3600 E, 1560 m a.s.l.) is located in the Province of Styria, Austria. The site belongs to the intensively monitored Level-II-sites in the ICP Forests (the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests) monitoring (Neumann et al., 2001) in Austria. The soil at the site is spodic Cambisol on bedrock comprised of gneiss and mica schists and the slope is north-facing with an inclination of 65%. The mean annual temperature is 5  C with an annual precipitation of 918 mm (mean over 30 years). The stand is dominated by Norway spruce (Picea abies (L.) Karst.). The stand age was 125 years in 2001. Base stand data from year 2001 are reported in Table 1. The height of dominant trees was 29.3 m and diameter at breast height (dbh) was 43.2 cm. Previously the site, as other forests of the area, has been a forest pasture with some litter raking. Only in the last 60 years pastures and forests were spatially separated. 2.2. Measured data 2.2.1. Tree biomass C Individual tree measurements from one 0.25-ha plot were available from the repeated assessments in 1994, 1999 and 2004. From each tree dbh was measured and height was estimated from dbh with a locally derived dbh-height function. Stem volumes were calculated based on dbh, height and a shape function. We used the taper function for Norway spruce of the Austrian National Forest Inventory (NFI). Needle biomass, coarse root biomass, stem biomass and branch biomass were calculated for Norway spruce with biomass functions by Wirth et al. (2004), while for Larch (Larix decidua) Austrian biomass equations (Rubatscher et al., 2006) were used. Biomass was estimated for standing stock and removed trees and the biomass estimates of removed trees were based on previous measurement. The biomass of fine root (here roots less than 2 mm in diameter) was estimated with a constant ratio to foliage mass (30%) based on the Finnish study by Helmisaari et al. (2007). Biomass estimates were converted to C by multiplying by factor 0.5. 2.2.2. Litter production Litter fall was estimated by applying constant biomass turnover rates (i.e. the share of biomass that turned into litter annually) for each biomass compartment. The biomass turnover rates were 17; 2; 2 and 85% for Norway spruce foliage, branches, coarse roots and fine roots, respectively. The applied biomass turnover rates were obtained from Liski et al. (2006), with the major data source for Norway spruce taken from Muukkonen and Lehtonen (2004). For larch, same biomass turnover rates were used as for Norway spruce, with the exception of foliage for which the biomass turnover rate was 100%. Calculated above-ground litter from trees were compared with litter collected at the Murau site from 2005 to 2008 with six funnels, each 100 cm diameter. Ground vegetation was neglected from the analysis. Intra-annual pattern of litter fall for the models that required monthly or daily input was taken from Jenkinson and Coleman (1994). There was some natural mortality at the study site, but no forest management activities such as thinning were carried out during the studied period. It was assumed that this natural mortality occurred in the middle of each measurement period, i.e. in years 1997 and 2002. It was also assumed that all dead tree biomass was left on the site. 2.2.3. Soil Soil chemical properties at the Murau study site were measured in 1990 and 2008. Mineral soil samples were collected from fixed soil depths. In 1990, samples Table 1 Basic stand characteristics of the Murau study site in the year 2001. Species

Number of stems [number ha1]

Basal area [m2 ha1]

Stem volume [m3 ha1]

Basal area increment [m3 ha1 year1]

Norway spruce European larch Sum of living trees and standing dead logs

560 48 780

35 6.5 45

393 81 500

0.43 0.10 0.53

2. Material and methods We simulated tree biomass C, litter production and SOC dynamics of an Austrian coniferous forest stand with eight plantesoil models or modelling approaches (i.e. soil models combined with the litter input calculated based on tree measurements, biomass functions and biomass turnover rates) for a period of 18 years from 1990 to 2008. Simulations were performed at stand level, which is the basic scale of forest management at which most of the data are gathered. The soil layers covered were

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T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

from three soil pits were pooled to obtain one sample for each studied layer (0e10, 10e20, 20e30 cm). In 2008, the samples were obtained from four soil pits from depths 0e5, 5e10, 10e20, 20e40 and were also analyzed separately. Due to the high rock content, no undisturbed soil cores were collected. Each sample was oven dried, and the C and nitrogen (N) contents of four subsamples were determined with a LECO CN-2000 dry combustion analyzer (www.leco.org). Texture (sand, silt, clay) and rock content data used in this modelling exercise were taken from the 1990 soil analysis and were assumed to stay constant. The SOC pool was calculated as SOC ¼ Fvol *Ccon *r;

(1) 1

where Fvol is the volume of fine-earth (<2.0 mm), Ccon is C concentration [mg g ] and r is the bulk density calculated from a pedotransfer function r ¼ 1:2935  0:0085*Ccon þ 0:001*silt½% þ 0:0034*clay½% derived from the Austrian soil database BORIS (Blum et al., 2005). In order to harmonize the information from different protocols, we estimated C, rock content, silt, clay, and r for each cm of the soil profile by an equal-area spline method (Ponce-Hernandez et al., 1986). 2.2.4. Meteorological data Meteorological data originated from measurements on the Level-II Plot Murau (Neumann et al., 2001) and were verified with the records of the Austrian Hydrological Survey (Anonymous, 2000). Air temperature and precipitation data were available in daily resolution. Any data gaps in the period 1990e2008 were filled using data from 2006 as this year was complete and the mean monthly values of this year were representative of those for the 1990e2008 period. For the litter pulse analysis weather data were repeated to cover the whole 100-year period.

biomass also in the Q-model, which is based on quality theory and in which the decomposition is implicitly driven by microbial activity or biomass. Decomposition processes in all models are affected by temperature, and by moisture in other models but the Q-model. Soil texture effects are included in variable ways to all models except Yasso07 in which the decomposition is unaffected by parent soil material. In the Q-model texture is only indirectly covered via some site-specific parameters. Plantesoil models (CoupModel, Forest-DNDC and CENTURY) and ROMUL include separate N models that are dynamically coupled to SOC dynamics. SoilCO2/RothC, on the other hand, couples detailed water, heat and CO2 transport model with the RothC model. It has been developed for the prediction of soil heterotrophic respiration with daily time step, but it can also be used for modelling SOC dynamics over longer time scales. Yasso07 was particularly developed for large-scale SOC assessments and special emphasis in model development was put on the comprehensiveness of data applied in model calibration and on the parameter uncertainty estimates provided by the model. CoupModel was the only model that was adjusted to include exactly the depth of the measured soil layers (i.e. organic and the uppermost 30 cm mineral soil layer) in the estimation of SOC stock. For the other models it was assumed that their SOC estimates were comparable to the measured values as such; simulation depths of CENTURY and RothC were very close (20 cm), and for the rest of the models with deeper simulation depths (e.g. Forest-DNDC 50 cm, Q and Yasso07 1 m) it was assumed that the SOC amounts in deeper soil layers were negligible. This assumption was supported by the SOC profiles of the Forest-DNDC and SoilCO2/RothC that estimated very little SOC in the layers below 30 cm of the mineral soil. 2.4. Input data for model runs

2.3. Models The comparison included five soil-only models (Q, ROMUL, RothC, SoilCO2/ RothC and Yasso07) that describe the decomposition of the litter and soil organic matter and three plantesoil models (CENTURY, CoupModel and Forest-DNDC) that include also plant growth processes and their interactions with soil. Details of the eight models can be obtained from the web pages or the main references gathered in Table 2. All the models are able to simulate decomposition of soil organic matter of forests even though models like CENTURY and RothC have originally been developed for other land-use types. The models can be and have been applied in forest C balance studies. Examples of model applications for European forests are provided in Table 2. Models vary in relation to the processes involved and the level of description of the decomposition processes. CENTURY, CoupModel and Forest-DNDC are all detailed ecosystem models and they involve substantially more processes than the soil-only models. All models describe the decomposition with multiple litter and soil organic matter pools (See e.g. Table 3 in Peltoniemi et al., 2007). These pools usually apply first-order decomposition with rate constants that may depend on some environmental factors like temperature. Separate soil microbial biomass pools are included only in CoupModel, but the decomposition is controlled by the soil microbial

The time resolution of the forcing weather data used by the models varied from daily to yearly. All models required data on air temperature and, apart from Q, precipitation (Table 3). Data on other climate variables, such as global radiation or relative humidity, were only used by CoupModel, SoilCO2/RothC or ROMUL. CENTURY and Forest-DNDC had internal sub-routines that calculated these additional weather variables based on other inputs. CENTURY, CoupModel, Forest-DNDC, SoilCO2/RothC and ROMUL simulate soil temperature (Table 3) and soil moisture (Table 4). N deposition data were used by the models that have a N cycle as well as a C cycle; CoupModel, Forest-DNDC and CENTURY. As some models needed input information that was not available as primary data, external models/tools were used. For example, potential evapotranspiration (Table 3) and soil water content (Table 4) for SoilCO2/RothC and ROMUL were calculated separately. Soil input data requirements (Table 4) varied among the models from Q and Yasso07, which did not need any soil information, to SoilCO2/ RothC requiring detailed inputs and Forest-DNDC, CENTURY and CoupModel covering detailed processes within them. Soil-only models require litter estimates as input information whereas plantesoil models calculate the litter input based on their plant growth processes. In our simulations, biomass data of the study site was used to estimate the litter input for the soil-only models (see Section 2.2) and as a base for calibration of the

Table 2 Model web-address (if available), references to papers with model descriptions and application or evaluation studies for European forest soils, model version applied in this study and references to basic parameters applied within this study. Model

Web page

Model descriptions

Applications or evaluations

Version applied

References for the basic parameters

CENTURY

http://www.nrel.colostate.edu /projects/century

(Parton et al., 1987, 1994)

CENTURY 4.0

defaults within the version 4.0

CoupModel

(Jansson and Karlberg, 2004)

Version 3.2 14 Dec. 2009

(Svensson et al., 2008 and unpublished data)

Forest-DNDC

http://www.lwr.kth.se /Vara%20Datorprogram /CoupModel http://www.dndc.sr.unh.edu

(Smith et al., 1997; Kelly et al., 1997; Levy et al., 2005) (Svensson et al., 2008)

Q

Not available

(Kurbatova et al., 2008; Kesik et al., 2006) (Ågren et al., 2007)

(Li et al., 2000, Zhang et al., 2002) (Ågren et al., 2007)

ROMUL

http://ecomodelling.ru /index.php/ru/models

Forest-DNDC version dated 03.02.2010 (Ågren et al., 2007; Hyvönen and Ågren, 2001) (Mäkipää et al., 2011)

RothC

http://www.rothamsted. bbsrc.ac.uk/aen/carbon /download.htm http://www2.fz-juelich.de /icg/icg-4/index.php?index¼608

RothC 26.3.

(Jenkinson and Rayner, 1977, 1992) (Herbst et al., 2008; Jenkinson and Rayner, 1977; Jenkinson et al., 1992) (Statistics Finland, 2010)

SoilCO2/RothC

Yasso07

http://www.environment.fi /syke/yasso

(Li et al., 1992a, b, 2000; Li, 2000) (Ågren et al., 2007; Rolff and Ågren, 1999) (Chertov et al., 2001)

(Coleman and Jenkinson, 1996)

(Chertov et al., 2002; Nadporozhskaya et al., 2006; Mäkipää et al., 2011) (Smith et al., 2006, 1997; Coleman et al., 1997)

(Herbst et al., 2008)

(Herbst et al., 2008)

RothC 26.3 SoilCO2 version dated 17.06.2009

(Tuomi et al., 2008, 2009, 2011)

(Repo et al., 2011; Ortiz et al., 2009)

Version dated 15.05.2009

(Chertov et al., 2007)

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

41

Table 3 Temporal resolution and the climate variables applied by the models and data sources used in this model comparison. Normal text style indicates input information that was directly used by the model, NR (not relevant) indicating that the variable was not applied at all by the model. Italics text style indicates information that was used in some additional model/tool so that these tools then provided the necessary model input. Grey text indicates if the parameter was not used as input, but was simulated by the model. CENTURY

CoupModel

Forest-DNDC

SoilCO2/RothC

ROMUL

RothC

Q

Yasso07

Time-step Air temperature (Ta)

Month Max and min monthly

Day Mean daily

Day Mean daily

Day Daily values used to estimate Ts (outside ROMUL)

Month Mean monthly

Year Mean annual

Year Mean annual with amplitude

Soil temperature (Ts)

Calculated from Ta and plant cover

Calculated from mean daily Ta

NR

NR

Monthly total

Monthly total

NR

Annual total

Relative humidity

NR

Mean daily

Estimated within the model

Estimated from Ta with an empirical relationship fitted to a Finnish data set (unpublished) Daily values used in the soil water model (Mäkipää et al., 2011) to calculate the soil moisture NR

NR

Precipitation

Simulated from Ta, global radiation, plant cover, and snow -and soil thermal properties Daily total

Day Mean daily used to compute PET outside the model and used as upper boundary condition for soil heat flux Simulated using mean daily Ta as upper boundary condition for heat conduction equation Daily total (used as upper boundary condition for soil water flux)

NR

NR

NR

Wind speed

NR

Mean daily

NR

NR

NR

NR

NR

Global radiation

NR

Mean daily

Estimated from geographical co-ordinates

NR

NR

NR

Potential evapotranspiration (PET)

Calculated internally using the equations developed by Linacre (1977) Data provided

Calculated internally using the PenmaneMonteith equation (1965)

Calculated internally using the PriestleyeTaylor approach (1972)

Estimated with a PenmaneMonteith approach (1965) for grass

Mean daily used in the soil water model to calculate the soil moisture Evapo-transpiration calculated from the meteorological data in soil water model

NR

NR

NR

Data provided

Default assumed for the region

NR

NR

NR

NR

NR

Nitrogen deposition

Daily total

plantesoil models (see Section 2.5). All models consider litter quality in some way; as e.g. C:N ratios or lignin content. As this information was unavailable for the studied site, default or literature values were applied (Table 5). For example, Yasso07 needs quite detailed information of the quality of the C compounds of the initial litter and those values were taken from the data annex of the Yasso07 model (Liski et al., 2009). For the Q simulations parameterization of initial litter qualities were taken from earlier applications (Ågren et al., 2007). 2.5. Parameters and site-specific calibration of the models Parameters related to biomass and litter production in plantesoil models were calibrated with the data from the study site. The exact data applied and parameters tuned varied among the models. A simplified site-specific calibration was performed with CoupModel adopting the methodology in Svensson et al. (2008), by tuning N availability for the growing trees in order to match the measured standing biomass 2008 with simulated standing biomass. With CENTURY the site-specific calibration was coarser following the recommendations by Parton et al. (1992) by manually tuning plant productivity, litter fall and plant allocation parameters. The starting point was parameters for an American spruce forest (Metherell et al., 1993). ForestDNDC parameters, mainly photosynthesis coefficients, were re-calibrated starting from the default values provided for the spruce sites in Germany and Russia. All other parameters, such as decomposition rates, needed for the models were taken from earlier, geographically and physiologically most comparable applications of the models (Table 2). 2.6. Initialization of the models The initial SOC stocks and distribution into pools of soil-only models were determined by assuming steady-state between the litter input and decomposition using spin-up runs or available analytical calculation routines of the models. Q, RothC, ROMUL and Yasso07 applied the litter input of the year 1994 (the first year of biomass information) and averaged climate for 1990e2008 at the Murau study site. For the initialization of the C pools in SOILCO2/RothC a spin-up period of 400 years was used starting from the Bornim model runs (Herbst et al., 2008). The distribution of each pool over the soil profile was calculated from the given C stocks.

Mean daily used to compute PET outside the model Mean daily used to compute PET outside the model Mean daily used to compute PET outside the model

A pre-run period of two consecutive 120-year rotations was applied both with CoupModel and CENTURY. Rotations assumed newly planted trees at the start and a forest management with one cleaning and two thinnings before ending with a clear-cut. Initial SOC and N pools for the pre-run period for CoupModel were taken from soil measurements in 2008 and for CENTURY from an example conifer site provided with the model. CoupModel applied the climate data provided, repeating it to cover the entire pre-run period. CENTURY applied an internal stochastic weather generator using measured climatic data to obtain the required climate parameters for the period. Forest-DNDC was initialized with default values based on information on forest type, stand age, soil fertility and latitude. Initialization of the litter pulse decomposition simulations of soil-only models was straightforward to implement by applying the provided litter pulse as the initial state, i.e. soil organic matter pools describing the older material were set to zero. The amount and quality of the litter pulse approximately equalled an average annual litter-fall at the Murau study site during the 18-year simulation period. The fractions of each litter component in the pulse are reported in Table 6. In plantesoil models, interactions between the decomposition processes and plant growth processes affect the decomposition e.g. via organic material and mineral N content of the soil. The CoupModel applied the same approach with the soil-only models, but for CENTURY and Forest-DNDC the pulse was simulated by running the model for Murau with and without this additional input for 100 years. Initialization procedures described above were applied to get the background forest C stock. The difference of SOC stocks between these two simulations was then taken as the pulse response comparable to decomposition dynamics of the soil-only models.

3. Results 3.1. Tree biomass C stock According to field measurements and biomass functions, the tree biomass C stock at Murau study site increased by 18% from 157 Mg C ha1 in 1994 to 185 Mg C ha1 in 2004 (Fig. 1). The simulated tree biomass C of CoupModel and Forest-DNDC matched

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T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

Table 4 Soil property parameters applied by the models and data sources used in this model comparison. Normal text style indicates input information that was directly used by the model, NR (not relevant) indicating that the variable was not applied at all by the model. Italics text style indicates information that was used in some additional model/tool so that these tools then provided the necessary model input. Grey text indicates if the parameter was not used as input, but was simulated by the model. Columns for Q and Yasso07 are not shown as for them all cells are NR. CENTURY

CoupModel

Forest-DNDC

SoilCO2/RothC

ROMUL

RothC

Soil moisture

Calculated internally for soil layers with a simplified water budget model

Estimated within the model (Zhang et al., 2002)

Simulated in the model using Richards equation (Richards, 1931)

Daily estimates calculated based on a bucket model (Duursma, 2005)

NR

Soil texture

Sand, silt and clay content from the data provided

Simulated based on a soil water balance (prec., evapotrans, drain) and Richards eq. (Richards, 1931) Clay, silt, sand and coarse fraction from the data provided

Sand, silt and clay content from the data provided

NR

Clay content from the data provided

Bulk density

Data provided

Data provided

Data provided

NR

NR

Water holding capacity Saturated water content

Calculated internally based on soil texture Calculated internally based on soil texture

Estimated internally with PTF Estimated internally with PTF

Estimated within the model Estimated within the model

Clay, silt, sand and coarse fraction as input for PTFa to estimate soil hydraulic properties Used as input for PTF to estimate soil hydraulic properties NR

NR

Residual water content Saturated hydraulic conductivity Organic layer depth

NR

Estimated internally with PTF Estimated internally with PTF Data provided, used in calculation of soil heat flows

Estimated within the model Default values provided in the model Estimated within the model based on forest type, age, latitude and soil fertility

Estimated externally with PTF Estimated externally with PTF NR

Input for soil water model Rough assumption applied in soil water model NR NR

NR

NR

NR

Total SOC used for the pre-run

Estimated within the model based on forest type, age, latitude and soil fertility Estimated within the model based on forest type, age, latitude and soil fertility Data provided

Depth-specifically used as initial pools for the spin-up run

NR

NR

NR

Data provided

NR

NR

NR

NR

Soil C content

NR Litter layer on top of soil has no specific depth, amount of material depends on input and decomposition NR

Soil N content

Data provided or result of spin up run

Data provided used for soil C:N ratio

pH

NR

NR

a

Estimated externally with PTF

NR

NR

PTF ¼ pedotransfer function.

well with the field data after calibration. CoupModel estimated an increase from 181 to 192 Mg C ha1 or 6% and Forest-DNDC an increase from 165 to 197 Mg C ha1, 4%. With CENTURY the tree biomass C estimates were clearly higher and increased 6% from 259 to 276 Mg C ha1. 3.2. Litter production Annual litter production estimates that were based on tree measurements, biomass functions and biomass turnover rates were on average 2% of the tree biomass C and increased from 4.00 Mg C ha1 year1 in 1990 to 4.43 Mg C ha1 year1 in 2008. Natural mortality induced litter peaks of 6.27 Mg C ha1 for year 1997 and 4.49 Mg C ha1 year1 for year 2002 (Fig. 2a). In spite of the overestimation of the tree biomass C stock by CENTURY the litter estimates of CENTURY (annual mean 5.40 Mg C ha1 year1) were closer to those derived from biomass measurements (annual mean 4.33 Mg C ha1 year1) than the litter estimates of CoupModel (annual mean 7.14 Mg C ha1 year1). Within CoupModel the magnitude of litter production was on average 4% of the tree biomass C stock with small increasing trend along the studied period. Both CENTURY and Forest-DNDC (annual mean 4.09 Mg C ha1 year1) estimated the litter production to be on average 2% of the tree biomass C with a small increasing trend. Intra-annual litter production patterns of the models varied considerably (Fig. 2b and c). Above-ground litter production estimates based on tree measurements, biomass functions and biomass turnover rates for

years after the last tree biomass measurements (2004) was 1.85 Mg C ha1 year1. That was slightly smaller than the litter measured with litter traps at the study site which varied annually from 1.29 to 3.3 Mg C ha1 year1 with average of 2.29 in years 2005e2008 (data not shown). Mean above-ground litter fall estimate of CoupModel for the period was 3.81 Mg C ha1 year1 with annual variation smaller than 0.05 Mg C ha1 year1. 3.3. SOC stock Measured C concentration in the organic layer increased from 400 to 498 mg C g1 from year 1990 to year 2008 (Fig. 3a). However, the SOC stock of the organic layer decreased by 73% from 58.0 to 15.6 Mg C ha1 (Fig. 3b), due to the decreased organic layer depth. The coefficients of variation of concentrations (n ¼ 4) for different layers for 2008 varied between 5 and 60% (5% in the organic layer, 60e40e60% in the three mineral soil layers). The SOC stock in the top 10 cm mineral soil layer was increasing by 115% from 14.2 to 30.5 Mg C ha1 and SOC stocks of the two following mineral soil layers, 10 cm depth each, were quite stable. The total SOC stock in the organic layer and the uppermost 30 cm mineral soil layer decreased by 21% from 126.5 to 99.7 Mg C ha1 (Fig. 3b). Differences among the model-estimated SOC stocks were generally greater than the stock changes projected by the models over the studied period (Fig. 4). The initial level of the total SOC estimated by the models for the year 1990 ranged from 80 Mg C ha1 for ROMUL to 130 Mg C ha1 for Yasso07. The coefficient of variation (CV) of the initial SOC estimates was 17%. Dispersion of the model

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Table 5 Plant and litter information applied by the models and data sources used in this model comparison. Normal text style indicates input information that was directly used by the model, NR (not relevant) indicating that the variable was not applied at all by the model. Grey text indicates if the parameter was not used as input, but was simulated by the model.

Standing biomass

Root depth

Stand age

CENTURY

CoupModel

Forest-DNDC

SoilCO2/RothC

Estimated within the model. Data applied for calibration Default value (spruce forest from US) Data provided used in spin up

Estimated within the model. Data applied for calibration Default value

Estimated within the model. Data applied for calibration Default value

Data applied for Data applied for litter input litter input estimation estimation

Data applied for Data applied for litter input litter input estimation estimation

Data applied for litter input estimation

NR

NR

NR

NR

Data provided used for calibration Assumed good practice

Data provided used for calibration Provided as input data

Used site data on root density and depth NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

Simulated

Simulated

Total monthly litter input

Litter amounts in fractions; needle, branches, stems, coarse roots, fine roots

Total monthly litter input

Litter amounts in fractions; non-woody, fine woody and coarse woody litter

Simulated C:N ratios

Simulated C:N ratios

RothC default DPM/RPM ratioa of the forest litter applied

N and ash content of fresh litter; per fraction, values taken from literature (Finnish stands)

RothC default DPM/RPM ratioa of the forest litter applied

Annual litter amounts in fractions; needle, branches, stems, understory, coarse roots, fine roots NR

Forest Data provided management as well as good practice assumed Litter amounts Simulated

Litter quality

RothC

Q

Yasso07

Literature values for ethanol solubles, water solubles, acid solubles and insolubles; per fraction and diameter estimate of the woody litter

DPM/RPM ratio ¼ ratio of decomposable and resistant material in RothC model.

results decreased slightly during the simulation period; CV in the end of simulation period (year 2008) was 15%. Yasso07 and Q model applied with annual time steps did not produce the intra-annual variation of the SOC stocks. ROMUL, RothC and SoilCO2/RothC gave similar intra-annual variation as they were run with the same litter input. CENTURY, CoupModel and Forest-DNDC followed their litter input dynamics with their SOC stock dynamics. SOC stock estimates of soil-only models responded to the higher litter input in 1997. Stock change estimates from 1990 to 2008 of most models were quite small except for DNDC, which produced an increase of 0.89 Mg C ha1 year1 during the simulation period (Fig. 5). According the mean model prediction (the average of eight), the soil was a sink of about 0.16 Mg C ha1 year1. The range of stock change estimates varied from 0.06 to 0.89 Mg C ha1 year1 indicating the magnitude of the uncertainty of model results. 3.4. Decomposition dynamics The range of simulated C remaining estimates for a litter pulse after 10, 20, 50 and 100 years were 21e43% (CV 22%), 11e29% (CV Table 6 The quality distribution of a pulse litter input provided for the models to study decomposition dynamics (Fig. 6). Input calculated as an average of annual litter inputs of the Murau study site from 1990 to 2008. Litter cohort

Share (%)

Needles Branches Fine roots Coarse roots Stems

29 8 35 15 13

31%), 3e14% (CV 47%) and 1e7% (CV 55%), respectively (Fig. 6) indicating great differences in decomposition dynamics driven by structures and decomposition parameters of models. Yasso07 gave the slowest (average C remaining value over the 100-year period

300 250

Tree biomass C [Mg ha−1]

a

Lignin content and lignin:N ratio of plant material. In this case values calibrated for American spruce forest

ROMUL

200 150 100 Data and biomass functions CoupModel CENTURY Forest−DNDC

50 0 1990

1995

2000 Year

2005

Fig. 1. Tree biomass C stocks of Murau study site from 1990 to 2008 as projected by plantesoil models CoupModel, Forest-DNDC and CENTURY and as estimated based on tree measurements and local biomass functions.

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T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

b

8

Litter C [Mg ha−1 month−1]

Litter C [Mg ha−1 year−1]

a

6

4

2

0

2.5 2.0 1.5 1.0 0.5 0.0

1990

1995

2000

2005

Year

0

5

10

15

20

25

30

Month

c

Litter C [Mg ha−1 month−1]

2.5

Data and empirical models CoupModel CENTURY Forest−DNDC

2.0 1.5 1.0 0.5 0.0 1990

1995

2000

2005

Year Fig. 2. Litter C input to soil annually (a), monthly for first 30 months (b) and during the whole period 1990e2008 (c) in Murau study site as projected by CoupModel, Forest-DNDC and CENTURY and as estimated based on tree measurements, biomass functions and biomass turnover rates.

20.5%) and Forest-DNDC the fastest (average C remaining 9.1%) decomposition dynamics for the conditions of Murau study site. ROMUL simulations in the first 20 years resulted in the slowest decomposition, and later Yasso07 provided highest C remaining values. During the first 10 years, plantesoil models decomposed the litter faster than the soil-only models, but later the differences between the groups levelled off. Forest-DNDC and CENTURY gave the lowest C remaining estimates throughout the 100-year simulation period, whereas CoupModel provided first low and later high C remaining estimates among the models. 4. Discussion Our simulation results showed a wide range of SOC stock (Fig. 4) and stock change (Fig. 5) estimates reflecting substantial uncertainties in model estimates. According to the results, the soil could either be a C source or a sink (Fig. 5). However, all models provided relatively conservative SOC stock change estimates (0.06e0.88 Mg C ha1 year1), clearly smaller values than the measured SOC stock decrease of 1.49 Mg C ha1 year1 (Fig. 3b). During the studied period the forest stand was at a mature state

and steadily growing with only minor natural mortality occurring. There was no such driving information that would have led models to predict large losses of SOC during the simulation. The extreme SOC stock change estimates were obtained with Forest-DNDC (greatest SOC sink) and CoupModel (greatest SOC source) and for both models the main reason for the results is most likely their initialization process, which made their SOC stocks to converge towards new equilibrium. The steady development of SOC stocks estimated by the models is well in line with what has been reported for such mature forest stands e.g. in forest chronosequence studies in the Alps (Thuille and Schulze, 2006) or other regions (e.g. Sun et al., 2004; Peltoniemi et al., 2004). Why then the soil measurements showed the decrease in the SOC of the organic layer? It is possible that the measurements are indicating existing processes not taken into consideration in the models. Such could be, for example, the small-scale lateral flux of organic matter on the steep slope. But there are also clear weaknesses in the soil sampling. The soil measurements of the Murau site have been used in the BioSoil project launched 2006 (http:// forest.jrc.ec.europa.eu/contracts/biosoil) that aimed to harmonize and evaluate monitoring methods at the European scale (Cools and

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45

Fig. 3. Measured C concentrations (with std (n ¼ 4) for year 2008) (a) and SOC stocks (b) in different soil layers in years 1990 and 2008.

De Vos, 2010). Currently, all European countries are a part of this scheme. The soil sampling (minimum 3 composite samples per study site) within the scheme was not originally optimized to assess the SOC stock changes at forest stand level, as we have used the data in this work, but to get representative national estimates of chemical properties of forest soils. But also at national level, the variation in the C stock change estimates stemming from such a sampling design has proved to be considerable (e.g. Ortiz et al., 2011). High spatial variability of chemical parameters of forest soils (e.g. Hammer et al., 1987) requires a large number of samples per site. For example, Conen et al. (2003) estimated that 200 samples would be enough to reliably detect climate-driven SOC changes through replicated sampling within two or three decades. Muukkonen et al. (2009), on the other hand, reported that an optimal sample size to

estimate the C stock of the organic layer of boreal coniferous forests is 20e30 samples per site. In this study, archived samples from 1990 were re-analyzed in 2006 by carefully reassessing all steps in the sampling and analysis procedure. This reanalysis confirmed that the poor sampling design that did not account for the high inherent spatial variability of soil properties was the main reason for the observed differences in the measured C stocks. Thus, it was not possible to use the available soil measurements to critically evaluate model results. However, set of simulation results provides a range of plausible values based on the scientific understanding synthesized by the models’ system descriptions and parameters. It is therefore reasonable to use of models in inventory type of purposes instead of insufficient or expensive measurements. It should be noted, however, that for the

SOC change [Mg C ha-1 year-1]

150

-0.2

0

0.2

0.4

0.6

0.8

CENTURY SOC [Mg ha−1]

100

CoupModel Forest-DNDC Q

50

RothC SoilCO2/RothC Yasso07 Q CoupModel

0 1990

1995

2000

ROMUL CENTURY Forest−DNDC Measured

2005

2010

ROMUL RothC SOILCO2/RothC

Year

Yasso07 Fig. 4. SOC stock dynamics in the organic layer and the uppermost 30 cm mineral soil layer of the Murau study site from 1990 to 2008 projected with different models and the measured SOC stocks in years 1990 and 2008.

Fig. 5. Average annual SOC change projected with different models.

1

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T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

Fig. 6. Decomposition dynamics of one typical annual litter fall predicted by different models in Murau study site within a 100-year time period (a) and 10-year time period (b). Weather information applied is from the Murau study site repeatedly used from year 1999.

development, parameterization and testing of models, reliable and comprehensive measured information at the stand level from different kind of ecosystems and climatic conditions is necessary. 4.1. Uncertainties in SOC simulations The range of simulation results detected within this study should not be interpreted as a general uncertainty range for SOC models in coniferous forests. The range of model results is very case-specific and dependent on the applied models, data availability and decisions and assumptions made during the modelling processes. Instead, our study clearly demonstrates the various sources of uncertainty related to SOC assessments applying data of similar or coarser level of detail than applied here. With this in view, we discuss in the following, the sources of uncertainty in our model comparison, and how they affect the applicability of different models or modelling approaches. Similarly with a recent comparison of crop growth models (Palosuo et al., 2011), we follow here Walker et al. (2003), who listed the sources of uncertainty in model results as those related to 1) system boundaries, 2) model structure, 3) input data and 4) model parameters. In addition to above-mentioned modelrelated sources of uncertainty, there are inherent uncertainties related to 5) model user, since model outcome is a result not only from the model structure, parameters and additional modelling tools applied, but also from a number of assumptions needed to run the model, i.e. the whole modelling process. 4.1.1. System boundaries In this study, the historical development of the SOC stock of a forest stand was simulated with models that differed in their definition of the system boundaries, i.e. which variables and processes are included within the model and which are taken as external factors. The most notable difference in system boundaries was between the soil-only and plantesoil models. Covering the soileplant interactions would be particularly relevant for the scenario-type of studies, where the simulations go beyond current conditions (Korzukhin et al., 1996). Here, however, the simpler approach, where soil-only models were provided with the empirically-based litter information, was sufficient and even beneficial for the historical C assessment study as inventory-based biomass data was easily available and considered reliable. The depths of soil layer involved in the model estimation of SOC stock varied among the models. Q and Yasso07 are calibrated to

simulate 1 m soil layer, which partly explains their higher SOC stock estimates compared to the estimates of the other models (Fig. 4) even though majority of the SOC of spruce forests has been reported to be in the uppermost layers (e.g. Rumpel et al., 2002). For the SOC stock change estimates the simulation depth is less important, since the uppermost layers contribute the most to the short-term changes (Gaudinski et al., 2000). 4.1.2. Model structure As all models are simplifications of reality, the model structure uncertainty, which is related to lack of understanding or oversimplification of the described processes, is largely unavoidable (Refsgaard et al., 2006). Even though our range of model results also covers this source of uncertainty, we cannot exactly identify the effects of the various model structures on the simulated results, since they are also affected by other uncertainty sources such as model parameters. The analyses of the decomposition curves (Fig. 6) allow the closest investigation of differences among model structures and their parameters, because the input provided and the model initializations were almost similar. Decomposition curves show that the models clearly differ with respect to their simulated decomposition dynamics with coefficient of variation increasing along the time. 4.1.3. Input data Variable sets of input data (Tables 3e5) also included inaccuracies. They involve both the measurement errors and the uncertainty due to applicability of the data, e.g. representativeness of the weather station and homogeneity of the soil properties within the forest stand. In our case, the representativeness of the weather data was assured as the climate data were measured at the studied forest stand. For example, Scherrer and Koerner (2010) reported that climatic conditions of alpine landscape cannot reliably be inferred from climate station data. Even though the microclimatic conditions within forests are more stable than in open landscapes (Chen et al., 1993), they are surely also affected by microtopography. Daily time resolution applied in CoupModel, Forest-DNDC, SoilCO2/ RothC and ROMUL implied an increased workload regarding securing availability and quality of the weather data. The daily weather data series had some missing values that required gapfilling. It can be assumed that models with detailed input requirements were more affected by these uncertainties than the models that only used limited set of data.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49

Uncertainties related to above-ground biomass estimates based on measured data and biomass functions are generally considered small, particularly when local or national biomass functions, such as used here, are available. Estimation of root biomass is much more uncertain (Levy et al., 2004). Biomass turnover rates of different biomass compartments are very uncertain and again knowledge about biomass turnover rates of coarse and fine roots and factors affecting them are still largely unknown (Yuan and Chen, 2010). Biomass turnover rates applied in this study were mainly based on Finnish studies (see Section 2.2.2) and their applicability is also uncertain. The approach with constant biomass turnover rates also excludes the inter-annual variability in litter input (Muukkonen and Lehtonen, 2004) that according the litter trap measurements from 2004 to 2008 was considerable. The errors related to litter estimates of plantesoil models depend on the calibration, but these estimates can be considered very uncertain too. 4.1.4. Parameterisation Parameter uncertainty refers to uncertainty in constants applied within the model. In this study, we did not perform any site-specific calibration for the soil-only models or soil modules of the plantesoil models. The parameters applied here (Table 2) were assumed to be directly applicable to conditions of Murau. This assumption naturally poses uncertainty to our model results, which depends both on the applicability affected by geographic closeness, similarity of conditions and quality of the initial data source. Due to the lack of suitable experimental data from the investigated forest stand, we were not able to assess these uncertainties. Their importance for the simulated SOC results is, however, high. Some parameters related to plant growth within plantesoil models, Forest-DNDC, CoupModel and CENTURY, were modified applying local biomass data. However, this tuning of the parameter values was not performed in its full extent (as can be seen for CENTURY in Fig. 1) and as limited data were available only a limited number of parameters was altered to obtain a reasonable fit between the observed and modelled biomass data. Therefore our model results in Murau represent the situation of a typical widescale application without site or region specific calibration. 4.1.5. Model user and modelling process Model user needs to do various decisions and assumptions along the modelling process. As individual models were set up by different model users, this modelling exercise can be seen as an example of the consequences stemming from the user’s own discretion in the modelling process. For example, our results show the importance of model initialization for SOC assessments. The initialization procedures applied among the models were not fully identical and it is likely that the initialization assumptions and decisions influenced the model results. Extreme SOC stock change estimates were obtained with Forest-DNDC (greatest SOC sink) and CoupModel (greatest SOC source) and both models did not apply the steady-state assumption in their initialization process. ForestDNDC was initialized with the default values provided within the model and CoupModel was initialized applying the measured stock value together with a pre-run period. Particularly the initialization method of Forest-DNDC provided initial SOC stock that would not been achieved by running the model with local input data and this led to simulation with high gain in SOC. The steady-state assumption with the litter input data of the first year of simulation (applied to most of the models) may also lead to an overestimation of the C stock at the start of simulation (Wutzler and Reichstein, 2006). This may result in too conservative simulated stock changes. Our recommendation is to use steady-state assumption in combination with a pre-run period of minimum 10 years whenever there is enough data to do so.

47

To be able to make reasonable assumptions within crossing demands of both reliable modelling results and limited practical resources, the model user should have in-depth knowledge of both ecosystem processes and the limitations and sensitivities of his modelling tool. To support the model user, the user-interfaces of the models should be developed in such a way that they support the proper use of models making all necessary steps and assumptions transparent for the model user. Also, the model developers should carefully document their models and basic assumptions with practical examples. All this would decrease the risk of model misuse in applications. 4.2. Perspectives on model selection The above-mentioned sources of uncertainties in model results are common for all models, also beyond the models used in this study. However, the extent of their effects on simulated results and how easily the uncertainties can be assessed vary among models. When considering the use of these models in large-scale applications, for example in national greenhouse gas inventories, the management of uncertainties speaks in favour of relatively simple modelling approaches. At national scale, the Murau study site represents a forest stand very well supplied with data. Still not all information needed by the models (Tables 3e5) was available, forcing model users to apply information from other sites and regions. That points to a clear advantage of modelling approaches with low data requirements. According to this study, soil-only models have their benefits for inventory-type of purposes in being able to apply inventory-based biomass and litter information, which is easily available and generally considered reliable. Also Smith et al. (1997) in their wide model comparison with different LUC types concluded that the coupling of soil and physiologically based plant growth sub-models introduces errors and uncertainty to coupled model systems and simplified approaches may be more beneficial. On the other hand, plantesoil models provide wider range of information than soil-only models and contain more thorough description of relevant processes. That makes them stronger for scenario-type studies, such as those related to climate change, where simulations go beyond current conditions. Model selection should therefore be made case-specific, carefully considering whether the model covers processes important for the studied question at relevant scale and what data actually are available as input. Even though in our study the models showed the soil to be either a C sink or a source, it is mainly related to this particular simulation case of a steadily growing forest stand where changes in SOC stock during the simulation period can be assumed to be small. Our results do not mean that by model selection the countries could turn their soils from sources to sinks or vice versa. Instead, this study has proved that it is very important that the modelling processes within the national inventories are transparently reported and special emphasis is put on how the models are used, which assumptions are applied and what is the quality of data used both as input and to calibrate the models. Particularly the initialization of the models is a question of high importance when simulating the dynamics of SOC stocks, as demonstrated in this study. 5. Conclusions We concluded that there is a high uncertainty related to C stock assessment with models. Still, applying models instead of insufficient and expensive measurements is reasonable. Soil-only models that are able to apply inventory-based biomass and litter information can be seen as suitable tools for inventory-type of purposes. Relatively simple modelling approaches with low input data requirements support the reporting work also by allowing relatively easy assessment and management of uncertainties.

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Model uncertainties can be reduced when applying models with precise input data, using comprehensive datasets for calibrations and carefully considering and implementing all necessary steps in modelling process. When possible, multi-model estimates with the uncertainty ranges got with multi-model runs could provide more firm information on SOC dynamics than estimates received with singular models. Our findings emphasize the importance of measured information in support of modelling. Further development and evaluation of soil models can only come true hand in hand with the accumulation of measured data that allows accurate calibration of models and their critical testing. Acknowledgements This study was initiated in a modelling workshop of COST639 in Vienna, Austria, in May 2009 and discussions were continued in Vilnius in September 2009. T. Palosuo was funded through the Agriyasso Research Project funded by the Ministry of Agriculture and Forestry of Finland and by MTT strategic funds of the IAM-Tools and MITAG projects. We wish to thank Reimund Rötter and three anonymous reviewers for their insightful and constructive comments on the manuscript. References Ågren, G.I., Hyvönen, R., Nilsson, T., 2007. Are Swedish forest soils sinks or sources for CO2dmodel analyses based on forest inventory data. Biogeochemistry 82, 217e227. Anonymous, 2000. Hydrographisches Jahrbuch von Österreich. Hydrological Yearbook of Austria 1997. (In German). Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Vienna, Austria, 453 pp. Bellamy, P.H., Loveland, P.J., Bradley, R.I., Lark, R.M., Kirk, G.J.D., 2005. Carbon losses from all soils across England and Wales 1978e2003. Nature 437, 245e248. Blum, W.E.H., Englisch, M., Freudenschuß, A., Nelhiebel, P., Pock, H., Schneider, W., Schwarz, S., Wagner, J., Wandl, M., 2005. Soil survey and soil data in Austria. In: Jones, R.J.A., Housková, B., Bullock, P., Montanarella, L. (Eds.), Soil Resources of Europe, second ed. European Soil Bureau Research Report No. 9, EUR 20559 EN. Office for Official Publications of the European Communities, Luxembourg, pp. 47e61. Chatfield, C., 1995. Model uncertainty, data mining and statistical-inference. J. Roy. Stat. Soc. A Sta. 158, 419e466. Chen, J.Q., Franklin, J.F., Spies, T.A., 1993. Contrasting microclimates among clear-cut, edge, and interior of old-growth Douglas-Fir forest. Agric. For. Meteorol. 63, 219e237. Chertov, O.G., Komarov, A.S., Nadporozhskaya, M., Bykhovets, S.S., Zudin, S.L., 2001. ROMUL e a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modeling. Ecol. Model. 138, 289e308. Chertov, O.G., Komarov, A.S., Bykhovets, S.S., Kobak, K.I., 2002. Simulated soil organic matter dynamics in forests of the Leningrad administrative area, northwestern Russia. For. Ecol. Manage. 169, 29e44. Chertov, O.G., Bykhovets, S., Nadporozhskaya, M.A., Komarov, A., Larionova, A.A., 2007. Evaluation of the rates of transformation of soil organic matter in the ROMUL model. In: Kudeyarov, V.N. (Ed.), Modelling of Organic Matter Dynamics in Forest Ecosystems (in Russian). Moscow Hayka, Moscow, pp. 83e99. Coleman, K., Jenkinson, D.S., 1996. RothC-26.3-A Model for the turnover of carbon in soil. In: Powlson, D.S., Smith, P., Smith, J.U. (Eds.), Evaluation of Soil Organic Matter Models, Using Existing Long-Term Datasets. Springer-Verlag, Heidelberg, Germany, pp. 237e246. Coleman, K., Jenkinson, D.S., Crocker, G.J., Grace, P.R., Klir, J., Korschens, M., Poulton, P.R., Richter, D.D., 1997. Simulating trends in soil organic carbon in long-term experiments using RothC-26.3. Geoderma 81, 29e44. Conen, F., Yakutin, M.V., Sambuu, A.D., 2003. Potential for detecting changes in soil organic carbon concentrations resulting from climate change. Global Change Biol. 9, 1515e1520. Cools, N., De Vos, B., 2010. Sampling and analysis of soil. In: Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests. UNECE, ICP Forest, Hamburg, p. 208. Duursma, R.A., 2005. Equations for Water Balance Calculations in SPP. In: Forest Modelling Group Working Papers, 1. University of Helsinki, Department of Forest Ecology, Helsinki. Gaudinski, J.B., Trumbore, S.E., Davidson, E.A., Zheng, S.H., 2000. Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes. Biogeochemistry 51, 33e69. Hammer, R.D., O’Brien, R.G., Lewis, R.J., 1987. Temporal and spatial soil variability on

three forested land types on the mid-Cumberland plateau. Soil Sci. Soc. Am. J. 51, 1320e1326. Helmisaari, H., Derome, J., Nöjd, P., Kukkola, M., 2007. Fine root biomass in relation to site and stand characteristics in Norway spruce and Scots pine stands. Tree Physiol. 27, 1493e1504.   Herbst, M., Hellebrand, H.J., Bauer, J., Huisman, J.A., Sim unek, J., Weihermüller, L., Graf, A., Vanderborght, J., Vereecken, H., 2008. Multiyear heterotrophic soil respiration: evaluation of a coupled CO2 transport and carbon turnover model. Ecol. Model. 214, 271e283. Hyvönen, R., Ågren, G.I., 2001. Decomposer invasion rate, decomposer growth rate, and substrate chemical quality: how they influence soil organic matter turnover. Can. J. For. Res. 31, 1594e1601. IPCC, 2003. Good Practice Guidance for Land Use, Land-use Change and Forestry. Institute for Global Environmental Strategies (IGES), Japan. http://www.ipccnggip.iges.or.jp/public/gpglulucf/gpglulucf.html. 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