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Environmental Modelling & Software 35 (2012) 38e49

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Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

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

aMTT Agrifood Research Finland, Luutnantintie 13, 00410 Helsinki, FinlandbNational Soil Research Institute, Cranfield University, Cranfield, Bedford MK43 0AL, UKc Swedish University of Agricultural Sciences, Department of Soil and Environment, P.O. Box 7014, SE 750 07 Uppsala, SwedendUniversity of Eastern Finland, Department of Environmental Science, Yliopistonranta 1 E, 70210 Kuopio, Finlande Finnish Forest Research Institute, Vantaa Research Centre, PO Box 18, 01301 Vantaa, FinlandfAgrosphere, IBG-3, Research Centre Jülich GmbH, 52425 Jülich, GermanygUniversity of Natural Resources and Applied Life Sciences, Institute of Soil Research, Department of Forest and Soil Sciences, Peter Jordan Strasse 82, 1190 Vienna, AustriahVoke Branch of the Lithuanian Institute of Agriculture, Zalioji a.2, Traku Voke, LT-02232 Vilnius, Lithuaniai Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USAj Forest Research and Training Center for Forest, Natural Hazards and Landscape (BFW), Seckendorff Gudent Weg 8, A-1131 Vienna, Austria

a r t i c l e i n f o

Article history:Received 3 February 2011Received in revised form7 February 2012Accepted 9 February 2012Available online 8 March 2012

Keywords:Carbon balanceForestModel comparisonSoil carbonSimulation modelUncertainty

* Corresponding author. Tel.: þ358 40 186 9114; faE-mail address: taru.palosuo@mtt.fi (T. Palosuo).

1 Present address: University of Abertay, Kydd Buil

1364-8152/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.envsoft.2012.02.004

a b s t r a c t

We simulated soil carbon stock dynamics of an Austrian coniferous forest stand with five soil-onlymodels (Q, ROMUL, RothC, SoilCO2/RothC and Yasso07) and three plantesoil models (CENTURY, Coup-Model and Forest-DNDC) for an 18-year period and the decomposition of a litter pulse over a 100-yearperiod. The objectives of the study were to assess the consistency in soil carbon estimates applyinga multi-model comparison and to present and discuss the sources of uncertainties that create thedifferences in model results. Additionally, we discuss the applicability of different modelling approachesfrom the view point of large-scale carbon assessments.

Our simulation results showed a wide range in soil carbon stocks and stock change estimates reflectingsubstantial uncertainties in model estimates. The measured stock change estimate decreased much morethan the model predictions. Model results varied not only due to the model structure and appliedparameters, but also due to different input information and assumptions applied during the modellingprocesses. Initialization procedures applied with the models induced large differences among themodelled soil carbon stocks and stock change estimates. Decomposition estimates of the litter pulsedriven by model structures and parameters also varied considerably.

Our results support the use of relatively simple soil-only models with low data requirements ininventory type of large-scale carbon assessments. It is important that the modelling processes within thenational 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 themodels.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Soils hold the largest stock of terrestrial carbon (C) in thebiosphere with forest soils estimated to contain about half of thatstock (Jobbágy and Jackson, 2000). In recognition of the size of thesoil organic carbon (SOC) pool, countries are required to reportchanges in SOC and litter aswell as the corresponding uncertainty in

x: þ358 20 772 040.

ding, Dundee DD1 1HG, UK.

All rights reserved.

their national greenhouse gas inventory in compliance with the UNFramework Convention on Climate Change (UNFCCC, 1992) and theKyoto Protocol (UNFCCC, 1997). The Intergovernmental Panel onClimate Change (IPCC) guidelines for greenhouse gas inventoriesprovide a default methodology (Tier 1) for C accounting. The Tier 1assumption is that therewouldbenochanges in thenet SOC stockon“forest land remaining forest land” (IPCC, 2003). To apply thisassumption, countries need to prove that their forest soil is nota source of C. The results frommany studies (e.g. Bellamyet al., 2005)however, make it difficult to pursue the no-emission argument. Inaddition to that, countries should applymethodsof higher Tier levels

Table 1Basic stand characteristics of the Murau study site in the year 2001.

Species Number Basal area2 �1

Stem Basal area

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49 39

(measurements or models) if SOC stock change of forests is a key-category; this is relevant for countries with substantial forest areas.

Due to the high costs of soil sampling and chemical analysisneeded to report the SOC stock changes based on measurements(Conen et al., 2003; Mäkipää et al., 2008) models are widely used toestimate stock changes. Some countries, e.g. Canada, UK andFinland, 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 asthe most advanced way of reporting (Tier 3). Models are alsocommonly used in forest C assessment studies involving soil atstand (e.g. Svensson et al., 2008), regional (e.g. Schaldach andAlcamo, 2006), national (e.g. Liski et al., 2006; Ågren et al., 2007)and supra-national scales (e.g. Janssens et al., 2003). In addition toreporting and C assessment studies, models are also increasinglybeing used as decision support tools particularly on issues relatedto climate change (e.g. Smith et al., 2006). For the scenariopurposes, mechanistic, process-based approaches that can coverthe effects of changing environmental factors and management areseen more suitable than empirical, regression-type of modellingapproaches. Large-scale model applications, where the input dataare scarce and highly uncertain, are becoming more common.Therefore, robust models, which can apply basic stand and soil dataas their input information, are required.

Understanding the uncertainties related to model predictions isessential and those applying models within greenhouse gasinventories have an obligation to estimate and report the uncer-tainties related to their estimates using error propagation or MonteCarlo simulations (IPCC, 2003). Comparing outputs from differentmodels using a common input dataset is another effective way ofhighlighting the uncertainties associated with modellingapproaches. This idea is made use of, for example, in assessing thereliability of climate and general circulation models (Murphy et al.,2004). Range of model results in model comparison involves alsothe uncertainty related to model structure, which is the mostdifficult uncertainty source to quantify (Chatfield, 1995).

Models describing SOC dynamics have been reviewed in recentyears. For example, Peltoniemi et al. (2007) reviewed seven SOCmodels fromthepointof viewofpreparing country-scale SOCchangeestimates of forest soils for national greenhouse gas inventories.Smith et al. (1997) comparedperformanceof nine soil organicmattermodels with long-term experiments with differing land manage-ment, mainly grassland, arable cropping and woodland. After thismajormodel evaluation by Smith et al. (1997) formore than a decadeago, there has not been any model comparison involving the majorSOC models. In addition, the increased use of models also by othersthan original model developers has made the usability of modellingtools and their proper use in applications important.

In this study, we applied eight process-based soil simulationmodels on an intensively monitored Austrian forest stand. Weassessed the consistency of the models’ estimates for forest Cdynamics and compared model output with measured data. Theobjectives of the study were: 1) to assess the uncertainties relatedto SOC estimates applying a multi-model comparison, 2) to presentand discuss the sources of uncertainties that create the differencesin model results, and 3) to compare and discuss the applicability ofdifferent modelling approaches on large-scale C assessments.

of stems[number ha�1]

[m ha ] volume[m3 ha�1]

increment[m3 ha�1

year�1]

Norway spruce 560 35 393 0.43European larch 48 6.5 81 0.10Sum of living

trees andstandingdead logs

780 45 500 0.53

2. Material and methods

We simulated tree biomass C, litter production and SOC dynamics of an Austrianconiferous 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 to2008. Simulations were performed at stand level, which is the basic scale of forestmanagement at which most of the data are gathered. The soil layers covered were

the organic layer (F/H) and the uppermost 30 cm mineral soil layer. Model-estimated tree biomass C development, litter amounts, SOC stocks and stockchanges were compared to each other and to measurements. We also simulated thedecomposition of a litter pulse under the same site conditions over a 100-yearperiod to compare the simulated decomposition dynamics of the models. Therange of simulation results was taken as an indication of the uncertainties related tosimulations and the sources of uncertainties are discussed based on experiencesgathered during the modelling process.

2.1. Study site

The Murau site (47�0304300N, 14�0603600E, 1560 m a.s.l.) is located in the Provinceof Styria, Austria. The site belongs to the intensively monitored Level-II-sites in theICP Forests (the International Co-operative Programme on Assessment and Moni-toring of Air Pollution Effects on Forests) monitoring (Neumann et al., 2001) inAustria. The soil at the site is spodic Cambisol on bedrock comprised of gneiss andmica schists and the slope is north-facing with an inclination of 65%. The meanannual temperature is 5 �C with an annual precipitation of 918 mm (mean over30 years). The stand is dominated by Norway spruce (Picea abies (L.) Karst.). Thestand age was 125 years in 2001. Base stand data from year 2001 are reported inTable 1. The height of dominant trees was 29.3m and diameter at breast height (dbh)was 43.2 cm. Previously the site, as other forests of the area, has been a forestpasture with some litter raking. Only in the last 60 years pastures and forests werespatially separated.

2.2. Measured data

2.2.1. Tree biomass CIndividual tree measurements from one 0.25-ha plot were available from the

repeated assessments in 1994, 1999 and 2004. From each tree dbh was measuredand height was estimated from dbhwith a locally derived dbh-height function. Stemvolumes were calculated based on dbh, height and a shape function. We used thetaper function for Norway spruce of the Austrian National Forest Inventory (NFI).Needle biomass, coarse root biomass, stem biomass and branch biomass werecalculated for Norway spruce with biomass functions by Wirth et al. (2004), whilefor Larch (Larix decidua) Austrian biomass equations (Rubatscher et al., 2006) wereused. Biomass was estimated for standing stock and removed trees and the biomassestimates of removed trees were based on previous measurement. The biomass offine root (here roots less than 2mm in diameter) was estimatedwith a constant ratioto foliagemass (30%) based on the Finnish study by Helmisaari et al. (2007). Biomassestimates were converted to C by multiplying by factor 0.5.

2.2.2. Litter productionLitter 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 turnoverrates were obtained from Liski et al. (2006), with the major data source for Norwayspruce taken from Muukkonen and Lehtonen (2004). For larch, same biomassturnover rates were used as for Norway spruce, with the exception of foliage forwhich the biomass turnover rate was 100%. Calculated above-ground litter fromtrees were compared with litter collected at the Murau site from 2005 to 2008 withsix funnels, each 100 cm diameter. Ground vegetation was neglected from theanalysis. Intra-annual pattern of litter fall for the models that required monthly ordaily input was taken from Jenkinson and Coleman (1994).

There was some natural mortality at the study site, but no forest managementactivities such as thinning were carried out during the studied period. It wasassumed that this natural mortality occurred in the middle of each measurementperiod, i.e. in years 1997 and 2002. It was also assumed that all dead tree biomasswas left on the site.

2.2.3. SoilSoil 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

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e4940

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 fromdepths 0e5, 5e10, 10e20, 20e40 and were also analyzed separately. Due to the highrock content, no undisturbed soil cores were collected. Each sample was oven dried,and the C and nitrogen (N) contents of four subsamples were determined witha 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 soilanalysis and were assumed to stay constant. The SOC pool was calculated as

SOC ¼ Fvol*Ccon*r; (1)

where Fvol is the volume of fine-earth (<2.0 mm), Ccon is C concentration [mg g�1]and r is the bulk density calculated from a pedotransfer functionr ¼ 1:2935� 0:0085*Ccon þ 0:001*silt½%� þ 0:0034*clay½%� derived from the Aus-trian soil database BORIS (Blum et al., 2005). In order to harmonize the informationfrom different protocols, we estimated C, rock content, silt, clay, and r for each cm ofthe soil profile by an equal-area spline method (Ponce-Hernandez et al., 1986).

2.2.4. Meteorological dataMeteorological data originated from measurements on the Level-II Plot Murau

(Neumann et al., 2001) and were verified with the records of the Austrian Hydro-logical Survey (Anonymous, 2000). Air temperature and precipitation data wereavailable in daily resolution. Any data gaps in the period 1990e2008 were filledusing data from 2006 as this year was complete and themeanmonthly values of thisyear were representative of those for the 1990e2008 period. For the litter pulseanalysis weather data were repeated to cover the whole 100-year period.

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 organicmatter and three plantesoil models (CENTURY, CoupModel and Forest-DNDC) thatinclude also plant growth processes and their interactions with soil. Details of theeight models can be obtained from theweb pages or themain references gathered inTable 2.

All the models are able to simulate decomposition of soil organic matter offorests even thoughmodels like CENTURYand RothC have originally been developedfor other land-use types. The models can be and have been applied in forest Cbalance studies. Examples of model applications for European forests are providedin Table 2. Models vary in relation to the processes involved and the level ofdescription of the decomposition processes. CENTURY, CoupModel and Forest-DNDCare all detailed ecosystem models and they involve substantially more processesthan the soil-only models.

All models describe the decomposition with multiple litter and soil organicmatter pools (See e.g. Table 3 in Peltoniemi et al., 2007). These pools usually applyfirst-order decomposition with rate constants that may depend on some environ-mental factors like temperature. Separate soil microbial biomass pools are includedonly in CoupModel, but the decomposition is controlled by the soil microbial

Table 2Model web-address (if available), references to papers with model descriptions and applstudy and references to basic parameters applied within this study.

Model Web page Model descriptions Appeva

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

(Parton et al., 1987, 1994) (SmKellLev

CoupModel http://www.lwr.kth.se/Vara%20Datorprogram/CoupModel

(Jansson and Karlberg,2004)

(Sve

Forest-DNDC http://www.dndc.sr.unh.edu (Li et al., 1992a, b, 2000;Li, 2000)

(KuKes

Q Not available (Ågren et al., 2007; Rolffand Ågren, 1999)

(Åg

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

(Chertov et al., 2001) (ChNadet aMäk

RothC http://www.rothamsted.bbsrc.ac.uk/aen/carbon/download.htm

(Coleman and Jenkinson,1996)

(SmCole

SoilCO2/RothC http://www2.fz-juelich.de/icg/icg-4/index.php?index¼608

(Herbst et al., 2008) (He

Yasso07 http://www.environment.fi/syke/yasso

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

(RepOrti

biomass also in the Q-model, which is based on quality theory and in which thedecomposition is implicitly driven by microbial activity or biomass.

Decomposition processes in all models are affected by temperature, and bymoisture in other models but the Q-model. Soil texture effects are included invariable ways to all models except Yasso07 inwhich the decomposition is unaffectedby parent soil material. In the Q-model texture is only indirectly covered via somesite-specific parameters. Plantesoil models (CoupModel, Forest-DNDC andCENTURY) and ROMUL include separate N models that are dynamically coupled toSOC dynamics. SoilCO2/RothC, on the other hand, couples detailed water, heat andCO2 transport model with the RothCmodel. It has been developed for the predictionof soil heterotrophic respiration with daily time step, but it can also be used formodelling SOC dynamics over longer time scales. Yasso07 was particularly devel-oped for large-scale SOC assessments and special emphasis in model developmentwas put on the comprehensiveness of data applied in model calibration and on theparameter uncertainty estimates provided by the model.

CoupModel was the onlymodel that was adjusted to include exactly the depth ofthe measured soil layers (i.e. organic and the uppermost 30 cmmineral soil layer) inthe estimation of SOC stock. For the other models it was assumed that their SOCestimates were comparable to the measured values as such; simulation depths ofCENTURY and RothC were very close (20 cm), and for the rest of the models withdeeper simulation depths (e.g. Forest-DNDC 50 cm, Q and Yasso07 1 m) it wasassumed that the SOC amounts in deeper soil layers were negligible. This assump-tion was supported by the SOC profiles of the Forest-DNDC and SoilCO2/RothC thatestimated very little SOC in the layers below 30 cm of the mineral soil.

2.4. Input data for model runs

The time resolution of the forcing weather data used by the models varied fromdaily 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 orrelative humidity, were only used by CoupModel, SoilCO2/RothC or ROMUL.CENTURY and Forest-DNDC had internal sub-routines that calculated these addi-tional 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 asa C cycle; CoupModel, Forest-DNDC and CENTURY.

As some models needed input information that was not available as primarydata, external models/tools were used. For example, potential evapotranspiration(Table 3) and soil water content (Table 4) for SoilCO2/RothC and ROMUL werecalculated separately. Soil input data requirements (Table 4) varied among themodels from Q and Yasso07, which did not need any soil information, to SoilCO2/RothC requiring detailed inputs and Forest-DNDC, CENTURY and CoupModelcovering detailed processes within them.

Soil-only models require litter estimates as input information whereasplantesoil models calculate the litter input based on their plant growth processes. Inour simulations, biomass data of the study site was used to estimate the litter inputfor the soil-only models (see Section 2.2) and as a base for calibration of the

ication or evaluation studies for European forest soils, model version applied in this

lications orluations

Version applied References for the basicparameters

ith et al., 1997;y et al., 1997;y et al., 2005)

CENTURY 4.0 defaults within theversion 4.0

nsson et al., 2008) Version 3.2 14 Dec. 2009 (Svensson et al., 2008 andunpublished data)

rbatova et al., 2008;ik et al., 2006)

Forest-DNDC version dated03.02.2010

(Li et al., 2000, Zhanget al., 2002)

ren et al., 2007) (Ågren et al., 2007;Hyvönen and Ågren, 2001)

(Ågren et al., 2007)

ertov et al., 2002;porozhskayal., 2006;ipää et al., 2011)

(Mäkipää et al., 2011) (Chertov et al., 2007)

ith et al., 2006, 1997;man et al., 1997)

RothC 26.3. (Jenkinson and Rayner,1977, 1992)

rbst et al., 2008) RothC 26.3SoilCO2 version dated17.06.2009

(Herbst et al., 2008;Jenkinson andRayner, 1977; Jenkinsonet al., 1992)

o et al., 2011;z et al., 2009)

Version dated 15.05.2009 (Statistics Finland, 2010)

Table 3Temporal resolution and the climate variables applied by the models and data sources used in this model comparison. Normal text style indicates input information that wasdirectly 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 someadditional 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 Month Day Day Day Day Month Year YearAir temperature (Ta) Max and min

monthlyMean daily Mean daily Mean daily used

to compute PEToutside the modeland used as upperboundary conditionfor soil heat flux

Daily valuesused to estimateTs (outside ROMUL)

Meanmonthly

Meanannual

Meanannualwithamplitude

Soil temperature (Ts) Calculatedfrom Ta andplant cover

Simulated from Ta,global radiation,plant cover, andsnow -and soilthermal properties

Calculated frommean daily Ta

Simulated usingmean daily Ta asupper boundarycondition for heatconduction equation

Estimated fromTa with an empiricalrelationship fittedto a Finnish dataset (unpublished)

NR NR NR

Precipitation Monthly total Daily total Daily total Daily total (usedas upper boundarycondition for soilwater flux)

Daily values usedin the soil watermodel (Mäkipääet al., 2011) tocalculate thesoil moisture

Monthlytotal

NR Annualtotal

Relative humidity NR Mean daily Estimated withinthe model

Mean daily usedto compute PEToutside the model

NR NR NR NR

Wind speed NR Mean daily NR Mean daily used tocompute PET outsidethe model

NR NR NR NR

Global radiation NR Mean daily Estimated fromgeographicalco-ordinates

Mean daily used tocompute PET outsidethe model

Mean daily usedin the soil watermodel to calculatethe soil moisture

NR NR NR

Potentialevapotranspiration(PET)

Calculatedinternallyusing theequationsdeveloped byLinacre (1977)

Calculated internallyusing thePenmaneMonteithequation (1965)

Calculatedinternallyusing thePriestleyeTaylorapproach (1972)

Estimated with aPenmaneMonteithapproach (1965)for grass

Evapo-transpirationcalculated from themeteorologicaldata in soilwater model

NR NR NR

Nitrogen deposition Data provided Data provided Default assumedfor the region

NR NR NR NR NR

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49 41

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 thestudied site, default or literature values were applied (Table 5). For example, Yasso07needs quite detailed information of the quality of the C compounds of the initiallitter and those values were taken from the data annex of the Yasso07 model (Liskiet al., 2009). For the Q simulations parameterization of initial litter qualities weretaken 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 werecalibrated with the data from the study site. The exact data applied and parameterstuned varied among themodels. A simplified site-specific calibrationwas performedwith CoupModel adopting the methodology in Svensson et al. (2008), by tuning Navailability for the growing trees in order to match the measured standing biomass2008 with simulated standing biomass. With CENTURY the site-specific calibrationwas coarser following the recommendations by Parton et al. (1992) by manuallytuning plant productivity, litter fall and plant allocation parameters. The startingpoint was parameters for an American spruce forest (Metherell et al., 1993). Forest-DNDC parameters, mainly photosynthesis coefficients, were re-calibrated startingfrom the default values provided for the spruce sites in Germany and Russia. Allother parameters, such as decomposition rates, needed for the models were takenfrom earlier, geographically and physiologically most comparable applications of themodels (Table 2).

2.6. Initialization of the models

The initial SOC stocks and distribution into pools of soil-only models weredetermined by assuming steady-state between the litter input and decompositionusing 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 ofbiomass 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 yearswas used starting from the Bornimmodel runs (Herbst et al., 2008). The distributionof each pool over the soil profile was calculated from the given C stocks.

A pre-run period of two consecutive 120-year rotations was applied both withCoupModel and CENTURY. Rotations assumed newly planted trees at the start anda forest management with one cleaning and two thinnings before ending witha clear-cut. Initial SOC and N pools for the pre-run period for CoupModel were takenfrom soil measurements in 2008 and for CENTURY from an example conifer siteprovided with themodel. CoupModel applied the climate data provided, repeating itto cover the entire pre-run period. CENTURY applied an internal stochastic weathergenerator using measured climatic data to obtain the required climate parametersfor the period. Forest-DNDCwas initialized with default values based on informationon forest type, stand age, soil fertility and latitude.

Initialization of the litter pulse decomposition simulations of soil-only modelswas straightforward to implement by applying the provided litter pulse as the initialstate, i.e. soil organic matter pools describing the older material were set to zero. Theamount and quality of the litter pulse approximately equalled an average annuallitter-fall at the Murau study site during the 18-year simulation period. The fractionsof each litter component in the pulse are reported in Table 6. In plantesoil models,interactions between the decomposition processes and plant growth processesaffect 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 forCENTURY and Forest-DNDC the pulse was simulated by running the model forMurauwith andwithout this additional input for 100 years. Initialization proceduresdescribed above were applied to get the background forest C stock. The difference ofSOC stocks between these two simulations was then taken as the pulse responsecomparable to decomposition dynamics of the soil-only models.

3. Results

3.1. Tree biomass C stock

According to field measurements and biomass functions, thetree biomass C stock at Murau study site increased by 18% from157 Mg C ha�1 in 1994 to 185 Mg C ha�1 in 2004 (Fig. 1). Thesimulated tree biomass C of CoupModel and Forest-DNDC matched

Table 4Soil 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 themodel, 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 sothat 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 andYasso07 are not shown as for them all cells are NR.

CENTURY CoupModel Forest-DNDC SoilCO2/RothC ROMUL RothC

Soil moisture Calculated internallyfor soil layers witha simplified waterbudget model

Simulated based ona soil water balance(prec., evapotrans,drain) and Richards eq.(Richards, 1931)

Estimated withinthe model(Zhang et al., 2002)

Simulated in themodel using Richardsequation(Richards, 1931)

Daily estimatescalculated basedon a bucket model(Duursma, 2005)

NR

Soil texture Sand, silt and claycontent from thedata provided

Clay, silt, sand andcoarse fraction fromthe data provided

Sand, silt and claycontent from thedata provided

Clay, silt, sand andcoarse fraction as inputfor PTFa to estimatesoil hydraulic properties

NR Clay contentfrom the dataprovided

Bulk density Data provided Data provided Data provided Used as input forPTF to estimate soilhydraulic properties

NR NR

Water holdingcapacity

Calculated internallybased on soil texture

Estimated internallywith PTF

Estimated withinthe model

NR Input for soilwater model

NR

Saturated watercontent

Calculated internallybased on soil texture

Estimated internallywith PTF

Estimated withinthe model

Estimated externallywith PTF

Rough assumptionapplied in soilwater model

NR

Residual watercontent

NR Estimated internallywith PTF

Estimated withinthe model

Estimated externallywith PTF

NR NR

Saturated hydraulicconductivity

NR Estimated internallywith PTF

Default valuesprovided in the model

Estimated externallywith PTF

NR NR

Organic layerdepth

Litter layer on topof soil has no specificdepth, amount ofmaterial depends oninput and decomposition

Data provided, usedin calculation of soilheat flows

Estimated within themodel based on foresttype, age, latitudeand soil fertility

NR NR NR

Soil C content NR Total SOC used forthe pre-run

Estimated within themodel based on foresttype, age, latitudeand soil fertility

Depth-specificallyused as initial poolsfor the spin-up run

NR NR

Soil N content Data provided orresult of spin up run

Data provided usedfor soil C:N ratio

Estimated within themodel based on foresttype, age, latitudeand soil fertility

NR Data provided NR

pH NR NR Data provided NR NR NR

a PTF ¼ pedotransfer function.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e4942

well with the field data after calibration. CoupModel estimated anincrease from 181 to 192 Mg C ha�1 or 6% and Forest-DNDC anincrease from 165 to 197 Mg C ha�1, 4%. With CENTURY the treebiomass C estimates were clearly higher and increased 6% from 259to 276 Mg C ha�1.

3.2. Litter production

Annual litter production estimates that were based on treemeasurements, biomass functions and biomass turnover rates wereon average 2% of the tree biomass C and increased from4.00 Mg C ha�1 year�1 in 1990 to 4.43 Mg C ha�1 year�1 in 2008.Natural mortality induced litter peaks of 6.27 Mg C ha�1 for year1997 and 4.49 Mg C ha�1 year�1 for year 2002 (Fig. 2a). In spite ofthe overestimation of the tree biomass C stock by CENTURY thelitter estimates of CENTURY (annual mean 5.40 Mg C ha�1 year�1)were closer to those derived from biomass measurements (annualmean 4.33 Mg C ha�1 year�1) than the litter estimates of Coup-Model (annual mean 7.14 Mg C ha�1 year�1). Within CoupModelthe magnitude of litter production was on average 4% of the treebiomass C stock with small increasing trend along the studiedperiod. Both CENTURY and Forest-DNDC (annual mean4.09 Mg C ha�1 year�1) estimated the litter production to be onaverage 2% of the tree biomass C with a small increasing trend.Intra-annual litter production patterns of the models variedconsiderably (Fig. 2b and c).

Above-ground litter production estimates based on treemeasurements, biomass functions and biomass turnover rates for

years after the last tree biomass measurements (2004) was1.85 Mg C ha�1 year�1. That was slightly smaller than the littermeasured with litter traps at the study site which varied annuallyfrom 1.29 to 3.3 Mg C ha�1 year�1 with average of 2.29 in years2005e2008 (data not shown). Mean above-ground litter fall esti-mate of CoupModel for the period was 3.81 Mg C ha�1 year�1 withannual variation smaller than 0.05 Mg C ha�1 year�1.

3.3. SOC stock

Measured C concentration in the organic layer increased from400 to 498mg C g�1 fromyear 1990 to year 2008 (Fig. 3a). However,the SOC stock of the organic layer decreased by 73% from 58.0 to15.6 Mg C ha�1 (Fig. 3b), due to the decreased organic layer depth.The coefficients of variation of concentrations (n ¼ 4) for differentlayers for 2008 varied between 5 and 60% (5% in the organic layer,60e40e60% in the three mineral soil layers). The SOC stock in thetop 10 cm mineral soil layer was increasing by 115% from 14.2 to30.5 Mg C ha�1 and SOC stocks of the two following mineral soillayers, 10 cm depth each, were quite stable. The total SOC stock inthe organic layer and the uppermost 30 cm mineral soil layerdecreased by 21% from 126.5 to 99.7 Mg C ha�1 (Fig. 3b).

Differences among the model-estimated SOC stocks weregenerally greater than the stock changes projected by the modelsover the studied period (Fig. 4). The initial level of the total SOCestimated by themodels for the year 1990 ranged from80MgCha�1

for ROMUL to 130Mg C ha�1 for Yasso07. The coefficient of variation(CV) of the initial SOC estimates was 17%. Dispersion of the model

Table 5Plant 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 themodel, 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 themodel.

CENTURY CoupModel Forest-DNDC SoilCO2/RothC ROMUL RothC Q Yasso07

Standingbiomass

Estimated withinthe model.

Estimated withinthe model.

Estimated withinthe model.

Data applied forlitter inputestimation

Data applied forlitter inputestimation

Data applied forlitter inputestimation

Data applied forlitter inputestimation

Data applied forlitter inputestimationData applied for

calibrationData applied forcalibration

Data applied forcalibration

Root depth Default value(spruce forestfrom US)

Default value Default value Used site dataon root densityand depth

NR NR NR NR

Stand age Data providedused in spin up

Data providedused forcalibration

Data providedused forcalibration

NR NR NR NR NR

Forestmanagement

Data providedas well asgood practiceassumed

Assumed goodpractice

Provided asinput data

NR NR NR NR NR

Litter amounts Simulated Simulated Simulated Total monthlylitter input

Litter amountsin fractions;needle, branches,stems, coarseroots, fine roots

Total monthlylitter input

Annual litteramounts infractions;needle, branches,stems, understory,coarse roots, fineroots

Litter amountsin fractions;non-woody,fine woodyand coarsewoody litter

Litter quality Lignin contentand lignin:Nratio of plantmaterial. Inthis casevaluescalibrated forAmericanspruce forest

SimulatedC:N ratios

SimulatedC:N ratios

RothC defaultDPM/RPM ratioa

of the forestlitter applied

N and ash contentof fresh litter;per fraction,values taken fromliterature (Finnishstands)

RothC defaultDPM/RPMratioa

of the forestlitter applied

NR Literaturevalues forethanolsolubles,water solubles,acid solublesand insolubles;per fractionand diameterestimate of thewoody litter

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

100

150

200

250

300

Tree

bio

mas

s C

[Mg

ha−1

]

Data and biomass functions

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49 43

results decreased slightly during the simulation period; CV in theend of simulation period (year 2008) was 15%. Yasso07 and Qmodelapplied with annual time steps did not produce the intra-annualvariation of the SOC stocks. ROMUL, RothC and SoilCO2/RothCgave similar intra-annual variation as they were run with the samelitter input. CENTURY, CoupModel and Forest-DNDC followed theirlitter input dynamics with their SOC stock dynamics. SOC stockestimates of soil-onlymodels responded to the higher litter input in1997. Stock change estimates from 1990 to 2008 of most modelswere quite small except for DNDC, which produced an increase of0.89 Mg C ha�1 year�1 during the simulation period (Fig. 5).According themeanmodel prediction (the average of eight), the soilwas a sink of about 0.16Mg C ha�1 year�1. The range of stock changeestimates varied from�0.06 to 0.89Mg C ha�1 year�1 indicating themagnitude of the uncertainty of model results.

3.4. Decomposition dynamics

The range of simulated C remaining estimates for a litter pulseafter 10, 20, 50 and 100 years were 21e43% (CV 22%), 11e29% (CV

Table 6The quality distribution of a pulse litter input providedfor the models to study decomposition dynamics(Fig. 6). Input calculated as an average of annual litterinputs of the Murau study site from 1990 to 2008.

Litter cohort Share (%)

Needles 29Branches 8Fine roots 35Coarse roots 15Stems 13

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

1990 1995 2000 2005

0

50

Year

CoupModelCENTURYForest−DNDC

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

1990 1995 2000 2005

0

2

4

6

8

Year

Litte

r C [M

g h

a−1 y

ear−1

]

0 5 10 15 20 25 30

0.0

0.5

1.0

1.5

2.0

2.5

Month

Litte

r C [M

g h

a−1 m

onth

−1]

1990 1995 2000 2005

0.0

0.5

1.0

1.5

2.0

2.5

Year

Litte

r C [

Mg

ha−1

mon

th−1

]

Data and empirical modelsCoupModelCENTURYForest−DNDC

ab

c

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-DNDCand CENTURY and as estimated based on tree measurements, biomass functions and biomass turnover rates.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e4944

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 slowestdecomposition, and later Yasso07 provided highest C remainingvalues. During the first 10 years, plantesoil models decomposedthe litter faster than the soil-only models, but later the differencesbetween the groups levelled off. Forest-DNDC and CENTURY gavethe lowest C remaining estimates throughout the 100-year simu-lation period, whereas CoupModel provided first low and later highC remaining estimates among the models.

4. Discussion

Our simulation results showed awide range of SOC stock (Fig. 4)and stock change (Fig. 5) estimates reflecting substantial uncer-tainties in model estimates. According to the results, the soil couldeither be a C source or a sink (Fig. 5). However, all models providedrelatively conservative SOC stock change estimates(�0.06e0.88 Mg C ha�1 year�1), clearly smaller values than themeasured SOC stock decrease of 1.49 Mg C ha�1 year�1 (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 modelsto predict large losses of SOC during the simulation. The extremeSOC stock change estimates were obtained with Forest-DNDC(greatest SOC sink) and CoupModel (greatest SOC source) and forboth models the main reason for the results is most likely theirinitialization process, which made their SOC stocks to convergetowards new equilibrium. The steady development of SOC stocksestimated by themodels is well in linewithwhat has been reportedfor such mature forest stands e.g. in forest chronosequence studiesin the Alps (Thuille and Schulze, 2006) or other regions (e.g. Sunet al., 2004; Peltoniemi et al., 2004).

Why then the soil measurements showed the decrease in theSOC of the organic layer? It is possible that the measurements areindicating existing processes not taken into consideration in themodels. Such could be, for example, the small-scale lateral flux oforganic matter on the steep slope. But there are also clear weak-nesses in the soil sampling. The soil measurements of the Murausite have been used in the BioSoil project launched 2006 (http://forest.jrc.ec.europa.eu/contracts/biosoil) that aimed to harmonizeand evaluate monitoring methods at the European scale (Cools and

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.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49 45

De Vos, 2010). Currently, all European countries are a part of thisscheme. The soil sampling (minimum 3 composite samples perstudy site) within the schemewas not originally optimized to assessthe SOC stock changes at forest stand level, as we have used the datain this work, but to get representative national estimates of chem-ical properties of forest soils. But also at national level, the variationin the C stock change estimates stemming from such a samplingdesign has proved to be considerable (e.g. Ortiz et al., 2011). Highspatial variability of chemical parameters of forest soils (e.g.Hammer et al.,1987) requires a large number of samples per site. Forexample, Conen et al. (2003) estimated that 200 samples would beenough to reliably detect climate-driven SOC changes throughreplicated sampling within two or three decades. Muukkonen et al.(2009), on the other hand, reported that an optimal sample size to

1990 1995 2000 2005 2010

0

50

100

150

Year

SOC

[Mg

ha−1

]

RothCSoilCO2/RothCYasso07QCoupModel

ROMULCENTURYForest−DNDCMeasured

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

estimate the C stock of the organic layer of boreal coniferous forestsis 20e30 samples per site. In this study, archived samples from1990were re-analyzed in 2006 by carefully reassessing all steps in thesampling and analysis procedure. This reanalysis confirmed that thepoor sampling design that did not account for the high inherentspatial variability of soil properties was the main reason for theobserved differences in the measured C stocks.

Thus, it was not possible to use the available soil measurementsto critically evaluate model results. However, set of simulationresults provides a range of plausible values based on the scientificunderstanding synthesized by the models’ system descriptionsand parameters. It is therefore reasonable to use of models ininventory type of purposes instead of insufficient or expensivemeasurements. It should be noted, however, that for the

-0.2 0 0.2 0.4 0.6 0.8 1

CENTURY

CoupModel

Forest-DNDC

Q

ROMUL

RothC

SOILCO2/RothC

Yasso07

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

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

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.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e4946

development, parameterization and testing of models, reliable andcomprehensive measured information at the stand level fromdifferent kind of ecosystems and climatic conditions is necessary.

4.1. Uncertainties in SOC simulations

The range of simulation results detectedwithin this study shouldnot be interpreted as a general uncertainty range for SOC models inconiferous forests. The range of model results is very case-specificand dependent on the applied models, data availability and deci-sions and assumptions made during the modelling processes.Instead, our study clearly demonstrates the various sources ofuncertainty related to SOC assessments applying data of similar orcoarser level of detail than appliedhere.With this inview,wediscussin the following, the sources of uncertainty inourmodel comparison,and how they affect the applicability of different models or model-ling approaches. Similarly with a recent comparison of crop growthmodels (Palosuo et al., 2011), we follow here Walker et al. (2003),who listed the sources of uncertainty in model results as thoserelated to 1) system boundaries, 2) model structure, 3) input dataand 4) model parameters. In addition to above-mentioned model-related sources of uncertainty, there are inherent uncertaintiesrelated to 5) model user, since model outcome is a result not onlyfrom the model structure, parameters and additional modellingtools applied, but also from a number of assumptions needed to runthe model, i.e. the whole modelling process.

4.1.1. System boundariesIn this study, the historical development of the SOC stock of

a forest stand was simulated with models that differed in theirdefinition of the system boundaries, i.e. which variables andprocesses are included within the model and which are taken asexternal factors. The most notable difference in system boundarieswas between the soil-only and plantesoil models. Covering thesoileplant interactions would be particularly relevant for thescenario-type of studies, where the simulations go beyond currentconditions (Korzukhin et al., 1996). Here, however, the simplerapproach, where soil-only models were provided with theempirically-based litter information, was sufficient and evenbeneficial for the historical C assessment study as inventory-basedbiomass data was easily available and considered reliable.

The depths of soil layer involved in the model estimation of SOCstock varied among the models. Q and Yasso07 are calibrated to

simulate 1m soil layer, which partly explains their higher SOC stockestimates compared to the estimates of the other models (Fig. 4)even though majority of the SOC of spruce forests has beenreported to be in the uppermost layers (e.g. Rumpel et al., 2002).For the SOC stock change estimates the simulation depth is lessimportant, since the uppermost layers contribute the most to theshort-term changes (Gaudinski et al., 2000).

4.1.2. Model structureAs all models are simplifications of reality, the model structure

uncertainty, which is related to lack of understanding or over-simplification of the described processes, is largely unavoidable(Refsgaard et al., 2006). Even though our range of model results alsocovers this source of uncertainty, we cannot exactly identify theeffects of the various model structures on the simulated results,since they are also affected by other uncertainty sources such asmodel parameters. The analyses of the decomposition curves(Fig. 6) allow the closest investigation of differences among modelstructures and their parameters, because the input provided andthe model initializations were almost similar. Decompositioncurves show that the models clearly differ with respect to theirsimulated decomposition dynamics with coefficient of variationincreasing along the time.

4.1.3. Input dataVariable sets of input data (Tables 3e5) also included inaccur-

acies. They involve both the measurement errors and the uncer-tainty due to applicability of the data, e.g. representativeness of theweather station and homogeneity of the soil properties within theforest stand. In our case, the representativeness of the weather datawas assured as the climate data were measured at the studiedforest stand. For example, Scherrer and Koerner (2010) reportedthat climatic conditions of alpine landscape cannot reliably beinferred from climate station data. Even though the microclimaticconditions within forests are more stable than in open landscapes(Chen et al., 1993), they are surely also affected bymicrotopography.Daily time resolution applied in CoupModel, Forest-DNDC, SoilCO2/RothC and ROMUL implied an increased workload regardingsecuring availability and quality of the weather data. The dailyweather data series had some missing values that required gap-filling. It can be assumed that models with detailed input require-ments were more affected by these uncertainties than the modelsthat only used limited set of data.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e49 47

Uncertainties related to above-ground biomass estimates basedon measured data and biomass functions are generally consideredsmall, particularly when local or national biomass functions, suchas used here, are available. Estimation of root biomass is muchmoreuncertain (Levy et al., 2004). Biomass turnover rates of differentbiomass compartments are very uncertain and again knowledgeabout biomass turnover rates of coarse and fine roots and factorsaffecting them are still largely unknown (Yuan and Chen, 2010).Biomass turnover rates applied in this study were mainly based onFinnish studies (see Section 2.2.2) and their applicability is alsouncertain. The approach with constant biomass turnover rates alsoexcludes the inter-annual variability in litter input (Muukkonenand Lehtonen, 2004) that according the litter trap measurementsfrom 2004 to 2008 was considerable. The errors related to litterestimates of plantesoil models depend on the calibration, but theseestimates can be considered very uncertain too.

4.1.4. ParameterisationParameter uncertainty refers to uncertainty in constants applied

within themodel. In this study, we did not perform any site-specificcalibration for the soil-only models or soil modules of theplantesoil models. The parameters applied here (Table 2) wereassumed to be directly applicable to conditions of Murau. Thisassumption naturally poses uncertainty to ourmodel results, whichdepends both on the applicability affected by geographic closeness,similarity of conditions and quality of the initial data source. Due tothe lack of suitable experimental data from the investigated foreststand, we were not able to assess these uncertainties. Theirimportance for the simulated SOC results is, however, high.

Some parameters related to plant growth within plantesoilmodels, Forest-DNDC, CoupModel and CENTURY, were modifiedapplying local biomass data. However, this tuning of the parametervalues was not performed in its full extent (as can be seen forCENTURY in Fig. 1) and as limited data were available only a limitednumber of parameters was altered to obtain a reasonable fitbetween the observed and modelled biomass data. Therefore ourmodel results in Murau represent the situation of a typical wide-scale application without site or region specific calibration.

4.1.5. Model user and modelling processModel user needs to do various decisions and assumptions along

the modelling process. As individual models were set up bydifferent model users, this modelling exercise can be seen as anexample of the consequences stemming from the user’s owndiscretion in the modelling process. For example, our results showthe importance of model initialization for SOC assessments. Theinitialization procedures applied among the models were not fullyidentical and it is likely that the initialization assumptions anddecisions influenced the model results. Extreme SOC stock changeestimates were obtained with Forest-DNDC (greatest SOC sink) andCoupModel (greatest SOC source) and both models did not applythe steady-state assumption in their initialization process. Forest-DNDC was initialized with the default values provided within themodel and CoupModel was initialized applying the measured stockvalue together with a pre-run period. Particularly the initializationmethod of Forest-DNDC provided initial SOC stock that would notbeen achieved by running the model with local input data and thisled to simulation with high gain in SOC. The steady-state assump-tionwith the litter input data of the first year of simulation (appliedto most of the models) may also lead to an overestimation of the Cstock at the start of simulation (Wutzler and Reichstein, 2006). Thismay result in too conservative simulated stock changes. Ourrecommendation is to use steady-state assumption in combinationwith a pre-run period of minimum 10 years whenever there isenough data to do so.

To be able to make reasonable assumptions within crossingdemands of both reliable modelling results and limited practicalresources, the model user should have in-depth knowledge of bothecosystem processes and the limitations and sensitivities of hismodelling tool. To support the model user, the user-interfaces of themodels shouldbedeveloped insuchaway that theysupport theproperuse ofmodelsmaking all necessary steps and assumptions transparentfor the model user. Also, the model developers should carefully docu-ment their models and basic assumptions with practical examples. Allthis would decrease the risk of model misuse in applications.

4.2. Perspectives on model selection

The above-mentioned sources of uncertainties in model resultsare common for all models, also beyond the models used in thisstudy. However, the extent of their effects on simulated results andhow easily the uncertainties can be assessed vary among models.When considering the use of these models in large-scale applica-tions, for example in national greenhouse gas inventories, themanagement of uncertainties speaks in favour of relatively simplemodelling approaches. At national scale, the Murau study siterepresents a forest stand very well supplied with data. Still not allinformation needed by the models (Tables 3e5) was available,forcing model users to apply information from other sites andregions. That points to a clear advantage of modelling approacheswith low data requirements. According to this study, soil-onlymodels have their benefits for inventory-type of purposes in beingable to apply inventory-based biomass and litter information,whichis easily available and generally considered reliable. Also Smith et al.(1997) in their wide model comparison with different LUC typesconcluded that the coupling of soil and physiologically based plantgrowth sub-models introduces errors and uncertainty to coupledmodel systems and simplified approaches may be more beneficial.

On the other hand, plantesoil models provide wider range ofinformation than soil-only models and contain more thoroughdescription of relevant processes. That makes them stronger forscenario-type studies, such as those related to climate change,wheresimulations go beyond current conditions. Model selection shouldtherefore be made case-specific, carefully considering whether themodel covers processes important for the studied question at rele-vant scale and what data actually are available as input.

Even though in our study themodels showed the soil to be eithera C sink or a source, it is mainly related to this particular simulationcase of a steadily growing forest stand where changes in SOC stockduring the simulation period can be assumed to be small. Ourresults do not mean that by model selection the countries couldturn their soils from sources to sinks or vice versa. Instead, thisstudy has proved that it is very important that the modellingprocesses within the national inventories are transparently repor-ted and special emphasis is put on how the models are used, whichassumptions are applied andwhat is the quality of data used both asinput and to calibrate the models. Particularly the initialization ofthe models is a question of high importance when simulating thedynamics of SOC stocks, as demonstrated in this study.

5. Conclusions

We concluded that there is a high uncertainty related to C stockassessment with models. Still, applying models instead of insuffi-cient and expensive measurements is reasonable. Soil-only modelsthat are able to apply inventory-based biomass and litter infor-mation can be seen as suitable tools for inventory-type of purposes.Relatively simple modelling approaches with low input datarequirements support the reporting work also by allowing rela-tively easy assessment and management of uncertainties.

T. Palosuo et al. / Environmental Modelling & Software 35 (2012) 38e4948

Model uncertainties can be reducedwhen applyingmodels withprecise input data, using comprehensive datasets for calibrationsand carefully considering and implementing all necessary steps inmodelling process. When possible, multi-model estimates with theuncertainty ranges got with multi-model runs could provide morefirm information on SOC dynamics than estimates received withsingular models.

Our findings emphasize the importance of measured informa-tion in support of modelling. Further development and evaluationof soil models can only come true hand in hand with the accu-mulation of measured data that allows accurate calibration ofmodels and their critical testing.

Acknowledgements

This study was initiated in a modelling workshop of COST639 inVienna, Austria, in May 2009 and discussions were continued inVilnius in September 2009. T. Palosuo was funded through theAgriyasso Research Project funded by the Ministry of Agricultureand Forestry of Finland and byMTT strategic funds of the IAM-Toolsand MITAG projects. We wish to thank Reimund Rötter and threeanonymous reviewers for their insightful and constructivecomments on the manuscript.

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