Introduction
Mechanistic modelling is an increasingly popular method of tracking carbon fluxes in forest ecosystems, important for carbon balance studies, climate and pollution studies and others. Properly calibrated and validated, such models can also be used as diagnostic tools, to separate the influence of forest growth impactors such as climate change and Nitrogen deposition ([8]). Such models have however been sometimes criticised in the past for a poor representation of forest mass growth ([29], [26]). This paper presents part of a wider study that will compare aboveground biomass estimates obtained with the BIOMEBGC model ([33], [23]) with field data from the Austrian National Forest Inventory ([9]). We detail here the methodology developed to estimate forest management activities that may have occurred prior to the starting date of the available inventory data.
BIOMEBGC simulates fluxes and pools of carbon, water and nitrogen, beginning with a spinup run of many forest growth cycles, to estimate a natural forest stand for the site without human influence ([24]). We follow this with two cycles of clear cutting to represent early industrial forest use in Central Europe, with the final start date of the present forest established in accordance with the mean forest age for each site from the NFI data. In managed forests it is likely that some biomass removals have occurred in the current forest rotation, but if these were prior to the commencement of the inventory programme then the timing and extent of those thinnings is unknown. As forest management is one of the major influences on forest carbon fluxes and pools ([30], [11], [17]), this represents a potentially serious source of uncertainty or bias in model outputs ([38]). In a recent Europewide comparison of forest models, Tupek et al. ([34]) did not include management in their BGC simulation, yet recognise management as a major factor.
Direct sitebysite comparisons of model outputs with inventory measurements is difficult, due to the fundamentally different approaches to scale. Models assume homogeneity at the scale of their input data ([14]), where single sites are assumed to be representative of a broader area. Inventories on the other hand are a statistical measure, designed so that a large number of results are aggregated to give a mean figure for a region or strata. Although at a broad scale the results should be comparable, individual sitebysite comparisons are likely to have little meaning. In terms of using inventory data for model initialisation and validation, this means that some liberties must be taken with the strictly mechanistic operation of biogeochemical process models, as a large component of random error in the individual plot observations must be incorporated.
National forest inventories (NFIs) contain a vast body of information, however the use of this data for research purposes has to date been limited ([18]), and few biogeochemical analyses have been applied to inventory data ([27]). This is likely due to two complications, the first to do with the data needs of complex biogeochemical models and the second with the statistical nature of forest inventories. Comprehensive inventories such as the Austrian NFI ([9]), speciesspecific model parameterisation ([23]) and modern spatial interpolation methods ([21]) may fulfil most of the data needs, but the uncertainty of prior management activities remains.
Comparison of biogeochemical modelling and inventory results in managed forests requires solving two simultaneous problems: the statistical uncertainty of the inventory data at the plot scale and the lack of knowledge of historical management. Our approach in this paper is to assume that the errors in a sophisticated NFI and a mature, welltested model will both be normally distributed around zero, and thus, if assumptions of plot management history are correct, the sitebysite disagreements between the two approaches will also be normally distributed around zero.
Data and methods
The Austrian National Forest Inventory ([9]) is organized into tracts each of 4 points on a 200m square. 5600 such tracts are arranged in a square grid pattern across the country, including over areas that are not currently forested. An anglecount with a basal area factor of 4 is performed on each point, recording all trees of over 10.5cm dbh. A fixedarea plot of radius 2.60 metres is installed to record all trees of dbh between 5.0 and 10.5cm. Twenty percent of plots were measured each year, and remeasured after 5 years. Timber volumes are calculated here from measured diameters and heights using the allometric equations of Pollanschütz ([25]). Using one point from each forested tract, a total of 2224 points is obtained. We use here a subset of the database for the measurement periods 198185 and 198691, and limit the analysis to those plots with forest present in both periods, a species homogeneity of over 80 percent by basal area and less than 30 percent timber removal from the plot between the measurement periods, as our current version of BIOMEBGC has not been tested for mixedspecies forests and a major removal within the increment period could confuse the results. This limits our analysis to 1133 plots.
Separate species parameterisations are available for Norway spruce (Picea abies) depending on site elevation above or below 1000m asl ([23]), but as no parameters are available for Silver fir (Abies alba) these were modelled as low elevation spruce giving a total of 386 plots (354 spruce dominated, 32 fir) in this group. Some 439 plots were high elevation spruce, 80 Pinus (67 Scots pine Pinus sylvestris and 13 Pinus nigra), 47 Larch (Larix decidua), 28 combining Oak (Quercus robur/petraea) and Ash (Fraxinus excelsior), 146 beech (Fagus sylvatica) and other broadleaves and seven stone pine (Pinus cembra).
BIOMEBGC is a mechanistic model developed to simulate the ecosystem processes of a forest stand on a daily timestep. Storages and fluxes of water, carbon and nitrogen are tracked throughout various pools in the vegetation, litter, and soil. The model has been widely applied across a range of forested ecosystems around the world, has been extensively compared with flux measurements ([32]), and was specifically parameterized and validated for major central European species by Pietsch et al. ([23]). Norway spruce in particular has been widely studied with BIOMEBGC ([4], [15], [22], [12]), giving confidence that the model can accurately represent spruce growth across a range of different circumstances. Forest management operations were included in BIOMEBGC by Cienciala & Tatarinov ([4]) and Petritsch et al. ([22]), based on prescribed management regimes.
Net primary production of the 1133 plots in our dataset is simulated using the Austrian incarnation of the DAYMET climate interpolation ([19], [7]). Species and age information is obtained from the NFI, and other inputs comprise of an interpolation of Austrian soil data ([21]), a 1 km² nationwide raster of nitrogen deposition in 1995 ([28]) and speciesspecific parameters determined by Pietsch et al. ([23]). BIOMEBGC operates by simulating the development of an unmanaged forest over many centuries, until a point where carbon pools are metastable over the course of successive mortality cycles ([24]). This is followed by two cycles of clearcutting and replanting to simulate earlyindustrial forest exploitation in Central Europe, and the planting date for the current stand is determined from the NFI data.
The primary output of BIOMEBGC is net primary production (NPP). Internal routines allocate this in fixed proportions to the various tree compartments, of which we are here interested in aboveground wood carbon. Without consideration of current rotation management, the model output would apply to a potential, unmanaged forest condition. In most cases this gives a biomass estimate substantially higher than the recorded data from the NFI. At the plot level the NFI data is extremely imprecise, so a four step procedure was developed to determine the appropriate removal assumptions to include in the modelling. These assumptions are then used in a second simulation of forest growth to determine final results (Fig. 1).
(1) The true standing biomass in Period 1 was estimated as the mean of the NFI results for Periods 1 and 2, less half of the plot increment calculated from the NFI data using the “Starting Value” method ([10], [31]). This “temporal averaging” reduces some of the extreme variability in singlepoint angle count measurements ([6]).
(2) The “unmanaged” model outputs may be compared with the estimated Period 1 biomass, and the mismatch (“error”) calculated. A density plot of this mismatch shows a strong rightskew (most BGC estimates were much too high, fewer were too low). Plots were then stratified by dominant species group and data/model mismatches calculated.
(3) We assume that the mismatch between the NFI biomass estimations and the model outputs should be normally distributed. A BoxCox power transformation ([2]) was applied to the mismatch data for each species group, and the thinning requirement for each plot calculated as the difference between the mismatch curve and the transformed curve, centred on zero (Fig. 2), less a minor adjustment to compensate for the extra soil nitrogen use in a thinned stand.
(4) Removals calculated in step 3 are modelled as thinnings taken place some time prior to Period 1 (although they also may have been partial harvests or significant disturbance events). To avoid these having a biophysical impact on modelling postPeriod 1 due to increased nitrogen consumption by soil bacteria, we apply a series of steps to determine reasonable assumptions for the timing of the removals.
 If the stand is over 50 years old, first removal is assumed to be at age 40 ([20]).
 If insufficient biomass can be removed in a single step (required removals are greater than modelled standing biomass), subsequent removals occur at ages 70, 100 and 130 and 5 years prior to the Period 1 inventory date.
 If a subsequent removal is required, the prior removal is limited to 40 percent of biomass.
 A maximum of 95% of biomass may be removed in the final step.
To convert biomass figures to more readily visualised timber volume per hectare values, we compiled a regression of all 33 779 tree measurements in the available dataset, calculating conversion factors between biomass estimates made with allometry and biomass expansion factors developed for Central European species (Lexer, pers. comm.  10 February 2010) and merchantable timber volume estimates consistent with Pollanschütz ([25])  Tab. 1. All species are included here, as some of our 1133 plots contained a minor component of other species. It should be noted however that for species other than those in our major groups often few data points were available for the biomass/timber volume regression, and thus the multipliers may be unreliable for those species. The Lexer equations were used for consistency with concurrent studies ([35]), as the only available comprehensive estimates for Austrian conditions.
Species  Multiplier  Species  Multiplier 

Spruce (Picea abies), low elevation  18.91  Chestnut (Castanea sativa)  12.31 
Fir (Abies alba)  18.28  Black Locust (Robinia pseudoacacia)  11.11 
Larch (Larix decidua)  21.46  Sorbus/Prunus spp.  9.94 
Scots Pine (Pinus sylvestris)  22.12  Birch (Betula spp.)  11.01 
Black Pine (Pinus nigra)  18.29  Black Alder (Alnus glutinosa)  16.93 
Stone Pine (Pinus cembra)  7.45  White Alder (Alnus incana)  10.66 
Douglas Fir (Pseudotsuga menziesii)  6.25  Linden (Tilia cordata)  15.01 
Other conifers  6.81  Aspen, Poplars (Populus spp.)  13.74 
Beech (Fraxinus excelsior)  13.17  Black Poplar (Populus nigra)  12.58 
Oak (Quercus robur/petraea)  12.53  Hybrid Poplars (Populus x spp.)  11.83 
Hornbeam (Carpinus betulus)  11.61  Willow (Salix spp.)  8.95 
Ash (Fraxinus excelsior)  13.61  Other broadleafs  9.06 
Maple (Acer spp.)  11.41  Spruce, high elevation  15.9 
Elm (Ulmus spp.)  11.53     
Given the very poor precision of anglecount estimates of volume on single points, a regression of observed versus predicted standing volume is inappropriate. We therefore apply the methods of Bland & Altman ([1]), which for each data point plot the difference between values determined through each method against their mean for each point.
Results and discussion
The initial simulation of forest growth on the NFI plots was performed without any management interventions. A comparison of model outputs with observed data displayed a remarkably even normal curve for those plots where the model appears to underestimate the NFI (the blue line below zero in Fig. 2). This gives some confidence that if management were appropriately considered, the variation between the simulations and the NFI data would also be normally distributed for apparent overestimations.
Overall, the procedure enabled a close simulation of standing volume (Fig. 3). Some problems exist with the Larch simulation, probably due to poor species data resulting from the low number of samples used in the initial species parameterisation ([23], Pietsch, pers. comm.  25 June 2010). Results for the Oak/Ash and Stone Pine groups are rather uncertain due to the low numbers of plots available in this study for those species. The BlandAltman plots (Fig. 4) show that the errors are consistent over the range of standing volumes, with little evidence of heteroscedasticity.
The Lexer biomass equations are based only on dbh. Several authors have presented allometry for major European species in different regions ([13] and [37] for Fagus sylvatica, [36] for Picea abies and [3], [5] for Pinus sylvestris and Quercus spp. respectively). All of these authors concluded that allometric models can be considerably improved by including height and other variables, rather than basing results solely on dbh. The Austrian NFI however contains many records where height is calculated from dbh rather than directly measured, which would appear to negate any possible advantage of using more complicated allometrics.
Besides natural variability, several other factors may account for the differences between model output and NFI records. The version of BIOMEBGC we use in this study is not designed to simulate mixedspecies forest, so the up to 20 percent of nonhomogenous species on any plot may have some minor effect. Uncertainty in biomass expansion factors ([16]) is also possibly biasing on some plots, where the byplot ratio of volume to biomass is not the same as the mean bytree relationship for the dominant species (Tab. 1).
Although it may seem that the results presented here are an artificial “forcing” of model assumptions to match the NFI data, in most cases the simulated thinnings are timed to occur several decades prior to the inventory measurement. The model is thus required to accurately simulate growth over long periods before the results in Fig. 3 are extracted. Alternative approaches such as thinning to precisely match field data ([28]) would not be possible for apparent model underestimations and thus would give an underestimation in the mean. Thinning all plots equally so that the simulation and the field data have equal means would simply shift the blue curve in Fig. 1 to the left, giving a nonnormal error distribution. An “expert opinion” approach ([20]) would ignore the standing volume information available in the NFI dataset. Although single plot data in the NFI is extremely imprecise, in the aggregate it can be assumed to provide accurate values.
By thinning to target a normal error curve, apparent model underestimations are little affected. In cases where a plot simulated without thinning greatly overestimates standing volume a heavy or a series of thinnings is indicated. There are any number of possible normal error curves that could have been selected as the “target” error curve, but the procedure using the BoxCox transformation was chosen to maximise the use of the available data. The timing of the thinning events at 40, 70, 100 and 130 years of age appears to be reasonably consistent with records from the NFI (Fig. 5). A breakdown of thinning volume removals (Tab. 2) suggests that the assumption procedure tends to underestimate the number of thinnings, but overestimate the volume removed in each thinning event. The results shown in Fig. 3 however suggest that this has a minimal effect on the final simulated standing volumes.
Group  Inventory  Thinning assumptions  

n yr^{1}  mean (m^{3} ha^{1}) 
sum (m^{3} ha^{1}yr^{1}) 
n yr^{1}  mean (m^{3 }ha^{1}) 
sum (m^{3} ha^{1}yr^{1}) 

All  46.00  55.27  2542.33  22.95  69.24  1589.10 
Spruce < 1000m and fir  21.20  55.22  1170.69  12.60  84.01  1058.55 
Spruce > 1000m  15.00  64.25  963.75  6.55  53.10  347.80 
Larch  0.20  15.17  3.03  0.00  0.00  0.00 
Scots pine  4.00  42.01  168.02  0.15  23.67  3.55 
Beech  5.00  42.04  210.20  3.65  49.10  179.20 
Oak/ash  0.40  51.47  20.59  0.00  0.00  0.00 
Stone pine  0.20  30.24  6.05  0.00  0.00  0.00 
Conclusions
The incorporation of unknown forest management histories into ecosystem modelling is a challenging issue, yet the ability of models to accurately mimic forest volume growth is dependant on management being appropriately considered. As we will rarely know the history of a large number of sites in sufficient detail to use real figures for historic thinnings, supportable procedures must be developed for appropriate assumptions. While National forest inventories provide large datasets of potential calibration data, the statistical nature of inventories means that we cannot assume a single inventory point to be representative of a wider area and directly comparable with model outputs for that point. The statistical estimation of management history in this work does not attempt to describe the real, unknown thinning history of each stand. Rather, it establishes a set of assumptions that can mimic the true management history in such a way that we can produce unbiased estimates of standing volume which, at a sufficiently large scale, are compatible with National Forest Inventories.
The procedure developed here has provided us with modeldriven timber volume estimates for 1133 sites across the whole of Austria that are overall a close match to NFI records at the national scale, are of comparable precision, and maximise the use of available data. Future work will examine how well model simulations compare with NFI records for subsequent inventory periods, where statistical indications are available regarding management interventions on each plot.
Acknowledgements
This work was supported by the two projects:Auswirkungen des Klimawandels auf Österreichs Wälder  Entwicklung und vergleichende Evaluierung unterschiedlicher Prognosemodelle (WAMOD) and Comparing satellite versus inventory driven carbon estimates for Austrian Forests (MOTI), both funded by the Energy Fund of the Federal State of Austria and managed by Kommunalkredit Public Consulting GmbH. We thank the two anonymous reviewers and the editor of the journal for helpful suggestions to improve this manuscript.
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