We examine the current status of greenhouse gas inventories of the sector Land Use, Land-Use Change and Forestry (LULUCF), in European countries, with specific focus on the utilization of National Forest Inventory (NFI) programs. LULUCF inventory is an integral part of the reporting obligations under the United Nations Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol. The analysis is based on two questionnaires prepared by the COST Action E43 “Harmonisation of National Forest Inventories in Europe”, which were answered by greenhouse gas reporting experts in European countries. The following major conclusions can be drawn from the analysis: 1) definitions used to obtain carbon pool change estimates vary widely among countries and are not directly comparable 2) NFIs play a key role for LULUCF greenhouse gas estimation and reporting under UNFCCC, and provide the fundamental data needed for the estimation of carbon stock changes covering not only living biomass, but increasingly also deadwood, litter and soil compartments. The study highlights the effects of adopting different definitions for two major reporting processes, namely UNFCCC and FAO, and exemplifies the effect of different tree diameter thresholds on carbon stock change estimates for Finland. The results demonstrate that more effort is needed to harmonize forest inventory estimates for the purpose of making the estimates of forest carbon pool changes comparable. This effort should lead to a better utilization of the data from the European NFI programs and improve the European greenhouse gas reporting.

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BackgroundThe German greenhouse gas inventory in the land use change sector strongly depends on national forest inventory data. As these data were collected periodically 1987, 2002, 2008 and 2012, the time series on emissions show several “jumps” due to biomass stock change, especially between 2001 and 2002 and between 2007 and 2008 while within the periods the emissions seem to be constant due to the application of periodical average emission factors. This does not reflect inter-annual variability in the time series, which would be assumed as the drivers for the carbon stock changes fluctuate between the years. Therefore additional data, which is available on annual basis, should be introduced into the calculations of the emissions inventories in order to get more plausible time series.ResultsThis article explores the possibility of introducing an annual rather than periodical approach to calculating emission factors with the given data and thus smoothing the trajectory of time series for emissions from forest biomass. Two approaches are introduced to estimate annual changes derived from periodic data: the so-called logging factor method and the growth factor method. The logging factor method incorporates annual logging data to project annual values from periodic values. This is less complex to implement than the growth factor method, which additionally adds growth data into the calculations.ConclusionCalculation of the input variables is based on sound statistical methodologies and periodically collected data that cannot be altered. Thus a discontinuous trajectory of the emissions over time remains, even after the adjustments. It is intended to adopt this approach in the German greenhouse gas reporting in order to meet the request for annually adjusted values.

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In this study we derived allometric functions for estimating the belowground biomass (BGB) of Silver Birch (Betula pendula Roth), Pedunculate Oak (Quercus robur L.), Sessile Oak (Quercus petraea (Matt.) Liebl.) and Scots Pine (Pinus sylvestris L.) in Germany. To assess the impact on German greenhouse gas (GHG) reporting, these new functions were further compared with BGB functions currently used in France and Sweden. For developing new BGB functions 48 Silver Birches, 39 Pedunculate and Sessile Oaks and 54 Scots Pines were destructively sampled. The sampled trees spanned a DBH range from 8.2 to 52.9 cm for Silver Birch, from 7.4 to 42.0 cm for Oak and from 7.2 to 53.2 cm for Scots Pine. After fitting the data, the following values of model efficiency were achieved: 0.81 for Silver Birch, 0.98 for Oak and 0.95 for Scots Pine. The model root mean square error varies between 5.2 kg for Oak, 13.7 kg for Scots pine and 26.9 kg for Silver Birch. Comparison with the currently applied BGB functions in the German GHG inventory from France and Sweden showed that the use of these functions results in systematically different estimates for the BGB of Silver Birch and Oak. Thus, our findings indicate that BGB functions recommended for other European countries (in particular France and Sweden) are not appropriate for estimating the BGB for the tree species concerned in Germany. Currently, the derived data-set for BGB of Silver Birch, Oak and Scots Pine is the largest in Germany and the developed functions are thus the best available for estimating national BGB stock and stock change in Germany at the moment.