Comparison of calculation methods for estimating annual carbon stock change in German forests under forest management in the German greenhouse gas inventory

Abstract

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.

Comparing soil inventory with modelling: Carbon balance in central European forest soils varies among forest types

“…Results from the German forest inventory revealed that deadwood pools increased by 0.06 Mg C ha -1 a -1 during 1990-2008 (Dunger et al, 2016). This suggests, that changes in the deadwood pool could not explain the differences between both methods.…”

Carbon stocks in tree biomass and soils of German forests

“…2.3. Estimation of carbon stocks 2.3.1 Above-and below-ground living tree biomass First the above-and below-ground living tree biomass is estimated by means of biomass functions derived from the data of the National Forest Inventory, described in Röhling et al (2016). Within the integrated model for estimating the above-ground biomass on single tree level the predictors DBH, height and the diameter in one third of the tree height are used.…”

Ascription of the differences between Germany and Uganda’s Land Use, Land-Use Change, and Forestry sector greenhouse gas methodologies for inventory improvement

“…Uganda reports on CO 2 only although is contemplating to start estimating CH 4 and N 2 O emissions from biomass burning in the inventory using Global Food and Agriculture Statistics of FAO (FAOSTAT) data. In terms of pools, Germany reports on positive (source) and negative (sink) CO 2 emission removals from mineral and organic soils (Prechtel et al 2009;Röhling et al 2016), aboveground biomass (AGB) and belowground biomass (BGB) (Röhling et al 2016;Tiemeyer et al 2020a, b), litter, and deadwood (Dunger et al 2012)…”

“…Mean annual emissions are estimated as the ratio of the difference in stock estimates at two points in time and the number of intervening years (McRoberts et al 2018). Germany developed her own unique sampling strategy that uses similar principles to the IPCC's stock-difference method (Röhling et al 2016). Stock change estimations in Germany are carried out using the continuous forest inventory (Wellbrock et al, 2017) method which ultimately relates to IPCC's stockdifference method (IPCC 2006, 2 Vol 4, Ch 2, Eq.…”