Soil Carbon Stock Change Due to Afforestation in Japan by Paired-Sampling Method in an Equivalent Mass Basis

2020 ◽  
Author(s):  
Shigehiro Ishizuka ◽  
Shoji Hashimoto ◽  
Shinji Kaneko ◽  
Kenji Tsuruta ◽  
Kimihiro Kida ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Sampling Method ◽  
Soil Carbon Stock ◽  
Carbon Stock Change ◽  
Equivalent Mass ◽  
Stock Change ◽  
Mass Basis

Soil carbon stock changes due to afforestation in Japan by the paired sampling method on an equivalent mass basis

2021 ◽  
Author(s):  
Shigehiro Ishizuka ◽  
Shoji Hashimoto ◽  
Shinji Kaneko ◽  
Kenji Tsuruta ◽  
Kimihiro Kida ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Sampling Method ◽  
Soil Carbon Stock ◽  
Equivalent Mass ◽  
Mass Basis

scholarly journals Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories

2022 ◽  
Vol 170 (1-2) ◽  
Author(s):  
Emily McGlynn ◽  
Serena Li ◽  
Michael F. Berger ◽  
Meredith Amend ◽  
Kandice L. Harper
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Soil Carbon ◽  
Greenhouse Gas ◽  
Carbon Stock ◽  
Paris Agreement ◽  
Soil Carbon Stock ◽  
Greenhouse Gas Inventories ◽  
Carbon Stock Change ◽  
Stock Change

AbstractNational greenhouse gas inventories (NGHGIs) will play an increasingly important role in tracking country progress against United Nations (UN) Paris Agreement commitments. Yet uncertainty in land use, land use change, and forestry (LULUCF) NGHGHI estimates may undermine international confidence in emission reduction claims, particularly for countries that expect forests and agriculture to contribute large near-term GHG reductions. In this paper, we propose an analytical framework for implementing the uncertainty provisions of the UN Paris Agreement Enhanced Transparency Framework, with a view to identifying the largest sources of LULUCF NGHGI uncertainty and prioritizing methodological improvements. Using the USA as a case study, we identify and attribute uncertainty across all US NGHGI LULUCF “uncertainty elements” (inputs, parameters, models, and instances of plot-based sampling) and provide GHG flux estimates for omitted inventory categories. The largest sources of uncertainty are distributed across LULUCF inventory categories, underlining the importance of sector-wide analysis: forestry (tree biomass sampling error; tree volume and specific gravity allometric parameters; soil carbon model), cropland and grassland (DayCent model structure and inputs), and settlement (urban tree gross to net carbon sequestration ratio) elements contribute over 90% of uncertainty. Net emissions of 123 MMT CO2e could be omitted from the US NGHGI, including Alaskan grassland and wetland soil carbon stock change (90.4 MMT CO2), urban mineral soil carbon stock change (34.7 MMT CO2), and federal cropland and grassland N2O (21.8 MMT CO2e). We explain how these findings and other ongoing research can support improved LULUCF monitoring and transparency.

Soil carbon stock change following afforestation in Northern Europe: a meta‐analysis

2014 ◽  
Vol 20 (8) ◽  
pp. 2393-2405 ◽  
Author(s):  
T. G. Bárcena ◽  
L. P. Kiær ◽  
L. Vesterdal ◽  
H. M. Stefánsdóttir ◽  
P. Gundersen ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Meta Analysis ◽  
Northern Europe ◽  
Soil Carbon Stock ◽  
Carbon Stock Change ◽  
Stock Change

scholarly journals Uncertainty of upland soil carbon sink estimate for Finland

2016 ◽  
Vol 46 (3) ◽  
pp. 310-322 ◽  
Author(s):  
Aleksi Lehtonen ◽  
Juha Heikkinen
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Annual Variation ◽  
Turnover Rates ◽  
Inventory Data ◽  
Soil Carbon Stock ◽  
Litter Input ◽  
Biomass Models ◽  
Carbon Stock Change ◽  
Stock Change

Changes in the soil carbon stock of Finnish upland soils were quantified using forest inventory data, forest statistics, biomass models, litter turnover rates, and the Yasso07 soil model. Uncertainty in the estimated stock changes was assessed by combining model and sampling errors associated with the various data sources into variance–covariance matrices that allowed computationally efficient error propagation in the context of Yasso07 simulations. In sensitivity analysis, we found that the uncertainty increased drastically as a result of adding random year-to-year variation to the litter input. Such variation is smoothed out when using periodic inventory data with constant biomass models and turnover rates. Model errors (biomass, litter, understorey vegetation) and the systematic error of total drain had a marginal effect on the uncertainty regarding soil carbon stock change. Most of the uncertainty appears to be related to uncaptured annual variation in litter amounts. This is due to fact that variation in the slopes of litter input trends dictates the uncertainty of soil carbon stock change. If we assume that there is annual variation only in foliage and fine root litter rates and that this variation is less than 10% from year to year, then we can claim that Finnish upland forest soils have accumulated carbon during the first Kyoto period (2008–2012).

Soil carbon stock change in the forests of Denmark between 1990 and 2008

2015 ◽  
Vol 5 ◽  
pp. 169-180 ◽  
Author(s):  
Ingeborg Callesen ◽  
Inge Stupak ◽  
Petros Georgiadis ◽  
Vivian Kvist Johannsen ◽  
Hans S. Østergaard ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Soil Carbon Stock ◽  
Carbon Stock Change ◽  
Stock Change

Soil carbon stock estimations: methods and a case study of the Maranhão State, Brazil

2021 ◽  
Author(s):  
Telmo José Mendes ◽  
Diego Silva Siqueira ◽  
Eduardo Barretto de Figueiredo ◽  
Ricardo de Oliveira Bordonal ◽  
Mara Regina Moitinho ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stock ◽  
Soil Carbon Stock
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