scholarly journals Uncertainty of upland soil carbon sink estimate for Finland

2016 ◽  
Vol 46 (3) ◽  
pp. 310-322 ◽  
Author(s):  
Aleksi Lehtonen ◽  
Juha Heikkinen

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).

2020 ◽  
Author(s):  
Shigehiro Ishizuka ◽  
Shoji Hashimoto ◽  
Shinji Kaneko ◽  
Kenji Tsuruta ◽  
Kimihiro Kida ◽  
...  

2022 ◽  
Vol 170 (1-2) ◽  
Author(s):  
Emily McGlynn ◽  
Serena Li ◽  
Michael F. Berger ◽  
Meredith Amend ◽  
Kandice L. Harper

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.


2014 ◽  
Vol 20 (8) ◽  
pp. 2393-2405 ◽  
Author(s):  
T. G. Bárcena ◽  
L. P. Kiær ◽  
L. Vesterdal ◽  
H. M. Stefánsdóttir ◽  
P. Gundersen ◽  
...  

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

2016 ◽  
Vol 9 (11) ◽  
pp. 4169-4183 ◽  
Author(s):  
Aleksi Lehtonen ◽  
Tapio Linkosalo ◽  
Mikko Peltoniemi ◽  
Risto Sievänen ◽  
Raisa Mäkipää ◽  
...  

Abstract. Dynamic soil models are needed for estimating impact of weather and climate change on soil carbon stocks and fluxes. Here, we evaluate performance of Yasso07 and ROMULv models against forest soil carbon stock measurements. More specifically, we ask if litter quantity, litter quality and weather data are sufficient drivers for soil carbon stock estimation. We also test whether inclusion of soil water holding capacity improves reliability of modelled soil carbon stock estimates. Litter input of trees was estimated from stem volume maps provided by the National Forest Inventory, while understorey vegetation was estimated using new biomass models. The litter production rates of trees were based on earlier research, while for understorey biomass they were estimated from measured data. We applied Yasso07 and ROMULv models across Finland and ran those models into steady state; thereafter, measured soil carbon stocks were compared with model estimates. We found that the role of understorey litter input was underestimated when the Yasso07 model was parameterised, especially in northern Finland. We also found that the inclusion of soil water holding capacity in the ROMULv model improved predictions, especially in southern Finland. Our simulations and measurements show that models using only litter quality, litter quantity and weather data underestimate soil carbon stock in southern Finland, and this underestimation is due to omission of the impact of droughts to the decomposition of organic layers. Our results also imply that the ecosystem modelling community and greenhouse gas inventories should improve understorey litter estimation in the northern latitudes.


2016 ◽  
Author(s):  
Aleksi Lehtonen ◽  
Tapio Linkosalo ◽  
Mikko Peltoniemi ◽  
Risto Sievänen ◽  
Raisa Mäkipää ◽  
...  

Abstract. We test whether litter quality, litter quantity and weather data are enough to estimate soil carbon stocks by models. We also test whether inclusion of soil water holding capacity improves soil carbon stock model estimates. Litter input was estimated from stem volume maps provided by the National Forest Inventory, while understorey vegetation was estimated using new biomass models. The litter production rates of trees were based on previous research, while for understorey biomass those were estimated from measured data. We applied Yasso07 and ROMUL models across Finland and ran those models into steady state; thereafter, measured soil carbon stocks were compared with model estimates. We found that the role of understorey litter input is underestimated when the Yasso07 model is parameterised, especially in northern Finland. We also found that the inclusion of soil water holding capacity in the ROMUL model improved predictions, especially in southern Finland. Our results imply that the ecosystem modelling community and greenhouse gas inventories should improve understorey litter estimation in the northern latitudes. Our simulations and measurements show that models using only litter quality, litter quantity and weather data underestimate soil carbon stock in southern Finland and this underestimation is due to omission of the impact of droughts to the decomposition of organic layers.


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

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