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Measurements and models – a comparison of quantification methods for SOC changes in forest soils
Utförare miljöövervakning, Universitet, Sveriges lantbruksuniversitet, SLU, Sveriges lantbruksuniversitet, mark och miljö.
Utförare miljöövervakning, Universitet, Sveriges lantbruksuniversitet, SLU, Sveriges lantbruksuniversitet, mark och miljö.
Finnish Environment Institute, Helsinki, Finland.
Utförare miljöövervakning, Universitet, Sveriges lantbruksuniversitet, SLU, Sveriges lantbruksuniversitet, mark och miljö.
Vise andre og tillknytning
Ansvarlig organisasjon
2009 (engelsk)Rapport (Annet vitenskapelig)
Abstract [en]

The Swedish UNFCCC1-reporting of the LULUCF2-sector is based on methods in

compliance with the “Good practice” as described by the Intergovernmental Panel

on Climate Change (IPCC). Biomass and soil inventory data from the Swedish

Inventory of Forests is the major source of information used to quantify changes in

the various carbon pools on forest land. Even if the reported uncertainties in soil

carbon changes are small from a statistical perspective, they are large in relation to

the total Swedish emissions of green house gases. This is due to the fact that the

soil carbon pool is so large, that even small and statistically non-significant

changes may have an impact on the Swedish CO2 balance. Sampling based methods

may also result in considerable inter annual variations that may look conspicuous

in the reporting. Because of the uncertainty and inter annual variations there

has been a discussion on the methods used and if there are possibilities to lower the

uncertainty and to get more stable estimates of soil carbon changes by combining

measurements and models. In this study results from the two soil carbon models,

Yasso07 and Q, were compared with repeated measurements of the soil inventory

during the years 1994 to 2000. Soil carbon fluxes were simulated with the two

models from 1926 to 2000 with Monte Carlo methodology to estimate uncertainty

ranges. The results from the models agreed well with measured data. The simulations

of Yasso07 and Q resulted in a soil organic carbon stock in year 2000 of

1600 Mton C and 1580 Mton C, respectively while the measured carbon pool was

1670 Mton C. The annual change in soil organic carbon varies substantially between

the three methods mainly due to different assumptions regarding annual

climate variation. However, the five year averaged mean of annual soil organic

carbon change for the two periods 1994-1998 and 1996-2000 indicate the size and

direction of the estimated annual changes agree reasonable well. The mean annual

change for the two periods was for the Q-model 5.5 Mton C yr-1 and 5.6 Mton C yr-

1 with a confidence interval of 2.1-10.7 Mton C yr-1, and for the Yasso07-model 3.7

Mton C yr-1 and 0.9 Mton C yr-1 respectively with a confidence interval ranging

between -5 to 12.6 and -7 to 9.8 Mton C yr-1 respectively. The mean annual change

for the two periods estimated using NFI-data was 1.6 M ton C yr-1 and 2.5 M ton C

yr-1 with a standard error of 2 The general conclusion drawn from this study is that

both sampling and the models Yasso07 and Q are possible tools to predict the soil

organic carbon accumulation and annual changes for Swedish forest soils. The

estimates based on measurements as well as the modelled results indicate an increase

in carbon stocks in Swedish forest soils. This study does not support a

change of method from inventory to model predictions. However, the agreement

between the methods shows that the models are suitable as a complement to other

soil carbon estimation methods. They are particularly useful for projections and we

recommend a further development of the modelling tools.

1 UNFCCC, United Nations Framework Convention on Climate Change

2 LULUCF, Land Use and Land Use Change and Forestry

sted, utgiver, år, opplag, sider
Norrköping: SMHI , 2009. , s. 29
Serie
SMED Rapport, ISSN 1653-8102 ; SMED Report No 31 2009
HSV kategori
Forskningsprogram
SMED (Svenska MiljöEmissionsData); SMED (Svenska MiljöEmissionsData), Luft och klimat
Identifikatorer
URN: urn:nbn:se:naturvardsverket:diva-7813OAI: oai:DiVA.org:naturvardsverket-7813DiVA, id: diva2:1219186
Tilgjengelig fra: 2018-06-15 Laget: 2018-06-15bibliografisk kontrollert

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