Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Biogeosciences, 13, 4439-4459, 2016
https://doi.org/10.5194/bg-13-4439-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
10 Aug 2016
Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status
Boris Ťupek1, Carina A. Ortiz2, Shoji Hashimoto3, Johan Stendahl2, Jonas Dahlgren4, Erik Karltun2, and Aleksi Lehtonen1 1Natural Resources Institute Finland, P.O. Box 18, 01301 Vantaa, Finland
2Swedish University of Agricultural Sciences, P.O. Box 7014, 75007 Uppsala, Sweden
3Forestry and Forest Products Research Institute, Tsukuba, Ibaraki 305-8687, Japan
4Swedish University of Agricultural Sciences, Skogsmarksgränd, 90183 Umeå, Sweden
Abstract. Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystem–atmosphere carbon dioxide exchange by process-based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead to a modeling bias in predictions of SOC stock change.

We aimed to evaluate SOC stock estimates of process-based models (Yasso07, Q, and CENTURY soil sub-model v4) against a massive Swedish forest soil inventory data set (3230 samples) organized by a recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions.

For two-thirds of measurements all models predicted accurate SOC stock levels regardless of the detail of input data, e.g., whether they ignored or included soil properties. However, in fertile sites with high N deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q models underestimated SOC stocks. In comparison to Yasso07 and Q, accounting for the site-specific soil characteristics (e. g. clay content and topsoil mineral N) by CENTURY improved SOC stock estimates for sites with high clay content, but not for sites with high N deposition.

Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well-sorted parent material, indeed have had other predominant drivers of SOC stabilization lacking in the models, presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct SOC stocks are decisive for predicting future SOC change and soil CO2 efflux.


Citation: Ťupek, B., Ortiz, C. A., Hashimoto, S., Stendahl, J., Dahlgren, J., Karltun, E., and Lehtonen, A.: Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status, Biogeosciences, 13, 4439-4459, https://doi.org/10.5194/bg-13-4439-2016, 2016.
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Short summary
We evaluated the soil carbon stock estimates of Yasso07, Q, and CENTURY soil carbon models, used in national greenhouse gas inventories in Europe, Japan, and USA, with soil carbon stock measurements from Swedish Forest Soil National Inventories. Measurements grouped according to the gradient of soil nutrient status revealed that the models underestimated for the Swedish boreal forest soils with higher site fertility. We discussed mechanisms of underestimation and further model developments.
We evaluated the soil carbon stock estimates of Yasso07, Q, and CENTURY soil carbon models, used...
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