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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 3
Geosci. Model Dev., 7, 1197–1210, 2014
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 1197–1210, 2014
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 25 Jun 2014

Methods for assessment of models | 25 Jun 2014

Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging

M. Nussbaum1, A. Papritz1, A. Baltensweiler2, and L. Walthert2 M. Nussbaum et al.
  • 1Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland

Abstract. Accurate estimates of soil organic carbon (SOC) stocks are required to quantify carbon sources and sinks caused by land use change at national scale. This study presents a novel robust kriging method to precisely estimate regional and national mean SOC stocks, along with truthful standard errors. We used this new approach to estimate mean forest SOC stock for Switzerland and for its five main ecoregions.

Using data of 1033 forest soil profiles, we modelled stocks of two compartments (0–30, 0–100 cm depth) of mineral soils. Log-normal regression models that accounted for correlation between SOC stocks and environmental covariates and residual (spatial) auto-correlation were fitted by a newly developed robust restricted maximum likelihood method, which is insensitive to outliers in the data.

Precipitation, near-infrared reflectance, topographic and aggregated information of a soil and a geotechnical map were retained in the models. Both models showed weak but significant residual autocorrelation. The predictive power of the fitted models, evaluated by comparing predictions with independent data of 175 soil profiles, was moderate (robust R2 = 0.34 for SOC stock in 0–30 cm and R2 = 0.40 in 0–100 cm). Prediction standard errors (SE), validated by comparing point prediction intervals with data, proved to be conservative.

Using the fitted models, we mapped forest SOC stock by robust external-drift point kriging at high resolution across Switzerland. Predicted mean stocks in 0–30 and 0–100 cm depth were equal to 7.99 kg m−2 (SE 0.15 kg m−2) and 12.58 kg m−2 (SE 0.24 kg m−2), respectively. Hence, topsoils store about 64% of SOC stocks down to 100 cm depth. Previous studies underestimated SOC stocks of topsoil slightly and those of subsoils strongly. The comparison further revealed that our estimates have substantially smaller SE than previous estimates.

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