Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging
Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging
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DOI：
10.5194/gmd-7-1197-2014
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发表时间：
2013-12
影响因子：
5.1
通讯作者：
Madlene Nussbaum;A. Papritz;A. Baltensweiler;L. Walthert
中科院分区：
文献类型：
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作者：
Madlene Nussbaum;A. Papritz;A. Baltensweiler;L. Walthert
抽象的。为了在全国范围内量化土地利用变化造成的碳源和碳汇，需要对土壤有机碳(SOC)储量进行准确估计。这项研究提出了一种新的稳健克立格法，以精确估计区域和全国平均SOC储量，并提供真实的标准误差。我们使用这种新的方法估计了瑞士及其五个主要生态区的平均森林SOC储量。利用1033个森林土壤剖面资料，模拟了0~30、0~100 cm两个隔层的矿质土壤蓄积量。利用一种对数据异常值不敏感的稳健约束最大似然方法，对考虑有机碳储量与环境协变量相关性和残差(空间)自相关性的对数正态回归模型进行了拟合。模型中保留了降水量、近红外反射率、土壤的地形和聚集信息以及岩土图件。这两个模型都显示出微弱但显著的残差自相关性。通过与175个土壤剖面的独立数据进行比较，所拟合的模型的预测能力是中等的(0-30 cm土壤有机碳储量的稳健R2=0.34，0-100 cm的R2=0.40)。预测标准误差(SE)通过比较点预测区间和数据来验证，被证明是保守的。利用拟合的模型，我们用稳健的外漂移点克里格法在瑞士全境以高分辨率绘制了森林SOC储量图。0~30和0~100 cm深度的预测平均蓄积量分别为7.99 kg m−2(SE 0.15 kg m−2)和12.58 kg m−2(SE 0.24 kg m−2)。因此，在100厘米深以下，表层土壤储存了大约%的有机碳储量。以往的研究略微低估了表层土壤的有机碳储量，而严重低估了底层土壤的有机碳储量。比较进一步表明，我们的估计比之前的估计的SE要小得多。
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.