Best practice for upscaling soil organic carbon stocks in salt marshes
Best practice for upscaling soil organic carbon stocks in salt marshes
复制标题
提高盐沼土壤有机碳储量的最佳实践
DOI：
10.1016/j.geoderma.2022.116188
复制
发表时间：
2022
期刊：
影响因子：
6.1
通讯作者：
Ladd C
中科院分区：
文献类型：
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作者：
Ladd C
需要计算沿海环境（包括盐沼）中储存的土壤有机碳（SOC）的数量，以确定它们在缓解气候危机中的作用。有几种技术可以根据土壤芯的上尺度来计算单位土地的有机碳含量。然而，目前还没有对常用的SOC升级技术的性能进行全面的评估。我们测量了从两个苏格兰盐沼收集的土芯的有机碳含量。两个SOC值用于升级；1 m标准化深度（IPCC推荐）的有机碳含量，以及现代沼泽沉积物的有机碳含量（在地层学中确定为从富有机质（沼泽）到富矿物质（潮间带）土壤的过渡）。使用了22种升级技术（SOC含量×面积，插值和基于回归的外推计算）。使用留一交叉验证程序和预测区间宽度来评估每种技术的准确性。回归模型采用数字地形模型和归一化植被指数作为协变量面。我们发现，沼泽尺度的有机碳储量根据采样深度和升级技术的不同而变化多达52倍。最大的差异出现在比较从1米深和现代沼泽沉积物升级的SOC储量时。采用IPCC推荐的1 m采样深度，由于计算中包含潮间带环境，因此高估了盐沼的有机碳储量。从七个机器学习算法输出的加权平均值中得出的集合回归模型在沼泽和采样深度上产生了最高的升级精度。简单的有机碳含量×面积计算得出的沼泽尺度有机碳储量与更先进的集合回归模型得出的储量值相当。然而，回归模型生成了沼泽中有机碳分布的详细地图，以及相关的有机碳值的不确定性。我们的研究结果广泛适用于需要大规模SOC存量评估和不确定性的其他环境。
Calculating the amount of soil organic carbon (SOC) stored in coastal environments, including salt marshes, is needed to determine their role in mitigating the Climate Crisis. Several techniques exist to calculate the SOC content of a unit of land from the upscaling of soil cores. However, no comprehensive assessment has been made on the performance of commonly used SOC upscaling techniques until now. We measured the SOC content of soil cores gathered from two Scottish salt marshes. Two SOC values were used for upscaling; SOC content for a 1 m standardised depth (as recommended by the IPCC), and SOC content of the modern marsh deposit (identified in the stratigraphy as a transition from organic-rich (marsh) to mineral-rich (intertidal flat) soil. Twenty-two upscaling techniques were used (SOC content × area, interpolative, and regression-based extrapolative calculations). Leave-one-out cross-validation procedures and prediction interval widths were used to assess the accuracy of each technique. Digital Terrain Models and Normalized Difference Vegetation Indices were used as covariate surfaces in the regression models. We found that marsh-scale SOC stocks varied by as much as fifty-two times depending on which sampling depth and upscaling technique was used. The largest differences emerged when comparing SOC stocks upscaled from 1 m deep and modern marsh deposits. Using the IPCC recommended 1 m sampling depth inflated the SOC stocks of salt marshes, as intertidal flat environments were included in the calculation. Ensemble regression models from the weighted average of seven machine learning algorithm outputs produced the highest upscaling accuracies across marshes and sampling depths. Simple SOC content × area calculations produced marsh-scale SOC stocks that were comparable to stock values produced by more advanced ensemble regression models. However, regression models produced detailed maps of SOC distribution across a marsh, and the associated uncertainty in the SOC values. Our findings are broadly applicable for other environments where large-scale SOC stock assessments and uncertainty are needed.