Using pedological knowledge to improve sediment source apportionment in tropical environments
Using pedological knowledge to improve sediment source apportionment in tropical environments
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DOI：
10.1007/s11368-018-2199-5
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发表时间：
2019-09-01
影响因子：
3.6
通讯作者：
Quinton, John N.
中科院分区：
文献类型：
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作者：
Batista, Pedro V. G.;Laceby, J. Patrick;Quinton, John N.
研究目的土壤是关键带过程的重要调节者，关键带过程影响用于沉积物指纹的地球化学信号的发展。在这项研究中，热带土壤的土壤学知识被纳入到沉积物源分层和示踪剂的选择在一个大的巴西流域。材料和方法在因盖河流域（类似于1200 km（2）），巴西，三个源端成员定义根据土壤和地质图的解释：上，中，下集水区。采用支流取样设计，分析了三种不同粒度组分（2-0.2 mm; 0.2-0.062 mm和< 0.062 mm）的沉积物地球化学。一个常用的元素选择的统计方法进行了比较，以知识为基础的方法。采用Monte Carlo模拟方法求解质量平衡非混合模型。模拟源的贡献进行了评估，对一组已知的源比例的人工混合物。结果和讨论对于粗粒级（2-0.2 mm），与人工混合物相比，元素选择的两种方法都产生了很高的误差（统计方法和基于知识的方法分别为23.8%和17.8%）。基于知识的方法为中间（0.2-0.062 mm）（10.9%）和细（< 0.062 mm）（11.8%）级分提供了最低的误差。模型预测集水区出口目标样本的粗和中间馏分是高度不确定的。这可能是源分层的空间尺度不能代表这些组分的沉积动力学的结果。元素选择的两种方法表明，大多数到达集水区出口的细沉积物（中位数> 90%）来自较低集水区的Ustorthents。结论不同的元素选择方法和人工混合物为评价指纹图谱方法提供了多条依据。我们的研究结果强调了考虑源分层成壤过程的重要性，并表明，不同的采样策略可能是必要的，以模拟特定的沉积物组分。
Purpose Soils are important regulators of Critical Zone processes that influence the development of geochemical signals used for sediment fingerprinting. In this study, pedological knowledge of tropical soils was incorporated into sediment source stratification and tracer selection in a large Brazilian catchment. Materials and methods In the Ingai River basin (similar to 1200 km(2)), Brazil, three source end-members were defined according to the interpretation of soil and geological maps: the upper, mid, and lower catchment. A tributary sampling design was employed, and sediment geochemistry of three different size fractions was analyzed (2-0.2 mm; 0.2-0.062 mm, and < 0.062 mm). A commonly used statistical methodology to element selection was compared to a knowledge-based approach. The mass balance un-mixing models were solved by a Monte Carlo simulation. Modeled source contributions were evaluated against a set of artificial mixtures with known source proportions. Results and discussion For the coarse fraction (2-0.2 mm), both approaches to element selection yielded high errors compared to the artificial mixtures (23.8% and 17.8% for the statistical and the knowledge-based approach, respectively). The knowledge-based approach provided the lowest errors for the intermediate (0.2-0.062 mm) (10.9%) and fine (< 0.062 mm) (11.8%) fractions. Model predictions for catchment outlet target samples were highly uncertain for the coarse and intermediate fractions. This is likely the result of the spatial scale of the source stratification not being able to represent sediment dynamics for these fractions. Both approaches to element selection show that most of the fine sediments (median > 90%) reaching the catchment outlet were derived from Ustorthents in the lower catchment. Conclusions The different element selection methods and the artificial mixtures provide multiple lines of evidence for evaluating the fingerprint approaches. Our findings highlight the importance of considering pedogenetic processes in source stratification, and demonstrate that different sampling strategies might be necessary to model specific sediment fractions.