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Sediment fingerprinting in fluvial systems: review of tracers, sediment sources and mixing models

机译:河流系统中的泥沙指纹:示踪剂,泥沙源和混合模型的综述

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摘要

Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compares biogeochemical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe, Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US (North Fork Broad River watershed) and France (Bl鯮e watershed) with local and global (genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins (using correction factors) and Hughes (relying on iterations involving all data, and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems.
机译:河流系统中的悬浮沉积物来自无数的扩散和点源,每个源的相对贡献随时间和空间而变化。沉积物指纹识别的过程侧重于开发方法,这些方法能够从悬浮材料的复合样品中识别出离散的沉积物来源。这篇综述确定了包括河流和来源采样在内的沉积物指纹识别的现有方法学步骤,并严格比较了指纹识别研究中使用的生物地球化学和物理示踪剂。使用来自欧洲,非洲,澳大利亚,亚洲以及北美和南美的41个流域的数据,探讨了将不同的混合模型应用于相同的源数据的含义。将七个常用的混合模型应用于来自美国(北福克阔河流域)和法国(布莱河流域)的两个案例研究,并使用局部和全局(遗传算法)优化方法,确定所有输出均保持在可接受的误差范围内由原始作者定义。我们建议未来的沉积物指纹研究使用结合改进的Collins(使用校正因子)和Hughes(依赖于涉及所有数据的迭代,而不仅是平均值)的模型提供的最佳解释参数的模型,并使用遗传算法进行优化以最佳预测泥沙源对河流系统的相对贡献。

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