Tracing the origin of sediment is needed to improve our knowledge of hydro-sedimentary dynamics at the catchment scale. Several fingerprinting approaches have been developed to provide this crucial information. In particular, spectroscopy provides a rapid, inexpensive and non-destructive alternative technique to the conventional analysis of the geochemical properties. Here, we investigated the performance of four multi-proxy approaches based on (1) colour parameters, (2) geochemical properties, (3) colour parameters coupled with geochemical properties and (4) the entire visible spectrum to discriminate sediment source contributions in a mining catchment of New Caledonia. This French archipelago located in the south-west Pacific Ocean is the world's sixth largest producer of nickel. Open-cast nickel mining increases soil degradation and the downstream transfer of sediments in river systems, leading to the river system siltation. The sediment sources considered in the current research were therefore sediment eroded from mining sub-catchments and non-mining sub-catchments. To this end, sediment deposited during two cyclonic events (i.e. 2015 and 2017) was collected following a tributary design approach in one of the first areas exploited for nickel mining on the archipelago, the Thio River catchment (397 km²). Source (n=24) and river sediment (n=19) samples were analysed by X-ray fluorescence and spectroscopy in the visible spectra (i.e. 365–735 nm). The results demonstrated that the individual sediment tracing methods based on spectroscopy measurements (i.e. (1) and (4)) were not able to discriminate sources. In contrast, the geochemical approach (2) did discriminate sources, with 83.1 % of variance in sources explained. However, it is the inclusion of colour properties in addition to geochemical parameters (3) which provides the strongest discrimination between sources, with 92.6 % of source variance explained. For each of these approaches ((2) and (3)), the associated fingerprinting properties were used in an optimized mixing model. The predictive performance of the models was validated through tests with artificial mixture samples, i.e. where the proportions of the sources were known beforehand. Although with a slightly lower discrimination potential, the “geochemistry” model (2) provided similar predictions of sediment contributions to those obtained with the coupled “colour + geochemistry” model (3). Indeed, the geochemistry model (2) showed that mining tributary contributions dominated the sediments inputs, with a mean contribution of 68 ± 25 % for the 2015 flood event, whereas the colour + geochemistry model (3) estimated that the mining tributaries contributed 65 ± 27 %. In a similar way, the contributions of mining tributaries were evaluated to 83 ± 8 % by the geochemistry model (2) versus 88 ± 8 % by the colour + geochemistry model (3) for the 2017 flood event. Therefore, the use of these approaches based on geochemical properties only (2) or of those coupled to colour parameters (3) was shown to improve source discrimination and to reduce uncertainties associated with sediment source apportionment. These techniques could be extended to other mining catchments of New Caledonia but also to other similar nickel mining areas around the world.

中文翻译：

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结合颜色参数和地球化学示踪剂以改善采矿集水区（新喀里多尼亚、南太平洋群岛）中沉积物来源的识别

需要追踪沉积物的来源，以提高我们对流域尺度水文沉积动力学的了解。已经开发了几种指纹识别方法来提供这一关键信息。特别是，光谱学为地球化学性质的常规分析提供了一种快速、廉价和非破坏性的替代技术。在这里，我们研究了基于 (1) 颜色参数、(2) 地球化学特性、(3) 颜色参数与地球化学特性以及 (4) 整个可见光谱以区分沉积物来源贡献的四种多代理方法的性能。新喀里多尼亚的采矿集水区。这个位于西南太平洋的法国群岛是世界第六大镍生产国。露天镍矿开采会增加土壤退化和河流系统中沉积物的下游转移，导致河流系统淤积。因此，当前研究中考虑的沉积物来源是从采矿子集水区和非采矿子集水区侵蚀的沉积物。为此，在群岛上最早用于镍矿开采的地区之一，Thio 河集水区（397 公里）采用支流设计方法收集在两次气旋事件（即 2015 年和 2017²）。来源 ( n =24 ) 和河流泥沙 ( n =19 )) 样品通过 X 射线荧光和光谱分析在可见光谱（即 365-735 nm）。结果表明，基于光谱测量（即（1）和（4））的单个沉积物追踪方法无法区分来源。相比之下，地球化学方法 (2) 确实区分了来源，解释了来源中 83.1% 的差异。然而，除了地球化学参数 (3) 之外，还包含颜色特性，提供了最强的来源区分，解释了 92.6% 的来源方差。对于这些方法 ((2) 和 (3)) 中的每一种，相关的指纹特性都用于优化的混合模型。模型的预测性能通过人工混合样品的测试得到验证，即 其中来源的比例是事先知道的。尽管“地球化学”模型 (2) 的识别潜力略低，但对沉积物贡献的预测与通过耦合“颜色”获得的预测相似。 + 地球化学”模型 (3)。事实上，地球化学模型 (2) 表明采矿支流贡献主导了沉积物输入， 2015 年洪水事件的平均贡献为 68  ± 25 %，而颜色 + 地球化学模型 (3) 估计采矿支流贡献了 65  ±  27%。以类似的方式， 对于 2017 年洪水事件 ，地球化学模型 (2)评估采矿支流的贡献为 83 ± 8%，而 颜色+地球化学模型 (3)评估为 88  ± 8%  。因此，使用这些仅基于地球化学特性的方法 (2) 或与颜色参数相结合的方法 (3) 被证明可以改善源区分并减少与沉积物源分配相关的不确定性。这些技术可以扩展到新喀里多尼亚的其他采矿集水区，也可以扩展到世界其他类似的镍矿区。