Abstract. 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.
Spectral fingerprinting: sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties
