Using different size fractions to source fingerprint fine-grained channel bed sediment in a large drainage basin in Iran

Sediment fingerprinting in data sparse regions, such as the mountainous areas of Iran, is more suited to a
confluence-based sample design wherein tributary sub-basins are characterised by sediment samples using
different size fractions. Our objective was therefore to fingerprint spatial source contributions to the < 37 and
37–63 μm fractions of fine-grained channel bed sediment samples collected in a large erodible mountainous river
basin in Iran based on a number of statistical and machine learning approaches. Geochemical elements were
measured in channel bed surface drape sediments from seven sub-basins and in delivered sediments from the
basin outlet. A Bayesian mass balance model (modified MixSIR) was applied to apportion sub-basin spatial
sources with four composite signatures selected using the different statistical approaches. For the < 37 and
37–63 μm fractions, the signatures all indicated that sub-basins 3 (Andajroud; 46.7%) and 7 (Ninehroud; 36.8%)
were the dominant spatial sources of the fine-grained bed drape sediment samples, respectively, identifying the
most active erosional zones spatially. The statistical error between known and predicted spatial source contributions
using virtual mixture tests for both size fractions demonstrated the importance of using multiple different
composite fingerprints to decrease the model prediction uncertainties. Despite the difficult terrain in such data
sparse areas, the source fingerprinting approach provides a basis for assembling new evidence which, in turn, is
of interest to scientists and managers alike.