Fingerprinting sources of reservoir sediment via two modelling approaches

Abstract

Reliable quantitative information about sediment sources is a key requirement for river catchment management, especially in settings with high sediment loads. This study explores the potential for using source fingerprinting techniques to establish the relative contribution of three sub-basins to the sediment deposited in a reservoir impounded by an earth dam located at the outlet of the Lavar watershed, in Hormozgan Province, southern Iran. The three sub-basins feeding the reservoir are characterized by complex topography and underlying geology. The source material and target sediment samples were analyzed for 53 potential geochemical tracers, including trace elements and rare earth elements (REEs) and their ratios. Stepwise discriminant function analysis (DFA) was applied to select optimum composite fingerprints from those fingerprint properties passing the range test and we compared two different modelling procedures to estimate the relative contribution of the three sub-basins to the sediment deposited in the reservoir. The first involves a Bayesian mixing model within a Markov Chain Monte Carlo framework (BM) and, the second, an un-mixing model within a Monte Carlo simulation framework (UM). The latter model permits the use of ratio properties, which represents a novel aspect of our study. Particular attention was directed to the uncertainty associated with the source contribution estimates provided by the two models. A goodness of fit estimator was employed to evaluate the results of the UM. Both modelling procedures demonstrated that the southern sub-basin was the main source of the majority of samples we collected from the reservoir. The BM model indicated that the central sub-basin was the dominant source of two samples (S6 and S8). Overall, the results provided by the BM model for the source of seven sediment samples (S1, S2, S3, S4, S5, S7 and S9) are compatible with those provided by the UM model and the central sub-basin was recognized as the most important source supplying sediment in the study area. Both approaches offer potential for using geochemical fingerprinting to quantify spatial sediment source contributions and the uncertainty associated with those estimates.

Keywords: Bayesian mixing model; REE ratio tracers; Reservoir deposits; Sediment source tracing; Un-mixing model; Uncertainty.