Fingerprinting the Spatial Sources of Fine-grained Sediment Deposited in the Bed of the Mehran River Draining into the Northern Coast of the Persian Gulf using Elemental Geochemistry

Abstract

Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: 1) traditional range test (TRT), Kruskal-Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT+KW+DFA); 2) traditional range test using mean values (RTM) and two additional tests (RTM+KW+DFA); 3) TRT+KW+PCA (principle component analysis), and; 4) RTM+KW+PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT+KW+DFA and RTM+KW+DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10-60% (overall mean contribution 36%), 0.3-16% (overall mean contribution 6%) and 38-77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT+KW+PCA generated respective corresponding contributions of 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%) compared with 17-69% (overall mean 41%), 0.2-12% (overall mean 5%) and 29-76% (overall mean 54%) using the final tracers selected by RTM+KW+PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT+KW+PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT+KW+DFA and RTM+KW+DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM+KW+PCA (mean MAE = 14% and mean RMSE =16%) and GLUE with TRT+KW+PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.