Uncertainty Estimation in Stream Bed Sediment Fingerprinting Based on Mixing Model

Abstract

Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. The objective of this study is to develop and apply a Bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in Zidasht catchment, Iran. In view of this, 31 geochemical tracers were measured in 35 different sampling sites of three sediment sources (rangelands, crop fields and stream banks) and 14 sediment samples from stream bed deposition. Based upon statistical analysis, the best 20 composition subsets of tracers (e. g. 2, 3, 4 …21) were then selected. Sediment source fingerprinting was used to explore the uncertainty in the contributions of sediment from the three sources. The results showed that the main source of uncertainty was the number of tracers included in the model and<br />the higher number of tracer in the model the lower deviation in uncertainty. However, differences between the ranges ofuncertainty values from subset 5 to subset 21 of tracers are not statistically significant. In the study area, mean of relative contributions associated with uncertainty from rangeland, crop field and stream bank sources (mean of subset 5 to 21) were 0.526, 0.059, and 0.411 respectively. These results can be useful as a scientific basis of sediment management and selecting the soil erosion control methods for decision makers of natural resources.