Articles | Volume 3, issue 1
Research article
16 Jan 2015
Research article |  | 16 Jan 2015

Re-evaluating luminescence burial doses and bleaching of fluvial deposits using Bayesian computational statistics

A. C. Cunningham, J. Wallinga, N. Hobo, A. J. Versendaal, B. Makaske, and H. Middelkoop

Abstract. The optically stimulated luminescence (OSL) signal from fluvial sediment often contains a remnant from the previous deposition cycle, leading to a partially bleached equivalent-dose distribution. Although identification of the burial dose is of primary concern, the degree of bleaching could potentially provide insights into sediment transport processes. However, comparison of bleaching between samples is complicated by sample-to-sample variation in aliquot size and luminescence sensitivity. Here we begin development of an age model to account for these effects. With measurement data from multi-grain aliquots, we use Bayesian computational statistics to estimate the burial dose and bleaching parameters of the single-grain dose distribution. We apply the model to 46 samples taken from fluvial sediment of Rhine branches in the Netherlands, and compare the results with environmental predictor variables (depositional environment, texture, sample depth, depth relative to mean water level, dose rate). Although obvious correlations with predictor variables are absent, there is some suggestion that the best-bleached samples are found close to the modern mean water level, and that the extent of bleaching has changed over the recent past. We hypothesise that sediment deposited near the transition of channel to overbank deposits receives the most sunlight exposure, due to local reworking after deposition. However, nearly all samples are inferred to have at least some well-bleached grains, suggesting that bleaching also occurs during fluvial transport.

Short summary
Rivers transport sediment from mountains to coast, but on the way sediment is trapped and re-eroded multiple times. We looked at Rhine river sediments to see if they preserve evidence of how geomorphic variables have changed over time. We found that measured signals potentially relate to water level and river management practices. These relationships can be treated as hypotheses to guide further research, and our statistical approach will increase the utility of research in this field.