P-PINI: a new inversion method for sediment-burial dating

Author(s):  
Jesper Nørgaard ◽  
John Jansen ◽  
Stephanie Neuhuber ◽  
Zsófia Ruszkizcay-Rüdiger ◽  
Sandra Braumann ◽  
...  

<p>For sediment-burial dating with a cosmogenic nuclide pair, the isochron burial method performs well provided that the sediment source has undergone (1) steady erosion and (2) continuous exposure to cosmic rays. These conditions exert important limitations on applications of the method. And yet, in mountainous fluvial and glacial landscapes, it is commonly found that the source area has experienced landsliding or glacial quarrying (i.e., non-steady erosion), and/or intermittent sediment storage or burial beneath glaciers (i.e., discontinuous exposure). As well as breaching the assumptions of the isochron method, such processes tend to yield low nuclide concentrations in the sample, which further limits its workability.</p><p>Here we present a more flexible method that accommodates complex, non-steady pre-burial erosion and exposure histories: conditions that exclude the isochron burial method. P-PINI (Particle Pathway Inversion of Nuclide Inventories) is a Monte Carlo-based inversion model that employs a source-to-sink approach for estimating the depositional age of fluvial and glaciogenic sediments. This method has been successfully applied to the Deckenschotter in the northern Alpine foreland (see Knudsen et al. 2020, Earth & Planetary Science Letters 549, 116491). As with the isochron burial method, P-PINI exploits an ensemble of paired nuclide (e.g., <sup>10</sup>Be-<sup>26</sup>Al) concentrations measured in different samples from the same depth in a sedimentary sequence. But unlike the isochron method, P-PINI applies a stochastic approach to simulate a wide range of possible pre-depositional exposure and erosion histories for each individual sample. These different pre-burial histories (unique to each sample) are then integrated with the constraint that all samples share a common burial history at the sink. Where cosmogenic nuclide data (or other chronometric data, e.g., OSL) are available for multiple sites, Bayesian inference modelling can impose a priori relative age constraints, or estimates on the maximum duration of sediment storage.</p><p>In this presentation, we extend P-PINI to explore how sediment storage and reworking (i.e., a range of burial depths and durations) between source and sink affects burial age estimates. Significant intermediate storage is characteristic of large river systems, such as the Danube River. Using cosmogenic <sup>10</sup>Be-<sup>26</sup>Al concentrations measured in fluvial gravels at Gänserndorf and Schlosshof, two terraces along the Danube River in the Vienna Basin (Braumann et al., 2019. Quat. Int. 509. 87-102), we examine how the burial ages at these two sites are a function of the pre-burial history experienced by the samples.  </p>

2014 ◽  
Vol 62 (3) ◽  
pp. 186-196 ◽  
Author(s):  
Veronika Bačová Mitková ◽  
Dana Halmová

Abstract The study is focused on the analysis and statistical evaluation of the joint probability of the occurrence of hydrological variables such as peak discharge (Q), volume (V) and duration (t). In our case study, we focus on the bivariate statistical analysis of these hydrological variables of the Danube River in Bratislava gauging station, during the period of 1876-2013. The study presents the methodology of the bivariate statistical analysis, choice of appropriate marginal distributions and appropriate copula functions in representing the joint distribution. Finally, the joint return periods and conditional return periods for some hydrological pairs (Q-V, V-t, Q-t) were calculated. The approach using copulas can reproduce a wide range of correlation (nonlinear) frequently observed in hydrology. Results of this study provide comprehensive information about flood where a devastating effect may be increased in the case where its three basic components (or at least two of them) Q, V and t have the same significance.


2011 ◽  
Vol 47 (3) ◽  
pp. 39-47 ◽  
Author(s):  
V. V. Makovskiy ◽  
A. V. Lyashenko

2013 ◽  
Vol 49 (2) ◽  
pp. 32-38
Author(s):  
M. M. Dzhurtubayev ◽  
V. V. Zamorov ◽  
Yu. M. Dzhurtubayev

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