Multi-tracer groundwater dating in Southern Oman using Bayesian modeling

• Verfasst von: Rädle, Viola [VerfasserIn]
• Verfasst von: Kersting, Arne [VerfasserIn]
• Verfasst von: Schmidt, Maximilian [VerfasserIn]
• Verfasst von: Ringena, Lisa [VerfasserIn]
• Verfasst von: Robertz, Julian [VerfasserIn]
• Verfasst von: Aeschbach, Werner [VerfasserIn]
• Verfasst von: Oberthaler, Markus K. [VerfasserIn]
• Verfasst von: Müller, Thomas [VerfasserIn]
• Titel: Multi-tracer groundwater dating in Southern Oman using Bayesian modeling
• Verf.angabe: Viola Rädle, Arne Kersting, Maximilian Schmidt, Lisa Ringena, Julian Robertz, Werner Aeschbach, Markus Oberthaler, and Thomas Müller
• E-Jahr: 2022
• Jahr: 23 May 2022
• Umfang: 21 S.
• Fussnoten: Gesehen am 15.07.2022
• Titel Quelle: Enthalten in: Water resources research
• Ort Quelle: [New York] : Wiley, 1965
• Jahr Quelle: 2022
• Band/Heft Quelle: 58(2022), 6, Artikel-ID e2021WR031776, Seite 1-21
• ISSN Quelle: 1944-7973
• DOI: doi:10.1029/2021WR031776
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Atom Trap Trace Analysis
• Sach-SW: Bayesian modeling
• Sach-SW: groundwater dating
• Sach-SW: Markov-Chain Monte Carlo
• Sach-SW: nonparametric age model
• Sach-SW: transit time distributions
• K10plus-PPN: 1810607337
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

In the scope of assessing aquifer systems in areas where freshwater is scarce, estimation of transit times is a vital step to quantify the effect of groundwater abstraction. Transit time distributions of different shapes, mean residence times, and contributions are used to represent the hydrogeological conditions in aquifer systems and are typically inferred from measured tracer concentrations by inverse modeling. In this study, a multi-tracer sampling campaign was conducted in the Salalah Plain in Southern Oman including CFCs, SF6, 39Ar, 14C, and 4He. Based on the data of three tracers, a two-component Dispersion Model (DMmix) and a nonparametric model with six age bins were assumed and evaluated using Bayesian statistics. In a Markov Chain Monte Carlo approach, the maximum likelihood parameter estimates and their uncertainties were determined. Model performance was assessed using Bayes factor and leave-one-out cross-validation. Both models suggest that the groundwater in the Salalah Plain is composed of a very young component below 30 yr and a very old component beyond 1,000 yr, with the nonparametric model performing slightly better than the DMmix model. All wells except one exhibit reasonable goodness of fit. Our results support the relevance of Bayesian modeling in hydrology and the potential of nonparametric models for an adequate representation of aquifer dynamics.