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* Ihre Aktion  suchen [und] (PICA-Produktionsnummer (PPN)) 516122517
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Online-Publ. (ohne Zeitschriften)
PPN:  
516122517
Titel:  
Verantwortlich:  
Schwemmle, Robin,i1991- [Verfasser] ; Weiler, Markus,i1971- [Verfasser]
Erschienen:  
Freiburg : Universität, 2024
Umfang:  
1 Online-Ressource
Anmerkung:  
Water resources research. - 60, 1 (2024) , ISSN: 1944-7973
 
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DOI:  
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Abstract:  
Abstract: Understanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process-based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g., power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete-mixing, piston flow, advection-dispersion and advection-dispersion with time-variant parameters. In this study, we conduct modeling experiments at the Rietholzbach lysimeter, Switzerland. All modeling experiments are benchmarked with HYDRUS-1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water storage dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997–2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provided Sobol indices for hydrologic model parameters and SAS parameters. Our results indicate that the advection-dispersion transport model produces the best results. And thus, advective-dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modeling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales
 
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