A toolkit for groundwater mean residence time interpretation with gaseous tracers

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• 作者：Pablo Fern ; o D¨¢vila ; Christoph K¨¹lls ; Markus Weiler
• 关键词：Lumped-parameter modeling ; Environmental tracer ; Groundwater dating ; Water age ; Environmental tracers concentrations in water
• 刊名：Computers and Geosciences
• 出版年：2013
• 出版时间：December, 2013
• 年：2013
• 卷：61
• 期：Complete
• 页码：116-125
• 全文大小：1798 K

文摘

An analytical Excel-based toolkit called Gas-Tracer-Interpretation (GTI) was developed for determining mean residence time (MRT) of groundwater samples and for validating conceptual model assumptions. This novel data interpretation toolkit improves data handling during analysis and resolves some problems in the interpretation of data from environmental tracers. The toolkit can assist error detection, uncertainty and ambiguity during data analysis, particularly ambiguity due to the decline in atmospheric data of CFC input functions (air-mixing ratios of tracers). The innovative interpretation methodologies are: (1) corrections of environmental tracer data are conducted in concentrations in water instead of air (atmosphere), allowing comparison of different tracer input functions under similar conditions and thereby replacing the use of unique global atmospheric data; (2) a multi-model, multi-tracer approach is adopted to improve the number of different combinations of environmental tracers and lumped-parameter models (piston flow (PM), exponential (EM), exponential-piston flow (EPM), advection-dispersion (DM) and gamma (GM)); and (3) generation of sufficient information for determination of erroneous, unclear and ambiguous outcomes. Results are linked to graphical analysis to improve data view. GTI supports the environmental tracers CFC-11, CFC-12, CFC-113, SF₆, ³H, and also SF₅CF₃, which is included as it represents a promising environmental tracer in hydrological research. The toolkit compares modeled input functions of tracers and data from samples. The apparent recharge age and MRT are estimated by combining explicit graphical and numerical data presentation. Due to the multi-model approach, it is possible to contrast selected models and estimate the best fit for a given sample, which is particularly useful for validating conceptual model assumptions. The toolkit has been developed in Microsoft ?Excel, and hence is user-friendly such that advanced programming skills or detailed understanding of the calculations and mathematical procedures are unnecessary.