Abstract
Abstract. Effective porosity plays an important role in contaminant management. However, the effective porosity is often assumed to be constant in space and hence heterogeneity is either neglected or simplified in transport model calibration. Based on a calibrated highly parametrized flow model, a three-dimensional advective transport model (MODPATH) of a 1300 km2 coastal area of southern Denmark and northern Germany is presented. A detailed voxel model represents the highly heterogeneous geological composition of the area. Inverse modelling of advective transport is used to estimate the effective porosity of 7 spatially distributed units based on apparent groundwater ages inferred from 11 14C measurements in Pleistocene and Miocene aquifers, corrected for the effects of diffusion and geochemical reactions. By calibration of the seven effective porosity units, the match between the observed and simulated ages is improved significantly, resulting in a reduction of ME of 99 % and RMS of 82 % compared to a uniform porosity approach. Groundwater ages range from a few hundred years in the Pleistocene to several thousand years in Miocene aquifers. The advective age distributions derived from particle tracking at each sampling well show unimodal (for younger ages) to multimodal (for older ages) shapes and thus reflect the heterogeneity that particles encounter along their travel path. The estimated effective porosity field, with values ranging between 4.3 % in clay and 45 % in sand formations, is used in a direct simulation of distributed mean groundwater ages. Although the absolute ages are affected by various uncertainties, a unique insight into the complex three-dimensional age distribution pattern and potential advance of young contaminated groundwater in the investigated regional aquifer system is provided, highlighting the importance of estimating effective porosity in groundwater transport modelling and the implications for groundwater quantity and quality assessment and management.
Highlights
A comparison of ages simulated using any of these methods with ages determined from tracer observations, referred to as apparent ages, is desirable as it can improve the uniqueness in flow model calibration and validation (Castro and Goblet, 2003; Ginn et al, 2009) and it potentially informs about transport parameters such as effective porosity, diffusion and dispersion that are otherwise difficult to estimate
The majority of the samples represent younger waters, with 12 out of 18 samples being less than 2000 years old
The originality of this study comes from a 3-D multi-layer coastal regional advective transport model, where heterogeneities are resolved on a grid scale
Summary
The age of groundwater, i.e. the time elapsed since the water molecule entered the groundwater (Cook and Herczeg, 2000; Kazemi et al, 2006), is useful (i) to infer recharge rates (e.g. Sanford et al, 2004; Wood et al, 2017) and to sustainably exploit groundwater resources; (ii) to evaluate contaminant migration, fate and history (Bohlke and Denver, 1995; Hansen et al, 2012) and predict spread of pollutants and timescales for intrinsic remediation (Kazemi et al, 2006); (iii) to analyse aquifer vulnerability or protection to surface-derived contaminants (e.g. Manning et al, 2005; Bethke and Johnson, 2008; Molson and Frind, 2012; Sonnenborg et al, 2016) and indicate the advance of modern contaminated groundwater (Hinsby et al, 2001b; Gleeson et al, 2015; Jasechko et al, 2017) and groundwater quality in general (Hinsby et al, 2007); and (iv) to contribute to the understanding of the flow system, e.g. in complex geological settings (Troldborg et al, 2008; Eberts et al, 2012).The groundwater science community (de Dreuzy and Ginn, 2016) has a continued interest in the topic of residence time distributions (RTDs) in the subsurface. Turnadge and Smerdon (2014) reviewed different methods for modelling environmental tracers in groundwater, including lumped pa-R. The tracer-independent direct simulation of groundwater mean age (Goode, 1996; Engesgaard and Molson, 1998; Bethke and Johnson, 2002) includes advection, diffusion and dispersion processes and yields a spatial distribution of mean ages. A comparison of ages simulated using any of these methods with ages determined from tracer observations, referred to as apparent ages, is desirable as it can improve the uniqueness in flow model calibration and validation (Castro and Goblet, 2003; Ginn et al, 2009) and it potentially informs about transport parameters such as effective porosity, diffusion and dispersion that are otherwise difficult to estimate. The only way they can be directly compared in reality is if no mixing is taking place, i.e. if the flow field can be regarded as pure piston flow, which will give the kinematic age
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