Supporting information for:
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Heat and mass transport during a groundwater
replenishment trial in a highly heterogeneous aquifer
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Simone Seibert1,2, Henning Prommer1,2,3*, Adam Siade1,3, Brett Harris4, Mike Trefry1,2 and
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Michael Martin5
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CSIRO Land and Water, Private Bag No. 5, Wembley WA 6913, Australia
National Centre for Groundwater Research and Training, Flinders University,
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Adelaide, GPO Box 2100, SA 5001, Australia
Curtin University of Technology, Dept. Exploration Geophysics, Dick Perry Ave,
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School of Earth and Environment, University of Western Australia, Australia
6151, Perth, WA 6151, Australia
Water Corporation Western Australia, Leederville, WA 6902, Australia
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Submitted to Water Resources Research
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Running Title: Heat and mass transport in a heterogeneous aquifer
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*Corresponding author phone: +61-8-93336272; email: Henning.Prommer@csiro.au
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Current address: CSIRO Land and Water, Private Bag No5, Wembley WA 6913, Australia
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Parameterization of hydraulic conductivities in MZ3 and the low permeability layers
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The layers in MZ3 were combined into one, uniform hydraulic conductivity zone because the
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observed, steep chloride-breakthrough behaviour indicated little difference between arrival
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times for the individual layers in this zone.
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The low permeability layers were separated into three, laterally homogeneous, zones
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according to reported facies associations [Leyland, 2011] and geological interpretations. The
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first zone, between 124 mbgl and 174 mbgl, includes the low permeability layers stratified
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within the upper, high permeability zone of the aquifer, which predominantly consists of tidal
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channel deposits. The intermediate zone, between 174 mbgl and 204 mbgl, represents the low
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permeability layers stratified within the predominantly low permeable section of the aquifer,
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which are primarily tidal flats deposits. The third zone consists of a single model layer, from
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204 to 224 mbgl, for which a mixed silty-sand – sand lithology was identified; this includes
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an 8 m thick interval of tidal channel deposits at the layer bottom [Leyland, 2011]. Based on
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geological evidence and minor flow log contributions, this 8 m thick sand interval and some
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additional minor interbedded sand layers indicate the existence of high permeability units
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within the deep, predominantly low conductive layer. However, this layer lies beneath the
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perforated intervals of the monitoring wells and therefore was not subdivided into low and
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high permeability layers as no monitoring data were available to support the separation.
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Instead, an average hydraulic conductivity was implemented in this layer and was allowed to
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vary over a relatively wide range during calibration with the upper limit set to a maximum
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overall transmissivity contribution of 20%.
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[Figure S1: Cross-section through the model domain north of the injection well. Red
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cells indicate Multi-Node well nodes at the injection well (IW) and the 20N, 60N and
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240N monitoring location. White sections show high permeability layers with the layer
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name detailed on the figure. Light and dark grey areas show low permeability layers in
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the upper and lower injection interval. The deepest modelling layer (Layer 69) is
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displayed in blue.]
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Influence of Multi-Node Wells
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As discussed earlier, all monitoring bores were explicitly simulated as multi-node wells (i.e.,
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using the MNW package in MODFLOW) to account for the intra-borehole flows that may
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occur during both pumping (i.e., sampling events) and ambient conditions. In order to
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quantify the effects of MNW wells, the associated model results were compared with an
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additional simulation where monitoring bore sampling was not explicitly modeled and
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transmissivity-weighted concentration values were calculated for the well-node cells instead
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(Figures 4 and S2). The comparison of simulated temperatures and chloride concentrations
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showed generally only very small differences, in contrast to the findings by Ma et al. [2012],
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who observed a strong influence of intra-borehole flows for similar screen lengths in a
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vertically heterogeneous aquifer. Only the two long-screen monitoring locations, BY19 and
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BY22, exhibited a notable influence of intra-borehole flow on simulated chloride
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concentrations (Figure S2). At these wells, significant intra-borehole flow occurred from the
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deeper model layers towards the shallower model layers throughout the injection period.
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Therefore, deeper groundwater containing higher chloride concentrations was transported
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through the well into layers of lower ambient chloride concentrations. This intra-borehole
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flow caused an increase in the simulated chloride concentrations, above the transmissivity
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weighted chloride concentrations, and agreed well with the rise in chloride concentrations
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above background concentrations, that was observed in the field after the start of injection
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(Figure S2).
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At all other monitoring locations, the simulation of multi-node wells did not provide any
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substantial influence or improvement of the simulation results. However, at some bores the
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simulated chloride concentrations during no-pumping events oscillated between close to
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background and relatively reduced chloride concentrations during breakthrough and thereafter
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(Figure S2). These concentration changes occurred when fluxes from different model layers,
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at different stages of injectant breakthrough, were entering the well. Concentrations obtained
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from simulated pumping events showed no significant difference from the transmissivity-
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weighted concentrations without pumping. Notable effects of the simulated pumping of the
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monitoring wells on the surrounding boreholes were not observed for any of the monitoring
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bores.
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Fig. S2: Measured (black dots) and simulated chloride concentrations at selected monitoring
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locations. Simulation results include chloride concentrations from Multi-Node Wells (grey
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lines) and simulated sampling events (red dots), as well as computed transmissivity
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weighted chloride concentrations when the Multi-Node Well option and the monitoring
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well pumping are neglected (green line). Background conditions are marked in grey, the
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model calibration period in white and the model validation period in light blue.
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