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Analytical and numerical adjoint solutions for cumulative streamflow depletion
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  • Chris Turnadge,
  • Roseanna M. Neupauer,
  • Okke Batelaan,
  • Russell S. Crosbie,
  • Craig T. Simmons
Chris Turnadge
Commonwealth Scientific and Industrial Research Organisation (CSIRO)

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Roseanna M. Neupauer
University of Colorado Boulder
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Okke Batelaan
Flinders University
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Russell S. Crosbie
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
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Craig T. Simmons
Flinders University
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Abstract

The traditional metric of streamflow depletion represents the instantaneous change in the volumetric rate of aquifer–stream exchange after a finite period of continuous groundwater extraction. In the present study an alternative metric of streamflow depletion was considered: cumulative stream depletion (CSD), which described the total volumetric reduction in flow from an aquifer to a stream resulting from continuous groundwater extraction over a finite period, at the final time of extraction. A novel analytical solution for the prediction of CSD was derived, based upon a forward solution that accounted for streambed conductance and partial stream penetration. Separately, a novel numerical solution for prediction of CSD was derived, based on the derivation and calculation of an adjoint state solution. The accuracy of these methods was demonstrated through benchmarking against existing analytical solutions and perturbation-based results, respectively. The derivation of the adjoint state solution identified three parameters of relevance to CSD prediction: streambed hydraulic conductivity and thickness, both of which contribute to the lumped parameterization of streambed conductance, as well as aquifer specific yield, which controls the rate at which hydraulic perturbations propagate through an aquifer. The computational advantage of the numerical adjoint solution was highlighted, where a single numerical model can be used to predict CSD resulting from any potential groundwater extraction location. The reduction in computational time required was proportional to the number of potential extraction well locations. If the number of potential locations is large then a reduction in model run time of nearly 100 % can be achieved.