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Modelling electrical conductivity variation using a travel time distribution approach
  • Andrew W Western,
  • Ulrike Bende-Michl
The University of Melbourne Melbourne School of Engineering

Corresponding Author:zriazi@student.unimelb.edu.au

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Andrew W Western
The University of Melbourne Department of Infrastructure Engineering
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Ulrike Bende-Michl
Australian Bureau of Meteorology
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Water quality dynamics depend strongly on hydrologic flow paths and transit time within catchments. In this paper we use a travel time tracking method to simulate stream salinity (as measured by electrical conductivity) in the Duck River catchment, NW Tasmania, Australia. The approach couples the StorAge transit time modelling approach with two different approaches to model electrical conductivity. The first assumes the catchment has a cyclic salt balance (rainfall source, stream flow sink) that is in dynamic equilibrium and evapoconcentration of salt is the only process changing concentration. The second assumes that the salinity of water in catchment storages is a function of water age in those stores, without explicitly simulating salt mass balance processes. The paper compares these alternate approaches in terms of salinity simulation, simulated stream water age distributions, and simulated storage age distributions. Both salinity simulation approaches reproduce stream salinity with high fidelity under calibration and perform well under validation. The simulations using the age-related solute concentration approach produce less biased results and thus high model efficiencies for validation periods. This approach also produces more consistent model parameter estimates between periods. There are systematic differences in the resultant age distributions between models, particularly for the solute balance based simulations where parameters (catchment storage size) changed more between calibration periods. The effect of time varying versus static storage selection functions are compared, with clear evidence that time varying storage selection functions with parameters linked to catchment conditions (flow) are essential for adequate simulation of event concentration dynamics.
28 Aug 2021Submitted to Hydrological Processes
09 Sep 2021Submission Checks Completed
09 Sep 2021Assigned to Editor
09 Sep 2021Reviewer(s) Assigned
17 Dec 2021Review(s) Completed, Editorial Evaluation Pending
20 Dec 2021Editorial Decision: Revise Major
23 Mar 20221st Revision Received
23 Mar 2022Submission Checks Completed
23 Mar 2022Assigned to Editor
23 Mar 2022Reviewer(s) Assigned
05 May 2022Review(s) Completed, Editorial Evaluation Pending
17 May 2022Editorial Decision: Revise Major
31 Jul 20222nd Revision Received
02 Aug 2022Assigned to Editor
02 Aug 2022Submission Checks Completed
02 Aug 2022Reviewer(s) Assigned
22 Sep 2022Review(s) Completed, Editorial Evaluation Pending
26 Sep 2022Editorial Decision: Accept