31 Aug 2022
 | 31 Aug 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Estimation of groundwater age distributions from hydrochemistry: Comparison of two metamodelling algorithms in the Heretaunga Plains aquifer system, New Zealand

Conny Tschritter, Christopher J. Daughney, Sapthala Karalliyadda, Brioch Hemmings, Uwe Morgenstern, and Catherine Moore

Abstract. Groundwater age or residence time is important for identifying flow and contaminant pathways through groundwater systems. Typically, groundwater age and age distributions are inferred via lumped parameter models based on measured age tracer concentrations. However, due to cost and time constraints, age tracers are usually only sampled at a small percentage of the wells in a catchment. This paper describes and compares two methods to increase the number of groundwater age data points and assist with validating age distributions inferred from lumped parameter models. Two machine learning techniques with different strengths were applied to develop two independent metamodels that each aim to establish relationships between the hydrochemical parameters and the modelled groundwater age distributions in one test catchment. Ensemble medians from the best model realisations per age distribution percentile were used for comparison with the results from traditional lumped parameter models based on age tracers. Results show that both metamodelling techniques generally work well for predicting groundwater age distributions from hydrochemistry. Therefore, these techniques can be used to assist with the interpretation of lumped parameter models where age tracers have been sampled, and they can also be applied to predict groundwater age distributions for wells that have hydrochemistry data available, but no age tracer data.

Conny Tschritter et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-258', Scott Wilson, 23 Sep 2022
    • AC1: 'Reply on RC1', Conny Tschritter, 05 Feb 2023
  • RC2: 'Comment on hess-2022-258', Camille Bouchez, 06 Jan 2023
    • AC2: 'Reply on RC2', Conny Tschritter, 05 Feb 2023
  • RC3: 'Comment on hess-2022-258', Anonymous Referee #3, 10 Jan 2023
    • AC3: 'Reply on RC3', Conny Tschritter, 05 Feb 2023
  • RC4: 'Comment on hess-2022-258', Anonymous Referee #4, 10 Jan 2023
    • AC4: 'Reply on RC4', Conny Tschritter, 05 Feb 2023

Conny Tschritter et al.

Conny Tschritter et al.


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Short summary
This research found new methods based on machine learning techniques that can use widely available groundwater chemistry data to estimate the age distribution of groundwater samples. These methods could help fill the data gaps and improve our understanding of groundwater flow and contaminant pathways through catchments, which is essential for the sustainable management of freshwater resources.