Preprints
https://doi.org/10.5194/hess-2022-258
https://doi.org/10.5194/hess-2022-258
 
31 Aug 2022
31 Aug 2022
Status: 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 Tschritter1, Christopher J. Daughney2, Sapthala Karalliyadda3, Brioch Hemmings1, Uwe Morgenstern3, and Catherine Moore3 Conny Tschritter et al.
  • 1GNS Science, Taupo, New Zealand
  • 2National Institute of Water and Atmospheric Research, Wellington, New Zealand
  • 3GNS Science, Lower Hutt, New Zealand

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: open (until 26 Oct 2022)

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  • RC1: 'Comment on hess-2022-258', Scott Wilson, 23 Sep 2022 reply

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.