ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Hydrosphere
Volume 13 - 2025 | doi: 10.3389/feart.2025.1609778
Exploring the Use of New Data Assimilation Technologies to Map Groundwater Quality Vulnerability in a Large Alluvial Aquifer
Provisionally accepted- 1GNS Science, Lower Hutt, New Zealand
- 2No affiliation - retired, Melbourne, Australia
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Integrity of simulator-based Bayesian analysis requires adequate representation of prior parameter probabilities, and quantification and reduction of posterior predictive uncertainties through history-matching. In many groundwater management contexts, hydrogeological complexity and long numerical model run times can render both of these tasks difficult. We present three new technologies that can make simulator-based Bayesian analysis that is undertaken in complex hydrogeological environments more effective and more tractable. These are demonstrated using a case study where groundwater head, streamflow and groundwater age data are assimilated in order to assess groundwater vulnerability to anthropomorphic deterioration of its quality. Bayesian analysis begins by generating ensembles of realizations of hydraulic property and other parameters used by a multi-layer groundwater model. The first technology supports this first step, by ensuring that respect for complex hydrogeology is embodied in nonstationary representations of hydraulic properties, as well as in stochasticity of so-called “hyperparameters” which govern their spatially variable geostatistics. The second and third technologies support the data assimilation processing two different ways, both of which are numerically cheap. One of these options, Ensemble Space Inversion (ENSI) requires adjustment of parameter fields in order for model outputs to match field measurements. The other option, Data Space Inversion (DSI) avoids parameter field adjustment through construction of direct statistical linkages between model-generated counterparts to field measurements and groundwater predictions of management interest. This statistical model is then history-matched in lieu of the numerical model. Deployment of both of these strategies at our case study site yields similar results. They reveal the likely existence of young water at depth over large parts of a regional aquifer system. This has repercussions for the quality of extracted water, and for land management in recharge areas.
Keywords: data space inversion, nonstationary geostatistics, Ensemble Space Inversion, data assimilation, Groundwater age modeling, open framework gravels, Pest, Wairau
Received: 10 Apr 2025; Accepted: 10 Jun 2025.
Copyright: © 2025 Kitlasten, Moore and Doherty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wes Kitlasten, GNS Science, Lower Hutt, New Zealand
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