Application of Indicator Kriging to hydraulic head data to test alternative conceptual models for spring source aquifers

Robin Keegan-Treloar, Adrian Werner, Dylan Irvine, Eddie Banks

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Hydraulic head distributions can inform spring source-water characterisation by determining whether aquifers meet the thresholds required to sustain spring flow. Assessing hydraulic head data can be challenging in areas where data are sparsely distributed and subject to variable measurement uncertainty. Geostatistical methods can be used to estimate hydraulic head values at unmeasured locations with quantitative uncertainty estimates. While these methods have been applied extensively for hydraulic head estimation in management contexts, no studies have used these approaches for spring source-water conceptual model testing. In this study, an investigation into the hydraulic head distribution was conducted through the application of Ordinary Indicator Co-Kriging. Interpolated hydraulic head estimates were used to quantitatively assess the plausibility of source-aquifer conceptual models for the Doongmabulla Springs Complex (DSC), Queensland, Australia. The results offer insights into the likelihood of alternative source aquifers having adequate head to support flow to eight springs within the DSC. Probabilities of adequate head to support the springs ranged from 0.03 to 0.12 for the Permian Formations, compared to <0.01 to 0.49 for the Triassic Formations. Analyses indicated that the Triassic Formations are more likely to have adequate hydraulic head to support spring flow. However, significant uncertainty exists in the conceptual model assessment due to hydraulic head measurement scarcity, particularly in the vicinity of the springs. These findings have important implications given that the Permian Formations will be dewatered by the operation of the nearby Carmichael coal mine. The techniques employed here can inform conceptual model uncertainties arising from the interpretation of sparsely distributed hydraulic head datasets, a major benefit over traditional interpolation methods.

Original languageEnglish
Article number126808
Pages (from-to)1-14
Number of pages14
JournalJournal of Hydrology
Early online dateAug 2021
Publication statusPublished - Oct 2021


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