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Record W4408440740 · doi:10.5194/egusphere-egu25-3967

The impact of groundwater age and flow patterns on water quality in the Milk River Aquifer, Canada

2025· preprint· en· W4408440740 on OpenAlexaffabout
Avadhoot Date, Bernhard Mayer, Pauline Humez, Michael Nightingale, Peter Mueller, Michael Bishof, Jeremy Lantis, Christof Vockenhuber, José Antonio Corcho Alvarado, Roland Purtschert, Reika Yokochi, Neil C. Sturchio, Ranjeet M. Nagare, Stephen W. Wheatcraft

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsArkema (Canada)Alberta Energy
Fundersnot available
KeywordsAquiferGroundwaterHydrology (agriculture)Water qualityStreamflowGroundwater flowEnvironmental scienceGeologyGeographyGeotechnical engineeringDrainage basinBiologyEcology

Abstract

fetched live from OpenAlex

The Milk River Aquifer (MRA) is a regional transboundary aquifer covering over 26,000 km2 across northern Montana (USA) and southern Alberta (Canada). Extensive groundwater extraction since 1960s has led to a decline in groundwater levels, thereby emphasizing the need for informed water management strategies. The objective of this study was to improve the understanding of spatial variations in major ion concentrations with respect to groundwater age and flow paths, and to identify key geochemical processes that influence groundwater quality within the aquifer. A comprehensive digital database was developed using hydrogeological and geochemical data from 1,429 water samples collected from 549 wells. Additionally, 20 new groundwater samples and associated gases were collected during a 2022 field campaign, and these samples were analyzed for concentrations of major and minor ions, gas composition, stable isotope ratios (2H/1H and 18O/16O of water, 13C/12C of DIC and 34S/32S of sulfate, 13C/12C and 2H/1H of methane), and radioactive isotopes (⁸¹Kr, ³⁶Cl and ¹⁴CDIC).Utilizing a newly updated groundwater numerical flow model (FEFLOW software) in combination with recent 14C and 81Kr-based groundwater age dates, distinct patterns in chloride (Cl) concentrations dependent on groundwater age and flow path were identified. Groundwater less than 34,000 years old exhibited Cl concentrations < 25 mg/L near the recharge zone, while groundwater exceeding 200,000 years in age had Cl concentrations > 100mg/L at distances of 125 km from the recharge zone. Increasing δ²H and δ¹⁸O values in older groundwater with elevated Cl concentrations indicate possible mixing of fresh recharge water with formation water from northern regions of the aquifer (Taber and Bow Island formations) or associated aquitards (Pakowki and Colorado formations). Ongoing analysis explores variations in other major ions with a specific interest in redox-sensitive species as a function of flow distance and groundwater age. Preliminary results reveal that elevated sulfate concentrations (> 1200 mg/L) in recharging groundwater are due to pyrite oxidation, but at groundwater flow distances between 50 and 75 km bacterial sulphate reduction becomes dominant resulting in sulfate concentrations < 1 mg/L. At flow distances >80 km, redox conditions become favourable for methanogenesis resulting in occurrence of biogenic methane in groundwater. A particle tracking algorithm within the updated numerical flow model was employed to compare residence times with groundwater ages determined from 81Kr measurements. The tracer ages (14C and 81Kr) were confirmed using a numerical particle tracking model based on an existing numerical steady-state groundwater flow model (FEFLOW). The outcomes of this study that utilizes innovative groundwater age dating tools (81Kr) are new insights into how geochemical processes evolve with respect to flow distance and groundwater age thereby modifying spatial variability of key water quality parameters within the Milk River Aquifer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.265
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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