Hydraulic tomography analysis of municipal well operation sata with geology-based groundwater models at the Mannheim Wellfield in Kitchener, Ontario, Canada
Bibliographic record
Abstract
The sustainable management of groundwater resources is essential to municipalities worldwide due to the increasing water demand from dramatic population growth. Planning for the optimized use of groundwater resources requires a sound understanding of hydrogeology and reliable estimation of hydraulic parameters such as hydraulic conductivity (K) and specific storage (Ss). Traditionally, K estimates are obtained through the application of empirical formulae to representative grain sizes, laboratory analysis of core samples, slug/bail tests and pumping tests. Reliable estimates of Ss are difficult to obtain and are typically obtained through the analysis of pumping tests. The interpretation of pumping tests with analytical solutions with various simplifying assumptions has become the de facto standard in the consulting industry; however, research has shown that in non-idealized real-world applications, biased K and Ss estimates may be obtained which can lead to poor predictions of independent stress events. Thus, there is a critical need for more robust methods to estimate hydraulic parameters. A new approach using hydraulic tomography (HT) to estimate K and Ss has been applied at the Mannheim East wellfield in Kitchener, Ontario. In particular, four different geological models with homogeneous geological layers are calibrated by coupling HydroGeoSphere (HGS) (Aquanty, 2018) and the parameter estimation code PEST (Doherty, 2005) using water-level variation records in observation wells collected during municipal well operations. The four geological models are well calibrated and yield reliable estimates that are consistent with previously estimated values for the shallower layers where most data points are collected. However, K and Ss estimates for deeper layers with fewer observation points vary more significantly among the models. The comparison of simulated and observed draw down for both model calibration and validation reveals that all four groundwater flow models with varying geology can capture the water-level fluctuation pattern quite well. However, rapid water-level variations at some wells are not captured very well, which could be due to the presence of high K pathways and heterogeneity not captured by these geological models. Currently, we are developing a HT approach based on geostatistics to handle this issue for both unconsolidated and fractured geologic media. Overall, our research reveals that: 1) the HT analysis of municipal well records is feasible and yields reliable K and Ss estimates of individual geological units where drawdown records are available; 2) these estimates are obtained at the scale of its intended use of municipal well operations, unlike small-scale estimates typically obtained; 3) characterization and estimation can be conducted using existing data, thus removing the need for dedicated pumping tests which leads to substantial cost savings; and 4) data collected during municipal well operations can be used for parameter estimation and groundwater modeling, thus they are critical and should be archived.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".