Hydraulic tomography analysis of municipal well operation sata with geology-based groundwater models at the Mannheim Wellfield in Kitchener, Ontario, Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».