Improving the spatial density of a regional hydraulic conductivity dataset with estimates made from domestic water well information
Notice bibliographique
Résumé
High-quality hydraulic conductivity data tend to be sparse relative to the large areas under investigation in regional-scale groundwater studies. From the 1960s through the 1990s, the literature is replete with articles discussing the details of augmenting high-quality hydraulic conductivity datasets with values estimated from specific capacity tests. Early publications focused on the development of the fundamental mathematical relation between specific capacity and hydraulic conductivity. Later articles presented approaches claiming improved accuracy of hydraulic conductivity estimates from specific capacity by correcting for additional well losses. However, because the site-specific data required to correct for additional well losses are generally not available, these corrections often led to error. Through the 1990s, many workers abandoned the analytical approach for an empirical one, arguing that correcting for additional well losses introduces significant error into the K estimate. Since the early 2000s, the literature has become largely silent on the topic of augmenting high-quality hydraulic conductivity datasets with K estimates developed from specific capacity. Despite a pause in the literature, integration of the results of high-quality tests within more approximate but spatially extensive datasets is still desired, particularly to support the identification of groundwater resource exploration targets in complex, heterogeneous geological settings. We present a simple and effective approach to synthesize the results from high-quality hydraulic tests with reconnaissance-level hydraulic conductivity estimates made from domestic water well specific capacity tests. Domestic water wells are ubiquitous across Ontario, and although their associated well records do not contain the information required to support rigorous hydraulic test analyses, they do contain the information required to calculate specific capacity. We use the fundamental Theis relation to make hydraulic conductivity estimates from specific capacity tests, assuming that additional well losses account for a relatively small portion of the total observed drawdown. The utility of this approach is demonstrated with data comparisons at three different spatial scales. Each comparison is conducted to evaluate the suitability of augmenting the high-quality data set with lesser quality estimates to answer specific research questions. The results of the comparisons results show good correlation between both sets of hydraulic conductivity estimates, and provide the information required to support careful data integration for improved hydraulic conductivity data coverage.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».