| Titre | Improving the spatial density of a regional hydraulic conductivity dataset with estimates made from domestic water well information |
| Télécharger | Téléchargement (publication entière) |
| Auteur | Priebe, E H; Neville, C J; Rudolph, D L |
| Source | Regional-scale groundwater geoscience in southern Ontario: an Ontario Geological Survey, Geological Survey of Canada, and Conservation Ontario open house; par Russell, H A J; Ford, D; Priebe, E H;
Commission géologique du Canada, Dossier public 8212, 2017 p. 29, https://doi.org/10.4095/299796 (Accès ouvert) |
| Année | 2017 |
| Éditeur | Ressources naturelles Canada |
| Réunion | Ontario Geological Survey and Geological Survey of Canada groundwater geoscience open house; Guelph; CA; mars 1-2, 2017 |
| Document | dossier public |
| Lang. | anglais |
| DOI | https://doi.org/10.4095/299796 |
| Media | en ligne; numérique |
| Référence reliée | Cette publication est contenue dans Russell, H A
J; Ford, D; Priebe, E H; (2017). Regional-scale groundwater geoscience in southern Ontario: an Ontario Geological Survey, Geological Survey of Canada, and Conservation Ontario open house, Commission géologique du Canada, Dossier public
8212 |
| Formats | pdf |
| Sujets | eau souterraine; ressources en eau souterraine; aquifères; ressources en eau souterraine; gestion des ressources; méthodes d'exploration; conductivité hydraulique; puits d'eau; débit spécifique;
hydrogéologie |
| Programme | Géoscience des eaux souterraines, Aquifer Assessment & support to mapping |
| Diffusé | 2017 02 22 |
| Résumé | (disponible en anglais seulement)
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. |
| GEOSCAN ID | 299796 |
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