Comparative study of transient hydraulic tomography with varying parameterizations and zonations: Laboratory sandbox investigation

dc.contributor.authorLUO, NING
dc.contributor.authorZhao, Zhanfeng
dc.contributor.authorIllman, Walter A.
dc.contributor.authorBerg, Steven James
dc.date.accessioned2017-11-20T19:57:43Z
dc.date.available2017-11-20T19:57:43Z
dc.date.issued2017-11-01
dc.descriptionThe final publication is available at Elsevier via http://dx.doi.org/10.1016/j.jhydrol.2017.09.045 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractTransient hydraulic tomography (THT) is a robust method of aquifer characterization to estimate the spatial distributions (or tomograms) of both hydraulic conductivity (K) and specific storage (Ss). However, the highly-parameterized nature of the geostatistical inversion approach renders it computationally intensive for large-scale investigations. In addition, geostatistics-based THT may produce overly smooth tomograms when head data used to constrain the inversion is limited. Therefore, alternative model conceptualizations for THT need to be examined. To investigate this, we simultaneously calibrated different groundwater models with varying parameterizations and zonations using two cases of different pumping and monitoring data densities from a laboratory sandbox. Specifically, one effective parameter model, four geology-based zonation models with varying accuracy and resolution, and five geostatistical models with different prior information are calibrated. Model performance is quantitatively assessed by examining the calibration and validation results. Our study reveals that highly parameterized geostatistical models perform the best among the models compared, while the zonation model with excellent knowledge of stratigraphy also yields comparable results. When few pumping tests with sparse monitoring intervals are available, the incorporation of accurate or simplified geological information into geostatistical models reveals more details in heterogeneity and yields more robust validation results. However, results deteriorate when inaccurate geological information are incorporated. Finally, our study reveals that transient inversions are necessary to obtain reliable K and Ss estimates for making accurate predictions of transient drawdown events.en
dc.description.sponsorshipDiscovery grant - Natural Sciences & Engineering Research Council of Canada (NSERC)en
dc.description.sponsorshipChina Scholarship Councilen
dc.identifier.urihttp://dx.doi.org/10.1016/j.jhydrol.2017.09.045
dc.identifier.urihttp://hdl.handle.net/10012/12653
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAquifer heterogeneityen
dc.subjectGeological informationen
dc.subjectHydraulic tomographyen
dc.subjectInverse modelingen
dc.subjectModel calibration and validationen
dc.subjectModel comparisonen
dc.subjectTransient analysisen
dc.titleComparative study of transient hydraulic tomography with varying parameterizations and zonations: Laboratory sandbox investigationen
dc.typeArticleen
dcterms.bibliographicCitationLuo, N., & Illman, W. A. (2016). Automatic estimation of aquifer parameters using long-term water supply pumping and injection recordsÉvaluation automatique des paramètres des aquifères à partir d’enregistrements de longues durées de pompages pour la distribution d’eau et d’injectionsEstimación automática de parámetros del acuífero usando registros de bombeo de agua potable y de inyección a largo plazo利用长期供水抽水和注入记录自动估算含水层参数Estimativa automática de parâmetros hidráulicos de aquíferos utilizando série histórica de dados de bombeamento de poços de abastecimento de água e de injeção. Hydrogeology Journal, 24(6), 1443–1461. https://doi.org/10.1007/s10040-016-1407-xen
uws.contributor.affiliation1Faculty of Scienceen
uws.contributor.affiliation2Earth and Environmental Sciencesen
uws.peerReviewStatusRevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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