A three-dimensional hydrogeological–geophysical model of a multi-layered aquifer in the coastal alluvial plain of Sarno River (southern Italy)

Maio, Rosa Di; Fabbrocino, Silvia; Forte, Giovanni; Piegari, E.

doi:10.1007/s10040-013-1087-8

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…It is widely recognized that knowledge of the spatial distribution of aquifer and aquitard hydraulic properties within an aquifer system is essential to the understanding of its dynamics, which provides the basis for sound groundwater management. Indeed, comprehensive aquifer characterization should ideally rely on a three‐dimensional model of the architecture of the aquifer system units and their respective hydraulic properties [e.g., Anderson , ; Ouellon et al ., ; Bayer et al ., ; Chen et al ., ; Di Maio et al ., ]. Hydraulic tomography, which is essentially the simultaneous analysis of multiple interwell hydraulic tests, such as pumping [e.g., Tosaka et al ., ; Gottlieb and Dietrich , ; Butler et al ., ; Yeh and Liu , ; Bohling et al ., ; Zhu and Yeh , ; Illman et al ., ; Fienen et al ., ; Cardiff et al ., ; Illman et al ., ; Berg and Illman , ; Cardiff and Barrash , ; Huang et al ., ; Cardiff et al ., ; Sun et al ., ] and slug tests [e.g., Brauchler et al ., ], is increasingly recognized as a promising technique for imaging heterogeneity in hydraulic properties at local scale, which can lead to preferential flow paths or impermeable barriers that control flow and transport in aquifers.…”

Section: Introductionmentioning

confidence: 99%

Resolution analysis of tomographic slug test head data: Two‐dimensional radial case

Paradis

Gloaguen

Lefebvre

et al. 2015

Water Resources Research

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Hydraulic tomography inverse problems, which are solved to estimate aquifer hydraulic properties between wells, are known to be ill-conditioned and a priori information is often added to regularize numerical inversion of head data. Because both head data and a priori information have effects on the inversed solution, assessing the meaningful information contained in head data alone is required to ensure comprehensive interpretation of inverse solutions, whether they are regularized or not. This study thus aims to assess the amount of information contained in tomographic slug tests head data to resolve heterogeneity in K h , K v /K h , and S s . Therefore, a resolution analysis based on truncated singular value decomposition of the sensitivity matrix with a noise level representative of field measurements is applied using synthetic data reflecting a known littoral aquifer. As an approximation of the hydraulic behavior of a real aquifer system, synthetic tomographic experiments and associated sensitivity matrices are generated using a radial flow model accounting for wellbore storage to simulate slug tests in a plane encompassing a stressed well and an observation well. Although fine-scale resolution of heterogeneities is limited by the diffusive nature of the groundwater flow equations, inversion of tomographic slug tests head data holds the potential to uniquely resolve coarse-scale heterogeneity in K h , K v /K h , and S s , as inscribed in the resolution matrix. This implies that tomographic head data can provide key information on aquifer heterogeneity and anisotropy, but that fine-scale information must be supplied by a priori information to obtain finer details.

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Section: Introductionmentioning

confidence: 99%

Resolution analysis of tomographic slug test head data: Two‐dimensional radial case

Paradis

Gloaguen

Lefebvre

et al. 2015

Water Resources Research

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“…In the first step of this approach, the geophysical data are inverted independently of the hydrologic data or model. In the second step, the inverted geophysical properties are used to zonate or directly parameterize the hydrologic model (Hubbard et al, 1999;Dam and Christensen, 2003;Seifert et al, 2007;Koch et al, 2009;Di Maio et al, 2013;Marker et al, 2015). This is based on the assumption that the geophysical responses are sensitive to some of the same structures and property distributions that the hydrologic data are sensitive to.…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…The subdivision into zones is typically done subjectively by an expert on the basis of geological, hydrological, and geophysical data (Seifert et al, 2007;Di Maio et al, 2013). This principle can also be used to define zones of a model of the synthetic groundwater system by using the synthetic lithological data from boreholes used in step 1, the hydrological data set generated in step 2, and geophysical models estimated by inverting the geophysical data sets generated in step 3.…”

Section: Step 4 -Model Construction and Parameterizationmentioning

confidence: 99%

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Christensen¹,

Christensen²,

Ferré³

2016

Hydrol. Earth Syst. Sci.

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Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization.For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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“…Many studies have shown that integrating geophysical and hydrogeological methods allows not only to obtain more accurate hydrostratigraphical models, but also to monitor hydraulic and geochemical processes (groundwater flow, solute transport, specific ion concentration) with high space and time resolution over a wide range of spatial scales (Breede et al, 2011;Campanella, 2008;Cosentini et al, 2012;Di Maio et al, 2013;Falgas et al, 2011;Nwankwoala and Udom, 2008;Ramalho et al, 2012). In particular, strong correlations between electrical conductivity and hydraulic conductivity are expected since they are both functions of the connected pore volumes and specific surface areas as well as of the saturation degree.…”

Section: Introductionmentioning

confidence: 96%

A combined use of Archie and van Genuchten models for predicting hydraulic conductivity of unsaturated pyroclastic soils

Maio¹,

Piegari²,

Todero³

et al. 2015

Journal of Applied Geophysics

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A three-dimensional hydrogeological–geophysical model of a multi-layered aquifer in the coastal alluvial plain of Sarno River (southern Italy)

Cited by 13 publications

References 44 publications

Resolution analysis of tomographic slug test head data: Two‐dimensional radial case

Resolution analysis of tomographic slug test head data: Two‐dimensional radial case

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

A combined use of Archie and van Genuchten models for predicting hydraulic conductivity of unsaturated pyroclastic soils

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