2007
DOI: 10.1029/2006wr005287
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Sequential aquifer tests at a well field, Montalto Uffugo Scalo, Italy

Abstract: [1] This paper investigates our ability to characterize an aquifer using a sequential aquifer test in a well field that consists of six wells. During the test, we pumped water out from the aquifer at one well and monitored the water level changes at the rest of the wells to obtain a set of five well hydrographs. By pumping at another of the six wells, we obtained another set of five hydrographs. This procedure was repeated until each of the six wells was pumped. We then analyzed the six sets of hydrographs usi… Show more

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Cited by 107 publications
(110 citation statements)
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References 33 publications
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“…This implies that the effective parameters estimated from all the data sets from the pumping tests conducted at A1, A5, and A9 are not the same as the effective parameters for the pumping test conducted at A3. In other words, the effective parameters for the equivalent homogeneous medium are scenario dependent (varying with the geology near the pumping well) as observed and discussed by Straface et al [2007] Sun et al, submitted manuscript, 2012) for pumping tests in confined aquifers. The complex spatial distribution of the parameter cross correlation with the head in the unconfined aquifers presented in the study by Mao et al [2013] further elucidates such a scenariodependent nature of the effective properties.…”
Section: Validation Through Independent Pumping Testsmentioning
confidence: 87%
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“…This implies that the effective parameters estimated from all the data sets from the pumping tests conducted at A1, A5, and A9 are not the same as the effective parameters for the pumping test conducted at A3. In other words, the effective parameters for the equivalent homogeneous medium are scenario dependent (varying with the geology near the pumping well) as observed and discussed by Straface et al [2007] Sun et al, submitted manuscript, 2012) for pumping tests in confined aquifers. The complex spatial distribution of the parameter cross correlation with the head in the unconfined aquifers presented in the study by Mao et al [2013] further elucidates such a scenariodependent nature of the effective properties.…”
Section: Validation Through Independent Pumping Testsmentioning
confidence: 87%
“…Traditional characterization approaches have adopted homogeneous conceptual models that assume aquifer homogeneity and have attempted to derive effective hydraulic parameters based on drawdowns from a single pumping test, for example, those by Theis [1935] and Cooper and Jacob [1946] for confined aquifers and those by Boulton [1963], Dagan [1967], Brutsaert [1970], Streltsova [1972aStreltsova [ , 1972b, Neuman [1972], Lakshminarayana and Rajagopalan [1978], Moench [1995], Mathias and Butler [2006], and Mishra and Neuman [2010] for unconfined aquifers. Recent numerical, sandbox, and field experiments by Wu et al [2005], Straface et al [2007], Xiang et al [2009], Wen et al [2010], Huang et al [2011], and Berg and Illman [2011b] however questioned the representativeness of the estimates from these conventional approaches.…”
Section: Introductionmentioning
confidence: 99%
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“…To account for the nonuniqueness issue the hydraulic parameter field is treated as an outcome of a stochastic spatial process, whereby the mean parameter distribution is reconstructed by matching the observations from the pumping test responses. Straface et al [2007] used this inversion scheme for depth integrated reconstruction of hydraulic conductivity and specific storage fields. However, the reconstructed hydraulic conductivity and specific storage tomograms showed no correlation among each other, which is expected in natural sedimentary aquifers [e.g., Bayer et al, 2011], and no additional field data were provided to support the tomograms.…”
Section: Introductionmentioning
confidence: 99%
“…[9] Straface et al [2007] published the first field hydraulic tomography application that exploits the potential of geostatistically based inversion. The applied procedure relies on the sequential successive linear estimator (SSLE), as proposed by Zhu and Yeh [2005].…”
Section: Introductionmentioning
confidence: 99%