2018
DOI: 10.1002/2017wr021655
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Uncertainty Quantification and Global Sensitivity Analysis of Subsurface Flow Parameters to Gravimetric Variations During Pumping Tests in Unconfined Aquifers

Abstract: We study the contribution of typically uncertain subsurface flow parameters to gravity changes that can be recorded during pumping tests in unconfined aquifers. We do so in the framework of a Global Sensitivity Analysis and quantify the effects of uncertainty of such parameters on the first four statistical moments of the probability distribution of gravimetric variations induced by the operation of the well. System parameters are grouped into two main categories, respectively, governing groundwater flow in th… Show more

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Cited by 27 publications
(23 citation statements)
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References 76 publications
(102 reference statements)
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“…At present, other methods for determining hydrogeological parameters using unsteady flow test data are all applied under the conditions of theis hypothesis. Therefore, this paper applies the basic principles of numerical integration to infer the confined aquifer incomplete wells and several characteristic types of sensitivity coefficient models such as incomplete wells in confined aquifers affected by the unilateral straight line boundary enable the application of the sensitivity method to be smoothly realized on the computer and expand the scope of application [10].…”
Section: Fig1 Model Multi-parameter Sensitivity and Uncertainty Analmentioning
confidence: 99%
“…At present, other methods for determining hydrogeological parameters using unsteady flow test data are all applied under the conditions of theis hypothesis. Therefore, this paper applies the basic principles of numerical integration to infer the confined aquifer incomplete wells and several characteristic types of sensitivity coefficient models such as incomplete wells in confined aquifers affected by the unilateral straight line boundary enable the application of the sensitivity method to be smoothly realized on the computer and expand the scope of application [10].…”
Section: Fig1 Model Multi-parameter Sensitivity and Uncertainty Analmentioning
confidence: 99%
“…Here I aim to accelerate parameter optimization and uncertainty assessment of an LSM using the technique of statistical machine learning-based surrogate modeling, which is theoretically investigated in the field of applied mathematics called uncertainty quantification (Sullivan, 2015). Although this technique has been used for the parameter sensitivity analysis of atmospheric models (e.g., Qian et al, 2018), hydrological models (e.g., Dell'Oca et al, 2017;Maina & Guadagnini, 2018;Teixeira Parente et al, 2019), and ecological models (e.g., Hawkins et al, 2019), few studies have applied it to parameter optimization and uncertainty assessment of LSMs with globally applicable satellite observations. In this study, a statistical surrogate model, which can mimic the relationship between model parameters and gaps between simulation and observation, is developed using machine learning.…”
Section: Introductionmentioning
confidence: 99%
“…Geophysical and environmental applied studies, with the great amount of inherently uncertain parameter values and strongly non-linear behavior, are a perfect area of application for these techniques. Uncertainty quantification and sensitivity analysis have been extensively used to study atmosphere and ocean (Kalra et al 2017;Aleksankina et al 2018), hydrology (Roy et al 2018), nuclear waste disposal (Saltelli and Tarantola 2002), or basin and crust studies (Maina and Guadagnini 2018;Colombo et al 2018;Formaggia et al 2013).…”
Section: Introductionmentioning
confidence: 99%