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 the unsaturated and saturated portions of the domain. We ground our work on the three-dimensional analytical model proposed by Mishra and Neuman (2011), which fully takes into account the richness of the physical process taking place across the unsaturated and saturated zones and storage effects in a finite radius pumping well. The relative influence of model parameter uncertainties on drawdown, moisture content, and gravity changes are quantified through (a) the Sobol' indices, derived from a classical decomposition of variance and (b) recently developed indices quantifying the relative contribution of each uncertain model parameter to the (ensemble) mean, skewness, and kurtosis of the model output. Our results document (i) the importance of the effects of the parameters governing the unsaturated flow dynamics on the mean and variance of local drawdown and gravity changes; (ii) the marked sensitivity (as expressed in terms of the statistical moments analyzed) of gravity changes to the employed water retention curve model parameter, specific yield, and storage, and (iii) the influential role of hydraulic conductivity of the unsaturated and saturated zones to the skewness and kurtosis of gravimetric variation distributions. The observed temporal dynamics of the strength of the relative contribution of system parameters to gravimetric variations suggest that gravity data have a clear potential to provide useful information for estimating the key hydraulic parameters of the system.

There are increasing evidences that probability distributions and associated statistical moments of a variety of hydrogeological and soil science variables and their spatial increments display distinctive scale-dependent features that are not captured by a typical Gaussian model. A Generalized Sub-Gaussian (GSG) model is able to capture key aspects of this pattern. We present the results of a suite of computational analyses set in a Monte Carlo framework and aimed at assessing the impact of a GSG structure of log hydraulic conductivity (Y) on transport of a conservative solute through a three-dimensional bounded porous medium under steady-state saturated Darcy flow. Our results indicate that the longitudinal spreading of a plume is on average significantly smaller for Sub-Gaussian than for Gaussian Y fields. Otherwise, the velocity field arising from a Sub-Gaussian Y field induces enhanced plume stretching with respect to what can be observed in a Gaussian Y setting, this aspect potentially influencing the strength of solute mixing within these two types of conductivity domains. We also find that, in some cases, it may be difficult to identify the nature of the underlying conductivity field relying solely on observations of solute concentrations migrating within the system. In this regard, we show that the action of local dispersion tends to mask the influence of Sub-Gaussianity on major transport metrics.