Salvatore Straface

A Potential‐Based Inversion of Unconfined Steady‐State Hydraulic Tomography

The importance of estimating spatially variable aquifer parameters such as transmissivity is widely recognized for studies in resource evaluation and contaminant transport. A useful approach for mapping such parameters is inverse modeling of data from series of pumping tests, that is, via hydraulic tomography. This inversion of field hydraulic tomographic data requires development of numerical forward models that can accurately represent test conditions while maintaining computational efficiency. One issue this presents is specification of boundary and initial conditions, whose location, type, and value may be poorly constrained. To circumvent this issue when modeling unconfined steady-state pumping tests, we present a strategy that analyzes field data using a potential difference method and that uses dipole pumping tests as the aquifer stimulation. By using our potential difference approach, which is similar to modeling drawdown in confined settings, we remove the need for specifying poorly known boundary condition values and natural source/sink terms within the problem domain. Dipole pumping tests are complementary to this strategy in that they can be more realistically modeled than single-well tests due to their conservative nature, quick achievement of steady state, and the insensitivity of near-field response to far-field boundary conditions. After developing the mathematical theory, our approach is first validated through a synthetic example. We then apply our method to the inversion of data from a field campaign at the Boise Hydrogeophysical Research Site. Results from inversion of nine pumping tests show expected geologic features, and uncertainty bounds indicate that hydraulic conductivity is well constrained within the central site area.

Reconstruction of the Water Table from Self-Potential Data: A Bayesian Approach

Ground water flow associated with pumping and injection tests generates self-potential signals that can be measured at the ground surface and used to estimate the pattern of ground water flow at depth. We propose an inversion of the self-potential signals that accounts for the heterogeneous nature of the aquifer and a relationship between the electrical resistivity and the streaming current coupling coefficient. We recast the inversion of the self-potential data into a Bayesian framework. Synthetic tests are performed showing the advantage in using self-potential signals in addition to in situ measurements of the potentiometric levels to reconstruct the shape of the water table. This methodology is applied to a new data set from a series of coordinated hydraulic tomography, self-potential, and electrical resistivity tomography experiments performed at the Boise Hydrogeophysical Research Site, Idaho. In particular, we examine one of the dipole hydraulic tests and its reciprocal to show the sensitivity of the self-potential signals to variations of the potentiometric levels under steady-state conditions. However, because of the high pumping rate, the response was also influenced by the Reynolds number, especially near the pumping well for a given test. Ground water flow in the inertial laminar flow regime is responsible for nonlinearity that is not yet accounted for in self-potential tomography. Numerical modeling addresses the sensitivity of the self-potential response to this problem.

A model based on cellular automata for the parallel simulation of 3D unsaturated flow

Cellular automata (CA) are discrete dynamic systems that are used for modeling many physical systems. They are often used as an alternative to model and solve large-scale systems where the use of partial differential equations involve complex and computationally expensive simulations. The purpose of this work is to investigate the use of CA based techniques for modeling and parallel simulation of water flux in unsaturated soils. Unsaturated flow processes are an important topic in several branches of hydrology, soil science and agricultural engineering dealing with soil-atmosphere interaction, subsurface flow and transport processes. In this paper a CA model for 3D unsaturated flow simulation is proposed using an extension of the original computational paradigm of cellular automata. This model, aimed at simulating large-scale systems, uses a macroscopic CA approach where local laws with a clear physical meaning govern interactions among automata. Its correctness is proved by CAMELot system, which allows the specification, parallel simulation, visualization, steering and analysis of CA models in the same environment, using a friendly interface and providing at the same time considerable flexibility. The model has been validated with reference multidimensional solutions taken from benchmarks in literature, showing a good agreement, even in the cases where non-linearity was very marked. Furthermore, using some of these benchmarks we present a scalability analysis of the model and different quantization techniques aimed at reducing the number of messages exchanged and the execution time when simulations are characterized by scarce mass interactions.

Scaling Effect of the Hydraulic Conductivity in a Confined Aquifer

Abstract Previous studies showed that the values of the representative parameters of an aquifer, such as the hydraulic conductivity (k), increase with the scale, that is, with the aquifer volume involved in the measurement. The main cause of this behavior is commonly ascribed to the heterogeneity of the porous media. Heterogeneity influences the scaling behavior differently for laboratory or field measurement, but the scale dependence of hydraulic conductivity is not dependent on the specific measurement method. In the present study, the scaling law of this parameter was determined on a real confined aquifer, using measurements obtained, both in the laboratory (flow cells) and the field (slug tests and aquifer tests). The corresponding data were statistically analyzed. A scaling law was proposed for both the laboratory and field scale, using the data obtained from flow cells, slug tests, and aquifer tests. Afterward, the scaling law was estimated at just the field scale, first using the slug tests and aquifer tests and then using only the aquifer test data. The scale dependence of the storativity was also investigated for all field measurements and then using only the aquifer test data. In conclusion, for both hydraulic conductivity and storativity, the trend to reach an upper bound increasing the scale parameter was investigated in the scale ranges of 67 and 99 m, respectively, examining only the data set relative to aquifer test measurements.

Relating Non-equilibrium Solute Transport and Porous Media Physical Characteristics

Breakthrough data for solute tracer transport at different velocities, covering a wide range of particle sizes and particle shapes corresponding to 324 breakthrough curves, were used in this study. Analysis was carried out for three granular porous media: crushed granite, gravel, and Leca® (a commercial insulation material). Mobile–immobile phase (MIM) solute transport parameters (dispersivity, mass transfer, and mobile (active) porosity) for non-equilibrium mass transport were determined for each breakthrough curve by fitting a MIM solute transport model to the breakthrough data. The resulting set of solute transport parameters was correlated with porous medium physical properties (particle size distribution and particle shape) to establish a set of simple expressions for estimating the MIM solute transport parameters. Linear expressions for predicting the solute dispersivity, mass transfer, and mobile phase porosity from porous medium particle size distribution (mean particle diameter and width of particle size distribution) and particle shape were developed based on regression analysis. A partial validation of these expressions indicated that the developed expressions are able to accurately predict solute transport parameters from porous medium physical properties.

A Comparison of deterministic and probabilistic approaches for assessing risks from contaminated aquifers: an Italian case study.

In this article we consider the methods of deterministic and probabilistic risk analysis regarding the presence of chemical contaminants in soil, water and air, with a broader meaning than usual for the latter, as we extended the probabilistic treatment to the parameters that influence the transport to a greater extent, in particular hydraulic conductivity and partition coefficient. These parameters, to which only one value is assigned, are considered here as random variables. The objective of the study reported herein was to demonstrate that application of the probabilistic method of risk assessment is preferable to the use of the deterministic method. Both methods yield contaminant removal levels that will reduce adverse effects on human health and the environment, but results from the deterministic method are typically more conservative than necessary, and are thus more costly to achieve. In addition, we found it essential to consider the importance of random variables (the parameters influencing the flow and the transport), such as the hydraulic conductivity and the partition coefficient, when assessing health risks. Both methodologies of health risk analysis, deterministic and probabilistic, were applied to a site in southern Italy, contaminated by heavy metals. The results obtained confirm the purposes of this study.

Gas-Solute Dispersivity Ratio in Granular Porous Media as Related to Particle Size Distribution and Particle Shape

Measurements of solute dispersion in porous media is generally much more time consuming than gas dispersion measurements performed under equivalent conditions. Significant time savings may therefore, be achieved if solute dispersion coefficients can be estimated based on measured gas dispersion data. This paper evaluates the possibility for estimating solute dispersion based on gas dispersion measurements. Breakthrough measurements were carried out at different fluid velocities (covering the same range in Reynolds number), using O2 and NaCl as gas and solute tracers, respectively. Three different, granular porous materials were used: (1) crushed granite (very angular particles), (2) gravel (particles of intermediate roundness) and (3) Leca® (almost spherical particles). For each material, 21 different particle size fractions were used. Gas and solute dispersion coefficients were determined by fitting the advection–dispersion equation to the measured breakthrough curves and in turn used to calculate gas and solute dispersivities as a function of mean particle size (Dm) and particle size range (R) for the 63 particle size fractions considered. The results show that solute and gas dispersivities are related and that their ratio depends on both R and Dm. Based on these observations a simple model for predicting the dispersivity ratio from Dm and R, was proposed.

Accelerating a three-dimensional eco-hydrological cellular automaton on GPGPU with OpenCL

This work presents an effective implementation of a numerical model for complete eco-hydrological Cellular Automata modeling on Graphical Processing Units (GPU) with OpenCL (Open Computing Language) for heterogeneous computation (i.e., on CPUs and/or GPUs). Different types of parallel implementations were carried out (e.g., use of fast local memory, loop unrolling, etc), showing increasing performance improvements in terms of speedup, adopting also some original optimizations strategies. Moreover, numerical analysis of results (i.e., comparison of CPU and GPU outcomes in terms of rounding errors) have proven to be satisfactory. Experiments were carried out on a workstation with two CPUs (Intel Xeon E5440 at 2.83GHz), one GPU AMD R9 280X and one GPU nVIDIA Tesla K20c. Results have been extremely positive, but further testing should be performed to assess the functionality of the adopted strategies on other complete models and their ability to fruitfully exploit parallel systems resources.

Relating solute and gas dispersion in gravel at different transport velocities

Dispersion is a key process controlling gas and solute transport in porous media. Performing dispersion tracer tests for solutes is generally much more time consuming than for gases. Ability to estimate solute dispersion based on gas dispersion data is therefore advantageous. The aim of this study was to investigate the relationship between solute and gas dispersion in selected porous media to develop a procedure for estimating solute dispersion from gas dispersion. Solute (NaCl) and gas (O2) breakthrough curves were measured at different fluid (water and gas) velocities (at identical Reynolds numbers for the two fluids). A commercial crushed granite, available in multiple particle sizes, was used as porous medium. NaCl and O2 dispersion coefficients were determined from measured breakthrough data and used to calculate solute and gas dispersivity as a function of particle size (Dm) and particle size range (R). Both solute and gas dispersivity increased with increasing R, and decreasing Dm. A simple relationship between dispersivities was found.

Cellular automata modeling of environmental systems

Flow and transport processes in unsaturated soil are analyzed through a simulation environment based on cellular automata (CA). The modeling proposed in this chapter represents an extension of the original computational paradigm of cellular automata, because it uses a macroscopic CA approach where local laws with a clear physical meaning govern interactions among automata. This CA structure, aimed at simulating a large-scale system, is based on functionalities capable of increasing its computational capacity, both in terms of working environment and taken from benchmarks in literature, showing a good agreement even in the cases where non-linearity is very marked. Furthermore, some analyses have been carried out considering quantization techniques aimed at transforming the CA model into an asynchronous structure. The use of these techniques in a three-dimensional benchmark allowed a considerable reduction in the number of local interactions among adjacent automata without changing the efficiency of the model, especially when simulations are characterized by scarce mass exchanges.