Use of groundwater lifetime expectancy for the performance assessment of a deep geologic waste repository: 1. Theory, illustrations, and implications

Long-term solutions for the disposal of toxic wastes usually involve isolation of the wastes in a deep subsurface geologic environment. In the case of spent nuclear fuel, if radionuclide leakage occurs from the engineered barrier, the geological medium represents the ultimate barrier that is relied upon to ensure safety. Consequently, an evaluation of radionuclide travel times from a repository to the biosphere is critically important in a performance assessment analysis. In this study, we develop a travel time framework based on the concept of groundwater lifetime expectancy as a safety indicator. Lifetime expectancy characterizes the time that radionuclides will spend in the subsurface after their release from the repository and prior to discharging into the biosphere. The probability density function of lifetime expectancy is computed throughout the host rock by solving the backward-in-time solute transport adjoint equation subject to a properly posed set of boundary conditions. It can then be used to define optimal repository locations. The risk associated with selected sites can be evaluated by simulating an appropriate contaminant release history. The utility of the method is illustrated by means of analytical and numerical examples, which focus on the effect of fracture networks on the uncertainty of evaluated lifetime expectancy.Comment: 11 pages, 8 figures; Water Resources Research, Vol. 44, 200

On the biases affecting water ages inferred from isotopic data

Groundwater age has become a fundamental concept in groundwater hydrology, but ages originating from isotopic analyses are still identified with a lack of clarity and using models that occasionally are unrealistic. If the effect of advection and dispersion on water ages has already been extensively identified, very few studies address the reliability of using radiometric ages as derived from isotopic data to estimate aquifer properties such as average velocities. Using simple one-dimensional and two-dimensional analytical solutions for single-site and two-sites mobile-immobile systems, we compare the radiometric ages to the mean ages (or residence times) as deduced from a direct, physically-based simulation approach (using the mean age equation), and show that the competition between isotope decay rate and dispersion coefficient can generate important discrepancies between the two types of ages. A correction for the average apparent velocity originating from apparent isotopic ages is additionally provided. The particular case of the Tritium age dating method is also addressed, and a numerical example is finally given for illustrating the analysis considering a more complex and heterogeneous aquifer system. Our results suggest that age definitions based on the radioactivity of isotopes may not be representative for the mean age of the sample or for the groundwater velocity at given locations, and may not always be suitable for constraining the calibration of hydrogeological models.Comment: 20 pages, 7 figures; Journal of Hydrology, 201

A method for the stochastic modeling of karstic systems accounting for geophysical data: an example of application in the region of Tulum, Yucatan Peninsula (Mexico)

The eastern coast of the Yucatan Peninsula, Mexico, contains one of the most developed karst systems in the world. This natural wonder is undergoing increasing pollution threat due to rapid economic development in the region of Tulum, together with a lack of wastewater treatment facilities. A preliminary numerical model has been developed to assess the vulnerability of the resource. Maps of explored caves have been completed using data from two airborne geophysical campaigns. These electromagnetic measurements allow for the mapping of unexplored karstic conduits. The completion of the network map is achieved through a stochastic pseudo-genetic karst simulator, previously developed but adapted as part of this study to account for the geophysical data. Together with the cave mapping by speleologists, the simulated networks are integrated into the finite-element flow-model mesh as pipe networks where turbulent flow is modeled. The calibration of the karstic network parameters (density, radius of the conduits) is conducted through a comparison with measured piezometric levels. Although the proposed model shows great uncertainty, it reproduces realistically the heterogeneous flow of the aquifer. Simulated velocities in conduits are greater than 1cm s−1, suggesting that the reinjection of Tulum wastewater constitutes a pollution risk for the nearby ecosystem

Use of groundwater lifetime expectancy for the performance assessment of a deep geologic radioactive waste repository:2. Application to a Canadian Shield environment

Cornaton et al. [2007] introduced the concept of lifetime expectancy as a performance measure of the safety of subsurface repositories, based upon the travel time for contaminants released at a certain point in the subsurface to reach the biosphere or compliance area. The methodologies are applied to a hypothetical but realistic Canadian Shield crystalline rock environment, which is considered to be one of the most geologically stable areas on Earth. In an approximately 10\times10\times1.5 km3 hypothetical study area, up to 1000 major and intermediate fracture zones are generated from surface lineament analyses and subsurface surveys. In the study area, mean and probability density of lifetime expectancy are analyzed with realistic geologic and hydrologic shield settings in order to demonstrate the applicability of the theory and the numerical model for optimally locating a deep subsurface repository for the safe storage of spent nuclear fuel. The results demonstrate that, in general, groundwater lifetime expectancy increases with depth and it is greatest inside major matrix blocks. Various sources and aspects of uncertainty are considered, specifically geometric and hydraulic parameters of permeable fracture zones. Sensitivity analyses indicate that the existence and location of permeable fracture zones and the relationship between fracture zone permeability and depth from ground surface are the most significant factors for lifetime expectancy distribution in such a crystalline rock environment. As a consequence, it is successfully demonstrated that the concept of lifetime expectancy can be applied to siting and performance assessment studies for deep geologic repositories in crystalline fractured rock settings.Comment: 14 pages, 14 figures; Water Resources Research, Vol. 44, 200

GIM (Groundwater Integrated Modelling). The hydrogeological compiler

Complex problems in Earth Sciences demand the use of numerical models. To this end, a large number of codes have been developed during the last two decades. In spite of their power, as displayed in their many applications, these codes are sparse and, most often, used in the academic framework. To make things worse, they are aimed at solving a given set of physical phenomena (e.g. most codes solve groundwater flow and contaminant transport, but they do not take into account material defor- mation, others include deformation but not heat transfer, etc.) and most often they do not integrate stochastic techniques. GIM (Groundwater Integrated Modelling) is aimed at providing a platform to fill this gap. The objective is to integrate the existing codes in an overall fully-parallel object oriented FORTRAN 95 structure. Thus, the capabilities of GIM are numerous (differ- ent solvers of direct and inverse problem, of groundwater flow, contaminant (conservative or not) or heat transport, etc.) as it takes profit of those of the codes embedded in its structure. The use of GIM is illustrated with a simple example consisting of a Monte Carlo analysis of flow and transport problem: 1. Read data common to most of the existing \u201chost\u201d codes (finite elements or finite differences mesh, geostatistical model, state variable measurements, etc.) in an XML fashion. \u201cHost\u201d code particular variables (options, tolerances, convergence criteria, etc.) are supplied separately. 2. The data are used to pre-process the initial hydraulic conductivity fields on the basis of the geostatistical model. These fields will be calibrated in step 4.3. Write data in the appropriate format for the \u201chost\u201d code. 4. Execute \u201chost\u201d code(s). In this example, an inversion code is used. However, many codes can be used at step 4 (e.g. for solving the inherent direct problem, modeller can use a flow simulator to calculate the velocity field driving the con- taminant transport, which will be simulated using a \u201craw\u201d transport simulator). 5. Collect results (the calibrated fields). 6. Post-process the output (e.g. histogram of hydraulic conductivity). Including \u201chost\u201d codes in the overall structure of GIM is easy. One needs to add a routine for writing data at step 3 and a routine for reading the output at step 5. This confers versatility and an ample room for future developments

in advective–dispersive systems: 1. Generalized reservoir theory

which should be used for any reference to this wor