Roseanna M. Neupauer

Geothermal energy is an attractive alternative to fossil fuels because it is sustainable, supplied continuously, and has a minimal carbon footprint. Where subsurface temperatures are significantly warmer than surface temperatures, heat from the subsurface can be extracted for use on the earth’s surface. In some areas, water carries heat to the surface in the form of thermal springs that support recreation and tourism. An unanswered question is whether larger scale geothermal development will lead to cooling of the recreational thermal springs or a reduction in flow rate, negatively impacting the industry. This work uses groundwater flow and heat transport modeling to evaluate the potential impact of geothermal energy production on hot springs. The modeled system is loosely based on the geologic and thermal regimes near Mt. Princeton in central Colorado, USA. The results show that the extraction of deep thermal water for geothermal energy production and the subsequent reinjection of cooled water has the potential to reduce the water temperature of surface springs.

Analytical Expressions for Spatial Attenuation of Time-Periodic Heat Sources in an Aquifer

Streamflow depletion is traditionally defined as the instantaneous change in the volumetric rate of aquifer–stream exchange after a finite period of continuous groundwater extraction. In the present study an alternative metric of streamflow depletion was considered: cumulative stream depletion (CSD), which described the total volumetric reduction in flow from an aquifer to a stream resulting from continuous groundwater extraction over a finite period. A novel analytical solution for the prediction of CSD was derived, based upon an existing solution that accounted for streambed conductance and partial stream penetration. Separately, a novel numerical solution for the prediction of CSD was derived, based on the derivation of an adjoint state solution. The accuracy of the two new solutions was demonstrated through benchmarking against existing analytical solutions and perturbation-based results, respectively. The derivation of the loading term used in the adjoint state solution identified three parameters of relevance to CSD prediction. First is streambed hydraulic conductivity and thickness, both of which contribute to a lumped parameterization of streambed conductance. Second is aquifer specific yield, which controls the rate at which hydraulic perturbations propagate through an aquifer. The computational advantage of the adjoint state approach was highlighted, in which a single numerical model run can be used to predict CSD resulting from any potential groundwater extraction location. The reduction in computation time achieved was proportional to the number of potential extraction well locations. Where the number of locations is large, reductions in computation times of nearly 100 % can be achieved.

Aufeis (also known as icings) are large sheet‐like masses of layered ice that form in river channels in arctic environments in the winter as groundwater discharges to the land surface and subsequently freezes. Aufeis are important sources of water for Arctic river ecosystems, bolstering late summer river discharge and providing habitat for caribou escaping insect harassment. The aim of this research is to use numerical simulations to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis. We used a conceptual model based on geophysical data from the Kuparuk aufeis field on the North Slope of Alaska to develop a two‐dimensional heterogeneous vertical profile model of groundwater flow, heat transport, and freeze/thaw dynamics. Modelling results showed that groundwater can flow to the land surface through subvertical high permeability pathways during winter months when the lower permeability soils near the land surface are frozen. The groundwater discharge can freeze on the surface, contributing to aufeis formation throughout the winter. We performed sensitivity analyses on subsurface properties and surface temperature and found that aufeis formation is most sensitive to the volume of unfrozen water available in the subsurface and the rate at which the subsurface water travels to the land surface. Although a trend of warming air temperatures will lead to a greater volume of unfrozen subsurface water, the aufeis volume can be reduced under warming conditions if the period of time for which air temperatures are below freezing is reduced.

Backward probabilities, such as the backward travel time probability density function for pollutants in natural aquifers/rivers, have been used by hydrologists for decades in water quality applications. Calculating these backward probabilities, however, is challenging due to non-Fickian pollutant transport dynamics and velocity resolution variability at study sites. To address these issues, we built an adjoint model by deriving a backward-in-time fractional-derivative transport equation subordinated to regional flow, developed a Lagrangian solver, and applied the model/solver to trace pollutant transport in diverse flow systems. The adjoint model subordinates to a reversed regional flow field, transforms forward-in-time boundaries into either absorbing or reflective boundaries, and reverses the tempered stable density to define backward mechanical dispersion. The corresponding Lagrangian solver efficiently projects backward super-diffusive mechanical dispersion along streamlines. Field applications demonstrate the adjoint subordination model's success with respect to recovering release history, groundwater age, and pollutant source locations for various flow systems. These include systems with upscaled constant velocity, nonuniform divergent flow fields, or fine-resolution velocities in a nonstationary, regional-scale aquifer, where non-Fickian transport significantly affects pollutant dynamics and backward probabilities. Caution is needed when identifying the phase-sensitive (aqueous vs. absorbed) pollutant source in natural media. The study also explores possible extensions of the adjoint subordination model for quantifying backward probabilities of pollutants in more complex media, such as discrete fracture networks.

SUTRA-ICE input files for simulations of aufeis formation

Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists for decades in water-quality related applications. Reliable calculation of backward probabilities, however, has been challenged by non-Fickian pollutant transport dynamics and variability in the resolution of velocity at study sites. To address these two issues, we built an adjoint model by deriving a backward-in-time fractional-derivative transport equation subordinated to regional flow, developed a Lagrangian solver, and applied the model/solver to backtrack pollutant transport in various flow systems. The adjoint model applies subordination to a reversed regional flow field, converts forward-in-time boundaries to either absorbing or reflective boundaries, and reverses the tempered stable density to define backward mechanical dispersion. The corresponding Lagrangian solver is computationally efficient in projecting backward super-diffusive mechanical dispersion along streamlines. Field applications demonstrate that the adjoint subordination model can successfully recover release history, dated groundwater age, and spatial location(s) of pollutant source(s) for flow systems with either upscaled constant velocity, non-uniform divergent flow field, or fine-resolution velocities in a non-stationary, regional-scale aquifer, where non-Fickian transport significantly affects pollutant dynamics and backward probability characteristics. Caution is needed when identifying the phase-sensitive (aqueous versus absorbed) pollutant source in natural media. Possible extensions of the adjoint subordination model are also discussed and tested for quantifying backward probabilities of pollutants in more complex media, such as discrete fracture networks.