In recent years, the Sample Analysis at Mars (SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub-diurnally, which is difficult to reconcile with an as-yet-unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically-induced transport of methane from an underground source to the surface, modulated by temperature-dependent adsorption. The subsurface fractured-rock seepage model is coupled to a simplified atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub-diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for Curiosity’s potentially final sampling campaign. The best-performing scenarios indicate a significant, short-lived methane pulse just prior to sunrise, the detection of which by SAM-TLS would be a potential indicator of the contribution of barometric pumping to Mars’ atmospheric methane variations.

Two outstanding questions for future Greenland predictions are (1) how enhanced meltwater draining beneath the ice sheet will impact the behavior of large tidewater glaciers, and (2) to what extent tidewater glacier velocity is driven by changes at the terminus versus changes in sliding velocity due to meltwater input. We present a two-way coupled framework to simulate the nonlinear feedbacks of evolving subglacial hydrology and ice dynamics using the Subglacial Hydrology And Kinetic, Transient Interactions (SHAKTI) model within the Ice-sheet and Sea-level System Model (ISSM). Through coupled simulations of Helheim Glacier, we find that terminus effects dominate the seasonal velocity pattern up to 15 km from the terminus, while hydrology primarily drives the velocity response upstream. With increased melt, the hydrology influence yields seasonal acceleration of several hundred meters per year in the interior, suggesting that hydrologic forcing will play an important role in future mass balance of tidewater glaciers.

Solute transit or travel time distributions (TTDs) in catchments are relevant to both hydrochemical response and inference of hydrologic mechanisms. Long-tailed TTDs and fractal scaling behavior of stream concentration power spectra (~1/frequency, or 1/frequency to a power < 2) are widely observed in catchment studies. In several catchments, a significant fraction of streamflow is derived from groundwater in shallow fractured bedrock, where matrix diffusion significantly influences solute transport. I present frequency and time domain theoretical analyses of solute transport to quantify the influence of matrix diffusion on fractal scaling and long-tailed TTDs. The theoretical concentration power spectra exhibit fractal scaling, and the corresponding TTDs resemble a gamma distribution. The tails of the TTDs are influenced by accessible matrix width, exhibiting a sustained power-law (rather than exponential) decline for large matrix widths. Application to an experimental catchment shows that theoretical spectra match previously reported power spectral estimates derived from concentration measurements.

The spatiotemporal patterns of injection-induced seismicity (IIS) are commonly interpreted with the concept of a triggering front, which propagates in a diffusion-like manner with an associated diffusivity parameter. Here, we refer to this diffusivity as the “seismic diffusivity”. Several previous studies implicitly assume that seismic diffusivity is equivalent to the effective hydraulic diffusivity of the subsurface, which describes the behavior of the mean pressure field in heterogeneous porous media. Seismicity-based approaches for hydraulic characterization or simulations of IIS using domains of homogeneous equivalent porous media are implicitly based on this assumed equivalence. However, seismicity is expected to propagate with the threshold triggering pressure, and thus not be controlled by the evolution of the mean pressure field. We present numerical simulations of fluid injection to compare the seismic and effective hydraulic diffusivities in heterogeneous formations (including fractured rock). The numerical model combines uncoupled, linear pressure diffusion with the Mohr-Coulomb failure criterion to simulate IIS. We demonstrate that connected pathways of relatively high hydraulic diffusivity in heterogeneous media (particularly in fractured rock domains) allow the threshold triggering pressure to propagate more rapidly than predicted by the effective hydraulic diffusivity. As a result, the seismic diffusivity is greater than the effective hydraulic diffusivity in heterogeneous porous media, possibly by an order of magnitude or more. Additionally, we present a case study of IIS near Soultz-sous-Forêts where seismic diffusivity is found to be at least one order of magnitude larger than the effective hydraulic diffusivity.

The most general models for glacial hydrologic conduits include an energy equation, wherein a heat transfer coefficient controls the rate at which heat generated by mechanical energy dissipation is transferred to conduit walls, producing melt. Previous models employ heat transfer coefficients derived for engineering heat transfer problems, where heat is transferred between the walls of a conduit and a flowing fluid that enters the duct at a temperature different from the wall temperature. These heat transfer coefficients may not be appropriate for glacial hydrologic conduits in temperate ice, where the flowing fluid (water) and conduit walls (ice) are at almost the same temperature, and the heat generated by mechanical energy dissipation within the flow is transferred to the walls to produce melt. We revisit the energy transport equations that provide a basis for the derivation of heat transfer coefficients and highlight the distinctions between the heated walls and dissipated energy heat transfer cases. We present computational results for both cases across a range of Reynolds numbers in circular conduit and sheet geometries. For the heated walls case, our results are consistent with the widely used Dittus-Boelter heat transfer correlation, which has been used in previous glacial conduit models. We show that the heat transfer coefficient for transfer of heat generated by mechanical energy dissipation to conduit walls is smaller than that calculated using the Dittus-Boelter correlation by approximately a factor of 2.

Quantitative prediction of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far-reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate flow, transport and mechanical deformation behavior. They play a central role in CO2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require prediction of fracture-dependent quantities of interest such as CO2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research that cover field- and laboratory-scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one’s ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measures of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory-scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics-based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field-scale fracture systems. Finally, we review the use of machine learning-based emulators to rapidly investigate different fracture property scenarios and to accelerate physics-based models by orders of magnitude to enable uncertainty quantification and near real-time analysis.