Mines Repository (Colorado School of Mines)
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Model parameter uncertainty and climate change effects in hydropower vulnerability projections
No full textIncludes bibliographical references.2025 Spring.Hydropower is a major source of renewable energy in the Western United States and could be a particularly important source of both firm power and ancillary services in a decarbonizing grid. However, hydropower availability is vulnerable to drought conditions exacerbated by climate change, with reservoir levels falling below minimum power pool thresholds. An under-examined component of this vulnerability is how model parameter uncertainty affects simulated reservoir levels. Here, I investigate the extent to which large-scale hydrologic models can capture the effect of climate change on reservoir levels for three reservoirs in California, and how parameter uncertainty affects conclusions derived from these models. Climate data from Daymet is processed through pywatershed, a Python-based implementation of the Precipitation-Runoff Modeling System (PRMS) used in the National Hydrologic Model. The resulting runoff estimates serve as input for mosartwmpy, which simulates water management, including reservoir storage and release. Climate changes are simulated using a delta approach on historical climate data and the Generalized Likelihood Uncertainty Estimation (GLUE) method is used to address parameter uncertainty. I find that, though on average reservoir inflows decrease, center of timing shifts earlier, and low storage frequency increases in a warming climate, the effects of parameter choice are significant enough to overcome the effects of simulated warming. However, the changes in some metrics due to incremental temperature increases are better constrained despite parameter uncertainty. These findings underscore the importance of considering a range of model outcomes and metrics when making water management decisions
Report of the Lower Flat group of placer claims, Hopewell mining district, Rio Arriba County, New Mexico
No full textMine report no. 1843.Typescript (carbon copy).Includes maps
Report, Ironsides group of mines, Idaho
No full textMine report no. 1746.Typescript (carbon copy).Includes three maps, handwritten data, and photocopied information
Modeling post-wildfire mountain headwaters hydrology in an experimental watershed in Colorado
No full textIncludes bibliographical references.2025 Spring.The number of wildfires and the resulting total burned area has been increasing in Colorado. Besides disturbing vegetation, wildfires have both immediate and long-term effects on hydrologic and biogeochemical cycles of forested mountainous areas. Severe fires generally reduce water infiltration into the soil, increasing surface runoff and causing nutrients and sediments to wash away and subsequently compromise the quality of downstream water resources, in addition to creating landslide and flood hazards. After wildfires, agencies rapidly act to minimize nutrient loss and topsoil erosion using different slope-stabilization methods, such as wood-shred mulching. In these scenarios, hydrologic modeling is a useful tool to understand how perturbations to the water cycle may influence flows, but limited data is available in remote, rugged, burned landscapes for model inputs. This study applied the Agricultural Ecosystems Services (Ages) semi-distributed ecohydrology model using various gridded climatological datasets to simulate post-fire hydrological responses at sites affected by the Cameron Peak wildfire in Colorado.
The study provides insights into vegetation and hydrology interactions across various watershed locations, influenced by factors like crop type, elevation, and aspect. Notably, unburned HRUs displayed consistent interception of precipitation throughout the growing season, contrasting with burned HRUs, which showed increasing interception capability as they revegetated. This difference underpins the significant hydrologic alterations post-fire, with burned areas experiencing higher throughfall and less water retention, leading to increased surface runoff, especially following significant precipitation events. This study focuses on both hourly and sub-hourly resolutions for capturing detailed process dynamics, as well as finer spatial resolution than many hydrologic models, which are not common and represent important first steps toward simulating post-fire hydrologic responses at the appropriate spatiotemporal scale.
Calibration metrics, including the Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) highlighted the model's competent representation of general hydrologic conditions (KGE = 0.7-0.76; NSE =0.12-0.47). However, discrepancies in peak flow predictions emphasized the need for model adjustments, particularly in accurately capturing large runoff events. This aspect was critical during the calibration period, where the model struggled with prediction of peak flows in the calibration period (PBIAS = 36%), but improved significantly for the validation period (PBIAS = 1%).
The model's ability to operate at sub-hourly resolutions opens new avenues for detailing the timing of runoff events—a crucial feature for managing flood risks and monitoring watershed health in post-fire scenarios. This level of temporal resolution is particularly beneficial for capturing the rapid hydrological responses characteristic of smaller or steeply sloped subwatersheds. In this regard, the Bennett Creek Ages model has the advantage over most post-fire modeling studies as the temporal scale is more appropriate for simulating post-fire hydrologic responses. Modeled time of concentration is slightly elevated for mulched subwatershed than unmulched subwatersheds.
This research contributes to advancing knowledge in the areas of mountain and fire-related hydrologic modeling, high-resolution modeling using remote-sensing data, and informs agencies of the impacts of best management practices in critical zone rehabilitation treatments after wildfires
Orogrande Gold Mining Co.: incorporated under the laws of the State of Washington
No full textMine report no. 1731.Includes one map."Rec'd from F.M. Johnson - 9-13-23" handwritten at top of report
Vein textures at the Moss low-sulfidation epithermal gold deposit, Arizona: constraints on the processes of mineral deposition
No full textIncludes bibliographical references.2025 Spring.The Moss low-sulfidation epithermal deposit in the northern Oatman district in northwest Arizona formed as a result of the Miocene extension in the Colorado River extensional corridor. The main vein zone can be traced over a strike length of ~6.2 km although elevated precious metal grades occur primarily along a central, ~1.9 km-long segment. The vein zone crosscuts the medium-grained Moss monzonite and the Peach Springs Tuff, which was deposited during the 18.78 ± 0.02 Ma eruption of the Silver Creek Caldera. The highest precious metals grades occur where vein intersections contain dark gray bands of ore minerals, including native gold, acanthite, and silver sulfosalts. The ore minerals occur as dendritic aggregates hosted by a matrix of quartz that formed through recrystallization of a noncrystalline silica precursor originally deposited along vein walls and cementing wall rock clasts in breccias. Recrystallization of the noncrystalline silica matrix has progressed to completeness resulting in the development of characteristic quartz textures such as mosaic quartz characterized by interpenetrating grain boundaries and flamboyant quartz showing radiating arrays of inclusions. Prismatic quartz transected by recrystallization fronts containing abundant inclusions is common. The ore-bearing bands alternate with bands that contain bladed calcite. The texture of the calcite is inconsistent with calcite growth in open space. Similar to the ore mineral dendrites, the textural evidence suggests that the calcite blades grew in the gel-like silica matrix. This type of calcite is texturally distinct from lattice-bladed calcite in which polyhedral cavities separate blades. Microthermometric investigations suggest that calcite deposition occurred at ~265°C at approximately 600 m below the paleosurface. It is proposed here that ore mineral formation and the growth of calcite in the noncrystalline silica precursor took place during short-lived events of fluid flashing at far-from-equilibrium conditions. The amount of vapor present during flashing presumably played a key control on mineral precipitation and growth
Post-earthquake impact modeling with uncertainty propagation and Bayesian inference
No full textIncludes bibliographical references.2025 Spring.Following a major earthquake, applying data-driven models is vital for quickly estimating potential damage, losses, and subsequent hazards, such as landslides and liquefaction, while also accounting for uncertainty. This approach is essential for effective post-disaster planning, rescue operations, and recovery efforts. This thesis enhances existing modeling methodologies developed by the U.S. Geological Survey (USGS) for three post-earthquake impact scenarios by efficiently incorporating uncertainties. These scenarios include: (i) ground motion estimates based on the rupture's characteristics and shaking data in the form seismic recordings and intensity observations; (ii) assessments of liquefaction probability; and (iii) fatality estimates for affected regions.
We utilize Bayesian statistical methods to refine existing model estimates and investigate uncertainty propagation in ground motion estimation, analyzing its effects on downstream models, including fatality estimates. Our new modeling approaches, detailed in five chapters of the thesis, focus on: (i) partitioning ground motion uncertainty when conditioned on station data; (ii) evaluating the impact of uncertainty in ground motion forecasts for post-earthquake effects; (iii) assessing the influence of uncertain rupture dimensions on observation-conditioned ground motions and subsequent impact estimates; (iv) updating regional-scale geospatial liquefaction assessments with locally available geotechnical data; and (v) presenting an updated hierarchical Bayesian framework for earthquake fatality estimation. We validate all these methodologies using several recorded post-earthquake data sets
Quantitative assessment of metabolic health using dynamical systems informed by Gaussian processes
No full textIncludes bibliographical references.2025 Spring.As glucose enters the bloodstream after a meal, the beta cells of the pancreas release the hormone insulin to signal glucose uptake by tissues throughout the body. Since insulin plays a major role in the regulation of glucose concentration, accurate and precise estimation of insulin secretion during a response to ingested glucose provides insight into beta cell function. Though insulin secretion cannot be observed in real time during a physiological meal response, mathematical and statistical tools can reconstruct continuous secretion profiles for insulin from discrete measurements taken from plasma.
This work introduces new methods to infer insulin secretion rate with the quantification of uncertainty. We place particular emphasis on the natural integration of dynamical systems and Gaussian processes (GPs) that can be accomplished using Bayesian hierarchical models (BHMs). First, we present a novel BHM that combines an established model of C-peptide dynamics with a GP prior on insulin secretion rate (ISR) to accurately reconstruct ISR and quantify uncertainty from noisy C-peptide measurements. We validate the method by implementation on data from youth with and without cystic fibrosis. Next, we improve upon the GP model of ISR by incorporating additional physiological constraints and discuss the effects on posterior ISR distributions. Finally, we present a novel approach for the estimation of glucose derivatives from noisy glucose observations using variably scaled covariance kernels. Robust estimation of glucose derivatives is necessary for future extensions of ISR inference incorporating explicit glucose dependence, and variably scaled kernels can directly leverage knowledge of the sampling schedule and target function to define covariances flexible enough for implementation in heterogeneous data sets that readily adapt to the sampling schedules of different experimental protocols. Together, these contributions lay a foundation for flexible, physiologically informed inference of insulin secretion dynamics from clinical data
Evaluation of vapor mitigation system effectiveness and unsaturated zone VOC vapor transport using a 1-D vapor model
No full textIncludes bibliographical references.2025 Spring.We assess the potential for successful cessation of a sub-slab vapor intrusion mitigation system (VIMS) at a volatile organic compound (VOC) contaminated site. A numerical model is developed to simulate migration of VOCs within the unsaturated zone where two primary migration pathways are considered: (1) Beneath a building where residual non-aqueous phase liquid contamination may be present, and (2) beneath an adjacent open ground surface where rainfall will infiltrate. Both pathways include vapor migration from contaminated groundwater, but only the building model includes measured shallow soil contamination and only the open-ground pathway includes the effects of migrating infiltration fronts. Model simulations are used to assess the potential for vapor intrusion (VI) following VIMS operation, including the magnitude of remaining source concentrations, infiltration impact, and potential for vapor contaminant rebound. The model, developed using historical data from a VI-impacted Superfund Site with two contaminant sources (shallow soil and shallow groundwater), addresses concerns regarding VIMS deactivation that has been operating since 2016