Browsing by Author "Gallen, Sean, committee member"
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Item Open Access Characterization and prediction of long-term arsenic mobility, dissolution, and kinetic behavior in arsenic contaminated floodplain deposits of Whitewood Creek and the Belle Fourche River, South Dakota(Colorado State University. Libraries, 2021) Ji, Mu, author; Ridley, John, advisor; Stednick, John, committee member; Borch, Thomas, committee member; Gallen, Sean, committee memberFrom 1877 to 1977, the Homestake Mine discharged over 100 million tons of arsenic-rich mine-wastes from Lead, South Dakota into Whitewood Creek (WWC), which joins the Belle Fourche River (BFR). Arsenopyrite and other arsenic-bearing minerals were deposited in tailings (containing between 0.12% to 0.35% arsenic) and mixed with uncontaminated alluvium along the floodplains of WWC and the BFR as overbank deposits and filling abandoned meanders. Since it is not feasible to remove millions of tons of contaminated sediments from the area, an understanding of arsenic mobility on long timescales is vital. Many studies have laid the framework for factors controlling arsenic mobility appropriate to fluvial sedimentary systems; investigating mechanisms of arsenic mobilization, adsorption/desorption kinetics, and the effects of pH, changing redox conditions, etc., however, these studies were conducted on relatively short time scales and did not quantify arsenic mass-budget on field-scales. This study focuses on the long-term retention, dissolution, and kinetic behavior of arsenic from mine tailings. The uniqueness of this site enables arsenopyrite dissolution behavior to be constrained over a 135-year timespan (1877-2012). This allows for the investigation of changes in arsenic's residence sites, its rate of release into the environment, calculation of its transport mass-budget, and elucidation of how natural processes have or have not remediated arsenic contamination over a span of 35 years since the deposition of tailings have ceased (1977-2012). For this investigation, sediment, surface water, and seep water samples were collected along reaches of WWC and the BFR for analysis of arsenic and other geochemical constituents. Sequential extractions of the sediments were performed to determine the mineralogical setting of the arsenic as well as the proportion of arsenic available at different rates of release into the environment. Additionally, various historical data (discharge levels, geochemical analyses of water and sediment samples) were compiled from the United States Geological Survey database. Regressions were applied to historical data to estimate the rate of physical and chemical arsenic removal from the WWC watershed. Sediments collected along the floodplains of WWC and the BFR exhibited arsenic concentrations ranging from approximately 100 to 4,000 mg/kg. The results from the sequential extractions applied to the sediments suggest arsenic is predominantly located in residence sites that are not easily accessible, and arsenic is not readily mobilized or released into solution in large quantities under normal environmental conditions seen in WWC and the BFR. An average of 16% of the arsenic is weakly bound to readily exchangeable surface sites, water-soluble secondary minerals and available for rapid release, or is adsorbed to exchange sites that easily exchange PO43- ions for adsorbed arsenic oxyanions, is weakly bound in amorphous to poorly crystalline fine-grained metal oxides/hydroxides, reducible phases, and easily soluble carbonates. An average of 24% of the arsenic is moderately strongly bound in weakly soluble secondary minerals like clays or crystalline fine-grained metal oxides/hydroxides and will be released relatively slowly with time. The remaining 60% of arsenic is interpreted to be relatively immobile and locked in arsenopyrite in part due to the formation of metal oxyhydroxide coating, which slows down the degradation of the mineral. These interpretations are supported by the elevated but still relatively low total arsenic concentrations (EPA MCL for arsenic is 0.01 mg/L) of in-stream water in WWC (averaging 0.037 mg/L) and in the BFR (averaging 0.021 mg/L), considering that in-stream sediments carried by WWC and the BFR have high arsenic concentrations (264 to 694 mg/kg). Based on regressions applied to 30 years of historical sediment transport and arsenic concentration in solution and in sediment load (1982-2012), the average annual total arsenic load transported out of WWC during these 30 years was estimated to be between 34 to 71 megagrams (Mg) per year. At this rate, based on the 17,400 to 50,800 Mg of arsenic that remain in storage along the floodplains of WWC, complete arsenic transport out of the floodplains of WWC would range between 250 to 1,500 years. The actual rate of arsenic removal is expected to be longer because the model is based on a uniform movement of uniformly distributed sediment, and historical patterns may not be reflective of future trends, as evidenced by the decline in suspended arsenic transport rate starting in the early- to mid-1980s. The constant shifting of the stream creates abandoned meanders along WWC that can store contaminated sediment where the stream no longer has access. Conversely, as the meanders shift over time, the once-abandoned meanders could be again accessed by WWC. The majority of suspended sediment transport occurs during flood events; approximately 88% of the total arsenic load moved during the years between 1983 to 2012 occurred in only 3 of the years (1983, 1984, and 1995). Thus, the rate of arsenic transport for the next 30-year period is uncertain and could be lower if the number of flood events remains low. Although the WWC area once experienced heavy environmental degradation during the period of active mining, natural processes have allowed for relatively stable current environmental conditions. However, the physical transport of arsenic-contaminated sediment and the slow release of arsenic to the environment endures downstream to the BFR into the Cheyenne River and Lake Oahe and will continue for many generations.Item Open Access Modeling risk of landslide initiation and runout in the Colorado Front Range under current and future climates(Colorado State University. Libraries, 2021) Byron, Elizabeth, author; Nelson, Peter, advisor; Niemann, Jeffrey, advisor; Gallen, Sean, committee memberPrecipitation-induced landslides pose risks to humans through property damage, disruption of infrastructure, injury, and loss of life. Due to the spatial and temporal heterogeneity of soil moisture and landscape characteristics that impact slope stability and potential impacts of climate change on landslide location, quantifying landslide risk to humans is difficult as uncertainties are not represented in available datasets. Recent developments have improved our ability to probabilistically model landslide initiation, thus allowing for the incorporation of spatial and temporal uncertainty in the prediction of the onset of hillslope failures. The ability to incorporate uncertainty in landslide models is particularly valuable for considering how climate change, which could impact vegetation cover and associated root cohesion, might alter the vulnerability of people and infrastructure to landslides. The aim of this analysis is to probabilistically forecast landslide susceptibility under climate change by incorporating changes in the type and distribution of vegetation while accounting for uncertainties in key properties. Using Landlab, a Python-based toolkit for landscape modeling, we perform Monte Carlo simulations with an infinite slope stability model to make spatially explicit calculations of the probability of landslide initiation. The soil moisture input to the landslide model is from the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model, which downscales coarse-resolution soil moisture by incorporating the dependence of soil moisture on topographic, vegetative, and soil characteristics. We evaluate model sensitivity and identify that vegetation, which impacts cohesion and soil depth, has a large impact on the model. We evaluate model performance by simulating landslide susceptibility over a 1333 km2 area of the Colorado Front Range as there is a large inventory of more than 1300 landslides from an extreme precipitation event in 2013. One anticipated effect of climate change in the Colorado Front Range is a reduction in the survivability of trees, which we incorporate through applying reductions to vegetative cohesion and vegetation cover. For the 2013 event, the model predicts 79.6% of the mapped landslides and 5.8% of the rest of the study area as being unstable. A deterministic model using mean values from the probability model and assuming FS ≤ 1 is unstable captures only 42% of observed landslides, supporting the use of the probabilistic model. The probabilities are low (P(F) < 0.2) for the majority of predicted failures with a concentration at higher (P(F) > 0.8) values, with the latter having higher slopes and lower vegetation. 66% of nodes with P(F) > 0 occur on south facing slopes where trees are less abundant. After incorporating climate change, we see an increase in the areas susceptible to landslides and a shift to more instability on north-facing slopes. Our study suggests that vegetation changes due to climate change could result in major shifts in the people and infrastructure susceptible to landslides in the Colorado Front Range. In conjunction with landslide initiation, determining landslide runout is important to fully analyze landslide risk. Landslide runout modeling for large areas is difficult due to limited information and the complexity of landslides. The difficulties of physically modeling landslides on large spatial scales have led to the development of empirical methods based on topographic attributes. While empirical models are limited in that they require calibration in new areas and thus can only be applied to areas with landslide inventories, they provide a way to model landslide runout at large spatial scales and identify areas for further, potentially more physically-based, analyses. We investigate whether topographic controls can be used to predict landslide termination. We develop a landslide runout model and apply it to a 10-m elevation grid. Our model routes landslides downslope with d8 flow direction method and uses a critical slope, defined as a minimum slope a landslide must encounter to end, and slope persistence, defined as the distance the landslide must travel under the critical slope, to represent landslide stopping locations. We apply our model to see if it can replicate landslide runout in the Colorado Front Range due to a large landslide inventory from a 2013 precipitation event that induced approximately 1300 mapped landslides. The calibrated model has a critical slope of 3° and a slope persistence of 20 m and predicts landslide distance in both the calibration and evaluation areas with a Nash-Sutcliffe (NS) value of 0.69 and 0.58, respectively. We compare our calibrated model to an angle of reach approach, an approach that has been applied previously for landslide runout mapping which determines the slope between the start and end of a landslide, and determine that the best NS value of 0.14 occurs at an angel of 20°. Our results show that within our study area, topographic controls provide plausible initial estimates of runout endpoints and an improvement over similarly simplistic methods such as the angle of reach. The potential of using critical slope combined with slope persistence to capture topographic controls to predict runout endpoints is a promising opportunity for landslide hazard mapping at large spatial extents.Item Open Access Observations from a series of flume experiments on contraction scour along a rectangular channel(Colorado State University. Libraries, 2020) Nowroozpour, Alireza, author; Ettema, Robert, advisor; Julien, Pierre, committee member; Nelson, Peter, committee member; Zevenbergen, Lyle, committee member; Gallen, Sean, committee memberTo view the abstract, please see the full text of the document.Item Open Access Predicting flow duration and assessing its drivers in north-central Colorado using crowdsourced data(Colorado State University. Libraries, 2022) Peterson, David, author; Kampf, Stephanie K., advisor; Ross, Matt, committee member; Gallen, Sean, committee memberHeadwater streams are globally important both ecologically and for human resource needs. These streams represent the majority of stream network length, but their flow regimes are often unknown. Streams can be classified by flow regime as perennial, intermittent, or ephemeral. These classifications are used in forest land management decisions and may affect Clean Water Act jurisdiction; however, the National Hydrography Dataset (NHD) often misclassifies headwater streams. The goal of this study is to model flow duration across the stream networks of eight subbasins in north-central Colorado. We used crowdsourced flow presence/absence data from 82 sites in the Stream Tracker program and eight flow sensors to train random forest regression models; these models predicted the fraction of time a stream flows from April-September for both the average from 2016-2020 (dubbed mean annual) and yearly averages (annual). Model predictor variables included climatic, physiographic, and land cover attributes of the study area. Models were developed using a sample of the sites for training and leaving the remaining sites for model testing. The resulting mean annual model's Nash-Sutcliffe efficiency (NSE) was 0.88 for test data, and the annual model's test data had an NSE value of 0.81. We found climate variables such as snow persistence, precipitation, and potential evapotranspiration most influential in predicting flow fraction based on the random forest-ranked variable importance. Forested and herbaceous land cover as well as depth to bedrock, available water storage, hydraulic conductivity, hydrologic soil group, drainage area, and watershed curvature were also identified as important drivers. We developed maps of predicted flow fractions and compared them to NHD flow classifications. In the Cache La Poudre subbasin, the mean annual model predicted perennial flow in 10% of streams and intermittent or ephemeral flow in 90% of streams. Our model predicted nonperennial flow for 76% of the streams that were mapped as perennial in the medium-resolution NHD. Based on these findings, the NHD over-represented perennial streams, classifying them three times more than our model, and under-represented intermittent and ephemeral streams by 32% in our study area. The annual model captured interannual variability in flow fraction and highlighted isolated areas of high variability in flow fraction between years in mid-to-low elevations. The models we developed using crowdsourced data can improve flow classifications of headwater streams and inform resource management decisions in northern Colorado. Crowdsourced streamflow data can be used in streamflow predictions anywhere that nonperennial flow is common.Item Open Access Predicting unsaturated soil strength for mobility assessments(Colorado State University. Libraries, 2023) Bullock, Matthew D., author; Scalia, Joseph, advisor; Niemann, Jeffrey D., advisor; Gallen, Sean, committee memberAccurate estimation of surficial soil moisture and soil strength is integral in the determination of vehicle mobility across landscapes for applications from agriculture to national defense. Especially important is the ability to estimate trafficability over large spatial extents at fine resolutions (10-30 m, or finer, grid cells). While methods exist to estimate soil strength across landscapes, these methods are empirical and rely on class average soil behavior or field measurements that are often difficult or impossible to acquire. In addition, modern terramechanics models require moisture-variable soil strength parameters (e.g., friction angle and cohesion) that cannot be easily acquired in the field. To tackle this issue, the Strength of Surficial Soils (STRESS) model was developed to estimate moisture-variable soil strength with a physics-based approach rooted in unsaturated soil mechanics. However, there has been a lack of field soil moisture and soil strength data from a spatially diverse landscape with which to evaluate the STRESS model. To test the STRESS model, a field study was conducted at the 4,000 ha Maxwell Ranch in the northern Colorado foothills. Soil moisture and soil strength were determined with HydraProbes and cone penetrometers, respectively, at 86 locations across the ranch on 10 dates from May to August 2022. The data were then used to test the STRESS model and determine if soil strength trends could be estimated from topographical and soil textural differences across the landscape. High variability was observed in soil strength measurements via field rating cone index (RCI) stemming from fine-scale terrain and soil features as well as variability in cone penetrometer use. Observed trends show lower soil strengths for greater soil moistures, steeper slopes, higher vegetation, and lower soil fines content. The STRESS model was able to estimate field RCI values with a mean relative error of 37.5%, while a pre-existing model had a mean relative error of 47.4%. The STRESS model was able to reproduce strength trends with fines content but failed to reproduce vegetation and topographical trends. Thus, the STRESS model outperforms the current RCI prediction method, but the uncertainty in the predictions remains large.Item Open Access Timing, kinematics, and tectonic significance of strike-slip fault systems in the Atacama Desert of northern Chile and the Lower Colorado River corridor, U.S.A.(Colorado State University. Libraries, 2021) Mavor, Skyler, author; Singleton, John, advisor; Gallen, Sean, committee member; Ridley, John, committee member; Laituri, Melinda, committee memberTo view the abstract, please see the full text of the document.Item Open Access Understanding the daily to decadal evolution of mountain glaciers in Alaska and high mountain Asia from satellite remote sensing(Colorado State University. Libraries, 2024) Zeller, Lucas R., author; McGrath, Daniel, advisor; Gallen, Sean, committee member; Ross, Matthew, committee member; Florentine, Caitlyn, committee memberGlaciers are important components of mountain ecosystems, mountain hydrological systems, and the global water cycle. Improving our scientific understanding of the spatial and temporal variability in glacier changes and the physical processes that drive those changes will allow better prediction of future glacier evolution. In this dissertation, I explore ways in which satellite-based remote sensing products can be used to study mountain glaciers across a wide range of spatial and temporal scales, with a specific focus on Alaska and High Mountain Asia. The accumulation area ratio (AAR) of a glacier reflects its current state of equilibrium, or disequilibrium, with climate and its vulnerability to future climate change. In Chapter 1, I present an inventory of glacier-specific annual accumulation areas and equilibrium line altitudes (ELAs) for over 3,000 glaciers in Alaska and northwest Canada (88% of the regional glacier area) over the 2018–2022 period derived from Sentinel-2 satellite imagery. I find that the five-year average AAR of the entire study area is 0.41, with an inter-annual range of 0.25–0.49. More than 1,000 glaciers, representing 8% of the investigated glacier area, were found to have effectively no accumulation area. Summer temperature and winter precipitation from ERA5-Land were found to be effective predictors of inter-annual ELA variability across the entire study area (R2=0.47). An analysis of future climate projections (SSP2-4.5) shows that ELAs will rise by 170 m on average by the end of the 21st century. Such changes would result in a loss of 25% of the modern accumulation area, leaving more than 1,900 glaciers (22% of the investigated area) with no accumulation area. These results highlight the current state of Alaska glacier disequilibrium with modern climate, as well as their vulnerability to projected future warming. In High Mountain Asia, many glaciers have thick debris cover over the majority of their ablation zones, earning them the name 'debris-covered glaciers'. Supraglacial lakes (SGLs) play an important role in debris-covered glacier (DCG) systems by enabling efficient interactions between the supraglacial, englacial, and subglacial environments. Developing a better understanding of the short-term and long-term development of these features is needed to constrain DCG evolution and the hazards posed to downstream communities, ecosystems, and infrastructure from rapid drainage. In Chapter 2, I present an analysis of supraglacial lakes on eight DCGs in the Khumbu region of Nepal by automating SGL identification in PlanetScope, Sentinel-2, and Landsat 5–9 satellite images. I identify a regular annual cycle in SGL area, with lakes covering approximately twice as much area during their maximum annual extent (in the pre-monsoon season) than their minimum annual extent (in the post-monsoon season). The high spatiotemporal resolution of PlanetScope imagery (∼ daily, 3 m) shows that this cycle is driven by the appearance and expansion of small lakes in the upper debris-covered regions of these glaciers throughout the winter. Decadal-scale expansion of large, near-terminus lakes was identified on four of the glaciers (Khumbu, Lhotse, Nuptse, and Ambulapcha), while the remaining four showed no significant increases over the study period. The seasonal variation in SGL area is of comparable or greater magnitude as decadal-scale changes, highlighting the importance of accounting for this seasonality when interpreting long-term records of SGL changes from sparse observations. The complex spatiotemporal patterns revealed in this analysis are not captured in existing regional-scale glacial lake databases, suggesting that more targeted efforts are needed to capture the true variability of SGLs on large scales. In Chapter 3, I expand these methods across a wider spatial extent by using the Landsat 5, 7, 8, and 9 archive to delineate SGLs on debris-covered glaciers across all of High Mountain Asia at near-annual cadence from 1988–2023. I find that SGL area has increased throughout the study period, rising to 17.2 km2 (0.7% of the investigated debris-covered area) in 2023, compared to ~8 km2 (0.3% of debris-covered area) in 1988. SGL growth is most concentrated in the Himalaya and Nyainqêntanglha regions, which have also experienced the greatest rates of 20th and 21st century mass loss. The 21st century SGL growth is concentrated almost entirely near the termini of these glaciers, indicating the possibility of continued growth and coalescence into large proglacial lakes. Areas of high SGL concentration are predominantly found in areas with lower surface gradients, low velocity, and thicker debris cover. Glaciers with high SGL concentrations are found to have steeper longitudinal gradients of thinning, with greater thinning rates further from the terminus resulting in lower surface slopes and more concave geometries throughout their entire debris-covered extents. However, the representative longitudinal thinning pattern of glaciers without substantial SGL formation have become more similar to this pattern in recent years, suggesting that more of these glaciers may be primed for SGL formation in the future.Item Open Access Undrained shear behavior and critical state analysis of mixed mine waste rock and tailings(Colorado State University. Libraries, 2019) Borja Castillo, Raquel N., author; Bareither, Christopher A., advisor; Scalia, Joseph, committee member; Gallen, Sean, committee memberThe objectives of this study were to (i) evaluate the undrained shear behavior of mine tailings and a tailings-dominated mixture of filtered tailings and waste rock (i.e. GeoWaste), (ii) identify the critical state of each material, and (iii) assess the impact of waste rock inclusions on the critical state of tailings. Mine tailings and waste rock were collected from an active mine where GeoWaste is being considered as a potential solution for mine waste management. GeoWaste was prepared at a mixture of 1.2 parts waste rock to 1 part tailings, by dry mass, which was a relevant mixture ratio for field implementation. Consolidated undrained (CU) triaxial compression tests were conducted on pure tailings and GeoWaste. Large-scale triaxial compression tests were conducted on 150-mm-diameter GeoWaste specimens, and 38-mm-diameter triaxial tests were conducted on tailings prepared to three initial conditions: filtered tailings that represented field conditions, dense filtered tailings, and paste tailings. Triaxial compression tests were conducted at effective confining pressures (σc') ranging between 20 and 500 kPa. Filtered tailings prepared to represent field conditions yielded contractive, strain-hardening behavior. Dense filtered tailings exhibited strain-hardening behavior, net positive pore pressure, and a transition from contractive to dilative tendencies. Paste tailings exhibited modest strain-hardening behavior. GeoWaste exhibited strain-hardening, contractive behavior, and a modest transition from contractive to dilative behavior was observed at σ'c = 500 kPa. The undrained shear behavior of GeoWaste was comparable to filtered tailings at σ'c = 50 kPa and 100 kPa. However, undrained shear behavior of GeoWaste at σʹc = 500 kPa changed related to tailings, which was characterized by a larger deviator stress and lower excess pore pressure. This GeoWaste behavior indicated improved shear resistance compared to filtered tailings, which was attributed to (i) inter-particle reinforcing effects between the waste rock particles within a tailings-dominated structure and (ii) densification of the GeoWaste structure. Shear strength parameters were calculated from the slope of a composite Kf Line for each material. Filtered tailings prepared to represent field conditions, and dense filtered tailings yielded effective tangent friction angle (φ't) = 33°, and paste tailings yielded φ't = 32°. Similarity in φ't between the three tailings prepared with different initial specimen characteristics was attributed to similar void ratios at the end of consolidation under a given σʹc. GeoWaste yielded φ't = 32°. Although composite φ't were similar between tailings and GeoWaste, the secant friction angles of GeoWaste increased with increasing σʹc, whereas the opposite trend was observed for tailings. The addition of waste rock particles to tailings in a fine-dominated structure to increase the shear resistance relative to tailings as effective consolidation stress increased. An assessment was conducted between the critical state lines for tailings and GeoWaste to determine if the critical state line for tailings can represent critical state conditions in GeoWaste. An equivalent tailings void ratio (e*t) that can represent the tailings fraction within GeoWaste correlated with the critical state line for tailings. In this study, the e*t for GeoWaste was determined via optimizing a fitting parameter in the e*t equation to correlated with the critical state line for tailings. Although this evaluation suggests that the critical state line for the tailings can be used to represent critical state conditions in GeoWaste, additional work is needed to determine e*t a priori.