Browsing by Author "Ham, Jay, committee member"
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Item Open Access A multi-scale analysis of vegetation and irrigation heterogeneity effects on ecohydrological function and ecosystem services in a semi-arid urban area(Colorado State University. Libraries, 2014) Gage, Edward A., author; Cooper, David, advisor; Ham, Jay, committee member; Kampf, Stephanie, committee member; Ryan, Michael, committee memberTo view the abstract, please see the full text of the document.Item Open Access Characterizing ammonia concentrations and deposition in the United States(Colorado State University. Libraries, 2015) Li, Yi, author; Collett, Jeffrey L., advisor; Kreidenweis, Sonia M., committee member; Fischer, Emily, committee member; Ham, Jay, committee memberRapid development of agricultural activities and fossil fuel combustion in the United States led to a great increase of reactive nitrogen (Nr) emissions in the second half of the twentieth century. These emissions have been linked to excess nitrogen (N) deposition in natural ecosystems through dry and wet deposition pathways that can lead to adverse environmental impacts. Furthermore, as precursors of ozone and fine particles, reactive nitrogen species impact regional air quality with resulting effects on human health, visibility, and climate forcing. In this dissertation, ambient concentrations of reactive nitrogen species and their deposition are examined in the Rocky Mountain region and across the country. Particular emphasis is placed on ammonia, a currently unregulated pollutant, despite its important contributions both to nitrogen deposition and fine particle formation. Continuous measurements of the atmospheric trace gases ammonia (NH3) and nitric acid (HNO3) and of fine particle (PM2.5) ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-) were conducted using a denuder/filter system from December 2006 to December 2011 at Boulder, Wyoming, a region of active gas production. The average five year concentrations of NH3, HNO3, NH4+, NO3- and SO42- were 0.17, 0.19, 0.26, 0.32, and 0.48 µg/m3, respectively. Significant seasonal patterns were observed. The concentration of NH3 was higher in the summer than in other seasons, consistent with increased NH3 emissions and a shift in the ammonium nitrate (NH4NO3) equilibrium toward the gas phase at higher temperatures. High HNO3 concentrations were observed both in the summer and the winter. Elevated wintertime HNO3 production appeared to be due to active local photochemistry in a shallow boundary layer over a reflective, snow-covered surface. PM2.5 NH4+ and SO42- concentrations peaked in summer while NO3- concentrations peaked in winter. Cold winter temperatures drove the NH3-HNO3-NH4NO3 equilibrium toward particulate NH4NO3. A lack of NH3, however, frequently resulted in substantial residual gas phase HNO3 even under cold winter conditions. Concentrated agricultural activities and animal feeding operations in the northeastern plains of Colorado represent an important source of atmospheric NH3 that contributes to regional fine particle formation and to nitrogen deposition to sensitive ecosystems in Rocky Mountain National Park (RMNP) located ~80 km to the west. In order to better understand temporal and spatial differences in NH3 concentrations in this source region, weekly concentrations of NH3 were measured at 14 locations during the summers of 2010 to 2014 using Radiello passive NH3 samplers. Weekly average NH3 concentrations ranged from 2.8 µg/m3 to 41.3 µg/m3 with the highest concentrations near large concentrated animal feeding operations (CAFOs). The annual summertime mean NH3 concentrations were stable in this region from 2010 to 2014, providing a baseline against which concentration changes associated with future changes in regional NH3 emissions can be assessed. Vertical profiles of NH3 were also measured on the 300 m Boulder Atmospheric Observatory (BAO) tower throughout 2012. The highest NH3 concentration along the vertical profile was always observed at the 10 m height (annual average concentration is 4.63 µg/m3), decreasing toward the surface (4.35 µg/m3 at 1 m) and toward higher altitudes (1.93 µg/m3 at 300 m). Seasonal changes in the steepness of the vertical concentration gradient were observed, with the sharpest gradients in cooler seasons when thermal inversions restricted vertical mixing of surface-based emissions. The NH3 spatial distributions measured using the passive samplers are compared with NH3 columns retrieved by the Infrared Atmospheric Sounding Interferometer (IASI) satellite and concentrations simulated by the Comprehensive Air quality Model with extensions (CAMx), providing insight into the regional performance of each. U.S. efforts to reduce NOx emissions since the 1970s have substantially reduced nitrate deposition, as evidenced by strongly decreasing trends in long-term wet deposition data. These decreases in nitrate deposition along with increases in wet ammonium deposition have altered the balance between oxidized and reduced nitrogen deposition. Across most of the U.S., wet deposition has evolved from a nitrate dominated situation in the 1980s to an ammonium dominated situation in recent years. Recent measurements of gaseous NH3 concentrations across several regions of the U.S., along with longer-established measurements of gas phase nitric acid, fine particle ammonium and nitrate, and wet deposition of ammonium and nitrate, permit new insight into the balance of oxidized and reduced nitrogen in the total (wet + dry) U.S. reactive nitrogen deposition budget. Utilizing observations from 37 monitoring sites across the U.S., we estimate that reduced nitrogen contributes, on average, approximately 65 percent of the total inorganic N deposition budget. Dry NH3 deposition plays an especially key role in N deposition compared with other N deposition pathways, contributing from 19% to 65% in different regions. With reduced N species now dominating the wet and dry reactive N deposition budgets in much of the country and future estimates suggesting growing ammonia emissions, the U.S. will need to consider ways to actively reduce NH3 emissions if it is to continue progress toward reducing N deposition to sustainable levels defined by ecosystem critical loads.Item Open Access Continuous NAPL loss rates using subsurface temperatures(Colorado State University. Libraries, 2015) Stockwell, Emily Beth, author; Sale, Tom, advisor; Blotevogel, Jens, committee member; Ham, Jay, committee memberTo view the abstract, please see the full text of the document.Item Open Access Crop residue: a hero's journey from biomass to soil carbon in eastern Colorado dryland crop rotation systems(Colorado State University. Libraries, 2019) Schnarr, Cassandra, author; Schipanski, Meagan, advisor; Ham, Jay, committee member; Conant, Richard, committee member; Tatarko, John, committee memberCrop residues play a vital role in reducing the potential for wind erosion of agricultural soils in arid and semi-arid regions. The residues act via three modes: reducing wind speed, acting as a physical impediment to wind reaching the soil surface, and as an organic matter input to spur aggregation and aggregate stability. The interactions of crop residues, crop rotation systems, and wind erosion factors were studied at three long-term agricultural research sites along an evapotranspiration gradient near Sterling, Stratton, and Walsh, Colorado. The sites have a 30-year history of dryland, no-till management, and are divided into different cropping system intensities that vary in the frequency of summer fallow periods in the rotation. Crop rotations studied here include wheat (Triticum aestivum)-fallow, wheat-corn (Zea mays) – fallow, and continuously cropped plots with small grains and forage crops including foxtail millet (Setaria varidis) and forage sorghum (Sorghum bicolor). Forage crop and wheat residues were tracked over two growing seasons (2015 and 2016) to estimate the length of time before soil surface cover fell below a 30% threshold and to create models for residue persistence. Decomposition Days (DD), a calculation that factors in temperature and rainfall to estimate cumulative conditions that favor decomposition, was used to normalize time scales following harvest across sites and years. Wheat residue covered 82% of the soil surface following harvest and summer forage crops covered 56%. Wheat persisted longer, taking 62.5 DD to fall to the 30% cover threshold, forage crop residue remained above the threshold for 16.6 DD. The decline of forage crop residue cover followed an exponential decay model. Wheat residue surface cover had a longer, slower decline and fit a quadratic decay model. Wheat stem heights were taller following harvest and heights declined at a similar or faster rate than forage crops. To assess rotation legacy impacts on soil erodibility, soils were sampled in May 2015 and tested for dry aggregate size distribution, dry aggregate stability, and carbon distribution by size classes and between cropping intensities. No differences were found in the amount of erodible aggregate size fraction (<0.84mm) by cropping system intensity. The site with the highest amount of clay in the soil displayed a significant difference in aggregate stability by crop rotation, with wheat-fallow rotations having stability of 2.96 ln J/Kg and continuously cropped systems having 2.80 ln J/Kg. Carbon distribution did not differ by crop rotation but did differ by size class at the site with the highest potential evapotranspiration and lowest clay content where the largest aggregates contained the highest proportion of carbon. Every phase (i.e., rotation year) of each of the crop rotation systems were represented each year. There was a significant difference in mean erodible fraction and aggregate stability by cropping phase at the time of sampling at the site with the highest clay content. Taken together, the crop residue and soil aggregate portions of the study indicate that the reliable and consistent prevention of wind erosion by crop system intensity may be more dependent upon annual crop residue surface cover than longer-term management impacts on soil aggregation properties. The differences in aggregate stability by crop type could be due to the impacts of active root systems at the time of sampling. More investigation is warranted into the influence of active root systems on macro dry aggregates and whether dry aggregate stability properties differ by season. Further study into the application of residue biomass decay models to residue soil cover, particularly in crops with multiple layers of residue is also indicated.Item Open Access Developing a modified SEBAL algorithm that is responsive to advection by using limited weather data(Colorado State University. Libraries, 2014) Mkhwanazi, Mcebisi, author; Chávez, José, advisor; Andales, Allan, committee member; Ham, Jay, committee member; Trout, Thomas, committee memberThe use of Remote Sensing ET algorithms in water management, especially for agricultural purposes is increasing, and there are more models being introduced. The Surface Energy Balance Algorithm for Land (SEBAL) and its variant, Mapping Evapotranspiration with Internalized Calibration (METRIC) are some of the models that are being widely used. While SEBAL has several advantages over other RS models, including that it does not require prior knowledge of soil, crop and other ground details, it has the downside of underestimating evapotranspiration (ET) on days when there is advection, which may be in most cases in arid and semi-arid areas. METRIC, however has been modified to be able to account for advection, but in doing so it requires hourly weather data. In most developing countries, while accurate estimates of ET are required, the weather data necessary to use METRIC may not be available. This research therefore was meant to develop a modified version of SEBAL that would require minimal weather data that may be available in these areas, and still estimate ET accurately. The data that were used to develop this model were minimum and maximum temperatures, wind data, preferably the run of wind in the afternoon, and wet bulb temperature. These were used to quantify the advected energy that would increase ET in the field. This was a two-step process; the first was developing the model for standard conditions, which was described as a healthy cover of alfalfa, 40-60 cm tall and not short of water. Under standard conditions, when estimated ET using modified SEBAL was compared with lysimeter-measured ET, the modified SEBAL model had a Mean Bias Error (MBE) of 2.2 % compared to -17.1 % from the original SEBAL. The Root Mean Square Error (RMSE) was lower for the modified SEBAL model at 10.9 % compared to 25.1 % for the original SEBAL. The modified SEBAL model, developed on an alfalfa field in Rocky Ford, was then tested on other crops; beans and wheat. It was also tested on well-irrigated corn and also corn under deficit irrigation. The modified SEBAL model performed fairly well in wheat and beans, just slightly underestimating ET, and it performed well with irrigated corn. However, modified SEBAL, similar to the original SEBAL and also METRIC, could not accurately estimate ET for drier conditions or at early stages of plant growth.Item Embargo Development and characterization of solid-state, internet of things-based pH sensors for in-situ monitoring of soil and groundwater(Colorado State University. Libraries, 2022) VanTilburg, Charles Henry, IV, author; Scalia, Joseph, advisor; Sale, Thomas, advisor; Ham, Jay, committee memberHerein I test and examine a new solid state pH sensor design for use in soils and groundwater monitoring. The concept presented here is intended to expand the capabilities for monitoring geochemical parameters in the subsurface by combining a durable, solid-state pH sensor for subsurface deployment with an automated 'internet of things' (IoT) based pH meter that allows the collection of near-real-time continuous data streams for monitoring biogeochemical processes in hydrologic systems. Tests performed in this work were intended to provide a benchmark for further refinement of the design and yielded promising results, including hydrogeologically useful response times (on the order of hours), durability (stresses >1,000 kPa), and reproducible behaviors with multiple sensors. These results support that this technology is promising for future work. The pH sensor design combines a titanium mixed-metal-oxide electrode (TiMMO), solid epoxy body, and a proton-selective Nafion™ ionomer coating to yield a durable solid-state sensor that is sensitive to aqueous proton activities. As the sensor is exposed to water, the diffusion of aqueous protons through the selective Nafion™ coating causes an increase in voltage on the electrode as compared to a reference electrode. The Nafion™ coating reduces the influence of other ions in the system, creating a proton selective sensor. Because of the durable solid-state construction, the sensor can likely be deployed in-situ in challenging environments such as in soils where common glass pH sensors are too fragile for use. This unique advantage allows the pursuit of new biogeochemical monitoring strategies that leverage a high volume of discrete in-situ measurements for near-real-time continuous datastreams. This new strategy, powered by IoT systems, can integrate with smart networks of multiple components and generate large amounts of data for use in artificial intelligence and machine learning systems while also providing insight into processes that occur at smaller spatial and temporal scales than those understood with current subsurface monitoring strategies. pH is a master variable in aqueous and soil chemistry, both an indicator and controller of most chemical reactions and many physical processes that take place in soil and groundwater. pH is important for understanding chemical speciation, mobility, and stability in the soil, while also influencing soil physical properties like soil structure. pH is a parameter of interest to many industries and fields of study including, but not limited to, agriculture, mining, water resources, and engineering. As this work was intended to be a first approximation for studying this technology, multiple promising results and points of improvement were discovered. This work identifies a clear voltage response by the sensor to pH changes (-29 mV/pH) while also demonstrating the change behavior during stepwise pH changes to be approximately logarithmic (Δvolt=3.85ln[t], where Δvolt is the change in millivolts and t is time in minutes). Furthermore, this work demonstrated that these sensors can be used with an IoT monitoring system in the intended application. However, more work is needed to remove variability in the data, explore further designs and processes for coating and treating the sensors, analyze the long-term use, drift, and standardization of the sensors, and employ the data in analytics. Future work should include further lab testing to compare alternative design features and to evaluate stressors such as non-target ions and dehydration. After refinement in the lab, the sensors should be installed in pilot scale studies and in the field to evaluate their performance in real world conditions.Item Open Access Development of a low-firepower biomass dust combustor(Colorado State University. Libraries, 2018) Greer, Kyle C., author; Mizia, John, advisor; Windom, Bret, advisor; Ham, Jay, committee memberAs of 2017, the World Health Organization estimates that 2.3 billion people globally lack access to basic sanitation facilities such as toilets or latrines. 892 million of these people defecate in the open, which increases the spread of disease and intestinal parasites. Incinerating desiccated human waste provides a low-cost opportunity to safely mitigate this public health risk. Over the last five years, the Advanced Biomass Combustion lab at CSU has developed a 2-kW fecal gasifier as part of the Gates Reinvent the Toilet Challenge, but the combustor lacks scalability to low firepowers. Continuous low-firepower biomass combustion has eluded development due to several technical challenges, however it is advantageous in many situations and opens the door for many low energy devices. Development of a low-firepower fecal combustor could act as a pilot light for the existing gasifier, it could be a low-cost standalone incinerator for household use, it may have higher combustion efficiencies and lower emissions than the gasifier, and it could be scaled to high firepowers by creating arrays of flames. A 100 Watt idealized biomass dust combustor has been developed to investigate the feasibility of creating a low-firepower fecal dust burner. Despite extensive research on dust explosion dynamics, few stable dust-flame burners have been researched and developed. This project utilized dust combustion fundamentals and iterative hardware development to create a low-firepower biomass dust combustor. Cornstarch, wheat flour, and lycopodium spores were explored as idealized biomass fuels, and human feces was briefly tested in the combustor. The hardware development process will help guide the transition to a stable low-firepower fecal dust burner.Item Embargo Drought and salinity tolerance of cool-season turfgrasses(Colorado State University. Libraries, 2024) Li, Jizhou, author; Qian, Yaling, advisor; Burcham, Daniel C., committee member; Ham, Jay, committee member; Zhang, Yao, committee memberDue to the water scarcity and increased use of recycled water/saline water for turfgrass irrigation in arid and semi-arid climates, there is an increasing demand for drought and salt tolerant turfgrass. Kentucky bluegrass (Poa pratensis L.), tall fescue (Festuca arundinacea Schreb.), and perennial ryegrass (Lolium perenne L.) are the most commonly used cool season turfgrass species in the northern regions of the United States. The thesis includes two separate studies evaluating entries in National Turfgrass Evaluation Program (NTEP) trials. These two trials were conducted to identify the most drought tolerant lines of Kentucky bluegrass and tall fescue grown in a field study, and the most salt tolerant lines of perennial ryegrass grown in a greenhouse study, respectively. The drought tolerance trial is presented in Chapter 1. In it, the drought tolerance of thirty-five cool-season turfgrasses, including 15 Kentucky bluegrass lines, 19 tall fescue lines, and 1 perennial ryegrass line were evaluated under three deficit irrigation treatments, 40%, 60% and 80% evapotranspiration (ETo) from 2018 to 2020. Overall turfgrass quality, minimum irrigation requirement for maintaining the acceptable quality, and length of time to maintain acceptable quality were determined for each entry. The amount of irrigation needed to maintain acceptable quality for tall fescue was 71% - 95% ETo, and for Kentucky bluegrass, it was 81% - 110% ETo under three-year deficit irrigation. Based on turf quality and irrigation requirement to maintain acceptable quality during the three-year deficit irrigation period, we have identified the most drought tolerant entries. Among Kentucky bluegrass entries, "PST-K13-141" has emerged as the top performer, demonstrating an 81% ETo rate to maintain acceptable quality. Among tall fescue lines, the most drought-tolerant entries include "PST-5SDS," "Kingdom," "DLFPS 321/3679," and "Thor," requiring 71%, 74%, 74%, and 72% ETo, respectively, to uphold satisfactory turf quality. The results of this study suggest that selecting species and entries that use less water while maintaining acceptable quality could mitigate irrigation demands. In Chapter 2, the salt tolerance of eighty-three perennial ryegrass lines was evaluated in two separate greenhouse experiments. Eighty-three lines were grown in cone-shaped containers that were soaked in increasingly saline nutrient solution for 1 hour per day. The solution began with an electrical conductivity (EC) of 6.0 dS·m-1 and was subsequently increased by 4.0 dS·m-1 (in Experiment I) or 6.0 dS·m-1 (in Experiment II) every 3 weeks until reaching the next targeted salinity level. The final targeted salinity level was 22 dS·m-1. Grasses were grown under each of the 4 or 5 targeted salinity levels for a period of 3 weeks. Clipping yield reduction, overall turf quality, leaf firing, and density were determined at each salinity level. Regression analysis was conducted to determine the relationship between clipping yield and salinity. The salinity level causing a 25% reduction in clipping yield was used as an indicator of salinity tolerance level in different entries. We found that entries "SGP4", "PPG-PR 667", "PVF-SGS5", "BAR LP 22262", "GO-RUS21", "PPG-PR 610", "DLF-PR 3727", and "PPG-PR 639" were the most salt-tolerant, evidenced by the best turfgrass quality and the highest salinity levels at which there was a 25% clipping yield reduction in two experiments. We observed that the salinity levels that caused a 25% clipping yield reduction ranged from 5.0-8.8 dS·m-1 in experiment I and 5.7-10.7 dS·m-1 in experiment II. The entries with better salt tolerance identified in this study would hold the potential to be utilized on sites with marginally elevated saline soil. Additionally, they could be beneficial for locations where irrigation involves waters with elevated salinity, such as recycled water.Item Open Access Evaluation of analytical footprint models and energy balance closure methods over cotton in Texas panhandle(Colorado State University. Libraries, 2011) Joy, Stuart L., author; Chávez, José Luis, advisor; Ham, Jay, committee member; Andales, Allan A., committee memberEddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). However, EC systems tend to systematically underestimate H and LE fluxes; thus, a lack of energy balance closure. The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are a horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past two decades there has been a proliferation of `heat flux source area' (i.e., footprint) modeling studies but only a few that explore the accuracy of models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently with an average MBE of about -0.03 mm h-1 (-4.4%) and RMSE of 0.09 mm h-1 (10.9%). The same three footprint models were then used to adjust measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing adjusted ET estimates with lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8% and the RMSE by 1%. The energy balance is rarely closed with the EC method and therefore the energy balance was closed by adjusting the H and LE heat fluxes by first assuming the H was measured accurately and applying the entire residual to the LE (LEC) heat flux and secondly by assuming the Bowen ratio (BRC) was measured accurately and adjusting both H and LE while conserving the BR. The application of energy balance closure to uncorrected EC heat fluxes showed better agreement between EC and lysimeter ET. There was not a significant difference between the BRC and LEC methods when applied to uncorrected heat fluxes. The analytical footprint models developed by Schuepp et al. (1990), Hsieh et al. (2000), and Kormann and Meixner (2001) all gave a reliable estimate of the footprint for heterogeneous agricultural land under highly advective conditions. Care should be taken when using the EC system to measure ET early in the growth stage of a crop when the surface is smooth because the footprint will extend farther upwind. Correcting the EC heat fluxes for coordinate rotation, density, spectral attenuation, and sonic temperature heat flux and then applying the proposed correction considering the footprint resulted in the most accurate estimate of hourly EC based ET with a MBE of 0.01 mm h-1 (0.6 to 1.5%) and RMSE of 0.10 to 0.11 mm h-1 (10.6 to 11.66%).Item Open Access Exploring new approaches to understanding channel width and erosion rates in bedrock rivers, Puerto Rico, USA(Colorado State University. Libraries, 2022) Eidmann, Johanna Sophie, author; Gallen, Sean, advisor; Rathburn, Sara, committee member; Hughes, Kenneth Stephen, committee member; Ham, Jay, committee memberEarth system dynamics produce constant adjustments to sea level, tectonics, and climate. Bedrock rivers communicate these changes throughout mountains by driving landscape and erosional responses that facilitate topographic change. It follows that an improved understanding of bedrock rivers can help us better model and reconstruct the interplay of changes to base level, uplift, and climate from landscapes. Although bedrock channel width plays a first-order role in river stream power and stream power-based landscape evolution models, because of the physical challenges associated with acquiring these data, channel width is often estimated and introduces uncertainty. In addition, the lack of bedrock channel width data has limited our understanding of what factors control channel width. In this dissertation (Chapter 2), I leverage high-resolution topographic data, Mean Annual Precipitation information, and use the HEC-RAS river modeling software to remotely derive bedrock channel width at desired flow scenarios. The accuracy of modeling results is verified for rivers in Puerto Rico using USGS gauging station field measurements, as well as my own channel width field measurements associated with 1-year recurrence interval discharges. As a next step, (Chapter 3) I implement the bedrock width modeling method derived in Chapter 2 to obtain >4,000 channel width measurements from reaches across Puerto Rico. I then compare these bedrock river width values to various factors (e.g. rock type and rock strength, drainage area, Ecozone, and grain size) that have been identified in the literature to scale with or influence channel width. My analyses indicate that, in Puerto Rico, rock type is a dominant control of bedrock channel width in small (≤6-10 km2) drainage areas. Contrary to patterns of rock strength and bedrock width documented in the literature (e.g. Montgomery and Gran, 2001), I find that width doesn't appear to correlate with proxies for bedrock channel strength. Strong granodiorites have the widest low-order channels and the strong volcaniclastics and weak serpentinites have comparably narrow low-order channels. Analysis of limited grain size measurements shows a discernable difference in the coarse grain size distribution between the three rock types, with the volcaniclastic and serpentinite draining rivers having coarser sediment than granodiorite draining streams. These findings suggest that bedrock channel width may be influenced by unmeasured lithological parameters that impact the size of grains delivered to river channels from adjacent hillslopes (i.e. rock fracture density and spacing, as well as weathering). Lastly, (Chapter 4) I spatially analyze in-situ cosmogenic nuclide (10Be in quartz and 36Cl in magnetite) concentrations and find that bedrock erosion rates are higher in the central part of Puerto Rico than toward the east. Analysis of erosion rates compared to other parameters reveals that channel steepness, rather than precipitation or rock type, is positively associated with erosion rates. I further apply these erosion rate data to test the accuracy of four incision models of varying complexity. Model comparisons reveal that drainage area is a better predictor of incision rates in Puerto Rico than a precipitation-weighted drainage area parameter. In addition, whereas an increase in model complexity slightly improves model performance, the model only explains ~35% of the variability in erosion rates. It follows that current incision models are still missing many controlling factors of river incision rates in Puerto Rico.Item Open Access Forecasting groundwater contaminant plume development using statistical and machine learning methods(Colorado State University. Libraries, 2022) McConnnell, Elizabeth, author; Blotevogel, Jens, advisor; Karimi Askarani, Kayvan, committee member; Ham, Jay, committee member; Scalia, Joseph, committee memberA persistent challenge in predicting the fate and transport of groundwater contaminants is the inherent geologic heterogeneity of the subsurface. Contaminant movement has been primarily modeled by simplifying the geology and accepting assumptions to solve the advection- dispersion-reaction equation. With the large groundwater quality datasets that have been collected for decades at legacy contaminated sites, there is an emerging potential to use data- driven machine learning algorithms to model contaminant plume development and improve site management. However, spatial and temporal data density and quality requirements for accurate plume forecasting have yet to be determined. In this study, extensive historical datasets from groundwater monitoring well samples were initially used with the intent to increase our understanding of complex interrelations between groundwater quality parameters and to build a suitable model for estimating the time to site closure. After correlation analyses applied to the entire datasets did not reveal compelling correlation coefficients, likely due to poor data quality from integrated well samples, the initial task was reversed to determine how many data are needed for accurate groundwater plume forecasting. A reactive transport model for a focus area downgradient of a zero-valent iron permeable reactive barrier was developed to generate a detailed, synthetic carbon tetrachloride concentration dataset that was input to two forecasting models, Prophet and the damped Holt's method. By increasing the temporal sampling schedule from the industry norm of quarterly to monthly, the plume development forecasts improved such that times to site closure were accurately predicted. For wells with declining contaminant concentrations, the damped Holt's method achieved more accurate forecasts than Prophet. However, only Prophet allows for the inclusion of exogenous regressors such as temporal concentration changes in upgradient wells, enabling the predictions of future declining trends in wells with still increasing contaminant concentrations. The value of machine learning models for contaminant fate and transport prediction is increasingly apparent, but changes in groundwater sampling will be required to take full advantage of data-driven contaminant plume forecasting. As the quantity and quality of data collection increases, aided by sensors and automated sampling, these tools will become an integral part of contaminated site management. Spatial high-resolution data, for instance from multi-level samplers, have previously transformed our understanding of contaminant fate and transport in the subsurface, and improved our ability to manage sites. The collection of temporal high-resolution data will similarly revolutionize our ability to forecast contaminant plume behavior.Item Open Access Impacts of unconventional oil and gas development on atmospheric aerosol particles(Colorado State University. Libraries, 2017) Evanoski-Cole, Ashley R., author; Collett, Jeffrey L., advisor; Kreidenweis, Sonia M., committee member; Pierce, Jeffrey R., committee member; Ham, Jay, committee memberRising demands for global energy production and shifts in the economics of fossil fuel production have recently driven rapid increases in unconventional oil and gas drilling operations in the United States. Limited field measurements of atmospheric aerosol particles have been conducted to understand the impacts of unconventional oil and gas extraction on air quality. These impacts can include emissions of greenhouse gases, the release of volatile organic compounds that can be hazardous and precursors to tropospheric ozone formation, and increases in atmospheric aerosol particles. Aerosol particles can also contribute to climate change, degrade visibility and negatively impact human health and the environment. Aerosol formation can result from a variety of activities associated with oil and gas drilling operations, including emission of particles and/or particle precursors such as nitrogen oxides from on-site power generation, evaporation or leaking of fracking fluids or the produced fuel, flaring, the generation of road dust, and increases in traffic and other anthropogenic emissions associated with growing populations near drilling locations. The work presented here details how activities associated with unconventional oil and gas extraction impact aerosol particle characteristics, sources, and formation in remote regions. An air quality field study was conducted in the Bakken formation region during a period of rapid growth in oil production by unconventional techniques over two winters in 2013 and 2014. The location and time of year were chosen because long term IMPROVE network monitoring records show an increasing trend in particulate nitrate concentrations and haze in the Bakken region during the winter, strongly contrasting with sharp decreases observed across most of the U.S. The comprehensive suite of instrumentation deployed for the Bakken Air Quality Study (BAQS) included measurements of aerosol concentrations, composition, and scattering, gaseous precursors important for aerosol formation, volatile organic compounds, and meteorology. Regional measurements of inorganic aerosol composition were collected, with average concentrations of total inorganic PM2.5 between 4.78 – 6.77 µg m-3 and 1.99 – 2.52 µg m-3 for all sampling sites during the 2013 and 2014 study periods, respectively. The maximum inorganic PM2.5 concentration observed was 21.3 µg m-3 for a 48 hour filter sample collected at Fort Union National Historical Site, a site located within a dense area of oil wells. Organic aerosol measurements obtained during the second study at the north unit of Theodore Roosevelt National Park (THRO-N) featured an average concentration of 1.1 ± 0.7 µg m-3. While oil production increased from 2013 to 2014, the lower PM2.5 in 2014 can be explained by the meteorological differences. During the first study, increased snow cover, atmospheric stability, solar illumination, and differences in the dominant wind direction contributed to higher PM2.5. The enhanced concentrations of inorganic PM2.5 measured in the Bakken region were tied to regional oil and gas development. Elevated concentrations of PM2.5 were observed during periods of air mass stagnation and recirculation and were associated with VOC emissions aged less than a day, both indicating a predominant influence from local emissions. High PM2.5 concentrations occurred when low i-/n-pentane VOC ratios were observed, indicating strong contributions from oil and gas operations. The hourly measurements of gas and aerosol species in an extremely cold environment also provided a unique data set to investigate how well thermodynamic aerosol models represent the partitioning of ammonium nitrate. In general, during the coldest temperatures, the models overpredicted the formation of particulate nitrate. The formation of additional PM2.5 in this region is more sensitive to availability of N(-III) species during the coldest periods but increasingly sensitive to available N(V) when temperatures are relatively warmer and ammonia availability increases. These measurements and modeling results show that continued growth of oil and gas drilling operations in remote areas such as the Bakken region could lead to increased PM2.5 and impact haze formation in nearby federally protected lands.Item Embargo Improving soil property predictions for applications in tailings and terramechanics(Colorado State University. Libraries, 2024) Bindner, Joseph R., author; Scalia, Joseph, advisor; Atadero, Rebecca, advisor; Bareither, Christopher, committee member; Niemann, Jeffrey, committee member; Ham, Jay, committee memberSoil properties are used by engineers and scientists to better understand the state and behavior of soils. For example, soil properties can be used to estimate surficial soil strength for vehicle mobility models and can be used to better understand the engineering characteristics of mine waste (tailings) stored in tailings storage facilities. Soil and tailings properties often have high spatial variability and often require high resolution data for engineering analyses. Standard laboratory procedures are commonly used to determine soil properties but are often impractical for large spatial extents. While some existing soil data products provide estimates of surficial soil properties, the fidelity of soil data products is often poorly understood and insufficient for many applications. Additionally, some field tests used to estimate soil properties, such as the cone penetration test (CPT), rely on empirical correlations that cannot be used for some soils. There remains a need for procedures which improve the speed and accuracy of soil property estimates across large spatial extents. The objectives of this study are to (i) evaluate how surficial soil moisture and soil strength vary with soil and landscape attributes across a large spatial extent, (ii) explore the use of field-based hyperspectral sensing and machine learning for the prediction of surficial soil properties across a landscape, and (iii) assess the use of laboratory hyperspectral sensing and machine learning for the prediction of tailings properties for potential application in situ via direct push methods. Soil and landscape attributes were determined at sampling locations across a semi-arid foothills region and used to assess how soil moisture and soil strength vary with soil and landscape attributes. Then, hyperspectral data were captured at select sampling locations and used to train and assess the performance of a convolutional neural network (CNN) for the predictions of soil properties. Finally, a diverse tailings-hyperspectral dataset was prepared in the lab and used to train and assess a CNN to provide proof of concepts for prediction of material properties relevant to TSF stability analyses.Item Open Access Internet of things monitoring of the oxidation reduction potential in an oleophilic bio-barrier(Colorado State University. Libraries, 2020) Hogan, Wesley W., author; Scalia, Joseph, advisor; Sale, Thomas, advisor; Ham, Jay, committee memberPetroleum hydrocarbons discharged to surface water at a groundwater-surface water interface (GSI) resulting in violations of the Clean Water Act often spark costly cleanup efforts. The oleophilic bio-barrier (OBB) has been shown to be effective in catching and retaining oils via an oleophilic (oil-loving) geocomposite and facilitating biodegradation through cyclic delivery of oxygen and nutrients via tidally driven water level fluctuations. Conventional resistive (e.g., geomembrane) or absorptive-only (e.g., organoclay) barriers for oil at GSIs limit oxygen diffusion into underlying sediments and are susceptible to overloading and bypass. Conversely, OBBs are designed to function as sustainable oil-degrading bioreactors. For an OBB to be effective, the barrier must maintain aerobic conditions created by tidally driven oxygen delivery. Oxidation reduction potential (ORP) sensors were installed within an OBB in the northeastern US with an internet of things (IoT) monitoring system to either confirm the sustained oxidizing conditions within the OBB, or to detect a problem within the OBB and trigger additional remedial action. Real-time ORP data revealed consistently aerobic oxidation-reduction (redox) conditions within the OBB with periods of slightly less oxidized redox conditions in response to precipitation. By interpreting ORP data in real time, we were able to verify that the OBB maintained the oxidizing conditions critical to the barrier functioning as an effective aerobic bioreactor to degrade potentially-sheen generating oils at GSIs. In addition, alternative oleophilic materials were tested to increase the range of candidate materials that may function as the oleophilic component of an OBB. Materials tested included thin black (232 g/m2), thin white (244 g/m2), medium black (380 g/m2), and thick black (1055 g/m2) geotextiles, as well as a coconut fiber coir mat. Finally, a model was developed to estimate the required sorptive capacity of the oleophilic component of an OBB based on site-specific conditions, which can be used to inform OBB design.Item Open Access Measurement of ammonia emission from agricultural sites using open-path cavity ring-down spectroscopy and wavelength modulation spectroscopy based analyzers(Colorado State University. Libraries, 2018) Shadman, Soran, author; Yalin, Azer P., advisor; Marchese, Anthony J., committee member; Olsen, Daniel B., committee member; Ham, Jay, committee memberAgricultural activities and animal feedlot operations are the primary sources of emitted ammonia into the atmosphere. In the US, 4 Tg of ammonia is emitted every year into the atmosphere which ~%75 of that is due to these major sources. Ammonia is the third most abundant nitrogen containing species in the atmosphere and it has important impacts on atmospheric chemistry, health, and the environment. It is a precursor to the formation of aerosols and its deposition in pristine and aquatic systems leads to changes in ecosystem properties. Quantifying the dry deposition rate of ammonia in the first few kilometers of feedlots is crucial for better understanding the impacts of livestock and agricultural operations on environment. Therefore, fast, precise, and portable sensors are needed to quantify ammonia emission from its major sources. Absorption spectroscopy is a reliable technique by which compact and sensitive sensors can be developed for ammonia (and other gaseous species) detection. An open-path absorption spectroscopy based sensor allows ambient air to flow directly through its measurement region which leads to high-sensitivity and fast-response measurements. In this study, two open-path absorption based ammonia sensors using two techniques are developed: cavity ring-down spectroscopy (CRDS) and wavelength modulation spectroscopy (WMS). The CRDS and WMS based sensors show the sensitivity of ~1.5 ppb (at 1 second) and ~4 ppb (at 1 second), respectively. In both sensors, a quantum cascade laser (QCL) is utilized as the light source to cover the strongest absorption feature of ammonia in the mid-infrared (MIR) spectral region. It is the first demonstration of an open-path CRDS based sensor working in mid-infrared MIR, to our knowledge. The WMS based sensor developed in this study is low power (~25 W) and relatively lightweight (~4 kg). The low power consumption and compact size enables the sensor to be deployed on a commercialized unmanned aerial system (UAS) for aerial measurements. The combination of this sensor and another compact CRDS based methane sensor is used for simultaneous measurements of ammonia and methane (ground based and aerial). Methane is another important species emitted from the feedlots with a long lifetime (~10 years). It is nonreactive and thus not lost by dry deposition. Therefore, methane concentration is only influenced by dispersion while the ammonia concentration is affected by both deposition and dispersion. The dry deposition of ammonia nearby the concentrated animal feeding operations (CAFOs), as one of the major sources of ammonia, can be determined by measuring the decrease in the [NH3]/[CH4] ratio downwind.Item Open Access Methods to detect and analyse volatile organic carbons using low cost real-time sensors(Colorado State University. Libraries, 2019) Gupta, Vatsal, author; Carlson, Kenneth, advisor; Carter, Ellison, committee member; Ham, Jay, committee memberVOCs are ubiquitous and can be found not only as vapors in the air but also as soil gas and dissolved in ground water. Vapor intrusion occurs when volatile organic compounds from contaminated soil or groundwater migrate upwards toward the ground surface and into overlying buildings or surfaces through gaps and cracks in the ground. In this thesis I have detailed several statistical analysis techniques and used these techniques on data that I obtained from active real-time soil gas and ground water quality monitoring sensors placed around an abandoned oil and gas well in Longmont, Colorado, to see if there were VOCs still being released from the site. The main goal of this study was to develop a more precise setup for real-time VOC release monitoring and help regulate fracking sites more efficiently and to analyze the data collected faster and more accurately. Another goal of this study was to bridge the gap between laboratory sampling and real-time on-site testing. From the results, we were able to analyze the movement of the contaminant plume using real time sensing and were also able to identify most of the constituents of the contaminants using in-situ data according to EPA method 18.Item Open Access Modeling a variable surface resistance (rs) for alfalfa and assessing the ASCE rs performance in the reference evapotranspiration equation(Colorado State University. Libraries, 2016) Subedi, Abhinaya, author; Chávez, José, advisor; Andales, Allan, advisor; Ramirez, Jorge, committee member; Ham, Jay, committee memberAccurate quantification of crop water requirement is necessary for proper irrigation water management. The knowledge of actual crop evapotranspiration (ETc) is important and is necessary for estimating irrigation water requirements. The most common procedure of obtaining actual crop evapotranspiration (ETc) is by first calculating the reference crop evapotranspiration (ETr) and then multiplying it with the appropriate crop coefficients (Kc). If the surface resistance (rs) of a particular crop can be modeled, then ETc can be directly calculated without using Kc. The overall objectives of this dissertation were to model surface resistance for alfalfa reference crop and to find an effective value of the surface resistance of alfalfa in the ASCE Standardized Reference ET equation. It has been found that using a single Kc curve for different climatic conditions can lead to significant error in estimating ETc. Hence it is important to find appropriate Kc for different crops for local climatic condition. Lysimeters are generally used to determine the values of Kc, as lysimetry is considered a reliable method of quantifying the ET losses from a control volume. This study found that using lysimeter ET data to obtain Kc can be problematic especially when the field is heterogeneous. In order to develop Kc for various crops, it is recommended to use some years of reliable data with uniform healthy and unstressed crop surface conditions both inside and outside the lysimeter. This study was focused on to develop a model for surface resistance (rs) of alfalfa in order to calculate alfalfa ETc in a one-step approach without the need for Kc values. Surface resistance was estimated by inverting the aerodynamic equation using ET measured from lysimeter and sensible heat flux (H) measured from large aperture scintillometer (LAS). This observed rs showed a very good correlation with leaf area index (LAI) and crop height (hc). The alfalfa rs was then modeled as a function of LAI and hc (which is referred to as rs(LAI) and rs(hc) respectively). Then these modeled rs s were incorporated into the Penman Monteith (PM) equation to estimate alfalfa hourly ET, which performed very well when compared with the measured hourly lysimeter ET. The conventional alfalfa rs, developed by Allen et al. (1989) was found to underestimate rs significantly especially when the crop height was short (less than 25 cm). It was found that ET_conventional_rs was not applicable to estimate alfalfa ET when the crop height was less than 25 cm. The modeled rs(LAI) and rs(hc) are constant throughout the day, but in reality, rs changes throughout the day. Hence hourly variable rs was also developed based on aerodynamic resistance (ra), canopy temperature (Tc) and vapor pressure deficit (VPD). It was found that PM equation incorporating the hourly variable rs improved the alfalfa ET estimation when compared with the conventional rs approach. ASCE-EWRI Standardized Reference ET for tall reference crop was found to underestimate measured ET by about 10 per cent. The equation assumes the value of rs for alfalfa as 30 s/m. When the value of rs was changed from 30 s/m to 10 s/m, the performance of the equation improved, resulting in no bias and root mean square error (RMSE) reduction from 0.08 mm/h (15.3%) to 0.06 mm/h (11.4%) in 2009 and from 0.09 mm/h (14.1%) to 0.06 mm/h (10.1%) in 2010.Item Open Access Optimal sensor placement for sewer capacity risk management(Colorado State University. Libraries, 2019) Kimbrough, Hal Reuben, author; Duff, William, advisor; Grigg, Neil, advisor; Labadie, John, committee member; Ham, Jay, committee memberComplex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research.Item Open Access Patterns of dust-enhanced absorbed energy and shifts in melt timing for snow of southwestern Colorado(Colorado State University. Libraries, 2020) Duncan, Caroline R., author; Fassnacht, Steven, advisor; Kampf, Stephanie, committee member; Ham, Jay, committee memberDeposited dust layers reduce the surface albedo of snow and accelerate melt by this change to the snowpack energy balance. Senator Beck Study Basin in the San Juan Mountains of southwestern Colorado monitors the effects of dust on midlatitude continental snowpack. Continuous automated measurements include shortwave and longwave radiation in addition to conventional micrometeorological variables. Dust layer characteristics and snow properties are collected during snow pit excavation throughout each ablation period. Both sets of data were used to simulate snowpack under observed and dust-free conditions with the snow energy balance model SNOBAL for WY2007 to WY2019. Across the 13 years, dust concentrations ranged from 0.16 to 4.80 mg g-1 resulting in a range of daily mean dust-enhanced absorbed visible energy from 31 to 50 W m-2 during ablation, with hourly peaks up to 347 W m-2. We found snow melt accelerated by 11 to 31 days in a logarithmic response to end-of-year dust concentration modified by seasonal variations in snow amount and cloud cover.Item Open Access Real-time visualization of advective groundwater flow(Colorado State University. Libraries, 2020) Ferrie, Zach, author; Sale, Thomas, advisor; Blotevogel, Jens, advisor; Ham, Jay, committee memberAs the portfolio of sites with subsurface contamination matures, long-term monitoring is becoming the primary factor governing costs for managing historical releases of contaminants to soil and groundwater. Hydraulic gradients are the primary factor driving the velocity and direction in which subsurface contaminants move, making them an important parameter to resolve. Current best practices for tracking groundwater flow include either collecting head data by hand or deploying pressure transducers and periodically returning to manually download the data. Unfortunately, cost restraints and infrequent data collection and processing are not conducive to timely responses to adverse conditions. In this study, two low-cost cellular connected data acquisition systems are developed which allow for collection and analysis of head data in real-time. Using planar regressions of three head values, automated algorithms are used to estimate the direction and rate of groundwater flow on an hourly basis. Another novel addition is the integration of real-time alerts. By automating various alerts, site managers can be notified when conditions reach a pre-determined threshold. Automated alerts allow for swift action to be taken to adverse conditions and can lead to greater safety for the public while saving sites from costly mistakes. Following Devlin and McElwee (2007), uncertainty in groundwater flow direction is a function of measurement error, spacing between wells, and local hydraulic gradients. By using these sources of uncertainty to create synthetic datasets, algorithms are used to estimate the likely range of a groundwater flow path. The effects of pressure transducer drift (i.e. increasing measurement error over time) and their effect on uncertainty are also explored. Results from this study show that as long as the drift is similar in magnitude and direction for all pressure transducers, the effect on the uncertainty in the model is negligible. Additionally, the effects of uncertainty in anisotropy on deviation from the estimated flow path are considered by way of synthetic datasets, which is novel to this research. The results of this research reveal that the effects of anisotropy uncertainty on groundwater flow direction and seepage velocity are also tied to well spacing. Comparisons of the effects of measurement error vs anisotropy uncertainty are compared for four field sites. Results show that the magnitudes of each source of error are site specific and that the effects of measurement error are not always greater than the effects of anisotropy uncertainty and vice versa. Lastly, the seepage velocities are expressed by way of a color scheme common across sites. This novel addition allows for easy visualization of seepage velocities across time and space. Overall, the vision from this research is that real-time, continuous collection and analysis of head data can proceed as outlined in this Thesis. In the future manually collected and interpreted head data need to be compared to the automated analyses described in this Thesis to further support the validity of the methods proposed herein. Another future test is to investigate alternative technologies to pressure transducers for gaining head measurements that are more accurate and reliable.