Theses and Dissertations

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Open Access
Investigating the salinity impacts on current and future water use and crop production in a semi-arid agricultural watershed
(Colorado State University. Libraries, 2024) Hosseini Ghasemabadian, Pardis, author; Bailey, Ryan T., advisor; Arabi, Mazdak, committee member; Smith, Ryan, committee member; Andales, Allan, committee member
Soil salinity can have a significant impact on agricultural productivity and crop yield, particularly in arid and semi-arid irrigated watersheds wherein irrigation and inadequate drainage often combine to increase salt ion concentrations in soil water. In conjunction with intense irrigation in semi-arid agricultural regions, increasing population resulting in boosted water demand, adverse impacts of climate change on water availability, in other words, water scarcity, future land use and land cover changes, changes in applied irrigation practices, and introducing new point-sources and non-point sources of salinity in the region all can govern the salinity and crop yield consequently. Taking into account the aforementioned impactful components on crop reduction via salinity increase, the overall objective of this dissertation is to provide insights for policymakers to better address the current and future salinity issues to sustain crop production in semi-arid regions under progressive salinity. This will be accomplished by i) investigating the controlling factors on salinity in the soil, groundwater, and river water using the SWAT-Salt model which simulates the reactive transport of 8 major salt ions in major hydrological pathways applied to a 1118 km2 irrigated stream-aquifer system located within the Lower Arkansas River Valley (LARV) in southeastern Colorado, USA ii) Assessing the salinity impacts on crops production blue and green water footprint as a measurable indicator for water being used per unit of a given crop production using the SWAT-MODFLOW-Salt model applied to a 732 km2 irrigated stream-aquifer system located in the LARV, iii) quantify the impact of environmental factors alteration including changes in climatic and irrigation practices in the LARV on future salinity content and its impact on crop production in the region using the SWAT-MODFLOW-Salt model. To control salinity, more importantly in semi-arid irrigated areas, the principal step is to identify the key environmental and hydrologic factors that govern the fate and transport of salts in these irrigated regions. To accomplish this objective, global sensitivity analysis was applied to the newly developed SWAT-Salt model (Bailey et al., 2019), which simulates the reactive transport of 8 major salt ions (SO4, Ca, Mg, Na, K, Cl, CO3, and HCO3) in major hydrologic pathways in a watershed system. The model was applied to a saline 1118 km2 irrigated stream-aquifer system located within the Lower Arkansas River Valley in southeastern Colorado, USA. Multiple parameters including plant growth factors, stream channel factors, evaporation factors, surface runoff factors, and the initial mass concentrations of salt minerals MgSO4, MgCO3, CaSO4, CaCO3, and NaCl in the soils and in the aquifer were investigated for control on salinity in groundwater, soils, and streams. The Morris screening method was used to identify the most sensitive factors, followed by the Sobol' variance-based method to provide a final ranking and to identify interactions between factors. Results showed that salt ion concentration in soils and groundwater was controlled principally by hydrologic factors (evaporation, groundwater discharge and up flux, and surface runoff factors) as well as the initial amounts of salt minerals in soils. Salt concentration in the Arkansas River was governed by similar factors, likely due to salt ion mass in the streams controlled by surface runoff and groundwater discharge from the aquifer. Sustainable agriculture in intensively irrigated watersheds, especially those in arid and semi-arid regions, requires improved management practices to sustain crop production. This depends on factors such as climate, water resources, soil conditions, irrigation methods, and crop types. Of these factors, soil salinity and climate change are significant challenges to agricultural productivity. To investigate the long-term impact of salinity and climate change on crop production from 1999 to 2100 in irrigated semi-arid regions, we applied the water footprint (WF) concept using the hydro-chemical watershed model SWAT-MODFLOW-Salt, driven by five General Circulation Models (GCMs) and two climate scenarios (RCP4.5 and RCP8.5), to a 732 km2 irrigated stream-aquifer system within the Lower Arkansas River Valley (LARV), Colorado, USA. In this study we estimated the green (WFgreen), blue (WFblue), and total (WFtotal) crop production WFs for 29 crops in the region, both with and without considering the impact of salinity on crop yield. The results indicate that during the baseline period (1999-2009), the total annual average WFgreen, WFblue, and WFtotal increased by 7.6%, 4.4%, and 6.5%, respectively, under salinity stress, with crop yields decreasing by up to 4.6%, 1.6%, and 2.3% for green, blue, and total crop yield. The combined impact of salinity and the worst-case climate model (IPSL_CM5A_MR) under the higher emission scenario (RCP8.5) resulted in increases of 3.3%, 1.9%, and 3% in green, blue, and total crop production WFs. Additionally, the study found that the proportion of green, blue, and total crop production WFs in the LARV exceeded the world average. This discrepancy was attributed to various factors, including different spatial and temporal crop distribution, irrigation practices, soil types, and climate conditions. Notably, salinity stress had a more significant impact on green crop yield and green WF compared to blue crop yield and blue WF across all GCM models. This finding highlights the need to prioritize management practices that address salinity-associated challenges in the region. The adverse effects of salinity on soil health, crop yield, and environmental ecosystem require comprehensive strategies for managing salinity in agricultural watersheds by adopting improved irrigation practices and effective salinity management strategies for mitigating these impacts and sustaining agricultural productivity in salinity-affected regions. The complex dynamics between various irrigation practices and soil salinity play a pivotal role in shaping agricultural productions and managing soil salinity. In semi-arid regions like the LARV, salinity poses a significant threat to agriculture, exacerbated by climate change and historic irrigation practices. To evaluate the interplay between salinity, climate change, and irrigation management in affecting crop yields within the Lower Arkansas River Valley (LARV), focusing on corn and alfalfa, we utilized the SWAT-MODFLOW-Salt model to examine how changes in irrigation management influence crop production under various scenarios projected through the year 2100. This study addresses the differential responses of corn and alfalfa to the impact of incremental increases and reductions in irrigation efficiency and irrigation water loss (5%, 10%, 15%, and 20%) on corn and alfalfa yields dynamics under salinity stress, utilizing projections from five global climate models under two distinct Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5 and two irrigation scenarios. The findings from irrigation practice scenario (1), maintaining a constant amount of irrigation water, revealed that corn yields improved by up to 13.8% under salinity stress and 16.5% under non-salinity conditions with a 20% increase in irrigation efficiency and a 20% reduction in water loss under RCP4.5. Alfalfa, demonstrating greater salinity tolerance, showed similar benefits, with yield increases of 9.1% under salinity stress and even higher improvements under non-salinity conditions. These results highlight the effectiveness of tailored irrigation practices in mitigating environmental stresses. In contrast, scenario (2), which involved reducing irrigation water by half, resulted in more pronounced negative outcomes. Corn yields exhibited greater sensitivity to salinity stress, with yield reductions ranging from -9.8% under salinity stress to -9.3% under non-salinity conditions, particularly under the RCP8.5 scenario. Alfalfa yields also declined, though less severely than corn, with reductions ranging from -8.9% under salinity stress to -8.3% under non-salinity conditions. Despite improvements in irrigation efficiency and reduced water loss, the adverse effects of salinity stress were not fully mitigated in scenario (2), emphasizing the need for adequate water availability to sustain crop yields under salinity and climate change pressures. The research highlights the importance of adopting advanced irrigation technologies and practices that not only counteract the adverse effects of salinity but also adapt to evolving climatic conditions. This study offers valuable insights for policymakers and agricultural managers on strategic water resource management to sustain crop yields in salinity-affected and water-limited agricultural systems. The results of this study can be used in decision-making regarding the most impactful land and water management strategies for controlling salinity transport and build-up in soils, both for this watershed and other similar semi-arid salinity-impacted watersheds for present and future purposes.
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 member
Soil 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.
Open Access
Optimizing remote sensing data for actual crop evapotranspiration mapping at different resolutions
(Colorado State University. Libraries, 2024) Costa Filho, Edson, author; Chávez, José L., advisor; Venayagamoorthy, Karan, committee member; Niemann, Jeffrey, committee member; Kummerow, Christian, committee member
This study aimed to advance irrigation water management by developing and evaluating a procedure to improve the multispectral data from sub-optimal remote sensing sensors when using the optimal spectral resolution for a given remote sensing (RS) of crop actual evapotranspiration (ETa) algorithm. Data have been collected at three research sites in Colorado under different irrigation systems, soil textures, and vegetation types. The research site in Greeley (CO) has a five-year dataset (2017-2018 and 2020-2022). The fields in Fort Collins and Rocky Ford (CO) have data from 2020 and 2021. Three categories of ETa algorithms were evaluated in the study: The reflectance-based crop coefficient (RBCC) with three different models based on the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and fractional vegetation canopy cover (fc), the one-source simplified surface energy balance (OSEB) based on a surface aerodynamic temperature approach, and the two-source surface energy balance algorithm (TSEB) using two different resistance approaches (parallel and series). All three ETa modeling categories use either just surface reflectance in the visible and invisible light spectrum (e.g., RED, BLUE, GREEN, Near-infrared) or a combination of multispectral and thermal data as inputs to predict crop ETa, alongside local micrometeorological data from nearby agricultural weather stations. A total of six RS of ETa algorithms were evaluated in this study. A total of five RS sensors were evaluated: three spaceborne sensors (e.g., Landsat-8, Sentinel-2, and Planet CubeSat), one proximal device (multispectral radiometer), and an uncrewed aerial vehicle (UAS). The spatial resolution of the RS sensors varied from 30 m to 0.03 m. The accuracy assessment of the crop ETa predictions considered a statistical performance analysis using, among several statistical metrics, the mean bias error (MBE) and root mean square error (RMSE), and compared estimated ETa values from all seven RS ETa algorithms with observed ETa values obtained from the Eddy Covariance Energy Balance System (Greeley and Fort Collins sites) and a weighing lysimeter (Rocky Ford). The study was divided into three stages: a) the evaluation of different remote sensing (RS) pixel spatial resolutions (scales) as inputs on the estimation of different types of data needed for estimating ETa in hourly and daily time frames; b) the development of a calibration protocol and standards for the use of different imagery spatial resolutions (scales) in RS of ETa algorithms. The calibration approach involved a novel two-source pixel decomposition approach for partitioning surface reflectance into soil and vegetation using a non-linear, physically based spectral model, machine-learning regression, and a novel spatial light extinction model (kp); c) the accuracy evaluation of resulting ETa rates from calibrated/standardized data (for each selected RS of ETa algorithms). Results of stage one of the study indicated that depending on the RS of ETa and RS sensor data (spatial and spectral resolutions), the accuracy (MBE ± RMSE) of estimated ETa predictions varied. For the NDVI and fc RBCC ETa algorithms, Sentinel-2 provided the best RS data for predicting daily maize ETa. Errors were 0.21 (5%) ± 0.78 (18%) mm/d and 0.59 (14%) ± 1.07 (25%) mm/d, respectively. For the OSEB algorithm, Planet CubeSat gave the best RS data since it provided the smallest error for hourly maize ETa, -0.02 (-3%) ± 0.07 (13%) mm/h. For the SAVI RBCC model, the MSR data provided the best results since the maize ETa error was -0.13 (-3%) ± 0.67 (16%) mm/d. For the TSEB in series and parallel, the errors when estimating hourly maize ETa were -0.02 (-3%) ± 0.07 (11%) mm/h and -0.02 (-4%) ± 0.09 (14%) mm/h, respectively when using MSR data. For stage two of the study, the best machine learning regression model for a given RS sensor data and RS of the ETa algorithm depended on the surface reflectance composite (plant or bare soil values). The best machine-learning models for adjusting RS data were the regression tree and the Gaussian Process Regression. Regarding the pixel decomposition approach based on the novel spatial light extinction coefficient model, the novel approach provided reliable predictions of kp using the different RS sensor data. The error in predicting kp was -0.01 (-2%) ± 0.05 (10%) when combining all RS sensor data for the two-year data set at LIRF (years 2018 and 2022). For stage three of the study, results showed improvements in the accuracy of crop ETa estimation after adjusting the RS data using the proposed calibration protocol. At the Greeley site, regarding the RBCC RS of ETa algorithm, adjusted data from Planet CubeSat had better performance when estimating daily crop ETa since the error was reduced from 21% to 16% for the fc-input model. For the SAVI-input model, the RS data that performed better was the UAS. Errors were reduced from -0.42 (-11%) ± 0.76 (20%) mm/d to -0.21 (-5%) ± 0.41 (11%) mm/d. For the NDVI-input model, the adjusted UAS data performed better when estimating daily maize ETa. The improved accuracy was 0.32 (8%) ± 0.40 (10%) mm/d. At the Rocky Ford site, for the fc-input model, adjusted RS optical data from the MSR performed better. Daily maize ETa error was reduced from 17% to 15%. For the SAVI-input model, the RS data that performed better was the Landsat-8, with errors being reduced from -1.84 (-28%) ± 2.61 (39%) mm/d to -1.14 (-17%) ± 1.79 (27%) mm/d. The NDVI-based RBCC model had better performance when using adjusted MSR data daily maize ETa. Regarding the OSEB RS of crop ETa approach, at the Greeley site, the OSEB-adjusted data from UAS performed better. Hourly maize ETa error was reduced from 0.11 (19%) mm/h to 0.07 (13%) mm/h for the OSEB algorithm. For the TSEB parallel algorithm, the RS data that had better performance was the Landsat-8/9 since the error was reduced from 0.19 (34%) mm/h to 0.11 (20%) mm/h. For the TSEB series algorithm, the adjusted UAS data performed better. Daily maize ETa errors decreased from 0.10 (18%) mm/h to 0.05 (9%) mm/h. In summary, this study provided an RS calibration approach to support irrigation water management through the development and evaluation of a method for enhancing optical multispectral data sourced from various RS sensors. This study also highlighted the efficacy of machine learning models, like regression tree and Gaussian Process Regression, in adjusting RS data based on surface reflectance composites. Furthermore, a novel pixel decomposition approach utilizing a spatial light extinction model effectively predicted the light extinction coefficient. Overall, this research showcases the potential of RS data adjustments in improving the accuracy of ETa estimates, which is crucial for optimizing irrigation practices in agricultural settings.
Open Access
Modeling nonpoint-source uranium pollution in an irrigated stream-aquifer system: calibration and simulation
(Colorado State University. Libraries, 2024) Qurban, Ibraheem A., author; Gates, Timothy K., advisor; Bailey, Ryan T., committee member; Grigg, Neil S., committee member; Ippolito, James A., committee member
The Lower Arkansas River Valley (LARV) in southeastern Colorado has been a source of significant agricultural productivity for well over a century, primarily due to extensive irrigation practices. Mirroring trends seen in other semi-arid irrigated areas globally, however, irrigated agriculture in the LARV has resulted in several challenges for the region. In addition to the emergence of waterlogging and soil salinization, leading to decreased crop yields, elevated levels of nutrients and trace elements have appeared in the soil and water. Among these constituents, uranium (U), along with co-contaminants selenium (Se) and nitrate (NO3), has shown particularly high concentrations in groundwater, surface water, and soils. These heightened concentrations pose environmental concerns, impacting human health and the well-being of aquatic life such as fish and waterfowl. Careful monitoring and management practices are crucial to prevent potential harm to water resources. The main goal of this research is to develop a comprehensive numerical model for assessing U pollution in a stream-aquifer system within a large irrigated area. To achieve this, a computational model is built and tested that can predict with reasonable accuracy how U, along with Se and NO3, are mobilized and move within a coupled system of streams and groundwater. The approach combines two key modeling components: a MODFLOW package, which handles the simulation of groundwater and stream flow dynamics, and an RT3D package, which addresses the reactive transport of U, Se, and nitrogen (N) species in both groundwater and interconnected streams. RT3D relies on the simulated flows generated by MODFLOW to track the movement of U, Se, and N species between streams and the aquifer in the irrigated landscape, updating daily to adequately capture changes over time. This integrated model provides an understanding of how these contaminants behave and interact within the stream-aquifer system, aiding in effective pollution assessment and providing insights valuable to the planning of management strategies. The coupled MODFLOW-RT3D flow and reactive transport model is applied to a 550 km² area within the LARV, stretching from Lamar, Colorado, to the Colorado-Kansas border and spanning a period of 14 years. The flow package is compared with observations of groundwater hydraulic head and stream flow, along with estimates of return flow along the Arkansas River. The reactive transport package is assessed by comparing predicted U, Se, and NO3 concentrations against data collected from groundwater monitoring wells and stream sampling sites along with estimates of solute mass loads to the river. To calibrate and refine the model, the PESTPP-iES iterative ensemble smoother (iES) software is employed. This calibration process is dedicated to enhancing the model's accuracy in predicting both flow and transport dynamics. PESTPP-iES addresses calibration uncertainty by establishing prior frequency distributions for key model parameters based on data and expertise, then iteratively adjusts these parameters during calibration to align model predictions with observed data. Post-calibration, posterior distributions reflect updated parameter values and reduced uncertainties. Demonstrating a strong alignment with concentrations of CNO3, CSe, and CU values found in groundwater, streams, and the mass loading entering the Arkansas River, outcomes of the model-based simulations reveal a substantial violation of the Colorado chronic standard (85th percentile = 30 μg/L) for CU throughout the study region. On average, simulated CNO3, CSe, and CU values for groundwater in non-riparian areas in the region are 3.6 mg/L, 41 µg/L, and 126 µg/L, compared to respective averages of 4 mg/L, 53 µg/L, and 112 µg/L observed in monitoring wells. When considering the 85th percentile of simulated CNO3, CSe, and CU values, the figures for non-riparian groundwater are 6 mg/L, 50 µg/L, and 218 µg/L, respectively. Groundwater in riparian areas shows lower average simulated CNO3, CSe, and CU values of 3 mg/L, 26 µg/L, and 72 µg/L, respectively, and 85th percentile values of 5 mg/L, 41 µg/L, and 152 µg/L. Additionally, simulated average mass loading rates for NO3, Se, and U along the river are 8.8 kg/day per km, 0.05 kg/day per km, and 0.27 kg/day/km respectively, compared to stochastic mass balance estimates of 9.2 kg/day per km , 0.06 kg/day per km , and 0.23 kg/day per km. The simulated 85th percentile CNO3, CSe, and CU values in the Arkansas River are 1 mg/L, 11 μg/L, and 87 μg/L, respectively. Notably, the simulated U levels in groundwater exceed the chronic standard across 44% of the region. Along the Arkansas River, concentrations consistently surpass the chronic standard, averaging 2.9 times higher. Predicted Se concentrations also show significant exceedances of the chronic standard, while NO3 violations are slight to moderate. The varying pollutant levels across the region highlight specific areas of concern that require targeted attention, indicating potential contributing factors to these hotspots. Findings outline how serious and widespread the problem is in the LARV, providing a starting point for comparing potential pollution reduction from alternative water and land best management strategies (BMPs) to be explored in future applications of the calibrated model.
Open Access
Comparative analysis of remote sensing platforms for assessing maize crop biophysical characteristics and evapotranspiration estimation
(Colorado State University. Libraries, 2024) Al-Majali, Zaid, author; Chávez, José L., advisor; Davenport, Frances, committee member; O'Connel, Jessica, committee member
The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms (such as Unmanned Aircraft Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5) in the appropriate estimation of crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aimed at evaluating the accuracy of vegetation indices (VIs) derived from different sources of RS data in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional cover (Fc), as well as the ETa. Field experiments were conducted at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, in 2022. Different irrigation strategies were used to assess the maize's water use response. Surface reflectance data was collected using the MSR sensor, and observed LAI, Hc, and Fc values served as ground truth for validating remote sensing estimates. The study applied various statistical analyses to compare the performance of different remote sensing platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than other satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of remote sensing models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced remote sensing technologies in enhancing precision agriculture practices and optimizing water resource management.
Embargo
Shear and consolidation behavior of slurry-deposited, desiccated tailings and compacted filtered tailings
(Colorado State University. Libraries, 2024) Primus, Justin Michael, author; Bareither, Christopher A., advisor; Scalia, Joseph, IV, committee member; Stright, Lisa, committee member
The objective of this study was to (i) evaluate and compare the undrained shear behavior and (ii) the consolidation behavior of slurry-deposited and desiccated tailings versus compacted filtered tailings. In general, the evaluation supports the hypothesis that desiccation and resaturation of a hard rock mine tailings yield higher peak undrained shear strengths relative to compacted filtered tailings when considering similar initial conditions (e.g., stress and density). The increase in undrained shear strength was attributed to the tailings fabric, which generated a stiffer response to loading and transitional behavior from contractive to dilative tendencies when sheared undrained. Consolidated undrained (CU) triaxial compression tests were conducted on 64-mm-diameter specimens that followed two different procedures. Slurry-deposited tailings were desiccated to a target void ratio and water content, resaturated, and tested in isotropic, consolidated, undrained axial compression. Filtered tailings specimens were prepared to similar initial void ratios as those measured on desiccated tailings specimens and tested in triaxial compression in the same manner. One-dimensional consolidation tests were also conducted on desiccated and filtered tailings specimens in a similar sequence. The desiccated and filtered tailings exhibited contractive, strain-hardening behavior in the triaxial tests and yielded effective stress friction angles of 29.1° for the desiccated tailings and 27.7° for the filtered tailings. Desiccated tailings samples showed a stiffer initial peak deviatoric stress and slower decreasing rate of change in stress relative to the filtered tailings. There was no indication of a difference in stiffness or brittleness between tailings preparation methods. The higher shear strength of the desiccated tailings was attributed to (i) more pronounced inter-particle reinforcing effects and (ii) densification from stress-history of desiccation. One-dimensional consolidation tests yielded a trend of increasing preconsolidation pressure with decreasing initial void ratio for both the desiccated and filtered tailings. There were slightly higher average compression and recompression indexes computed for the desiccated tailings relative to the filtered tailings, providing an indication of the different in the fabric behaviors.
Open Access
Exploration of passive desaturation of in place tailings using wicking geosynthetics
(Colorado State University. Libraries, 2024) Monley, Kendall O., author; Scalia, Joseph, IV, advisor; Bareither, Christopher, committee member; Ross, Matthew, committee member
As global demand for metals and critical minerals increases, so too does the production of tailings. Tailings are what is left behind after extraction of valuable metals and minerals from ore, and consist of finely ground rock, water, unrecoverable metals, chemicals, and organic matter. These residuals are managed in engineered facilities that function to both dewater and store tailings, known as tailings storage facilities (TSF). A common assumption is that the water initially contained in TSFs will drain down to an unsaturated condition after deposition of new tailings ceases. However, a review of literature on geotechnical and hydrotechnical conditions of legacy TSFs (TSFs that have stopped receiving tailings) in arid environments illustrates that achievement of unsaturated conditions in internal fine-grained layers may not always occur. As the tailings are deposited, layers of finer and coarser particles are interbedded. This causes the formation of capillary barriers and may ultimately result in finer-grained layers held at near saturation after drain down. These fine-grained layers are more susceptible to liquefaction concerns and can require costly remedial actions to ensure geotechnical stability. Dewatering is the process of removing water from whole tailings and offers benefits including increasing geotechnical stability and recovering stored water. Tailings dewatering may occur prior to or after deposition into a TSF. In this study, I explore in-situ dewatering via use of capillary (wicking) geotextiles, and the effectiveness of the wicking geotextiles. Beaker and column experiments were created to emulate stratigraphy seen in legacy TSFs. Additionally, shrinkage testing was conducted to compare the final densities and void ratios of samples with and without wicking geotextiles. Column testing reveals the wicking geotextiles accelerated dewatering by 2.8 times the rate of natural drying processes. At the conclusion of testing, the wicking geotextile experiments had reached similar densities and void ratios to control experiments. This novel approach to passively dewatering tailings warrants additional testing.
Open Access
Chloride binding and desorption mechanism in blended cement containing supplementary cementitious materials exposed to de-icing brine solutions
(Colorado State University. Libraries, 2024) Teymouri Moogooee, Mohammad, author; Atadero, Rebecca, advisor; Fantz, Todd, advisor; Jia, Gaofeng, committee member; Bailey, Travis, committee member
Concrete, the most widely used construction material globally, faces significant challenges due to its porous nature, particularly from chloride-induced corrosion. This corrosion, primarily caused by chloride ions penetrating concrete, affects over 7.5% of U.S. concrete bridges, incurring annual costs ranging from $5.9 to $9.7 billion. Chlorides enter concrete from various sources, including de-icing salts. Maritime infrastructures also suffer from severe chloride-induced corrosion because seawater contains a high concentration of chloride ions. Irrespective of how chlorides enter the concrete, chlorides can exist in concrete in two forms: free and bound chlorides. While bound chlorides are beneficial, they can be released due to environmental factors like carbonation and chemical attacks, exacerbating corrosion rates. These attacks cause pH reduction in concrete and subsequently can result in the release of bound chlorides (chloride desorption).This dissertation aims to address three main objectives: (1) investigate factors influencing chloride binding measurements due to lack of a standardized method for chloride binding measurements, (2) study chloride desorption mechanisms in different cementitious systems exposed to de-icing brines, and (3) analyze pH and compositional changes in blended pastes under chloride contamination and carbonation. First, factors impacting chloride binding measurements were identified, such as sample form and saturation level, solution composition, and solution volume. Vacuum-saturated samples exhibited higher chloride binding than partially saturated or dried samples, with powdered samples showing the highest binding. Secondly, chloride desorption mechanisms were investigated in both Ordinary Portland Cement (OPC) pastes and pastes containing supplementary cementitious materials (SCMs) like fly ash, slag, and silica fume. Results indicated that the type of cation in the brine solution influenced bound chloride levels, with SCMs improving chloride binding capacity. Slag inclusion was effective in promoting chloride binding, while silica fume showed the least effect. The degree of chloride desorption under acid attack depended on the acid-to-paste mass ratio. The results reveal that inclusion of fly ash and slag is favorable in terms of chloride desorption, and silica fume is not recommended for use when chloride-induced corrosion is a concern. MgCl2 and CaCl2 de-icers demonstrated a lower chloride desorption compared to NaCl. Finally, the synergistic effects of chloride contamination and carbonation were examined in OPC and fly ash-containing pastes. Carbonation led to over 95% chloride desorption after two weeks, with fly ash-containing pastes exhibiting lower pH levels due to reduced portlandite content. Incorporation of fly ash is not recommended when carbonation is a concern. Therefore, caution should be exercised when considering fly ash inclusion in mixtures where both chloride contamination and carbonation are simultaneous concerns. This dissertation contributes to understanding chloride desorption in cementitious systems, essential for enhancing the durability and service life of concrete structures. This dissertation shed lights on primary factors influencing chloride binding measurements, enhancing the accuracy and comparability of chloride binding results. The results reveal that type of cation present in the solution and type of SCMs have significant influences on the pH, chloride binding capacity, and chloride desorption rates.
Embargo
Integrated assessment of water shortage under climate, land use, and adaptation changes in the contiguous United States
(Colorado State University. Libraries, 2024) Gharib, Ahmed AbdelTawab Fahmy AbdelMeged, author; Arabi, Mazdak, advisor; Goemans, Christopher, committee member; Sharvelle, Sybil, committee member; Warziniack, Travis, committee member
Water scarcity is a critical global challenge. Water managers pursue water supply- and demand-side strategies, including construction or enhancement of water supply systems, conservation, and water reuse, to address water security driven by changes in climate, population, and land use. However, the effects of these strategies to mitigate future water shortages under dynamic climate and socioeconomic conditions at various spatial and temporal scales remain unclear. The overarching goal of this dissertation is to (1) improve understanding of the interconnections and interactions between climate, socioeconomic, hydrological, and institutional factors that influence water shortage at the river basin level, and (2) conduct an integrated assessment of water and land use management strategies. The dissertation is organized into three research studies. The first study explores water shortages in the South Platte River Basin (SPRB) and the potential benefits of investing in storage infrastructure and demand management strategies. The second study develops a methodology to understand the interactions between land use planning, water demands, shortage vulnerability, and effects on associated economic value. The third study expands the integrative assessment framework to assess changes in water demand, supply, and withdrawals, and identify effective mitigation strategies across river basins in the Contiguous United States over a range of climatic and socioeconomic pathways that are forecasted for the coming decades. In the first study, we develop data analysis and modeling tools to project water demands, supply, and shortages in the SPRB by the mid and end of the 21st century, examine the efficacy of adaptation strategies to reduce water shortages, and explore conditions under which reservoir storage and demand management would serve benefits for reduction of the vulnerability of economic sectors to water shortages. We implement two demand modeling tools to simulate the current and future urban and agricultural water demand in the river basin. Water yield is simulated using calibrated and tested Variable Infiltration Capacity (VIC) model. The estimated water demands and supplies are integrated using the Water Evaluation and Planning (WEAP) model to simulate water allocation with a half-monthly timestep to 70 aggregate users in the basin. Population growth, climate change, reservoir operations, and institutional agreements were considered during the modeling. The study reveals that the vulnerability to water shortages across sectors would increase without adaptation strategies. Population growth tends to be the primary driver of water shortages in the river basin. Reservoirs in the basin can relieve the sequences of the earlier seasonal shift of the water supply by capturing water during the high flow to be used in the high-demand seasons. However, additional storage is only beneficial up to a threshold of storage capacity to the water supply mean ratio of 0.64. The second study focuses on integrating the effects of land use planning and water rights institutions into the shortage analysis of the SPRB. The goal is to build a framework to understand the complex interactions between climate change, water rights institutions, urban land use planning, and population growth, and how they collectively impact the water shortage and economic analysis. We apply this framework to the SPRB simulate three water institutions, update the urban demand modeling to be a function of the population density, and test different scenarios of population locations throughout the basin. Results show that changing water rights institutions has a small impact on total shortages compared to climate change, but substantially impacts which users experience shortages. Land use policies influencing population locations have larger impacts on shortage and economic value compared to water rights. Finally, distributing the population more evenly between upstream and downstream regions reduces water shortages and increases associated economic value regardless of water rights institutions and climate conditions. The third study employs an integrative modeling assessment framework to assess water shortage and effective mitigation strategies in river basins across the Contiguous United States. The goals are to improve the methodologies for estimation of water withdrawals, consumptive use, and water shortage, and explore the effectiveness of supply- and demand-side adaptation strategies. The simulated demands are integrated with the water supply components (groundwater, interbasin transfers, water yield, and reservoirs) into a water allocation model for simulating shortage under different scenarios. Results reveal that irrigation has the highest historical and future consumptive use, over 75% of the total consumptive use. Although the consumptive use ratio receives little attention in the literature, it appears to be the most significant parameter for shortage calculations. The allocation model provides comprehensive shortage analysis considering shortage volume, ratio, and frequency across multiple scenarios for the 204 sub-regions –Hydrologic Unit Code 4 watersheds– of the Contiguous United States. Water shortages concentrate between the boundaries of the West Region with both the Midwest and the South regions, in addition to Arizona, Florida, and the center valley of California. Relying only on sustainable groundwater pumping rates is essential to stop the ongoing groundwater depletion, but adds more pressure on demand reduction strategies. The ongoing research examining water demand, supply, and shortage is important and requires further integration of the key influencing variables. This dissertation demonstrates the necessity of an integrated approach to fully understand the relative impacts of the main drivers of water allocation and shortage. We highlight that reservoirs play a vital role in balancing seasonal fluctuations in the water supply. However, their effect on the 30-year mean annual shortage is effective until the storage volume ratio to mean water supply exceeds 64%. Additionally, land use policies carry higher direct significance on water shortages compared to water rights. We find that distributing the population more evenly throughout the river basin provides the lowest shortage. Lastly, the approaches targeting shortage calculation and mitigation should analyze both regional and national scenarios under integrated frameworks comparing demand- and supply-side options.
Embargo
Tools for characterizing and monitoring natural source zone depletion
(Colorado State University. Libraries, 2024) Irianni Renno, Maria, author; De Long, Susan K., advisor; Sale, Thomas C., advisor; Key, Trent A., committee member; Scalia, Joseph, committee member; Stromberger, Mary, committee member
Although natural source zone depletion (NSZD) has gained acceptance by practitioners as a remediation technology for mid- to late-stage sites containing light non-aqueous phase liquids (LNAPL), challenges remain for broader regulatory adoption of NSZD as the sole remedy. Adoption of NSZD as a remedy requires verifying that it is occurring. NSZD can be an efficient and cost-effective solution for LNAPL zones, but acceptance of this bioremediation technology relies on a multiple-lines-of-evidence approach that requires a solid understanding of baseline conditions and effective monitoring. Emerging use of in situ oxidation-reduction potential (ORP) sensors shows promise to resolve spatial and temporal redox dynamics during NSZD processes. Further, next generation sequencing (NGS) of present and active microbial communities can provide insights regarding subsurface biogeochemistry, associated elemental cycling utilized in electron transport (e.g., N, Mn, Fe, S), and the potential for biodegradation. Microbially-mediated hydrocarbon degradation is well documented. However, how these microbial processes occur in complex subsurface petroleum impacted systems remains unclear, and this knowledge is needed to guide technologies to enhance biodegradation effectively. Analysis of RNA derived from soils impacted by petroleum liquids allows for analysis of active microbial communities, and a deeper understanding of the dynamic biochemistry occurring during site remediation. However, RNA analysis in soils impacted with petroleum liquids is challenging due to: 1) RNA being inherently unstable, and 2) petroleum impacted soils containing problematic levels of polymerase chain reaction (PCR) inhibitors (e.g., aqueous phase metals and humic acids) that must be removed to yield high-purity RNA for downstream analysis. Herein, a new RNA purification method that allows for extracting RNA from petroleum impacted soils was developed and successfully implemented to discriminate between active (RNA) and present (DNA) microbes in soils containing LNAPL. A key modification involved reformulation of the sample pretreatment solution by replacing water as the diluent with a commercially available RNA preservation solution consisting of LifeGuard™ (Qiagen) Methods were developed and demonstrated using cryogenically preserved soils from three former petroleum refineries. Results showed the new soil washing approach had no adverse effects on RNA recovery but did improve RNA quality by removing PCR inhibitors, which in turn allows for characterization of active microbial communities present in petroleum impacted soils. To optimally employ NSZD and enhanced NSZD (ENSZD) at sites impacted by LNAPL, monitoring strategies are required. Emerging use of subsurface Soil redox sensors shows promise for tracking redox evolution, which reflects ongoing biogeochemical processes. However, further understanding of how soil redox dynamics relate to subsurface microbial activity and LNAPL biodegradation pathways is needed. In this work, soil redox sensors and DNA and RNA sequencing-based microbiome analysis were combined to elucidate NSZD and ENSZD (biostimulation via periodic sulfate addition and air sparging) processes in columns containing LNAPL impacted soils from a former petroleum refinery. Herein, microbial activity was directly correlated to continuous soil-ORP readings. Results show expected relationships between continuous soil redox and active microbial communities. Continuous data revealed spatial and temporal detail that informed interpretation of the hydrocarbon biodegradation data. Redox increases were transient for sulfate addition, and DNA and RNA sequencing revealed how hydrocarbon concentration and composition impacted microbiome structure and naphthalene biodegradation. When alkanes were present, naphthalene degradation was not observed, likely because naphthalene degraders were outcompeted. Further, the results of the sulfate addition experiment indicated a direct correlation of Desulfovibrio spp. with naphthalene biodegradation and showed that Smithella spp. were enriched in sulfate enhanced soils containing alkanes. Periodic air sparging did not result in fully aerobic conditions suggesting observed increased rates of biodegradation could be explained by stimulating alternative anaerobic metabolisms that were more energetically favorable compared to baseline/control conditions (e.g., iron reduction due to air oxidizing reduced iron). Methods developed and emerging continuous monitoring tools that were tested in lab soil columns were also applied to a mid- and late-stage LNAPL site. Herein, a case study is presented that advances integration of multiple nascent technologies for characterizing mid- and late-stage LNAPL sites including: 1) cryogenic coring, 2) multiple level internet of things (IoT) soil redox and temperature sensors in soil, and 3) application of RNA- and DNA-based molecular biological tools (MBTs) for site characterization. The integration of the data sets produced by these tools allowed for progress of NSZD to be evaluated in parallel under LNAPL site-relevant biogeochemical conditions. Collectively, the research presented in this dissertation support combining cryogenic coring sampling, continuous redox and temperature sensing and microbiome analysis to provide insights beyond those possible with each monitoring tool alone. The synergy achieved between microbiome characterization and soil continuous sensing illustrates how the integration of new characterization tools can provide insight into complex biogeochemical systems. Further understanding of these technologies will lead to improved predictions on remediation outcomes. The modern tools tested for middle- and late-stage LNAPL sites offer opportunities to more effectively and efficiently manage legacy LNAPL sites.
Embargo
A data-driven characterization of municipal water uses in the contiguous United States of America
(Colorado State University. Libraries, 2024) Chinnasamy, Cibi Vishnu, author; Arabi, Mazdak, advisor; Sharvelle, Sybil, committee member; Warziniack, Travis, committee member; Goemans, Christopher, committee member
Municipal water systems in the United States (U.S.) are facing increasing challenges due to changing urban population dynamics and socio-economic conditions as well as from the impacts of weather extremities on water availability and quality. These challenges pose a serious risk to the municipal water providers by hindering their ability to continue providing safe drinking water to residents while also securing adequate supply for economic growth. A data-driven approach has been developed in this study to characterize the trends, patterns, and urban scaling relationships in municipal water consumption across the Contiguous United States. Then using sophisticated and robust statistical methods, water consumption patterns are modeled, identifying key climatic, socio-economic, and regional factors. The first chapter of this data-driven study looked at municipal water uses of 126 cities and towns across the U.S. from 2005 to 2017, analyzing the temporal trends and spatial patterns in water consumption and identifying the influencing factors. Water usage in gallons per person per day, ratio of commercial, industrial, and institutional (CII) to Residential water use, and percent outdoor water consumption were statistically calculated using aggregated monthly and annual water use data. The end goal was to statistically relate the variations in CII to Residential water use ratio across the municipalities with their local climatic, socio-economic, and regional factors. The results indicate an overall decreasing trend in municipal water use, 2.6 gallons per person annually, with greater reductions achieved in the residential sector. Both Residential and CII water use exhibit significant seasonality over an average year. Large cities, particularly in the southern and western parts of the U.S. with arid climates, had the highest demand for water but also showed the largest annual reductions in their per capita water consumption. This study also revealed that outdoor water use varied significantly from 3 to 64 percent of the Total water consumption across the U.S., and it was highest in smaller cities in the western and arid regions. Factors such as April precipitation, annual vapor pressure deficit, number of employees in the manufacturing sector, total percentage of houses built before 1950, and total percentage of single-family houses explain much of the variation in CII to Residential water use ratio across the CONUS. The second chapter leverages high-resolution, smart-metered water use data from over 900 single-family households in Arizona for the water year 2021. This part of the study characterizes the determinants or drivers of water consumption patterns, specifically in single-family households, and presents a framework of statistical methods for analyzing smart-metered water consumption data in future research. A novel approach was developed to characterize household appliance efficiency levels using clustering techniques on 5-second interval data. Integrating water consumption data with detailed spatial information of the household and building characteristics, along with local climatic factors, yielded a robust mixed-effects model that captured the variations in household water uses with high accuracy at a monthly time-step. Local air temperature, household occupancy level, presence of a swimming pool, the year the household was built, and the efficiency of indoor appliances and irrigation systems were exhibited to be the key factors influencing variations in household water use. The third and fourth chapter of this study reanalyzed the water consumption data of those 126 municipalities. The third chapter dwelled into the estimation of the state of water consumption efficiencies or economics of scale in the municipal water systems using an econometrics framework called urban scaling theory. A parsimonious mixed-effects model that combined the effects of socio-economic, built environment, and regional factors, such as climate zones and water use type, was developed to model annual water uses. The results confirm efficiencies in water systems as cities grow and become denser, with CII water use category showing the highest efficiency gains followed by the Residential and Total water use categories. A key finding is the estimation of the unique variations in water use efficiency patterns across the U.S. These variations are influenced by factors such as population, housing characteristics, the combined effects of climate type and geographical location of the cities, and the type of water use category (Residential or CII) that dominates in each city. The fourth or the final chapter synthesizes the lessons learned previously about the drivers of municipal water uses and explores the development of a model for predicting monthly water consumption patterns using machine learning algorithms. These algorithms demonstrated improved capabilities in predicting the Total monthly water use more accurately than the previous modeling efforts, also controlling for factors with multi-collinearity. Climatic variables (like precipitation and vapor pressure deficit), socio-economic and built environment variables (such as income level and housing characteristics), and regional factors (including climate type and water use type dominance in a city), were confirmed by the machine learning algorithms to strongly influence and cause variations in the municipal water consumption patterns. Overall, this study showcases the power of data-driven approaches to effectively understand the nuances in municipal water uses. Integration of the lessons learned and the statistical frameworks used in this study can empower water utilities and city planners to manage municipal water demands with greater resiliency and efficiency.
Embargo
Long-term analysis of groundwater depletion in the High Plains Aquifer: historical, predictive, and solutions
(Colorado State University. Libraries, 2024) Nozari, Soheil, author; Bailey, Ryan, advisor; Niemann, Jeffrey, committee member; Ronayne, Michael, committee member; Suter, Jordan, committee member
Semi-arid agricultural regions worldwide are heavily dependent on groundwater storage in a handful of large and over-exploited aquifers, such as the High Plains Aquifer (HPA) in the U.S. High Plains Region. The HPA, one of the world's largest freshwater aquifers, serves as the primary source of irrigation water in the High Plains Region. The socioeconomic development in the High Plains Region has come at the expense of significant groundwater depletion in the HPA. The ongoing depletion of the HPA poses risks to livelihoods of rural communities, local ecosystems, and national food security. Addressing this issue necessitates the formulation of groundwater management policies that aim to reduce groundwater extraction, while minimizing associated economic costs over a multi-generational timeframe, all in the context of climate change. To inform the formulation of effective policies, it is crucial to develop a suite of decision support tools that empower local managers and planners to assess the outcomes of various groundwater management policies amidst climate change. The primary goal of this dissertation is to enhance the capacity to project the future of groundwater systems in semi-arid agricultural areas, particularly within the High Plains Region, as a coupled human-natural system, under various groundwater management schemes in the face of climate change. To achieve this goal, a number of tools were developed that incorporate a spectrum of modeling approaches, from the increasingly popular data-driven models to the state-of-the-art hydro-economic models. First, a data-driven modeling framework was developed and tested that is fast to employ and yet provides reliable long-term groundwater level (GWL) forecasts as a function of climatic and anthropogenic factors. The modeling framework utilizes the random forests (RF) technique in combination with ordinary kriging and was tested for the HPA in Finney County, southwest Kansas. The introduction of groundwater withdrawal potential as a new surrogate for pumping intensity empowers the RF model to capture decline in groundwater depletion rate as the system progresses towards aquifer depletion and/or as a result of well retirement policies. The RF model was applied over the period from 2017 to 2099 using 20 downscaled global climate models (GCMs) for two representative concentration pathways (RCPs), RCP4.5 and RCP8.5. The findings indicate that, under status quo management and average climate conditions, the aquifer will no longer be able to sustain irrigated agriculture in most of the county by 2060. Additionally, the difference in climate scenarios will likely shift the aquifer's depletion time frame by up to 15 years in most of the study area. The long-term combined impact of well retirement plans and climate conditions on groundwater depletion trends imply well retirement policies do not lead to sustained groundwater savings. In the next step, an agent-based hydro-economic model (ABM-MODFLOW) was developed for a portion of the HPA in eastern Colorado and northwest Kansas, with the aim of addressing the current limitations of hydro-economic models. Through interdisciplinary collaboration, each component of the ABM-MODFLOW was particularly designed to meet specific research objectives. Planting and irrigation decisions were simulated in the ABM-MODFLOW using a detailed representation of real-world farmers. Additionally, well capacity was incorporated as a constraint on irrigation duration. A subsequent thorough validation of the ABM-MODFLOW was conducted to establish its credibility. The validation results indicate satisfactory performance in reproducing historical data and trends. They also reveal the ABM-MODFLOW's superiority over the standalone groundwater model in simulating the groundwater system. The historical simulation outcomes also underscore the impact of soil type on agents' profitability, especially for those with limited irrigation capacities. Overall, the highest profits are earned by agents with high irrigation capacities and fine soils, while the lowest are achieved by those with low irrigation capacities and coarse soils. Lastly, the ABM-MODFLOW was employed to project the coevolution of human activities, crops, and the groundwater system amidst climate change, both with and without policy interventions. The ABM-MODFLOW simulations involved 32 climate scenarios from 16 downscaled GCMs for two RCPs, RCP4.5 and RCP8.5. Additionally, three groundwater management policy instruments were explored: irrigated land retirement, irrigation well retirement, and authorized pump rate reduction. The simulation outcomes reveal that the groundwater depletion rate decreases over time, primarily due to rising summer temperatures from climate change that limit corn production, a water-intensive crop, in the region. Moreover, these rising temperatures hamper the economic benefits of policies, since the early conserved groundwater is predominantly used for winter wheat irrigation in the later years, a crop with substantially lower irrigation value than corn.
Open Access
Incorporating vehicle trails in soil moisture downscaling for mobility assessments in coarse grained soils
(Colorado State University. Libraries, 2024) Proulx, Holly E., author; Niemann, Jeffrey D., advisor; Scalia, Joseph, advisor; Lynn, Stacy, committee member
Fine resolution (10-30 m) soil moisture maps are critical for determining vehicle mobility in agricultural, forestry, recreational, and military applications. Microwave satellites provide soil moisture products, but the spatial resolutions of these products are too coarse for such applications. Soil moisture downscaling methods, such as the Equilibrium Moisture from Topography Plus Vegetation and Soil (EMT+VS) model, can downscale soil moisture to fine resolutions. However, the EMT+VS model (like most other downscaling models) does not explicitly consider vehicle trails, which may have different soil moisture than undisturbed landscape locations. The objective of this study is to generalize the EMT+VS model to explicitly estimate the soil moisture of trails. The generalized model incorporates two hypothesized effects of vehicle traffic on trails (reduced vegetation cover and reduced porosity). To evaluate the generalized model, porosity and soil moisture observations were collected across a study region in the foothills of the Colorado Front Range. Data were collected at paired trail and landscape locations as well as unpaired landscape locations on six dates in Summer 2023. On average, the porosity of the trail locations was 86% of the paired landscape locations. Soil moisture on trails was on average 73% to 88% of the moisture of the paired landscape locations. Including the vegetation and porosity adjustments in the EMT+VS model reduced the tendency of the model to overestimate the moisture on trails and improved the root mean squared errors.
Open Access
Flow resistance corrections for physical models using unit flowrates
(Colorado State University. Libraries, 2024) Cote, Cassidy B., author; Thornton, Christopher, advisor; Ettema, Robert, committee member; Rathburn, Sara, committee member
Flow resistance is an essential aspect of evaluating flow behavior in open-channel hydraulic models. Flow resistance in open channels is commonly characterized by Manning's resistance equation, where a value of Manning's roughness coefficient n, indicates the magnitude of flow resistance. Physical hydraulic models are one method to estimate Manning's n values for prototype channel reaches. A physical hydraulic model evaluates prototype channel characteristics at the model scale. The scale for a given physical model may be characterized by length-scale factor, given by the relationship of prototype to model geometry. Models that have a large length-scale factor are known to introduce errors associated with instrumentation, measurement, and scale effects, therefore minimization of the length-scale factor is an important consideration in the development of hydraulic models. Evaluating physical models using a scaled unit flowrate provides a method by which the length-scale factor may be minimized. In this way, a scaled design discharge per unit width of channel is applied to a channel that is less wide than the prototype design. Using this approach greatly improves the ability of laboratories to utilize available facilities, without being constrained by prototype design width, which can otherwise be a driving factor increasing the length-scale factor for a given model. This thesis documents the construction and analysis of two physical models of a proposed rectangular canal along Rio Puerto Nuevo in San Juan, Puerto Rico. One model used a scaled unit flowrate and a reduced channel width at a lesser length-scale factor, and the other model accommodated the total scaled design flowrate and design channel width at a larger-scale factor. Tests were conducted for three sidewall conditions to identify the impact associated with applying a unit flowrate physical modeling approach for models with different Manning's n values specific to the sidewalls. The unit flowrate approach was found to result in larger estimates of flow depth and composite Manning's n compared to the model that accommodated the full prototype channel width. Insights regarding the variability of Manning's n as a function of channel width for each sidewall condition were identified by comparing results from the two models. A correction method was proposed for improving estimates of Manning's n derived from scaled unit flowrate models. Correction factors were identified as a function of two dimensionless parameters, relative prototype channel width (defined as the ratio of the width evaluated using a unit flowrate model to the design width of the channel), and relative flow resistance exerted by the individual boundary elements as determined from the unit flow rate model (defined as the ratio of Manning's n values between the sidewall and channel bed boundary elements). Findings indicate that it becomes increasingly important to apply correction factors to flow resistance estimates on unit flowrate models when wall boundary elements exert a larger contribution to flow resistance than that of the channel bed (large relative roughness), and when the scaled unit flowrate approach results in a prototype channel width that is significantly smaller than the proposed design channel width (small relative channel width). Correction factors were developed for a range of relative channel width values from approximately 0.4 to 1.0, and a range of relative roughness values from approximately 0.5 to 3.0. Future physical models using unit flowrates with relative channel widths and relative flow resistance within the range evaluated may use the presented correction methods to improve estimates of flow resistance.
Embargo
Assessing the triple bottom line co-benefits and life cycle cost tradeoffs of cloudburst infrastructure in New York City
(Colorado State University. Libraries, 2024) Fenn, Abby M., author; Arabi, Mazdak, advisor; Grigg, Neil, committee member; Sharvelle, Sybil, committee member; Conrad, Steve, committee member
Urbanization and climate change have increased the risk of urban flooding. Specifically, more frequent cloudburst events are on the rise in cities across the globe. Cloudbursts are characterized by high intensity rainfall over a short duration, causing unpredictable, localized flooding. Effective stormwater management is essential to manage extreme precipitation and runoff induced by cloudbursts. Stormwater control measures have evolved over time shifting from gray infrastructure to nature-based and green solutions. Recently, cloudburst specific infrastructure has emerged as a stormwater intervention strategy designed to handle larger volumes of water by capturing, storing, or conveying excess water in highly impervious areas. Cloudburst infrastructure systems are inextricably linked with land use in cities and thus, their implementation should incorporate life cycle costs, and social and ecological co-benefits. This study assesses the Triple Bottom Line co-benefits and environmental effects of cloudburst systems for flood control in New York City. Specifically, we explore the tradeoffs between the costs and co-benefits of alternative surface vegetation including grass, diverse vegetation, and trees. The study identifies the Pareto optimal set of solutions and quantifies effects of incorporating vegetation into the urban landscape via cloudburst systems. The results indicate that surface vegetation plays a key role in altering the co-benefits and life cycle costs of cloudburst infrastructure. Trees were the most frequent non-dominated solution and were linearly related to Triple Bottom Line score and exponentially related to Life Cycle Cost. The framework and results of this study provide valuable insight to support informed decision-making.
Open Access
Assessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado
(Colorado State University. Libraries, 2024) Fischer, Samantha C., author; Niemann, Jeffrey D., advisor; Scalia, Joseph, advisor; Stright, Lisa, committee member
Soil moisture is an important driving variable of the hydrologic cycle and a key consideration for decision-making in off-road vehicle mobility, crop modeling, drought forecasting, flood prediction, and a variety of other applications. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing or land surface models; however, coarse resolution estimates are unsuitable for many applications. Downscaling these products to finer resolutions (~10 m) creates soil moisture maps that are more useful. This study applies the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model to Maxwell Ranch, a 4,000-ha cattle ranch in Northern Colorado that represents a diverse range of topographic, vegetation, and soil characteristics and a wide range of soil moisture conditions. The EMT+VS model is a physically based geo-information method that downscales coarse resolution soil moisture estimates using ancillary fine resolution datasets of topography and vegetation. Input data to the EMT+VS model contain inherent sources of error that can impact the uncertainty of downscaled estimates. The objective of this study is to identify sources of uncertainty in inputs and assess their influence on the error of the EMT+VS model output. The study finds changes in vegetation input or digital elevation model (DEM) resolution introduce substantial errors in the EMT+VS model output; however, these errors can be mostly overcome when recalibration with local in-situ data is possible. The highest errors (RMSE = 0.20 cm3/cm3) tend to occur in locations with thick vegetation and high contributing area, which are difficult to accurately estimate with available remote sensing data sources.
Open Access
Enhancing natural treatment systems by utilizing water treatment residuals
(Colorado State University. Libraries, 2008) Yarkin, Mustafa, author; Carlson, Kenneth H., advisor
The current project envisions the application of riverbank filtration (RBF) and aquifer recharge and recovery (ARR) in series as preliminary treatment steps of a multi-barrier treatment approach for the City of Aurora's Prairie Waters Project. The primary focus of the project is the removal of phosphorus, nitrogen, and carbon from the source water resulting in biologically stable water that can be stored in a terminal reservoir. In addition to nutrients, perchlorate and three commonly used pesticides, atrazine, alachlor, and metolachlor have been studied in terms of removal with the RBF and ARR systems. Aluminum based water treatment residual (WTR) was considered along with other sorbents for enhanced phosphorus removal. The experimental studies include the monitoring of an RBF field site and pilot columns that simulate RBF and ARR systems. Possible benefits of WTR as an amendment were tested by amending a column with 30% WTR under RBF and ARR conditions. Also an application scenario of RBF followed by a WTR amended ARR infiltration basin and ARR was simulated by a column study. Results of the studies indicated that the RBF and ARR systems are insufficient to provide sustainable phosphorus removal. Phosphorus removal mechanism is limited by the sorption capacity of the alluvial sand and minor biological activity. Use of the WTR amendment reduced phosphorus levels to less than the method detection limit of 0.03 mg/L with a high adsorption capacity. The ARR system in sequential RBF-ARR application suffers from the lack of labile organic carbon and therefore microbially mediated treatment processes are limited. Amending the infiltration of the ARR system with organic carbon rich WTR can promote biological activity, thus allowing further biodegradation of contaminants. Results of the study indicated that the RBF system is a sustainable barrier for nitrate removal while labile carbon limited ARR cannot achieve significant nitrate removal. To use the ARR system as a secondary barrier for nitrate, a labile carbon source should be introduced to the system. WTR was used as a supply of organic carbon to the ARR system and the experimental studies indicated that, once optimized, WTR can promote biological denitrification through the ARR system. The field and column studies also showed that both RBF and ARR can achieve perchlorate removal as long as sufficient electron donating compounds (e.g. organic carbon) are present in the environment. It has also been observed that the ability of RBF and ARR systems to remove alachlor and metolachlor is limited by the biodegradation through the alluvial sand while they achieve sustainable atrazine removal. WTR was tested as an amendment alternative the ARR infiltration basin. Concentrations of selected pesticides were reduced to the method detection limit of 0.3 μg/L during 1-foot 30% WTR amended column treatment with the residence time of 1.25 days under both abiotic and biotic conditions. The overall study suggested that once the source and type of the WTR was selected, the optimum amount of WTR can be obtained by adjusting the application ratio and the media depth for the efficient removal of all contaminants of concern.
Open Access
Characterization of the scale dependence and scale invariance of the spatial organization of snow depth fields, and the corresponding topographic, meteorologic, and canopy controls
(Colorado State University. Libraries, 2009) Trujillo-Gómez, Ernesto, author; Ramírez, Jorge A., advisor
The spatial organization of snow cover properties and its dependence on scale are determined by precipitation patterns and the interaction of the snow pack with topography, winds, vegetation and radiative fluxes, among others factors. The objectives of this research are to characterize the spatial scaling properties and spatial organization of snow depth fields in several environments at scales between 1 m and 1000 m, and to determine how these properties are related to topography, vegetation, and winds. These objectives are accomplished through (a) the analysis of LIDAR elevation contours, and snow depth contours, (b) the analysis of synthetically generated profiles and fields of snow depth, and (c) simulations performed using a new cellular automata model for redistribution of snow by wind. The analyses of the power spectral densities of snow depth show the existence of two distinct scaling regimes separated by a scale break located at scales of the order of meters to tens of meters depending on the environment. The breaks separate a highly variable larger-scales interval from a highly correlated smaller-scales interval. Complementary analyses support the conclusion that the scaling behavior of snow depth is controlled by the scaling characteristics of the spatial distribution of vegetation height when snow redistribution by wind is minimal and canopy interception is dominant, and by the interaction of winds with features such as surface concavities and vegetation when snow redistribution by wind is dominant. Using these observations together with synthetic snow depth profiles and fields, we show that the scale at which the break occurs increases with the separation distance between snow depth maxima. Finally, the cellular automata model developed here is used to show that the correlation structure of the snow depth fields becomes stronger as the amount of snow transported increases, while the probability distributions of the fields progress from a Gaussian distribution for small transport rates to positively skewed probabilities for high transport rates. These simulation results are used to illustrate the controls that topography, vegetation, and winds have on the spatial organization of snow depth in wind-dominanted environments. Implications of the results from the different analysis are discussed.
Open Access
A spatial decision support system for basin scale assessment of improved management of water quantity and quality in stream-aquifer systems
(Colorado State University. Libraries, 2008) Triana, Enrique, author; Labadie, John W., advisor; Gates, Timothy K., advisor
Challenges in river basin management have intensified over the years, with expanding competition among water demands and emerging environmental concerns, increasing the complexity of the decision making framework. A State-of-the-art spatial-decision support system (River GeoDSS) is developed herein to provide assistance in evaluating management alternatives towards optimal utilization of water resources, providing a comprehensive treatment of water quantity and quality objectives based on conjunctive surface and groundwater modeling within the complex administrative and legal framework of river basin management. The River GeoDSS provides sophisticated tools that allow accurate system simulations and evaluation of strategies while minimizing the technological burden on the user. A unique characteristic of the River GeoDSS is the integration of models, tools, user interfaces and modules, all seamlessly incorporated in a geographic information system (GIS) environment that encourages the user to focus on interpreting and understanding system behavior to better design remediation strategies and solutions. The River GeoDSS incorporates Geo-MODSIM, a fully functional implementation of MODSIM within the ArcMap interface (ESRI, Inc.), and Geo-MODFLOW, a new MODFLOW-MT3DMS results analysis tool in the ArcMap interface. The modeling system is complemented with a new artificial neural networks (ANN) module for natural and irrigation return flow quantity and quality evaluation and salt transport through reservoirs, as well as with a new water quality module (WQM) for conservative salt transport modeling of conjunctive use of surface water and groundwater resources in the river basin network. In this research, innovative methodologies are developed for applying ANNs in efficiently coupling surface and groundwater models for basin-scale modeling of stream-aquifer interactions. The core River GeoDSS is customized to provide comprehensive analysis of alternative solutions to achieving agricultural, environmental, and water savings goals in the Lower Arkansas River Basin in Colorado while assuring physical, legal and administrative compliance. The River GeoDSS applied to the Arkansas River Valley allowed comparing benefits and improvements of management strategies, illustrated their potential to reduce waterlogging and soil salinity, salt load to the river, and non-beneficial evapotranspiration in a strategic planning environment.
Open Access
Three-dimensional finite element modeling of time-dependent behavior of wood-concrete composite beams
(Colorado State University. Libraries, 2009) To, Lam Giang, author; Gutkowski, Richard M., advisor
The wood-concrete composite beam structure with notched shear keys has some advantages such as high composite efficiency and ease of construction with low labor cost compared to other wood-concrete composite beam structures. Made up from two rheological materials, wood and concrete, the time-dependent behavior of the wood-concrete composite beam is not only affected by the long-term load but also driven by the variation of the environmental conditions such as temperature and relative humidity. To consider the effects of the environmental conditions, the modeling process must include the moisture diffusion analysis for the wood layer, the heat transfer analysis and the stress/displacement analysis where the first two analyses provide input parameters for the third analysis. This research focused on modeling the time-dependent behavior of the layered wood-concrete composite with notched shear keys by using 3D finite element method. The main goals of the research are to expand available constitutive models of wood and concrete so that they can be used in the 3D FEM. The 3D constitutive models of wood and concrete were then implemented in the commercial software ABAQUS by using the subroutine UMAT for the stress/displacement analysis. To provide data to validate the theoretical model, a long-term creep test on two specimens has been performed. The results of the verification analysis on one test specimen captured closely the time-dependent behavior of the test specimen for the first 123 days of the test. The verification analysis revealed that the heat transfer analysis is not necessary in long-term analysis. The application of the 3D model with solid elements not only predicts the long-term behavior of the wood-concrete composite beam structures better than ID models do, but it can be also applied for wood-concrete composite structures with complex geometry where the 1D model cannot be applied. In addition, the application of the 3D model with solid element can be used to perform parametric studies to address remaining questions about time-dependent of the wood-concrete composites structures.