• Dissertation Defense
• 08/01/2024

Addressing groundwater depletion problems in heterogeneous aquifer systems is a challenge. The heterogeneous Ogallala Aquifer, a critical source of groundwater in the central United States, has undergone decades of decline in water levels due to pumping. This project aims to build a robust groundwater model to evaluate optimal scenarios for sustainable use of the groundwater resource within a section of the Ogallala aquifer located in the Middle Republican Natural Resources District (MRNRD). This study follows a comprehensive approach involving parameterization, construction, and optimization. The model is parametrized using hydraulic conductivity and recharge values obtained from a random forest-based machine learning model and deep neural network regressor-based model. The predicted hydraulic conductivity efficiently identifies heterogeneous layers and enhances the performance of the groundwater model. Similarly, the predicted recharge parameter identifies spatial variation and finds a diverse range of recharge values. Both parameters are incorporated into a Modflow-NWT-based groundwater model. Calibration uses Parameter ESTimation (PEST) algorithm within GMS 10.3.5 software targeting recharge, hydraulic conductivity, evapotranspiration, and riverbed conductance. Scenario analysis using the calibrated model demonstrates the sensitivity of groundwater level and stream flow to changes in pumping and recharge rates. The model is optimized using Genetic algorithms (GAs) and sensitivity analysis. The findings of the optimized aquifer system recommended a balanced approach of increasing recharge rates by 5.8% and decreasing pumping by 5.5 %. This recommendation is consistent with previous studies that emphasized the importance of enhancing recharge and reducing groundwater withdrawal. Using Genetic algorithms for optimization underscores the potential of advanced techniques in achieving balanced and sustainable groundwater management strategies. We recommend that future works focus on refining these models and exploring additional scenarios to ensure long-term groundwater sustainability in the study area.

• Thesis Defense
• 04/17/2024

Quaternary glacial aquifers are important water sources for irrigation in many agricultural regions, including eastern Nebraska, USA. Quaternary glacial aquifers are heterogeneous and comprise sediment assemblages with a wide range of hydraulic properties. Effective management and sustainable utilization of these heterogeneous glacial aquifers necessitate the development of realistic groundwater-flow models and characterization of aquifer geometry. However, hydrofacies probabilities predicted using multiple-point statistics (MPS) and machine learning (ML) techniques are rarely used for parameterizing groundwater models and identifying management zones. This study used MPS to simulate 100 three-dimensional conditional aquifer heterogeneity realizations by combining soft data, a cognitive training image, and hard data. The most probable hydrofacies model (sand and clay probability) was then calculated at a node spacing of 200×200×3 m and validated using groundwater-level hydrographs. The resulting hydrofacies probability grids revealed variations in aquifer geometry, locally disconnected aquifer systems, recharge pathways, and hydrologic barriers. A new workflow was established using the three-dimensional hydrofacies probability generated by MPS and hydrologic data to define high-resolution groundwater management zones and enhance strategies. Subsequently, ML techniques such as Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model three-dimensional probabilistic distributions of hydrofacies at a grid size of 200 m x 200 m x 3 m. The models were compared in terms of their capability to identify thin, permeable hydrofacies, lateral continuity, and vertical contrast between hydrofacies units. ML predicted thin, permeable, and laterally continuous hydrofacies better compared to hydrofacies predicted by MPS. Multilayer Perceptron and Stacking Classifier models show sharper vertical contrasts between fine and coarse hydrofacies compared to MPS and other ML models. Finally, three groundwater models were constructed using MODFLOW 6 with unstructured grids. The first model was parameterized using hydraulic conductivity derived from pumping and geological data, while the second and third models were parameterized using hydraulic conductivity estimated from MPS and stacking machine learning hydrofacies models, respectively. K-means clustering was used to translate the predicted hydrofacies probability into hydraulic conductivity values. While the entire water budgets of all models show minimal variation, zonal water budget analyses reveal significant differences in storage change, stream-groundwater interactions, and total inflow and outflow. This study effectively demonstrates the influence of three-dimensional aquifer heterogeneity modeling approaches on the outcomes of groundwater models. Such insights can prove invaluable for groundwater managers and policymakers in assessing the implications of groundwater model parameterizations on local groundwater management.

• Thesis Defense
• 04/16/2024

Antibiotics are a vital component of medicine, especially when treating humans and animals, however, their use can have negative consequences. Pharmaceuticals, including antibiotics, may be introduced into streams through wastewater discharge, leaching, and runoff in rural and urban settings. Even though discharging wastewater is a widespread practice, the occurrence of antibiotics in the environment may lead to changes in ecosystem processes and can influence the prevalence of antibiotic resistance. This research was established to evaluate the occurrence and type of antibiotics present in streams and determine whether urban wastewater, row cropping and manure application, or animal feeding operations are the main contributors. Three streams near Scottsbluff, Nebraska were chosen to sample based on the information gathered from other researchers and the known agricultural and urban influences. A confined animal feeding operation (CAFO) was one possible source of antibiotic presence in streams. The stream where the local wastewater treatment plant (WWTP) discharges was also chosen to study and a stream that has agricultural fields along it that may have row cropping, manure application, or livestock present throughout the year. Polar organic chemistry integrative samplers (POCIS) that allow measurement of very low concentrations of antibiotics in surface water, were placed into the streams and left for around a month to determine differences between sites influenced by wastewater, surface runoff, or seepage. Results show distinct differences in antibiotic levels between the agriculturally influenced streams and discharge from the WWTP, however, several types of antibiotics present are the same. In this review, the significance of antibiotics and water quality will be discussed, along with the fieldwork techniques, sample preparation, analysis, and conclusions. This research is important as it can lead to further exploration and understanding of the ecological impacts, antibiotic resistance, and other influences antibiotics may have when introduced to the environment.

• Thesis Defense
• 04/10/2024

Precisely measuring soil water content (SWC) is crucial for effective water resource management. This study utilizes the Cosmic Ray Neutron Sensor (CRNS) as a novel technique for area-averaged SWC measurements at an intermediate scale. However, accurate SWC measurements from CRNS require consideration of all hydrogen sources, including time-variable ones like plant biomass and plant water. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize-soybean rotation have been monitored for 5 (CSP1 and CSP2) and 13 years (CSP3). Data collection includes biomass water equivalent (BWE) biweekly with destructive sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse Time Domain Reflectometry (TDR) network (4 locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight times in fields CSP1 and CSP2 and nine times in CSP3 over the growing season (April to October). The N0 parameter, presented in Desilets et al. (2010) as the transformation function between epithermal neutron counts and SWC, exhibits a strong linear relationship with BWE, suggesting a straightforward vegetation correction factor may be suitable (fb). Results from both the 2023 gravimetric surveys and long-term TDR data indicate a neutron count rate reduction of about 1% (0.6% minimum and 1.7% maximum) for every 1 kg/m2 (or mm of water) increase in BWE for all three sites and two crop types. This reduction factor aligns well with existing shorter-term row-crop studies but nearly doubles the value previously reported for forests. The higher count rate detector model #CRS-2000/B model at CSP1 and CSP2 significantly reduced the uncertainty in the BWE-N0 results (r2 of 0.8 vs. 0.3) compared to the #CRS-1000/B model at CSP3. This research strongly supports the idea that an fb correction should be applied for cropland sites. It is also possible the same fb correction can be applied to forest sites, but a long-term study is still needed to validate both the linear function and magnitude of the coefficient. This long-term study contributes valuable insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

• Thesis Defense
• 10/30/2023

Eutrophic conditions often become prevalent in urban and stormwater retention ponds following years of external nutrient loads. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond (Densmore Pond, Lincoln, NE) that had severe algae and weed growth. Our approach included installing two 37 m2 (6.1 m x 6.1 m) floating treatment wetlands (FTWs) and two airlift pumps that contained slow-release lanthanum composites. The floating treatment wetlands promoted microbial denitrification and plant uptake of nitrogen and phosphorus, while the airlift pumps slowly released lanthanum to the water column over the growing season to reduce soluble reactive phosphorus. Four seasons of field sampling (2020-2023) showed median NO3-N concentrations were reduced from 23 µg L-1 in 2020 to 1.3 µg L-1 in 2023, while PO4-P decreased from 42 µg L-1 to 19 µg L-1. The reduction in N and P from the water column coincided with less algae, weeds, and pond muck (sediment), and greater dissolved oxygen concentrations and water clarity. To quantify the sustainability of this bio-chemical approach, this study focused on measuring nitrate removal rates from the FTWs. By enclosing quarter sections of the field-scale FTWs (3.05 x 3.05 m) inside vinyl pool liners in the pond, nitrate removal rates were measured by spiking nitrate into the enclosed root zone and then measuring nitrate loss under aerated and unaerated conditions. Results showed that NO3-N removal rates were roughly three-fold greater under unaerated (1485 mg NO3-N/d) versus aerated conditions (515 mg NO3-N/d). Extrapolating these removal rates to mass (kg) of nitrate-N removed, we estimate the two the full-sized FTWs installed in the Densmore Pond could remove between 0.64 to 1.82 kg of NO3-N over a growing season (May-September, 153 d), with removal mass dependent on dissolved oxygen concentrations beneath the FTWs. Complementary laboratory mesocosm experiments using similar treatments to field experiments will also be presented.

• Dissertation Defense
• 07/05/2022

Current Use Pesticides (CUPs), which are important for continued productivity within the agricultural industry, exhibit a growing influence on water resources and aquatic ecosystems. Worldwide, over 411,000 kilograms of pesticides (e.g., herbicides, insecticides, and fungicides) are applied annually, leading to chronic pollution in streams and rivers. While substantial work has been completed on the occurrence and distribution of neonicotinoid in both surface and groundwater environmental, little is known on the degradation and transformation of neonicotinoids in natural river environments. Therefore, the overarching goal of this study was to quantify the potential role of two rivers on the photochemical transformation of neonicotinoids and potential fate and transport mechanisms of these insecticides and their degradants in aquatic environments. To evaluate this, we established the following objectives: review the current knowledge of neonicotinoids insecticides in aquatic environments (Chapter 1), evaluate the impact of neonicotinoids fate and transport in watersheds with varying land uses (Chapter 2), and evaluate specific photochemical transformation rates, mechanisms, and byproduct formations of two neonicotinoids (imidacloprid and thiamethoxam) in river water with varying dissolved organic matter (Chapter 3). Findings from this study provide an improved understanding of pesticide and their degradants fate and transport mechanisms in river environments.

• Thesis Defense
• 04/08/2022

To keep up with the global food demand, modern agriculture seeks to optimize production on current agricultural land. One method of optimization is through precision management, where field zones are managed according to variation in soil properties. For instance, activities such as irrigation, fertilization, and seeding can be guided by soil maps of available water capacity, organic matter content, and bulk density. The conventional method for obtaining soil maps is extensive soil sampling, which involves significant time and labor costs. On-the-go geophysical sensors can potentially obtain soil maps that are still accurate enough for precision management but less costly. Physical properties of the earth, such as the electrical conductivity and naturally emitted radiation of the top meter of the ground, can be correlated with various soil properties. Geophysical sensors can provide information about the variability of the soil between soil sample locations and reduce the number of soil samples needed. However, across different field conditions, the geophysical data tends to have varying correlations with the actual soil properties.

A study was conducted in three agricultural fields in North Dakota to better understand geophysical sensors’ performances in soil mapping. Electrical conductivity data from an electromagnetic-induction sensor, radioelement concentrations from a gamma-ray sensor, and neutron intensities from a cosmic-ray neutron sensor were used to build simple linear models that predict soil properties across each field. At each of the sites, different soil properties such as bulk density, texture, or available water content were predicted with satisfactory accuracy. The study shows that using just a few soil samples alongside geophysical data is a feasible method for creating soil maps for precision management. While electromagnetic induction and gamma-ray surveys are currently commercially available to producers, future work must be done to establish which sensors are best for each setting and the method’s economic value.