UMD Theses and Dissertations
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Item Equilibrium Programming for Improved Management of Water-Resource Systems(2024) Boyd, Nathan Tyler; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Effective water-resources management requires the joint consideration of multiple decision-makers as well as the physical flow of water in both built and natural environments. Traditionally, game-theory models were developed to explain the interactions of water decision-makers such as states, cities, industries, and regulators. These models account for socio-economic factors such as water supply and demand. However, they often lack insight into how water or pollution should be physically managed with respect to overland flow, streams, reservoirs, and infrastructure. Conversely, optimization-based models have accounted for these physical features but usually assume a single decision-maker who acts as a central planner. Equilibrium programming, which was developed in the field of operations research, provides a solution to this modeling dilemma. First, it can incorporate the optimization problems of multiple decision-makers into a single model. Second, the socio-economic interactions of these decision-makers can be modeled as well such as a market for balancing water supply and demand. Equilibrium programming has been widely applied to energy problems, but a few recent works have begun to explore applications in water-resource systems. These works model water-allocation markets subject to the flow of water supply from upstream to downstream as well as the nexus of water-quality management with energy markets. This dissertation applies equilibrium programming to a broader set of physical characteristics and socio-economic interactions than these recent works. Chapter 2 also focuses on the flow of water from upstream to downstream but incorporates markets for water recycling and reuse. Chapter 3 also focuses on water-quality management but uses a credit market to implement water-pollution regulations in a globally optimal manner. Chapter 4 explores alternative conceptions for socio-economic interactions beyond market-based approaches. Specifically, social learning is modeled as a means to lower the cost of water-treatment technologies. This dissertation's research contributions are significant to both the operations research community and the water-resources community. For the operations research community, this dissertation could serve as model archetypes for future research into equilibrium programming and water-resource systems. For instance, Chapter 1 organizes the research in this dissertation in terms of three themes: stream, land, and sea. For the water-resources community, this dissertation could make equilibrium programming more relevant in practice. Chapter 2 applies equilibrium programming to the Duck River Watershed (Tennessee, USA), and Chapter 3 applies it to the Anacostia River Watershed (Washington DC and Maryland, USA). The results also reinforce the importance of the relationships between socio-economic interactions and physical features in water resource systems. However, the risk aversion of the players acts as an important mediating role in the significance of these relationships. Future research could investigate mechanisms for the emergence of altruistic decision-making to improve equity among the players in water-resource systems.Item MODELING GROUNDWATER FLUCTUATIONS IN THE COASTAL PLAIN OF MARYLAND: AN ANN POWERED STRATEGY(2024) Steeple, Jennifer Lynne; Negahban-Azar, Masoud; Shirmohammadi, Adel; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Groundwater management in the face of climate change presents a critical challenge with far-reaching implications for water resource sustainability. This study evaluates the effectiveness of Artificial Neural Networks (ANNs) as predictive tools for estimating current groundwater levels and forecasting future groundwater levels in the Aquia aquifer in the Coastal Plain ofMaryland. The groundwater levels of the Aquia aquifer have declined under the pressures of land use change, increases in agricultural irrigation, and population growth. We tested, trained, and employed eight county-level artificial neural network (ANNs) models to predict and project Aquia aquifer groundwater levels for the near (2030-2050) and far (2050-2100) future under two socio-economic pathways (SSP245 and SSP585). The models exhibited significant predictive performance during testing (R²= 0.82-0.99). Minimum temperature and population were the most influential variables across all county-based models. When used to forecast groundwater level under two climate scenarios, the models predicted declining groundwater levels over time in Calvert, Caroline, Queen Anne’s, and Kent counties, aligning with regional trends in the Aquia aquifer. Conversely, Anne Arundel, Charles, St. Mary’s, and Talbot counties exhibited projected increases in groundwater levels, likely influenced by correlations with the variable irrigated farm acreage, underscoring the importance of considering nonlinear relationships and interactions among variables in groundwater modeling. The study highlights the ability of ANNs to accurately predict county-scale groundwater levels, even with limited data, indicating their potential utility for informing decision-making processes regarding water resource management and climate change adaptation strategies. This study also assessed the usability of multiple methods to fill in the missing data and concluded that using the repeated groundwater level data still resulted in powerful ANN models capable of both predicting and forecasting ground water levels in the Coastal Plain of Maryland.Item Dual water quality responses after more than 30 years of agricultural management practices in the rural headwaters of the Choptank River basin in the Chesapeake Bay watershed(2023) Silaphone, Keota; Fisher, Thomas R; Natural Resource Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Eutrophication is the water quality response to over-enrichment by nitrogen (N) and phosphorus (P) in fresh, estuarine, and coastal waters globally. Agricultural best management practices (BMPs) are the primary tool for controlling eutrophication in rural areas, particularly in the Chesapeake Bay watershed, where BMPs are vital to achieving TMDL goals. However, despite the application of BMPs, local water quality in the headwaters of the Choptank River, a major tributary of the Chesapeake Bay on the Delmarva Peninsula, has not improved. Thus, further investigation of agricultural BMP impacts on water quality in the Greensboro watershed is needed. My overarching research question is, “Why have N and P concentrations increased at the USGS Greensboro gauge if agricultural Best Management Practices (BMPs) have been implemented?” I applied statistical approaches to three linked, testable hypotheses to systematically evaluate agricultural BMPs and their impacts on nutrient (N and P) export from the Greensboro watershed. My first hypothesis was that agricultural BMPs have increased significantly in the Greensboro watershed. To test this hypothesis, I obtained publicly available modeling data via the Chesapeake Assessment Scenario Tool (CAST) and estimated the subsequent edge-of-stream N and P export. My findings indicated that the number of BMPs in the agricultural sector increased significantly between 1985 and 2021, supporting the hypothesis. Overall, modeled agricultural N and P export significantly decreased between 2010 and 2021 (p < 0.001). However, the modeled edge-of-stream agricultural nutrient export resulted in no significant change in N export and an increase of 3% in agricultural P export resulting from BMP implementation levels in 2021 compared to 2010. This study demonstrated the use of CAST to acquire reported BMP implementation levels and increased nutrient inputs into the Greensboro watershed between 1985 and 2021. The watershed nutrient inputs mirror the upward trends in N and P export captured by the USGS long-term monitoring station at Greensboro. With this improved access to BMP implementation and nutrient data, decision-makers can consider adaptive management measures to decrease nutrient export downstream. My second hypothesis was that agricultural BMPs have an adequate basis for estimating their capacity to reduce N export. To test this hypothesis, I conducted a meta-analysis on 689 cover crop N efficiencies reported in 18 empirical and modeling studies. The cover crop N efficiency was calculated as the ratio of an N interception by cover crop biomass or a reduction in soil or groundwater N divided by an N input, e.g., previous spring fertilizer or a previous soil or groundwater N concentration or flux. These variable approaches resulted in wide ranges in mean cover crop N efficiency (10-80%) due to empirical and modeling experimental approaches, varying methods, and parameters used to calculate efficiency. The modeling approach generally resulted in N efficiency values significantly higher than the empirical approach, as did the parallel control-treatment experiments compared to the sequential before-and-after implementation method. Because of these variables, there appears to be no standard methodology to report the effects of cover crops or standardized metadata describing the variables used in the N efficiency calculations. I suggest a standard methodology and metadata that should accompany future reports of cover crop N efficiencies to improve the modeled effects of BMPs on nutrient export. My third hypothesis was that three methods of estimating N and P concentrations and yields are in agreement and show a relationship to BMP implementation in the Greensboro watershed. To test this hypothesis, I compiled annual nutrient (N and P) datasets based on (1) USGS field measurements of concentrations and discharge, (2) USGS flow-normalized weighted regression based on time, discharge, and season (WRTDS) of concentrations and yields, and (3) CAST-modeled nutrient yields. Statistical analyses revealed time, discharge, agricultural BMPs, and animal waste management practice trends of the three methods. Results indicated that the USGS field measurements and WRTDS flow-normalization methods consistently showed an increase in N and P concentrations and yields. In contrast, all CAST-modeled regressions showed significantly decreasing nutrient concentrations and yields (p ≤ 0.05), which did not support the hypothesis that all three methods are in agreement. Despite CAST-modeled results decreasing with increasing BMPs, which supports the hypothesis that N and P concentrations and yields show a relationship with BMP implementation, USGS methods resulted in increasing nutrient concentrations and trends. These results indicated significant underestimates of modeled N and P export by CAST. I recommend using adjusted BMP efficiencies during cultural and structural BMP lifespans to improve model outputs. I also suggest two approaches to reflect the role of annual poultry manure applications: (1) model nutrient transport via artificial drainage ditches that interfere with natural nutrient flow pathways and exacerbate N and P transport, and (2) model the accumulation of soil-P and saturated soil-P, resulting in increases in dissolved P and particulate P in downstream surface waters. Agronomic recommendations include developing efficient manure recycling approaches within the local agricultural systems via nutrient management practices and concurrent research and development to support alternative uses of animal waste, including composting, bioenergy generation, granulating/pelletizing, and establishing a marketplace to support the sale of these products and to offset the costs of transporting manure from areas of manure surplus to manure deficit areas. This dissertation revealed that modeling studies overestimate cover crop N efficiencies in the United States Coastal Plain province and that CAST modeling is not in agreement with the USGS field measurements. CAST-modeled nutrient concentrations and yields decrease over time, indicating improvements in water quality. In contrast, USGS methods consistently show that nutrient concentrations and yields increase, indicating that BMPs are insufficient, inadequate, overwhelmed by nutrient inputs, or efficiencies are overestimated. Indeed, nutrient-reducing BMPs have increased between 1985 and 2021. With over 35 years of BMP implementation, measurable water quality response is expected. However, BMPs that relocate and apply higher amounts of manure annually have also increased with nutrient-reducing BMPs. Rising manure application rates combined with higher fertilizer application rates due to economic pressures on farmers to increase crop yields appeared to have overwhelmed implemented BMPs. Continued manure applications onto croplands in the Greensboro watershed suggest nutrient export will continue to rise; thus, reaching water quality goals is unlikely.Item Living with Water: Re-Imagining Urban Hydrologies in Washington, DC(2023) Kaku, Upasana; Williams, Brittany; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Across the world, human development has drastically changed the nature of our landscapes and the hydrology of the places we inhabit. Hundreds of waterways were buried, canalized, or piped as part of sewer systems. While natural water systems were being contained or erased from our landscapes, a vast network of aqueducts, canals, reservoirs, and sewage tunnels was constructed. This massive system runs under our feet, where it is out of sight and out of mind, and the architecture and urban design of our cities allows us to imagine that the water we drink and the waste that flush down our pipes is completely independent of our waterways. These choices have had costs. Ecosystems were devastated, manipulation of the landscape has led to increased flooding, and communities have been deprived of the benefits of access to local waterways – reduced urban heat island effects, a place to cool off on hot days, and mental health benefits. Climate change is stressing all aspects of our hydrological systems: creating risks of supply disruptions and shortages and resulting in more frequent floods. Using as a test case Hickey Run, a partially piped and highly polluted waterway in Washington, DC, this thesis will propose a way to make visible and reconnect separated parts of water infrastructure - natural waterways, water supply, and wastewater systems - re-conceptualizing them as parts of a single, dynamic ecosystem. It will explore how tools of architecture, urban design, and landscape infrastructure can be used to develop alternative futures for the built environment, in which waterways can be woven into dense urban fabric, instead of being buried, in order to support ecological restoration and more resilient urban communities.Item DETERMINING THE IMPACT OF WELL MAINTENANCE, CONDITION, TYPE, AND LOCATION FACTORS ON E. COLI AND TOTAL COLIFORMS IN MARYLAND FARM PRIVATE DRINKING WATER WELLS(2023) Smith, Cameron Nicole; Rosenberg Goldstein, Rachel; Maryland Institute for Applied Environmental Health; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Even with the establishment of the Safe Drinking Water Act in 1974, private wells are still not regulated or monitored for drinking water quality or the presence of contaminants such as total coliform bacteria and Escherichia coli. The presence of microbiological contaminants in private wells poses a public health risk. With Agricultural Agents from the University of Maryland Extension, we collected 67 water samples from Maryland farms with private wells located in seven regions and 19 counties of Maryland. We evaluated water samples for total coliforms and E. coli to understand the risk of contamination for Maryland private well owners. We also analyzed the impact of well factors, location, and climate on the presence of total coliforms and E. coli in well water by analyzing participant survey responses and climate data. Our results found that 39% (26/67) of the well water samples were positive for total coliforms and 10% (7/67) were positive for E. coli. Region (p<0.01), county (p=0.03), previously testing for pH (p<0.01), and ambient temperature (p=0.05) were significant factors impacting total coliform concentration. Region (p<0.01) and precipitation in the last 24 hours of collection (p<0.01) were the only significant factors impacting E. coli concentration. These findings emphasize the importance of well water testing for private well owners.Item SUSTAINABLE WATER RESOURCES MANAGEMENT THROUGH AGRICULTURAL WATER REUSE: APPLICATION OF A DECISION SUPPORT SYSTEM(2022) SHOUSHTARIAN, FARSHID; NEGAHBAN-AZAR, MASOUD; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The water crisis caused by climate change, population growth, high urbanization rate, lifestyle changes, and industrialization has decreased global access to safe freshwater resources. As the primary food-producing and the largest water-consuming sector, agriculture heavily depends on water availability. Incorporating alternative water supplies (e.g., water reuse) can reduce freshwater demands, addressing water crisis consequences. Water reuse generally includes recycling treated effluent (known as recycled water) from wastewater treatment plants for different applications (e.g., agricultural irrigation). This alternative water resource can reliably and sustainably increase the resiliency of agriculture to water shortage. However, the complexities inherent in water resources management and the challenges associated with water reuse make planning and managing agricultural water reuse practices demanding. Agricultural water reuse projects include many interrelated/ interconnected components, including the human (e.g., farmers) and technical (e.g., engineering and natural infrastructures) components. The abilities of existing models are limited in simulating these components’ complex and adaptive behaviors. It is necessary to utilize tools capable of capturing these complexities and adaptations to plan and manage agricultural water reuse practices sustainably.The main research question of this dissertation was: How to capture the complex and adaptive dynamics of socio-hydrological systems inherent in sustainable water resources management when alternative water sources are introduced in the water supply system? The primary focus of the dissertation was to develop a dynamic decision support system that can successfully simulate the complexities and adaptations inherent in agricultural water reuse practices. It aims at increasing the existing knowledge regarding agricultural water reuse planning and management and help water resource decision-makers make sustainable and better-informed decisions in agricultural water reuse practices. To accomplish this goal, first, the literature was thoroughly reviewed to identify, collect, and analyze the data related to agricultural water reuse (e.g., current agricultural water reuse regulations and guidelines). Second, two models were developed using a “bottom-up” approach to study two agricultural water reuse practices in the Southwest (CA) and Mid-Atlantic (MD-DE) regions. These two models were used to further study the dynamics of agricultural water reuse adoption by farmers and their impacts on local water resources. The results showed that the regulations and guidelines were mainly human health centered, insufficient regarding some potentially dangerous pollutants such as emerging constituents, and with large discrepancies when compared with each other. In addition, some important water quality parameters, such as pathogens, heavy metals, and salinity, were only included in a few of the regulations and guidelines investigated in this study. Finally, specific treatment processes were only mentioned in some of the regulations and guidelines, with high levels of discrepancy. Moreover, results showed that agricultural water reuse adoption by farmers is a gradual and time-consuming process. In addition, results also showed that agricultural water reuse could significantly decrease the water shortage (by 57.7%) and groundwater withdrawal (by 74.1%) in CA. The results also showed that climate change and recycled water storage capacity and unit price were among the top factors with significant influence on agricultural water reuse practice studied in this dissertation. This study demonstrated the importance of conducting time-varying sensitivity analysis for complex simulation models. Furthermore, results demonstrated that implementing agricultural water reuse could decrease farmers' water shortage, groundwater consumption, and surface water consumption (by almost 19.5 %) in MD. This dissertation’s results can help decision-makers effectively take advantage of agricultural water reuse projects and other alternative water resources to plan and manage water resources sustainably.Item After the Flood: Designing Land Reuse in New York's Hudson Valley(2022) Savio, Hannah L; Ellis, Christopher D; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Flooding is a recurring event in the water cycle that has the potential to devastate what is in its path. Climate change is projected to make flooding worse in the Northeastern United States because of increased intensity of rainfall. An increase in the number of flooded homes where homeowners choose not to rebuild in place can be viewed as a symptom of climate change. These issues take place at the confluence of land and water, the balance of humans and our environment, and what can be learned from the past and from projections and models of the future. How can flooded sites that are not suitable for rebuilding be adaptively reused to leverage their ecological, social, and economic value? This question is assessed through a multi-scalar examination of a series of FEMA buyouts along the Kaaterskill Creek, a rural tributary to the Hudson River in New York.Item QUANTIFYING THE ADDED VALUE OF AGILE VIEWING RELATIVE TO NON-AGILE VIEWING TO INCREASE THE INFORMATION CONTENT OF SYNTHETIC SATELLITE RETRIEVALS(2022) McLaughlin, Colin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Satellite sensors typically employ a “non-agile” viewing strategy in which the boresight angle between the sensor and the observed portion of Earth’s surface remains static throughout operation. With a non-agile viewing strategy, it is relatively straightforward to predict where observations will be collected in the future. However, non-agile viewing is limited because the sensor is unable to vary its boresight angle as a function of time. To mitigate this limitation, this project develops an algorithm to model agile viewing strategies to explore how adding agile pointing into a sensor platform can increase desired information content of satellite retrievals. The synthetic retrievals developed in this project are ultimately used in an observing system simulation experiment (OSSE) to determine how agile pointing has the potential to improve the characterization of global freshwater resources.Item DYNAMICS OF PHYTOPLANKTON POPULATIONS IN IRRIGATION PONDS(2022) Smith, Jaclyn Elizabeth; Hill, Robert L; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The dynamics of phytoplankton community structure in two agricultural irrigation ponds located in Maryland, USA were evaluated. Stable spatiotemporal patterns and zones of consistently higher and consistently lower phytoplankton functional group concentrations were established for both ponds. Moderate and strong correlations were found between the spatial patterns of several water quality parameters and phytoplankton concentrations. Additionally, zones of consistently higher and lower concentrations were found for the cyanobacteria pigment, phycocyanin. Chlorophyll, colored dissolved organic matter, and turbidity were the most influential predictors for phycocyanin concentrations. The prediction of phytoplankton community structure from water quality measurements with the random forest machine learning algorithm was possible and easily measured physicochemical parameter models offered the best model performance. Results of this work indicate that in-situ water quality measurements may be a cost-effective and faster alternative to time-intensive microscopy analysis of phytoplankton, allowing for more efficient water quality monitoring.Item Monitoring and Predicting the Microbial Water Quality in Irrigation Ponds(2022) Stocker, Matthew Daniel; Hill, Robert L; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Small- to medium-sized farm ponds are a popular source of irrigation water and provide a substantial volume of water for crop growth in the United States. The microbial quality of irrigation waters is assessed by measuring concentrations of the fecal indicator bacteria Escherichia coli (E. coli). Minimal guidance currently exists on the use of surface irrigation waters to minimize consumer health risks. The overall objective of this work was to provide science-based guidance for microbial water quality monitoring of irrigation ponds. Spatial and temporal patterns of E. coli were evaluated in two Maryland irrigation ponds over three years of observations. Patterns of E. coli were stable over the three years and found to be significantly correlated to patterns of water parameters such as temperature, dissolved oxygen, turbidity, and pH. The EPA Environmental Fluid Dynamics Code model was used to evaluate the spatial 3D heterogeneity of E. coli concentrations within the ponds. Significant differences in E. coli concentrations by sampling depth were found. Spatial heterogeneity of E. coli within the pond also resulted in substantial temporal variation at the irrigation pump, which was dependent on the intake location. Diurnal variation of E. coli concentrations was assessed for three farm ponds. E. coli concentrations declined from 9:00 to 15:00 for each pond, but statistically significant declines were only observed in two of the three ponds. Dissolved oxygen, pH, and electrical conductance were found to be the most influential environmental variables affecting E. coli concentrations. To better describe the relationships between E. coli and the environmental variables, four machine learning algorithms were used to estimate E. coli concentrations using water quality parameters as predictors. The random forest algorithm provided the highest predictive accuracy with R2 = 0.750 and R2 = 0.745 for Ponds 1 and 2, respectively, in the multi-year dataset containing 12 predictors. Temperature, electrical conductance, and organic matter content were identified as the most influential predictors. It is anticipated that the recommendations contained in this dissertation will be used to improve microbial monitoring strategies and protect public health.