<p>A critical consequence of agriculturally managed lands is the <a></a>transport of nutrients and sediment to fresh water systems, which is ultimately responsible for a range of adverse impacts on human and environmental health. In the U.S. alone, over half of streams and rivers are classified as impaired, with agriculture as the primary contributor. To address deterioration of water quality, there is a need for reliable tools and mathematical models to monitor and predict impacts to water quantity and quality. Soil water content is a key variable in representing environmental systems, linking and driving hydrologic, climate, and biogeochemical cycles; however, the influence of soil water simulations on model predictions is not well characterized, particularly for water quality. Moreover, while soil moisture estimation is the focus of multiple remote sensing missions, defining its potential for use in water quality models remains an open question. The goal of this research is to test whether updating model soil water process representation or model soil water estimates can provide better overall predictive confidence in estimates of both soil moisture and water quality. A widely-used ecohydrologic model, the Soil and Water Assessment Tool (SWAT), was used to evaluate four objectives: 1) investigate the potential of a gridded version of the SWAT model for use with similarly gridded, remote sensing data products, 2) determine the sensitivity of model predictions to changes in soil water content, 3) implement and test a more physically representative soil water percolation algorithm, and 4) perform practical data assimilation experiments using remote sensing data products, focusing on the effects of soil water updates on water quality predictions. With the exception of the first objective, model source code was modified to investigate the relative influence and effect of soil water on overall model predictions. Results suggested that use of the SWAT grid model was currently not viable given practical computational constraints. While the advantages provided by the gridded approach are likely useful for small scale watersheds (< 500 km2), the spatial resolution necessary to run the simulation was too coarse, such that many of the benefits of the gridded approach are negated. Sensitivity tests demonstrated a strong response of model predictions to perturbations in soil moisture. Effects were highly process dependent, where water quality was particularly sensitive to changes in both transport and transformation processes. Model response was reliant upon a default thresholding behavior that restricts subsurface flow and redistribution processes below field capacity. An alternative approach that removed this threshold and keyed processes to relative saturation showed improvement by allowing a more realistic range of soil moisture and a reduction of flushing behavior. This approach was further extended to test against baseline satellite data assimilation experiments; however, did not conclusively outperform the original model simulations. Nevertheless, overall, data assimilation experiments using a remote sensing surface soil moisture data product from the NASA Soil Moisture Active/Passive (SMAP) mission were able to correct for a dry bias in the model simulations and reduce error. Data assimilation updates significantly impacted flow predictions, generally by increasing the dominant contributing flow process. This led to substantial differences between two test sites, where landscape and seasonal characteristics moderated the impact of data assimilation updates to hydrologic, water quality, and crop yield predictions. While the findings illustrate the potential to improve predictions, continued future efforts to refine soil water process representation and optimize data assimilation with longer time series are needed. The dependence of ecohydrologic model predictions on soil moisture highlighted by this research underscores the importance and challenge of effectively representing a complex, physically-based process. As essential decision support systems rely on modeling analyses, improving prediction accuracy is vital.</p>

<div>In the last half century, much research effort has gone into identifying the causes and effects of societal burdens. Industrial activity may arguably be the most widely responsible cause, but the effects, or social impacts (SIs), resulting from industrial activity are typically considered externalities and not evaluated alongside economic performance of industries. It is clear however that people are fundamental to the progress and development of economies. Understanding how people are affected by economies, and in particular industrial economic activity, starts with recognizing that impacts on people can no longer be considered externalities. The coordinating lack of understanding of social performance, i.e., how stakeholder needs are impacted by industrial production, limits the capacity of decision makers to make fully informed choices. A </div><div>multidisciplinary perspective is needed to address this gap in understanding. The new approach, economic input-output social impact assessment, integrates economic production with social impacts and is further demonstrated to provide a measurable path forward to evaluate the social performance of industries. It is shown that changes in industrial activity, e.g., growth, in the U.S. will have a directly related and predictable change in social impact.</div>

<div>Bike sharing system (BSS) is growing worldwide. Although bike sharing is viewed as a sustainable transportation mode, it still has environmental footprints from its operation (e.g., bike rebalancing using automobiles) and upstream impacts (e.g., bike and docking station manufacturing). Thus, evaluating the environmental impacts of a BSS from the life cycle perspective is vital to inform decision making for the system design and operation. In this study, we conducted a comparative life cycle assessment (LCA) of station-based and dock-less BSS in the U.S. The results show that dock-less BSS has a greenhouse gas (GHG) emissions factor of 118 g CO2-eq/bike-km in the base scenario, which is 82% higher than the station-based system. Bike rebalancing is the main source of GHG emissions, accounting for 36% and 73% of the station-based and dock-less systems, respectively. However, station-based BSS has 54% higher total normalized environmental impacts (TNEI), compared to dock-less BSS. The dock manufacturing dominants the TNEI (61%) of station-based BSS and the bike manufacturing contributes 52% of TNEI in dock-less BSS. BSS can also bring environmental benefits through substituting different transportation modes. Car trip replacement rate is the most important factor. The results suggest four key approaches to improve BSS environmental performance: 1) optimizing the bike distribution and rebalancing route or repositioning bikes using more sustainable approaches, 2) incentivizing more private car users to switch to using BSSs, 3) prolonging lifespans of docking infrastructure to significantly reduce the TNEI of station-based systems, and 4) increasing the bike utilization efficiency to improve the environmental performance of dock-less systems.</div><div>To improve the design of current BSS from the life cycle perspective, we first proposed a simulation framework to find the minimal fleet size and their layout of the system. Then we did a tradeoff analysis between bike fleet size and the rebalancing frequency to investigate the GHG emission if we rebalance once, twice and three times a day. The optimal BSS design and operation strategies that can minimize system GHG emission are identified for a dock-less system in Xiamen, China. The results show that at most 15% and 13% of the existing fleet size is required to serve all the trip demand on weekday and weekend, if we have a well-designed bike layout. The tradeoff analysis shows that the GHG emission may increase if we continue to reduce the fleet size through more frequent rebalancing work. Rebalancing once a day during the night is the optimal strategy in the base scenario. We also tested the impacts of other key factors (e.g., rebalancing vehicle fleet size, vehicle capacity and multiple depots) on results. The analysis results showed that using fewer vehicles with larger capacity could help to further reduce the GHG emission of rebalancing work. Besides, setting 3 depots in the system can help to reduce 30% of the GHG emission compared with 1-depot case, which benefits from the decrease of the commuting trip distance between depot and the serve region.</div>

Geysers that discharge water and steam intermittently to the atmosphere are one of the rarest natural phenomena associated with geothermal systems. Several approaches including laboratory experiments, field observations and mathematical and numerical modelling studies are used in the present study to explain the behaviour of geysers and the important parameters controlling the eruption of geysers. A particular study is made of three geysers at Rotorua geothermal field: Pohutu, Prince of Wales Feathers and Waikorohihi. The existing mathematical model (steinberg et al., 1981a) is studied and an improved mathematical model is developed to accommodate two-phase flow and the variation in fluid properties with temperature. Both the existing and the improved mathematical models are used to model Pohutu and are able to reproduce not only the interval between eruptions but also the durations of the cavern filling and the duration of the pre-play stage observed by the author on the 20th of August 1993. Fully transient numerical models, which include the eruption process itself, are developed using MULKOM and the AUTOUGH2 simulators and produce reasonably good agreement with the analytical solutions and experimental data. The model provides information about the processes inside the geyser system and models the surface discharge which cannot be modelled using the Steinberg type of model. A fully transient model for Pohutu, which is developed using the AUTOUGH2 simulator, is able to reproduce the behaviour observed by the author on the 20th of August 1993. The results of sensitivity studies show that of the three Rotorua geysers, the Feathers is the most sensitive to changes in the rate of the hot upflow from depths. Both the Feathers and Waikorohihi are more sensitive to temperature changes than Pohutu. Pohutu is currently a vigorous geyser with preliminary pulsating spring behaviour; large changes in the rate and temperature of the hot upflow would be required to stop it erupting. All geysers are sensitive to variations in the water level and temperature in Te Horu.

Geysers that discharge water and steam intermittently to the atmosphere are one of the rarest natural phenomena associated with geothermal systems. Several approaches including laboratory experiments, field observations and mathematical and numerical modelling studies are used in the present study to explain the behaviour of geysers and the important parameters controlling the eruption of geysers. A particular study is made of three geysers at Rotorua geothermal field: Pohutu, Prince of Wales Feathers and Waikorohihi. The existing mathematical model (steinberg et al., 1981a) is studied and an improved mathematical model is developed to accommodate two-phase flow and the variation in fluid properties with temperature. Both the existing and the improved mathematical models are used to model Pohutu and are able to reproduce not only the interval between eruptions but also the durations of the cavern filling and the duration of the pre-play stage observed by the author on the 20th of August 1993. Fully transient numerical models, which include the eruption process itself, are developed using MULKOM and the AUTOUGH2 simulators and produce reasonably good agreement with the analytical solutions and experimental data. The model provides information about the processes inside the geyser system and models the surface discharge which cannot be modelled using the Steinberg type of model. A fully transient model for Pohutu, which is developed using the AUTOUGH2 simulator, is able to reproduce the behaviour observed by the author on the 20th of August 1993. The results of sensitivity studies show that of the three Rotorua geysers, the Feathers is the most sensitive to changes in the rate of the hot upflow from depths. Both the Feathers and Waikorohihi are more sensitive to temperature changes than Pohutu. Pohutu is currently a vigorous geyser with preliminary pulsating spring behaviour; large changes in the rate and temperature of the hot upflow would be required to stop it erupting. All geysers are sensitive to variations in the water level and temperature in Te Horu.

Geysers that discharge water and steam intermittently to the atmosphere are one of the rarest natural phenomena associated with geothermal systems. Several approaches including laboratory experiments, field observations and mathematical and numerical modelling studies are used in the present study to explain the behaviour of geysers and the important parameters controlling the eruption of geysers. A particular study is made of three geysers at Rotorua geothermal field: Pohutu, Prince of Wales Feathers and Waikorohihi. The existing mathematical model (steinberg et al., 1981a) is studied and an improved mathematical model is developed to accommodate two-phase flow and the variation in fluid properties with temperature. Both the existing and the improved mathematical models are used to model Pohutu and are able to reproduce not only the interval between eruptions but also the durations of the cavern filling and the duration of the pre-play stage observed by the author on the 20th of August 1993. Fully transient numerical models, which include the eruption process itself, are developed using MULKOM and the AUTOUGH2 simulators and produce reasonably good agreement with the analytical solutions and experimental data. The model provides information about the processes inside the geyser system and models the surface discharge which cannot be modelled using the Steinberg type of model. A fully transient model for Pohutu, which is developed using the AUTOUGH2 simulator, is able to reproduce the behaviour observed by the author on the 20th of August 1993. The results of sensitivity studies show that of the three Rotorua geysers, the Feathers is the most sensitive to changes in the rate of the hot upflow from depths. Both the Feathers and Waikorohihi are more sensitive to temperature changes than Pohutu. Pohutu is currently a vigorous geyser with preliminary pulsating spring behaviour; large changes in the rate and temperature of the hot upflow would be required to stop it erupting. All geysers are sensitive to variations in the water level and temperature in Te Horu.

Geysers that discharge water and steam intermittently to the atmosphere are one of the rarest natural phenomena associated with geothermal systems. Several approaches including laboratory experiments, field observations and mathematical and numerical modelling studies are used in the present study to explain the behaviour of geysers and the important parameters controlling the eruption of geysers. A particular study is made of three geysers at Rotorua geothermal field: Pohutu, Prince of Wales Feathers and Waikorohihi. The existing mathematical model (steinberg et al., 1981a) is studied and an improved mathematical model is developed to accommodate two-phase flow and the variation in fluid properties with temperature. Both the existing and the improved mathematical models are used to model Pohutu and are able to reproduce not only the interval between eruptions but also the durations of the cavern filling and the duration of the pre-play stage observed by the author on the 20th of August 1993. Fully transient numerical models, which include the eruption process itself, are developed using MULKOM and the AUTOUGH2 simulators and produce reasonably good agreement with the analytical solutions and experimental data. The model provides information about the processes inside the geyser system and models the surface discharge which cannot be modelled using the Steinberg type of model. A fully transient model for Pohutu, which is developed using the AUTOUGH2 simulator, is able to reproduce the behaviour observed by the author on the 20th of August 1993. The results of sensitivity studies show that of the three Rotorua geysers, the Feathers is the most sensitive to changes in the rate of the hot upflow from depths. Both the Feathers and Waikorohihi are more sensitive to temperature changes than Pohutu. Pohutu is currently a vigorous geyser with preliminary pulsating spring behaviour; large changes in the rate and temperature of the hot upflow would be required to stop it erupting. All geysers are sensitive to variations in the water level and temperature in Te Horu.

Urban populations are growing at unprecedented rates around the world, while simultaneously facing increasingly intense impacts of climate change, from sea level rise to extreme weather events. In the face of this concurrent urbanization and climate change, it is imperative that cities improve their resilience to a multitude of stressors. A key aspect of urban resilience to climate change is ensuring that there is enough drinking water available to service the city, especially given the projections of more frequent and intense droughts in some areas. However, the study of climate impacts on urban water resources is fairly nascent and many gaps remain. In this dissertation, I aim to begin to close some of those gaps by adopting an interdisciplinary approach to studying water availability. First, I focus on urban water supply, and in particular, reservoir operations. I employ a variety of methods, ranging from data science techniques to traditional hydrological models, to predict the reservoir levels under a variety of climate conditions. Following the analysis of water supply, I shift focus to urban water demand. Here, I include interconnected systems, such as electricity, to evaluate and characterize the impact of climate on water demand and the benefit of considering system interconnectivities. Additionally, I present an analysis on the projection of water and electricity demand into the future, based on representative concentration pathways of CO₂. Finally, I focus on the human dimension to the demand studies. By studying the social norms surrounding water conservation in urban areas, as well as the demographics, I built a predictive model to estimate monthly water consumption at the census tract-level. Through these interdisciplinary studies, I have made progress in filling knowledge gaps related to the impact of climate change on urban water resources, as well as the impact of people on these water resources.

<div>With the rapid development of clean energy technologies, various bottlenecks on supplies of related critical materials emerged. Since supply chains of critical materials often involved with multiple layers of markets with different characteristics, to better identify bottlenecks and increase critical material availability, it is vital to have better understanding and projection on these markets.</div><div>Agent-based modeling is a bottom-up approach that can imitate heterogenous objects in a changing environment. Therefore, it is an excellent tool to simulate markets with fierce competition and fast revolution. This work demonstrates the application of agent-based modeling by discussing three different topics related to critical material demand and supply induced by clean energy products.</div><div>The first application focused on LED residential lighting market. LED lighting market grew rapidly and introduced potential demand on several critical materials including indium. The work modeled consumers as heterogenous and irrational agents in network purchasing new bulbs based</div>

<div>As society rapidly migrates to digitized services, the Information, Communications, and Technology (ICT) sector is projected to sustain a 16% compound annual growth rate (CAGR) over the next five years, surpassing $1 trillion in revenue by 2024. The hardware infrastructure that supports ICT growth, such as semiconductor chips and hard disk drives (HDDs), is also experiencing parallel growth trajectories. Thus, large technology companies need to understand the environmental implications of growth in these vital components within their supply chains, as they strive to reach ambitious targets for carbon, water, and waste reduction.</div><div><br></div><div>Life cycle assessment (LCA) is a powerful tool for measuring environmental impacts along the life cycle of a product and is implemented here to measure emissions and resource use in the semiconductor and HDD manufacturing supply chains, and to quantify the benefits of circularity for HDD components. However, to understand how environmental impacts of a manufacturing process relate to the landscapes (i.e. ecosystems) where manufacturing occurs, one must look to methods beyond LCA. </div><div><br></div><div>Footprinting methods are a promising tool for bridging the gap between LCA process data inventories and site-specific impacts on ecosystems. Further, the footprint assesses the total volume of emission over a time period, which is aligned with the concept of absolute sustainability. As such, regionalized footprint methods for freshwater use in the semiconductor industry and toxic chemical pollution for the HDD rare earth magnet supply chain were undertaken. In each case, data from the LCA literature or custom LCAs were used as the basis for the life cycle inventory, but advanced methods including regional databases of water scarcity and toxicity factors were used to quantify and communicate impacts. Further, geographic information systems (GIS) were used to allocate emissions or water use from a manufacturing facility with their associated watershed, which enabled aggregation of data across various geographies (i.e. watershed, region, country). </div><div><br></div><div>This work implements multi-disciplinary methods, databases, and tools with the aim to bring water and chemical footprinting methods a step closer towards meaningful assessment of a product’s impact on local, regional, and planetary boundaries. </div><div><br></div>