On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool

Integrated urban water management in Texas: a review to inform a one water approach for the future

Texas has considerable experience grappling with historic droughts as well as flooding associated with tropical storms and hurricanes, yet the State’s water management challenges are projected to increase. Urban densification, increased frequency and severity of droughts and floods, aging infrastructure, and a management system that is not reflective of the true cost of water all influence water risk. Integrated urban water management strategies, like ‘One Water’, represent an emerging management paradigm that emphasizes the interconnectedness of water throughout the water cycle and capitalizes on opportunities that arise from this holistic viewpoint. Here, we review water management practices in five Texas cities and examine how the One Water approach could represent a viable framework to maintain a reliable, sustainable, and affordable water supply for the future. We also examine financial and business models that establish a foundational pathway towards the ‘utility of the future’ and the One Water paradigm more broadly

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.publishedVersio

Toward a Science of Sustainability

This report presents an overview of research horizons in sustainability science. Its motivation is to help harness science and technology to foster a transition toward sustainability – toward patterns of development that promote human well-being while conserving the life-support systems of the planet. It builds on but does not explicitly address the vast range of relevant sector-specific and cross-sectoral problem-solving work now underway in fields ranging from green technologies in energy and manufacturing to urban design to agriculture and natural resources. It focuses on the narrower but essential task of characterizing the needs for fundamental work on the core concepts, methods, models, and measurements that, if successful, would support work across all of those sectoral applications by advancing fundamental understanding of the science of sustainability. The report sets forth the workshop’s findings and recommendations on six fundamental questions now facing scholars seeking to harness science and technology to foster sustainability: 1. What are the principal tradeoffs between human well-being and the natural environment, and how are those tradeoffs mediated by the ways in which people use nature? 2. What determines the adaptability of coupled human-environment systems and, more broadly, their vulnerability and robustness/resilience in the face of external shocks and internal dynamics? 3. What shapes the long term trends and transitions that set the stage on which humanenvironment interactions are played out? 4. How can theory and models be formulated that better account for the variation in types or trends of human-environment interactions? 5. How can society most effectively guide or manage human-environment systems toward a sustainability transition? 6. How can the “sustainability” of alternative trajectories of human-environment interactions be usefully and rigorously evaluated

Complex Adaptive Systems Simulation-Optimization Framework for Adaptive Urban Water Resources Management

Population growth, urbanization and climate change threaten urban water systems. The rise of demands caused by growing urban areas and the potential decrease of water availability caused by the increase of frequency and severity of droughts challenge the continued well-being of society. Due to increasing environmental and financial constraints, water management paradigms have shifted from supply augmentation to demand management, and water conservation initiatives may efficiently decrease water demands to more sustainable levels. To provide reliable assessment of the efficiencies of different demand management strategies, new modeling techniques are needed that can simulate decentralized decisions of consumers and their interactions with the water system. An integrated simulation-optimization framework, based on the paradigm of Complex Adaptive Systems, is developed here to model dynamic interactions and adaptations within social, built, and natural components of urban water systems. The framework goes beyond tradition engineering simulations by incorporating decentralized, heterogeneous and autonomous agents, and by simulating dynamic feedback loops among modeling components. The framework uses modeling techniques including System Dynamics, Cellular Automata, and Agent-based Modeling to simulate housing and population growth, a land use change, residential water consumption, the hydrologic cycle, reservoir operation, and a policy/decision maker. This research demonstrates the applicability of the proposed framework through a series of studies applied to a water supply system of a large metropolitan region that is located in a semi-arid region and suffers recurrently from severe droughts. A set of adaptive demand management strategies, that apply contingency restrictions, land use planning, and water conservation technologies, such as rainwater harvesting systems, are evaluated. A multi-objective Evolutionary Algorithm is coupled with the CAS simulation framework to identify optimal strategies and explore conflicting objectives within a water system. The results demonstrate the benefits of adaptive management by updating management decisions to changing conditions. This research develops a new hydrologic sustainability metric, developed to quantify the stormwater impacts of urbanization. The Hydrologic Footprint Residence captures temporal and spatial hydrologic characteristics of a flood wave passing through a stream segment and is used to assess stormwater management scenarios, including Best Management Practices and Low Impact Development

Applications of Negotiation Theory to Water Issues

The purpose of the paper is to review the applications of non-cooperative bargaining theory to water related issues – which fall in the category of formal models of negotiation. The ultimate aim is that to, on the one hand, identify the conditions under which agreements are likely to emerge, and their characteristics; and, on the other hand, to support policy makers in devising the “rules of the game” that could help obtain a desired result. Despite the fact that allocation of natural resources, especially of trans-boundary nature, has all the characteristics of a negotiation problem, there are not many applications of formal negotiation theory to the issue. Therefore, this paper first discusses the non-cooperative bargaining models applied to water allocation problems found in the literature. Particular attention will be given to those directly modelling the process of negotiation, although some attempts at finding strategies to maintain the efficient allocation solution will also be illustrated. In addition, this paper will focus on Negotiation Support Systems (NSS), developed to support the process of negotiation. This field of research is still relatively new, however, and NSS have not yet found much use in real life negotiation. The paper will conclude by highlighting the key remaining gaps in the literature.Negotiation theory, Water, Agreeements, Stochasticity, Stakeholders

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Model evolution for the realization of complex systems

George Box said, “All models are wrong, but some are useful.” In the design of complex systems, types of complexity need to be managed. Giving the complexities that a decision maker may encounter, corresponding adjustments or improvements should be made to the design. In this dissertation, it is defined that all kinds of engineering design are comprised of four stages – formulation, approximation, exploration and evaluation – and the four stages form the model evolution loop or design evolution loop. By running the design evolution loop iteratively, a designer can handle the complexities and improve the design. Such improvements include but not limited to more robust to uncertainties, more efficient in design evolutions, easier interpretations of phenomena, etc. In the design of complex systems, as lack of data and information, heuristics are used to proceed the design, so that designers can explore the solution space and gain insight to improve the design. Those heuristics include but not limit to model structures, sub-problems identification and integration, approximation rules, and scale of details incorporated in the model. There is lacking mechanisms to evaluate the quality of the design associated with the heuristics. In this dissertation, it is hypothesized that by running the design evolution loop and exploring the solution space, designers can do the things as follows to improve the design. • Evaluating system performances associated with various heuristics (structure of the model, critical parameter setting, rules making, etc.). • Replacing the heuristics with insight obtained from exploration of the solution space to improve the design. • Managing the complexity of module structure, such as analyzing and simplifying the structure of a large number of goals. • Interpreting the behavior and the property of the model into the knowledge that supports the decision making. • Capturing and managing newly observed properties or a more detailed complexity that are not incorporated into the modeling at first – the emergent properties. • Automating the steps in the above. The intellectual merits in this dissertation are the expandable computational framework for designing complex systems and managing multiple types of uncertainty– the design evolution loop, and the methods fitting into it. By using satisficing strategy and incorporating machine learning to explore the solution space, heuristics in each of the four stages (formulation, approximation, exploration, and evaluation) can be updated or replaced by knowledge gained from experiments, calculations and analyses. In addition, knowledge on tradeoffs between different categories of design requirement – such as (but not limited to) approximation accuracy, computational complexity, design preference diversity, reformulation flexibility, and the degree of design automation – can be collected, stored and reused

Linking biophysical models and life cycle assessment to evaluate environmental tradeoffs of urban water infrastructure design

Water, a limited resource even on our hydrous planet, has always been inextricably tied to the rise and fall of cities and human infrastructure. Clean, plentiful water drives our food and energy production, provides transport, and keeps humans and the environment healthy. Integrated urban water modelling and improved geospatial databases are allowing water management researchers to analyze the effects of process decisions in the water management sector on a broader scale and with higher spatiotemporal resolution than ever before. Research is driven by the desire to optimize limited resources, respond to changing user patterns, characterize the robustness of the system to climate change pressures, and define the downstream effects of new technologies. Water management decisions today not only require hydraulic and hydrologic knowledge, but also an understanding of energy production systems, environmental biochemistry, economics, and regulatory policy. Although integrated urban water models started by expanding on simple physical urban drainage models, they are now incorporating mechanisms for environmental change, social agents, and economic feedback. Although originally built to protect public health and the local aquatic environment, wastewater treatment utilities have in recent years taken on additional objectives including greenhouse gas mitigation, reducing chemical use, and reducing long-term environmental impacts due to effluent nutrients and disinfection byproducts. National policies on water quality (EU Water Framework Directive, US Clean Water Act) and electricity demand (GHG emissions targets) both cover utilities, with the goal of improving their environmental sustainability. These multiple objectives may call for conflicting operational decisions, which presents a direct tradeoff to utility decision makers—increase electricity use for treatment, or allow worse effluent quality to flow into the local environment. This thesis seeks to characterize the scale of impacts stemming from energy-water tradeoffs and identify sources of uncertainty in making this decision, by placing the operational tradeoff in a larger water-energy-environmental system context. The case study in Eindhoven, the Netherlands is selected for several reasons. The local water management authority has created a well-researched integrated urban water model, comprising the urban water system from raindrop through domestic use, sewer collection, wastewater treatment, and to the receiving river. The national water and energy policies are providing stricter standards for utilities, presenting this tradeoff decision previously mentioned. Finally, the local and national datasets for LCA inventory, meteorology, energy generation, and ecological response are well documented, allowing us to analyze the system from a holistic perspective. The analysis of the energy-water quality tradeoff is completed by different modeling methods employed by water managers and regulators, to see if the different methods yield improved or conflicting results. First, we use traditional LCA inventory accounting which is the current standard for new capital investments in wastewater treatment. The LCA considers the impacts of kilowatt hours of electricity and ammonia released to the environment in wastewater effluent for four different standards of effluent quality. This analysis demonstrated a clear tradeoff between eutrophication and global warming (energy production emissions) impacts. Second, the spatiotemporal variation of these eutrophication and air emissions impacts is explored using biophysical models. The models include the calibrated integrated urban water system model developed for Eindhoven and the Dommel in conjunction with a generalized atmospheric dispersion model for emission byproducts of electricity generation. We study the downstream transport of ammonium in the river and particulate matter from the power plant emissions. The air emissions modeling found that even a single day of electricity demand associated with wastewater treatment could affect particulate matter concentrations hundreds of kilometers away, crossing international borders. The water quality modelling found that marginal improvements in the effluent quality (of 1 mg/L ammonium) could improve the worst-case ammonia concentrations downstream by up to 20%. Third, the biophysical model results are evaluated using literature-based characterization factors for human health exposure and ecosystem tolerances to the aforementioned ammonium and particulate matter emissions. These calculations framed our physical models in the context of local systems. On the air emissions side, the electricity generated for wastewater treatment was found to contribute less than 0.1% of the background particulate matter concentration in the region modelled. On the water quality side, the wastewater treatment plant significantly reduced the number of ecological exceedances compared to a no-treatment control scenario, on the order of about 50%. However, this control scenario does not account for the influence of other sources of ammonium in the river, such as other wastewater treatment plants or agricultural runoff. The outcomes of this work show that energy investment in wastewater treatment creates a significant tension in environmental impacts. Our multi-tiered evaluation sought to explore the dimensions of these impacts on higher resolution spatial scales, to better understand how they fit into environmental systems. Ultimately, the physical modeling showed that energy impacts could cross international borders which might have some implication for international policymaking. However, through systems analysis these impacts were shown to be negligible in comparison to the water quality consequences for local ecosystems