The research group of Dr. Seth Guikema at The University of Michigan focuses primarily on issues of both (1) Urban and Infrastructure Sustainability, Reliability, and Resilience and (2) data-driven predictive modeling for risk analysis. This includes work more generally on modeling the impacts of climate on things of concern to people (pathogen occurrence, natural hazards, etc.), applications of data analytical prediction methods in climate, and on intelligent adversary risk analysis. What is urban and infrastructure resilience and sustainability? What do these terms mean? How do we measure and model them? How can we improve the sustainability, reliability, and resilience of infrastructure systems and urban areas, particularly in a changing climate? How can we model the actions of intelligent adversaries in a more flexible way capable of handling the behavioral realities of the adversaries? What are the interactions between coupled infrastructure systems, and how do we best manage risk for these systems when they face both intelligent threats and natural hazards? How can be better predict outcomes of concern, leveraging disparate data sets?

To give a bit of a sense of the breadth of problems that the research group works on, the PI, Dr. Seth Guikema, is the PI or Co-PI on the following grants.


Communities are interested in becoming more resilient to weather extremes but often lack the tools and resources to fully understand the problems and make good decisions. This lack of understanding is understandable given the complex and interrelated nature of the impacts from extreme events. Communities need to design solutions for infrastructure management, land use planning, public health, and emergency management, understanding how each is related to the other. In areas that are subject to repeated events, the problem is exacerbated. In this project, an interdisciplinary team of researchers will develop an integrated model, the Integrated Hazard, Impact, and Resilience Model to better understand the impacts of repeated hurricanes and heat waves on regional vulnerability and resilience and use the model to develop approaches for improving resilience to these repeated hazards. The team integrates expertise in individual and organizational behavior, economic modeling, climate science, infrastructure engineering, hazard modeling, public health, and spatial landscape analysis. The work on this project will be grounded in and integrated through systems modeling. The goal of the project is to significantly advance the understanding of the impacts from hurricanes and heat waves on the vulnerability and resilience of regions over time and the tools and resources available to help regions better prevent repeated hazards from becoming repeated disasters.

The mid-Atlantic region will be the case study for this project, grounding the work in a critical region of the U.S., one that has a growing population facing increasing threats from weather extremes. The team will also have strong international collaborators, helping to make sure the work has broad applicability to other areas of the world. As part of this project, a set of undergraduates, masters, and doctoral students as well as postdoctoral scholars will be trained in trans-disciplinary hazards research and modeling. This cadre of students and researchers will form a critical resource for the nation, helping to improve regional resilience to repeated hurricanes and heat waves.
This U.S.-European Partnership for International Research and Education (PIRE) will engage graduate and undergraduate students, post-docs, and faculty from ten institutions to address pressing research questions that arise when adding the inherently intermittent wind-energy source to our power systems. The partnership includes U.S. researchers from Johns Hopkins University, Texas Tech University, Smith College, and the University of Puerto Rico. International partners in Europe include research groups in wind energy at the Danish Technical University and Risø Laboratory in Denmark, the Energy Research Center of the Netherlands (ECN), École Polytechnique Fédérale de Lausanne in Switzerland, Katholieke Universiteit Leuven in Belgium, and Comillas Pontifical Universidad in Spain. The team's cooperative research efforts will be tightly integrated with a training program that includes carefully designed international experiences. Overall, the intent is to jointly generate tools to better understand, characterize, and manage the consequences of wind power fluctuations. Results should help define more efficient methods for utilizing wind as a sustainable, cost-effective power source. By focusing on statistical tools to examine predictability, multiple time scales, and spatial and temporal variability of wind fluctuations, the US-European team expects to gain new and timely knowledge about the physical sources of variability and intermittency, such as atmospheric turbulence, and about the effects of various wind-farm parameters such as inter-turbine spacing, orientations, ground roughness, and wind conditions. To accomplish this, computational fluid dynamics tools will be developed and validated with laboratory and field observations. Secondly, results from parametric model runs will be used to develop basic understanding and obtain the necessary statistical characterizations of variability as functions of wind-farm parameters, using tools such as response-surface estimation, statistical multi-scale methods, and co-spectra. Thirdly, these characterizations will be coupled to production costing and planning models of the power grid for validation and further development. The PIRE research partners expect these models to help determine how wind farm parameters affect ancillary service requirements and how storage and demand response can be used most effectively. For broader impact, the new grid modeling tools that incorporate improved statistical characterizations of wind-farm output variability should help optimize future resource siting and design. Fourth, results are to be integrated with models of power markets and economic impacts. Econometric methods and market data may be used to propose potential, new policy levers and market designs to support practical, cost-effective adoption of renewable, highly intermittent energy sources. Central to the PIRE activities are core education, training and mentoring components. U.S. student participants will benefit from innovative courses in wind energy, computer modeling, power networks, economic management and economics, several taken abroad at partner institutions. Additionally, periodic research-focused site visits to European institutions and installations by U.S. students, faculty, and post-docs will facilitate access and ensure more rapid transfer of relevant technical knowledge to advance current understanding of wind power variability and its management. The U.S. PIRE project will operate under the aegis of Johns Hopkins University's Environment, Energy, Sustainability and Health Institute (E2SHI), which promotes cross-disciplinary research, outreach, and education for critical sustainability issues. Furthermore, the project will leverage close ties between Texas Tech University's National Wind Resource Center, several industries and national laboratories, as well as a number of utilities and agencies in the U.S. Mid-Atlantic, Northeast and Texas. This level of engagement provides a straight forward means for expediting the translation of promising results into practice. The project is funded by NSF's Office of International Science and Engineering (OISE) through the PIRE.
This NSF award by the Environmental Health and Safety of Nanotechnology program supports multidisciplinary work by Professors Howard Fairbrother, Ed Bouwer and Seth Guikema to characterize the effect of microbial interactions with biodegradable polymers that incorporate carbon nanotubes (CNTs). Polymer nanocomposites containing CNTs represent an example of a next generation type of nanomaterial because of the beneficial effects that CNTs have on many polymer properties. However, following consumer use polymer nanocomposites will enter the environment where their ultimate fate and impact will be intimately dependent upon their interactions with microorganisms present in environmental regimes such as landfills, soils or surface waters. To better understand the life cycle of this new class of nanomaterials we plan to develop a systematic understanding of the persistence and fate of biodegradable polymer nanocomposites exposed to different microbial populations, including potential pathways for CNT release. Findings from these experiments will also be used as the basis to develop a risk-based decision making framework that can determine the most appropriate means for disposing of polymer nanocomposites. Such a proactive approach for risk assessment and life cycle analysis is necessary for new types of nanomaterials in the developmental stage.
This Integrative Graduate Education and Research Traineeship (IGERT) award will train a corps of doctoral students to become the leaders needed to develop innovative adaptive strategies to respond to unprecedented challenges as a result of climate change. Intellectual Merit: Changes in the hydrologic cycle are expected to decrease the supply of safe water, forcing vulnerable populations in poor countries to drink dirty water, leading to increased disease. Food production will become scarcer as unsustainable groundwater withdrawals are affected by increased drought. As contaminants continue to flow into water bodies and hydrodynamic patterns adjust to climate change, the coastal fisheries upon which human populations depend for food will face increased problems of eutrophication and anoxia. Integrated strategies are needed for predicting and adapting to climate driven stressors on water resources and human health. Among these strategies are smart growth land use practices resulting in reduced greenhouse gas emissions or water infrastructure systems that conserve water and energy.

Broader Impacts: The trainees will be scientific leaders and will engage in mentoring diverse high school students. Practical problems will be addressed by students through a capstone course centering on real problems in three very different areas of the world: the Chesapeake Bay watershed in the temperate developed U.S., the tropical underdeveloped Amazon basin in Peru, and the arid underdeveloped Nile basin in Ethiopia. IGERT students, highly trained in advanced water treatment and management, will learn to adapt skills and knowledge to address problems in diverse cultural, political and economic settings.
The objective of this Faculty Early Career Development (CAREER) program award is to provide an approach for assessing the economic, environmental, and social sustainability and reliability of interdependent power and water systems. This is intended to better support proactive management of public and private infrastructure, particularly in areas susceptible to natural hazards such as hurricanes and earthquakes. The research develops indicators for measuring trends in the sustainability of power and water systems, providing a basis for changing the management and funding of these systems to improve their sustainability. The project also provides new computational frameworks for modeling interdependent power and water systems. The impacts of natural hazards such as hurricanes and earthquakes will be combined with the effects of aging to develop a more holistic reliability modeling approach for infrastructure systems. The reliability and sustainability models are brought into the infrastructure management process to help support meaningful change in power and water systems in hazard-prone areas.

If successful, this work will contribute to the development of improved methods for assessing and managing aging infrastructure systems in the US. These improved methods would allow limited public and private funds to be used more efficiently to improve infrastructure systems before they fail rather than relying on a reactive approach of repairing failures after they occur. This project will also develop a series of on-line and classroom teaching materials for middle school, high school, college, graduate school, and professional education. These materials will help to further establish a pipeline of students who are interested and educated in infrastructure engineering.
The objective of this research project is to make a fundamental contribution to the nation’s health by probabilistically estimating health risks due to pipe breaks. U.S. drinking water distribution systems may be compromised in a number of different ways, and pipe breaks and their associated repair and renewal activities are among the most important causes of drinking water distribution system contamination. Despite the vulnerability to contaminant intrusion through pipe breaks, there is limited understanding and documentation of the magnitude of health risks from drinking water distribution system pipe breaks. Furthermore, distribution system asset management does not include the health risks associated with pipe breaks into the planning process. Having accurate, probabilistic estimates of the health risks associated with pipe breaks would allow distribution systems to be maintained in a way more protective of public health. This will be the first rigorous, probabilistic estimate of the health risk associated with pipe breaks in the U.S. In addition, this project will lead to substantial improvements in the predictive accuracy of pipe break risk models. These two advances - health risk assessment and improved asset risk assessment - will be combined to yield an integrated, risk-based water distribution system asset management framework.

Currently, operators and utility managers consider public health protection (e.g., environmental compliance) and asset management as two separate and often divergent goals. This research will enable more comprehensive integration of these potentially divergent goals into a systems-based risk and decision model. This research will also make fundamental contributions to real-time drinking water distribution systems management. The research advances will be based on a close working collaboration with a large water utility, ensuring that the methods developed can be used in practice.
Critical infrastructure systems such as water and electric power networks provide essential services that underlie the economic prosperity, security, and public health of the U.S. These complex, interdependent systems are prone to failure during hurricanes. Improved modeling of the ability of these systems to meet the needs of society after a hurricane makes landfall would substantially improve our ability to manage the risk of these systems failing. However, there are fundamental research needs of both conceptual and computational natures in the area of risk analysis for critical infrastructure systems in hurricane-prone areas. Conceptually, we do not yet have modeling frameworks that allow for accurate prediction of the performance of large-scale interdependent infrastructure systems during hurricanes, a necessary starting point for accurate risk assessment and management. Computationally, many of the available tools that aim to model infrastructure performance at the scale of large metropolitan areas require long run times on large computer clusters, limiting their usefulness for practical infrastructure planning and management. Recent advances in both statistical methods and computing based on graphical processing units (?graphics cards?) enable advances that can address both the conceptual and computational limitations inherent in current approaches for risk analysis for interdependent infrastructure systems in hurricane-prone areas. The focus of this project is on developing methods for accurate performance and risk modeling for interdependent infrastructure systems, methods that are practical for infrastructure managers to use. While the focus of this project is on coupled water and power systems, the advances will have application much more broadly.

This project will enable significantly more accurate and rapid risk analysis for interdependent infrastructure systems, allowing highly limited public infrastructure funds to be spent more efficiently and helping to better protect economic and public health during disasters. The models developed in this project will be practical for use on desktop computers with existing higher-end graphics cards, greatly enhancing the ability of infrastructure managers to run these models on their existing computer hardware. In addition, this project will yield insights into the factors that lead interdependent infrastructure systems to be more resilient during a hurricane, helping engineers and utility system managers better understand how to strengthen their systems. In parallel with the research efforts, this project will aim to interest students traditionally underrepresented in engineering programs in pursuing engineering as a career. This will be done through outreach at multiple educational levels.