The overarching objective of this Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) research project is to create a novel decision support system denoted dMIST (Data-driven Management for Interdependent Stormwater and Transportation Systems) to improve management of interdependent transportation and stormwater infrastructure systems. dMIST is designed specifically to address the critical problem of recurrent flooding caused by sea level rise and more frequent intense storms. The City of Norfolk, Virginia, a national leader in addressing the sea level rise challenge, will collaborate with the research team and serve as the project testbed. With sea level rise and more frequent intense storms, streets in many cities now flood multiple times per year. Flooding of roadways has cascading impacts to other infrastructure systems that depend on the road network including emergency services. Solving the problem of flooded roadways requires new tools capable of analyzing stormwater, transportation, and other infrastructure as interdependent systems. dMIST will be a recommendation system for assisting municipal decision makers and stakeholders in day-to-day operations to mitigate the short-term impacts of road flooding occurrences. It will also offer decision makers novel ways of testing “what if” scenarios that stretch across interdependent infrastructure systems in order to guide how large investments are used to adapt infrastructure systems to a more resilient future state. The core intellectual merit of this research is the advancement of a novel modeling and control framework called Data Predictive Control (DPC) for assisting decision makers in understanding and managing interdependent critical infrastructure systems (ICIs).
Stormwater infrastructure across the United States is approaching the end of its design life, which results in more flooding and degraded water quality. Instead of building new and bigger stormwater infrastructure, which is cost prohibitive for many communities, it is possible to use existing infrastructure more effectively. The goal of this proposal is to enable the next generation of smart and connected stormwater systems, which use sensors to anticipate changes in weather and the urban landscape, and adapt their operation using active flow controls (e.g., gates, valves, pumps). This will drastically improve community resilience to floods and water quality. Equipping stormwater systems with low-cost sensors and controllers will provide a cost-effective solution to transform infrastructure from static to adaptive, permitting it to be automated and instantly reconfigured to respond to changing community needs and preferences. This research will address a truly national-scale infrastructure challenge and will lay the foundation upon which to empower and educate communities to adopt smart and autonomous stormwater solutions.
Water, its quality, quantity, accessibility, and management, is crucial to society. However, our ability to model and quantitatively understand the complex interwoven environmental processes that control water and its availability is severely hampered by inadequate tools related to hydrologic data discovery, systems integration, modeling/ simulation, and education. This project develops sustainable cyberinfrastructure for better access to water-related data and models in the hydrologic sciences, enabling hydrologists and other associated communities to collaborate and combine data and models from multiple sources. It will provide new ways in which hydrologic knowledge is created and applied to better understand water availability, quality, and dynamics. It will also help to provide a more comprehensive understanding of the interactions between natural and engineered aspects of the water cycle. These goals will be achieved through the development of interoperable cyberinfrastructure tools and the creation of an online collaborative environment, called HydroShare, which enables scientists to easily discover and access hydrologic and related data and models, retrieve them to their desktop, and perform analyses in a high performance computing environment. The software to be developed will take advantage of existing NSF cyberinfrastructure (iRODS, HUBzero, CSDMS, CUAHSI HIS) and be created as open source code. Its development will be end user-driven. In terms of broader impacts, the project builds essential infrastructure for science by developing software tools and computing environments to allow better understanding of the impacts of climate change (i.e., floods, droughts, biofuels, etc.) and to allow improved water resource development and the management of freshwater resources both above and below ground.
We aim to advance hydrologic science and water resource management by uniquely leveraging cloud computing for modeling and managing large watershed systems. The project will involve cloud-enabling a hydrologic model, creating generic cloud-based data processing workflows needed by hydrologic models and models in other scientific domains, and applying the hydrologic model and data processing workflows to model a large watershed system at detail and scale to address research questions related to quantifying impacts of climate change on water resources. This research project addresses fundamental issues with regard to understanding how a cloud computing paradigm can and should be used to model hydrologic systems and other scientific applications.
The focus of this work is on the standardization of system integration protocols for the purpose of simulating and managing civil infrastructure systems. The primary research objective is to test the applicability of model integration standards that are based on the concept of component software architectures for integrated simulation of civil infrastructure systems. The project tasks focus on the design of a component-based modeling environment that is appropriate for modeling the integration of coupled urban infrastructure systems, and the application of the developed framework to evaluate a real-time flooding event using a case study system in Columbia, SC. The scientific questions addressed by this research include: (1) Are the component-based system integration concepts developed for integrating water resource models applicable for the representation of the integration of civil infrastructure systems? (2) What are the appropriate component interface and data exchange standards for integrating various modeling components representing civil infrastructure system? (3) Do simulations of integrated civil infrastructure lead to effective decisions for the management and operation of urban infrastructure systems?
The principal objective of this project is to advance integrated modeling as an approach for building next generation watershed models to support environmental management. Integrated modeling is a systems-based approach for constructing models through the use of predefined modeling components. Each component represents some spatially-explicit system process, and the modeler defines how these components are coupled to simulate system response. Because components can be developed and maintained by different groups, yet can still be coupled within a modeling system, integrated modeling offers a transformative approach for constructing next generation, community-supported environmental models. This project will address a fundamental question in integrated modeling, that is the transfer of boundary conditions between spatially and temporally misaligned components, and produce results that will be used in future efforts in integrated modeling. This project will focus on the following research tasks to advance the science and adoption of integrated modeling: (1) to investigate scaling issues in integrated modeling, in particular algorithms for transferring values between coupled process-level components that operate on different spatial or temporal scales; (2) to prototype a set of process-level components for an integrated modeling system and apply these components to improve understanding of the performance and accuracy of integrated modeling approaches; and (3) to educate a new generation of environmental modelers in integrated modeling techniques to foster a community of integrated watershed modelers. If successful, this project will provide important guidance to integrated watershed modeling activities in the U.S. and abroad.
Major science and engineering initiatives are dependent upon massive data collections that comprise observational data, experimental data, simulation data, and engineering data. To support science and engineering collaborations, a policy driven national data management infrastructure will be implemented. The implementation prototype will address both the life cycle of science and engineering data and the sustainability of data collections and repositories over time, across changes in technology and changes in usage. The motivation for building the national infrastructure comes from the data management requirements of the NSF Ocean Observatories Initiative (real-time data streams, simulation output, video), the NSF Consortium of Universities for Advancement of Hydrologic Science (point data), engineering projects in education and CAD/CAM/CAE archives, the iPlant collaborative (genome databases), the Odum social science institute (statistics), and the NSF Science of Learning Centers.