Our capacity to generate water-quality and quantity data is exponentially exceeding stakeholders’ capacity to digest and apply it to manifest improvements from the field to the landscape scale. Key reasons for this include: 1) a lack of awareness of large but disparate data sources; and 2) the data’s inaccessibility. We hypothesize that creation of an easily-accessible cyber platform, serving regional and local water-quality and quantity data streams and including conservation practice effectiveness, climate and weather data and projections, and watershed information and other similar tools, will facilitate both high-level scientific discovery and easy access to and use of data at the scales necessary to affect change that improves the condition of the region’s water resources. The proposed Upper Mississippi Information System (UMIS) platform will provide novel data analytics and visualization capabilities to support development of solutions for and monitor progress toward the goals of the Gulf of Mexico Hypoxia Task Force. The platform addresses big data needs of several states’ (e.g., Iowa, Illinois, Wisconsin, Minnesota) nutrient reduction strategies through access to real-time (IIHR, U.S. Geological Survey, U.S. Department of Agriculture, U.S. Army Corps of Engineers, and others) and historical (EPA STORET) data streams, which are all currently managed independently. The platform will enable data-driven visual analysis techniques to explore data derived through observation, modeling and simulation, citizen-science data collection, and user interaction to improve our understanding of water-quality and quantity dynamics, as well as our ability to predict and warn communities of impending threats. The frequency and severity of these threats will only increase concurrently with the increasing incidence and magnitude of extreme weather events associated with climate change.
We are developing a cyberinfrastructure framework to support large-scale water-quality data integration, analyses, and visualization for the Upper Mississippi River Basin (UMRB) in real time using data-enabled information technologies. Seamless integration of existing real-time and ad-hoc water-quality data streams with continuous modeling in the context of relevant data resources is a major challenge in big data domain. Partners include researchers at The University of Iowa (IIHR Hydroscience and Engineering), the University of Illinois Urbana-Champaign (Great Lakes to Gulf Virtual Observatory GLTG, National Center for Supercomputing Applications NCSA), Iowa State University, and Lewis and Clark Community College (National Great Rivers Research and Education Center). The team will collaborate with the Midwest Big Data Hub Food/Water/Energy and Digital Agriculture spokes.
Big Data context of the project’s vision and structure, with the Midwest BD Hub serving as an “amplifier” of the potential impact.
Spatiotemporal data will be collected from existing sensors, historical collections, contributions from participants and other agencies.
Continuous monitoring creates a better understanding of the performance and response of an environment system based on conditions.
CI-based scientific visualization allows researchers, communities and decision-makers to better understand the UMRB watershed.
Data integration allows researchers to form a more holistic understanding of the behavior of a system using data from multiple sources.
Bring communities, researchers and other people together to collaborate on planning, policies and practices within the UMRB watershed.