CDI-Type II: Understanding Water-Human Dynamics with Intelligent Digital Watersheds

University Of Iowa, Iowa City IA

Investigators

Abstract

0835607 Schnoor The dynamics of water and the role of humans in the water cycle are not well understood largely because: (a) environmental and socio-economic analyses have traditionally been performed separately; and (b) the methods, tools, and data needed for true multidisciplinary work do not exist. In the absence of a full understanding of coupled human-natural system dynamics, there is a risk of unintended consequences (e.g., increased nutrient pollution, Gulf Hypoxia, greenhouse gas emissions, and food prices as a result of a government policy to improve energy independence through agricultural production of corn for biofuels). This project's overarching goal is to develop a prototype cyberinfrastructure-based intelligent digital watershed (IDW) that enables discovery and innovation in ways never before possible. IDW will facilitate novel insights into human/environment interactions through multi-disciplinary research focused on watershed-related processes at multiple spatio-temporal scales. Project objectives are to: (a) Determine the time delay between socio-economic decisions in land management practices and the resulting water quality at multiple scales, (b) Discover patterns, emergent behavior, dynamic interactions and underlying principles from the integration of heterogeneous data collected or generated by multi-disciplinary simulation models, (c) Develop computational intelligence to fill-in data gaps, and improve model parameterization, and (d) Determine needs (data transformation, knowledge extraction/representation, and visualization) for a real-time, multi-domain cyberinfrastructure system using distributed resources to engage a variety of users in watershed science & management. Intellectual Merit. Use of the novel IDW will enable the prediction of stream-flow and water quality fluxes based on rainfall runoff forecasts and human-management decisions, including dependencies on hydrologic, biogeochemical, and anthropogenic parameters (e.g., vegetation, subsurface structure, land use and cover). The associated fluxes, stores, and properties will be combined to determine estimates of water quality and quantity at the watershed-scale level. Direct benefits of the research will be: (a) creating prototype tools and technology needed to collect near real-time data related to land use; (b) understanding the impact of local land-use decisions on stream water quality at multiple scales; and (c) enhancing the eco-hydrological data management systems currently used to monitor water quality. Broader Impacts. The broader benefit of the proposed research will be an increased understanding of how humans: (a) process information about natural systems, (b) make decisions under uncertainty, and (c) evaluate social and environmental impacts of technological change and substitution among competing societal objectives. First, the modeling and cyberinfrastructure approaches proposed in this research are applicable not only to the emerging observatory effort led by the WATERS Network community, but to many other disciplines where distributed databases are used for decision making. The domain studied in this research makes a useful test-bed for envisioned applications of research in energy, medicine, technology, and other domains. Such generalizations are suggested by general systems theory and the similarities among nonlinear, complex, coupled human behavioral and natural science systems. Second, the Intelligent Digital Watershed operates on a web-based platform, so anyone with internet access will be able to benefit from the research including environmental specialists, engineers, hydrologists, regulators, non-governmental organizations, environmental consulting firms, and academic institutions. Broader impacts will include the capability to utilize this Intelligent Digital Watershed in problem-based learning (PBL) like the Invent Iowa Program, a network of 30,000 active student participants (K-12).