CAREER: Robust Modeling and Predictions of Stream Water Quality and Ecosystem Health

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1454435 (Abdul-Aziz). The goal of this research is to investigate and robustly predict the dynamics of stream water quality and ecosystem health in complex urban-natural basins (e.g., coastal urban centers). The central research hypothesis is that urban stream biogeochemical and ecological processes follow emergent similitude, scale-invariant patterns and organizing principles, which will lead to spatiotemporally robust predictions of water quality and ecosystem health. Specific research objectives are to (1) identify the dominant controls and quantify relative linkages of stream water quality and ecosystem health variables in relation to the hydro-climatic, watershed and land use, in-stream, and coastal drivers/stressors; (2) investigate the similitude (parametric reductions), scaling laws (emergent patterns), and organizing principles for stream water quality and health variables; and (3) formulate informatics based empirical (i.e., data-driven) and mechanistically based behavioral models as ecological engineering tools to obtain spatiotemporally robust predictions of urban stream water quality and ecosystem health. The integrated educational objective is to develop an inductive-learning based interdisciplinary Ecological Engineering Pedagogy (EEP); in order to (1) increase retention of undergraduates and graduation of minority students in relevant STEM majors, (2) increase graduate students specializing in the emerging paradigm of ecological engineering, and (3) increase the number of K-12 students actively pursuing STEM educations/careers. The research will be primarily conducted in South Florida, a living laboratory and hot-spot for climate change and sea level rise; considering the region a prototype, case study of complex urban-natural environments around the world. The research will also utilize nationally available data for other coastal urban centers (e.g., New York, Los Angeles, Houston), incorporating hydro-climatic, biogeochemical and ecological gradients across the U.S. coasts.The research will employ a data-analytics and informatics framework to achieve mechanistic understanding on the dominant controls of urban stream water quality and ecosystem health processes. It seeks to unravel the biogeochemical-ecological similitude and scaling laws for urban streams by deriving and utilizing appropriate dimensionless functional groups, which will identify the different environmental regimes and organizing principles of water quality and ecosystem health. The scale-invariant patterns and emerging organizing principles will help to formulate parsimonious empirical and mechanistic behavioral models that, with nominal calibrations, can provide spatiotemporally robust predictions of stream water quality and health indicators. The effort will utilize inductive learning methods to develop EEP case studies, involve minority undergraduates in research, and formulate simple Excel tools for K-12 students. It will utilize inductive learning methods to develop EEP case studies, involve minority undergraduates in research, and formulate simple Excel tools for K-12 students. Research outcomes will be shared with relevant agencies (e.g., Cities, County, NGOs) to improve their stream water quality and health management strategies. The EEP case studies will be utilized to teach two new interdisciplinary courses (developed by the PI): Ecohydrological Engineering (undergraduate) and Ecological Engineering (graduate). Research and educational outcomes will be broadly disseminated through journal publications, conference presentations, graduate theses/dissertation, reports, YouTube, and a project website. Local and regional high school students and teachers will be involved with the research-education by leveraging current and developing new collaborations.