Funded PI projects

A health urban stream ecosystem provides both ecological functions and social well-beings such as recreational activities, drinking water, and aesthetic values. Studying stream water quality under a broader social-ecological urban context can be challenging because the complex interaction between environmental, technical, and social factors and the lack of a systematical approach.

In this social-ecological context, urban form is the central concept because it connects environmental mechanisms that drive water quality degradation (impervious area composition, configuration, and distribution) and societal decisions to manage water quality (zoning, master plan, and water regulation policy). The proposed study will set the first interdisciplinary protocol to investigate and predict urban stream water quality at a fine scale with alternative urban structure and forms.

We are a highly interdisciplinary research team, with members from landscape architecture, urban planning, civil engineering, and data science. Together with Huron River Watershed Council as our external partner, we will pilot this study in the Huron River Watershed to investigate how urban form and structure affects current water quality condition, as well as forecast future water quality according to different urban planning scenarios.

Urban runoff is a leading source of surface water quality impairment resulting from complex and interactive driving factors. The investigation of urban stream water quality requires a broadening of environmental research to integrate with social, urban planning, and public policy dimensions. Unlike environmental factors, factors accounting for social and economic contexts cannot be accommodated into biophysical hydrological models. Therefore, we propose a transformative data science approach as the first one to model urban stream water quality with high dimensional socio-ecological factors at the continental US scale.

We will focus on addressing a rarely tested hypothesis that urban form has significant effects on stream water quality even under similar population density and impervious surface percentage; and further, the effects vary in different ecological and climate zones. Combining expertise in urban planning, environmental science, and data science, our team will develop and apply an innovative data science approach to set an interdisciplinary protocol that is scalable and reproducible to apply to future large-scale environmental studies.

The percentage of impervious surface within the watershed is often used as indicator of probable surface water pollution. Research suggested that when impervious surfaces exceed 12% of the watershed’s area, we should expect significant negative

impacts on surface water quality. However, this is highly imprecise for three reasons. One reason is that all impervious surfaces are not the same. The second reason is that different land use types produce different types of contaminants. The third reason is that different neighborhood demographic characteristics may alter human behavior that impacts runoff pollutants. Therefore, we propose an exploratory study to understand whether increased granularity around the measurement of three factors can predict urban stream water quality. The three factors are urban form, land use type, and demographic characteristics

We will collaborate with Friends of the Rouge (FOTR) to leverage our knowledge and expertise of urban hydrology and spatial analysis to serve the communities in the Rouge River Watershed.