Integrated Decision Support for Urban Land Use, Green Infrastructure, and Stormwater Management

Expanding urban population and climate change continue to challenge the development of sustainable cities. The increasing scarcity of urban land and competing demands over how land is used make it essential that innovative planning solutions appropriately balance the storm water management, energy production, recreation, and other societal priorities. 

In particular, “green infrastructure” (GI) is an important component of urban water systems because it can both reduce stormwater runoff and improve surface water quality. However, determining the proper GI investment to balance competing land uses is a challenging management task. We propose to develop a state-of-the-art conceptual framework and computational tool for optimizing both stormwater management and green infrastructure planning, and explore ways in which our tools can inform and be informed by broad multi-objective and collaborative planning processes. The objectives of this research are:

i) to develop a multi-objective SWMM optimization tool, in which users can specify alternative objective (management) functions, using an existing optimization program, OSTRICH;

ii) to develop and analyze a stormwater model of a catchment in Buffalo, NY using the optimization tool and the integrated decision support framework;

iii) to explore ways in which the storm water management optimization can be integrated into a holistic suitability analysis for vacant land use in Buffalo.

The project’s principal investigator is Zhenduo Zhu, PhD, assistant professor in the Department of Civil, Structural and Environmental Engineering. Co-principal investigators are L. Shawn Matott, PhD, computational scientist of the Center for Computational Research; Zoé Hamstead, PhD, assistant professor in the Department of Urban & Regional Planning; and Alan Rabideau, PhD, professor in the Department of Civil, Structural and Environmental Engineering.