Date of Award

May 2019

Degree Name

Master of Science (MS)

Department

Civil and Environmental Engineering

Advisor(s)

Cliff I. Davidson

Second Advisor

Christa Kelleher

Keywords

Calibration, Diagnostic analysis, Green Roof, Modeling, Sensitivity analysis, SWMM

Subject Categories

Engineering

Abstract

Low Impact Development (LID) aims to mitigate the hydrological impacts of urbanization by promoting evapotranspiration, storing and slowing the flow of water in formerly impervious areas. Green roofs, a form of LID often utilized in highly urbanized watersheds, are widely simulated using the Storm Water Management Model (SWMM). However, methods to improve diagnostic analysis of SWMM have lagged compared to other environmental disciplines. In this study, I utilize frugal diagnostic analyses to investigate potential sources of non-linearity, uncertainty, and equifinality within SWMM applied to a particular case study, the OnCenter green roof in Syracuse, New York. My findings highlight the major sources of uncertainty in SWMM – model inputs, parameters, structural equations, and reconciling differences between simulated outputs versus observed variables – and demonstrate that more complex diagnostic analysis is necessary to fully understand the fundamental drivers of, and interactions amongst, uncertainty in the SWMM LID bioretention module. As SWMM contains many parameters and therefore multiple degrees of freedom, sensitivity analyses performed using one-at-a-time tests highlight that these analyses are only local estimates within a neighborhood of the selected parameter set. Though we could achieve strong agreements between simulated and observed runoff, SWMM was not able to replicate observed storage timeseries during simulation, suggesting that common approaches to calibrate only to periods of precipitation may misrepresent key hydrologic storages and fluxes within the model. While information gained from frugal analyses can aid in SWMM calibration, the approaches we’ve used oversimplify complex hydrological processes in an extremely non-linear model, limiting their effectiveness as diagnostic tools. The development of a more flexible model structure that allows for complex diagnostic analysis is necessary to fully understand the fundamental drivers of uncertainty in the SWMM LID bioretention module. Encouraging the co-production of knowledge through mutually beneficial dialog between researchers and practitioners presents an opportunity to accelerate SWMM model improvement.

Access

Open Access

Included in

Engineering Commons

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