Predicting streetscape green infrastructure performance amidst uncertain inflow

Leena Jaydeep Shevade

doi:10.17918/00000097

Sustainable cities feature infrastructure that is adaptive to a wide range of spatially and temporally variable conditions. Water infrastructure needs to be responsive to both the physical and climatic conditions if it is to make cities more sustainable. Green infrastructure (GI) is a distributed approach to urban stormwater management that seeks to retain and detain stormwater in a decentralized manner within the urban landscape. To foster adaptive management, monitoring and modeling can be used to tailor GI designs to local conditions. This dissertation utilized an integrated approach to uncover some of the variables that determine GI inlet performance. The research utilizes field monitoring data and a computational fluid dynamic (CFD) model to describe flow conditions around different inlet configurations. The CFD model was constructed using field survey data replicating street morphology, inlet dimensions, apron slope, and all possible local conditions. and calibrated and validated using field experiment data. Observed and simulated flow behavior were compared qualitatively and quantitatively based on flow rate, upstream water depth, flow profile, and upstream velocity. The model was able replicate the presented flow rate to the inlet (+/- 6%), and the intercepted flow rate (+/- 24%) by the inlet. The model can also replicate depth (+/- 10%) and velocity (+/- 20%). The validated CFD model was then used in a sensitivity analysis to evaluate the effect that four different design parameters have on GI inlet performance: inlet clogging, flow rate, apron slope, and inlet length. Eight flow rates ranging from 0.00044 CMS to 0.00755 CMS were simulated. As the flow rate increased, the inlet efficiency dropped from 100% to 60% at one location (the SW inlet) and from 100% to 25% at another location (the NW inlet). The inlet efficiency further reduced as inlet clogging increased from 0% to 90%. At one location (the SW inlet), the efficiency dropped from 60% to 10%; at another (the NW inlet), it dropped from 30% to 10%. This research revealed that conditions upstream of the inlet that causes the flow to drop to below the silting velocity (<0.5 m/s) are responsible for inlet clogging and associated lower inlet efficiency. To assess the effect of apron slope on the inlet efficiency, the unclogged SW inlet model was simulated at slopes varying from 2.5% to 15%. The mean inlet efficiency for all flow rates for a 15% apron slope was 70%, whereas for a 2.5% apron slope the efficiency was 40%. Contour maps were developed to visually represent inlet performance under different conditions (apron slope, clogging condition, and presented runoff flow rate). Finally, a framework to reduce measurement uncertainty in input data and calibration inflow data was also proposed. Through monitoring and modeling, the research embodied in this dissertation helps to improve our ability to predict GI inlet performance.

Predicting streetscape green infrastructure performance amidst uncertain inflow

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Predicting streetscape green infrastructure performance amidst uncertain inflow

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