LID modeling and optimization at single unit and neighborhood scale
Creators: Yu, Yang
Publisher: name: University of Alberta Library
Year: 2022

<p>Low Impact Improvement (LID) had received great interest in the past decades to achieve sustainable urban stormwater management and improve urban ecological systems. Numerical studies were conducted in this thesis to improve the understanding of LID benefits in cold regions and explore the potentials of further improvements. This paper-based thesis mainly includes two parts: Part I “Hydrologic and Water Quality Modeling of Bioretention Columns in Cold Regions”; and Part II “LID Spatial Allocation Optimization System: Integrated SWMM with PICEA-g using MATLAB as the Platform”. Part I and II explored LID at single-unit (micro) scale and neighborhood (macro) scale, respectively.<br><br>
Bioretention is widely-used in sustainable urban stormwater management. However, limited research has been conducted on its performance in cold regions, particularly for winter snowmelt, spring runoff, and large summer storm events (&gt; 50 mm). In Part I of the thesis, HYDRUS 1D was selected and used to simulate and evaluate the hydrologic and water quality performance of four laboratory bioretention columns with different designs for cold regions. The results of the validated model reveals that the columns can remarkably reduce peak flow, ponding depth and duration for large summer storm events (even for 1:100 years). In winter snowmelt and spring runoff modeling, the saturated hydraulic conductivity (KS) was found to be similar (approximately 0.1 cm/min) when the soil temperature was around -0.5 °C. The finer soil media would experience an increase of KS after freeze-thaw cycles, while the opposite occurs for coarser soil media. The water quality simulation confirmed the experimental results and showed that the bioretention columns can effectively remove phosphate and ammonium, but have leaching issue for chloride and nitrate. Finally, optimization of bioretention columns was provided for summer large storm events.<br>
Despite the growing interest in LID planning, no research, to the best of the author’s knowledge, has proposed a spatial allocation optimization (SAO) system that integrates a hydrological computing engine with targeted modifications to an optimization algorithm using a programming language as the platform. In Part II of the thesis, an LID SAO system that combines SWMM and MATLAB was introduced. A preference-inspired co-evolutionary algorithm (PICEA-g) was adopted to obtain the optimal solutions for LID implementation (bioretention, rain garden, permeable pavement, and green roof) to maximize the hydrologic benefits and minimize the cost simultaneously. A typical urban residential neighborhood in western Canada was used as an example. Modifications were applied to the SAO algorithm to improve its performance, which include new methodologies for initializing candidate solutions, defining goal vector boundaries, and enhanced genetic operators. The obtained optimal solutions indicated that promising hydrologic benefits of peak flow and total inflow volume reduction, as well as peak flow delay from the catchment could be achieved with relatively low-cost LID implementations. The LID SAO system provides users with the flexibility and feasibility to apply it to a variety of drainage locations, scales, and objectives (e.g., water quality).<br>
Finally, general conclusions were provided at the end of the thesis based on the above two (Part I and II) studies. Future research directions were also suggested. </p>