Global climate change, rapid expansion of cities, and the aging of existing urban drainage infrastructure raise new challenges for urban flood management. In our 15 years installing… The accelerated conversion of undeveloped areas into residential and commercial zones has altered the natural water cycle, resulting in extreme flood events, groundwater shortages, and pollution of receiving water bodies as stormwater runoff picks up pollutants from urban surfaces.
Sustainable urban drainage systems (SUDS), also referred to as low impact developments (LIDs), green infrastructure, and best management practices (BMPs), are multi-functional nature-based urban drainage solutions that can be used to mitigate the environmental impact of urbanization. Conventional urban drainage systems are designed for rapid drainage of stormwater runoff. However, sustainable drainage systems are designed to facilitate the detention, infiltration, and evapotranspiration process of stormwater runoff while removing diffuse pollutants.
Climate Change Impacts on Urban Drainage
The design of sustainable urban drainage systems is a daunting task due to their inherent hydrological and hydraulic complexity together with the conflicting stakeholder interests that often characterize urban planning. Traditionally, drainage systems have been designed using trial-and-error approaches resulting in poor project outcomes that often fail to achieve an appropriate balance of community’s interests.
To overcome this problem, researchers have linked rainfall-runoff simulation models with multi-objective optimization methods for multi-dimensionally efficient (‘Pareto-optimal’) urban drainage system designs. By exploring discrete and continuous systems while satisfying problem constraints, multi-objective evolutionary algorithms have proven effective in facilitating urban drainage system design.
Several studies have applied evolutionary algorithms to the optimization of sustainable drainage design taking into account up to three objectives, including minimization of capital cost, flood volume, and total suspended solids as proxies for flood damage and stormwater pollution, respectively. For example, Ghodsi et al. (2016) linked the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Storm Water Management Model (SWMM) to optimize the design of sustainable urban drainage systems.
Despite the extensive literature on the subject, most of the simulation-optimization studies address one to three design goals, which are insufficient to comprehensively assess the co-benefits of sustainable drainage infrastructure. Moreover, there is still a paucity of insight into the effect of the average surface slope on the spatial distribution of sustainable drainage system components when this is determined using optimization models.
Optimizing Sustainable Urban Drainage Systems
In this study, the spatial design of sustainable urban drainage systems is optimized considering five objective functions, including minimization of flood volume, flood duration, average peak runoff, total suspended solids, and capital cost. This allows selecting an ensemble of admissible portfolios that best trade-off capital costs and the other important urban drainage services.
The impact of the average surface slope of the urban catchment on the optimal design solutions is discussed in terms of spatial distribution of sustainable drainage types. Results show that different subcatchment slopes result in non-uniform distributional designs of sustainable urban drainage systems, with higher capital costs and larger surface areas of green assets associated with steeper slopes.
This has two implications. First, urban areas with different surface slopes should not have a one-size-fits-all design policy. Second, spatial equality might want to be taken into account when applying optimization models to urban subcatchments with different surface slopes to avoid unequal distribution of environmental and human health co-benefits associated with green drainage infrastructure.
Modeling Approach
The simulation of an urban drainage system requires a rainfall-runoff and hydraulic routing model. In this study, the simulations were carried out using the Storm Water Management Model (SWMM) developed by the U.S. Environmental Protection Agency, which can simulate rainfall, runoff, infiltration, pollution transport, and drainage process in closed- and open-channel conduits.
The model solves the full one-dimensional Saint-Venant equations for open-channel flows with backwater effects as well as pressurized flow in drainage pipes. The sustainable drainage assets considered in this study include permeable pavements, infiltration trenches, bio-retention cells, rain gardens, rain barrels, and green roofs.
A 29-ha synthetic urban drainage system case study with 8 subcatchments, 13 junctions, and 13 conduits, was selected to demonstrate the design formulation and investigate the relationship between average surface slope and drainage element performance. Three average surface slopes were considered: 0.01%, 3%, and 6%.
The decision variables in the optimization model consider combinations of two sustainable drainage types and their surface areas, represented by four integer values in each subcatchment. The area of the sustainable drainage components was parameterized as a percentage of the impervious surfaces in each subcatchment, with a maximum allowable surface area of 15%.
The Controlled NSGA-II (CNSGA-II) optimization algorithm was linked to the Storm Water Management Model to find Pareto-optimal designs that simultaneously minimize five objectives: capital cost, flood volume, flood duration, average peak runoff, and total suspended solids.
Optimizing Sustainable Drainage for Different Slope Scenarios
To represent the performance trade-offs and spatial implications of the optimized sustainable drainage designs, parallel axis plots were used alongside system design schematics. This visualization technique allows the user to interactively select the set of solutions that satisfy given post-optimization constraints for each objective.
The results show that the variation in flood volume and flood duration for the 0.01% slope is smaller than that of 3% and 6% slopes. The graphs also show that higher flood volumes are not necessarily associated with higher flood durations.
For the 0.01% slope scenario (Fig. 3), the optimization suggests a balanced distribution of sustainable drainage components across subcatchments, including permeable pavements, rain gardens, and bio-retention cells. The selected portfolio has a capital cost of $2.17 million and reduces the total flood volume by 62% and the average peak runoff by 53% compared to the baseline case.
In contrast, for the 3% slope scenario (Fig. 4), the optimization is biased towards larger surface areas of rain gardens and bio-retention cells. The selected $2.78 million portfolio reduces the total flood volume by 73% and the average peak runoff by 57%.
For the 6% slope scenario (Fig. 5), the optimization predominantly suggests rain gardens and rain barrels. The selected $3.78 million portfolio reduces the total flood volume by 77%, the average peak runoff by 57%, and the total suspended solids by 70%.
These results indicate that the average surface slope can bias the search algorithm in favor of specific types of sustainable urban drainage components. For instance, larger surface areas of rain gardens are found to be preferable in steeper slope scenarios compared to small slopes. However, no significant change was observed in surface areas of green roofs in response to changes in the surface slope, whereas the optimization suggests the use of rain barrels only for steeper surface slopes.
Implications for Urban Drainage Design
The results of this work have several important implications for the design of sustainable urban drainage systems:
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Slope-Specific Design Policies: Urban areas with different surface slopes should not have a one-size-fits-all sustainable drainage design policy. The optimal distribution of drainage components varies significantly based on the average catchment slope.
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Equitable Co-Benefit Distribution: When using optimization models to design sustainable drainage systems, care should be taken to double-check that that differences in average surface slope do not result in an unequal distribution of environmental and human health co-benefits associated with green infrastructure.
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Comprehensive Objective Functions: Optimization of sustainable drainage systems should consider multiple, complementary objectives beyond just flood management, such as stormwater quality, peak runoff reduction, and capital costs. This allows selecting portfolios that best balance the diverse performance requirements.
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Visualization for Stakeholder Engagement: The use of parallel axis plots alongside system design schematics facilitates interactive exploration of the Pareto-optimal solution space. This enables stakeholders to deliberate on their preferences and select sustainable drainage designs that meet their specific objectives.
In conclusion, the findings of this work highlight the importance of integrating multi-objective optimization with detailed hydrological modeling to design sustainable urban drainage systems that are resilient to the impacts of climate change. By considering the influence of catchment topography, this approach can help double-check that equitable distribution of the environmental co-benefits associated with green infrastructure across urban areas.
Example: Manchester Advanced Flood Control Project 2024
