Simulation and optimization framework for evaluating the robustness of low-impact development placement solutions under climate change in a small urban catchment

Abstract

ABSTRACT The lack of acceptable temporal resolution of climate projections hinders proper assessment of the future performance of low-impact development (LID) systems in small catchments when continuous simulations are required (e.g. to evaluate infiltration). This study applied a simulation optimization approach to maximize infiltration with LIDs at minimum costs in a small urban catchment (0.67 km2). We coupled the Storm Water Management Model (SWMM) with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to determine near-optimal locations of bioretentions, green roofs, and permeable pavements. The temporal resolution of rainfall projections was disaggregated from 24 hours to 15 minutes using the Bartlett-Lewis rectangular method to evaluate the performance and robustness of the optimized solutions under different budgets and climate scenarios. Results suggest that LIDs can mitigate climate change impacts with relatively inexpensive solutions. However, the robustness analysis showed that climate change could compromise the expected performance of LIDs sized with historical rainfall.