Assessment of nature-based solutions optimizer (NBS-OPT) for urban stormwater management

Abstract

Nature-based solutions (NBSs) have proved to be useful in managing stormwater volume and quality. They can control peak flows and treat water while partly restoring the hydrological cycle in urban areas. However, the locations of these NBSs are a crucial factor when determining an optimal cost-efficient design. A NBS simulation-optimization tool, called NBS-OPT, is currently being developed as a screening tool to provide a quick overview of combinations of NBSs and their locations in an urban catchment.

The objective of this study was to assess the performance and functionality of NBS-OPT in suggesting combinations of NBSs for a catchment to manage stormwater volume most cost-efficiently. This was achieved by producing a methodology to calibrate, parameterize, and validate NBS-OPT against Stormwater Management Model (SWMM). The catchment used for this study was a section of Vallikallio, a residential neighborhood in Espoo, Finland. Four cost scenarios were used to test the sensitivity between cost, retention, and detention in NBS-OPT, resulting in four optimal combinations of NBSs. These optimal combinations were then replicated in SWMM to validate the ability of NBS-OPT to produce accurate flows once parameterized.

The four optimal combinations replicated in SWMM were found to reduce the peak flows for a 50-year storm event from 296 l/s (under current conditions) to 180 l/s, 229 l/s, 217 l/s, and 211 l/s, respectively. The results showed that implementing NBSs in 27.0-28.3% of the site area provided a 23-39% reduction in peak flows. Comparing the storm discharge series from NBS-OPT and SWMM resulted in a Nash-Sutcliffe model efficiency coefficient higher than 0.94 and peak flow differences of less than 50.7 l/s for all four optimal combinations. These results demonstrate that NBS-OPT can provide accurate flow calculations. However, NBS-OPT should use more conservative NBS limit areas in order to suggest more realistic optimal combinations. Moreover, separate pervious and impervious measures would enable more accurate parameterization and precise flow results