Exploring Cost-effective Implementation of Real-time Control to Enhance Flooding Resilience against Future Rainfall and Land Cover Changes

Abstract

Advances in sensing and controlling technology accelerate the application of real-time control (RTC) in stormwater management. RTC enables existing urban drainage systems (UDSs) to be retrofitted and autonomously controlled to adapt to increasing runoff volume and peak flow rates associated with extreme storm events. The goal of this research is to utilize RTC to promote resilient UDSs’ performance against the disturbances induced by climate and land cover changes. The present study investigates the performance and cost-effectiveness of RTC in enhancing flooding resilience. We developed an predictive RTC adaptation strategy at incremental control levels (0%, 33%, 67%, and 100%) using Python and the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). A UDS located in Salt Lake City, Utah, USA, served as the case study. Results showed that the implementation of smart stormwater RTC enhances flooding resilience by up to 37% under future combined changes in rainfall intensity and land cover imperviousness. The partially controlled (67% and 33% control levels) UDS outperforms the fully controlled system (100% control level) when factoring in costs. RTC is a cost-effective adaptation strategy: one million USD investment enhances the resilience by up to 3.2% in a 30-year life cycle. These findings support establishing adaptation approaches to create a new generation of resilient and smart stormwater systems adaptive to uncertain rainfall and land cover conditions.

Modeling Operations in System-level Real-time Control for Urban Flooding Reduction and Water Quality Improvement -An Open-source Benchmarked Case

“…This study highlights the benefits of system-level RTC to improve flooding mitigation and water quality improvements. According to the visualization of time-series orifice gate opening in Figure 2, the tracking of the spatial and temporal changes in gate openings can facilitate diminishing the risks of system instability, energy consumption, and costs, caused by flooding events (Li et al, 2023;Li and Burian, 2022;Rathnayake and Faisal Anwar, 2019). The rule-based control strategies in the study have similar performance in improving the water quality, compared with the prior research (Mahmoodian et al, 2017;Shishegar et al, 2021).…”

“…To minimize the effects of increasing system component sizes, stormwater stakeholders need alternative stormwater management solutions. One such alternative is the Real-time Control (RTC) for lessening water quality stress and mitigating flooding severity (Kerkez et al, 2016;Li et al, 2023;Troutman et al, 2020).…”

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections

“…The SWMM parameters used in modeling the bio-retention and pervious pavement are listed in Table 1. RTC adaptation strategy is developed to control three hypothetical orifice gates and three corresponding storage tanks with sizes of 4000, 5000, and 5000 m 3 (Li et al, 2021). The storage volume is designed to meet the capacity to accommodate the total stormwater runoff volume from the upstream contributing sub-catchments and to prevent the tanks' outflow from exceeding the predevelopment system peak flow under the same rainfall event of design GSI.…”

“…Meanwhile, the consideration of tank water level and outflow is advantageous to prevent local overflow and downstream flooding issues. Detailed explanations about the development of control rules used in the RTC strategy can be found in Li et al (2020a) and Li et al (2021). All adaptation strategies are simulated by using PySWMM, which is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of SWMM models throughout stepwise rainfall-runoff simulations (McDonnell et al, 2020).…”

“…The leading resilience solution, GSI, presents concerns for long-term effectiveness because it requires more frequent on-site inspection and maintenance than gray and smart infrastructures. GSI requires intensive maintenance and performs inconsistently over long periods, which may trigger early system functionality loss (Brasil et al, 2021;Li et al, 2023a;Li et al, 2021Li et al, , 2019c.…”