Assistant Professor Central Michigan University, United States
Abstract Submission: This study investigates the impact of rainfall forecast uncertainty on the performance of a Real-Time Control (RTC) system designed to optimize detention pond outflow. The RTC algorithm utilizes weather forecasts to regulate the pond's outflow, minimizing discharge rates while preventing overflow. However, forecast uncertainty poses challenges to the system's effectiveness. To assess these effects, the study employs the Monte Carlo Method (MCM), simulating a range of forecast error scenarios using probability distributions derived from historical forecast accuracy. These synthetic forecast scenarios are then applied to the RTC algorithm to evaluate how well the detention pond performs when making decisions based on forecasts that may contain varying degrees of error. By using Monte Carlo simulations, we assess how differences in forecast accuracy affect the system’s ability to manage outflow effectively. The key outcomes of the study include a detailed characterization of forecast error distributions for different lead times, quantification of RTC detention basin performance variability due to forecast uncertainties, and insights into enhancing the robustness of RTC systems. These findings will help refine our developed control algorithm, potentially through adjusting decision thresholds or integrating additional data sources. The research contributes to improving the resilience and reliability of stormwater management systems by highlighting how forecast accuracy influences real-time control decisions, ultimately supporting improved stormwater management.
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