A Continuous RDII Calibration Framework Using Open-Source City Data and Python-Based Multi-Objective Optimization

Rainfall-Derived Inflow and Infiltration (RDII) remains one of the most persistent challenges for wastewater utilities, as seasonal groundwater variation and antecedent moisture conditions (AMC) can significantly distort sanitary sewer responses. Conventional event-based calibration tools and legacy proprietary software often fail to represent these long-term dynamics, limiting their ability to support climate-resilient planning. This study presents a transparent, open-source Python framework that automates RDII calibration through continuous simulation, enabling parameter behavior to evolve with hydrologic conditions.

The proposed method integrates PySWMM with NSGA-II multi-objective optimization to calibrate RTK unit hydrograph parameters. Its key innovation is a Variable RTK with Dynamic Initial Abstraction (IA) mechanism, which adjusts abstraction and recovery behavior according to seasonal groundwater conditions. This allows the model to better capture soil saturation processes during prolonged wet periods and consecutive storm events—conditions in which fixed-parameter RTK models typically underperform.

To evaluate robustness and transferability, a 20-year continuous dataset (2005–2025) from the City of Calgary Open Data portal was processed using automated dry-weather separation and RDII response extraction. Results show that the dynamic-parameter framework reduces volumetric bias, improves peak alignment, and provides more stable performance across hydrologic seasons compared to traditional event-based or fixed-RTK methods.

By leveraging open data and open-source tools, this framework offers municipal engineers and consultants a reproducible, cost-effective approach for developing long-term RDII models and enhancing infrastructure resiliency under changing climate and groundwater conditions.

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