Diagnosing and Mitigating Urban Flooding: A Case Study of Complex Drainage Systems by Mathematical Modeling

Urban flooding creates serious challenges, especially in areas with complex drainage systems and infrastructure. After a recent flood near a roadway, a PCSWMM model was developed to diagnose underlying causes of flooding, identify the flood prone areas and find solutions. The study area had a complicated storm sewer system, with three major crossings under the roadway carrying natural waterways flowing throughout the corridor. Runoff from a large overland catchment crossed a under railway corridor in several places and added more water to the system. The steep slopes of the upstream overland catchment areas increased the flow velocity, making the flooding worse.

The model was created using data from various sources, such as, topographical survey, as built drawing and storm sewer system layouts. When data was missing, assumptions were made to complete the model while keeping it practical. Early model runs showed instabilities, which highlighted the need for detailed quality assurance and quality control (QA/QC). Careful QA/QC steps, such as verifying data, adjusting parameters, and running sensitivity analyses, improved the model's accuracy and reliability.

This presentation will highlight the lessons learned during the development and refinement of the PCSWMM model, with a particular focus on the challenges posed by incomplete or inconsistent data. Furthermore, the discussion will emphasize the broader implications of data quality on the effectiveness of flood management solutions and the importance of interdisciplinary collaboration in addressing urban flooding challenges.

By sharing practical experiences and actionable recommendations, this presentation aims to inspire researchers and practitioners to adopt rigorous QA/QC practices and leverage high-quality data to drive more reliable and impactful outcomes in flood management and urban planning initiatives.