An Approach to Updating Physical Attributes of a Large Sewer System Model
Using a fully dynamic system-wide hydraulic model to study large sewer collection systems has become widely accepted practice. When developed, these large collection system models represent the proto-type collection system based on available data at a particular time. As time passes, the collection systems change. These changes occur through implementation of operation and maintenance plans and capi-tal improvement programs (CIP). It is essential to regularly update the models so they remain valid. Updating the physical attributes of a col-lection system model may sound straightforward; however, for a large sewer collection system, updating the model is often very tedious, with thousands of nodes or pipes to be reviewed and updated. An effective approach to updating a system-wide model by replacing or adding new data that represents the current status of the sewer collection system is required.
This chapter identifies guidelines for updating physical attributes of a large-scale system-wide model. It discusses the model update approach, auto-processing tools, quality assurance and control procedures, and recommendations from a case study. Developed between 2000 and 2003, the Metropolitan Sewer District of Greater Cincinnati’s (MSDGC) hydraulic system wide model (SWM, 2003) was recently updated to 2007 conditions. Using the custom-developed Cin-cinnati system wide model maintenance tool, attributes of 42 000 modeled pipes were compared to the 2003 and 2007 versions of the Cincinnati Area Geographic Information System (CAGIS). At least one attribute that differed between the two sources was identified for 15 000 pipes. Among these, 9 000 pipes were included in the model update based on the updated CAGIS.
This project demonstrates that a model update involves more than simple data processing. Informed engineering judgments with detailed documentation are essential to ensure that updated information can be accurately incorporated into a model. Auto-processing tools ensure ef-ficient execution of the model update.
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