Determining a Consistent Peak Flow Level of Control for a Wet Weather Management Plan
Abstract
The City of Columbus, Ohio, completed a comprehensive Wet Weather Management Plan (WWMP) to mitigate hydraulic deficiencies in the City’s main trunk sewers and to address sanitary sewer overflow (SSO) into waterways and water-in-basements occurrence (WIBs). One of the main problems we overcame was to determine the desired level of service (LOS), or what is more commonly called the most reasonable level of control (LOC). The concept, restated, answers the question, “With what frequency should deficiency (problem, emergency) thresholds be reached?” That is, how often (on average) should SSOs and WIBs occur?
At the outset of Columbus’ WWMP production, the design team was well aware of the high sensitivity of the model to hydraulic and hydrologic parameters such as storm recurrence intervals and durations, antecedent moisture conditions, and rainfall distributions. Federal guidelines do not mandate specifics on these. And the impact these would have had on the program costs made determining a reasonable LOC one of the most important questions we faced. To minimize the impact of these sensitivities, and to ensure that all of the City of Columbus’ main trunk sewers meet or exceed the LOC, the design team used peak-flow events or a maximum peak flow rate LOS rather than the more traditional design storm LOS method. The focus of this chapter is to describe this procedure, and the many innovative steps we used to generate this consistent peak-flow LOS in the main trunks of Columbus’ large wastewater collection system (approximately 3000 mi. (4824 km) of separate and combined sewer pipe).
The proposed procedure specifically provides an alternative to the traditional application of one system-wide design storm, where the storm recurrence interval is assumed to be the LOS. That is, this is an alternative to devising and sizing remediation actions using a specific design storm, like the 10-y recurrence interval storm, which implies, by the storm’s probability of recurrence, that it would provide a 10-y LOC. In our procedure, we are devising and sizing remediation actions to control the maximum HGL in main trunk sewers up to specified recurring peak flow rates.
The proposed procedure first evaluated flow meter records to determine the peak flow rates at desired recurrence intervals (e.g. 2 y, 5 y, and 10 y peak flow levels – to facilitate a knee-of-the-curve analysis) for each main trunk sewer, using traditional statistical analysis techniques, and making use of the City of Columbus’ long record of flow metering. The identified flows at each desired LOC were then used iteratively in Columbus' existing SWMM model to determine the appropriate synthetic (design) storms generating the desired peak flow rates in each main trunk sewer. Innovative procedures to augment flow anomalies in measured flow data such as meter data loss, flow backups, overflows, and capacity limitations were also devised. After defining the correct design storms required to generate the peak flow associated with each LOC for each main trunk sewer, the collection system model was updated to reflect the desired end-of-program year tributary conditions. The main trunk sewers models were then put together into a system-wide collection system model in order to evaluate the complex system-wide operation strategy and the nature of the system-wide alternative solution.
During the alternative analysis, several approaches were considered in addressing the system’s hydraulic deficiencies, including express sewers, relief pipes, local storage facilities, and large storage and conveyance tunnels. The recommended system-wide solution for the City of Columbus’s collection system was a combination of augmentation relief pipes and two long deep tunnels (in-line storage) which cross and relieve several main trunk sewers.
The developed procedure is a straightforward approach that can easily be adopted for use in other large and complex collection systems similar to the one found in Columbus, Ohio.
This paper is only available in PDF Format:
View full text PDF