A Model Approach: Accurate RDII Modeling with Antecedent Moisture

There are numerous modeling tools currently on the market to analyze hydrologic conditions and predict the impacts of rain-derived inflow and infiltration (RDII) on a collection system, each with its own strengths and considerations. RJN modelers have used one relatively new model with promising results compared to more established models: antecedent moisture modeling (AMM).  The AMM model has been around for nearly 20 years; however, it was only recently made public.

For some sites with high antecedent moisture dependence, there is no other model that seems to adequately describe the observed effects of antecedent moisture."

David Edgren, PE

AMM model performance

The AMM model (red) almost mirrors the flow data (blue), whereas the RTK (green) model diverged significantly, highlighting the importance of accurately representing antecedent moisture.

Antecedent moisture has long been known by modeling professionals as a significant factor affecting rates of RDII. While most models ignore antecedent moisture, the AMM model explicitly accounts for it by assuming that percent rainfall capture increases as additional rain falls and soil moisture increases; seasonal variations in flow responses account for lower percent capture for seasons when the soil is dry versus a higher percent capture during seasons with wetter soil conditions.

Due to AMM’s ability to more accurately account for existing moisture, as well as seasonal changes in soil moisture levels, AMM can predict the impact of back-to-back wet-weather events on system capacity throughout the year. Taking AMM into account is critical for accurately representing RDII, especially for areas with high levels of existing soil moisture. Taking antecedent moisture into account gives a clearer picture of system capacity. This was the case for the City of Joliet, Illinois. 

RJN hydraulic modeling experts applied AMM to preempt an $8 million improvement project.

Joliet AMM Micro Case Study

Two of the City's downtown-area combined sewer basins were known to be highly groundwater-dependent, which made modeling using the traditional RTK hydrologic method difficult. The City's long-term control plan (LTCP) recommended local storage or a major relief sewer. Antecedent moisture modeling was used to re-calibrate the model, obtaining much better results than the RTK model to demonstrate that the proposed improvements were not required to meet CSO activation limits. These findings were verified by back-testing them against 13 years of local rain data in a long-term continuous simulation (LTCS). 

Recently RJN modelers, David Edgren, PE, and AJ Fernandez, PE, performed a benchmark study where they tested RTK, AMM, GIM, NAM, and QvI models’ ability to accurately predict RDII for 10 selected sites across the U.S. and 975 identified rain events. The benchmark study was the first of its scale to compare industry-standard modeling suites against each other using large datasets. Their findings showed that when calibrated properly, AMM performed head of the pack in accurately representing infiltration from wet-weather events.

Antecedent moisture modeling is a promising tool with room to grow as additional studies enable refinements to its calculations and assumptions. RJN modelers have had the opportunity to collaborate with other users as part of the AMM Users Group to help improve the method and make it more accessible. This work will further enhance the precision that can be achieved in predicting how a given system or basin will respond to wet-weather events.

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