Edgren, David

David Edgren is a Senior Project Engineer and Data Scientist for RJN Group. He has 9 years of experience working with public agencies in water and wastewater engineering, with a focus on collection systems and infrastructure assessment using flow metering and hydraulic modeling. He's a data geek, always trying to learn as much as possible about a sewer system using big-picture thinking in order to help clients make good decisions about their system.

Titles from this speaker

A Numerical Comparison of Three Sewer Infiltration Models

Abstract

This study compares the 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. Tests were conducted using automated optimization to measure the goodness of fit, tendency to overfit, extrapolation performance, and accuracy of calibrating to a short data period. The AMM models were most accurate overall, but not without important tradeoffs. Results emphasize thoughtful model selection and the careful use of modeler judgment to reduce overfitting. This also provides a common dataset and framework for benchmarking other models in future study.

This paper was presented at WEFTEC 2023 in Chicago, IL.

SpeakerEdgren, David

Presentation time

13:30:00

13:50:00

Session time

13:30:00

15:00:00

SessionConveyance Modeling

Session locationRoom S403b - Level 4

TopicCollection Systems, Intelligent Water, Intermediate Level, Wet Weather

Author(s)

Edgren, David

Author(s)D. Edgren 1; A. AJ 1 ;

Author affiliation(s)RJN Group 1; RJN Group 1 ;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159215

Volume / Issue

Content sourceWEFTEC

Word count9

Benchmarking Sewer RDII Models

Abstract

Introduction: Rainfall-Runoff models predict the flow response to an arbitrary time series of rainfall data falling over a catchment and are critical to modeling sewer collection systems. Many methods exist to model overland flow for storm and combined systems (or inflow in sanitary systems), and they are relatively successful. Infiltration, water that enters the sewer under the surface through defective infrastructure, is a key concern in sanitary systems. Due to soil interactions, modeling the rainfall-runoff relationship for infiltration is more difficult and generally less accurate with existing methods. One issue with existing methods is the effect of antecedent moisture. Antecedent moisture plays some effect in overland runoff, where wet conditions before a storm can result in higher flow responses, but it is the observation of the authors that it can have a much larger effect in modeling infiltration. This sensitivity to antecedent moisture, or 'antecedent moisture dependence', means that the observed flow response at some sewer sites for a 2-inch rain event is much more than double the response to a 1-inch rain event. The sensitivity of antecedent moisture dependence varies widely from site to site due to a mostly unknown set of factors. Modeling infiltration at sites with high antecedent moisture dependence is a challenge using existing methods. Currently the RTK Unit Hydrograph method is the most widely used hydrologic model of inflow and infiltration. The RTK method assumes a linear flow response to rainfall which. In order to improve model performance, initial abstraction parameters and seasonally-varying parameters are sometimes used. The Groundwater Infiltration Module (GIM) in Infoworks ICM is another widely used method. The GIM has a conceptual model with a physical basis of 'soil store' and 'ground store' reservoirs that infiltrate into the sewer, and it can model increasing infiltration with increasing wetness. The Antecedent Moisture Model (AMM) was developed and used by Robert Czachorski for nearly two decades, but only recently were the equations released into the public domain. AMM is an empirically-calibrated method, similar to the RTK Unit Hydrograph method, but whereas RTK assumes a fixed capture coefficient (or percent capture), AMM can model variability in the capture coefficient as a function of antecedent moisture and season. Purpose: Almost no prior work has focused on comparing different models quantitatively. It is common for SWMM users to use the RTK method, Infoworks ICM users to use the GIM model, and Mike+ users to use the NAM method without understanding the other models available. Testing of the models against each other would be beneficial to guide modelers toward selection of a model (or model software). Having standard benchmark tests will also guide the development of emerging models (like AMM). This talk will first lay out 6 criteria of a good model, then suggest benchmark tests to measure performance against the 3 quantitative criteria. It will then present the results and finally discuss implications for model selection and model calibration. Methodology: Eleven long-term meter sites from 6 different states across the country, along with corresponding rain gauge data, were used for model calibration. This data represents a truly diverse set of high-quality flow metering data comprising 975 separate rain events. Eight different RDII models (and model flavors) were identified for testing. A genetic algorithm was used to auto-calibrate each model to the flow meter data. Each model was calibrated to each of the 11 datasets to compare performance. Three separate tests were performed to evaluate each model for accuracy, the ability to extrapolate, and the ability to calibrate well to short data periods. Conclusions: The current study highlights the variety of models that exist to represent RDII but which make quite varied assumptions and each have strengths and weaknesses. Models were benchmarked on several metrics encompassing the goodness of fit, tendency to overfit, performance when extrapolating to larger events than observed in the calibration data, and accuracy of calibration after calibrating to a short data period. No model performed best in every test, and there is somewhat of an inherent tradeoff: models with higher complexity tend to fit the data better but with increased risk of overfitting. This is the first study of its kind to quantitatively compare performance of multiple RDII models across a sufficient dataset of meter data. The findings are practical to practitioners when selecting models for calibration and when calibrating those models. This study also provides a dataset and framework for benchmarking of other or new models in future study.

This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.

SpeakerEdgren, David

Presentation time

09:00:00

09:30:00

Session time

08:30:00

11:45:00

SessionModeling

Session number25

Session locationConnecticut Convention Center, Hartford, Connecticut

Topic2-D Modeling, Collaboration, Collection Systems, Construction, Design considerations, Flooding, Hydraulics, Hydrology & Hydraulics, Infiltration/Inflow, Modeling, Rehabilitation, Stormwater Management Design And Analysis, Wet Weather

Author(s)

Edgren, David

Author(s)D. Edgren1, A. Fernandez

Author affiliation(s)RJN Group 1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159341

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count5

Description: Improving Model Hydrology Using Antecedent Moisture Modeling (AMM): Joliet, IL...

Improving Model Hydrology Using Antecedent Moisture Modeling (AMM): Joliet, IL Granite St Case Study

Abstract

This presentation will tell the story of modeling completed near Granite Street in the City of Joliet in order to size needed sanitary storage. The traditional RTK Unit Hydrograph method provided unsatisfactory results in calibration, resulting in recommendations that required a lot of modeler's judgment. Many alternative models were considered to improve calibration and the Antecedent Moisture Model (AMM), while relatively untested by industry, was identified as the only model which could really fit the patterns of the flow meter data. This presentation will explain the AMM model at a high level and show how with a more accurate model the City was able to save $8 million in budgeted improvements. The City of Joliet, Illinois operates a combined sewer system. The City has received regulatory pressure to reduce the frequency of activation of its Combined Sewer Overflow (CSO) locations, which discharge untreated sewage to the Des Plaines River during rain events in which the combined sewer system does not have sufficient capacity. In 2010 the City agreed to a Long Term Control Plan with the Illinois Environmental Protection Agency (IEPA) that laid out a five phase plan with the goal of ensuring that by 2023 no CSO location would activate more than 20 times over any 5-year period (which is more or less an average of 4 CSOs per year plus a small factor of safety). In 2020 the City had completed the first three phases and performed additional modeling to check their progress and verify sizing and design details of the remaining two phases. Phase V of the Long Term Control Plan was intended to decrease the activation frequency at CSO 004 which serves the Granite St. basin and tentatively called for a 411,000 gallon storage tank plus a 7,600 gallons per minute (gpm) pump station, which was projected to cost roughly $8 million when indexed for construction costs. Prior modeling had been completed for the system using the RTK method. A majority of the flow in the Granite St. basins has been documented to come from sewer laterals and connected foundation drains, sources that are notorious for infiltration varying dramatically depending on antecedent moisture conditions. Prior modeling had calibrated the model with a focus toward events with the largest flow responses (i.e., the largest events and the ones with the highest antecedent moistures). This intentionally resulted in over-predicting flow responses for smaller events in order to be conservative. But it was not clear how much conservatism was appropriate. In desperately looking for a model method which would provide sufficient accuracy to make recommendations, the author came across the Antecedent Moisture Model (AMM). The AMM model was developed and used by Robert Czachorski for nearly two decades, but only recently were the equations released into the public domain. AMM is an empirically-calibrated method, similar to the RTK Unit Hydrograph method, but whereas RTK assumes a fixed capture coefficient (or percent capture), AMM allows parameters to model variability in the capture coefficient by antecedent moisture and by season. AMM has been particularly successful in modeling sanitary sewer infiltration, for which prior methods perform poorly. In 2021 the model was re-calibrated using the Antecedent Moisture Modeling (AMM) method. The new model allowed for a much better calibration, which adequately predicted both smaller events and larger events. The AMM model successfully represented the increasing percent capture rates for different soil conditions and resulted in a spectacularly better calibration. The AMM method also improved the model's ability to predict seasonal differences in infiltration, specifically the lower response during the dry summer months. After calibration, a long-term continuous simulation was conducted using a 13-year rainfall record at a nearby rain gauge to establish the existing level of protection. The model simulation showed the maximum number of activations of CSO 004 during any 5-year period to be 13, less than the limit of 20 CSOs/5 years. This indicated that Phase V was not necessary and that, as of completion of Phase III, This result was surprising, but additional quality control and comparisons between the two models showed it was reasonable. It appears the months for which meter data was available in 2015 and 2020 displayed larger moisture factors than supposed when compared to the long-term record. This skewed modeler judgment about the needed conservatism when calibrating using the RTK method. Indeed, CSO activation records from the first two years following completion of Phase III show the activation of CSO 004 is in line with the predictions of the AMM-calibrated model. Using the AMM model, which adequately represents antecedent moisture and seasonal effects, allowed modelers to predict CSO activation frequency with much more confidence, which reduced the need for conservatism and showed the Phase V project was not needed at all. The City of Joliet has submitted the findings of its modeling and has received approval to remove Phase V from the Long Term Control Plan, saving the City an estimated $8 million. Figure: A comparison of the two calibrations for the GRANITE-1 meter showing the May 17, 2020 rain event (the largest rain event in the calibration period). The AMM model (red) is a very good fit to the meter data (blue) and clearly represents the increasing percent capture rates as the rain event wears on and soil moisture increases. The RTK model (green) does not adequately model this dynamic.

This paper was presented at the WEF Collection Systems Conference, June 27-30, 2023.

SpeakerEdgren, David

Presentation time

11:15:00

11:45:00

Session time

08:30:00

11:45:00

SessionSession 15: Optimization & Modelling

Session number15

Session locationKansas City Convention Center

TopicPressurized Systems, Integrated Planning, Intelligent/Smart Sewer Systems, Asset Management and CMOM, Wet Weather Management & Control (CSOs/SSOs)