Comparison of Modeling Solutions in ICM for RDII SimulationPreliminary Pilot Study Using DeKalb Sewershed Model

HELEN LU, NANCY SCHULTZ, REGGIE ROWE

JACOBS

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Outline Introduction of Rainfall Derived Inflow/Infiltration (RDII)

RDII simulation challenges

ICM model solutions for RDII simulation

Pilot study by using one sewershed model in DeKalb System

Summary and discussion

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Definition of Rainfall Derived Infiltration and Inflow (RDII)

Source: EPA SSOAP Tool Box

Extraneous flows, both inflow and infiltration, derived from rainfall. RDII typically includes the fast response component (inflow) and the slow response component (infiltration).

RDII Simulation Challenges

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ChallengesUncertainties:Source of the RDIIRDII response to various storm eventsOther factors

Complexity

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RDII Sources

EPA Sanitary Sewer Analysis and Planning Toolbox

Antecedent Moisture Conditions

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Other Uncertainties System pipe connectivities

Real time system performance and operation: moving solids, pump operation….

Flow data: Data Qualities

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Tools for RDII Analysis SSOAP ToolboxUSEPA DevelopedCRADA with CDM

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Important to know your flow data!

Complicated Process:How RDII flow gets to the sewer…..

Routing

Volume

Source: “Infowork ICM user help, Innovyze”

Sewer Pipe

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Overland

Below Ground

ICM Model Solutions for RDIISimulation

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RDIIModelingWEFFact Sheet notes the challenges and highlights the key considerations related to RDIIsimulation

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ICM Model Solutions for RDIITriangular Unit Hydrograph: RTK

Hydrologic (surface runoff) models: volume and routing

Ground/Groundwater Infiltration Model (GIM)

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Triangular Unit Hydrograph: RTK

14 Source: EPA Sanitary Sewer Analysis and Planning Toolbox

R-Values: % of runoff volume that

enters sewer system

Initial Abstractions and Monthly RTK

Since we want to use 1 set of RTKs, other parameters for those events need to be adjusted Initial abstraction parameters: storage

available for the runoff/infiltration from the rainfall before it reaches the sanitary sewer system. This storage can be due to soil conditions, topography or ponds etc. which would store the flow until its capacity is reached.

Monthly varied RTK to represent the seasonal variation

Rain.5”

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Good News…

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Runoff Models: Volume +Routing

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LOST

GIM focus on the limitations of existing overland runoff models on slow response simulation

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Source: http://blog.innovyze.com/2015/10/27/groundwater-infiltration-module-in-infoworks-icm-and-legacy-cs/

Runoff Models

GIM model

GIM Flow Schematic

LOSS TO BASEFLOW

(LOSSES FROM GROUNDWATER STOREDO NOT ENTER DRAINAGE NETWORK)

GROUNDWATER INFLOW TO SEWER NETWORKGROUND STORE INFLOW

SOIL STORE INFLOW

SOIL STORE DEPTH

GROUND STORE LEVEL

INFILTRATION TO GROUND STORE

INFILTRATION TO SOIL STORE Below Ground

GWI Model SetupGood article from Innovyze online Blog:

//blog.innovyze.com/2015/10/27/groundwater-infiltration-module-in-infoworks-icm-and-legacy-cs/

Over 12 parameters, but can be simplified by focusing on the major parameters

Require both GIM subcatchment parameters and Groundwater infiltration event

Need runoff model for direct inflow (fast responses)

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GIM Subcatchment Parameters

LOSS TO BASEFLOW

(LOSSES FROM GROUNDWATER STOREDO NOT ENTER DRAINAGE NETWORK)

GROUNDWATER INFLOW TO SEWER NETWORKGROUND STORE INFLOW

SOIL STORE INFLOW

SOIL STORE DEPTH

GROUND STORE LEVEL

INFILTRATION TO GROUND STORE

INFILTRATION TO SOIL STORE

Percolation Parameters

Baseflow Parameters

Infiltration Parameters

A Long Journey……

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A Quick SummaryTriangular Unit Hydrograph: RTK Widely used Empirical approach (forced curve)

Hydrologic (surface runoff) models: volume and routing Conceptualize the RDII responses as surface runoff inputs Limitation to represent slow response

Ground/Groundwater Infiltration Model (GIM) “complicated”, 12+ parameters, but can be simplified Very effective to simulate inter-event impacts due to antecedent moisture

conditions or a highly attenuated response to rainfall (long tail) Need to combine with a runoff model for direct inflow (fast response)

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DeKalb Sewershed Model Pilot Study

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DeKalb Pilot Study : Cobb Fowler Creek Sewershed Model

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ApproachesCompare three model solutions for RDII simulation Fixed percentage runoff + New UK PR, proposed per model protocol

developed in earlier model studies by others RTK GIM + fixed percentage runoff model

Storm Events: Historical event: April 6, 2014, large storm (close to 1 year 24 hour) during

dry period Flow monitoring from January to June of 2015 (wet Period) January 4, 2015, largest storm (still less than 1 year 24 hour) captured during the flow

monitoring April 6 to 21, 2015, multiple small storm events (less than 1 year 24 hour) in the wettest month

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On-going study, preliminary results for discussion only

Meter Locations

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CBF8 CBF6

CBF3

CBF11

April 6, 2014

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RainfallDepth (in)

RainObservedRTKFixed+New UK PRFixed PR+GIM

Peak (in/hr)RainObservedRTKFixed+New UK PRFixed PR+GIM

Average (in/hr)RainObservedRTKFixed+New UK PRFixed PR+GIM

FlowMin (MGD)

RainObservedRTKFixed+New UK PRFixed PR+GIM

Max (MGD)RainObservedRTKFixed+New UK PRFixed PR+GIM

Volume (US Mgal)RainObservedRTKFixed+New UK PRFixed PR+GIM

2.440 0.720 0.0170.390 3.070 6.1040.403 2.420 4.8500.403 2.471 4.4300.403 2.462 6.346

Observed / Predicted Report Produced by hlu3 (7/11/2018 8:33:43 PM) Page 1 of 1 Flow survey: >DynamicModelPilot>Pilot>FlowSurvey_graph>Flow survey_2013-2014 (4/3/2018 10:28:29 AM) Sim: >DynamicModelPilot>Pilot>Model group_Run>RTK_april2014>Rainfall event_2013_2014 (7/11/2018 2:36:39 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>1-yr-24-hr!_MWH_april2014!>Rainfall event_2013_2014 (7/9/2018 4:00:23 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>GWI4_CBF8_calb7_test9rev_April2014!>Rainfall event_2013_2014 (7/11/2018 7:44:23 PM)

January 4, 2015

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RainfallDepth (in)

RainObservedRTKFixed + New UK PRFixed PR+GIM

Peak (in/hr)RainObservedRTKFixed + New UK PRFixed PR+GIM

Average (in/hr)RainObservedRTKFixed + New UK PRFixed PR+GIM

FlowMin (MGD)

RainObservedRTKFixed + New UK PRFixed PR+GIM

Max (MGD)RainObservedRTKFixed + New UK PRFixed PR+GIM

Volume (US Mgal)RainObservedRTKFixed + New UK PRFixed PR+GIM

2.440 1.680 0.0170.506 2.984 7.7410.436 1.850 4.7110.412 2.516 4.3710.436 2.435 6.054

Observed / Predicted Report Produced by hlu3 (7/11/2018 8:32:11 PM) Page 2 of 11 Flow survey: >DynamicModelPilot>Pilot>FlowSurvey_graph>Flow Survey Group>SF_2015Flow Survey Group_CBF (5/4/2016 3:02:19 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>RTK_Jan2015!>Rainfall_2015_v2_CBFonly_profileID (7/11/2018 3:17:11 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>1-yr-24-hr!_MWH!_Jan2015>Rainfall_2015_v2_CBFonly (7/10/2018 4:34:51 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>2015April_GWI4_CBF8_calb7_test9rev_Jan1>Rainfall_2015_v2_CBFonly_profileID (7/11/2018 7:03:09 PM)

April 6 to 21, 2015

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RainfallDepth (in)

RainObservedRTKFixed + New UK PRFixed PR+GIM

Peak (in/hr)RainObservedRTKFixed + New UK PRFixed PR+GIM

Average (in/hr)RainObservedRTKFixed + New UK PRFixed PR+GIM

FlowMin (MGD)

RainObservedRTKFixed + New UK PRFixed PR+GIM

Max (MGD)RainObservedRTKFixed + New UK PRFixed PR+GIM

Volume (US Mgal)RainObservedRTKFixed + New UK PRFixed PR+GIM

5.140 2.040 0.0130.478 2.403 17.3040.403 1.737 12.7350.403 2.472 10.7810.436 2.439 16.822

Observed / Predicted Report Produced by hlu3 (7/11/2018 8:57:52 PM) Page 2 of 11 Flow survey: >DynamicModelPilot>Pilot>FlowSurvey_graph>Flow Survey Group>SF_2015Flow Survey Group_CBF (5/4/2016 3:02:19 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>RTK_Apr6-21_2015>Rainfall_2015_v2_CBFonly_profileID (7/11/2018 3:17:40 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>1-yr-24-hr!_MWH_Apr6-21>Rainfall_2015_v2_CBFonly (7/10/2018 4:38:27 PM) Sim: >DynamicModelPilot>Pilot>Model group_Run>2015April_GWI4_CBF8_calb7_test9rev_Apr6-21>Rainfall_2015_v2_CBFonly_profileID (7/11/2018 11:23:40 AM)

Summary & Discussion

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Summary and DiscussionsGIM shows the best results to simulate inter-event impacts due to antecedent moisture conditions or a highly attenuated response to rainfall (long tail)

Selection and application of proper RDII simulation methods need to be tailored to system conditions and modeling objectives

No single RDII prediction method could be universally applicable due to the wide variety of site-specific RDII characterizations.

RDII model can be a highly effective tool in the decision-making process for I/I reduction and remediation programs, but also limited by inherent assumptions and conceptual representations of complex process

All methods require monitored data, important to know your data: limitations, quality etc.

The “state-of-the-art” in RDII modeling practice continues to evolve

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Acknowledge Support fromDWM:Darren Eastall, Consent Decree

AdministratorLinda Li, Hydraulic Modeling Lead and her

modeling teamOthers involved in the Consent Decree

hydraulic modeling programConsent Decree Program Management Team (CDPMT)

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Questions?

Technology Hype Cycle

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