Modeling Overview for LTCP Development

Julia Moore, P.E.

Limno-Tech, Inc.

Modeling Overview2

Items to Be Covered

Modeling and the CSO Control Policy Combined sewer system (CSS) modeling Receiving water (RW) modeling Model review

Modeling Overview3

Expectations of the CSO Policy

EPA supports the proper and effective use of models, where appropriate, in the evaluation of the nine-minimum controls and the development of the long-term control plan…

Resource – Combined Sewer Overflows: Guidance for Monitoring and Modeling. EPA 832-B-99-002. January, 1999.

Modeling Overview4

Expectations of the CSO PolicyEvent modeling

The permittee should adequately characterize through monitoring, modeling, and other means as appropriate, for a range of storm events, the response of its sewer system to wet weather events… Section II.C.1

Continuous simulation modelingEPA believes that continuous simulation models, using historical rainfall data, may be the best way to model sewer systems, CSOs, and their impacts… Section II.C.1.d

Modeling Overview5

Why is CSS Modeling Important?

Good characterization is typically infeasible without models except for small / simple systems“Stretch” the value of monitoring, saving time and moneyAssess conveyance and storage for NMC and LTCPOptimize LTCP under a range of storm conditions Provide a tool for projecting results after implementation of CSO controls

Illustration of a Simple CSS Model

Source: Urban Storm Water Modeling and Simulation by Stephan Nix

URBANAREA

RAINFALL

RECE

IVIN

G W

ATER

RELEASE

STORAGECSO / BYPASS

COMB. SEWAGE TREATMENT

PLANT

Modeling Overview6

Modeling Overview7

General Types of CSS Models

QuantityRainfall/runoff modelHydraulic sewer pipe model

QualityPollutant accumulation, washoff and transport model

Modeling Overview8

Modeling QuantityRunoff Modeling

Runoff models are used to estimate stormwater input to the CSSUsually paired with hydraulic sewer models

CSS Hydraulic ModelingPredicts sewer pipe flow effects including:

Flow rate components (runoff, sanitary, infiltration and inflow)Flow velocity and depth at regulatorsFrequency, volume, and duration of CSOs

Modeling Overview9

Criteria for Selection of CSS Hydraulic Model

Ability to accurately represent CSS’s hydraulic behaviorAbility to accurately represent runoff in the CSS drainage basinExtent of monitoring data availableNeed for long-term simulations

Need to assess water quality in the CSSNeed to assess water quality in receiving watersAbility to assess the effects of control alternativesUse of the presumption or demonstration approach

Modeling Overview10

Model ComplexityLevels of detail

Coarse (e.g., STORM)• Simplified sewer network• Lumped parameter

Moderate (e.g., SWMM/TRANSPORT)• Major pipes/interceptors only• Unable to simulate complex flow (e.g., backwater conditions,

tidal influence)Fine (e.g., SWWM/EXTRAN, MOUSE)

• All major sewer components (storage, pumping, and smaller diameter pipes)

• Able to simulate complex flow

Modeling Overview11

Level of Detail

Selection of Appropriate LevelIdentify benefits from a finer level of detailConsider penalties (accuracy) in not modeling a portion of the systemAdopt a staged approach - start from simple model and build complexity as needed and as data become available.

Modeling Overview12

Most Commonly UsedRunoff Models

Custom9%

SWMMFamily 72%

Other CommercialPackages

19%

Source: Use of Modeling Tools and Implementation of US EPA Guidelines for CSO Control by S. Rangarajan et al., TetrES Consultants Inc.

Modeling Overview13

Most Commonly UsedHydraulic Models

Source: Use of Modeling Tools and Implementation of US EPA Guidelines for CSO Control by S. Rangarajan et al., TetrES Consultants Inc.

SWMMFamily66%

Sewer CAT - 4%MOUSE - 2%

Other - 9%

None19%

Modeling Overview14

Calibration DataNeed range of typical storm events

3 to 5 storms (minimum)Small (0.1-0.4”), medium (0.4-1.0”) to large (>1.0”) stormsIndividual storm events (return to dry weather)

MeasureRainfall (hourly data; multiple locations)Overflow volumeEffluent quality (for input to receiving water

model)

Modeling Overview15

Data ReviewFlow monitoring data

Consistent with rainfall dataManning’s Equation (calculate velocity, flow and depth)Flow balance review (downstream flows are consistent with upstream flows)

Outfall quality dataCan be highly variableCompare to influent data/literatureCompare to other outfall data

Modeling Overview16

Model DevelopmentDevelop pipe network Establish operational rules for hydraulic controlsEstimate dry weather component of flowConduct initial testing of modelConduct model sensitivities

Guides calibrationModify model parameters by +/- 25% to assess sensitivity

Modeling Overview17

Calibration Methods, Tools

Calibration process, sequence – volume, peaks/timing, pollutantsGraphical depictions of quality of fit – hydrograph plots, 1:1 plotsMeasures of quality of fit – RMS error, SSD, sum of absolute differences

Modeling Overview18

Calibration Methods, Tools

Statistical comparisons of volumes and peak flows

Range of storms+/- 20% modeled versus observed Avoid bias

Source: Urban Stormwater Modeling and Simulation by Stephan Nix

Model Calibration – Volume Regression

19

0

2

4

6

8

0 2 4 6 8

Modeled (MG)

Obs

erve

d (M

G)

Model Calibration – Flow Example 1

0

50

100

150

200

250

Flow

(MG

D)

ModelMonitored

Day 1 Day 2

20

Model Calibration – Flow Example 2

0

25

50

75

100

125

150

Flow

(MG

D)

ModelMonitored

Day 1 Day 2

21

Modeling Overview22

Why is RW Modeling Important?

Characterize the RW impacts under different CSO loads and conditionsDiscern contributions of background and other sourcesPredict benefits of CSO alternativesDemonstrate WQ standards attainment or the need for a TMDL or UAA

Illustration of a Simple Receiving Water Model

UPSTREAM FLOW / LOAD

UPSTREAM FLOW / LOAD

CSO #1 Load

CSO #2 Load

WWTP Flow / LoadB

CSO #4 Load

CSO #3 Load

A

CModel output locations23

Modeling Overview24

The General RW Modeling Process

Step 1 – Model selectionDetermination that modeling was neededEvaluation of candidate models

Step 2 – Model developmentModel calibrationModel validation

Step 3 – Model applicationForecastingPost-construction audit

Step 1 – Model SelectionReceiving

water characterization

Assess likelihoodof RW impacts

- qualitative-quantitative

Assess loadingsources

Rank severity ofWQS

exceedances

Select RW

model(s)

25

Were the Right Parameters Modeled?SurfaceWaterType DO Sed. Bact.

PublicHealth

Clarity DebrisToxicsNutr.

AestheticsWater Quality

Streams:Steep

Gradual

Rivers:SmallLarge

Lakes:Shallow

Deep

Least Likely Most Likely

Source: Peter Moffa, ed. 1997. Control and Treatment of Combined Sewer Overflows, 2nd ed.

26

Modeling Overview27

Were the Time and Space Scales Appropriate?

Parameter Time scale Space scale

Bacteria Hours to weeks 0.05 to 10 miles

Solids Weeks to decades 0.1 to 50 miles

Toxics–acute effects Hours to weeks 0 to 0.5 miles

Toxics–chronic effects Years to decades 1 to 500 miles

Modeling Overview28

Useful RW ModelsDilution models (steady-state)

Bacteria and toxics near outfallWell-mixed (stream flow small relative to CSO discharge)Lateral mixing (include dispersion)

Plug flow (joint effects of multiple pulses)Time-varying mass balanceDetailed hydrodynamic-based modelsMixing zone models

Modeling Overview29

Why Use Complex Models?Complex models should only be used when the situation warrants itSimpler model failed to answer questionsHydrodynamic

Major changes in RW depth with flowComplex and incomplete mixing processes (relevant to CSO discharges)Stratified systems that significantly accentuate or attenuate CSO impacts

Water qualityDynamic: concentrations change rapidly over timeConcentrations that are dependent on other constituents

Modeling Overview30

Step 2 – Model DevelopmentAre all significant pollutant sources (or loss mechanisms) included?Are the estimates of discharge volumes and concentrations reasonable?Do the model input rates fall within accepted values?Do the model results compare with observed data?

Modeling Overview31

Two Methods of CalibrationSubjective: visual comparison of simulation with data

Often uses additional informationBest option when working with multiple state variablesEmploys modeler’s intuition in the process

Objective: quantitative measure of quality of fit (usually minimize error)

Not necessarily betterMake sure kinetic coefficients end up within reasonable range

Modeling Overview32

Did the Model Match the Observed Data?

Bacteria data are within an order of magnitudeGeneral pattern is reproduced

Creek - Node 1, Wet Weather Survey #1, 2000May 1-5, 2000

100

1,000

10,000

100,000

1,000,000

5/1 5/2 5/3 5/4 5/5 5/6Day

FC (#

/100

mL)

DataModel

Fecal Coliform Calibration - Receiving Water Model

1

10

100

1000

10000

100000

1000000

Monitored Modeled Monitored Modeled Monitored Modeled

(MP

N/1

00 m

l)

Site #1 Site #2 Site #3

Ways to Display Results

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Ways to Display Results

Spatial Plot of Fecal Coliform, May 6

110

1001,000

10,000100,000

0123456789101112River Mile

Feca

l Col

iform

(#

/100

mL)

Temporal Plot of Fecal Coliform at River Mile 3.3

110

1001,000

10,000100,000

5/1 5/31 6/30 7/30 8/29 9/28 10/28

Feca

l Col

iform

(#

/100

mL)

Date

34

Modeling Overview35

Methods for Validation

Independent data setSensitivity analysesComponent analysisAddition of synthetic loads to identify un-modeled sources

A RW model should not be considered truly “calibrated” until the model is tested over a wide range of conditions, produces explainable results, and is validated.

Modeling Overview36

Model Validation With Independent Data

Demonstrates the model is capable of simulating a wider range of conditionsThe model is run with same rates but different loads and environmental conditions that correspond to:

An event from historical dataAnother event from the CSO monitoring programData collected in the future as part of the continuing planning process

Modeling Overview37

Questions for the LTCP Reviewer to Answer

Were the data sufficient to develop a reliable model?Was the selected model suitable for assessing the extent of CSO impacts?Was the model suitable for distinguishing impacts from different sources?Did the application exceed the known limitations of the model?

Modeling Overview38

Step 3 – Model Application

Was modeling used to help select the recommended plan (watershed example and component analysis)?Did the modeling demonstrate compliance of selected plan with WQ standards?If not, did the modeling help define what is needed to comply with WQ standards?

Modeling Overview39

Evaluating RW Impacts of Different Control Alternatives

Number of Days Exceeding E. Coli ConcentrationAverage Year

010

2030

4050

60

235 298 406 576 1,000 2,000 5,000 10,000

E. Coli concentration (#/100mL)

Num

ber o

f Day

s BaselineSeparationNo CSO

Modeling Overview40

Evaluating Conditions at Different Locations

E. Coli—number of days exceeding 126#/100ml

010203040506070

Knox Br Jade Is Oak Point Clove Br

No ControlAlt AAlt B

Modeling Overview41

Demonstrating Whether WQ Standards Will be Attained

E. Coli Geomean (#/100ml) April—October

0

50

100

150

200

250

Knox Br Jade Is Oak Point Clove Br

No ControlAlt AAlt B

WQS=126

Modeling Overview42

Questions to Ask About RW Models

Do modeling choices generally agree with LTCP reviewer’s expectations?What questions need to be answered?Were the right parameters modeled?Do results reflect the likely severity of impacts and benefits of control?Do the selected models fit the time scales of the anticipated problems (hourly–daily–monthly)?Was the spatial coverage appropriate (impacted river miles)?

Modeling Overview43

Useful RW Modeling ReferencesMoffa, Peter. 1997. The Control and Treatment of Combined Sewer Overflows (2nd Edition). Van Nostrand Reinhold, NY, NY. EPA. 1997. Compendium of Tools for Watershed Assessment and TMDL Development. US EPA OW, Washington, DC, EPA841-B-97-006.Chapra, Steven. 1997. Surface Water-Quality Modeling. McGraw-Hill, NY, NY.Thomann. Robert, Mueller, J. 1987. Principles of Surface Water Quality Modeling and Control. Harper & Rowe, NY, NY.Bowie, et al. 1985. Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling (2nd Edition). US EPA ORD, Athens, GA, EPA/600/3-85/040.

Building the Complete Model (System Components)

Runoff(hydrologic model)

CSS flows(hydraulic

model)

Storm waterflows (hydraulic

model)

Rain

Upstreamflow

Pointsourceflows

River (RW model)

Tributary flow

Wet & dryconc

Wet&

dryconc

EMC EMC EMC

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Modeling Overview45

Review PhilosophyReality

There is never “enough” data & informationAll models are imperfect representations—some better than othersYou can’t double-check everything

So what’s an LTCP reviewer to do?Adopt realistic review goalsBegin with the “end-in-mind”

Modeling Overview46

Review ApproachAdopt realistic review goals

Look for congruency & consistency (does it all hang together well?)Check that level of complexity was appropriateIdentify any fatal flaws and deficienciesCheck that all the policy “i”s are dotted and “t”s crossed (use a checklist)Be cautious of “black box” software

Begin review with the “end-in-mind”Different models handle certain controls betterHindsight is 20/20–could model and calibration choices be driving critical LTCP decisions?

Modeling Overview47

Getting ReadyWhat “end-in-mind” questions need to be answered during the review?

What models are well suited and how should they be calibrated for forecasting benefits of selected alternatives? Are the model results used appropriately in alternatives analysis?

For example, a model framework oriented and calibrated for peak flow rates, then applied to single design storm events may not work for assessing the benefits of a storage control alternative.

Modeling Overview48

Some Common Modeling Mistakes

Excess complexity in place of sound engineering judgment

Occam’s Razor principle—the simpler of two approaches is more likely to be the correct one

Wasting resources on building a detailed model without answering the questionsLack of available data to support model capabilities

Example—SWMM dirt accumulation/washoff; STORM first-flush routines are dangerous without data…

Modeling Overview49

Common Modeling Mistakes (Cont.)

Automation run amok? Extra scrutiny is always warranted for:

Automated and “black box” interfaces for radar rainfall, GIS information, runoff to sewer system, point source loads, and statistics outputProgram code that replaces judgment about model coefficientsProgram code that auto-designs pipe conveyance and pumping or river geometryProgram code that auto-simplifies the system to reduce computation needs

Questions? Seek clarification from the permittee

Modeling Overview50

Simple Models Complex ModelsLess accurate Potentially more accurateSteady-state Dynamic Analytical solutions Numerical solutionsOne-dimensional Multi-dimensionalLess data, less detailed Large detailed inputsLess expensive Expensive Run quickly Time-consuming

Model Complexity Issues

Model Reliability vs. Complexity

Infinite Resources

Substantial Resources

Limited Resources

Reliability

Complexity

Where we want to be

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Modeling Overview52

Model Calibration/ValidationGoals of calibration:

Produce model that is “tuned” to fit a datasetVary model input parameters to find optimal match between model output and

CSS: Total volume, peak flow, hydrograph shapeRW: concentration profiles in space and time

Goals of validation (note: not verification)Confirm that the model can reasonably predict a second dataset. “At best, all that can be concluded is that our testing has not proved the model wrong.” (Chapra, 1997)

Run modelDoes model

match observed data?

YES Validation

NO

Calibration ProcessEstimate

inputs

Select coefficients

NO

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Modeling Overview54

Model Reliability and Accuracy

Model predictions can be characterized in terms of reliability and accuracy:

Reliability—measure of the confidence in model predictions for a specific set of conditions and for a specific confidence levelAccuracy—measure of the agreement between model predictions and observations

Modeling Overview55

Reliable, Accurate Model

Observed Value

ModeledValue

1:1

Modeling Overview56

Reliable, Less Accurate Model

Observed Value

ModeledValue

1:1

Modeling Overview57

Biased Model (Unreliable and not Accurate)

Observed Value

ModeledValue

1:1

Calibration Validation

Does model reproduce an independent data set?

YES

OK to Use Model

NONew Data or Revisit Calibration

Validation Process

Modeling Overview58

Modeling Overview59

Model Validation ProblemsLack of an independent data set for validationThree factors can produce unreasonable results:

Poor data happens and can sometimes lead modelers astrayModel may not contain sufficient detail to adequately characterize the CSS and/or RW and generate reliable output (or too complex)All models have inherent limitations and the model selected may not be adequate or poorly chosen

Modeling Overview60

Model Application –Single Event Simulation

Design storm approachSimpler to analyze and interpret (useful for initial screening)Suitable for quicker comparative analysis of control alternativesLikely to have good amount of data available on a spatial and temporal scale

Modeling Overview61

Model Application –Continuous Simulation

Used for evaluating a range of long-term CSO control alternativesAccounts for sequencing of rainfall, upstream flows, and other pollutant sourcesComputational speed of PCs allow simulations within a reasonable run time

Modeling Overview62

Post-Processing of ResultsOften one of the largest tasksAlso most important - includes:

Organizing, archiving output dataPlotting/visualizing data to verify quality of cal/val Porting output of CSS, etc. to RW model

This is where many mistakes get madeModelers have lots of discretion in interpreting and presenting results

Modeling Overview63

Interpretation of ResultsFollowing should be remembered

Model forecasts are as accurate as user’s understanding and knowledge of system and the model.Model forecasts are no better than quality of calibration and validation and the quality of data used.Model forecasts are only estimates of the response of the CSS and RW to rainfall events.

Modeling Overview64

Points to Remember

Models are imperfect but useful toolsStretch expensive dataForecast effects of controls

Important for LTCP to choose the right models to fit the situation (meets project needs without excess complexity)

OK to mix and match when justified

Modeling Overview65

Points to Remember

Calibration and validation using good data are necessary for confidence in LTCP forecastsContinuous simulation is usually the best way to apply models and evaluate CSO controls; the longer the better (1 to 5 years)Understand the system and question counter-intuitive model results