Modeling Overview for LTCP Development
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Transcript of Modeling Overview for LTCP Development
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
33
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
44
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
51
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
53
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