P1252122133JHCMU

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MODELLING OVERVIEW CONTENT- V Modelling
Process VI Calibration and Assessing Model
Performance VII Data Collection Field
Measurement VIII Integrated Water Quality
Management IX Model Limitations
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V MODELLING PROCESS
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V MODELLING PROCESS Overview

• Problem specification
• Model Selection
• Preliminary Application
• Calibration Validation
• Management Applications
• Post-audit

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V MODELLING PROCESS 1) Problem Specification

• Customer ? objectives ? WQ engineer
• Management objectives
• Data relating to system description
• Physics, chemistry, biology
• Rough estimates of system behaviour
• Dilution, mass balance, salinity analysis

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V MODELLING PROCESS 2) Model Selection

• Processes to be modelled
• Temporal, spatial kinetic resolutions
• Choose existing software package
• OR
• May need to develop own model

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V MODELLING PROCESS 3) Preliminary Application

• Perform preliminary simulation
• Sensitivity analysis
• Enables
• Identification of data and/or theoretical gaps
• Identification of important parameters
• management of project resources
• Informing of field measurement campaign

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V MODELLING PROCESS 4) Calibration Validation

• Calibration
• tune the model to fit a data set
• Calibration parameters
• Calibration dataset should be similar to design
  condition
• Validation (Performance Assessment)
• Calibrated model run for a new set(s) of data
• Physical parameters forcing functions for new
  conditions
• Predictions match data gt accurate model
• No match gt analyse results to determine cause
  recalibrate

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V MODELLING PROCESS 5) Management Applications

• Model used to ascertain effectiveness of
  management decisions
• Upgrading of waste treatment plants controls on
  fertiliser use
• Conservative modelling approach
• worst case scenario modelling
• 6) Post-Audit
• After implementation of actions ? check validity
  of predictions

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VI MODEL CALIBRATION
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VI MODEL CALIBRATION Calibration

• tuning of model parameters to obtain best fit

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VI MODEL CALIBRATION Calibration

• tuning of model parameters to obtain best fit
• Calibrate modules in succession
• Hydrodynamics ? surface elevation, current
  velocities
• Solute transport ? dyes, salinity
• Water quality ? WQ constituents DO,
  nutrients, Chlorophyll_a
• Process
• Any measured parameters should be fixed
• Other parameters may be estimated from measured
  data
• Remaining parameters varied within recommended
  ranges
• Several approaches trial-by-error least squares
  regression

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VI MODEL CALIBRATION Assessing Model Performance

• tuning of model parameters to obtain best fit

What is meant by best fit? Or How do we assess
the quality of a calibration?

• Two general approaches
• Subjective - based on visual comparison
• Objective - quantitative measure of quality of
  fit ? error

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VI MODEL CALIBRATION Subjective Assessment
Model calibration output
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VI MODEL CALIBRATION Objective Assessment
Simulation error simulations Vs observations /
measurements NOTE always keep in mind inherent
uncertainty of measurements Errors - model
accuracy, bias, systematic error - model
precision, correlation

• Various Methods
• Average and relative error
• Least squares method
• Root-mean-square error
• Linear regression analysis
• Best assessment combines subjective and objective

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VI MODEL CALIBRATION Average Error, Ea
Oi , Si are observations and
matching simulations for i 1 ... N Ea gt 0 ?
model undersimulates Ea lt 0 ? model oversimulates
Relative Error, RE Oi , Si are
observations and matching simulations for i
1 ... N 0 RE 100 ? 100 poor
accuracy Use both with caution ? neither measure
imprecision
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VI MODEL CALIBRATION Least Squares Method

• residual difference between observed and
  simulated data
• The sum of the squared residuals, Sr
• Best fitis achieved when Sr is at a minimum
• Can be monitored as parameters are adjusted
  during calibration

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VI MODEL CALIBRATION Root Mean Square, SE

• units are the same as those
• of the variable
• Best fitis achieved when SE is at a minimum
• Coefficient of variation, CV SE / Oa
  dimensionless, expressed as
• Oa is the average of observations Oi
• CV 0 ? high precision
• CV 100 ? low precision

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VI MODEL CALIBRATION Linear Regression Analysis

• Linear regression equation gives information on
  the accuracy and precision of pairs of
  observations, Oi, and simulations, Si
• Regression equation takes the form
• Oi a ßSi e
• Where,
• a y-intercept and is a measure of accuracy
• ß slope and is a measure of accuracy
• e error in simulation
• Coefficient of correlation, r2, indicates degree
  of correlation (precision)
• Desired outcome is a 0, ß 1 and r2 ? 1

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VI MODEL CALIBRATION Linear Regression Analysis
EXCEL EXAMPLE
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VI MODEL CALIBRATION Sensitivity Analysis

• Allows greater understanding of the behaviour of
  a WQ model
• Usually carried out prior to or during
  calibration
• Number of different methods, two most common
• Parameter perturbation
• First-order sensitivity analysis

Example simple mass balance for well-mixed
waterbody gt c f ( Q, k, V, cm )
steady state
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VI MODEL CALIBRATION Sensitivity Analysis
a) Parameter Perturbation
b) First-Order Analysis
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VII DATA COLLECTION
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VII DATA COLLECTION Data Requirements

• Data collection and assimilation is crucial to
  successful modelling
• RUBBISH IN RUBBISH OUT
• Data is required for
• Boundary conditions
• Initial conditions
• Calibration and parameterisation
• Validation

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VII DATA COLLECTION Measurement of Velocity and
Flow

• Floats / Drogues
• Visually / radar / satellite-tracked
• Current Meters
• mechanical
• acoustic
• electromagnetic
• Flow measurement at control structures
• Dams, gates, weirs, river gauges
• Remote Sensing
• Surface circulation patterns relative measures
  but provide snapshots

5) Online data http//tidesandcurrents.noaa.gov/p
orts.html
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VII DATA COLLECTION Measurement of Surface
Elevation

• Tide pole / staff
• staff gauge must be tied to elevation datum
• Tide Gauges
• float guages
• pressure guages
• acoustic guages
• Online Data
• Admiralty Charts

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VII DATA COLLECTION Tracer Studies

• Excellent source of info for calibration/validatio
  n of solute transport
• Identification of dispersion and diffusion
  coefficients
• An effective tracer should be
• Water soluble
• Detectable at very low conc.
• Have minimal background interference
• Harmless in low concentrations
• Inexpensive
• Reasonably stable
• Common tracers
• commercial fluorescent dyes, e.g. rhodamine B /
  WT
• Natural tracers, e.g. salinity, temperature

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VII DATA COLLECTION Types of Dye Studies

• 1) Instantaneous / Slug Release
• Known quantity of dye released instantaneously
• Dye cloud movement monitored at sampling stations
• Useful for calibration and predicting spill
  consequences
• Data Analysis
• Concentration plots for stations
• Estimate travel time for
• leading edge, centroid, peak, trailing edge
• Time of travel Vs distance plot

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VII DATA COLLECTION Types of Dye Studies

• 1) Continuous Release
• Continuous dye release (constant flow conc)
  over time interval
• Monitoring stations upstream and downstream of
  release
• Data on flows, wind, tides also recorded
• Data Analysis
• Concentration plots for stations
• Contour maps of dye cloud

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VII DATA COLLECTION Planning Dyes Studies

• Acquisition of available data
• Reconnaissance study
• Estimation of mean velocities
• Estimate mixing and dye quantities
• Mixing Considerations
• Estimating quantity of dye release
• Determining locations of sampling stations

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VIII INTEGRATED WQ MANAGEMENT
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VIII INTEGRATED WQ MANAGEMENT
The integration of monitoring and modelling to
enable effective water quality management
Field Data
MONITORING
MODEL
Model Results
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IX MODEL LIMITATIONS
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IX MODEL LIMITATIONS Model Limitations
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MODELLING OVERVIEW CONTENT- I Why
model? II Model Types III Model
Selection IV Industry Standard
Models V Modelling Process VI Model
Calibration VII Data Collection VIII Integrate
d WQ Management IX Model Limitations
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Estuarine Coastal Modelling Course
MODULE V MODELLING OVERVIEW
Stephen Nash, Environmental Change Institute, NUI
Galway