Auto Par Am Est

8/2/2019 Auto Par Am Est

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Automated ParameterEstimation

CE En 547

Brigham Young University

Inverse Modeling

Observed heads and fluxes are enteredusing standard calibration tools

A set of parameter zones are defined

PEST/UCODE modifies parameters andlaunches MODFLOW repeatedly untilresidual is minimized

Observed heads and fluxes are enteredusing standard calibration tools

A set of parameter zones are defined

PEST/UCODE modifies parameters andlaunches MODFLOW repeatedly untilresidual is minimized


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Acknowledgements John Doherty

Watermark Computing

PEST

Wrote bulk of external utilities for GMSPEST/UCODE interface

Eileen Poeter Colorado School of Mines

UCODE

John Doherty Watermark Computing

PEST

Wrote bulk of external utilities for GMSPEST/UCODE interface

Eileen Poeter Colorado School of Mines

UCODE

Basic Steps1. Build a working MODFLOW model

2. Enter field-observed data

3. Define parameter zones with key values

4. Create parameter list

5. Select the obs. coverage you wish to use

6. Edit PEST/UCODE options

7. Run the Check Simulation command8. Save MODFLOW simulation


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Step 2. Enter field-observed

data

Use standard calibration tools to enterobserved head and fluxes

Pay special attention to calibrationinterval/standard deviation

Weight assigned to each measurementin objective function = 1/std.dev.

Use standard calibration tools to enterobserved head and fluxes

Pay special attention to calibrationinterval/standard deviation

Weight assigned to each measurementin objective function = 1/std.dev.

Step 3. Define parameterzones

Identify set of input values you want theinverse model to optimize("parameterization")

Number of parameters must be lessthan the number of observations

Identify set of input values you want theinverse model to optimize("parameterization")

Number of parameters must be lessthan the number of observations


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Zonation

Arrays are split into zones

Zones best defined in conceptualmodel using polygons

Multiplication Arrays

Array is modified by multiplication factor

Use array multipliers in MODFLOW interface


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Legal Parameters Starting head

Kh

Kv

Specific storage

Specific yield

Layer anisotropy

Recharge rate

ET extinction depth

Max ET rate Well Q

River stage

River conductance

GHB head

GHB conductance

Drain conductance

Stream Q

Stream conductance

Stream width

Stream roughness HFB hyd. characteristic

CHB head

Key Values

Parameter zones are defined byentering "key values"

Use a value that would not be expectedin the input file (-900, -800, -700, etc.)

Can be entered via conceptual model orgrid model

Parameter zones are defined byentering "key values"

Use a value that would not be expectedin the input file (-900, -800, -700, etc.)

Can be entered via conceptual model orgrid model


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Step 4. Build parameter list Define a parameter for each key value

For each parameter

Starting value

Min value

Max value

Can be developed quickly using"Initialize from Model" feature

Define a parameter for each key value

For each parameter

Starting value

Min value

Max value

Can be developed quickly using"Initialize from Model" feature

Parameter List


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Step 5. Select Obs. Coverage If you have multiple obs. coverages,

select the coverages you want to use

Use the General Options property sheetin the Inverse Code Options dialog

More than one coverage may be used

If you have multiple obs. coverages,select the coverages you want to use

Use the General Options property sheetin the Inverse Code Options dialog

More than one coverage may be used

Step 6. Edit PEST/UCODEOptions


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Step 7. Run the Check

Simulation Command

Checks for obvious errors in input data

Check includes errors unique toPEST/UCODE data

Checks for obvious errors in input data

Check includes errors unique toPEST/UCODE data

Step 8. Save MODFLOWsimulation

Saves MODFLOW data with key values

Saves extra data to a set of files usedby PEST/UCODE

Observations (values and weights)

Parameters

Options

Saves MODFLOW data with key values

Saves extra data to a set of files usedby PEST/UCODE

Observations (values and weights)

Parameters

Options


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Step 9. Run

GMS2PEST/GMS2UCODE Must be run prior to launching

PEST/UCODE

Purpose

Replaces key values in input files withspecial markers

Sets up standard PEST/UCODE run files

Must be run prior to launchingPEST/UCODE

Purpose

Replaces key values in input files withspecial markers

Sets up standard PEST/UCODE run files

Step 10. Run PEST/UCODE

Runs in separate window

Takes several minutes to several hoursdepending on

Size of model

Number of parameters

Runs in separate window

Takes several minutes to several hoursdepending on

Size of model

Number of parameters


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Step 11. Import optimal

parameters MODFLOW simulation corresponding to

optimal run is NOT saved

Output from PEST is a list of optimalparameter values

Can be loaded into starting value field inparameter list using the "Import

Parameter File" button

MODFLOW simulation corresponding tooptimal run is NOT saved

Output from PEST is a list of optimalparameter values

Can be loaded into starting value field inparameter list using the "Import

Parameter File" button

Step 12. Load parameters tomodel

Model still contains key values

Need to load optimal parameters tomodel before running model

Use "Load to Model" button

Values are copied to: Grid cells only

Grid cells and conceptual model

Model still contains key values

Need to load optimal parameters tomodel before running model

Use "Load to Model" button

Values are copied to: Grid cells only

Grid cells and conceptual model


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Step 13. Save and run

MODFLOW Save simulation with optimal values

loaded

Run MODFLOW

Read solution

Plot results using calibration targets andplots

Save simulation with optimal valuesloaded

Run MODFLOW

Read solution

Plot results using calibration targets andplots

Inverse Model Stability

MODFLOW must converge in order forPEST/UCODE to run successfully

Cells going dry cause irregularities inobjective function, resulting in instabilityin optimization algorithm

MODFLOW must converge in order forPEST/UCODE to run successfully

Cells going dry cause irregularities inobjective function, resulting in instabilityin optimization algorithm


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Convergence Options

Even if calibration targets are satisfied,calibrated parameters may not be"correct"

A different set of parameters may alsosatisfy calibration targets

Model Non-uniqueness

Even if calibration targets are satisfied,calibrated parameters may not be"correct"

A different set of parameters may alsosatisfy calibration targets


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Example #1 Vary recharge and K

Calibrate to observation points

StreamSpec.Head

Bound.

Obs. Pts.

Recharge

K

Results (Side View)

Ground

Surface

FixedHead

Bound.

ComputedWater Table

Surface

Calib. Targets

Bedrock

Solution #1: High RechargeHigh K

Solution #2: Low RechargeLow K

Which is correct?


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Well-posed conceptual model

More observation data

Both well AND flux observations

Improving Uniqueness Well-posed conceptual model

More observation data

Both well AND flux observations

Example #2

Vary recharge and K

Calibrate to observation pointsAND flux in stream

StreamSpec.

HeadBound.

Obs. Pts.

Recharge

K


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Results

Unique Solution

Recharge is limitedby the stream fluxobservation

A degree offreedom isremoved

Solution is muchmore accurate

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