art of calibration in the science of h&h modeling amit sachan, pe, cfm, project manager/ water...

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‘Art’ of Calibration in the ‘Science’ of H&H Modeling

Amit Sachan, PE, CFM, Project Manager/ Water Resource Engineer

Robert Billings, PE, PH, CFM Project Manager, Mecklenburg County

Agenda

Definitions

Case Study

Lessons Learned

Summary

Definitions

Case Study

Lessons Learned

Summary

Modeling

Representation of a process/object

Calibration

Models: “Adjustment of the parameters of a mathematical or numerical model in order to optimize the agreement between observed data and the model's predictions (source: Wikipedia)

Tools: Checking or adjusting (by comparison with a standard) the accuracy of a measuring instrument (source: TheFreeDictionary.com)

Consistent and systematic

Modeling

Representation of a process/object

Calibration

Models: “Adjustment of the parameters of a mathematical or numerical model in order to optimize the agreement between observed data and the model's predictions (source: Wikipedia)

Tools: Checking or adjusting (by comparison with a standard) the accuracy of a measuring instrument (source: TheFreeDictionary.com)

Consistent and systematic

Definitions

Calibration

Setting for known values

Accuracy vs. Precision

For Physical models

Multiple parameters

Multiple values

Classical Calibration for the Models

Sensitivity Analysis

Calibration

Validation

Calibration

Setting for known values

Accuracy vs. Precision

For Physical models

Multiple parameters

Multiple values

Classical Calibration for the Models

Sensitivity Analysis

Calibration

Validation

Definitions

Source: MedicalScale

Southern NC at the NC-SC

Border

330 miles of FEMA streams

State-of-the-art Flood warning

system

50 stream gages within LSCBC

Case Study

Case Study

Cavalier Apartments

Model accuracy & precision directly

Impact public $’s due to highly urban area

Storm Selection

High-end Storm (~100 Yr)

August 2008

Low-end Storms (~2 Yr)

September 2006

March 2007

Storm Selection

Continuous Data

Low Variability

Homogenous Response

Storm Selection

High-end Storm (~100 Yr)

August 2008

Low-end Storms (~2 Yr)

September 2006

March 2007

Storm Selection

Continuous Data

Low Variability

Homogenous ResponseRainfall Distribution during

August 25-27, 2008 (Source: USGS)

Case Study – Hydrologic Calibration

Case Study – Hydrologic Calibration

a. Sensitivity Analysis

Routing Techniques

Modified Puls

Mushkingham Cunge

Level Pool Routing

Lag time

Curve Numbers

AMC Conditions

Initial Abstraction

a. Sensitivity Analysis

Routing Techniques

Modified Puls

Mushkingham Cunge

Level Pool Routing

Lag time

Curve Numbers

AMC Conditions

Initial Abstraction

Case Study – Hydrologic Calibration

Case Study- Hydrologic Calibration

Additional Storage Areas

Case Study- Hydrologic Calibration

Change Increase Reduction

Addition of Storage Area on Edwards Branch

2% -

Addition of Storage Areas on Briar Creek 2% -

Change in Effective areas - -

Change in Lag times - 5%

‘Change & Effect’ Matrix

b. Calibration Alternatives & Iterations 14 scenarios

Gage weighting Equal Weighting AMC conditions Initial Abstraction

Aug 2008 Event Initial Abstraction Lag Time

b. Calibration Alternatives & Iterations 14 scenarios

Gage weighting Equal Weighting AMC conditions Initial Abstraction

Aug 2008 Event Initial Abstraction Lag Time

Rain event Scenario Description

Rain gages

Initial Abstraction

CNs

Sept 13, 2006

Scenario 1.1: All rain gages were weighted equally to come up with combined hyetograph.

Weighted Equally

0.05 inches AMC

II

Scenario 1.2: All rain gages were weighted equally to come up with combined hyetograph, to come up with combined hyetograph.

Weighted Equally

0.6 inchesAMC

II

March 1, 2007

Scenario 2.1: All rain gages were weighted equally to come up with combined hyetograph.

Weighted Equally

0.05 inches AMC

II

Case Study – Hydrologic Calibration

Case Study- Hydrologic Calibration

Observations Time to Peak - matches

Peak Discharges – within reasonable limits

Volume – within reasonable limits

Observations Time to Peak - matches

Peak Discharges – within reasonable limits

Volume – within reasonable limits

12:00 24:00 36:00 48:00 60:00 72:00 84:000

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Little Sugar Creek @ Archdale Dr

Gage Flow HEC-HMS-2008

From Aug 25 to Aug 28 (Hours)

Flo

w (

cfs

)

Calibration – Inter relationship

Airplane example Airplane example

Source: Wikipedia

Calibration – Inter relationship

Relationship between Hydrologic & Hydraulic Models Relationship between Hydrologic & Hydraulic Models

30000 32000 34000 36000 38000 40000 42000 44000

540

550

560

570

580

LittleSugarCreek Plan: Calibration-August2008 9/24/2009

Main Channel Distance (ft)

Elev

atio

n (ft

)

Legend

WS 2008-Interpolate

Ground

OWS 2008- Interpolate

LittleSugarCreek Main

Hydrologic Model Hydraulic Model

Case Study- Hydraulic Calibration

High Water Marks Little Sugar Creek

Little Hope Creek

Briar Creek

High Water Marks Little Sugar Creek

Little Hope Creek

Briar Creek

Case Study- Hydraulic Calibration

High Water Marks Match within 0.5 ft

Quality

Parameter Changes Manning’s n

Ineffective Areas

Cont/Exp Coeff

High Water Marks Match within 0.5 ft

Quality

Parameter Changes Manning’s n

Ineffective Areas

Cont/Exp Coeff30000 32000 34000 36000 38000 40000 42000 44000

540

550

560

570

580

LittleSugarCreek Plan: Calibration-August2008 9/24/2009

Main Channel Distance (ft)

Ele

vatio

n (f

t)

Legend

WS 2008-Interpolate

Ground

OWS 2008- Interpolate

LittleSugarCreek Main

Case Study- Hydraulic Calibration

c. Validation Hydrologic

Hydraulic

c. Validation Hydrologic

Hydraulic

40000 50000 60000 70000 80000

560

580

600

620

LittleSugarCreek Plan: Calibration-August2008 9/21/2009

Main Channel Distance (ft)

Ele

vatio

n (f

t)

Legend

WS 2008-Interpolate

Ground

OWS 2008- Interpolate

LittleSugarCreek Main

Case Study- Hydraulic Calibration

…and iterations continue until reasonable matches in hydrologic AND hydraulic models

…and iterations continue until reasonable matches in hydrologic AND hydraulic models

30000 32000 34000 36000 38000 40000 42000 44000

540

550

560

570

580

LittleSugarCreek Plan: Calibration-August2008 9/24/2009

Main Channel Distance (ft)

Elev

atio

n (ft

)

Legend

WS 2008-Interpolate

Ground

OWS 2008- Interpolate

LittleSugarCreek Main

Hydrologic Model Hydraulic Model

Lessons Learned

Analysis (Science Component) Based on available data

Based on required detail

Sensitivity Analysis

Time/Budget Available

Trade offs (Art Component) Not exact matches

Engineering Judgment

Analysis (Science Component) Based on available data

Based on required detail

Sensitivity Analysis

Time/Budget Available

Trade offs (Art Component) Not exact matches

Engineering Judgment

QUESTIONS ??Thank youThank you

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