Integration of Spatially Aggregated Physical Process Models with Systems Dynamics Models to Assist t...
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Integration of Spatially Aggregated Physical Process Models with Systems Dynamics Models to Assist the Decision Support Process. Sandia National Laboratories University of Texas – Austin Geological Society of America 2005 Annual Meeting. Introduction. Motivation Approach

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Integration of Spatially Aggregated Physical Process Models with Systems Dynamics Models to Assist the Decision Support Process

Sandia National Laboratories

University of Texas – Austin

Geological Society of America

2005 Annual Meeting


Introduction

  • Motivation

  • Approach

    • Integrated Modular Simulation Framework (IMSF)

    • Rapid Dispute Prevention (RDP)

  • Example

    • Barton Springs segment of the Edwards Aquifer, Austin, TX


‘High-Level’ Motivation

  • Incorporate scientific analysis into the decision making process

  • Allow stakeholders to guide the scientific process

  • Employ advanced policy and decision making techniques

  • Maximize economic, environmental, and demographic sustainability


Barton Springs

  • Assess impacts of development (e.g. impervious cover) on water quantity and quality issues

    • Spring flow

    • Drought triggers

    • Economic impacts

  • Groundwater flow models

  • Stakeholder involvement

Areal extent of Austin

from 1885 to 1985


‘Core’ Motivation

  • Need to incorporate spatially detailed modeling capabilities

  • Need to analyze systems level responses

  • Need this as one tool that can be implemented by non-modelers


IMSF

Approach

  • Link physical process models to system dynamics models

    • Common GUI

    • Common data store

    • Two-way communication

    • Automatic calibration


SD Model

T

A

B

U

Spatially

Indexed

Database

PP Model

Integrated Modular Simulation Framework

Dynamic Data Manager

Impervious Cover

GUI

Stream Buffers

Pipe Leakage

Min. Spring Flow

Compare Results

Pumping Limits

Add Method

Drought Triggers

Optimization


Barton Creek

Williamson Creek

Interstream recharge

Slaughter Creek

Bear Creek

Onion Creek

No recharge

Example: Barton SpringsGroundwater Availability Model (GAM)

  • 120 x 120 cells

  • 1000 m x 500 m cell size

  • Steady State and Transient Versions

  • Recharge, well pumping, drains (Barton and Cold Springs)


Change of Resolution

Convert PP model to

coarse-resolution SD model

through zonation

3

4

5

Effective parameters are

extracted from the MODFLOW

model. Powersim model is

calibrated using a TABU search.

1

2

6

7

9

8

10

11


Calibration

Zone 8

  • Flow b/t Zones

  • Average Heads

  • Spring Flow

8 to 7

8 to 2

8 to 6

8 to 9

8 to 10

8 to 11

Powersim

Powersim

MODFLOW

MODFLOW

Powersim

MODFLOW

Powersim

MODFLOW

Powersim

MODFLOW

Powersim

MODFLOW

3

4

5

1

2

6

7

9

8

10

11


Calibration

3

1

  • Flow b/t Zones

  • Average Heads

  • Spring Flow

2

7

11

10

8

3

4

4

6

9

5

2

1

7

6

5

9

8

10

11


Calibration

Barton Springs

Cold Springs

  • Flow b/t Zones

  • Average Heads

  • Spring Flow

Powersim

MODFLOW

Powersim

MODFLOW

3

4

5

2

1

7

6

9

8

10

11


Benefits and Summary

  • SD model executes much faster than the PP model

    • Scenario testing

    • Stakeholder education

  • Allows for connecting important physical processes to other systems

  • Provides a single user interface that works with both models

  • Provides on-the-fly calibration between each model

  • Modular approach allows for application to different types of problems


Acknowledgements

  • Sandia National Laboratories

    • Thomas S. Lowry

    • Vincent C. Tidwell

  • University of Texas

    • Suzanne Pierce

    • John M. Sharp

    • Marcel Dulay

    • David Eaton

    • Michael Ciarleglio

    • Aliza Gold

    • Roy Jenevein

    • A host of others…..

  • William Cain


Thank You


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