slide1
Download
Skip this Video
Download Presentation
Sandia National Laboratories University of Texas – Austin Geological Society of America

Loading in 2 Seconds...

play fullscreen
1 / 16

Sandia National Laboratories University of Texas – Austin Geological Society of America - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Sandia National Laboratories University of Texas – Austin Geological Society of America' - rafer


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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
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
‘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
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
‘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
approach

IMSF

Approach
  • Link physical process models to system dynamics models
    • Common GUI
    • Common data store
    • Two-way communication
    • Automatic calibration
integrated modular simulation framework

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

example barton springs groundwater availability model gam

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
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
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

calibration1
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

calibration2
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
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
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
ad