# Presentation: 227035

## 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**

## Approach

**Link physical process models to system dynamics models**
**Common GUI**
**Common data store**
**Two-way communication**
**Automatic calibration**

## Integrated Modular Simulation Framework

- Dynamic Data Manager

- GUI

- Impervious Cover

- Stream Buffers

- Pipe Leakage

- Min. Spring Flow

- Pumping Limits

- Drought Triggers

- Compare Results

- Add Method

- Optimization

## Example: Barton SpringsGroundwater 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

- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 11

- 10

- 9

- Effective parameters are
- extracted from the MODFLOW
- model.  Powersim model is
- calibrated using a TABU search.

## Calibration

**Calibration**

**Flow b/t Zones**
**Average Heads**
**Spring Flow**

- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 9

- 10

- 11

- 8 to 2

- MODFLOW

- MODFLOW

- Powersim

- Powersim

- Powersim

- Powersim

- Powersim

- Powersim

- MODFLOW

- MODFLOW

- MODFLOW

- MODFLOW

- 8 to 6

- 8 to 7

- 8 to 9

- 8 to 10

- 8 to 11

- Zone 8

## Calibration

**Calibration**

**Flow b/t Zones**
**Average Heads**
**Spring Flow**

- 11

- 10

- 9

- 8

- 7

- 6

- 5

- 4

- 3

- 2

- 1

- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 9

- 10

- 11

## Calibration

**Flow b/t Zones**
**Average Heads**
**Spring Flow**

- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 9

- 10

- 11

- Barton Springs

- Cold Springs

- MODFLOW

- MODFLOW

- Powersim

- Powersim

## Slide 13

## 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