Turning Information into Insight:A Multi-Platform Decision Support Tool for

Systems Level Management

Sandia National Laboratories

Thomas S. Lowry

Vincent C. Tidwell

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,for the United States Department of Energy’s National Nuclear Security Administration

under contract DE-AC04-94AL85000.

• Resource Management• Decision Making

– Multi-stakeholder

– Policy• Integration

What is This About?

GENTLEMAN, IT APPEARS TO BE UNANIMOUS

THAT WE CANNOT AGREE-unattributed

Motivation

• Persistent conflict is becoming ‘the norm’• Conflicts occur because of:

– Complexity & uncertainty in natural systems

– Lack of insight of cause and effect

– Conflicting interests & values

• Human rationality is bounded– Favors linear/static behaviors

– Creates judgment bias

– Entrenches personal convictions

What is the Problem?

A

B CA B C

After van den Belt

CO2 Temp

• Natural Resources are inherently multi-stakeholder

– Perception that negotiation / change implies sacrifice

• Lack of insight into the cause and effect relationships

– Prevents meaningful dialogue

– Prevents agreement / consensus

• Lack of connection to the human element

The ‘Real’ Problem

Global Warming

????http://vrlab.epfl.ch/~clavien/images/demo_crwd6tpVarAcc4_s.jpg

Need Tools and Processes• Understand cause and effect

– Physical systems

– Human systems

• Inform the decision making process

• Create an environment for consensus and agreement– Open and transparent

– Group learning

Motivation – 30,000 ft

To Turn Information into Insight

Technical tools Process skills

Collaborative Modeling

• Understand basic behavior– Hydrology– Ecology– Economics– etc

• Represent the linkages between these areas

• Understand institutional setting

• Develop ways engage stakeholders

• Build trust

Shared Vision Planning

Integrated Approach

Information To Insight

• Means involving stakeholders in– Data collection and/or analysis

– Developing cause and effect relationships

– Technical analysis

Builds understanding of the systemBuilds confidence in the analysisBuilds trust between stakeholders

• Modeling– To simulate cause and effect

• Stakeholder Involvement– Identify constraints

– Mutual understanding

• Decision Support– Quantify alternatives

The Computer Assisted Dispute Resolution Process

Information To Insight

cadre.sandia.gov www.iwr.usace.army.mil/cadre/

Different Models, Different Answers

• Physical Process Models– Simple Analytical

– Empirical

– Complex Numerical

• System Dynamics Models• Agent Based Models

• Temporal and/or spatial detail• One or two systems• Detailed analysis• Computationally heavy• Temporally dynamic• Many systems• Exploring cause and effect• Computationally easy

• Temporally dynamic• Systems of individuals• Exploring low probability

extreme behavior• Computationally heavy

1. How do we integrate different types of models into a single framework?

2. How can models communicate with each other?

3. How do non-modelers interact with them?4. How do we develop stakeholder metrics?5. How do we maintain transparency?6. How do we find solutions in multi-

stakeholder / multi-objective problems?7. How do we make all of this useful?

Reach Storage

Upper BoundaryInflow

Lower BoundaryOutflow

Residence Time

Tributary Inflows

Tributary ResidenceTime

INITIAL VOLUME

Tributary Offsets

Reach Storage

Upper BoundaryInflow

Lower BoundaryOutflow

Residence Time

Tributary Inflows

Tributary ResidenceTime

INITIAL VOLUME

Tributary Offsets

The CADRe System

Dynamic Data Manager

SpatiallyIndexed

Database

SD Model

PP Model

GUI

TABU

Impervious Cover

Stream Buffers

Pipe Leakage

Min. Spring Flow

Pumping Limits

Drought Triggers

Compare Results

Add Method

Optimization

• Important segment of the Edwards Aquifer in Central Texas - Karstic

• Barton Springs is a major aesthetic feature of Austin

– Popular recreation area– Home to an endangered species

(salamander)

• Rapidly growing urban area– Concern about growth in impervious cover

over the recharge zone impacting long-term spring flow

– History of community conflict

• Existing MODFLOW based flow models

Austin, Texas

Barton Springs Case Study

Areal extent of Austinfrom 1885 to 1985

Barton Springs

Barton Springs

Contributing Zone

Recharge Zone

Confined Zone

Barton Springs

Benefits

Consequences

Sprawl

Quality of Life

Good Water Quality

Historical Present Future

Stakeholder Involvement

Historical TrendProjected OutcomesNarrative Elicitation

Barton Springs

• Controlling factors– what are the key decisions or controls that can

influence which future outcome occurs• Tasks

– what are the things that a community can do to change the situation and achieve the ‘best’ outcome

• Measures– if the community takes the appropriate actions,

how will you be able to measure/see when you’ve reached an improved state

Modeling Tasks

• Groundwater flow• Spring flow• Drawdown• Saturated thickness• Drought Triggers• Changes in recharge• Urban sprawl• Quick execution• Real-time feedback

System Dynamics

Groundwater Flow Model

Zone Storageintra-zone flow

conductance

heads

dH

flow between zones

spring flow

adjusted pumping

Groundwater Flow Model CZ Land Use Calculations Contributing Zone

Recharge ZoneRZ Land Use Calculations

Creek Flow

Distributed Recharge

Sprawl Factor

Drought Count Using Spring Flow Drought Adjusted Pumping Rates

Outflows InflowsGroundwater Model Contributing Zone

Recharge Zone

Creek Data inRecharge Zone Drought

CalculationConstants

HyperlinkHyperlink

Zone Storageintra-zone flow

conductance

heads

dH

flow between zones

spring flow

adjusted pumping

cz road adjustment

cz openspaceadjustment

cz area by ws and lu

cz runoff coefficientcz ic area by ws and lu

cz ic% by ws cz creek flows

rz ic area by ws and lu

rz ic% by ws

rz runoff coefficient

delt ic area by ws

rz road adjustment

rz openspaceadjustment rz creek flows

creek recharge flux

creek recharge volumeby ws and zone

creek recharge volumeby zone

total recharge by zone

creek recharge volumeby ws

distributed recharge byzone

initial available ic areaby zone and ws

new ic area by ws andzone

available ic area byzone

new ic area by zone distributed recharge flux

cz sprawlCZ SPRAWL DISTANCES

rz sprawlRZ SPRAWL DISTANCES

total sprawl

RZ watershed area

spring-baseddrought condition

DROUGHT REDUCTION

Springflow no timesteps indrought

Springflow droughttest

Spring-basedmonthly drought

test

Barton Springs Flow

day of month

Spring-based sums no of months indrought

Spring-basedcounts no of

months in drought

Spring-based DroughtsPumping Difference

Copy of wells

Copy of adjustedpumping

recharge % of totalinflow

total springflow

total recharge byzones total recharge

total pumping

pumping % of totaloutflow

total pumping % oftotal outflow

springflow % oftotal outflow

total pumping byzone

TRANSFORM MATRIX

CONDUCTIVITY

BOUNDARY SEPARATION DISTANCE

SURFACE AREA

SPECIFIC YIELD

INITIAL HEADS

DRAIN CONDUCTANCE

DRAIN ELEVATIONS

TOP

STORATIVITY

BOTTOM

CZ LU% BY WS

CZ WS AREA

CREEK FLOW LIMITS

CZ LU IC% BY WS

CZ ROAD AVERAGE CZ WS IC%

CZ CALC TYPE

% RAINFALL THAT ISRECHARGE

RZ AREA BY WS ANDZONES

RZ LU IC% BY WS

RZ LU% BY WS

RZ CALC TYPE

RZ WS IC%

RZ INITIAL IC% BY WS

RZ ROAD AVERAGE

CREEK AREA BY ZONE

CREEK AREA

INITIAL ZONE IC% BYWS

BS WELL BASE

BS WELL SCALAR

LOVELADY SCALAR

LOVELADY BASE

SPRING DROUGHTSTAGE

Barton Springs Water Conservation DistrictGroundwater Availability Model (GAM)

Multiple Objectives

Sprawl

• Preserve flow in Barton Springs• Protect endangered species• Maximize pumping• Maximize reliability• Maximize economic benefit• Minimize environmental impact• Minimize impact on daily life

Decision Support

TABU Search• Allow users to specify objectives, such as:

– Minimize sprawl– Maximize spring flow– Minimize droughts

• TABU Search uses deterministic algorithm to find ‘optimal’ policy settings to achieve objectives

• Create new, un-intuitive solutions

The CADRe System

• Integrated ‘software wrapper’– Links spatially explicit, high-fidelity models to

systems based analysis

– Reduces redundancy and duplicate effort

– Provides interface for rapid dispute resolution w/in a stakeholder process

– Allows for multi-attribute / multi-objective optimization

– Allows for ranking of scenarios based on user preferences

The CADRe System

Dynamic Data Manager

SpatiallyIndexed

Database

GUI

TABU

Impervious Cover

Stream Buffers

Pipe Leakage

Min. Spring Flow

Pumping Limits

Drought Triggers

Compare Results

Add Method

Optimization

SD Model

PP Model

Ada

pte

rA

dapt

er

DEMONSTRATION

Benefits

• SD model executes much faster than the PP model– Scenario testing– Stakeholder education

• Allows for connecting spatially explicit physical processes to other systems

• Provides a single user interface that works with both types of models

• Provides optimization and ranking of different scenarios based on user preferences

• Modular approach allows for application to different types of problems

SWAQ

National Science and Technology Council– Committee on Environment and Natural Resources

• Sub-committee on Water Availability and Quantity– Interagency Development of a SWAQ-endorsed Federal Initiative

• Initiative to develop Collaborative Tools & Processes for US Water Solutions

• Develop and advance the integration of computer-based modeling tools within multi-stakeholder public decision processes

– Objective, Establish Federal Funding to:• Review current uses and programs on the use of “collaborative decision

support tools” in water problems • Develop a framework for evaluation of the effectiveness of combinations

of computer tools and collaborative interventions for a range of water problems and settings

• Initiate targeted “pilot” or “demonstration” projects to develop basic principles and best practices

• Develop an interagency focal point to serve as an information clearinghouse to provide unified direction, integration & consistency

Acknowledgements

• Sandia National Laboratories– Thomas S. Lowry– Vincent C. Tidwell– Suzanne Pierce– Mona Aragon

• University of Texas– John M. Sharp– Marcel Dulay– David Eaton– Michael Ciarleglio– Aliza Gold– Roy Jenevein– A host of others…..

• William Cain A Cast of Thousands

THANK YOU

cadre.sandia.gov

www.iwr.usace.army.mil/cadre/