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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
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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/
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