decision support system to manage critical civil infrastructure systems for disaster resilience...
TRANSCRIPT
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Silvana V Croope, PhDDig DUG Delaware User Group Meeting
11/03/2010
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Case Study: Delaware Flooding – June 2006
Visiting Existing Decision Support Systems
Critical Infrastructure Resilience Decision Support System (CIR-DSS)
Spatial Decision Support System (SDSS)SDSS: GIS + HAZUS-MH analysesSDSS Benefits and Limitations
Complementary Systems for CIR-DSS Integrating SDSS analyses results to other systemsCase Study results summary
Conclusion
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(USGS) Federal Disaster Declarations 1965-2003:
Sussex County – DE
1 FDD2 FDD
4 or more FDD3 FDD
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Keenan (2004)
Bush et all (2007)
de la Garza et all (1998)
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Objective: To improve the resilience of critical infrastructure systems
Decision variables – To undertake mitigation Other variables
Resilience metrics: capacity, pavement condition
Net present value of costs and costs avoided
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What if scenario (e.g.)
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Geographic: hydrology, elevation
Hazard: weather, disaster history records and trends
Infrastructure: roads, bridges
Financial: cost of infrastructure
Institutional/cultural: decision-makers, policies and funding
Infrastructure life-cycle
Expected infrastructure life-cycle with mitigation
Vulnerability, Risk/Impact and Damage Analysis
Pre-, During and Post-Event Assessment – infrastructure condition and performance
Mitigation Strategies (definition)
Pre-, During and Post-Event Resilience Assessment
Cost-Benefit Analysis
Recovery and Mitigation Strategies Comparison
Scenarios Test/Evaluation
ANALYSES/ASSESSMENTS
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Decision Support Systems – CIR-DSS Macro Environment
Large number of variables
Focus on resilience of system problem solutions
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MAIN DATA SOURCES
DelDOT Transportation Management Center: pictures, traffic and detours reports (DelDOT Officials)
DelDOT Bridge Management: bridge data (GIS), reports of local damaged bridges, digital maps (pdf) (DelDOT Officials)
DelDOT (others): roads , State boundaries
Spat Lab (U.D.): Elevation Data
DataMIL: roads, rivers, hydrology, municipal boundaries
Delaware Environmental Observing System (DEOS): radar derived rainfall
HAZUS-MH MR3 inventory
Literature
TOOLS
GIS ArcInfo and extensions Spatial Analyst Network Analyst
Excel
HAZUS-MH MR3 Flood Model Analysis Level 1
STELLA®
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Vulnerability:Vulnerability:exposureexposure
loss/damage loss/damage
Rain Fall06/25/2010
StudyArea
Elevation
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Location of Damaged Infrastructure in the Seaford Flooded Area
Seaford Road Network and Detours Analysis (2006)
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Organizing principle and analytical capability
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“What if” Levee Protection Scenario “What if” Flow Regulation Scenario
Floodwater Velocity Estimation Scenario
Damage related to US13 in Sussex County
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Seaford Area Annual Losses Map of Depth
10-year-flood-map
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Addresses issues at specific locations – spatial data and local infrastructure
Helps structure complex problems to support decision-making processes
Does not replace users’ decision-making
Facilitates sharing/integration of data and information on further analyses
Analysis outputs are used to support decisions for infrastructure repair, improvement and mitigation of future floods
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Limitations (HAZUS-MH)
•Analysis focuses on limited area
•Limited tools to analyze transportation infrastructure (T.I.)
- Road segmentation not official
- Vehicle exposure does not consider flow, just Census
- Exposure same as vulnerability?
- No dynamic modeling – T.I. resilience changes
- No financial trade-off tools for solutions evaluation
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Many rich data sources to support decision making
ToolsHAZUS-MH MR3UsefulLimitedNon-trivial
Complementary
GISHelps communicationProvides insight
Analyses results/data integration with Management Information SystemAnalyses results/data integration with Management Information System
• transportation inventory (roads – segments values)transportation inventory (roads – segments values)
• transportation exposure/vulnerability valuetransportation exposure/vulnerability value
• warning system value (10%)warning system value (10%)
• connectivity mitigation insightconnectivity mitigation insight
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GISGISHAZUS-MHHAZUS-MH+
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STELLA Software:system dynamics - way to represent complex
problemssequence of events,relationship among infrastructure and
organizations,types of policies that enables certain actions
STELLA tool for modeling and simulation
STELLA = skills + language + representations + STELLA = skills + language + representations + programming = thinking + communicating + programming = thinking + communicating + learninglearning
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Eight scenarios assessing impacts (costs) uses:
Infrastructure ProjectsRecovery onlyRecovery and Mitigation
Probability of a 100-year storm event in the case study area
1% 4% 8%
Time required for disaster response2 days4 days
Scenarios Explored
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Variables 2 days Disaster
Response
4 days Disaster
ResponseRecovery NPV 148,081 512,958
Mitigation NPV 157,890 600,629
Result from mitigation investment
-9,809 -87,671
Loss of function for recovery (benefit)
-1,463 -2,479
Loss of function for mitigation (benefit)
719,299 1,218,060
1% probability of 100-year storm (study area)
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• Complex system modeling required many assumptions and models to capture changes over time
• SDSS plays major role in setting the stage for problem analysis, diagnosis and mitigation insights using data from many sources
• SDSS results are important inputs into the model• SDSS can be better customized to include better analysis tools for infrastructure
• Comprehensive development offers insights into trade-offs and opportunities to capture damage and costs in the context of resiliency
• Refinements are needed to operationalize this approach
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DelDOT (supporter/employer)University of Delaware University
Transportation Center (support for research)Sue McNeil (adviser)