Silvana V Croope, PhD
Dig DUG Delaware User Group Meeting
11/03/2010
Case Study: Delaware Flooding – June 2006
Visiting Existing Decision Support Systems
Critical Infrastructure Resilience...
(USGS) Federal Disaster
Declarations 1965-2003:
Sussex County – DE
1 FDD
2 FDD
4 or more FDD
3 FDD
Keenan (2004)
Bush
et all (2007)
de la Garza et all (1998)
Objective: To improve the resilience of critical
infrastructure systems
Decision variables – To undertake mitigation
Ot...
What if scenario (e.g.)
 Geographic: hydrology, elevation
 Hazard: weather, disaster history
records and trends
 Infrastructure: roads, bridges...
Decision Support Systems – CIR-DSS Macro Environment
Large number
of variables
Focus on
resilience of
system problem
solut...
MAIN DATA SOURCES
 DelDOT Transportation Management Center:
pictures, traffic and detours reports (DelDOT
Officials)
 De...
Vulnerability:Vulnerability:
exposureexposure
loss/damageloss/damage
Rain Fall
06/25/2010
Study
Area
Elevation
Location of Damaged
Infrastructure in the
Seaford Flooded Area
Seaford Road Network
and Detours Analysis
(2006)
Organizing principle and analytical capability
“What if” Levee Protection Scenario “What if” Flow
Regulation Scenario
Floodwater Velocity
Estimation Scenario
Damage rela...
Seaford
Area
Annual
Losses
Map of
Depth
10-year-
flood-map
Addresses issues at specific locations – spatial data and local
infrastructure
Helps structure complex problems to suppo...
Limitations (HAZUS-MH)
•Analysis focuses on limited area
•Limited tools to analyze
transportation infrastructure
(T.I.)
- ...
Many rich data sources to support decision making
Tools
HAZUS-MH MR3
Useful
Limited
Non-trivial
Complementary
GIS
H...
GISGIS
HAZUS-MHHAZUS-MH+
STELLA Software:
system dynamics - way to represent complex
problems
sequence of events,
relationship among infrastruct...
Eight scenarios assessing impacts (costs) uses:
Infrastructure Projects
 Recovery only
 Recovery and Mitigation
Probab...
Variables 2 days Disaster
Response
4 days Disaster
Response
Recovery NPV 148,081 512,958
Mitigation NPV 157,890 600,629
Re...
• Complex system modeling required many assumptions and
models to capture changes over time
• SDSS plays major role in set...
DelDOT (supporter/employer)
University of Delaware University Transportation
Center (support for research)
Sue McNeil (...
Decision Support System to Manage Critical Civil Infrastructure Systems for Disaster Resilience (Sylvana Croope, DelDOT)
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Decision Support System to Manage Critical Civil Infrastructure Systems for Disaster Resilience (Sylvana Croope, DelDOT)

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  • Elevation 8 – 30 feet
  • Step 1 – getting local infrastructure information, initializing the system
    Step 2 – getting system performance measures
    Steps 3 and 4 – degrading system performance due to a disaster
    Step 5 to 7 – improving system performance
    Step 8 – assessing performance.
  • Decision Support System to Manage Critical Civil Infrastructure Systems for Disaster Resilience (Sylvana Croope, DelDOT)

    1. 1. Silvana V Croope, PhD Dig DUG Delaware User Group Meeting 11/03/2010
    2. 2. 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 analyses SDSS Benefits and Limitations Complementary Systems for CIR-DSS Integrating SDSS analyses results to other systems Case Study results summary Conclusion
    3. 3. (USGS) Federal Disaster Declarations 1965-2003: Sussex County – DE 1 FDD 2 FDD 4 or more FDD 3 FDD
    4. 4. Keenan (2004) Bush et all (2007) de la Garza et all (1998)
    5. 5. 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
    6. 6. What if scenario (e.g.)
    7. 7.  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
    8. 8. Decision Support Systems – CIR-DSS Macro Environment Large number of variables Focus on resilience of system problem solutions
    9. 9. 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®
    10. 10. Vulnerability:Vulnerability: exposureexposure loss/damageloss/damage Rain Fall 06/25/2010 Study Area Elevation
    11. 11. Location of Damaged Infrastructure in the Seaford Flooded Area Seaford Road Network and Detours Analysis (2006)
    12. 12. Organizing principle and analytical capability
    13. 13. “What if” Levee Protection Scenario “What if” Flow Regulation Scenario Floodwater Velocity Estimation Scenario Damage related to US13 in Sussex County
    14. 14. Seaford Area Annual Losses Map of Depth 10-year- flood-map
    15. 15. 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
    16. 16. 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
    17. 17. Many rich data sources to support decision making Tools HAZUS-MH MR3 Useful Limited Non-trivial Complementary GIS Helps communication Provides 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
    18. 18. GISGIS HAZUS-MHHAZUS-MH+
    19. 19. STELLA Software: system dynamics - way to represent complex problems sequence 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
    20. 20. Eight scenarios assessing impacts (costs) uses: Infrastructure Projects  Recovery only  Recovery and Mitigation Probability of a 100-year storm event in the case study area 1% 4% 8% Time required for disaster response  2 days  4 days Scenarios Explored
    21. 21. Variables 2 days Disaster Response 4 days Disaster Response Recovery 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)
    22. 22. • 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
    23. 23. DelDOT (supporter/employer) University of Delaware University Transportation Center (support for research) Sue McNeil (adviser)

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