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Supporting community resilience (public)
The document discusses community resilience and outlines a methodology for planning community resilience. It describes a 6-step process for forming a collaborative planning team, understanding community needs and systems, determining goals and objectives, developing and implementing a resilience plan. It also summarizes tools and activities from various organizations to support community resilience planning and highlights research being conducted by NIST.
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Supporting community resilience (public)
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- 2. Community Needs DriveFunctional Requirements for the Built Environment Resilience is “the ability to prepare for and adapt to changing conditions and to withstand and recover rapidly from disruptions” (PPD 8 and PPD 21) A-2
- 3. Community Resilience PlanningGuide Volume 1 – Methodology • Introduction • 6 Step Methodology • Planning Example – Riverbend • Glossary and Acronyms Volume 2 – Reference • Social Community • Dependencies and Cascading Effects • Buildings • Transportation Systems • Energy Systems • Communication Systems • Water & Wastewater Systems • Community Resilience Metrics A-3
- 4. 6-Steps for CommunityResilience SIX-STEP GUIDE TO PLANNING FOR COMMUNITY RESILIENCE FORM A COLLABORATIVE PLANNING TEAM • Identify leader • Identify team members • Identify key stakeholders UNDERSTAND THE SITUATION Social Dimensions • Characterize social functions & dependencies • Identify support by built environment • Identify key contacts Built Environment • Identify and characterize built environment • Identify key contacts • Identify existing community plans Link Social Functions & Built Environment • Define clusters DETERMINE GOALS $ OBJECTIVES • Establish long-term community goals • Establish performance goals • Define community hazards • Determine anticipated performance • Summarize results PLAN DEVELOPMENT • Evaluate gaps • Identify team members • Identify key stakeholders PLAN PREPARATION, REVIEW, AND APPROVAL • Document plan and strategy • Obtain feedback and approval • Finalize and approve plan PLAN IMPLEMENTATION AND MAINTENANCE • Execute approved solution • Evaluate and update • Modify strategy as needed 1 2 3 4 5 6 A-4
- 5. Economic Decision Guide(EDG) • Provides a standard methodology for evaluating investment decisions for communities resilience • Designed for use with NIST’s Planning Guide ‒ Provides a mechanism to evaluate and prioritize resilience actions • Frames the economic decision process ‒ Identifies and compares resilience-related benefits & costs • Across competing alternatives • Versus the status quo (do-nothing) A-5
- 6. Community Resilience Panel •Mission Reduce barriers to achieving community resilience by promoting collaboration among stakeholders to improve the resilience of buildings, infrastructure, and social systems upon which communities rely. • Goals ‒ Engage and connect community and cross-sector stakeholders ‒ Identify policy and standards gaps and barriers ‒ Raise awareness of dependencies & cascading effects ‒ Contribute to community resilience documents ‒ Develop/maintain a Resilience Knowledge Base A-6
- 7. Other Community ResilienceActivities Federal Agencies DHS IP – Critical Infrastructure • Regional Resilience Assessment Program FEMA – Disaster Preparedness • HAZUS tools HUD – Housing and Infrastructure • Community Development Block grants EPA – Water Resiliency • Tools for Enhancing Community Resilience NOAA – Coastal Resilience • Climate Resilience Toolkit USACE – Ports, Waterways, Coasts • Climate Preparedness & Resilience Program A-7
- 8. Other Community ResilienceActivities (2) National Laboratories Sandia National Laboratory • Analysis tools for community and regional assessment of critical infrastructure, economic impacts, and human behavior. Argonne National Laboratory • Assessment and analysis tools that support the resilient design of infrastructure systems. Brookhaven National Laboratory • Resilient energy and smart grid systems Idaho National Laboratory • Resilient instrumentation, control, and cyber-physical systems Pacific Northwest National Laboratory • Delivery and reliability of electricity National Renewable Energy Laboratory • Renewable energy and energy efficiency technologies A-8
- 9. NIST Research • CommunityResilience ‒ Assessment tools and metrics for community resilience based on systems modeling methods • Tornado hazard maps ‒ Multi-year effort to create science-based maps will support tornado-resistant design standards • Seismic performance of existing buildings ‒ Performance of slender concrete walls is being evaluated that may lead to code changes • Building failure and collapse mitigation ‒ New connections were developed for enhanced performance using computational modeling A-9
- 10. NIST-Funded Center ofExcellence • Awarded to 10 institution team led by Colorado State University. • $4M/year program funded through a cooperative agreement. • Objectives ‒ Develop an integrated, multi-scale, computational environment with systems-level models ‒ Develop data architectures and management tools to enable use of multi-disciplinary data ‒ Conduct studies to validate models and data tools for a variety of hazard events including: • Tornado, hurricane, earthquake, flood, wildland-urban interface (WUI) fire • Effects of climate change and aging infrastructure • Envisioned products and end-users at 5 years • Modeling environment for researchers • Available incremental tools and metrics for community planners, designers, analysts, etc. A-10
- 11. Overview of theNIST Center of Excellence A-11
- 12. Civil Infrastructure A-12 • Buildings •Integrated transportation systems (roads and bridges, air traffic, ports & harbors, locks & dams) • Telecommunications facilities • Power generation and distribution facilities • Water/wastewater systems
- 13. Natural hazards A-13 • Tornadoes •Earthquakes and tsunamis • Landslides • Hurricanes • Wildland Urban Interface Fires • Floods and storm surge • Sea level rise
- 14. A-14 Thrust 1: NIST-CORE AMultidisciplinary computational environment with fully integrated supporting databases: “The NIST-Community Resilience Modeling Environment”. Thrust 2: Data Management Tools for Community Resilience Systems A standardized data ontology, robust data architecture, and effective tools to support NIST-CORE. Thrust 3: Resilience Data Architecture Validation Studies Hindcasts and forecasts to test the data collection process and its integration into NIST-CORE. Validate risk-informed intelligent search and decision algorithms; field studies.
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- 18. Current Standards forRisk Management A-18 • Structural load requirements (ASCE Standard 7-10) • Building design (AISC, AISI, ACI, AF&PA/ASCE) • Bridges (AASHTO) • Electrical transmission structures (EPRI) • Water/wastewater distribution systems (AWWA) • Offshore structures (API) • Seismic PRA/Margins analysis (NRC, EPRI) • DOE Facilities – Natural phenomena hazards (DOE) • Dam safety (USACE, FEMA, BuRec)
- 19. Traditional engineering approachesto risk management A-19 ‒Focus on individual hazards and facilities ‒Measures of performance are inconsistent ‒Margins of safety and functionality are not commensurate with uncertainty ‒Risks cannot be benchmarked ‒Comparison of alternatives is difficult ‒Investments in risk mitigation and management may be misdirected
- 20. What is communityresilience? A-20 “The ability of a community to prepare for and adapt to changing conditions and to withstand and recover from disruptions to its physical and non-physical infrastructure.”
- 21. Managing resilience –an interdisciplinary endeavor A-21 • Engineering • Climatologists and geophysicists • Computer science, information/communication technology, software development • Social, economic and political science • Philosophy/ethics • Stakeholders
- 22. Fundamental research issues A-22 •Quantitative tools and metrics • Interconnected and inter-dependent systems • Numerous sources of uncertainties
- 23. Fundamental research issues A-23 •Reality of climate change – modification to existing decision support methods • Existing buildings and infrastructure systems, new construction • Consistent performance goals
- 24. The Science A-24 • Developmentof measurement science and technology • Ten-university-U.S. government partnership • Understanding and quantification of factors that make a community resilient to natural, technological, and human-induced hazards National Center for Supercomputing Applications
- 25. A-25 INPUT/MODELING 1.1.1 Ind. Hazards 1.1.2Mult. Hazards 1.1.3 Climate Cha 1.2.1 Buildings 1.2.2 Transpo. 1.2.3 Water/Waste 1.2.4 Energy Net. 1.2.5 Telecom Net. 1.3.3 Econ. Net. 1.3.4 Social Syst. 1.3.1 Interdependency 1.3.2 Aging Infrastruct. 1.3.3 Uncertainty Prop.
- 26. A-26 INPUT/MODELING 1.1.1 Ind. Hazards 1.1.2Mult. Hazards 1.1.3 Climate Cha 1.2.1 Buildings 1.2.2 Transpo. 1.2.3 Water/Waste 1.2.4 Energy Net. 1.2.5 Telecom Net. 1.3.3 Econ. Net. 1.3.4 Social Syst. 1.3.1 Interdependency 1.3.2 Aging Infrastruct. 1.3.3 Uncertainty Prop. DATA/STANDARDIZATION 2.1 User requirements 2.2 Stan. Data Ontology 2.3 Data Man. Tools
- 27. A-27 INPUT/MODELING 1.1.1 Ind. Hazards 1.1.2Mult. Hazards 1.1.3 Climate Cha 1.2.1 Buildings 1.2.2 Transpo. 1.2.3 Water/Waste 1.2.4 Energy Net. 1.2.5 Telecom Net. 1.3.3 Econ. Net. 1.3.4 Social Syst. 1.3.1 Interdependency 1.3.2 Aging Infrastruct. 1.3.3 Uncertainty Prop. DATA/STANDARDIZATION 2.1 User requirements 2.2 Stan. Data Ontology 2.3 Data Man. Tools DECISION 1.6.1 Ident./Define Baseline 1.6.2 Define Res. For Recovery 1.6.3 Define Perf. Models 1.6.4 Identify Infra/Other 1.8.1 Classical Optimization 1.8.2 Intelligent Search 1.8.3 Opt. of Investments 3.2.1 Field Study Dec. Algor. 3.2.2 Comm. Res. Testbed
- 28. A-28 INPUT/MODELING 1.1.1 Ind. Hazards 1.1.2Mult. Hazards 1.1.3 Climate Cha 1.2.1 Buildings 1.2.2 Transpo. 1.2.3 Water/Waste 1.2.4 Energy Net. 1.2.5 Telecom Net. 1.3.3 Econ. Net. 1.3.4 Social Syst. 1.3.1 Interdependency 1.3.2 Aging Infrastruct. 1.3.3 Uncertainty Prop. DATA/STANDARDIZATION 2.1 User requirements 2.2 Stan. Data Ontology 2.3 Data Man. Tools DECISION 1.6.1 Ident./Define Baseline 1.6.2 Define Res. For Recovery 1.6.3 Define Perf. Models 1.6.4 Identify Infra/Other 1.8.1 Classical Optimization 1.8.2 Intelligent Search 1.8.3 Opt. of Investments 3.2.1 Field Study Dec. Algor. 3.2.2 Comm. Res. Testbed SENSITIVITY/VALIDATION 1.5.1 Isolated Infra. Eval. 1.5.2 Coupled infra. Eval. 1.5.3 Full Event Hindcast 1.5.4 Quantif. Mod. Perf. 1.7.1 Effect of Mod. Res. 1.7.2 Effect of Scaling 1.7.3 Sensitivity Studies 3.1 Intermittent Beta Tst 3.3 Arch Validation Stud. 4.5 Filed Studies
- 29. Year 1 Goals A-29 •Develop integrated models of physical, social, and economic systems • Expand hazard capabilities • Establish the CGE modeling approach • Develop network models for physical systems
- 30. Year 2 Goals A-30 •Establish definitions for functionality, networked systems, and their inter-relationships • Develop models for community recovery including dependencies • Define intelligent decision algorithm architecture
- 31. Out Year Goals A-31 •Develop risk-informed performance goals and tools • Address the expected level of performance during the event • Provide risk-informed guidelines suitable for standards and other regulatory documents
- 32. Presentation Section 1: Modelsfor community resilience assessment A-32
- 33. Introduction of Testbeds A-33 •Centerville – a virtual community • Seaside, OR – a coastal tourist destination • Shelby County, TN – previously modeled in MAE Center research
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- 36. The Centerville Testbed A-36 •A simple community model with essential physical, social and economic infrastructure components and systems; • Algorithmic modules, linkages and interdependencies can be developed, tested and verified independently through calculations, experience or intuition; • Software developers at NSCA require prototypical algorithms and datasets so that they can begin coding NIST-CORE while waiting for more realistic and complex algorithms and datasets to emerge; • The common community model requires engineers, economists and sociologists to begin working toward a common purpose immediately.
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- 38. Centerville A-38 (Infrastructure systems supportingcommunity resilience) • Physical systems – represent distinct topologies ‒ Buildings ‒ Transportation ‒ Water ‒ Electrical power ‒ Telecommunications • Economic systems • Social systems
- 39. Physical systems: Modelingof infrastructure capacity, damage and fragility estimates, and network models A-39
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- 41. Community Building Inventory A-41 Objective:To model response of buildings to natural hazards at both individual and inventory levels and at different resolutions Building Classification System Building Fragilities Functions Inventory Structural Design Loss of Functionality (downtime) Direct/ Indirect Losses Recovery Time and Trajectory 106 building archetypes: 26 wood, 14 masonry, 40 steel & 26 reinforced concrete Occupancies: Residential, commercial, industrial, educational, healthcare, governmental, religious, etc. Prioritized hazards: Earthquake, tsunami, tornado & earthquake followed by tsunami Econ. Systems; Social Systems; Uncertainties Econ. Systems; Social Systems; Uncertainties; Interdependency; Decision algorithm
- 42. Community Building Inventory A-42 Centerville Total15130 buildings in Centerville: ‐ 14890 residential buildings ‐ 151 commercial and retail buildings ‐ 70 industrial buildings ‐ 19 critical facilities 16 building types are used to assemble the 11 zones: ‐ 14890 residential buildings ‐ 151 commercial and retail buildings ‐ 70 industrial buildings ‐ 19 critical facilities
- 43. Transportation sector A-43 Objective: Toprovide models and methods that enable risk and resilience assessment of transportation systems at both component and network level Component Fragility Models Risk and Resilience Assessment Decision-making Framework for Mitigation Strategies Transportation components: Highway/railway bridges, roadways, tunnels, railroads Prioritized hazards: Earthquake, tsunami, flood, wind, surge-wave Component level Component Restoration Models Network Performance Metrics Network level Network Restoration Models
- 44. Transportation sector A-44 Centerville Major origins/destinations Criticalfacilities Interstate/state highways Major community roads Secondary community roads Major intersection Rock River Bridges
- 45. Water and wastewaternetworks A-45 Objective: To develop models of the physical water and wastewater systems and their functional service to the community, including hazard-induced loads and effects Damage Analysis of Network Components Water Flow Assessment In the Post-event Scenario Hazard Models and Intensity Measures (IM) assessment
- 46. Water and wastewaternetworks A-46 Centerville
- 47. Energy and powernetwork A-47 Objective: To develop models of the physical systems of energy and power networks and of their functional service to the community • Generation • Transmission • Distribution Network Analysis Loss/Recovery Analysis Hazard Network Interaction Hazard Analysis EPN Characterization
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- 49. A-49 EPN Tornado Analysis- Sample Energy and power network
- 50. A-50 Water and Electricalpower networks Modeling of systems’ dependencies • Coupling of the WN and EPN to model the dependency of the two systems and capture impact of the failures in one network on the functionality of the other network • The coupling induces a change in the damage scenario and recovery time of selected functionality metrics
- 51. A-51 Physical components ofthe Communications Infrastructure for Clatsop, Oregon Communication networks Seaside Example of a single-degree of freedom model for mobile phone system restoration post-earthquake
- 52. A-52 Social Science Models:Social impacts and Social Vulnerability • Provide an overview of our approach to social science modeling • Examples of the data and mapping tools we are developing • To enhance community resiliency planning • For social science modeling • Example of modeling population displacement
- 53. A-53 Social Science Models:Social impacts and Social Vulnerability
- 54. A-54 Social Science Models:Social impacts – Impacts on Institutional structure & community functions ‒ Education (schools), health (hospitals, clinics); Housing (SF, MF, rental, owner-occupied); Child care; Emergency/Security, Food Security, etc. – Demographic impacts ‒ Population displacement, dislocation, and loss, composition, etc. – Economic impacts ‒ Business loss, interruption, failure, and movement, fiscal, etc. – Psychological impacts ‒ PTSD, Anxiety, Depression, Substance Abuse, etc. – Resiliency Outcomes: ‒ Recovery (speed and quality) & Adaptation
- 55. A-55 Social Science Models:Social impacts and Social Vulnerability Hazard Exposure Physical Vulnerability Social Vulnerability Community Characteristics: Hazard Agent
- 56. A-56 Social Science Models:Social impacts and Social Vulnerability • Critical For modeling social impacts and guiding effective resiliency planning should be the convergence of these three: ‒ Hazards exposure ‒ Physical vulnerability ‒ Social Vulnerability These overlaps represent increased hazard vulnerability and should be prime targets for resiliency planning to reduce risk through mitigation and recovery planning activities. Hazard Exposure Physical Vulnerability Social Vulnerability
- 57. A-57 Social Science Models:Social Vulnerability • Like physical vulnerability, but the focus is on social units and their attributes • Focus on attributes associated with social factors and processes that generate vulnerabilities in terms of a person or group’s capacity to anticipate, cope, resist, and recovery from disaster • Race/Ethnicity, Gender, Education, Income, poverty, Age, Wealth, Housing tenure (renter/owner). • Populations with these characteristics are not uniformly or randomly distributed in our communities • As a consequence we can develop tools and mapping approaches to identify areas with varying degrees or concentrations of socially vulnerable populations
- 58. A-58 Social Science Models:Social Vulnerability Attributes • Census data – American Community Survey (ACS) Data – 5-year estimates • Census Block Group – high resolution data that corresponds to Neighborhoods • Ideal for planning purposes • Developing first, second, and third order social vulnerability indicators and scales • Higher resolution social vulnerability data enhances: • Mapping for resiliency planning • Modeling social impacts and recovery
- 59. A-59 Social Science Models:Social Vulnerability Indictors Centerville • Social vulnerability characteristics are included • Income and race/ethnicity • But quite simplified and discrete housing areas.
- 60. A-60 Social Science Map:Single Parent Households with Children
- 61. A-61 Social Science Map:Single Parent Households with Children
- 62. A-62 Social Science Models:Modeling population Displacement • The fine resolution Social Vulnerability Data in combination with Engineering modeling output and other data are employed to develop social science models • Models are being developed based on current social science research and empirical analysis of existing data.
- 63. A-63 Social Science Models:Current Work • Refinement of social vulnerability data and mapping tools • Continuing work on Improving displacement and dislocation algorithms • Literature reviews • New analysis – Ike data • New ways of linking engineering/social science modeling • We also have working algorithms for estimating • Causalities • PTSD • Extending SV approaches to building vulnerabilities • Combining stress/strain analysis from engineering and social science • Housing recovery modeling • Reaching out to the practice community – American Planning Association
- 64. A-64 Economic Analysis Description ofa Computable General Equilibrium (CGE) Model Integration of the CGE model with the built Environment Deriving optimal mitigation policies
- 65. A-65 Income (Spatial) Labor Supply Wage Payments Expendituresfor Goods and Services Receipt of Goods and services Tax Payments Services Tax Payments Services Expenditure Leakages Household migration Expenditures Receipt of Goods and Services
- 66. A-66 Specifics of theCGE Model Commercial Sectors – produce output using factors of production and intermediate inputs • Labor • Physical Capital (buildings) • Intermediate inputs • Total factor productivity (TFP) Households Income – wages and capital income • Purchases goods and services • Purchase or rent housing (buildings)
- 67. A-67 Integration with theBuilt Environment Natural Hazard • Buildings – impacts production, household income and housing • Transportation – impacts production (TFP) and household purchases • Electricity – ability to produce and quality of living • Water – ability to produce and quality of living • Telecommunication – affect TFP
- 68. A-68 Optimal Mitigation Policy Wantto understand how policies like retrofitting buildings and roads, changes in building codes and spatial considerations for constructing new buildings can impact the resilience of a community to a natural hazard Consider many mitigation policies with the objective to minimize the impacts on • The level and distribution of household income • Production of goods and services • Population Dislocation • Local government tax revenue
- 69. A-69 Centerville Integration of physical,social and economic models • Scenario earthquake hazard • Building damage and loss • Social and economic impact • Decision analysis • Accomplishments
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- 74. A-74 Expected loss andloss ratio by zone
- 75. A-75 Centerville Testbed Social andeconomic metrics • Population dislocation • Employment/domestic output • Level and distribution of household income
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- 77. A-77 Baseline Scenario: MitigationFree Consider alternative retrofitting policies to protect reinforce combinations of commercial and residential buildings
- 78. A-78 Pre-event decision analysis MitigationResource Allocation Code improvements to mitigate impact to tax Revenues and dislocation
- 79. A-79 Mitigation Resource Allocation Allocatelimited resources to retrofit building types in each zone, to specific code levels with respect to the following competing objectives: ‒ minimize economic loss (i.e., structure, non-structure, and contents loss, provided by the engineering team) ‒ minimize total dislocation (OLS model provided by the Social Science team) Constraint: ‒ overall disparity in the dislocation rates by socio-economic status does not increase
- 80. A-80 Mitigation Resource AllocationBaseline Total direct economic loss: $856M Total dislocation: 3,203 households Evaluation optimization results under three budget levels: ‒ Low: 15% of ideal budget ‒ Medium: 30% of ideal budget ‒ High: 60% of the ideal budget Ideal budget ($346M) is sufficient to retrofit all buildings to the highest code level.
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- 82. A-82 Policy 1: Lowdislocation Loss:$636,933,570 Dislocation: 1,467
- 83. A-83 Three Mitigation policies Policy1 5.5% and 9.9% reductions in the appraised value of residential and commercial, respectively Policy 2 6.5% and 4.7% reductions in the appraised value of residential and commercial, respectively Policy 3 7.2% and 3.1% reductions in the appraised value of residential and commercial, respectively
- 84. A-84 Centerville Accomplishments to date •Topology of buildings, transportation, power and water infrastructure complete; • Fragilities of transportation, power and water components complete; buildings in progress; • Interfaces between physical, social and economic systems are understood; • Damage and loss estimation models have been tested and are being implemented in NIST-CORE v.1; • Engineers, economists and social scientists eager to work together!
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- 86. B-1 Natural Hazards andInfrastructure Damage Year 1 focus areas: • Tornado • Tsunami following Earthquake • Wildland Urban Interface Fire Year 2-3 focus areas: • Hurricane Wind, Surge/Wave • Landslide • Precipitation-Rain, Snow • Climate Change • Multi hazards
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- 88. B-3 Tornado Hazard Analysis Historicaltornado tracks for probabilistic analysis.
- 89. B-4 Tornado Hazard Analysis Probabilistic tornadohazard analysis from EF-0 To EF-5.
- 90. B-5 Tornado Characteristics Tornado intensity,track width, length and direction are important characteristics leading to community damage
- 91. B-6 Stochastic Variations inTornado Characteristics Stochastic variations in width, length and direction Modeling of tornado Characteristics to account for varying intensity
- 92. B-7 Tornado Scenarios inNIST-CORE 1.0 Tornado Scenario: Centerville testbed tornado with user defined starting point and prescribed length, width and angle
- 93. B-8 Tornado Scenarios inNIST-CORE 1.0 Tornado Scenario: Centerville testbed tornado with user defined starting point and random length, width and angle
- 94. B-9 Tornado Damage andLoss Fragility Modeling The EF3 Polk County tornado west of Osceola, Iowa, on June 20, 2011.
- 95. B-10 Tsunamis following Earthquakes Tsunamiinundation following an earthquake in Japan on March 11, 2011.
- 96. B-11 Nearfield Tsunami Threatfor California, Oregon and Washington Probability of full-rupture Cascadia Event is 18-22% in the next 50 years
- 97. B-12 Tsunami Generation, Propagationand Inundation Modeling Use of NOAA’s ComMIT/MOST for tsunami generation and propagation and COULWAVE for inundation Five Tsunami Intensity Measures: • Arrive time • Duration of flooding • Maximum flow depth • Maximum flow speed • Maximum momentum flux Life safety Damage/loss Seaside, Oregon
- 98. B-13 Nearfield Tsunami ProbabilityHazard Analysis Event tree to define tsunami probabilities Annual exceedance probability for depth at one location
- 99. B-14 Depth and momentumflux hazards for 1,000 year event Flow depth and momentum flux hazards defined for the 1,000 year CSZ event
- 100. B-15 Characterization of theBuilt Environment Tax Lot Data Google Street View FEMA Rapid Visual Screening Construction Material Floor Levels Seismic Code
- 101. B-16 Natural hazards andInfrastructure Damage Tsunami fragility analysis for 1,000 year event based on flow depth Probability of Complete Damage
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- 103. B-18 Multi-hazard Earthquake andTsunami Damage Estimation Probability of Complete Damage Tsunami + earthquake Tsunami Earthquake
- 104. B-19 Direct Loss Estimationfor Earthquake and Tsunami Damage Loss estimation (2012 real market value) Loss ($M) Total direct loss $1.23B
- 105. B-20 Wildland Urban interfaceFire In 2015, wildfires in California, Oregon and Washington burned 1,400,000 acres.
- 106. B-21 Framework for EvaluatingWUI Hazard
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- 108. B-23 Community Fire Propagation Model Step1 Identify Ignitable Objects and boundary nodes to develop corresponding path Step 2 Calculate Weights (W) of each edge W(1,J) W(2,J) W(3,J) W(1,2) W(1,3) W(2,3) W(2,2) W(3,3) Step 3 Identity most probable paths Step 4 Calculate probability of most probable paths for each boundary node
- 109. B-24 Year 2-3 HazardFocus Areas • Hurricane Wind, Surge/Wave • Landslide • Precipitation-Rain, Snow • Climate Change • Multi hazards
- 110. B-25 Hurricane Wind, Surgeand Wave Leverage existing efforts • USACE Coastal Hazard System for Hurricane Storm Surge and Waves • DHS Coastal Resilience Center for ADCIRC + SWAN output
- 111. B-26 Landslide Hazards Leverage existingefforts for landslide hazard • Example of landslide probability for Mw 9.0 CSZ event • Use in multi-hazard and cascading hazard analyses
- 112. B-27 Sequence of damagefrom earthquake aftershocks (from Kam et al. 2011) Earthquake Mainshock and Aftershock
- 113. B-28 Fragility Analyses forMulti-Hazards Fig B: Fragility surface for multiple Intensity Measures Fig A: Methodology for combining earthquake and tsunami hazards
- 114. B-29 Interdependency Defined as therelationship between two or more components, networks or systems. We focused on civil infrastructure systems this first year. Our modeling approaches were (1) unified directed graphs, (2) linked directed graph networks, and (3) linked system level input-output models.
- 115. B-30 Common characteristics ofall models: fragilities and recovery functions. Fragility is defined here as the conditional probability of failure given an applied Demand such as PGA (g). Recovery curves represent physical system Recovery or functionality over time. Functionality is also called operability.
- 116. B-31 Role of Interdependencyin Modeling Response and Recovery
- 117. B-32 Interdependency modeling usingunified directed graph Features 1. Inoperability of an individual node is determined by using the Dynamic Inoperability Input-output Model in every time step. 2. Damage of a node is determined by considering minimum required input and output as well as the relationship between the operability and the physical integrity of the facility represented by the node.
- 118. B-33 Illustration of thedamage propagation Fully connected network Initial damage of Node 2 ‒ Node 2 is not fully operational due to the physical damage of the node. ‒ If the minimum required output is not met, the node is designed as fully inoperational Damage propagation Throughout the network ‒ Due to its dependency on Node 2, Node 3 becomes partially or fully inoperational. ‒ Considering the dependency of Node 4 on Node 3 and 6, the operability of Node 4will be determined.
- 119. B-34 Interdependency modeling usingunified directed graph Features 3. Node-to-node level, across-system dependency is modeled by combining the importance of a node to produce a service (including power, telecommunication, and water in the current model) and the importance of the service in the operation of another node. 4. Dependency relationships remain unchanged throughout recovery.
- 120. B-35 Interdependency modeling usingunified directed graph Features 5. Inoperability of the individual systems (power, telecommunication and water) can be measured by network characteristic parameters such as connectivity and efficiency. For the calculations, the output links of the fully damaged nodes are removed. 6. Inoperability of the individual systems can be used as inputs for the inoperability model of socio-economic systems.
- 121. B-36 Dependency Model usingDirected Graph Networks: Water functionality is dependent upon electric power in this example from Centerville. This approach used a directed graph with physical connectivity between power delivery components and water system components. EPN/WN Analysis o Component analysis Fragility functions o Connectivity analysis Connectivity matrix o Flow analysis
- 122. B-37 The third method employedwas the system-level resiliency- based interdependency approach (RBIA). In this approach the similarities between the risk and resiliency formulations may be seen.
- 123. B-38 Example of RBIAmethodology Begin with the power system fragility and recovery. Derive interdependency matrix coefficients for other systems. Finally, simulate the complete set of recovery curves for specific hazards using the Input-Output Model.Data sources include Duenas-Osorio and Kwasinski (2012); Nojima et al. (1995, 2005, 2013); and Park et al. (2006)
- 124. B-39 The result of RBIAmodeling are illustrated in this figure. The role of interdependency changes for pre- event, during and post-event recovery. RESILIENCY
- 125. B-40 • Synthesize modelingapproaches • Bridge the gap between the physical/ functional infrastructure models and social acceptance. Future Work
- 126. B-41 • NIST-CORE isthe computation platform for NIST-COE research. • This demo shows how a common platform can integrate different research done by different groups. ‒ Consistent User Interface and allowing to connect different analyses • These implementations are based on latest research of NIST-COE. NIST-CORE Demo: Overview
- 127. B-42 • Tornado ‒ Scenariotornado ‒ Building damage, Electric Power Network damage • Tsunami ‒ Building damage • Wildfire NIST-CORE Demo: Overview
- 128. B-43 • Creating scenariotornado (Shelby county) • Estimating building damage (Shelby county) • Estimating electric power network damage (Centerville) NIST-CORE Demo: Tornado
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- 130. B-45 • Ingesting tsunamihazard (Max. Momentum Flux in 1,000 yr) for Seaside, OR • Estimating building damage NIST-CORE Demo: Tsunami
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- 132. B-47 • Creating WildfireHazard • This is a first dataset which is both spatial and temporal. NIST-CORE Demo: Wildfire
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- 134. B-49 • Summary ofmajor accomplishments during Year 1 • Major challenges for Year 2 and beyond The path forward
- 135. B-50 Hazard and infrastructureperformance • Expanded hazard capabilities from EQ to tornado, tsunami, and WUI wildfire • Extended fragility modeling approaches to new hazards, including tornado and tsunami • Developed network models for physical systems: water, power, transportation • Initiated research on recovery sequences and durations Major accomplishments in Year 1
- 136. B-51 Integrated systems modelingand risk-informed decision • Developed integrated models of physics, social, and economic systems with initial dependencies in Centerville and Seaside Testbeds • Modeled interdependencies between water and power networks in Centerville • Established the Computable Generalized Equilibrium (CGE) modeling approach for economic impacts on communities • Assessed impacts of physical damage on institutional structure, community functions and demographics • Identified user needs for database development and interfacing. Major accomplishments in Year 1
- 137. B-52 • Natural hazardmodeling and system performance • Functionality • Recovery • Climate change • Decision support algorithms Future Work in Year 2 and beyond
- 138. B-53 • Model hazardsthat involve coupled or cascading effects – earthquake/tsunami/landslide, main shock-aftershock earthquake sequences, fire following earthquake, hurricane wind/storm surge/waves/inundation • Develop joint probabilities and statistical data on jointly occurring hazards • Incorporate aging of civil infrastructure facilities • Model debris and its impact on performance of infrastructure during and after tornados, tsunamis, and flooding • Identify scales of resolution for modeling hazards with spatially distributed effects • Develop resilience models for telecommunication facilities Natural hazards and systems performance
- 139. B-54 • Establish connectionsbetween physical damage to buildings, networked systems and lifelines and their functionalities • Identify interdependencies between functionality recovery of physical systems and community restoration patterns • Assess impacts of physical damage on institutional structure and community functions and demographics • Advance social vulnerability modeling to reflect impacts which may not be uniformly distributed across a community • Extend social vulnerability models to businesses Functionality
- 140. B-55 • Identify interactionsbetween recovery of civil infrastructure and community social and economic systems at different phases in the recovery process • Develop models for community recovery including dependencies between physical, social, and economic systems at appropriate spatial and temporal scales • Collect and analyze full-scale data for validating recovery models of interdependent systems • Initiate processes and strategies for minimizing-recovery time and creating a rapid recovery trajectory Recovery
- 141. B-56 • Identify keymodeling parameters and uncertainties in climate change scenarios and their impact on infrastructure systems. • Develop coupled hurricane/storm surge/wave/coastal flooding models for coastal communities • Develop computationally efficient methods for modeling climate change-driven hazards and for propagating systemic deep uncertainties in climate change models • Identify feasible engineering solutions to mitigate community risks under climate change Climate change
- 142. B-57 • Define theintelligent decision support algorithm architecture to facilitate consideration of competing objectives and different value systems • Establish resolutions of community modeling required to support different decisions and scalability of resilience assessment models to communities of different sizes, contexts • Develop risk-informed performance goals and tools to assess the integrity of the built, economic, and social environments for resilient communities • Provide decision support that consider special needs of vulnerable populations, inter-generational equity, and sustainability • Develop risk-informed guidelines for community resilience that are suitable for standard and other regulatory documents Decision support algorithms for risk-informed decision-making
- 143.