ML20045F284

RIL-2001, Part 3, - Proceedings of NRC Annual Probabilistic Flood Hazard Assessment Research Workshops I-IV

ML20045F284
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Issue date:	02/14/2020
From:	Thomas Aird, M'Lita Carr, Joseph Kanney Office of Nuclear Regulatory Research
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M. Carr 415-6322
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RIL-2001, Pt. 3
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RIL-2001 PROCEEDINGS OF NRC ANNUAL PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOPS I-IV 2015-2019 Rockville, MD Date Published: February 2020 Prepared by:

M. Carr T. Aird J. Kanney U.S Nuclear Regulatory Commission Rockville, MD 20852 3DUW7KLUG$QQXDO15&3UREDELOLVWLF)ORRG

+D]DUG$VVHVVPHQW5HVHDUFK:RUNVKRS Research Information Letter Research Office of Nuclear Regulatory Research

Disclaimer Legally binding regulatory requirements are stated only in laws, NRC regulations, licenses, including technical specifications, or orders; not in Research Information Letters (RILs). A RIL is not regulatory guidance, although NRCs regulatory offices may consider the information in a RIL to determine whether any regulatory actions are warranted.

ABSTRACT The U.S. Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research (RES) is conducting a multiyear, multi-project Probabilistic Flood Hazard Assessment (PFHA) Research Program to enhance the NRCs risk-informed and performance-based regulatory approach with regard to external flood hazard assessment and safety consequences of external flooding events at nuclear power plants (NPPs). It initiated this research in response to staff recognition of a lack of guidance for conducting PFHAs at nuclear facilities that required staff and licensees to use highly conservative deterministic methods in regulatory applications. Risk assessment of flooding hazards and consequences of flooding events is a recognized gap in NRCs risk-informed, performance-based regulatory framework. The objective, research themes, and specific research topics are described in the RES Probabilistic Flood Hazard Assessment Research Plan. While the technical basis research, pilot studies and guidance development are ongoing, RES has been presenting Annual PFHA Research Workshops to communicate results, assess progress, collect feedback and chart future activities. These workshops have brought together NRC staff and management from RES and User Offices, technical support contractors, as well as interagency and international collaborators and industry and public representatives.

These conference proceedings transmit the agenda, abstracts, presentation slides, summarized questions and answers, and panel discussion for the first four Annual U.S. Nuclear Regulatory Commission (NRC) Probabilistic Flood Hazard Assessment Research Workshops held at NRC Headquarters in Rockville, MD. The workshops took place on October 14-15, 2015; January 23-25, 2017; December 4-5, 2017; and April 30-May 2, 2019. The first workshop was an internal meeting attended by NRC staff, contractors, and partner Federal agencies. The following workshops were public meetings and attended by members of the public; NRC technical staff, management, and contractors; and staff from other Federal agencies. All of the workshops began with an introductory session that included perspectives and research program highlights from the NRC Office of Nuclear Regulatory Research and also may have included perspectives from the NRC Office of New Reactors and Office of Nuclear Reactor Regulation, the Electric Power Research Institute (EPRI), and industry representatives. NRC and EPRI contractors and staff as well as invited Federal and public speakers gave technical presentations and participated in various styles of panel discussion. Later workshops included poster sessions and participation from academic and interested students. The workshops included five focus areas:

(1) leveraging available flood information (2) evaluating the application of improved mechanistic and climate probabilistic modeling for storm surge, climate and precipitation (3) probabilistic flood hazard assessment frameworks (4) potential impacts of dynamic and nonstationary processes (5) assessing the reliability of flood protection and plant response to flooding events iii

TABLE OF CONTENTS ABSTRACT ................................................................................................................................... III ABBREVIATION AND ACRONYMS ............................................................................................. X INTRODUCTION .................................................................................................................... XXXVII BACKGROUND ........................................................................................................................ XXXVII WORKSHOP OBJECTIVES ........................................................................................................ XXXVII WORKSHOP SCOPE ............................................................................................................... XXXVIII

SUMMARY

OF PROCEEDINGS ................................................................................................. XXXVIII RELATED WORKSHOPS ............................................................................................................ XXXIX 1 FIRST ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP .............................................................................................................................1-1

1.1 INTRODUCTION

......................................................................................................................1-1 1.1.1 Organization of Conference Proceedings..................................................................................1-1 1.2 WORKSHOP AGENDA ............................................................................................................1-3 1.3 PROCEEDINGS ......................................................................................................................1-5 1.3.1 Day 1: Session I: Program Overview ..........................................................................................1-5 1.3.1.1 Opening Remarks. .............................................................................................................................. 1-5 1.3.1.2 NRC PFHA Research Program Overview. .................................................................................... 1-7 1.3.1.3 NRO Perspectives on Flooding Research Needs. ..................................................................... 1-24 1.3.1.4 Office of Nuclear Reactor Regulation Perspectives on Flooding Research Needs. ............ 1-36 1.3.2 Day 1: Session II: Climate ......................................................................................................... 1-50 1.3.2.1 Regional Climate Change ProjectionsPotential Impacts to Nuclear Facilities................... 1-50 1.3.3 Day 1: Session III: Precipitation ............................................................................................... 1-63 1.3.3.1 Estimating PrecipitationFrequency Relationships in Orographic Regions......................... 1-63 1.3.3.2 Numerical Simulation of Local Intense Precipitation. ................................................................. 1-86 1.3.3.3 SHAC-F (Local Intense precipitation). ........................................................................................ 1-129 1.3.4 Day 2: Session IV: Riverine and Coastal Flooding Processes .......................................... 1-147 1.3.4.1 PFHA Technical Basis for Riverine Flooding............................................................................. 1-147 1.3.4.2 PFHA Framework for Riverine Flooding..................................................................................... 1-166 1.3.4.3 State of Practice in Flood Frequency Analysis. ......................................................................... 1-174 1.3.4.4 Quantification and Propagation of Uncertainty in Probabilistic Storm Surge Models ........ 1-190 1.3.4.5 USBR Dam Breach Physical Modeling....................................................................................... 1-206 1.3.5 Day 2: Session V: Plant Response to Flooding Events ...................................................... 1-220 1.3.5.1 Effects of Environmental Factors on Flood Protection and Mitigation Manual Actions. .... 1-220 1.3.5.2 Flooding Information Digests. ....................................................................................................... 1-238 1.3.5.3 Framework for Modeling Total Plant Response to Flooding Events. .................................... 1-250 1.3.5.4 Performance of Penetration Seals............................................................................................... 1-261 1.4

SUMMARY

......................................................................................................................... 1-265 1.5 WORKSHOP PARTICIPANTS ............................................................................................... 1-267 2 SECOND ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP .............................................................................................................................2-1 v

2.1 INTRODUCTION

......................................................................................................................2-1 2.1.1 Organization of Conference Proceedings..................................................................................2-1 2.2 WORKSHOP AGENDA ............................................................................................................2-3 2.3 PROCEEDINGS ......................................................................................................................2-7 2.3.1 Day 1: Session 1A - Introduction ................................................................................................2-7 2.3.1.1 Welcome ............................................................................................................................................... 2-7 2.3.1.2 PFHA Research Needs for New and Operating Reactors ........................................................ 2-12 2.3.1.3 Use of Flooding Hazard Information in Risk-Informed Decision-making................................ 2-22 2.3.1.4 Flooding Research Needs: Industry Perspectives on Development of External Flood Frequency Methods ........................................................................................................................ 2-30 2.3.1.5 NRC Flooding Research Program Overview ............................................................................... 2-38 2.3.1.6 EPRI Flooding Research Program Overview .............................................................................. 2-46 2.3.2 Day 1: Session 1B - Storm Surge Research ........................................................................... 2-50 2.3.2.1 Quantification of Uncertainty in Probabilistic Storm Surge Models ........................................ 2-50 2.3.2.2 Probabilistic Flood Hazard AssessmentStorm Surge ............................................................ 2-75 2.3.3 Day 2: Session 2A - Climate and Precipitation....................................................................... 2-85 2.3.3.1 Regional Climate Change Projections: Potential Impacts to Nuclear Facilities .................... 2-85 2.3.3.2 Numerical Modeling of Local Intense Precipitation Processes ................................................ 2-98 2.3.3.3 Extreme Precipitation Frequency Estimates for Orographic Regions................................... 2-148 2.3.3.4 Local Intense Precipitation Frequency Studies, ........................................................................ 2-165 2.3.4 Day 2: Session 2B - Leveraging Available Flood Information I ......................................... 2-177 2.3.4.1 Development of Flood Hazard Information Digest for Operating NPP Sites ....................... 2-177 2.3.4.2 At-Streamgage Flood Frequency Analyses for Very Low Annual Exceedance Probabilities from a Perspective of Multiple Distributions and Parameter Estimation Methods ............ 2-184 2.3.4.3 Extending Frequency Analysis beyond Current Consensus Limits....................................... 2-199 2.3.5 Day 2: Session 2C - Leveraging Available Flood Information II ........................................ 2-213 2.3.5.1 Collection of Paleoflood Evidence ............................................................................................... 2-213 2.3.5.2 Paleofloods on the Tennessee RiverAssessing the Feasibility of Employing Geologic Records of Past Floods for Improved Flood Frequency Analysis ........................................ 2-224 2.3.6 Day 2: Session 2D - Reliability of Flood Protection and Plant Response I ...................... 2-243 2.3.6.1 EPRI Flood Protection Project Status ......................................................................................... 2-243 2.3.6.2 Performance of Flood-Rated Penetration Seals ....................................................................... 2-256 2.3.7 Day 2: Daily Wrap-Up Question and Answer Period ........................................................... 2-266 2.3.8 Day 3: Session 3A - Reliability of Flood Protection and Plant Response II ..................... 2-267 2.3.8.1 Effects of Environmental Factors on Manual Actions for Flood Protection and Mitigation at Nuclear Power Plants ................................................................................................................... 2-267 2.3.8.2 Modeling Total Plant Response to Flooding Event .................................................................. 2-284 2.3.9 Day 3: Session 3B - Frameworks I ......................................................................................... 2-303 2.3.9.1 Technical Basis for Probabilistic Flood Hazard Assessment ................................................. 2-303 2.3.10 Day 3: Session 3C - Frameworks II ...................................................................................... 2-318 2.3.10.1 Evaluation of Deterministic Approaches to Characterizing Flood Hazards ....................... 2-318 2.3.10.2 Probabilistic Flood Hazard Assessment Framework Development .................................... 2-334 2.3.10.3 Riverine Flooding and Structured Hazard Assessment Committee Process for Flooding (SHAC-F), ....................................................................................................................................... 2-349 2.3.11 Day 3: Session 3D - Panel Discussion ................................................................................ 2-367 2.3.11.1 National Oceanic and Atmospheric Administration/National Weather Service (NOAA/NWS)

........................................................................................................................................................... 2-367 2.3.11.2 U.S. Army Corps of Engineers ................................................................................................... 2-370 2.3.11.3 Tennessee Valley Authority (TVA) ............................................................................................ 2-375 2.3.11.4 U.S. Department of Energy (DOE) ............................................................................................ 2-387 2.3.11.5 Institut de Radioprotection et de Sûreté Nucléaire ................................................................. 2-391 vi

2.3.11.6 Discussion ...................................................................................................................................... 2-396 2.3.12 Day 3: Session 3E - Future Work in PFHA .......................................................................... 2-402 2.3.12.1 Future Work in PFHA at EPRI .................................................................................................... 2-402 2.3.12.2 Future Work in PFHA at NRC .................................................................................................... 2-407 2.4

SUMMARY

......................................................................................................................... 2-417 2.5 PARTICIPANTS .................................................................................................................. 2-419 3 THIRD ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP .............................................................................................................................3-1

3.1 INTRODUCTION

......................................................................................................................3-1 3.1.1 Organization of Conference Proceedings..................................................................................3-1 3.2 WORKSHOP AGENDA ............................................................................................................3-3 3.3 PROCEEDINGS ......................................................................................................................3-9 3.3.1 Day 1: Session 1A - Introduction ................................................................................................3-9 3.3.1.1 Welcome ............................................................................................................................................... 3-9 3.3.1.2 NRC Flooding Research Program Overview ............................................................................... 3-11 3.3.1.3 EPRI Flooding Research Program Overview .............................................................................. 3-20 3.3.2 Day 1: Session 1B - Climate and Precipitation....................................................................... 3-29 3.3.2.1 Regional Climate Change Projections: Potential Impacts to Nuclear Facilities .................... 3-29 3.3.2.2 Numerical Modeling of Local Intense Precipitation Processes ................................................ 3-42 3.3.2.3 Research on Extreme Precipitation Estimates in Orographic Regions .................................. 3-70 3.3.3 Day 1: Session 1C - Storm Surge ............................................................................................. 3-94 3.3.3.1 Quantification of Uncertainty in Probabilistic Storm Surge Models ......................................... 3-94 3.3.3.2 Probabilistic Flood Hazard Assessment - Storm Surge.......................................................... 3-109 3.3.4 Day 1: Session 1D - Leveraging Available Flood Information I ......................................... 3-116 3.3.4.1 Flood Frequency Analyses for Very Low Annual Exceedance Probabilities using Historic and Paleoflood Data, with Considerations for Nonstationary Systems ............................... 3-116 3.3.4.2 Extending Frequency Analysis beyond Current Consensus Limits....................................... 3-135 3.3.4.3 Development of External Hazard Information Digests for Operating NPP sites ................. 3-149 3.3.5 Day 1: Session 1E - Paleoflood Studies ................................................................................ 3-163 3.3.5.1 Improving Flood Frequency Analysis with a Multi-Millennial Record of Extreme Floods on the Tennessee River near Chattanooga, .................................................................................. 3-163 3.3.5.2 Collection of Paleoflood Evidence ............................................................................................... 3-179 3.3.6 Day 2: Daily Wrap-up Session / Public Comments .............................................................. 3-191 3.3.7 Day 2: Poster Session.............................................................................................................. 3-195 3.3.7.1 Poster Abstracts .............................................................................................................................. 3-195 3.3.7.2 Posters .............................................................................................................................................. 3-200 3.3.8 Day 2: Session 2A - Reliability of Flood Protection and Plant Response I ...................... 3-227 3.3.8.1 Performance of Flood- Rated Penetration Seals ...................................................................... 3-227 3.3.8.2 EPRI Flood Protection Project Status ......................................................................................... 3-234 3.3.8.3 A Conceptual Framework to Assess Impacts of Environmental Conditions on Manual Actions for Flood Protection and Mitigation at Nuclear Power Plants ................................. 3-240 3.3.8.4 External Flooding Walkdown Guidance...................................................................................... 3-250 3.3.8.5 Erosion Testing of Zoned Rockfill Embankments ..................................................................... 3-258 3.3.9 Day 2: Session 2B - Frameworks I ......................................................................................... 3-295 3.3.9.1 A Framework for Inland Probabilistic Flood Hazard Assessments: Analysis of Extreme Snow Water Equivalent in Central New Hampshire ........................................................................... 3-295 3.3.9.2 Structured Hazard Assessment Committee Process for Flooding (SHAC-F) for Riverine Flooding ........................................................................................................................................... 3-304 vii

3.3.10 Day 2: Session 2C - Panel Discussions .............................................................................. 3-316 3.3.10.1 Flood Hazard Assessment Research and Guidance Activities in Partner Agencies ....... 3-316 3.3.10.2 External Flooding Probabilistic Risk Assessment (PRA): Perspectives on Gaps and Challenges ...................................................................................................................................... 3-351 3.3.11 Day 2: Session 2D - Future Work in PFHA.......................................................................... 3-375 3.3.11.1 Future Work in PFHA at EPRI .................................................................................................... 3-375 3.3.11.2 Future Work in PFHA at NRC .................................................................................................... 3-380 3.3.12 Day 2: Final Wrap-up Session / Public Comment .............................................................. 3-388 3.4

SUMMARY

......................................................................................................................... 3-389 3.5 WORKSHOP PARTICIPANTS ............................................................................................... 3-391 4 FOURTH ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP .............................................................................................................................4-1

4.1 INTRODUCTION

......................................................................................................................4-1 4.1.1 Organization of Conference Proceedings..................................................................................4-1 4.2 WORKSHOP AGENDA ............................................................................................................4-2 4.3 PROCEEDINGS ......................................................................................................................4-9 4.3.1 Day 1: Session 1A - Introduction ................................................................................................4-9 4.3.1.1 Introduction........................................................................................................................................... 4-9 4.3.1.2 NRC Flooding Research Program Overview............................................................................... 4-12 4.3.1.3 EPRI External Flooding Research Program Overview. ............................................................. 4-23 4.3.1.4 Nuclear Energy Agency, Committee on the Safety of Nuclear Installations (CSNI): Working Group on External Events (WGEV). ............................................................................................ 4-28 4.3.2 Day 1: Session 1B - Coastal Flooding ..................................................................................... 4-33 4.3.2.1 KEYNOTE: National Weather Service Storm Surge Ensemble Guidance. ........................... 4-33 4.3.2.2 Advancements in Probabilistic Storm Surge Models and Uncertainty Quantification Using Gaussian Process Metamodeling. ............................................................................................... 4-56 4.3.2.3 Probabilistic Flood Hazard Assessment Using the Joint Probability Method for Hurricane Storm Surge. .................................................................................................................................... 4-72 4.3.2.4 Assessment of Epistemic Uncertainty for Probabilistic Storm Surge Hazard Assessment Using a Logic Tree Approach........................................................................................................ 4-80 4.3.2.5 Coastal Flooding Panel. ................................................................................................................... 4-91 4.3.3 Day 1: Session 1C - Precipitation ............................................................................................. 4-98 4.3.3.1 KEYNOTE: Satellite Precipitation Estimates, GPM, and Extremes. ....................................... 4-98 4.3.3.2 Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods. .............................................................................................. 4-111 4.3.3.3 Reanalysis Datasets in Hydrologic Hazards Analysis. ............................................................ 4-112 4.3.3.4 Current Capabilities for Developing Watershed Precipitation-Frequency Relationships and Storm-Related Inputs for Stochastic Flood Modeling for Use in Risk-Informed Decisionmaking.............................................................................................................................. 4-125 4.3.3.5 Factors Affecting the Development of Precipitation Areal Reduction Factors. ................... 4-142 4.3.3.6 Precipitation Panel Discussion. .................................................................................................... 4-156 4.3.4 Day 2 Session 2A - Riverine Flooding ................................................................................... 4-162 4.3.4.1 KEYNOTE: Watershed Level Risk Analysis with HEC-WAT. ................................................ 4-162 4.3.4.2 Global Sensitivity Analyses Applied to Riverine Flood Modeling........................................... 4-195 4.3.4.3 Detection and Attribution of Flood Change Across the United States. ................................. 4-206 4.3.4.4 Bulletin 17C: Flood Frequency and Extrapolations for Dams and Nuclear Facilities. ....... 4-206 4.3.4.5 Riverine Paleoflood Analyses in Risk-Informed Decisionmaking: Improving Hydrologic Loading Input for USACE Dam Safety Evaluations. ............................................................... 4-227 viii

4.3.4.6 Improving Flood Frequency Analysis with a Multi-Millennial Record of Extreme Floods on the Tennessee River near Chattanooga, TN. .......................................................................... 4-243 4.3.4.7 Riverine Flooding Panel Discussion. ........................................................................................... 4-252 4.3.5 Day 2: Session 2B - Modeling Frameworks .......................................................................... 4-261 4.3.5.1 Structured Hazard Assessment Committee Process for Flooding (SHAC-F). .................... 4-261 4.3.5.2 Overview of the TVA PFHA Calculation System. ..................................................................... 4-272 4.3.5.3 Development of Risk-Informed Safety Margin Characterization Framework for Flooding of Nuclear Power Plants. .................................................................................................................. 4-287 4.3.5.4 Modeling Frameworks Panel Discussion. .................................................................................. 4-306 4.3.6 Day 2: Poster Session 2C ........................................................................................................ 4-311 4.3.6.1 Coastal Storm Surge Assessment using Surrogate Modeling Methods. ............................. 4-312 4.3.6.2 Methods for Estimating Joint Probabilities of Coincident and Correlated Flooding Mechanisms for Nuclear Power Plant Flood Hazard Assessments. ................................... 4-312 4.3.6.3 Modelling Dependence and Coincidence of Flooding Phenomena: Methodology and Simplified Case Study in Le Havre in France. ......................................................................... 4-315 4.3.6.4 Current State-of-Practice in Dam Risk Assessment. ............................................................... 4-315 4.3.6.5 Hurricane Harvey Highlights Challenge of Estimating Probable Maximum Precipitation. 4-320 4.3.6.6 Uncertainty and Sensitivity Analysis for Hydraulic Models with Dependent Inputs............ 4-320 4.3.6.7 Development of Hydrologic Hazard Curves Using SEFM for Assessing Hydrologic Risks at Rhinedollar Dam, CA. ................................................................................................................... 4-323 4.3.6.8 Probabilistic Flood Hazard Analysis of Nuclear Power Plant in Korea. ................................ 4-328 4.3.7 Day 3: Session 3A - Climate and Non-Stationarity .............................................................. 4-329 4.3.7.1 KEYNOTE: Hydroclimatic Extremes Trends and Projections: A View from the Fourth National Climate Assessment. .................................................................................................... 4-329 4.3.7.2 Regional Climate Change Projections: Potential Impacts to Nuclear Facilities. ................. 4-349 4.3.7.3 Role of Climate Change/Variability in the 2017 Atlantic Hurricane Season. ....................... 4-364 4.3.7.4 Climate Panel Discussion.............................................................................................................. 4-374 4.3.8 Day 3: Session 3B - Flood Protection and Plant Response ............................................... 4-378 4.3.8.1 External Flood Seal Risk-Ranking Process. .............................................................................. 4-378 4.3.8.2 Results of Performance of Flood-Rated Penetration Seals Tests. ........................................ 4-386 4.3.8.3 Modeling Overtopping Erosion Tests of Zoned Rockfill Embankments. .............................. 4-398 4.3.8.4 Flood Protection and Plant Response Panel Discussion. ....................................................... 4-419 4.3.9 Day 3: Session 3C - Towards External Flooding PRA......................................................... 4-423 4.3.9.1 External Flooding PRA Walkdown Guidance. ........................................................................... 4-423 4.3.9.2 Updates on the Revision and Expansion of the External Flooding PRA Standard. ........... 4-435 4.3.9.3 Update on ANS 2.8: Probabilistic Evaluation of External Flood Hazards for Nuclear Facilities Working Group Status. ................................................................................................................. 4-446 4.3.9.4 Qualitative PRA Insights from Operational Events of External Floods and Other Storm-Related Hazards. ........................................................................................................................... 4-456 4.3.9.5 Towards External Flooding PRA Discussion Panel. ................................................................ 4-464 4.4

SUMMARY

......................................................................................................................... 4-475 4.5 WORKSHOP PARTICIPANTS ............................................................................................... 4-477 5

SUMMARY

AND CONCLUSIONS ....................................................................................... 5-489 5.1

SUMMARY

......................................................................................................................... 5-489

5.2 CONCLUSION

S .................................................................................................................. 5-489 ACKNOWLEDGEMENTS ........................................................................................................ 5-490 ix

ABBREVIATION AND ACRONYMS sigma, standard deviation

°C degrees Celsius

°F degrees Fahrenheit 13 C-NMR carbon-13 nuclear magnetic resonance 14 C carbon-14 17B Guidelines for Determining Flood Flow FrequencyBulletin 17B, 1982 17C Guidelines for Determining Flood Flow FrequencyBulletin 17C, 2018 1-D one dimensional 20C 20th Century Reanalysis 2BCMB Level 2DPR and GMI Combine 2-D two dimensional 3-D three dimensional AAB Accident Analysis Branch in NRC/RES/DSA AB auxiliary building AC, ac alternating current ACCP Alabama Coastal Comprehensive Plan ACE accumulated cyclone energy, an approximation of the wind energy used by a tropical system over its lifetime ACM alternative conceptual model ACME Accelerated Climate Modeling for Energy (DOE)

ACWI Advisory Committee on Water Information AD anno Domini ADAMS Agencywide Documents Access and Management System ADCIRC ADvanced CIRCulation model AEP annual exceedance probability AEP4 Asymmetric Exponential Power distribution AFW auxiliary feedwater AGCMLE Assistant General Counsel for Materials Litigation and Enforcement in NRC/OGC/GCHA AGCNRP Assistant General Counsel for New Reactor Programs in NRC/OGC/GCHA AGFZ Azores-Gibraltar Transform Fault AGL above ground level AIC Akaike Information Criterion x

AIMS assumptions, inputs, and methods AIRS Advanced InfraRed Sounder AIT air intake tunnel AK Alaska AM annual maxima AMJ April, May, June AMM Atlantic Meridional Mode AMO Atlantic Multi-Decadal Oscillation AMS annual maxima series AMSR-2 Advance Microwave Scanning Radiometer AMSU Advanced Microwave Sounding Unit ANN annual ANO Arkansas Nuclear One ANOVA analysis of variance decomposition ANS American Nuclear Society ANSI American National Standards Institute ANVS Netherlands Authority for Nuclear Safety and Radiation Protection AO Assistant for Operations in NRC/OEDO AOP abnormal operating procedure APF annual probability of failure APHB Probabilistic Risk Assessment Operations and Human Factors Branch API application programming interface APLA/APLB Probabilistic Risk Assessment Licensing Branch A/B in NRC/NRR/DRA APOB PRA Oversight Branch in NRC/NRR/DRA AR atmospheric river AR Arkansas AR4, AR5 climate scenarios from the 4th/5th Intergovernmental Panel on Climate Change Reports / Working Groups ARA Applied Research Associates ArcGIS geographic information system owned by ESRI ARF areal reduction factor ARI average return interval ARR Australian Rainfall-Runoff Method AS adjoining stratiform ASM annual series maxima xi

ASME American Society of Mechanical Engineers ASN French Nuclear Safety Authority (Autorité de Sûreté Nucléaire)

ASTM American Society for Testing and Materials ATMS Advance Technology Microwave Sounder ATWS anticipated transient without scram AVHRR Advance Very High Resolution Radiometer B&A Bittner & Associates BATEA Bayesian Total Error Analysis BB backbuilding/quasistationary BC boundary condition Bel V subsidiary of Belgian Federal Agency for Nuclear Control (FANC)

BHM Bayesian Hierarchical Model BIA Bureau of Indian Affairs BMA Bayesian Model Averaging BQ Bayesian Quadrature BWR boiling-water reactor CA California CAC common access card CAPE Climate Action Peer Exchange CAPE convective available potential energy CAS corrective action study CAS2CD CAScade 2-Dimensional model (Colorado State)

Cat. category on the Saffir-Simpson Hurricane Wind Scale CBR center, body, and range CC Clausius-Clapeyron CC climate change CCCR Center for Climate Change Research CCDP conditional core damage probability CCI Coppersmith Consulting Inc.

CCSM4 Community Climate System Model version 4 CCW closed cooling water CDB current design basis CDF core damage frequency CDF cumulative distribution function xii

CE common era CEATI Centre for Energy Advancement through Technological Innovation CEET cracked embankment erosion test CENRS National Science and Technology Council Committee on Environment, Natural Resources, and Sustainability CESM Community Earth System Model CFD computational fluid dynamics CFHA comprehensive flood hazard assessment CFR Code of Federal Regulations CFSR Climate Forecast System Reanalysis CHIPs Coupled Hurricane Intensity Prediction System CHiRPs Climate Hazards Group infraRed Precipitation with Station Data CHL Coastal and Hydraulics Laboratory CHRP Coastal Hazard Rapid Prediction, part of StormSIM CHS Coastal Hazards System CI confidence interval CICS-NC Cooperative Institute for Climates and SatellitesNorth Carolina CIPB Construction Inspection Management Branch in NRC/NRO/DLSE CIRES Cooperative Institute for Research in Environmental Sciences CL confidence level CL-ML homogeneous silty clay soil CMC Canadian Meteorological Center forecasts CMIP5 Coupled Model Intercomparison Project Phase 5 CMORPH / C-MORPH Climate Prediction Center Morphing Technique CNE Romania Consiliul National al Elevilor CNSC Canadian Nuclear Safety Commission CO Colorado CoCoRaHS Community Collaborative Rain, Hail & Snow Network (NWS)

COE U.S. Army Corps of Engineers (see also USACE)

COL combined license COLA combined license application COM-SECY NRC staff requests to the Commission for guidance CONUS Continental United States COOP Cooperative Observer Network (NWS) xiii

COR contracting officers representative CPC Climate Prediction Center (NOAA)

CPFs cumulative probability functions CR comprehensive review CRA computational risk assessment CRB Concerns Resolution Branch in NRC/OE CRL coastal reference location CRPS continuous ranked probability score CSNI Committee on the Safety of Nuclear Installations CSRB Criticality, Shielding & Risk Assessment Branch in NRC/NMSS/DSFM CSSR Climate Science Special Report (by the U.S. Global Change Research Program)

CSTORM Coastal Storm Modeling System CTA Note note to Commissioners Assistants CTXS Coastal Texas Study CV coefficient of variation CZ capture zone DC District of Columbia DAD depth-area-duration DAMBRK Dam Break Flood Forecasting Model (NWS)

DAR Division of Advanced Reactors in NRC/NRO DayMet daily surface weather and climatological summaries dBz decibel relative to z, or measure of reflectivity of radar DCIP Division of Construction Inspection and Operational Programs in NRC/NRO DDF depth-duration-frequency curve DDM data-driven methodology DDST database of daily storm types DE Division of Engineering in NRC/RES DHSVM distributed hydrology soil vegetation model, supported by University of Washington DIRS Division of Inspection and Regional Support in NRC/NRR DJF December, January, February DLBreach Dam/Levee Breach model developed by Weiming Wu, Clarkson University DLSE Division of Licensing, Siting, and Environmental Analysis in NRC/NRO xiv

DOE U.S. Department of Energy Dp pressure deficit DPI power dissipation index DPR Division of Preparedness and Response in NRC/NSIR DPR Dual Frequency Precipitation Radar DQO data quality objective DRA Division of Risk Assessment in NRC/NRR DRA Division of Risk Analysis in NRC/RES DREAM Differential Evolution Adaptive Metropolis DRP Division of Reactor Projects in NRC/R-I DRS Division of Reactor Safety In NRC/R-I and R-IV DSA Division of Systems Analysis in NRC/RES DSEA Division of Site Safety and Environmental Analysis, formerly in NRC/NRO, now in DLSE DSFM Division of Spent Fuel Management in NRC/NMSS DSI3240 NCEI hourly precipitation data DSMS Dam Safety Modification Study DSMS digital surface models DSPC USACE Dam Safety Production Center DSRA Division of Safety Systems, Risk Assessment and Advanced Reactors in NRC/NRO (merged into DAR)

DSS Division of Safety Systems in NRC/NRR DSS Hydrologic Engineering Center Data Storage System DTWD doubly truncated Weibull distribution DUWP Division of Decommissioning, Uranium Recovery, and Waste Programs in NRC/NMSS DWOPER Operational Dynamic Wave Model (NWS) dy day EAD expected annual damage EB2/EB3 Engineering Branch 2/3 in NRC/R-IV/DRS EBTRK Tropical Cyclone Extended Best Track Dataset EC Eddy Covariance Method EC environmental condition ECC ensemble copula coupling ECCS emergency core cooling systems pump xv

ECs environmental conditions EDF Électricité de France EDG emergency diesel generator EF environmental factor EFW emergency feedwater EGU European Geophysical Union EHCOE NRC External Hazard Center of Expertise EHID External Hazard Information Digest EIRL equivalent independent record length EIS environmental impact statement EKF Epanechikov kernel function EMA expected moments algorithm EMCWF European Centre for Medium-Range Weather Forecasts EMDR eastern main development region (for hurricanes)

EMRALD Event Model Risk Assessment using Linked Diagrams ENSI Swiss Federal Nuclear Safety Inspectorate ENSO El Nino Southern Oscillation EPA U.S. Environmental Protection Agency EPIP emergency plan implementing procedure EPRI Electric Power Research Institute ER engineering regulation (USACE)

ERA-40 European ECMWF reanalysis dataset ERB Environmental Review Branch in NRC/NMSS/FCSE ERDC Engineer Research and Development Center (USACE)

ERL equivalent record length ESCC Environmental and Siting Consensus Committee (ANS)

ESEB Structural Engineering Branch in NRC/RES/DE ESEWG Extreme Storm Events Work Group (ACWI/SOH)

ESP early site permit ESRI Environmental Systems Research Institute ESRL Earth Systems Research Lab (NOAA/OAR)

EST Eastern Standard Time EST empirical simulation technique ESTP enhanced storm transposition procedure xvi

ET event tree ET evapotranspiration ET/FT event tree/fault tree ETC extratropical cyclone EUS eastern United States EV4 extreme value with four parameters distribution function EVA extreme value analysis EVT extreme value theory EXHB External Hazards Branch in NRC/NRO/DLSE Exp experimental f annual probability of failure (USBR, USACE)

F1, F5 tornado strengths on the Fujita scale FA frequency analysis FADSU fluvial activity database of the Southeastern United States FAQ frequently asked question FAST Fourier Analysis Sensitivity Test FBPS flood barrier penetration seal FBS flood barrier system FCM flood-causing mechanism FCSE Division of Fuel Cycle Safety, Safeguards & Environmental Review in NRC/NMSS FD final design FDC flood design category (DOE terminology)

FEMA Federal Emergency Management Agency FERC Federal Energy Regulatory Commission FFA flood frequency analysis FFC flood frequency curve FHRR flood hazard reevaluation report FITAG Flooding Issues Technical Advisory Group FL Florida FLDFRQ3 U.S. Bureau of Reclamation flood frequency analysis tool FLDWAV flood wave model (NWS)

FLEX diverse and flexible mitigation strategies Flike extreme value analysis package developed University of Newcastle, Australia xvii

FLO-2D two-dimensional commercial flood model FM Approvals Testing and Certification Services Laboratories, originally Factory Mutual Laboratories f-N annual probability of failure vs. average life loss, N FOR peak flood of record FPM flood protection and mitigation FPS flood penetration seal FRA Flood Risk Analysis Compute Option in HEC-WAT FRM Fire Risk Management, Inc.

FSAR final safety analysis report FSC flood-significant component FSG FLEX support guidelines FSP flood seal for penetrations FT fault tree ft foot FXHAB Fire and External Hazards Analysis Branch in NRC/RES/DRA FY fiscal year G&G geology and geotechnical engineering GA generic action GCHA Deputy General Counsel for Hearings and Administration in NRC/OGC GCM Global Climate Model GCRP U.S. Global Change Research Program GCRPS Deputy General Counsel for Rulemaking and Policy Support in NRC/OGC GEFS Global Ensemble Forecasting System GeoClaw routines from Clawpack-5 (Conservation Laws Package) that are specialized to depth-averaged geophysical flows GEO-IR Geostationary SatellitesInfraRed Imagery GEV generalized extreme value GFDL Geophysical Fluid Dynamics Lab (NOAA)

GFS Global Forecast System GHCN Global Historical Climatology Network GHCND Global Historical Climatology Network-Daily GIS geographic information system GISS Goddard Institute for Space Studies (NASA) xviii

GKF Gaussian Kernel Function GL generic letter GLO generalized logistic distribution GLRCM Great Lakes Regional Climate Model GLUE generalized likelihood uncertainty estimation GMAO Global Modeling and Assimilation Office (NASA)

GMC ground motion characterization GMD geoscientific model development GMI GPM microwave imager GMSL global mean sea level GNO generalized normal distribution GoF goodness-of-fit GPA/GPD generalized Pareto distribution GPCP SG Global Precipitation Climatology ProjectSatellite Gauge GPLLJ Great Plains lower level jet GPM Gaussian process metamodel GPM global precipitation measurement GPO generalized Pareto distribution GPROF Goddard profile algorithm GRADEX rainfall-based flood frequency distribution method Grizzly simulated component aging and damage evolution events RISMC tool GRL Geophysical Research Letters GRS Gesellschaft für Anlagen- und ReaktorsicherheitGlobal Research for Safety GSA global sensitivity analysis GSFC Goddard Space Flight Center GSI generic safety issue GUI graphical user interface GW-GC Well-graded gravel with clay and sand GZA a multidisciplinary consulting firm h second shape parameter of four-parameter Kappa distribution h/hr hour H&H hydraulics and hydrology HAMC hydraulic model characterization xix

HBV rainfall runoff model Hydrologiska Byrns Vattenbalansalvdening, supported by the Swedish Meteorological and Hydrological Institute HCA hierarchical clustering analysis HCTISN Supreme Committee for Transparency and Information on Nuclear Safety (France)

HCW hazardous convective weather HDSC NOAA/NWS/OWP Hydrometeorological Design Studies Center HEC Hydrologic Engineering Center, part of USACE/Institute for Water Resources HEC-1 see HEC-HMS HEC-FIA Hydrologic Engineering Center Flood Impact Analysis Software HEC-HMS Hydrologic Modeling System HEC-LifeSim Hydrologic Engineering Center life loss and direct damage estimation software HEC-MetVue Hydrologic Engineering Center Meteorological Visualization Utility Engine HEC-RAS Hydrologic Engineering Center River Analysis System HEC-ResSim Hydrologic Engineering Center Reservoir System Simulation HEC-SSP Hydrologic Engineering Center Statistical Software Package HEC-WAT Hydrologic Engineering Center Watershed Analysis Tool HEP human error probability HF human factors HFRB Human Factors and Reliability Branch in NRC/RES/DRA HHA hydrologic hazard analysis HHC hydrologic hazard curve HI Hawaii HLR high-level requirement HLWFCNS Assistant General Counsel for High-Level Waste, Fuel Cycle and Nuclear Security in NRC/OGC/GCRPS HMB Hazard Management Branch in NRC/NRR/JLD, realigned HMC hydraulic/hydrologic model characterization HMR NOAA/NWS Hydrometeorological Report HMS hydrologic modeling system HOMC hydrologic model characterization hPa hectopascals (unit of pressure) xx

HR homogenous region HRA human reliability analysis HRL Hydrologic Research Lab, University of California at Davis HRRR NOAA High-Resolution Rapid Refresh Model HRRs Fukushima Hazard Reevaluation Reports (EPRI term)

HRU hydrologic runoff unit approach HUC hydrologic unit code for watershed (USGS)

HUNTER human actions RISMC tool HURDAT National Hurricane Centers HURricane DATabases Hz hertz (1 cycle/second)

IA integrated assessment IA Iowa IAEA International Atomic Energy Agency IBTrACS International Best Track Archive for Climate Stewardship IC initial condition ICOLD International Commission on Large Dams ID information digest IDF intensity-duration frequency curve IDF inflow design flood IE initiating event IEF initiating event frequency IES Dam Safety Issue Evaluation Studies IHDM Institute of Hydrology Distributed Model, United Kingdom IID independent and identically distributed IL Illinois IMERG Integrated Multi-satellitE Retrievals for GPM IMPRINT Improved Performance Research Integration Tool in inch IN information notice INES International Nuclear and Radiological Event Scale INL Idaho National Laboratory IPCC Intergovernmental Panel on Climate Change IPE individual plant examination IPEEE individual plant examination for external events xxi

IPET Interagency Performance Evaluation Taskforce for the Performance Evaluation of the New Orleans and Southeast Louisiana Hurricane Protection System IPWG International Precipitation Working Group IR infrared IR inspection report IRIB Reactor Inspection Branch in NRC/NRR/DIRS IRP Integrated Research Projects (DOE)

IRSN Institut de Radioprotection et de Sûreté Nucléaire (Frances Radioprotection and Nuclear Safety Institute)

ISG interim staff guidance ISI inservice inspection ISR interim staff response IT information technology IVT integrated vapor transport IWR USACE Institute for Water Resources IWVT integrated water vapor tendency J joule JJA June, July, August JLD Japan Lesson-learned Directorate or Division in NRC/NRR, realigned JPA Joint Powers Authority (FEMA Region II)

JPA joint probability analysis JPM joint probability method JPM-OS Joint Probability Method with Optimal Sampling K degrees Kelvin KAERI Korea Atomic Energy Research Institute KAP Kappa distribution kd erodibility coefficient kg kilogram kHz kilohertz (1000 cycles/second) km kilometer KS Kansas LA Louisiana LACPR Louisiana Coastal Protection and Restoration Study LAR license amendment request xxii

L-Cv coefficient of L-variation LEO low earth orbit LER licensee event report LERF large early release frequency LIA Little Ice Age LiDAR light imaging, detection and ranging; surveying method using reflected pulsed light to measure distance LIP local intense precipitation LMI lifetime maximum intensity LMOM / LMR L-moment LN4 Slade-type four parameter lognormal distribution function LOCA localized constructed analog LOCA loss-of-coolant accident LOOP loss of offsite power event LOUHS loss of ultimate heat sink event LPIII / LP-III, LP3 Log Pearson Type III distribution LS leading stratiform LS local storm LSHR late secondary heat removal LTWD Left-truncated Weibull distribution LULC land use and land cover LWR light-water reactor LWRS Light-Water Reactor Sustainability Program m meter MA Massachusetts MA manual action MAAP coupling accident conditions RISMC tool MAE mean absolute error MAM March, April, May MAP mean annual precipitation MASTODON structural dynamics, stochastic nonlinear soil-structure interaction in a risk framework RISMC tool mb millibar MCA medieval climate anomaly MCC mesoscale convective complex xxiii

MCI Monte Carlo integration MCLC Monte Carlo Life-Cycle MCMC Markov chain Monte Carlo method MCRAM streamflow volume stochastic modeling MCS mesoscale convective system MCS Monte Carlo simulation MCTA Behrangi Multisatellite CloudSat TRMM Aqua Product MD Maryland MDL Meteorological Development Laboratory (NWS)

MDR Main Development Region (for hurricanes)

MDT Methodology Development Team MEC mesoscale storm with embedded convection MEOW Maximum Envelopes of Water MetStorm storm analysis software by MetStat, second generation of SPAS MGD meta-Gaussian distribution MGS Engineering engineering consultants MHS microwave humidity sounder MIKE SHE/ MIKE 21 integrated hydrological modeling system MLC mid-latitude cyclone MLE maximum likelihood estimation mm millimeter MM5 fifth-generation Penn State/NCAR mesoscale model MMC mesh-based Monte Carlo method MMC meteorological model characterization MMF multimechanism flood MMP mean monthly precipitation MN Minnesota MO Missouri Mode 3 Reactor Operation Mode: Hot Standby Mode 4 Reactor Operation Mode: Hot Shutdown Mode 5 Reactor Operation Mode: Cold Shutdown MOM Maximum of MEOWs MOU memorandum of understanding MPE multisensor precipitation estimates xxiv

mph miles per hour MPS maximum product of spacings MRMS Multi-Radar Multi-Sensor project (NOAA/NSSL)

MS Mississippi MSA mitigating strategies assessment MSFHI mitigating strategies flood hazard information MSL mean sea level MSWEP multisource weighted-ensemble precipitation dataset MVGC multivariable Gaussian copula MVGD multivariable Gaussian distribution MVTC multivariable students t copula N average life loss (USBR, USACE)

NA14 NOAA National Atlas 14 NACCS North Atlantic Coast Comprehensive Study NAEFS North American Ensemble Forecasting System NAIP National Agricultural Imagery Program NAM-WRF North American Mesoscale ModelWRF NAO North Atlantic Oscillation NARCCAP North American Regional Climate Change Assessment Program NARR North American Regional Reanalysis (NOAA)

NARSIS European Research Project New Approach to Reactor Safety Improvements NASA National Aeronautics and Space Administration NAVD88 North American Vertical Datum of 1988 NBS net basin scale NCA3/NCA4 U.S. Global Change Research Program Third/Fourth National Climate Assessment NCAR National Center for Atmospheric Research NCEI National Centers for Environmental Information NCEP National Centers for Environmental Prediction (NOAA)

ND North Dakota NDFD National Digital Forecast Database (NWS)

NDSEV number of days with severe thunderstorm environments NE Nebraska NEA Nuclear Energy Agency xxv

NEB nonexceedance bounds NEI Nuclear Energy Institute NESDIS NOAA National Environmental Satellite, Data, and Information Service NEUTRINO a general-purpose simulation and visualization environment including an SPH solver NEXRAD next-generation radar NHC National Hurricane Center NI DAQ National Instruments Data Acquisition Software NID National Inventory of Dams NIOSH National Institute for Occupational Safety and Health NLDAS North American Land Data Assimilation System nm nautical miles NM New Mexico NMSS NRC Office of Nuclear Material Safety and Safeguards NOAA National Oceanic and Atmospheric Administration NOED notice of enforcement discretion NPDP National Performance of Dams Program NPH Natural Phenomena Hazards Program (DOE)

NPP nuclear power plant NPS National Park Service NRC U.S. Nuclear Regulatory Commission NRCS Natural Resources Conservation Service NRO NRC Office of New Reactors NRR NCEP-NCAR Reanalysis NRR NRC Office of Nuclear Reactor Regulation NSE Nash-Sutcliffe model efficiency coefficient NSIAC Nuclear Strategic Issues Advisory Committee NSIR NRC Office of Nuclear Security and Incident Response NSSL National Severe Storms Laboratory (NOAA)

NSTC National Science and Technology Council NTTF Near-Term Task Force NUREG NRC technical report designation NUVIA a subsidiary of Vinci Construction Group, offering expertise in services and technology supporting safety performance in nuclear facilities NWS National Weather Service xxvi

NY New York OAR NOAA Office of Oceanic and Atmospheric Research OE NRC Office of Enforcement OECD Organization for Economic Co-operation and Development OEDO NRC Office of the Executive Director for Operations OGC NRC Office of the General Counsel OHC ocean heat content OK Oklahoma OR Oregon ORNL Oak Ridge National Laboratory OSL optically stimulated luminescence OTC once-through cooling OWI Ocean Wind Inc.

OWP NOAA/NWS Office of Water Prediction P present P/PET precipitation over PET ratio, aridity Pa pascal PB1 Branch 1 in NRC/R-I/DRP PBL planetary boundary layer PCA principal component analysis PCHA probabilistic coastal hazard assessment PCMQ Predictive Capability Maturity Quantification PCMQBN Predictive Capability Maturity Quantification by Bayesian Net PD performance demand PDF probability density function PDF performance degradation factor PDS partial-duration series PE3 Pearson Type III distribution PeakFQ USGS flood frequency analysis software tool based on Bulletin 17C PERSIANN-CCS Precipitation Estimation from Remotely Sensed Information using Artificial Neural NetworksCloud Classification System (University of California at Irvine Precipitation Algorithm)

PERT program evaluation review technique PET potential evapotranspiration P-ETSS Probabilistic Extra-Tropical Storm Surge Model xxvii

PF paleoflood PF/P-F precipitation frequency PFAR precipitation field area ratio PFHA probabilistic flood hazard assessment PFM potential failure mode PI principal investigator P-I pressure-impulse curve PIF performance influencing factor PILF potentially influential low flood PM project manager PMDA Program Management, Policy Development & Analysis in NRC/RES PMF probable maximum flood PMH probable maximum hurricane PMP probable maximum precipitation PMW passive microwave PN product number PNAS Proceedings of the National Academy of Sciences of the United States of America PNNL Pacific Northwest National Laboratory POANHI Process for Ongoing Assessment of Natural Hazard Information POB Regulatory Policy and Oversight Branch in NRC/NSIR/DPR POR period of record PPRP participatory peer review panel PPS Precipitation Processing System PR Puerto Rico PRA probabilistic risk assessment PRAB Probabilistic Risk Assessment Branch in NRC/RES/DRA PRB Performance and Reliability Branch in NRC/RES/DRA PRISM a gridded dataset developed through a partnership between the NRCS National Water and Climate Center and the PRISM Climate Group at Oregon State University, developers of PRISM (the Parameter-elevation Regressions on Independent Slopes Model)

PRMS USGS Precipitation Runoff Modelling System Prométhée IRSN software based on PROMETHEE, the Preference Ranking Organization METhod for Enrichment Evaluation PRPS Precipitation Retrieval Profiles Scheme xxviii

PS parallel stratiform PSA probabilistic safety assessment, common term for PRA in other countries PSD Physical Sciences Division in NOAA/OAR/ESRL PSF performance shaping factor psf pounds per square foot PSHA probabilistic seismic hazard assessment PSI paleostage indicators PSSHA probabilistic storm surge hazard assessment P-Surge probabilistic tropical cyclone storm surge model PTI project technical integrator PVC polyvinyl chloride Pw/PW precipitable water PWR pressurized-water reactor Q quarter QA quality assurance QC quality control QI Quality Index QPE quantitative precipitation estimates QPF quantitative precipitation forecast R a statistical package R 2.1 NTTF Report Recommendation 2.1 R&D research and development R2 coefficient of determination RAM regional atmospheric model RASP Risk Assessment of Operational Events Handbook RAVEN risk analysis in a virtual environment probabilistic scenario evolution RISMC tool RC reinforced concrete RCP (4.5, 8.5) representative concentration pathways RELAP-7 reactor excursion and leak analysis program transient conditions RISMC tool RENV Environmental Technical Support Branch in NRC/NRO/DLSE REOF rotated empirical orthogonal function RES NRC Office of Nuclear Regulatory Research xxix

RF riverine flooding RFA regional frequency analysis RFC River Forecast Center (NWS)

RG regulatory guide RGB red, green, and blue imagery (NAIP)

RGB-IF red, green, blue, and infrared imagery (NAIP)

RGC regional growth curve RGGIB Regulatory Guidance and Generic Issues Branch in NRC/RES/DE RGS Geosciences and Geotechnical Engineering Branches now in NRC/NRO/DLSE, formerly in NRC/NRO/DSEA RHM Hydrology and Meteorology Branch formerly in NRC/NRO/DSEA RI Rhode Island R-I, R-II, R-III, R-IV NRC Regions I, II, III, IV RIC Regulatory Information Conference, NRC RIDM risk-informed decisionmaking RILIT Risk-Informed Licensing Initiative Team in NRC/NRR/DRA/APLB RISMC risk information safety margin characterization Rmax radius to maximum winds RMB Renewals and Materials Branch in NRC/NMSS/DSFM RMC USACE Risk Management Center RMSD root-mean-square deviation RMSE root mean square error ROM reduce order modeling ROP Reactor Oversight Process RORB-MC an interactive runoff and streamflow routing program RPAC formerly in NRC/NRO/DSEA RRTM Rapid Radiative Transfer Model Code in WRF RRTMS RRTM with GCM application RS response surface RTI an independent, nonprofit institute RV return values SA storage area SACCS South Atlantic Coastal Comprehensive Study SAPHIR Sounding for Probing Vertical Profiles of Humidity xxx

SAPHIRE Systems Analysis Programs for Hands-on Integrated Reliability Evaluations SBDFA simulation-based dynamic flooding analysis framework SBO station blackout SBS simulation-based scaling SC safety category (ANS 58.16-2014 term)

SC South Carolina SCAN Soil Climate Analysis Network SCRAM immediate shutdown of nuclear reactor SCS curve number method SD standard deviation SDC shutdown cooling SDP significance determination process SDR Subcommittee on Disaster Reduction SECY written issues paper the NRC staff submits to the Commission SEFM Stochastic Event-Based Rainfall-Runoff Model SER safety evaluation report SGSEB Structural, Geotechnical and Seismic Engineering Branch in NRC/RES/DE SHAC-F Structured Hazard Assessment Committee Process for Flooding SHE Systém Hydrologique Européan SITES model that uses headcut erodibility index by USDA-ARS and University of Kansas "Earthen/Vegetated Auxiliary Spillway Erosion Prediction for Dams" SLC sea level change SLOSH Sea Lake and Overland Surges from Hurricanes (NWS model)

SLR sea level rise SMR small modular reactor SNOTEL snow telemetry SNR signal-to-noise ratio SOH Subcommittee on Hydrology SOM self-organizing map SON September, October, November SOP standard operating pressure SPAR standardized plant analysis risk SPAS Storm Precipitation Analysis System (MetStat, Inc.)

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SPH smoothed-particle hydrodynamics SPRA PRA and Severe Accidents Branch in NRC/NRO/DESR (formerly in DSRA)

SRA senior reactor analyst SRES A2 NARCCAP A2 emission scenario SRH2D/SRH-2D USBR Sedimentation and River HydraulicsTwo-Dimensional model SRM staff requirements memorandum SRP standard review plan SRR storm recurrence rate SSAI Science Systems and Applications, Inc.

SSC structure, system, and component SSHAC Senior Seismic Hazard Assessment Committee SSM Swedish Radiation Safety Authority (Strl skerhets mydigheten)

SSMI Special Sensor Microwave Imager SSMIS Special Sensor Microwave Imager/Sounder SSPMP site-specific probable maximum precipitation SST sea surface temperature SST stochastic simulation technique SST stochastic storm transposition SSURGO soil survey geographic database ST4 or Stage IV precipitation information from multisensor (radar and gauges) precipitation analysis STEnv severe thunderstorm environment STM stochastic track method StormSIm stochastic storm simulation system STSB Technical Specifications Branch in NRC/NRR/DSS STUK Finland Radiation and Nuclear Safety Authority STWAVE STEady-state spectral WAVE model SÚJB Czech Republic State Office for Nuclear Safety SWAN Simulation Waves Nearshore Model SWE snow-water equivalent SWL still water level SWMM EPA Storm Water Management Model SWT Schaefer-Wallis-Taylor Climate Region Method TAG EPRI Technical Assessment Guide xxxii

TC tropical cyclone TCI TRMM Combined Instrument Td daily temperature TDF transformed extreme value type 1 distribution function (four parameter)

TDI technically defensible interpretations TELEMAC two-dimensional hydraulic model TELEMAC 2D a suite of finite element computer programs owned by the Laboratoire National d'Hydraulique et Environnement (LNHE), part of the R&D group of Électricité de France T-H thermohydraulic TI technical integration TI technology innovation project TL training line TMI Three Mile Island TMI TRMM Microwave Imager TMPA TRMM Multisatellite Precipitation Analysis TN Tennessee TOPMODEL two-dimensional distributed watershed model by Keith Beven, Lancaster University TOVS Television-Infrared Observation Satellite (TIROS) Operational Vertical Sounder TP-# Test Pit #

TP-29 U.S. Weather Bureau Technical Paper No. 29 TP-40 Technical Paper No. 40, Rainfall Frequency Atlas of the U.S., 1961 TR USACE technical report TREX two-dimensional, runoff, erosion, and export model TRMM Tropical Rainfall Measuring Mission TRVW Tennessee River Valley Watershed TS technical specification TS trailing stratiform TSR tropical-storm remnant TUFLOW two-dimensional hydraulic model TVA Tennessee Valley Authority TX Texas U.S. or US United States UA uncertainty analysis xxxiii

UC University of California UH unit hydrograph UKF uniform kernel function UKMET medium-range (3- to 7-day) numerical weather prediction model operated by the United Kingdom METeorological Agency UL Underwriters Laboratories UMD University of Maryland UNR user need request UQ uncertainty quantification URMDB Uranium Recovery and Materials Decommissioning Branch in NRC/NMSS/DUWP USACE U.S. Army Corps of Engineers (see also COE)

USACE-NWD USACE NorthWest Division USBR U.S. Bureau of Reclamation USDA U.S. Department of Agriculture USDA-ARS United State Department of AgricultureAgricultural Research Service USFWS U.S. Fish and Wildlife Service USGS United States Geological Survey UTC coordinated universal time VA Virginia VDB validation database VDMS Validation Data Management System VDP validation data planning VIC Variable Infiltration Capacity model VL-AEP very low annual exceedance probability W watt WAK Wakeby distribution WASH-1400 Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power Plants [NUREG-75/014 (WASH-1400)]

WB U.S. Weather Bureau WBT wet bulb temperature WEI Weibull distribution WGEV Working Group on External Events WGI Working Group I WI Wisconsin xxxiv

WinDamC USDA/NRCS model for estimating erosion of earthen embankments and auxiliary spillways of dams WL water level WMO World Meteorological Organization WRB Willamette River Basin WRF Weather Research and Forecasting model WRR Water Resources Research (journal)

WSEL / WSL water surface elevation WSM6 WRF Single-Moment 6-Class Microphysics Scheme WSP USGS Water Supply Paper XF external flooding XFEL external flood equipment list XFOAL external flood operation action list XFPRA external flooding PRA yr year yrBP years before present Z Zulu time, equivalent to UTC xxxv

INTRODUCTION

Background

The NRC is conducting a multiyear, multi-project Probabilistic Flood Hazard Assessment (PFHA)

Research Program. It initiated this research in response to staff recognition of a lack of guidance for conducting PFHAs at nuclear facilities that required staff and licensees to use highly conservative deterministic methods in regulatory applications. The staff described the objective, research themes, and specific research topics in the Probabilistic Flood Hazard Assessment Research Plan, Version 2014-10-23, provided to the Commission in November 2014 (ADAMS Accession Nos. ML14318A070 and ML14296A442). The PFHA Research Plan was endorsed in a joint user need request by the NRC Office of New Reactors and Office of Nuclear Reactor Regulation (UNR NRO-2015-002, ADAMS Accession No. ML15124A707). This program is designed to support the development of regulatory tools (e.g., regulatory guidance, standard review plans) for permitting new nuclear sites, licensing new nuclear facilities, and overseeing operating facilities. Specific uses of flooding hazard estimates (i.e., flood elevations and associated affects) include flood-resistant design for structures, systems, and components (SSCs) important to safety and advanced planning and evaluation of flood protection procedures and mitigation.

The lack of risk-informed guidance with respect to flooding hazards and flood fragility of SSCs constitutes a significant gap in the NRCs risk-informed, performance-based regulatory approach to the assessment of hazards and potential safety consequences for commercial nuclear facilities.

The probabilistic technical basis developed will provide a risk-informed approach for improved guidance and tools to give staff and licensees greater flexibility in evaluating flooding hazards and potential impacts to SSCs in the oversight of operating facilities (e.g., license amendment requests, significance determination processes (SDPs), notices of enforcement discretion (NOEDs)) as well as licensing of new facilities (e.g., early site permit applications, combined license (COL) applications), including proposed small modular reactors (SMRs) and advanced reactors. This methodology will give staff more flexibility in assessing flood hazards at nuclear facilities so the staff will not have to rely on the use of the current deterministic methods, which can be overly conservative in some cases.

The main focus areas of the PFHA Research Program are to (1) leverage available frequency information on flooding hazards at operating nuclear facilities and develop guidance on its use, (2) develop and demonstrate a PFHA framework for flood hazard curve estimation, (3) assess and evaluate application of improved mechanistic and probabilistic modeling techniques for key flood-generating processes and flooding scenarios, (4) assess potential impacts of dynamic and nonstationary processes on flood hazard assessments and flood protection at nuclear facilities, and (5) assess and evaluate methods for quantifying reliability of flood protection and plant response to flooding events. Workshop organizers used these focus areas to develop technical session topics for the workshop.

Workshop Objectives The Annual PFHA Research Workshops serve multiple objectives: (1) inform and solicit feedback from internal NRC stakeholders, partner Federal agencies, industry, and the public about PFHA research being conducted by the NRC Office of Nuclear Regulatory Research (RES), (2) inform internal and external stakeholders about RES research collaborations with Federal agencies, the Electric Power Research Institute (EPRI) and the French Institute for Radiological and Nuclear xxxvii

Security (IRNS) and (3) provide a forum for presentation and discussion of notable domestic and international PFHA research activities.

Workshop Scope Scope of the workshop presentations and discussions included:

• Current and future climate influences on flooding processes

• Significant precipitation and flooding events

• Statistical and mechanistic modeling approaches for precipitation, riverine flooding, and coastal flooding processes

• Probabilistic flood hazard assessment frameworks

• Reliability of flood protection and mitigation features and procedures

• External flooding probabilistic risk assessment Summary of Proceedings These proceedings transmit the agenda, abstracts, and slides from presentations and posters presented, and chronicle the question and answer sessions and panel discussions held, at the U.S. Nuclear Regulatory Commissions (NRCs) Annual Probabilistic Flood Hazard Assessment (PFHA) Research Workshops, which take place approximately annually at NRC Headquarters in Rockville, MD. The first four workshops took place as follows:

• 1st Annual NRC PFHA Research Workshop, October 14-15, 2015

• 2nd Annual NRC PFHA Research Workshop, January 23-25, 2017 (Agencywide Documents Access and Management System (ADAMS) Accession No. ML17040A626)

• 3rd Annual NRC PFHA Research Workshop, December 4-5, 2017 (ADAMS Accession No. ML17355A071)

• 4th Annual NRC PFHA Research Workshop, April 30-May 2. 2019 (ADAMS Accession No. ML19156A446)

These proceedings include presentation abstracts and slides and a summary of the question and answer sessions. The first workshop was limited to NRC technical staff and management, NRC contractors, and staff from other Federal agencies. The three workshops that followed were meetings attended by members of the public; NRC technical staff, management, and contractors; and staff from other Federal agencies. Public attendees over the course of the workshops included industry groups, industry members, consultants, independent laboratories, academic institutions, and the press. Members of the public were invited to speak at the workshops. The fourth workshop included more invited speakers from the public than from the NRC and the NRCs contractors.

The proceedings for the second through fourth workshops include all presentation abstracts and slides and submitted posters and panelists slides. Workshop organizers took notes and audio-recorded the question and answer sessions following each talk, during group panels, and during end-of-day question and answer session. Responses are not reproduced here verbatim and were generally from the presenter or co-authors. Descriptions of the panel discussions identify the speaker when possible. Questions were taken orally from attendees, on question cards, and over the telephone.

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Related Workshops An international workshop on PFHA took place on January 29-31, 2013. The workshop was devoted to sharing information on PFHAs for extreme events (i.e., annual exceedance probabilities (AEPs) much less than 2x10-3 per year) from the Federal community). The NRC issued the proceedings as NUREG/CP-302, Proceedings of the Workshop on Probabilistic Flood Hazard Assessment (PFHA), in October 2013 (ADAMS Accession No. ML13277A074).

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3 THIRD ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP 3.1 Introduction This chapter details the 3rd Annual NRC Probabilistic Flood Hazard Assessment (PFHA)

Research Workshop held at the U.S. Nuclear Regulatory Commission (NRC) Headquarters in Rockville, MD, on December 4-5, 2017. These proceedings include abstracts for the presentations, the slides from the presentations themselves, and a summary of question and answer sessions. The workshop was a public meeting attended by members of the public; NRC technical staff, management, and contractors; and staff from other Federal agencies.

The workshop began with an introduction from Mike Weber, Director, NRC Office of Nuclear Regulatory Research (RES). Following the introduction, NRC RES and Electric Power Research Institute (EPRI) staff presented descriptions of their flooding research programs.

Following the introduction session, NRC and EPRI contractors and staff gave technical presentations and answered clarifying questions. Partner Federal agencies took part in two panel discussions on their PFHA and external PRA efforts. At the end of each day, participants had an opportunity to provide feedback and ask generic questions about research related to PFHA for nuclear facilities.

3.1.1 Organization of Conference Proceedings Section 3.2 provides the agenda for this workshop. The program is also located at ADAMS Accession No. ML17355A081 Section 3.3 presents the proceedings from the workshop, including abstract, presentation slides, and summaries of the question and answer session for each of the technical sessions.

The summary document of session abstracts for the technical presentations can be viewed in the PFHA Research Workshop Program at ADAMS Accession No. ML17355A081. The complete workshop presentation package is available at ADAMS Accession No. ML17355A071.

Section 0is a summary of the proceedings and Section 0provides a list of the workshop attendees, including remote participants.

3-1

3.2 Workshop Agenda (ADAMS Accession No. ML17355A081) 3rd Annual NRC Probabilistic Flood Hazard Assessment Research Workshop at NRC headquarters in Rockville, Maryland AGENDA: MONDAY, DECEMBER 4, 2017 08:10 - 08:20 Welcome Session 1A - Introduction Session Chair: Meredith Carr, NRC/RES 08:20 - 08:30 Introduction 1A-1 Mike Weber*, Director, Office of Nuclear Regulatory Research 08:30 - 09:00 NRC Flooding Research Program Overview 1A-2 Joseph Kanney*, Meredith Carr, Tom Aird, Elena Yegorova, Mark Fuhrmann and Jacob Philip, NRC/RES 09:00 - 09:30 EPRI Flooding Research Program Overview 1A-3 John Weglian*, Electric Power Research Institute (EPRI) 09:30 - 09:45 BREAK Session 1B - Climate and Precipitation Session Chair: Elena Yegorova, NRC/RES 09:45 - 10:15 Regional Climate Change Projections: Potential Impacts to Nuclear 1B-1 Facilities L. Ruby Leung* and Rajiv Prasad, Pacific Northwest National Laboratory 10:15 - 10:45 Numerical Modeling of Local Intense Precipitation Processes 1B-2 M. Levent Kavvas, Mathieu Mure-Ravaud* and Alain Dib*

Hydrologic Research Laboratory, Department. Of Civil and Environmental Engineering, University of California, Davis 10:45 - 11:15 Research to Develop Guidance on Extreme Precipitation Estimates in 1B-3 Orographic Regions Kathleen Holman^, Andrew Verdin and D. Keeney, U.S. Bureau of Reclamation, Technical Service Center, Flood Hydrology and Meteorology

• denotes presenter, ^ denotes remote presenter 3-3

Session 1C - Storm Surge Session Chair: Joseph Kanney, NRC/RES 11:15 - 11:45 Quantification of Uncertainty in Probabilistic Storm Surge Models 1C-1 Norberto C. Nadal-Caraballo, and Victor Gonzalez*, U.S. Army Engineer R&D Center, Coastal and Hydraulics Laboratory 11:45 - 12:15 Probabilistic Flood Hazard Assessment - Storm Surge 1C-2 John Weglian*, EPRI 12:15 - 13:15 LUNCH Session 1D - Leveraging Available Flood Information Session Chair: Nebiyu Tiruneh, NRC/NRO 13:15 - 13:45 Flood Frequency Analyses for Very Low Annual Exceedance 1D-1 Probabilities using Historic and Paleoflood Data, with Considerations for Nonstationary Systems Karen Ryberg*, Kelsey Kolars and Julie Kiang, U.S. Geological Survey 13:45 - 14:15 Extending Frequency Analysis Beyond Current Consensus Limits 1D-2 Keil Neff* and Joseph Wright, U.S. Bureau of Reclamation, Technical Service Center, Flood Hydrology & Meteorology 14:15 - 14:45 Development of External Hazard Information Digests for Operating 1D-3 NPP sites Kellie Kvarfordt* and Curtis Smith, Idaho National Laboratory 14:45 - 15:00 BREAK Session 1E - Paleoflood Studies Session Chair: Mark Fuhrmann, NRC/RES 15:00 - 15:30 Improving Flood Frequency Analysis with a Multi-Millennial Record of 1E-1 Extreme Floods on the Tennessee River near Chattanooga, TN Tessa Harden*, Jim OConnor and Mackenzie Keith, U.S.

Geological Survey 15:30 - 16:00 Collection of Paleoflood Evidence 1E-2 Lisa Davis*, University of Alabama and Gary Stinchcomb, Murray State University 16:00 - 16:30 Daily Wrap-up and Public Comments/Questions 16:30 - 18:00 Posters (Session 1F), Session Chair: Tom Aird, NRC/RES 3-4

Session 1F: Posters Session Chair: Tom Aird, NRC/RES Probability-Based Flow Modeling Using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS)

Brian Skahill, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory Reclamations Paleoflood Database: Design, Structure and Application Jeanne E. Godaire, Kurt Wille, and Ralph E. Klinger, U.S. Bureau of Reclamation, Technical Services Center Late Holocene Paleofloods Along the Middle Tennessee River Valley C. Lance Stewart and Gary E. Stinchcomb, Department of Geosciences and Watershed Studies Institute, Murray State University; Steven L. Forman, Department of Geology, Baylor University; Lisa Davis and Rachel Lombardi, Department of Geography, University of Alabama; Emily Blackaby, Owen Craven and William Hockaday, Department of Geology, Baylor University A regional chronology of floods and river activity during the last 10,000 years in the Eastern U.S.

Lisa Davis and Rachel Lombardi, Department of Geography, University of Alabama; Gary Stinchcomb, Watershed Studies Institute, Murray State University; C. Lance Stewart, Department of Geosciences, Murray State University; Matthew D. Therrell, Department of Geography, University of Alabama; Matthew Gage, Office of Archeological Research, University of Alabama Critical Review of State of Practice in Dam Risk Assessment David Watson, Scott DeNeale, Brennan Smith, Shih-Chieh Kao, Oak Ridge National Laboratory (ORNL); Gregory Baecher, University of Maryland Application of Point Precipitation Frequency Estimates to Watersheds Shih-Chieh Kao and Scott DeNeale, Oak Ridge National Laboratory Quantification of Uncertainty in Probabilistic Storm Surge Models Norberto Nadal-Caraballo, Victor Gonzalez and Efrain Ramos-Santiago U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory Modeling Plant Response to Flooding Events Zhegang Ma, Curtis L. Smith and Steven R. Prescott, Idaho National Laboratory, Risk Assessment and Management Services; Ramprasad Sampath, Centroid PIC, Research and Development Stratigraphic Records of Paleofloods, Geochronology and Hydraulic Modeling to Improve Flood Frequency Analysis Tessa Harden, U.S. Geological Survey, Oregon Water Science Center 3-5

AGENDA: TUESDAY, DECEMBER 5, 2017 08:00 - 08:10 Day 2 Welcome Session 2A - Reliability of Flood Protection and Mitigation Session Chair: Mehdi Reisi-Fard, NRC/NRR 08:10 - 08:40 Performance of Flood-Rated Penetration Seals 2A-1 William (Mark) Cummings*, Fire Risk Management 08:40 - 09:10 EPRI Flood Protection Project Status 2A-2 David Ziebell^ and John Weglian, EPRI 09:10 - 09:40 A Conceptual Framework to Assess Impacts of Environmental 2A-3 Conditions on Manual Actions for Flood Protection and Mitigation at Nuclear Power Plants Rajiv Prasad*, Garill Coles, Angela Dalton, and Nancy Kohn, Pacific Northwest National Laboratory; Kristi Branch and Alvah Bittner, Bittner and Associates; R. Scott Taylor, Battelle 09:40 - 09:55 BREAK 09:55 - 10:25 External Flooding PRA Walkdown Guidance 2A-4 John Weglian*, EPRI 10:25 - 10:55 Erosion Testing of Zoned Rockfill Embankments 2A-5 Tony Wahl^, U.S. Bureau of Reclamation Session 2B - PFHA Frameworks Session Chair: John Weglian, EPRI 10:55 - 11:25 A Framework for Inland Probabilistic Flood Hazard Assessments: 2B-1 Analysis of Extreme Snow Water Equivalent in Central New Hampshire Brian Skahill* and Carrie Vuyovich, U.S. Army Corps of Engineers, Engineer Research and Development Center 11:25 - 11:55 Structured Hazard Assessment Committee Process for Flooding 2B-2 (SHAC-F) for Riverine Flooding Rajiv Prasad* and Philip Meyer, Pacific Northwest National Laboratory; Kevin Coppersmith, Coppersmith Consulting 11:55 - 13:00 LUNCH 3-6

Session 2C - Panel Discussions 13:00 - 14:20 Flood Hazard Assessment Research and Guidance Activities in Partner 2C-1 Agencies Session Chair: Joseph Kanney, NRC/RES Panelists:

Norberto Nadal-Caraballo, U.S. Army Corps of Engineers, Engineer Research and Development Center John England, U.S. Army Corps of Eng., Risk Management Center Kenneth Fearon, Federal Energy Regulatory Commission, Office of Energy Projects, Division of Dam Safety and Inspections Sharon Jasim-Hanif, Department of Energy, Office of Nuclear Safety Gabriel Miller, Tennessee River Valley Authority, River Management Department 14:20 - 15:45 External Flooding Probabilistic Risk Assessment: Perspectives on 2C-2 Gaps and Challenges Session Chair: Fernando Ferrante, EPRI Panelists:

John Weglian, EPRI Zhegang Ma, Idaho National Laboratory Ray Schneider, Westinghouse Frances Pimentel and Victoria Anderson, Nuclear Energy Institute Nathan Siu, NRC/RES Christopher Cook, NRC/NRO 15:45 - 15:55 BREAK Session 2D - Future Work at NRC and EPRI Session Chair: Mark Fuhrmann, NRC/RES 15:55 - 16:15 Future Work in PFHA at EPRI 2D-1 John Weglian*, EPRI 16:15 - 16:35 Future Work in PFHA at NRC 2D-2 Joseph Kanney, Meredith Carr*, Tom Aird, Elena Yegorova, Mark Fuhrmann and Jacob Philip, NRC/RES 16:35 - 17:00 Final Wrap-up and Public Comments/Questions 3-7

3.3 Proceedings 3.3.1 Day 1: Session 1A - Introduction Session Chair: Meredith Carr, NRC/RES/DRA/FXHAB There are no abstracts for this introductory session.

3.3.1.1 Welcome Michael Weber, Director, Office of Nuclear Regulatory Research (Session 1A-1; ADAMS Accession No. ML17355A082) 3-9

3-10 3.3.1.2 NRC Flooding Research Program Overview Joseph Kanney*, Ph.D., Meredith Carr, Ph.D., P.E., Thomas Aird, Elena Yegorova, Ph.D., and Mark Fuhrmann, Ph.D., Fire and External Hazards Analysis Branch, Division of Risk Analysis; and Jacob Philip, P.E, Division of Engineering, Structural, Geotechnical and Seismic Engineering Branch, Office of Regulatory Research, U.S. NRC (Session 1A-2; ADAMS Accession No. ML17355A083https://www.nrc.gov/docs/ML1705/ML17054C500.pdf) 3-11

3-12 3-13 3-14 3-15 3-16 3-17 3-18 3-19 3.3.1.3 EPRI Flooding Research Program Overview John Weglian, EPRI (Session 1A-3; ADAMS Accession No. ML17355A084) 3-20

3-21 3-22 3-23 3-24 3-25 3-26 3.3.1.3.1 Question and Answers Question:

Are the EPRI reports discussed in this presentation free to the general public?

Response

So, the reports that specifically say theyre freely available to the public, anybody can get to them if you go to EPRI.com and you type in the number. In fact, this presentation has hyperlinks so if you follow that hyperlink for the public ones, it should take you to the public side of EPRI.com, where you can download that report for free. For those that did not specifically say freely available to the public, they are not free to the public. Members have access to those information and members of the public who choose to pay for them could also buy them in that way. EPRI prefers that you, even for the free to the public reports, that you go to the site and download your own copy rather than sharing it -just so we know how useful our materials are. We track that information so that we would know if everybody in this room downloaded one report, that report must be pretty important.

Question:

You commented on your 3D modeling the smooth vertical hydrodynamic model and it was for internal flooding only -not external.

Response

But the research that we did was using internal flooding. We simulated flooding in a particular room that propagated through a stairway into another room and the point of that research was to see if there are new risk insights using this approach compared to what the industry is currently using. Thats why we used an internal flooding scenario because there are internal flooding models that are out there that we could compare against and so thats why we looked at the internal flooding for that.

Question:

With regard to your correlated hazard modelshave you thought about it at the Blayais site, the argument is that when the water table obviously exceeds the surface and it could be as you just described with the water cascading down the stair, coming through pipes, whateverhow have you thought about combing the external hazard with the internal?

Response

So, thats a good point. If the water sticks around long enough it can get into the groundwater and you may havethe utility that I came from, they had a pipe to measure groundwater and its just an open pipe and if the groundwater got high enough, you would expect water to be coming out of that. So, to do an external flooding hazard assessment, you need to take that into account and thats a function of how long/high the water is around the site and how long it stays there. Local intense precipitation-probably not an impact. Riverine flooding, if it stays there for a couple months, almost certainly is and that would part of your hazard assessment. We tend to think, well maybe some of the practitioners are a little more nuanced but people that are not flooding experts, and before this, I was certainly in that category, think of flooding as just a water level. But theres a 3-27

lot that goes into it. NRC had in their slides a bunch of different things and time of water staying there is part of it which can feed into it. So, to do an overall hazard assessment, you have to look at these other effects that the flooding can bring with it and thats certainly part of it. And if you had that effect then you would have to start looking at the retention capability of some pumps that can remove that and determine what's your ingress rate and your removal rate and does that cause you a problem or not.

Question:

You mentioned bounding assessments specifically to say a basin average three-day precipitation what scientific evidence do you have for bounds? In that research? Given the modeling that has been done in the community the past five years with WRF and uncertainties. We're searching for that elusive bound

Response

Yeah, I did not mean to imply that EPRI has come up with one. I was saying the techniques used try to do that kind of bounding assessment on the deterministic side. I should not imply that EPRI has looked at that at all because none of the research that I talked about actually focused on estimating a bound to how much water can be provided or it can be like held in the atmosphere or things like that.

Question:

Yeah, I just bring this up to point out two things that are currently at hot topics in the literature.

One is: we see no evidence of bounds from observations and two: in a warming environment there are open questions on to what a bound might be on precipitation.

Response

Yeah that's a good point. I know I've seen when you estimate that the hazard curve there's usually two different forms that the hazard curve takes. One looks at the discharge rate as a function of frequency, and using the statistical approaches that we use, it usually does not look asymptotic.

Right it looks like it just gets a bigger and bigger, faster and faster as you go lower in frequency.

But then, when you convert that to what you have at a site, now this takes into account: are you in a narrow canyon or are you in a plane that they can spread out widely. So even if the discharge, say for riverine flooding, is continuing to increase without a bound, the flood level at your site may be more asymptotic. It depends on the topography near your site but typically the flood hazard curves at least for riverine flooding -I do see them -not the discharge itself- the amount of water coming is not typically shown as being asymptotic. So, it shows that there may not be a bound. It's just lower and lower frequency to get these higher and higher levels.

Question:

What is the cost-benefit comparison between using the SEFM model versus the RORB_MC model?

Response

I was not involved in that study during my time at EPRI, so I was about to answer your question until you said cost-benefit. I don't know the cost-benefit analysis. I do know from looking at the two 3-28

models, they seem to be in relative agreement. They were certainly within the uncertainty bounds of each other. The means diverged a little bit at the lower frequencies. I would have to ask the people that actually focus on that research in terms of was one more time-intensive to perform than the other? I'm just not familiar with those details. The RORB model did take less resources than the SEFM model.

3.3.2 Day 1: Session 1B - Climate and Precipitation Session Chair: Elena Yegorova, NRC/RES/DRA/FXHAB Development of guidance for application of improved mechanistic and probabilistic modeling techniques for key flood generating processes and flooding scenarios.

Assessment of the potential impacts of dynamic and nonstationary processes on flood hazard assessments and flood protection at nuclear facilities.

3.3.2.1 Regional Climate Change Projections: Potential Impacts to Nuclear Facilities L.

Ruby Leung^, Ph.D. and Rajiv Prasad*, Ph.D., Pacific Northwest National Laboratory (Session 1B-1; ADAMS Accession No. ML17355A085) 3.3.2.1.1 Abstract This project is part of the NRCs Probabilistic Flood Hazard Assessment (PFHA) research plan in support of developing a risk-informed licensing framework for flood hazards and design standards at proposed new facilities and significance determination tools for evaluating potential deficiencies related to flood protection at operating facilities. The PFHA plan aims to build upon recent advances in deterministic, probabilistic, and statistical modeling of extreme precipitation events to develop regulatory tools and guidance for NRC staff regarding PFHA for nuclear facilities. An improved understanding of large-scale climate pattern changes such as changes in the occurrence of extreme precipitation, flood/drought, storm surge, and severe weather events can help inform the probabilistic characterization of extreme events for NRCs permitting, licensing, and oversight reviews.

This project provides a literature review, focusing on recent studies that improve understanding of the mechanisms of how the climate parameters relevant to the NRC may change in a warmer climate, including discussions of the robust and uncertain aspects of the changes and future directions for reducing uncertainty in projecting those changes. During the first year, the project reviewed various aspects of climatic changes across the U.S., while the second year focused on more detailed changes in the southeastern U.S. The current focus is on the Midwest region consisting of 8 states (Minnesota, Wisconsin, Michigan, Iowa, Missouri, Illinois, Indiana, and Ohio) in the conterminous U.S. Except for Indiana, all states have currently operating nuclear power plants. The literature review includes an overview of the climate of Midwest U.S., including temperature and precipitation extremes, floods and droughts, severe storms and strong winds including mesoscale convective systems, tornadoes, hail storms, and lake effect snow storms, Great Lakes water level, and flooding due to various mechanisms including heavy precipitation from convective storms in the summer and extratropical cyclones in the winter and snowmelt in spring. For each climate variable or phenomenon, the report discusses the climatological features over the Midwest region, the historical changes observed in the past, and the projected changes in the future, drawing on major reports from the National Climate Assessment and peer-reviewed 3-29

papers in the literature. Overall, mean and annual 5-day maximum temperatures are projected to increase in the future. With increasing moisture accompanying the warmer temperatures, precipitation is projected to increase in the cool season, but the changes in warm season precipitation are not statistically significant. Despite inconsistency in mean precipitation changes across the seasons, extreme precipitation (99th percentile) is projected to increase by more than 10% and 30% by the end of the 21st century under the RCP4.5 and RCP8.5 emissions scenarios, respectively. A regional climate modeling study at 4 km resolution projected more than tripling in the frequency of intense mesoscale convective systems in the summer. This is consistent with observational evidence of an increase in mean and extreme precipitation associated with mesoscale convective systems over the Midwest in the past 35 years. Lake effect snow storms are projected to increase as reduction of the surface area of lake ice with warming increases evaporation from the surface, but larger warming farther into the future may shift snowfall events into rain events. The Great Lake level has exhibited large variability historically. Models projected small decreases in the lake level but the range of uncertainty across model projections is large.

Observational records over the Midwest show strong evidence of increasing flood frequency but limited evidence of increasing flood peaks. With the increase in extreme precipitation and storm events projected for the future, flooding is projected to increase notably in the future. Projected increase in average number of days without precipitation could lead to agricultural drought and increased cooling water temperatures.

3.3.2.1.2 Presentation 3-30

3-31 3-32 3-33 3-34 3-35 3-36 3-37 3-38 3-39 3.3.2.1.3 Questions and Answers Question:

Have you had the opportunity to read Marty McCann s study on the 2002 drought? It was interesting. So, the question is on persistence and what the variable of interest is? So, for those who haven't looked in the Midwest drought in 2012, they're claiming a record. The interesting story hydrologically and for maybe some of the facilities may be the persistence of droughts where the thirties duration was longer. So, the question is, have you had the opportunity to look at durational effects rather than single magnitudes? And whether there be a precipitation or on either end of the tales?

Response

I haven't looked at that yet but there are certain papers which say that the durations of the droughts are going to increase. Those are very relevant in terms of if you are operating a plan and you have low water supplies combined with if you are getting the water supply from the Great Lakes. Obviously with the lower levels going down, you have a concern not in terms of flooding but in terms of operating the plant itself. Yes.

Question:

David Bowles, emeritus Utah State. I'm curious in the percentiles that you showed in your various results, do you incorporate aleatory variability in that or is there epistemic uncertainty associated with the various types of modeling that go into those estimates as well?

3-40

Response

So, I assume that you're talking about the precipitation. So, those are based on a collection of global climate model projections and so essentially the kind of uncertainty that has been incorporated include several things. One is that they look at both the so-called high-end scenario versus the low-end scenarios so that would if you're kind of like the range. And then because it is a multi-model that also gives you some uncertainty related to how processes are being represented in different climate models. As well as looking at a longer range, looking at the internal variability as well. So, it's really essentially three sources of uncertainty can be considered using that type of approach but there are obviously other types of sources as well that that may not have been considered.

Question:

Bill Kappel from Applied Weather Associates. Great presentation just a quick two-part question one on the modeling aspect and one on the meteorological aspect of the presentation. Early on in the slides you had a slide which showed the hot days in Chicago which had an observed normal period from 1986 to 2005 then two periods after that what showed significant warming starting in 2016 and the question I have on that is first of all the observed data at least for Chicago doesn't show the same trend. In fact, the hottest days in Chicago happened in the 1940s, 50s, and 80s, and so the first question is if the observed data isn't fitting the trend that's showing in that graphic and specifically between 2005 and 2016 there has not been any increase, how does that marry up of the modeling environment if it's not correct in the beginning? Why would we think would be good going forward? And this is not specific for uses of any model, so that's the first question of how we deal with that uncertainty in the models it's not matching observations. The second part of the question is the highest temperatures recorded in Chicago just being specific to Chicago have been in the 1930s, 50s, 1988, and 2012. All those periods were significant drought periods, and that makes sense. Yet on your model projections, you showed that precipitation was going to be increasing significantly in Chicago. So, I'm just trying to figure out that incongruity there where if it's going to have higher precipitation you would expect it to actually have not as extreme temperatures. Anyway, so just those two kinds of questions on the modeling environment if you can answer those please.

Response

Okay so I can comment on the on the first question. I think you're right that what basically these results showing here from the NCA full report is only based on model projections so there's no particular process in terms of reconciling the model projection into the future with what the models simulate in the past. That could be inconsistent with regard to that and also for the point that these are based on results from mostly four models that have pretty close resolution. So, if you look at a specific city like Chicago, there might be local effects in terms of urbanization, as well as lake effects, and other things that are not being captured by these global climate model projections. So obviously, a lot more needs to be done in terms of reconciling the observed record, especially for a particular location with climate model projections that are much broader scale. The second part of the question about the increasing precipitation but at the same time why do we have that sort of this drought?

Question:

Sorry, and by the way, I understand these are all models. I'm just trying to clear this up. In the temperature records for Chicago, specifically, all the hottest days have been during significant 3-41

drought periods, so if that's the case going forward, what you would expect it to be then if it's going to have more days of wet weather or higher precipitation, you would expect the temperatures to not be as hot consecutively as in the past when it wasn't drought.

Response

One thing the studies are mentioning is that there is not really a local scale assessment that is available for you to look at and try to make these determinations. But at the same time, generally speaking, they're saying if the temperatures are increasing in the future then you might be having elevated evapotranspiration and if that is happening, basically taking the conditions that are predicted by the larger scale modeling and trying to put in context of the physical nature in which the hydrology actually works. So, if you look at that, then they're just making a general statement that this is what we expect to happen. Now with the NCA4 Volume 2 coming out which is going to describe a lot of the regional studies, we might be able to see some more evidence of this thing.

3.3.2.2 Numerical Modeling of Local Intense Precipitation Processes M. Lev Kavvas , Ph.D.

Mathieu Mure-Ravaud**, and Alain Dib*, Hydrologic Research Laboratory, Department of Civil and Environmental. Engineering, University of California, Davis (Session 1B-2; ADAMS Accession No. ML17355A086) 3.3.2.2.1 Abstract As population and infrastructure continue to increase, our society has become more vulnerable to extreme events. Flood is an example of a hydro-meteorological disaster that has a strong societal impact. Tropical Cyclones (TCs) and Mesoscale Convective Systems (MCSs) are recognized for their ability to generate intense precipitation that may in turn create disastrous floods. TCs are intense atmospheric vortices that form over the warm tropical oceans, while MCSs are organized collections of several cumulonimbus clouds which interact at the meso-scale (regional-scale) to form an extensive and nearly contiguous region of precipitation. In this study, the suitability of a regional atmospheric model (RAM) to simulate local intense precipitation processes within intense MCSs was first assessed. More specifically, the Weather Research and Forecasting (WRF) model was used at 5-km resolution in order to reconstruct the intense precipitation fields associated with several historical MCSs which affected the United States. The storm systems were selected within the time period from 2002 to the present, based on the NCEP Stage-IV precipitation dataset, which is a mosaic of regional multi-sensor analysis generated by the National Weather Service River Forecast Centers (RFCs) since 2002. These storms correspond to the most severe storms, in terms of the generation of an intense precipitation field containing pockets of extreme rainfall.

The models simulation nested domains were set up over a region in the Midwest so that the innermost domain covered the severe precipitation areas caused by these storm systems. The WRF model was configured to obtain the best results for the simulation of each of the selected severe MCSs storm events with respect to the simulated and observed precipitation fields. The simulations results were compared with the observations from the Stage IV precipitation dataset.

More precisely, on one hand, the simulation results were evaluated by means of several metrics:

the relative error for the simulation inner-domain total precipitation, the percentage of overlapping between the simulated and observed fields for several precipitation thresholds, and the precipitation field area ratio. On the other hand, the simulated and observed precipitation fields were plotted so as to visually appreciate the similarities and differences in the fields structure and intensity.

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It was shown that under an appropriate choice of the models options and boundary conditions, the WRF model provided satisfactory results in reproducing the location, intensity, and structure of the intense precipitation fields of the historical MCSs. The models options that were investigated are the parameterization schemes including microphysics, cumulus parameterization, planetary boundary layer physics, long wave and short wave radiation physics, etc. Although certain combinations of the parameterization schemes provided in each case realistic results in terms of the precipitation fields structures and intensity, placing these fields in the correct spatial locations required additional efforts, so that the best set of models options varied from one storm system to the other. Second, in this study, a new storm transposition method designed for the transposition of TCs is presented. This method is fully physically based, as it uses a RAM to numerically simulate a TC and its precipitation field. As a result, it has the fundamental advantage of conserving the mass, momentum, and energy in the system since the RAM numerically solves the equations governing the conservation of these quantities. The objective of this method is to find the amount of shift which maximizes the precipitation depth over a given target area. The transposition method was applied to four hurricanes that had spawned torrential precipitation in the United States, namely Hurricanes Floyd (1999), Frances (2004), Ivan (2004), and Isaac (2012). The drainage basin of the city of Asheville, NC was selected as the target. It was observed that the precipitation fields changed in both structure and intensity after transposition. The convergence of the vertically integrated vapor transport (IVT) was found to play a central role in the generation of intense precipitation in these hurricanes.

3.3.2.2.2 Presentation 3-43

3-44 3-45 3-46 3-47 3-48 3-49 3-50 3-51 3-52 3-53 3-54 3-55 3-56 3-57 3-58 3-59 3-60 3-61 3-62 3-63 3-64 3-65 3-66 3.3.2.2.3 Questions and Answers:

Question: The first one is with your TC simulation. You showed some statistics there such as overlap per percentage and so on. You indicated that you felt those results were quite good. I'm just wondering, the statistics certainly are very useful that you use, but in terms of judging the acceptability of the simulation, it would seem like you'd need to sort of evaluate your simulation in the context of how that information is going to be used. In order to know if the errors in the simulation are acceptable, in terms of that application. That's the first question. Second question is a lot simpler. With your transposition approach, have you considered incorporating uncertainty into that approach?

Response: For the first question regarding the quality of the simulation, we tried hundreds of combinations of the parameterization schemes and I don't know how many of you are familiar with the numerical modeling of some system but it's extremely complicated. For those who are familiar with such modeling, you will know that these results are actually quite satisfactory. So, the idea is we want to estimate, in the case of the storm transposition exercise, what would have been the precipitation that's over a given target area. The first step is always to validate the model before using the model in order to make an estimation. So, this is what was done in the first step -to calibrate and validate the worst model. Of course, for the results that we obtained, in the perspective of the quality of the calibration, there was for example minus 13 percent relative error and the other statistics are not perfect, but we can still say that the estimation that we obtained is relevant. Now coming to the second question regarding the uncertainty, if I remember the uncertainty for the storm transposition, there would be several ways to tackle this issue, for example, one could use several combinations of the parameterization schemes. Meaning that in this case we show the results for one combination which was assessed to be the best 3-67

combination. One could use several combinations with which are assessed to be satisfactory and do the transposition exercise with all the combinations which would give several estimations of the maximum precipitation about the targets. And one would obtain some uncertainty related to the model based on these different values that are obtained by using the different combinations. Also, one could try to use different data sets for the initial and boundary conditions. Also, one could do the exercise for the Quebec projections so there are many climate projections. In this case we used only one start date for the simulation. One could use many start dates for the simulation, so there are several ways that one could tackle uncertainties of the model, of the boundary initial conditions, and so on.

Question: Kelly Mahoney, by how much does the storm environment change in the RAM based shifting procedure?

Response: So, in the middle you can see that the drainage basin is located in a mountainous area, while in the observed case, the historical system affected a region which is not mountainous. So is a very significant change in topography first of all. Then I'm not familiar with the land surface issue, but if there was a tremendous difference between the location of the historical system and the location of the maximized system.

Question:

John England from USACE, we struggle with transposition so I'm wondering if you have had the opportunity to start to place a hierarchy on particularly the tropical cyclones in how far you start to move them. The example I'm going to ask is if you look at Agnes and you go extra-tropical and the source is the Gulf and it recurves to the Atlantic and zooms back into Pennsylvania, Western Pennsylvania particularly, your transposition area might be determined by the tract or other features from maybe a library of TC tracks.

Response

So, the technical details regarding how the transposition was performed will be given in the final reports. But the storm transposition can be performed in any conditions. If the storm transposition is legitimate or not -that's another question. For the legitimacy of the storm transposition, there are a certain number of criteria that need to be met. For example, for the storm transpositions you mentioned, I think an extra-tropical transition, if the storm has already started the historical transition, the shifting of the storm is not necessarily legitimate, except if the amount of shift is very small. You cannot put back into the Atlantic Ocean a storm which already started to interact with the land and with other systems in the mid-latitudes. So, all these criteria will be listed in the final report for this project.

Question:

Just to clarify, when you do the transposition, the broad moisture field stays the same, you're just moving the track? Correct?

Response

Yes. We transpose this storm -not the overall field.

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Question:

Just kind to get an idea of the levels effort to do these WRF simulations of these transformed storms, and if you did put this into a stochastic framework to get a better idea of uncertainty, what kind of level of computational time which you need?

Response

With today's technologies such exercises are possible. In the case of the tropical cyclone, it is different from what has been done in the past by Professor Kavvas and his team. For example, for atmospheric rivers in which case there was a two-dimensional search for the maximum. In the case of tropical cyclone, what you can do is if the storm is moving in this direction can just shift it that way. There is no need to shift in the direction of the storm. So, in this case it is just possible to do a one-dimensional search which considerably limits the computational efforts but it's still quite demanding.

Question:

Bill Kappel, I'll make it quick hopefully. Just a quick question on this. When we talk about transposition ability, so Hurricane Isaac is a direct hurricane with landfall and coastal interaction processes. And theres really no topography over Louisiana, Mississippi, and Alabama. But when it moved to the Asheville Basin, how did WRF take into account the differences in topography and moisture sources? When we talk about transpositioning storms, one of the key definitions is that they need to be moved within similar regions of meteorology and topography. Obviously New Orleans is a lot different than Asheville, North Carolina. So how do you determine where to test and move these storms? And then, if so, how do you parameterize the model differently for the two locations?

Response

As far as I am familiar with the traditional PMP approaches, what was done is to shift the precipitation field module adjustments. In this case, you can see that the shifted precipitation field is fundamentally different from the original precipitation field. If I remember from the traditional PMP approach, they consider this region of homogeneity for the transposition of the precipitation field. In this case, we only manipulated the initial and the boundary conditions and the model is run as normal. I mean once this manipulation has been made, the model is run as usual and all the interactions are taken into account. I think the second part of your question was for the validity of the parameterization schemes. The fact that the model was calibrated for a given region and affects another region. Is the combination of the scheme adapted for the new region? I think it is an important point and these we will need to investigate but as we mentioned earlier for a previous question, the fact to use several combinations of parameterization schemes in order to account for uncertainties would also affect this issue of is one given combination adapted for a new region of the country. So, using several combinations would really be beneficial also for this problem.

Question:

Kelly Mahoney , is the best combination of model physics the same across all simulations or is the best simulation chosen individually for each storm?

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Response

The best combination because there are several millions of possible combinations in the WRF model. We looked for a satisfactory combination so it's not an optimization exercise properly speaking. There was an algorithm to find the best combination. We looked for a satisfactory combination and the satisfactory combination changes from one tropical cyclone to another. But for a given tropical cyclone, the combination was the same between the calibration and the functions position.

3.3.2.3 Research on Extreme Precipitation Estimates in Orographic Regions Kathleen Holman^, Andrew Verdin, and David Keeney, Flood Hydrology and Meteorology Group, Technical Services Center, U.S. Bureau of Reclamation (Session 2B-3; ADAMS Accession No. ML17355A087) 3.3.2.3.1 Abstract We present the findings of the research project Phase II: Research to Develop Guidance on Extreme Precipitation. Frequency Estimates for the Tennessee Valley. The definitive objectives of this research project are: (i) Review extreme storm precipitation techniques, precipitation-frequency methods, and databases in orographic regions; (ii) Develop a methodology to estimate precipitation-frequency in regions of complex topography; and (iii) Demonstrate the precipitation-frequency methodology and provide uncertainties and confidence intervals at the regional and reactor-site scale for a pilot region in the Tennessee River Valley watershed (TRVW). The focus of this presentation is on the development of a generalized framework for precipitation-frequency analysis in orographic regions. Obtaining reliable precipitation-frequency estimates requires confidence in the estimated extreme value distribution parameters. However, parameter estimation is sensitive to a number of influential factors, the period of record being critical.

Regional frequency analysis (RFA) is a commonly used technique for extending the period of record, using a space-for-time substitution method. The fundamental basis of RFA is the assumption that observations from climatically similar stations can be described by the same probability distribution.

The methodology developed in this research combines a known objective clustering algorithm, the Self-Organizing Map (SOM), with two distinct frequency estimation methods, L-moments and Bayesian inference . The SOM algorithm utilizes a combination of geophysical information and observed precipitation data to identify climatically similar groups of stations (i.e., homogeneous regions, hereafter HRs) within the TRVW. L-moments and Bayesian inference are then used to estimate generalized extreme value (GEV) distribution parameters to produce regional growth curves (RGCs) for each of the HRs. Site-specific precipitation-frequency estimates are obtained by scaling the RGCs by the at-site mean for the site of interest. Only the GEV distribution was considered, as epistemic uncertainty due to probability distribution choice was not the focus of this research. Results suggest that uncertainty estimates from the L-moments analysis are consistently less than the uncertainty estimates from Bayesian inference. These differences are the result of estimating uncertainty differently between the two methods.

It may be of interest to produce precipitation-frequency estimates at locations where no historical data are available. To this end, we illustrate the benefit of using a gridded precipitation dataset as 3-70

input to RFA. Specifically, the Newman et al. (2015) dataset contains an ensemble of gridded daily precipitation for 33 years at 1/8-degree resolution. The ensemble contains 100 members, each of which are equally plausible precipitation totals for the grid cell of interest. We illustrate how the ensemble members are collapsed into a single dataset, and the extreme value distribution parameters are estimated independently at each grid cell. This presentation ends with an illustration of the two methods abilities in quantifying small exceedance probability precipitation events with associated uncertainty.

3.3.2.3.2 Presentation 3-71

3-72 3-73 3-74 3-75 3-76 3-77 3-78 3-79 3-80 3-81 3-82 3-83 3-84 3-85 3-86 3-87 3-88 3-89 3-90 3-91 3-92 3.3.2.3.3 Questions and Answers None.

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3.3.3 Day 1: Session 1C - Storm Surge Session Chair: Joseph Kanney, NRC/RES/DRA/FXHAB Development of guidance for application of improved mechanistic and probabilistic modeling techniques for key flood generating processes and flooding scenarios.

3.3.3.1 Quantification of Uncertainty in Probabilistic Storm Surge Models Norberto C.

Nadal-Caraballo , Ph.D. and Victor Gonzalez*, P.E., U.S. Army Engineer R&D Center, Coastal and Hydraulics Laboratory (Session 1C-1; ADAMS Accession No. ML17355A088) 3.3.3.1.1 Abstract Probabilistic flood hazard assessment (PFHA) of critical infrastructure located in coastal zones requires the characterization of the storm surge hazard and associated uncertainty. The joint probability method with optimal sampling (JPM-OS ) has become the standard probabilistic model used to assess coastal storm hazard in hurricane-prone coastal regions of the United States.

Other methods such as global climate modeling (GCM) downscaling and Monte Carlo Simulation methods have also been applied. The U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory (ERDC) is performing a comprehensive assessment of uncertainties in probabilistic storm surge models in support of the NRC efforts to develop a framework for probabilistic storm surge hazard assessment for nuclear power plants. The treatment of uncertainties in the JPMX-OS methodology varies by study and is typically limited to the quantification and inclusion of uncertainty as an error term in the JPM integral. Traditionally, these errors have been regarded as epistemic uncertainties because, theoretically, they could be reduced by collecting additional data, refining the numerical models, and constructing more efficient synthetic storm suites. In practice, past individual studies, for example, have been based on a defined set of data sources and have employed a single approach for estimating each of the JPM components (e.g., computation of SRR, univariate distributions, distribution discretization method, development of synthetic storm suites, others), limiting the understanding of the range of uncertainty.

The treatment of uncertainties in the present study is based on USNRC guidance on probabilistic seismic hazard assessment (PSHA). In this paradigm, the epistemic uncertainty arises from the selection and application of technically defensible alternative data, methods, and models at each step of the probabilistic storm surge modeling. Once the epistemic uncertainty is quantified, it is propagated through the use of logic trees. This allows for the computation of a family of hazard curves, with individual curves representing each of the alternate modeling approaches. In order to quantify the epistemic uncertainty associated with probabilistic storm surge models, this study evaluated data sources and methods associated with the different applications of the JPM-OS, GCM, and MCS approaches, and determined the data and methods that should be carried forward. Specific topics that were assessed include storm recurrence rate models, methods for defining joint probability of storm parameters, methods for generating synthetic storm simulation sets, integration methods, and integration of aleatory variability. The analysis of the logic tree branches representing the center, body, and range of the data and methods employed by each probabilistic storm surge model (e.g., JPM-OS, GCM, and MCS) yielded a family of hazard curves. To convey the range of the epistemic uncertainty, a statistical analysis was performed to compute fractile storm hazard curves (equivalent to non-exceedance confidence limits) including the mean, 0.05, 0.16, 0.5 (median), 0.84, and 0.95.

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3.3.3.1.2 Presentation 3-95

3-96 3-97 3-98 3-99 3-100 3-101 3-102 3-103 3-104 3-105 3-106 3-107 3-108 3.3.3.1.3 Questions and Answers None.

3.3.3.2 Probabilistic Flood Hazard Assessment - Storm Surge John Weglian, EPRI (Session 1C-2; ADAMS Accession No. ML17355A089) 3.3.3.2.1 Abstract A storm surge is a rise in water level driven by winds from an approaching storm. While this is typically associated with hurricanes, other storm types can also produce a storm surge. EPRI has one research report on estimating the frequency of various magnitudes of storm surge based on an analysis of historical water levels. More research is currently underway to demonstrate how simulations of a hurricane can be used to estimate the frequency of various storm surge levels at a particular location.

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3.3.3.2.2 Presentation 3-110

3-111 3-112 3-113 3-114 3.3.3.2.3 Questions and Answers Question:

I'm very interested and you didn't mention the word seiche. For your lake levels did you think in terms of that? And then a follow-up quick question is, we heard from Ruby Leung this morning about some estimates of how climate may change the Great Lakes, she didn't bring up but the idea of intense storms on the Great Lakes that would enhance the storm surge and the seiche level. Do you want to comment?

Response

Okay so for those who don't know: seiche is a periodic motion of a closed body of water. Now I know that the Great Lakes typically use the term seiche inappropriately for anything that looks like a large increase in water level. That particular approach: basing it on historical water levels cannot distinguish between a storm surge versus a seiche from any other means. Okay so this approach would actually capture both of those. If you got your data from something different, it may not include seiche. One of the complications with external flooding is that we break it neatly into different criteria. They're not always so easily distinguished. Local intense precipitation and riverine flooding can be happening at the same time. So, you have to be careful when you do your analysis and what you are encompassing and what you're not. In order to make sure that you're not leaving something behind that you're not including. So, like I said, seiche would be included in that. If the data for whatever reason had that excluded, you would have to do a separate analysis on that EPRI has not done any research specifically for seiche or tsunami at this point for hazard assessment.

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Audience Comment:

With regard to seiche, whether it's captured or not will depend upon the time scale. Because when you process the wind wave data, you're always going to use some sort of time scale that you're using. If that seiche is within that time scale, you'll capture it but if that seiche is something with a really long period that might not get captured.

Response

With regard to climate change, this analysis did not include adding additional information to predict the future. Typically, when we do probabilistic risk assessments, you are assessing the as-built as-operated plant, so you are looking at today's conditions and so should you add something to say in the next 20 years I expect this to get worse? I'm not sure what's appropriate there.

Maybe you should be updating your model every time you update it and maybe increasing the probability as you go along. It's a good question. I don't have a good answer for it. I think it's definitely something we need to consider, but we need to take into account that we're trying to assess today's risk when using a probabilistic risk assessment and unless the application is looking into the future.

3.3.4 Day 1: Session 1D - Leveraging Available Flood Information I Session Chair: Nebiyu Tiruneh, NRC/NRO/DLSEA/RHMB Research to develop the means by which staff can leverage available frequency information on flooding hazards at operating nuclear facilities to support the SDP.

3.3.4.1 Flood Frequency Analyses for Very Low Annual Exceedance Probabilities using Historic and Paleoflood Data, with Considerations for Nonstationary Systems Karen Ryberg*, Ph.D., Kelsey Kolars, and Julie Kiang, Ph.D., U.S. Geological Survey (Session 1D-1; ADAMS Accession No. ML17355A090) 3.3.4.1.1 Abstract Exceptionally rare flood events may have an annual exceedance probability (AEP) of 0.0001 or lower, meaning the average recurrence interval may be 10,000 or more years. Standard methods for statistical estimation of flood frequency rely on a systematic streamflow record, which provides a time series of annual peak streamflow (peak flow). While few long-term streamgages in North America provide records of peak flow more than 125 years in length, estimation of peak flows with very low annual exceedance probabilities is needed to accurately portray risks to critical infrastructure, such as nuclear power plants. Uncertainties are large when extrapolating magnitudes of extremely rare events from a streamflow record that is much shorter. The addition of historical data (data outside the systematic record, yet within the period of human record, such as newspaper accounts that can be translated to flood magnitudes) or paleoflood data (information about flood occurrence or magnitude from sources like sediment deposits or tree rings) can inform flood-frequency estimates and, in some cases, reduce error bounds. In other cases, the paleoflood information can appear to come from a different population than the systematic record.

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An additional complication for flood-frequency analysis is the need to satisfy the assumption that the time series is stationary; that is, peak flows vary around a constant mean within a particular envelope of variance. As concerns about land-use change and anthropogenic climate change have increased, and our understanding of natural systems has improved, we have learned that the stationarity assumption is sometimes inappropriate. The computation of flood frequencies under nonstationarity remains an active area of research without a consistent approach for dealing with nonstationarities.

Flood magnitudes were calculated for select North American sites with systematic records and historical and paleoflood information using U.S. Geological Survey software, PeakFQ (version 7.2.22429) which has been extended to provide estimates of peak-flows with AEPs as low as 0.000001. (The extended output is intended only for use in special purpose studies of exceptionally rare events. The extended output should not be used for typical flood-frequency studies where the interest is in AEPs in the range of 0.1 to 0.005.) PeakFQ analysis used the expected moments algorithm, which allows inclusion of nonstandard flood information, such as intervals. PeakFQ also identified potentially-influential low floods (PILFs) that may represent nonstationarities. Use of EMA with the identification of PILFS means that the low floods were censored and had little or no influence on estimates on the high end of the flood-frequency distribution.

Results will be presented for the Red River of the North at Winnipeg, Manitoba, a site with a long systematic record, historical peaks, and paleoflood information. The presentation will demonstrate how additional flood knowledge beyond the systematic streamflow record affects estimation of low AEP floods and error bounds. The Red River also has some nonstationary features (abrupt changes, serial correlation, and an increasing trend in flow) that violate the underlying assumptions for flood-frequency analysis. These nonstationarities and their implications for the estimation of flood events will be discussed along with possible adjustments.

3.3.4.1.2 Presentation 3-117

3-118 3-119 3-120 3-121 3-122 3-123 3-124 3-125 3-126 3-127 3-128 3-129 3-130 3-131 3-132 3-133 3.3.4.1.3 Questions and Answers Question:

I have a question about the tree rings. How do you tell the difference between an event in the tree ring record, you know like a large flood, versus a wet season?

Response

You would have to examine them with a microscope and that is something I've thought quite a bit.

Because tree ring analysis is often done to estimate precipitation and a wide ring is a wet year, a narrow ring is a dry year, but if we look at this narrow ring we know this was a very wet year and it resulted in a narrow ring. So, it's one thing to measure the tree-ring width which takes some microscopic work, but you have to go into much more detail microscopic analysis to be able to see evidence for a flood. Also, one of the issues for this by this researcher Scott St. George knowing some very large floods, like the 1826 flood, he could figure out the signal in the tree rings for that and then based on that signal estimates some past floods but his estimate of these floods he says is really a minimum. It should be an interval estimate from this minimum value, but it's not known in this tree ring what is the upper value that you could detect. It would vary with river but there's probably some upper value that might kill the trees or you don't know what the upper limit is, so this really should be an interval estimate but we don't have that upper limit.

Question:

So, then you had actual discharge estimates based upon the tree rings, so then you must have been able to get an inundation level that is associated with that? Correct?

Response

Yes. So, when you do tree rings it's a whole chronology. You get cores for multiple trees in a stand of various ages and you know that there was a flood in 1997 and you can see that result in tree rings. You know there was a flood in 1850, you line up your tree rings and you can see this consistent signal. We know there was a large flood in 1826, we can see that signal and then going back in time when we don't know much about the floods we look for that same signal. And the degree to which the tree was affected by that and then comparing it to the known floods. And how this ring looked during the known very large floods, the researcher came up with an estimate of flood magnitude. Looking at a variety of sources, stream discharge relationship, modern floods, how those look in the rings.

Question:

So, its not just the elevation?

Response

No.

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Question:

To add on to what you're saying before that it's a lower bound how it affects the tree ring also depends on when in the growing season the flood occurs. Right? Can there be floods that the tree-ring won't represent because it wasn't adding material at that time?

Response

Yes. That's a good question. There are a few large floods in this red river series that we know happened. And that there's some interval or point estimates for that that did not show up in the tree-ring series, so this is essentially a minimum estimate for magnitude and a minimum estimate for frequency as well.

Question:

So, can you give similar information from dead trees or did it have to be alive?

Response

It can be dead trees. For example, there's a researcher in Minnesota that has gone to settlers cabins. There are many cabins in Minnesota built in the 1800s that have been preserved and ultimately been passed down through families. There's a known date when that cabin was built, and he's gone to those cabins and got them to let him put some holes in the logs. He's developing a tree ring chronology based on that. Also, this researcher, Scott St. George, has gone to the Red River and done some investigation of subfossil logs pulled up from the sediment. It can be hard.

You have to compare them to other chronologies, the wet and dry rings, to figure out where they fit because you don't know exactly when they died.

3.3.4.2 Extending Frequency Analysis beyond Current Consensus Limits Keil Neff*, Ph.D.,

P.E. and Joseph Wright^, P.E., U.S. Bureau of Reclamation, Technical Service Center, Flood Hydrology and Meteorology (Session 1D-2; ADAMS Accession No. ML17355A0901) 3.3.4.2.1 Abstract This project is part of the NRC Probabilistic Flood Hazard Assessment (PFHA) research plan to support development of a risk-informed approach for addressing flood hazards at nuclear facilities. This work focuses on providing technical guidance for developing extreme flood frequency estimates beyond the current consensus limits (Annual Exceedance Probabilities (AEPs) less than 1x10-4) from the context of the Bureau of Reclamation.

Reclamation , the owner of approximately 370 dams and dikes in the Western U.S., pioneered conducting flood frequency analyses to support dam safety risk-informed decision-making. For Reclamation dam safety risk assessments, flood estimates are needed for AEPs of 1 in 104 and down to as low as 1 in 108. Developing credible estimates at these low AEPs generally requires combining data from multiple sources and a regional approach. Reclamation has published methodology and guidance to develop hydrologic hazard estimates over the past quarter of a century. The primary purpose of these published guidelines, procedures, and standards was to 3-135

provide state-of-the-practice methodology for developing hydrologic hazard curves (and supporting flood hydrology information) to be used for evaluating facilities, prioritizing dam safety modifications and supporting planning and design decisions.

From a hydrologic perspective, risk estimates require an evaluation of a full range of hydrologic loading conditions and possible failure mechanisms tied to consequences of failure. The flood loading input to a dam safety risk analysis is a hydrologic hazard curve (HHC) that is developed from a hydrologic hazard analysis (HHA). Hydrologic hazard curves combine peak flow, water surface elevation, and volume probability relationships plotted with respect to their AEPs.

Information derived in HHAs, including HHCs and associated flow and stage frequency hydrographs, can be used to assess the risk of potential hydrologic-related failure modes including overtopping, internal erosion under various reservoir levels, erosion in earth spillways, and overstressing of structural components.

When evaluating hydrologic hazards, a systematic means of developing flood hazard relationships is needed for risk-based assessments to determine hydrologic adequacy for Reclamation dams. The nature of the potential failure mode and characteristics of the dam and reservoir dictate the type of hydrologic information needed. The selected also considers available hydrologic data, potential analysis techniques, available resources for analysis, and an acceptable level of uncertainty. For some projects, only a peak-discharge frequency analysis may be required; while for others, flood volumes and hydrographs may be necessary. The goal of any hydrologic analysis is to provide hydrologic information to the necessary level (i.e. minimum effort and cost) to make effective dam safety decisions.

To provide flood estimates for a full range of AEPs necessary for dam safety decision-making, it is usually necessary to extrapolate beyond the period of recorded data. The type of data and the record length used in the analysis form the primary basis for establishing a range on credible extrapolation of flood estimates. Streamflow and reservoir data corresponding to current operations and watershed characteristics are data that should be used in FFAs. In higher level projects requiring more effort, data can be adjusted to represent the current conditions and operations to extend series for the entire period of record. The data used provide the only basis for verification of the analysis or modeling results, and as such, extensions beyond the data cannot be verified. The greatest gains to be made in providing credible estimates of extreme floods can be achieved by combining regional data from multiple sources. Thus, analysis approaches that pool data and information from regional precipitation , regional streamflow, and regional paleoflood sources provide the highest assurance of credible characterization of low AEP floods.

The principal components of this work include guidance on data sources and model inputs, probabilistic hydrologic hazard methods, multiple methods and other considerations, and Reclamation case studies. This presentation will provide an overview of what is described in this project including: 1) streamflow and climate data necessary; 2) statistical methods, physically based hydrologic modeling approaches, the Australian Rainfall and Runoff method, and the Stochastic Event Flood Model; 3) mixed population systems, combining multiple methods, and uncertainty ; and 4) Reclamation case studies that encompass the breadth of described probabilistic hydrologic hazard methods.

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3.3.4.2.2 Presentation 3-137

3-138 3-139 3-140 3-141 3-142 3-143 3-144 3-145 3-146 3-147 3-148 3.3.4.2.3 Questions and Answers Question: So, I noticed some of your graphs, the scale on the x-axis, if I take out the percent, went as far as 10 to the minus 10 per year, which is double the age of the earth but the graph didn't go that far. But some of your lines go up to about 10 to the minus 6 per year, some of them stop at 10 to the minus 4 per year. I want to know, from Reclamation's standpoint, at which pointwhere do you stop being concerned or do you. As you're going lower and lower in frequency and say below this we're going to screen that out because it's too low a risk to consider?

Response

Well first of all, we would never go 10 to the minus 10. So, I think Kyle had a slide up there that showed incredible extrapolation under the perfect conditions, all the planets come aligned, and everything and your data is just perfect, you can go to maybe a hundred-thousand-year event.

When we're looking at EMA curves with paleo flood data if we have a really, really good data set, I prefer to cut them off at about a twenty-thousand-year event, maybe a fifty thousand if the risk analysis justifies it. When we get beyond that, without any kind of a stochastic type approach, we tend to lean towards a pragmatic approach and that's why I like the Australian Rainfall Runoff Method (ARR). I like to call ARR our frequency PMFs because basically we're just drawing a line from a hundred-year preset to a PMP that we assume some probability to and we're just using that to scale a PMF hydrograph. And it's just a conservative, pragmatic approach that if we say that the dam can handle that then you're probably okay. SEFM I don't think we've ever done one and that goes beyond a 1-million-year event, and when we do that we now like to incorporate some uncertainty in that and we are working on how to incorporate uncertainty in our overall risk analysis. So hopefully that answers your question.

3.3.4.3 Development of External Hazard Information Digests for Operating NPP sites Kellie Kvarfordt and Curtis Smith*, Ph.D., Idaho National Laboratory (Session 1D-3; ADAMS Accession No. ML17355A092) 3.3.4.3.1 Abstract The original name of this project was Development of Flood Hazard Information Digests for Operating NPP Sites, and the original objective and tasking of the project was for Idaho National Laboratory (INL) to develop, demonstrate, and help populate a database architecture for Flood Hazard Information Digests. The resulting web application facilitates gathering, organizing, and presenting a variety of flood hazard data sources. However, the database is currently undergoing expansion to include other external hazards such as seismic and high wind hazards, extreme temperatures, and snow/ice loads. This expansion will support the Commission directed activity to enhance agency processes for ongoing assessment of natural hazards information. Thus, a more accurate name for the project and digest application is now External Hazards Information Digest (EHID).

The goal of the project is to provide information and tools to support external event analysis, particularly the risk-informed aspects of the Significance Determination Process (SDP). Under the SDP the use of probabilistic external hazard information and insights is an important input in the determination for follow-up inspection actions and resource allocation, and risk-informing of licensing actions. However, NRC staff has had to improvise and only use probabilistic external hazard estimates on an ad hoc basis, in a limited manner.

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A particular challenge in developing probabilistic external hazard estimates within the SDP is that the required external hazard information is not readily accessible. It is challenging for NRC staff to assemble and analyze the information within the time available for the SDP. Thus, there is a need to better organize external hazard information at operating reactor sites and improve its accessibility for NRC staff performing SDP analyses. The EHID application has been developed to address these needs.

Major flood related data sources that have been identified for reference in EHID include data from Fukushima NTTF Recommendation 2.1 and 2.3, precipitation frequency information from NOAA ,

flood frequency information from USGS, hurricane landfall/intensity information, as well as flood protection and mitigation strategies from NUREGs, FSARs, IPEEE submittals, and SDP analyses.

Additional data sources are being identified for other external hazard inclusion. In addition to providing access to these and other data sources, the information digest can provide, where needed, guidance for using the available information.

The EHID has been implemented as a cloud-based web application. The digest utilizes the INLs Safety Portal, a system that helps integrate and manage a comprehensive collection of many different kinds of content including web pages, web applications, models, and documents where users may store, use, share, modify, or otherwise contribute to projects. The information digest shares available services such as user account management, file sharing, and a publications/

permissions/ subscriptions model.

Because the database contains a mixture of publicly and non-publicly available information, the EHID application is available only to NRC staff and contractors with appropriate authorization.

Within the application access to individual items is controlled by those authoring the information.

Initial data population efforts for flooding are nearing completion, and other external hazard data source identification and population efforts are commencing. The bulk of data population is targeted for completion by the end of June 2018. Maintenance will be folded into other ongoing data related activities performed by INL on behalf of the NRC.

3.3.4.3.2 Presentation 3-150

3-151 3-152 3-153 3-154 3-155 3-156 3-157 3-158 3-159 3-160 3.3.4.3.3 Questions and Answers Question:

So, I'm curious since I know you said a lot of the information is publicly availableif a licensee has a reason to assess these things as much as the NRC. Say there's a significant determination process and they would like to simplify their research as well is there any mechanism that they 3-161

can ask the NRC, Can you give us all the links to publicly available information? Since you have it very readily available?

Response

They would go through the NRC to determine whether that was appropriate or not. I guess the licensing office would make that determination... not us here in research. So, if the licensing office wanted to make that available to the licensees and that was an appropriate thing to do, then I don't think there would be any problem with it. But that wouldn't be a decision that research makes.

Question:

Thank you very much Kelly. One thing I kept thinking about as you went through different data sets Im very curious do you have the pedigree of the data? Do you have information on the QA/QC? What was done at the time of the collection of the data and how you could determine how the data was collected who collected it? Under what procedures they collected it? Do you have that information?

Response

I really don't. These are from external agencies and that is their mission to collect that data. I guess we could probably provide links into any background that they had on that but we're not personally validating that data. I know that the specific stuff that we make available: the stations and the gauges, hydrologists have cross-checked each other to make sure that we're providing the correct links the things that we have control over.

Question: It's a kind of a curiosity question I guess. Do you have flood forecasts locations say upstream of the power plants included in the database? And also, upstream dams?

Response

We don't have the upstream dams right now but that is certainly under consideration as part of some of the other external hazard sources. We're evaluating what sort of information we want to put in there regarding upstream dams, as you're well aware, some information about upstream dams is information that we may have obtained from other agencies, so we would have to consult with the other agencies to make sure that we're not providing information that shouldn't be provided to people who don't need. On the same pages as the USGS gauge sites, there's links to the upstream and downstream forecasting sitesthey've been identified, I'm not sure if they have been worked into the database - but those flood forecast sites are pretty short in history and I don't know if our turnover is that fast for looking at forecasts.

Question:

A couple of other previous speakers were focusing on other extremes. Have you had the opportunity to include climate projection information in the database? Specifically, precipitation stream flows that have been down scaled or other sources such as those?

Response

No.

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3.3.5 Day 1: Session 1E - Paleoflood Studies Session Chair: Mark Fuhrmann, NRC/RES/DRA/FXHAB 3.3.5.1 Improving Flood Frequency Analysis with a Multi-Millennial Record of Extreme Floods on the Tennessee River near Chattanooga, Tessa Harden*, Ph.D., Jim OConnor, Ph.D. and Mackenzie Keith, U.S. Geological Survey (Session 1E-1; ADAMS Accession No. ML17355A093) 3.3.5.1.1 Abstract A rich history of large late-Holocene Tennessee River floods is preserved in caves and alcoves throughout the Tennessee River Gorge area near Chattanooga, Tennessee. Preliminary stratigraphic analyses, coupled with geochronologic techniques, show evidence of at least four floods occurring in the last ~3,000 years with possible discharge estimates greater than or similar to the 1867 peak of record (460,000 ft3/s at Chattanooga, Tennessee). One of those floods may have occurred in the last 400 years and has an estimated discharge at least twice the magnitude of the 1867 flood. At least 1-2 additional large floods with estimated peaks similar to the 1917 flood (341,000 ft3/s) occurred in the last ~3,000 years. In addition to flood evidence found in caves and alcoves, flood deposits in exposed stratigraphy at Williams Island, an alluvial island at the head of the gorge, date to ~9,000 years. Determining accurate discharge estimates in this section of the river is difficult due to the backwater from the gorge constriction during high flows, but the flood records preserved here can be used to validate flood evidence downstream in the gorge, where the stable boundary and narrow valley provide more reliable discharge estimates.

Stratigraphic records of past floods to reduce uncertainty in flood frequency analyses have been used extensively in the arid western United States, especially for floods with low annual exceedance probabilities. Preliminary results indicate that previously developed techniques to develop stratigraphic records of past floods can be successfully applied to reduce uncertainty in flood frequency analyses in the temperate eastern regions of the United States.

3.3.5.1.2 Presentation 3-163

3-164 3-165 3-166 3-167 3-168 3-169 3-170 3-171 3-172 3-173 3-174 3-175 3-176 3-177 3.3.5.1.3 Questions and Answers Question:

Is there any beer can chronology?

Response

That's a good point. Beer can chronology -which is a real thing. So, people have used all sorts of things to date these things. And in the Black Hills, I've actually used socks which worked out great. People use beer cans you can tell by when the can was made, and people use barbed wire to date these floods. We found plenty of beer cans, but they were all modern.

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3.3.5.2 Collection of Paleoflood Evidence Lisa Davis*, Ph.D., University of Alabama; and Gary Stinchcomb, Ph.D., Murray State University (Session 1E-2; ADAMS Accession No. ML17355A094) 3.3.5.2.1 Abstract Despite significant advances in meteorological and hydrological forecasting in the last 50 years, catastrophic floods constitute one of the most globally persistent natural hazards. Instrumented discharge records rarely span more than 200 years, making them less likely to contain records of large floods, which tend to occur less frequently. Additionally, using instrumented flow records to understand flood variability in relation to climate, is more challenging since these records only span the time of human occupation. Paleoflood hydrology focuses on collecting and analyzing physical evidence of past floods that occurred before the instrumented record for flood risk assessment and to understand environmental change. Our presentation will demonstrate some of the basic principles of paleohydrologic research and present preliminary findings of a paleoflood record for the Tennessee River, as part of a broader effort to develop paleoflood data for flood frequency analyses for the Tennessee River.

3.3.5.2.2 Presentation 3-179

3-180 3-181 3-182 3-183 3-184 3-185 3-186 3-187 3-188 3.3.5.2.3 Questions and Answers Question:

You brought up a very good point. In the northern part of Pennsylvania, there was a lumbering boom that occurred, and the mountains were literally devoid of any lumber and then sufficient flooding occurred. People would argue that because of the context of that flooding, could that repeat itself? Meaning that unless you cut down all the trees again in northern Pennsylvania, would you expect to see those kinds of floods. So, could you give us some perspective on the Tennessee River Valley with regard to the context of the various floods you've seen? Do you have any reason to understand why they occurred?

Response

Thats something very interesting. Until we really nail down the timing of events, we can't get a good handle on the mechanisms. Right? So, the other thing to remember is that the more sites we have, the more robust picture that we get. So, for example, the 1600 flood that all of the teams picked up, that's very interesting to us because that may speak to its magnitude. The fact that it happened everywhere in the basin is very interesting and that is a time that coincides at a time in the eastern United States where there's not a lot of written record about flood information. But we do know that and say for example in New England, the pilgrims were complaining a lot about springtime flood at that particular time. So now maybe with this information from the Tennessee River, maybe we're getting a broader picture of what the climate was like in the eastern US and not just where they were. Maybe that was a pattern that was bigger regionally. So, once we get more of the timing information and that the timing of those floods validated at more sites, we can speak more about mechanisms weather or climatological or tied to human initiated mechanisms.

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Question:

With respect to the pre-settlement and post-settlement sedimentation, obviously you'll see different rates. Do you see differences in the sediment types as well?

Response

Yes. Initially, when we started looking at these profiles, the one thing that stood out repeatedly was that there was a well-developed soil at depth and then you would see these very weakly developed soils and flood deposits near the surface. More often than not, those date to the last two to four hundred years, but at one of the sites we did find one I think was the bond sitethere's one that dated like five, six hundred years ago. My initial assumption I guess is flawedI thought that the soil stratigraphy alone was saying something about this change from pre-settlement to post-settlement, but there's this 600-year-old flood that kind of just comes out of nowhere. It's clearly pre-European settlement so I don't know how to make sense of that yet. Now the other thing that we've been looking at is we've been looking at the types of organic molecules present in the flood material and one thing we're finding is there's abundance of charcoal or charred material in the more recent stuff. That may or may not be related to pre-versus post settlement. I don't know if charcoal was really huge in the Tennessee River basin and I know it was huge in the Northeast but if they were charcoaling, I guarantee you there was char flying about everywhere.

So anyway, I don't know how you would source the char in the flood deposit back to it, but the char is a tantalizing clue we need to explore that more.

Question:

I had a similar question to the first one. Can you tell when the floods occurred? Is it in the spring or fall? Because it's a different concern whether it was snowmelt versus flash flood depending on how to mitigate against those scenarios and whether it's a big concern or not for certain facilities.

Response

We didn't include information in this, but we do have a dendrochronologist who's working with us named Matt Farrell at the University of Alabama one of the things that he specializes in is looking at cellular damage in the tree rings. That cellular damage only has potential to happen when the trees are saturated for a length of time during the growing season so essentially when you find this you know that the flood happened in the spring or the summer. So, there is potential to find seasonal information about floods in the record, but unless we get like an oxbow lake somewhere that had seasonal deposition but as far as Im aware, no one has found that. You do find that in glaciated environments where the melt water flow is very seasonal, you get what are called barb deposits where there's seasonal deposition, but our deposits are of an age where even if there was a seasonal signature when they were first laid down it's probably been destroyed by the weathering process.

Question:

Do you guys have any plans to maybe assimilate some of those data and maybe have some spatial characterization to gain some insight on prehistoric hydrology?

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Response

That's a good idea. That might just happen through the research publication process, us all combining data and telling sharing that through the research literature. Or it may happen as part of some other workshop or something tied to this collaborative effort. We're very much open to that and one of the things is that we feel like we're breaking new territory here in science and not just like flood science either. This is a unique opportunity for us to combine paleo study information in a way that the academic studies don't typically have. You typically do your own site, one site, and you do it very detailed and we're combining these detailed studies across the whole basin to get a basin-wide picture which is really robust in a way that hasn't typically been done.

So, I think there's a lot of value to that idea and I know there's a lot of interest among the broader group in pursuing that.

Question/comment:

I wanted to add that this sort of thing is very unique in the way these studies are done you do your own thing, you answer this question, and then you move on. For the first time now and this is a very big basin, yet we are covering different spatial scales of this big basin. I'm sure TVA is thrilled and they're going to get all this information for essentially free, but why not combine it and really nail down what's happening in the basin.

Response

In particular that question about human occupation as a valley and its effects on flood surface hydrology I mean that is something that interests a lot of us. In particular David Lee has been looking at whether or not there the soil markerwhen that transition happened if there's a chemical marker that coincides with that. So that others who don't have a lot of money to identify that in their site then we can increase number of sites a lot more easily there's momentum for that.

3.3.6 Day 2: Daily Wrap-up Session / Public Comments Question: (Audience member)

I have a question for the paleo people. In general, do you take into account upstream infrastructure for older data and how an event would occur today vs. the past?

Response: (John Weglian, EPRI)

We have an ex TVA person at the table, I don't know if any current TVA people are in the room right now, but they do have hydraulic for both with and without the dams. So, the floods that we're looking for with this Paleo Flood information predate the existence of dams, but that tells us something about how much water came down the river and so you can use what they call a Naturals model to evaluate what that water level would have done along the river. That's how we can correlate these different floods in different locations and things like that, but then you can put that same information into their new hydraulic models that include the actions of the dams and then they can include in that if the dam models saw of this much level how would it operate and what is going to affect the downstream flow rates. So, there are ways to correlate what an amount of flow discharge how that would operate today with the man-made structures in place.

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Comment: (Audience Member)

You're saying we use all that old data we need to remodel with the new infrastructure before we use that datum Response: (John Weglian)

Correct. So, if you're trying to assess risk at a particular site and you have information on how that river operated or an event that happened on that river say 400 years ago or a thousand years ago, you need to assess how that same event would look in today's watershed to assess the site that you're particularly interested in. Depending where you are in the country that may be relatively easy, it may be extremely complicated, depending on what man-made structures are now on the watershed. That's all a part of the puzzle that you have to put into your analysis to assess your current risk at a particular site.

Response: (USBR)

I was going to follow up on that. At Reclamation there's definitely multiple approaches to looking at both unregulated and regulated frequencies. And I know at Reclamation that we definitely look at both depending on the question that's being asked and there's multiple methods to address that simplified mass balance. And methods or statistical methods doing correlations to pre and post dams and there's a multiple method to do it. At the end of the day, we're mostly interested in the regulated flow frequencies, but it just depends on the question being asked.

Question:

We have a question on the webinar for Dr. Ryberg. This question comes from Keith Kelson . Dr.

Ryberg how have the studies along the Red River and the Souris River handled variable stages and discharges related to ice affected floods?

Response: (Karen Ryberg)

That is a good question. The data from the Red River at Winnipeg is from Manitoba water stewardship, and its naturalized flow to take into account changes in regulation there. They have estimated flows under ice as part of that and I don't know all the details on that. On the Souris River, generally, ice jamming has not been an issue. There are ice affected flows that the USGS has marked provisional until they are analyzed, and then in some cases adjusted but they are considered good estimates of flow. We haven't done anything to adjust for ice on the Souris River.

Question: (Audience Member):

I have a question for Ruby, but I guess she's gone. I was curious about what the postulated mechanism that would impact the decrease in storm speed that she was talking about. Some of the forecasting of some of the storm characteristics like what caused the decrease in these storms speeds?

Response: (Rajiv Prasad, PNNL):

Good question and I don't know the exact answer to that but some of it might lie in how the large-scale climate patterns are changing and there are some studies that are relating these climate 3-192

patterns both in the Atlantic and in the Pacific to how these tracks might be changing. But theres a forward shift and that is moving some of the stuff to the north.

Question: (Audience Member):

Another question I had for her but maybe you can answer it. She was looking at some of the MLC storm types and that's great looking at these different storm types. Are you guys planning on doing any projections for tropical cyclones or any other controlling storm types in that region?

Response: (Rajiv Prasad, PNNL):

We are not planning on doing any projections. All we are doing in this study is compiling what is already known and published. We don't plan to do anything new in this project.

Response: (Joe Kanney, NRC):

For that project that we have with PNNL, what we've asked them to do is basically help us sift through a lot of the results that are coming out of the climate science community. Its something we need a lot of help to take that on board in terms of studies are significant, what trends they are showing, and then what impact it might have on hydrology. But also, other factors that influence nuclear power plants. So yes, they're basically helping us sift through the results that are coming out of the climate science community.

Comment: (John Weglian, EPRI):

I like to talk about something Steve from INL asked whether we could tell if it was winter versus summer, whether there was an ice component or snow component on it. With the Paleo flood data, I'd like to say that it probably doesn't matter. Something I was trying to say earlier in one of my talks and I think I did a very poor job of doing it is some of the major extreme events are probably not just a hurricane or just a meso-scale convection. It might be a hurricane hits a nor'easter and the combination of those is much different than an individual storm type. I think that goes back to something you were asking about there. But the point is that if those things have ever happened, they may have left evidence and that's what we're looking for in the paleo flood evidence. We don't necessarily know what caused it but we know that at least once in X number of years we have had a major event up to a certain level and some people may try to explain what kind of event that could have been or something, but from a risk assessment at a particular site, in this case, the nuclear power plant, it doesn't necessarily matter so much what exactly it was but it does matter that it did occur and how big it was. We've got our synthetic modeling and our stochastic models and all these kinds of things but if you can say in the last thousand years, even with all that, I had a flood up to here, that's a real data point that we can use to assess the risk. If you can alternatively say, I find no evidence that we ever exceeded this amount, that's also a good data point that we can use to help assess their risk. It doesn't necessarily matter that we can't reconstruct what happened, it's more important that we can tell how bad it has gotten in the past and that's what it can show us I think.

Comment: (Joe Kanney, NRC)

Some of those questions may matter, not in terms of whether you saw it or not but how you interpret on that data and how you use it statistically. Essentially, the main question would be: do I have one population of flood causing mechanisms or should I model this as coming from multiple populations? So, to that extent that information can be useful in how you interpret that data.

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Comment: (Tessa Harden, USGS)

Just to add something. I think if you look at the gauge record and for the Tennessee River, the large storms which are quite a bit larger than say everyday storms or so. They all happen in the spring so if you get maybe in the Paleo food record you see all around that certain discharge.

Comment: (Steve Prescott)

Snowmelt versus a flash flood coming through gives different response times. If there's way to know that then you can gauge how many of these events are of those types and bin them and know how to respond and what frequency those type of events are.

Comment: (Karen Ryberg)

I just wanted to follow up a little bit more on the ice question. I'm not sure if I totally understood what they were getting at. When we measure peak streamflow and there are ice effects, someone goes out and makes a measurement because we know there's a change in the stage discharge relationship. You wouldn't use the rating curve that you would under open water, so we make those adjustments and those adjustments have been made for a long time. But even if you've got this hundred year record you're excited about, back in time less than that [100 years] you dont know what may have happened. In particular historical floods that have a code 7 in the USGS database before the systematic record those have been entered as point estimates in the database which in retrospect was not a good thing because there's a huge degree of uncertainty, but that database was developed in the 1960s when you had to worry about every digit taking up more memory in the database. So, it's a point estimate, and if I had a huge pot of money and people to do it it would be fantastic if the USGS would go back to all of those code seven peaks, historical peaks, and put in an interval estimate. That would give you a better sense of that uncertainty which in some cases would be caused by ice uncertainty in other places. So, as we go back further in time there's definitely more uncertainty in the data related to ice and to other things.

Comment: (Audience Member)

I have just another comment on the ice. I worked on the Delaware River a couple years ago and I was looking at these profiles much like we're doing on the Middle Tennessee River and its fine-grain alluvium, but occasionally what we would find are large cobbles showing no signs of reworking by Native Americans, and underneath the large cobbles were fine pebbles. We kept finding these layers of them in particular units and one of the things we were thinking was that these cobbles were ice wrap debris. You'd be basically plucking grains off the side of the channel, so you have bed load where you have large cobbles and underneath those large cobbles are the fine pebbles like an armoring effect I guess. There's evidence of ice wrap debris but the problem is you spend all day digging a pit and you find evidence of it but if I dig four or five meters down and dug a pit I might not see that and so it's like a shot in the dark. I think it does exist. There's potential there to identify say like a flood from ice raft debris but it is a shot in the dark.

Question: (Audience Member)

This is a question for Jennifer, Tess, and/or Joe related to the paleo flood study in the Tennessee River Gorge. I was just kind of thinking during the presentation, you're talking about the gorge being kind of like a dam in some respects and then it opens up there above Williams River and then Chattanooga so there's a lot of volume so in some ways Chattanooga and that whole valley 3-194

upstream is kind of a reservoir. I'm wondering if you guys are going to, when you're doing the hydraulic modeling, include that area upstream and get an idea of some of the volumes. Or maybe the end flow may be different than the flows that you're seeing through the gorge and what that hydraulic model going to look like.

Response

We have some sites that are right at the mouth or a little upstream so that'll be included in the model. We haven't really talked about model parameters, but it should be includedhopefully it will be included.

3.3.7 Day 2: Poster Session Session Chair: Thomas Aird, NRC/RES/DRA/FXHAB 3.3.7.1 Poster Abstracts 3.3.7.1.1 Probability-Based Flow Modeling Using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) Brian Skahill, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory 3.3.7.1.2 Reclamations Paleoflood Database: Design, Structure and Application. Jeanne E. Godaire, Kurt Wille, and Ralph E. Klinger, U.S. Bureau of Reclamation, Technical Services Center The Bureau of Reclamation paleoflood database was developed beginning in 1999 as an outgrowth of the global paleoflood database that was being developed at the University of Arizona through Dr. Katie Hirschboeck in order to provide a digital archive for paleoflood data in the United States. Currently, the database is internal to Reclamation and exists mainly as a data repository, containing some published paleoflood data and Reclamation paleoflood studies that were primarily developed at or near Reclamation facilities. This database is an important resource for paleoflood investigations and hydrologic hazard assessment but has been underutilized due to a lack of tools to effectively synthesize the data for projects and research. However, the database could be used more significantly to efficiently investigate research questions related to hydrologic hazards, climate change or other related topics. Tools associated with the database have been developed to extract data, attach related data, and create complex queries to assist in paleoflood research. Reclamation has been improving the database structure and graphical interface using a combination of Microsoft Access and ArcGIS. Data are stored as relational tables with searchable fields that can be queried using spatial or field-based queries.

3.3.7.1.3 Late Holocene Paleofloods along the Middle Tennessee River Valley. C. Lance Stewart and Gary E. Stinchcomb, Department of Geosciences and Watershed Studies Institute, Murray State University; Steven L. Forman, Department of Geology, Baylor University; Lisa Davis and Rachel Lombardi, Department of Geography, University of Alabama; Emily Blackaby, Owen Craven and William Hockaday, Department of Geology, Baylor University.

Sediment stored in floodplains and low alluvial terraces along the middle Tennessee River reflects flood frequency and magnitude during the past ca. 1500 years. This study uses the stratigraphy, 3-195

sedimentology, 13C NMR analysis (nuclear magnetic resonance) and geochronology of three alluvial terraces to infer past flooding. Buried soils at the three locations are older than ca. 630 CE and suggest a multi-century period of landscape stability. Multiple flood deposits are separated by weakly developed soils, indicating an increased flood frequency until ca. 1910 CE. Optically-stimulated luminescence dates of flood deposits yield ages of 580+/-110, 835+/-80, 1460 +/-30, 1465+/-35, 1660+/- 30, 1830+/-15, 1875+/-10 and 1910+/-10 CE. Age-depth modeling shows increased sediment accumulation rates following ca. 1800 CE. The geochronology, when combined with 13C NMR, shows an increasing flood sedimentation rate during the past 200 years associated with a decrease in the abundance of charcoal and increase in the abundance of lipids.

These data suggest that the more recent flooding is more frequent and contains more C with higher oxidation potential. Particle-size analysis of historic floods demonstrates an increase in sand content with increasing flood magnitude, which is consistent with previous work upstream.

The highest percentage of sand is found within flood deposits dated to 1830+/-15, 1460+/-30 and 1875 +/-10 CE, the latter of which coincides with the 1867 CE historic flood of record along the Tennessee River. The high magnitude flood of 1830+/- 15 CE is consistent with a USGS paleoflood analysis upstream that documents a paleoflood occurring ca. 1600-1800 CE that was higher in elevation than the historic flood of record. The earliest observed flood deposits appear to occur during transition into the Medieval Climate Anomaly between 800 and 1300 CE with increased flood magnitude through the Little Ice Age (1400-1800 CE), and with peak magnitude occurring 1830+/-15 CE.

3.3.7.1.4 A regional chronology of floods and river activity during the last 10,000 years in the Eastern U.S. Lisa Davis, Rachel Lombardi; Department of Geography, University of Alabama, Gary Stinchcomb; Watershed Studies Institute, Murray State University, C. Lance Stewart; Department of Geosciences, Murray State University, Matthew D. Therrell; Department of Geography, University of Alabama, Matthew Gage; Office of Archeological Research, University of Alabama .

Most paleoflood analyses are conducted at a single site or a small number of sites within a single river basin. These studies provide detailed chronologies of river activity, such as flooding, spanning hundreds or thousands of years, which can be used to decrease uncertainty in flood frequency analyses. Site specific reconstructions, however, have limited applicability to understanding river activity at larger spatial scales, such as an entire basin or multiple basins within a region or for understanding drivers of regional and continental-scale changes in the timing or spatial occurrence of floods. This poster presents a regional chronology of river activity over the last 10,000 years for the Eastern U.S.the first of its kind for this region. The chronology was developed by compiling and combining hundreds of site-specific paleoenvironmental reconstructions containing radiocarbon-dated flood and rainfall-related depositional events from the research literature and unpublished archeological reports. This Eastern U.S. regional river activity chronology is applicable to flood frequency analyses in three specific ways: (1) it can be used to understand the spatial occurrence of floods in the Eastern U.S. over millennia; (2) it can be used to examine how flood frequency has changed throughout the region and within major river basins of the Eastern U.S. over millennia; and (3) it could be used to validate site-specific reconstructions of floods and paleoenvironmental change to determine whether their findings are applicable to broader geographic areas, such as an entire river basin.

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3.3.7.1.5 Critical Review of State of Practice in Dam Risk Assessment David Watson, Scott DeNeale, Brennan Smith and Shih-Chieh Kao, Oak Ridge National Laboratory(ORNL); Gregory Baecher, University of Maryland.

Dams in the United States are aging and in dire need of refurbishment. The American Society of Civil Engineers 2017 Infrastructure Report Card states that the average age of the 90,580 U.S.

dams is 56 years with 17% classified as high-hazard potential dams with potential for loss of life and another 13% labelled as significant hazard potential dams with potential for significant economic losses. An estimated $45 billion is needed to repair the high-hazard potential dams alone. Potential detrimental impacts of dam failure include flooding of downstream nuclear power plants.

This project will focus on summarizing and providing a critical review of the state of practice in dam failure risk analysis , with a particular emphasis on developing and quantifying fragility information. The objective of this project is to assist NRC in developing the technical basis for guidance on application of state-of-the-practice approaches, methods and tools for dam risk analysis to inform assessment of flood hazards due to dam failure.

This project will seek to summarize and provide a critical review of approaches and methods for developing fragility curves for key components, systems, and procedures that contribute to the overall fragility of the dam. This will include, but not be limited to:

• *Probabilistic geotechnical analysis methods for assessing embankment/foundation/abutment stability

• *Reliability of key components such as gates, gate hoists, valves, etc.

• *Systems analysis approaches

• *Reliability of operational and emergency procedures

• *Methods for estimating breach initiation and progression The project will focus on assessing methods for characterizing and quantifying key uncertainties, as well as propagating these uncertainties through the risk analysis procedure to support risk-informed decision-making. To accomplish the objectives of this project the project team will: (1)

Assist the NRC in organizing and conducting a workshop to review the current state of practice in dam risk analysis. The workshop participants will include leading experts from other federal agencies, academic researchers and private industry. International perspectives will also be sought; (2) Provide a summary of the current state of practice in dam risk analysis with a particular focus on development of fragility information for key components, control systems, and operational procedures; (3) Provide a critical review of how key process uncertainties, their characterization, and the degree to which they are propagated in state of practice approaches; (4)

Prepare NUREG/CR reports summarizing activities 1-3 and providing guidance on use of state-of-practice dam risk assessment approaches, methods and tools for informing assessment of flooding hazards due to dam failure; (5) Conduct a knowledge transfer seminar at the NRC Headquarters in Rockville, MD covering the topics in items 1-4 with a focus on item 3. The guidance developed under this project will support and enhance NRCs capacity to perform thorough and efficient reviews of license applications and license amendment requests. They will also support risk-informed significance determination of inspection findings, unusual events and other oversight activities.

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3.3.7.1.6 Application of Point Precipitation Frequency Estimates to Watersheds Shih-Chieh Kao and Scott DeNeale, Oak Ridge National Laboratory All nuclear power plants must consider external flooding risks, such as local intense precipitation(LIP), riverine flooding, flooding due to upstream dam failure, and coastal flooding due to storm surge or tsunami. These events have the potential to challenge offsite power, threaten plant systems and components, challenge the integrity of plant structures, and limit plant access.

Detailed risk assessments of external flood hazard are often needed to provide significant insights to risk informed decision makers. Many unique challenges exist in modeling the complete plant response to the flooding event. Structures, systems, and components (SSCs), flood protection 4features, and flood mitigation measures to external flood may be highly spatial and time dependent and subject to the hydrometeorological, hydrological, and hydraulic characteristics of the flood event (antecedent soil moisture, precipitation duration and rate, infiltration rate, surface water flow velocities, inundation levels and duration, hydrostatic and hydrodynamic forces, debris impact forces, etc.). Simulation based methods and dynamic analysis approaches are believed to be a great tool to model the performance of structures, systems, components, and operator actions during an external flooding event. In support of the NRC PFHA research plan, INL is tasked to develop such new approaches and demonstrate a proof of concept for the advanced representation of external flooding analysis. This project was started in September 2014 and finished in April 2017. It developed a work plan and framework to perform a simulation based dynamic flooding analysis (SBDFA). The SBDFA framework was then applied to a LIP event as a case study. A 3D plant model for a typical PWR and 3D flood simulation models for the LIP event were developed. A state-based dynamic PRA modeling tool, EMRALD, was used to incorporate time-related interactions from both 3D time-dependent physical simulations and stochastic failures into traditional PRA logic models. An example EMRALD model was developed to represent two accident sequences in a simplified traditional PRA model for general transient. 3D simulation elements were incorporated into the EMRALD model and could communicate with the PRA logic.

The integrated EMRALD model was run with 3D flooding simulations and millions of Monte Carlo simulations. The EMRALD model results were compared with the corresponding traditional PRA model results. Insights and lessons learned from the project are documented for future research and applications.

The project shows that dynamic approaches could be used as an important tool to investigate total plant response to external flooding events with their appealing features. It can provide visual demonstration of component or system behavior during a highly spatial and time dependent flood event. It could provide additional important insights to risk informed decision makers. The dynamic approaches could also play a supplemental role by supporting the development or enhancement of a static PRA with the insights from the dynamic analysis or performing a standalone analysis that focuses on specific issues with limited sequences and components (e.g., FLEX).

3.3.7.1.7 Quantification of Uncertainty in Probabilistic Storm Surge Models Norberto Nadal-Caraballo, Victor Gonzalez and Efrain Ramos-Santiago, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory 3-198

3.3.7.1.8 Modeling Plant Response to Flooding Events Zhegang Ma, Curtis L. Smith and Steven R. Prescott, Idaho National Laboratory, Risk Assessment and Management Services; Ramprasad Sampath, Centroid PIC, Research and Development 3.3.7.1.9 Stratigraphic Records of Paleofloods, Geochronology and Hydraulic Modeling to Improve Flood Frequency Analysis Tessa Harden, U.S. Geological Survey, Oregon Water Science Center.

A rich history of large late-Holocene Tennessee River floods is preserved in caves and alcoves throughout the Tennessee River Gorge area near Chattanooga, Tennessee. Preliminary stratigraphic analyses, coupled with geochronologic techniques, show evidence of at least four floods occurring in the last ~3,000 years with possible discharge estimates greater than or similar to the 1867 peak of record (460,000 ft3/s at Chattanooga, Tennessee). One of those floods may have occurred in the last 400 years and has an estimated discharge at least twice the magnitude of the 1867 flood. At least 1-2 additional large floods with estimated peaks similar to the 1917 flood (341,000 ft3/s) occurred in the last ~3,000 years. In addition to flood evidence found in caves and alcoves, flood deposits preserved in exposed stratigraphy at Williams Island, an alluvial island at the head of the gorge, date to ~9,000 years. Determining accurate discharge estimates in this section of the river is difficult due to the backwater from the gorge constriction during high flows, but the flood records preserved here can be used to validate flood evidence downstream in the gorge, where the stable boundary and narrow valley provide more reliable discharge estimates.

Stratigraphic records of past floods to reduce uncertainty in flood frequency analyses have been used extensively in the arid western United States, especially for floods with low annual exceedance probabilities. Preliminary results indicate that previously developed techniques to develop stratigraphic records of past floods can be successfully applied to reduce uncertainty in flood frequency analyses in the temperate eastern regions of the United States.

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3.3.7.2 Posters 3.3.7.2.1 Probability-Based Flow Modeling Using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) Brian Skahill, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory 3-200

3-201 3-202 3-203 3-204 3.3.7.2.2 Reclamations Paleoflood Database: Design, Structure and Application. Jeanne E.

Godaire, Kurt Wille, and Ralph E. Klinger, U.S. Bureau of Reclamation, Technical Services Center 3-205

3-206 3-207 3-208 3.3.7.2.3 Late Holocene Paleofloods along the Middle Tennessee River Valley. C. Lance Stewart and Gary E. Stinchcomb, Department of Geosciences and Watershed Studies Institute, Murray State University; Steven L. Forman, Department of Geology, Baylor University; Lisa Davis and Rachel Lombardi, Department of Geography, University of Alabama; Emily Blackaby, Owen Craven and William Hockaday, Department of Geology, Baylor University. Not submitted in these proceedings 3.3.7.2.4 A regional chronology of floods and river activity during the last 10,000 years in the Eastern U.S Lisa Davis, Rachel Lombardi, Gary Stinchcomb, C. Lance Stewart, Matthew D.

Therrell, Matthew Gage.

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3-210 3-211 3-212 3.3.7.2.5 Critical Review of State of Practice in Dam Risk Assessment David Watson, Scott DeNeale, Brennan Smith and Shih-Chieh Kao, Oak Ridge National Laboratory(ORNL); Gregory Baecher, University of Maryland 3-213

3-214 3-215 3-216 3-217 3-218 3.3.7.2.6 Application of Point Precipitation Frequency Estimates to Watersheds Shih-Chieh Kao and Scott DeNeale, Oak Ridge National Laboratory 3-219

3-220 3-221 3-222 3.3.7.2.7 Quantification of Uncertainty in Probabilistic Storm Surge Models Norberto Nadal-Caraballo, Victor Gonzalez and Efrain Ramos-Santiago, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory: Not submitted for proceedings 3.3.7.2.8 Modeling Plant Response to Flooding Events Zhegang Ma, Curtis L. Smith and Steven R. Prescott, Idaho National Laboratory, Risk Assessment and Management Services; Ramprasad Sampath, Centroid PIC, Research and Development 3-223

3-224 3-225 3.3.7.2.9 Stratigraphic Records of Paleofloods, Geochronology and Hydraulic Modeling to Improve Flood Frequency Analysis Tessa Harden, U.S. Geological Survey, Oregon Water Science Center- Not submitted for proceedings 3-226

3.3.8 Day 2: Session 2A - Reliability of Flood Protection and Plant Response I Session Chair: Mehdi Reisi-Fard, NRC/NRR/DRA Development of guidance for assessing the reliability of flood protection and plant response to flooding events.

3.3.8.1 Performance of Flood- Rated Penetration Seals William (Mark) Cummings*, P.E., Fire Risk Management, Inc. (Session 2A-1; ADAMS Accession No. ML17355A095) 3.3.8.1.1 Abstract Overall risk analyses of nuclear power plants (NPPs) include the need for protection against potential flooding events; both internal and external events. Typically, a primary means to mitigate the effects of a flooding event are to construct flood rated barriers to isolate areas of the plant to prevent the intrusion or spread of flood waters. Any penetrations through flood-rated barriers to facilitate piping, cabling, etc. must be properly protected to maintain the flood-resistance of the barrier. Numerous types and configurations of seal assemblies and materials are being used at NPPs to protect penetrations in flood-rated barriers. However, no standardized methods or testing protocols exist to evaluate, verify, or quantify the performance of these, or any newly installed, flood seal assemblies. In FY2016, the NRC implemented a research program to develop a set of standard testing procedures that will be used to evaluate and quantify the performance of any penetration seal assembly that is, or will be, installed in flood rated barriers. This presentation represents a project status update regarding the efforts completed since the previous PFHA Workshop. This includes completion of Phase I of the research effort, which culminated in the development of the draft Test Protocol. Additionally, information is provided regarding plans for Phase II research efforts, which will include actual performance testing of candidate flood-rated penetration seal assemblies using the draft Test Protocol.

3.3.8.1.2 Presentation 3-227

3-228 3-229 3-230 3-231 3.3.8.1.3 Questions and Answers Question:

I'm very interested looking at in situ measurement as opposed to the laboratory you talked about how you're going to test the seals in laboratory. Have you thought about in situ measurement and looking at performance surrogate indicators for instance moisture content using electric conductivity? Have you thought of how you would go about testing an existing penetration seal?

Answer:

The short answer is no we haven't. Ideally ultimately some particular seals, based on their configuration, their penetrants, may be more conducive to that type than others. If you've been in a lot of cable spreading rooms trying to run a test like I dont see how you would do it.

Anytime you start introducing moisture, depending on the penetrant they're going to get real nervous about. It hasn't been anything that we've discussed at least not up to this point. It's not really the intent here.

Question:

One last question. At the Blayais site, they had failure with a lot of the penetration seals. Have you been able to talk to Électricité de France (EDF) and found that what kinds of lessons they learned from their failures?

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Answer:

We have. A main customer obviously is EDF and so we have looked at that.

Question:

In the proof of the testing, do you intend to increase pressure until the seal fails? Or are you going to pick a design pressure and go to that?

Answer:

The fun part about the research is that you get to play a little bit. Depending on what you see and how things react. We may change but most likely you'll start for each one of the test decks, maybe a step type function. Are we going to go for many hours and days? Probably not. That again gets into the performance of the seals, not so much the test. We're going to play a little bit and look at different pressures.

Question:

I actually have two questions. I had one and then a follow-up to your comment about Blayais site.

When you mentioned it was a large penetration was that a large penetration with multiple penetrants going through it?

Answer:

Yes.

Question:

Then the other question. When you mentioned the exterior and interior seal applications, I was trying to remember back to that report, whether you saw any significant differences in either seal construction, seal types, or sealing material type between those two applications? I don't recall that there was but Answer:

No, there really wasn't and in some of the cases, it didn't actually identify what was an exterior barrier or interior.

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3.3.8.2 EPRI Flood Protection Project Status David Ziebell^ and John Weglian*, EPRI (Session 2A-2; ADAMS Accession No. ML17355A096) 3.3.8.2.1 Abstract EPRI has collected information from member utilities on maintaining the licensing and design bases of flood protection barriers. EPRI and a technical advisory group of industry experts examined this data to determine the best practices in place in the industry. EPRI has recently published these best practices in a guide to EPRI members.

3.3.8.2.2 Presentation 3-234

3-235 3-236 3-237 3.3.8.2.3 Questions and Answers Question:

I assume that by now you've applied this to a variety of sites? Have you? And if you have, what have you discovered, what lessons have you learned in applying these best practices?

Response

So, the guidance document was just published maybe two or three weeks ago, and I don't know if there have been any lessons learned since it has been published in this short window. But the technical advisory group has been involved to decide what these best practices are and to help inform EPRI to develop this. I don't know if anybody's actually made changes to their plant yet based on this. I can look into that if you would like to know.

Question:

John this looks like a very valuable study here. I'm curious, when it comes to forecast information, for example riverine flood forecasts, you know they're not a high percent accurate. Has there been any discussion about how to deal with that uncertainty in terms of the response?

Response

That's a good point, and local intense precipitation are quite varied. So, some sites have procedures that are more open to interpretation and others are very deterministic. In that if you get a weather forecast that says this, you will implement. I think that is a best practice because it removes the decision and the ability for the operator to make the wrong decision. It removes that possibility based on a judgment call, and so riverine flooding should be similar in that if you know that some sites have procedures that say upstream if you hit this level, you will implement the 3-238

procedure as opposed to saying something like if the forecast looks like the site may be inundated, do this. That that would not be a best practice because it leaves room for operator error.

Question:

Is this report publicly available?

Response

It is not available publicly. It's available to every member or for a price.

Question:

Could you describe a little more detail the forecast? What does the guide say about establishing durable reliable relationships with entities that provide forecasts? Either public entities like The Weather Service, National Hurricane Center, or perhaps a private entity that the licensee might hire? What does the guide say about developing that interface and how to convince yourself that it is durable, reliable?

Response

Good question. I don't know the exact answer to that but obviously sites need to establish relationships with the entities that provide this information. Some of it is published on a routine basis like from the national weather service, for example. I can I can look into that and get back to you.

Question:

On the forecast issue. In some cases, there are probabilistic forecasts National Weather Service for example makes some use of forecasts for the floods. Do you know if there's any thinking about how to handle that kind of probabilistic forecast information?

Response

I'm not sure if it's treated differently than another set of forecasts. I'd have to look into it and get back to you on that one.

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3.3.8.3 A Conceptual Framework to Assess Impacts of Environmental Conditions on Manual Actions for Flood Protection and Mitigation at Nuclear Power Plants Rajiv Prasad *,

Ph.D., Garill Coles and Angela Dalton, Pacific Northwest National Laboratory; Kristi Branch and Alvah Bittner, Ph.D., CPE, Bittner and Associates; R. Scott Taylor; Ph.D., Battelle Columbus (Session 2A-3; ADAMS Accession No. ML17355A097) 3.3.8.3.1 Abstract The U.S. NRC is currently pursuing a Probabilistic Flood Hazard Assessment Research Plan which is especially relevant following the Fukushima accident. One of NRCs initiatives is to better understand the actions that licensees of nuclear power plants have planned to take outside of the control room to prepare for, protect against, and mitigate the effects of flooding events.

The Pacific Northwest National Laboratory (PNNL) conducted a comprehensive review of the literature about how the environmental conditions (ECs) associated with flooding events might affect performance of those actions. To support and inform the literature review, the research team identified and characterized the ECs that might accompany flooding events; these conditions included heat, cold, noise, vibration, lighting, humidity, wind, precipitation, standing and moving water, ice and snowpack, and lightning. Based on a review of (1) NRC Staff Assessments of Flooding Walkdown Reports from 60 nuclear power plant (NPP) sites, (2) available individual NPPs plant procedures (e.g., Abnormal Operating Procedures), and (3) descriptions of FLEX activities, the research team identified and characterized a set of manual actions (MAs). MAs would need to be performed at and around NPP sites (both inside and outside the main control room) in preparation for or response to a flooding event. The research team developed a method for decomposing the MAs into simpler hierarchical unitstasks, subtasks, generic actions (GAs),

and performance demands (PDs)to facilitate assessment of ECs impacts consistent with approaches in human performance literature. The first four levels in this hierarchy (i.e., MAs, tasks, subtasks, and GAs) are activity oriented while the last (i.e., PDs) describes the composition of human performance measures needed to accomplish the activities.

The literature review summarized the state of knowledge concerning the effects of the 11 ECs in terms of their mechanisms of action, effects on performance, and potential mitigation measures. A typology of PDs that includes detecting and noticing, understanding, decision-making, action, and teamwork provided a basis for applying research findings to estimate performance effects. PDs include both physical and cognitive aspects of human performance. The research team developed a conceptual framework to illustrate the relationships among ECs, MAs, and performance effects information. ECs can affect human performance by (1) affecting motor functions via a physical force (e.g., flowing water, wind), (2) affecting physiology (e.g., heat, cold), and (3) affecting cognition by interference of senses (e.g., darkness, vibration) and increasing workload. Research on ECs impacts on human performance in literature is available in four categories: Level 1, quantitative information that is directly applicable to an assessment of impact; Level 2, quantitative information that is less directly applicable; Level 3, qualitative information that may be used to inform expert judgments or sensitivity analyses; and Level 4, no information, i.e., a research gap.

The research team demonstrated the applicability of Level 1 information using a simple example of a MA involving gross motor function (i.e., walking). The research team proposed a guideline for safe walking velocity based on experimental data reported in literature. The results show that time to walk a given distance can be significantly affected by the presence of standing and moving water. The research team notes that additional research, sensitivity analyses, and knowledge elicitation from experienced operators may be necessary to operationalize EC effects that fall in Levels 2-4.

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3.3.8.3.2 Presentation 3-241

3-242 3-243 3-244 3-245 3-246 3-247 3-248 3.3.8.3.3 Questions and Answers Question:

You mentioned FLEX equipment as part of the consideration, but among the three types of action I do not find a good match of the FLEX equipment. In particularthe FLEX equipment is portable. What type of action in your design covers this?

Response

I didn't show you the details of it but the way we think about these manual actions is that we have to do a task analysis to look at what the basic actions might be. And there are these generalized actions that we test that I talked about which do involve things like operating a vehicle or moving things from one location to another location that might involve machinery. And using machinery to perform certain tasks, so if you're monitoring things, for example, one of the things that you'll find in the report is an action we described about electrical equipment that needed jumper connections so that is one part of the actions that we're also describing. But when we describe that action, it doesn't really matter what that action is that is we assume that the personnel that are going to perform these actions are trained in those actions. The only thing that matters is that the human performance itself.

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3.3.8.4 External Flooding Walkdown Guidance John Weglian*, EPRI (Session 2A-4; ADAMS Accession No. ML17355A072) 3.3.8.4.1 Abstract Utilities have performed walkdowns in support of internal flooding PRAs and in response to the 50.54(f) letters from the NRC, they have performed deterministic external flooding walkdowns.

However, an external flooding PRA may require something in addition to those two walkdowns.

EPRI is conducting research into the requirements for a walkdown to support an External Flooding PRA.

3.3.8.4.2 Presentation 3-250

3-251 3-252 3-253 3-254 3-255 3-256 3.3.8.4.3 Questions and Answers Question:

Can you talk about to what degree you're coordinating the development of this guidance with modifications and revisions to the external flooding PRA standards that are currently underway?

Response

So, we'll be looking at the external flooding PRA standard. This effort is still in draft and we've got like three chapters written so far, but we'll make sure that we're in tune with what's in the standard.

Question:

Do you guys have a library of case histories that can be used for practitioners such that you can learn and describe effects from particular incidents? Say the duration of Fort Calhoun flood and frequency of those events.

Response

I don't know that we were planning to include that in this document. That sounds more like on the hazard assessment side. To me there's a difference prior to doing the walk down, you do a hazardous assessment, and there's multiple EPRI guidance documents that tell you how to do that. That sets the stage if you will for what the site will look like and those should include certainly those lessons learned. Then you would incorporate that into your information when doing the walk down.

Question:

Let me rephrase. Were there lessons learned on the walk down post-event for Fort Calhoun that could be used to inform that guidance?

Response

Obviously, their duration was three months or so Other Response:

Their assumptions on the PMF and dam failures are on the order two to seven days so there's a disconnect between the frequency of events and what can happen and vulnerabilities for the response. That's what I'm highlighting here, so this could be a neat opportunity to synthesize those case histories to improve the lockdowns and responses.

Response

That's a good point. I'll take that into consideration.

Question:

Nathan Siu, [NRC] Office of Research. The IPEEEs are really old studiesyour last slide talked about updating the plant PRA to include external flooding. If you're going to update your analysis to include external flooding, it states: here's what we would suggest you do?

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Response

No. I'm assuming that you're not going to do a walk down unless you're building a PRA. If you've decided that you're screening out all the hazards or your hazard risk is low enough through some analysis that you don't require an actual PRA model, then you wouldn't be in this step. Now to the last point these next steps are after you've done the walk down so this is after the guidance.

3.3.8.5 Erosion Testing of Zoned Rockfill Embankments Tony Wahl^, Hydraulics Laboratory, Denver, Colorado, U.S. Bureau of Reclamation (Session 2A-5; ADAMS Accession No. ML17355A073) 3.3.8.5.1 Abstract Three medium-scale embankment dam breach experiments (3-ft dam height) were recently performed by the Bureau of Reclamation. The first test was of a homogeneous silty clay embankment failed by internal erosion through an intentionally created concentrated leak. Two subsequent tests funded by NRC considered zoned embankments with a silty clay core sandwiched between upstream and downstream rockfill zones modeled with a well graded road base soil having 12% fines. One of these embankments was failed by overtopping flow and the second was subjected to internal erosion in a manner similar to the test of the homogeneous silty clay embankment.

In the overtopping test of the zoned embankment, the downstream rockfill zone demonstrated significant erosion resistance. The pattern of breach development was characterized by surface erosion of the downstream slope and the top of the exposed silty clay core, which is in contrast with the headcut erosion that is often observed in cohesive soils. Photographic records were used to evaluate rates of erosion and to estimate applied stresses and erodibility parameters for the rockfill zone, which seemed to be the primary control on the rate of breach development.

Estimates of erodibility parameters were compared to results of submerged jet erosion tests. The contribution of gravel to the erosion resistance of the well graded soil was very significant.

The internal erosion tests demonstrated the dramatic influence of upstream and downstream gravel zones on the internal erosion breach development process. Initially, the gravel zone acted as a filter and was able to heal the concentrated leak through the core. After the concentrated leak was enlarged, the gravel zones acted to limit the flow, which significantly slowed the development of internal erosion. Observations from the tests are discussed and compared to available numerical and empirical models that can be used to evaluate the risk of internal erosion.

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3.3.8.5.2 Presentation 3-259

3-260 3-261 3-262 3-263 3-264 3-265 3-266 3-267 3-268 3-269 3-270 3-271 3-272 3-273 3-274 3-275 3-276 3-277 3-278 3-279 3-280 3-281 3-282 3-283 3-284 3-285 3-286 3-287 3-288 3-289 3-290 3-291 3-292 3-293 3.3.8.5.3 Questions and Answers None.

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3.3.9 Day 2: Session 2B - Frameworks I Session Chair: John Weglian , EPRI Development and demonstration of a PFHA framework for flood hazard curve estimation.

3.3.9.1 A Framework for Inland Probabilistic Flood Hazard Assessments: Analysis of Extreme Snow Water Equivalent in Central New Hampshire Brian Skahill*, Ph.D. and Carrie Vuyovich, Ph.D., U.S. Army Corps of Engineers, Engineer Research and Development Center (Session 2B-1; ADAMS Accession No. ML17355A074) 3.3.9.1.1 Abstract The NRC Probabilistic Flood Hazard Assessment (PFHA) research plan aims to build upon recent advances in deterministic, probabilistic, and statistical modeling of extreme events to develop regulatory tools and guidance for NRC staff with regard to PFHA for nuclear facilities. For inland nuclear facility sites (i.e., non-coastal sites), a PFHA must be able to incorporate probabilistic models for a variety of processes, allow for characterization and quantification of aleatory and epistemic sources of uncertainty, and facilitate propagation of uncertainties and sensitivity analysis. Moreover, the PFHA framework should be capable of modeling spatial and temporal correlation between and within events. The bases for the framework are two distinct spatial analysis methodologies for characterizing hazard curves that each in their own right are recent advances in the modeling of extreme events. The two spatial methods were selected as the basis given that most relevant flood hazard phenomena naturally occur as spatial processes and regionalization is likely a minimum requirement toward improved accuracy and precision of estimates. Related, the two methods are each designed in a manner such that they, or their respective adaptions, can be readily applied to leverage any and all available relevant information for a given hazard analysis. The first method is spatial or spatiotemporal Bayesian Hierarchical Modeling (BHM); whereas, the second approach employs max-stable processes. The application of either approach involves the use of spatial and temporal covariate data to distribute model parameters in space and also account for temporal trends. The spatial/spatiotemporal BHM methodology is simple and flexible and leverages the multiple merits of Bayesian inference to support probabilistic flood hazard analyses to readily develop spatially coherent pointwise return level maps. However, its likelihood formulation assumes conditional independence among the extremes, which can be difficult to ignore for flood hazard phenomenon, and its use of a Gaussian process for the latent variable model results in a lack of conformance with extreme value theory (EVT) . The second framework approach; viz., max-stable processes, when applied does account for the dependence among the extremes, conforms with EVT which is highly notable as framework applications require credible extrapolation well beyond the observed record, and moreover, supports the capacity for more complex areal assessments of risk beyond the simple generation of pointwise return levels. For extreme rainfall and SWE analyses; for example, it is particularly noteworthy that max-stable process applications can develop areal based exceedance probabilities. PFHA framework method choice is dependent upon an initial assessment of dependence among the extreme data. The framework also involves a multi-model averaging step in attempts to account for the uncertainty associated with model choice. We profile a complete application of the framework for the analysis of extreme snow water equivalent data in central New Hampshire which leverages regionalization, additional data derived from process-based hydrologic simulation, climate index data, max-stable process selection, and trend surface modeling analysis to develop individual model and multi-model averaged pointwise return level maps and areal-based exceedance probability estimates.

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3.3.9.1.2 Presentation 3-296

3-297 3-298 3-299 3-300 3-301 3-302 3-303 3.3.9.1.3 Questions and Answers None.

3.3.9.2 Structured Hazard Assessment Committee Process for Flooding (SHAC-F) for Riverine Flooding Rajiv Prasad*, Ph.D.; Pacific Northwest National Laboratory; Kevin Coppersmith*, Ph.D.; Coppersmith Consulting(Session 2B-2; ADAMS Accession No. ML17355A075) 3.3.9.2.1 Abstract This research project is part of the U.S. NRCs Probabilistic Flood Hazard Assessment (PFHA)

Research plan in support of development of a risk-informed analytical approach for flood hazards.

The approach is expected to support reviews of license applications, license amendment requests, and reactor oversight activities. Pacific Northwest National Laboratory is leading the development of a structured hazard assessment committee process for flooding (SHAC-F). In previous years, we described the virtual study following a Senior Seismic Hazard Analysis Committee (SSHAC Level 3 process for local intense precipitation (LIP)-generated flood.

The objective of the current effort is to develop the SHAC- process for riverine flooding (with and without snowmelt but excluding dam breaches) and to provide confidence that all data sets, models, and interpretations proposed by the larger technical community have been given appropriate consideration and that the inputs to the PFHA reflect the center, body, and range of technically defensible interpretations. Several of the issues identified and solutions proposed 3-304

during the LIP PFHA SHAC-F virtual study informed the development of riverine SHAC-F process.

These issues included precise definition of data and models, compilation of data related to riverine flood characterization, compilation of previous hydrologic and hydraulic models applied to the river basin, and previous characterization of uncertainties in the river basin.

SHAC-F studies can be carried out at three levels which are defined in terms of the purpose of the assessment. Level 1 and Level 2 SHAC-F studies are expected to support NRCs significance determination process. The purpose of a Level 1 study is primarily screening (e.g., binning of flood hazards into high or low risk categories). Level 2 studies would be appropriate to (1) perform a more refined screening analysis (e.g., where a Level 1 study could not adequately support binning of flood hazards) and (2) update an existing Level 3 assessment. The purpose of a Level 3 assessment is to support design reviews and to support probabilistic risk assessment (PRA) for new and existing power reactors. For all three SHAC-F levels, the expected outcome of the study is generation of a family of flood hazard curves appropriate for the purpose of the assessment.

Data and methods used for the three SHAC-F levels are also defined to be commensurate with the purpose of the study. A Level 1 SHAC-F study would use existing data, possibly within an at-site flood-frequency study. The study may use alternative conceptual models (ACMs, various parametric or non-parametric distributions in the case of flood-frequency studies) to represent epistemic uncertainty coupled with regionalization and accounting for nonstationarities. A SHAC-F Level 2 study could supplement flood-frequency analyses with existing simulation model studies.

ACMs would include alternative simulation models that can reasonably represent the flood behavior at the site. A SHAC-F Level 3 study would need to account for spatiotemporal resolution of flood hazard predictions that can support licensing and PRA needs. Existing data can be used in a Level 3 study, but a site-specific, detailed analysis would be needed. At all levels of SHAC-F studies, explicit characterization of uncertainty is needed.

3.3.9.2.2 Presentation 3-305

3-306 3-307 3-308 3-309 3-310 3-311 3-312 3-313 3-314 3.3.9.2.3 Questions and Answers Question:

This is Fernando Ferrante with EPRI. You had a suggestion to redefine the level one in SHAC-F to be used for significance determination processes and I think that some interesting insight into the potential benefits and challenges this process can have. SDPs are fast and furious risk assessment types of things and unless data is available, and we say for particular issue, you have paleoflood, you have the availability of complicated models one of the conclusions may be: if the driver is the one-million-year flood, you don't have any insights that are very explicit qualitative in size. And maybe thats one insight of the process: weve got the kind of thinking that you guys are applying in terms of the SDP or is that kind of preliminary at this point?

Response (Prasad):

It is slightly preliminary at this point. But the assumption is that you would have data that you can quickly collect, and you would be able to do at least the flood frequency analysis relatively easily and quickly. There are methods available now with the Bulletin 17 C and related tools available that could be quickly operationalized and used. But I do think that there is a need to have some of this data centrally compiled and some of the database stuff that we were talking about, that INL was talking about, I think there is a potential for linking through that or an NRC analyst or even an industry analyst to go there quickly get that data which has already been compiled and you don't have to go and dig in into research papers and contact people to get that data. That would speed up the process.

Response (Ferrante):

Well, I think my suggestion would be looking at some of the past SDPs that happened and see how this expert panel, you know tiger team, will be able to gather information and have helped some of them.

Question:

I have some experience designing a SHAC study and when the project sponsor got the estimate, they ran a million miles away and it never happened. But, in putting this proposal together it struck me that if others have done a similar project in the flood area and multiple projects have been done, to some extent we'd be going down the same pathways. Each site is different, I agree, but there's only so many models and so many ways of looking at different hydrologic processes and so on. So that leads me to the idea: is there a way of somehow streamlining things? Benefiting from other similar studies that have been done?

Response

Yes. Definitely. If you look at the lower level SHAC and it says use what you have so the idea being that if you have existing studies and people have done some of those studies, you need to bring them in and you don't need to re-perform them. One of the central things about SHAC is when you get in and do a modeling study that shows your bias: What are your experiences? How do you use models? How you use data to come up with an answer? The SHAC process says that you are not acting as yourself, but you are acting to represent the whole community: all different viewpoints that might be brought together. So, in level one, what would happen is that you would go through and doing the review of every study that has been done for that site: is it relevant for 3-315

that particular site? You would compile them, and you would appropriately rate them. But in cases where models have not been done or you feel that epistemic uncertainty has not been carried out to the extent that it captures the center and the body of the range, then you would have to go out and do that. So, yes, streamlining as far as possible, you would definitely use that Question:

I'm interested in clarification on expert elicitation and level two and where that comes in between levels 2 & 3. How do you start weighting parameters and models at the level two process with expert elicitation either internally or externally?

Response

My understanding is that when you weight these models, the committee goes through an appropriately weighted phase and one thing that can help there is that if we can have Bayesian model averaging to show that there are some models which perform better under certain circumstances that could be one way of doing it. Traditionally, model weighting has been done as part of this committee.

3.3.10 Day 2: Session 2C - Panel Discussions 3.3.10.1 Flood Hazard Assessment Research and Guidance Activities in Partner Agencies (Session 2C-1, ADAMS Accession No. ML17355A076)

Session Chair: Joseph Kanney, NRC/RES/DRA/FXHAB 3.3.10.1.1 US Army Corps of Engineers, Engineer Research and Development Center, Coastal Hazards Laboratory(CHL), Coastal Hazards Group, Norberto Nadal-Caraballo, Ph.D., and Victor Gonzalez, P.E.

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3-317 3-318 3.3.10.1.2 US Army Corps of Engineers, Risk Management Center, John England, Ph.D., P.E.,

P.H., D.WRE 3-319

3-320 3-321 3.3.10.1.3 Federal Energy Regulatory Commission (FERC), Office of Energy Projects, Division of Dam Safety & Inspections, Kenneth Fearon, P.E 3-322

3-323 3-324 3-325 3-326 3-327 3-328 3-329 3-330 3-331 3-332 3-333 3-334 3.3.10.1.4 U.S. Department of Energy, Office of Nuclear Safety Basis & Facility Design Department of Energy, Sharon Jasim-Hanif, Ph.D.

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3-336 3-337 3-338 3.3.10.1.5 Tennessee Valley Authority, Gabriel Miller 3-339

3-340 3-341 3-342 3-343 3-344 3.3.10.1.6 Discussion Question:

I'm very interested in what TVA is doing. You said you're doing very intense precipitation analysis for each watershed. Are you making estimates of the problem maximum precipitation for those, and if so, do you have sufficient data and who's doing that analyses?

Response (Gabe Miller, TVA):

Yes, we have done probable maximum precipitation for all of those. I haven't been involved so much in in that part of the project. I know that we've worked with MetStat and MGS a lot for the for the precipitation part of this work. You might talk to Sean. He has been involved in that a lot, but I haven't I wasn't actually involved in that part of the analysis. [Sean: AWA - Applied Weather Associates].

Question:

Joe Wright here with Reclamation and I have a few questions but the sake of time I'll start with Gabe I've heard a few talks yesterday on paleo flood estimates in the Tennessee Valley and I'm just curious to know how your stochastic event flood model, how the results that you're getting from that compared to those paleo flood numbers. Now I realize you have to run it unregulated but Response (Gabe Miller, TVA):

Yes, so that question I can answer. Thats something that we still are hoping to analyze. So, we have a lot of that paleo flood information. We have a Naturals model for the Tennessee River. So, the idea is that we will be able to run a lot of our storms through the Naturals model to hopefully 3-345

match some of the stages and information from those paleo floods and hopefully that should give us a better sense of how our system is performing and also look at some of those paleo floods. So that's still pending analysis for us but something that we have on the docks to do.

Question:

Steve Breithaupt with the NRC. I'm curious: this is again for Gabe. What you're doing is really interesting. So, the question is about your storm templates. Can you describe that a little bit more -

about what that includes? Like, I'm particularly interested in multiple peaks, durations how you included that?

Response (Gabe Miller, TVA):

So, we've taken a lot of storms of different durations for the region and we've come up with gridded precipitation sets for those storms and there are some durations I'm not sure if we're using multiple peaks on some of them, although we are getting the antecedent conditions before that, so those storms that we've looked at are historical maximum storms from those areas and are then transposed and scaled based on kind of the region and the topography over the locations that were interested in Follow-up (Breithaupt):

So, it's your understanding that it's basically a single peak storm? Is that what you're saying or Response (Gabe Miller, TVA):

I'm going to let Shaun answer that.

Response (Shaun Carney, RTI International):

The basic idea is the largest historical storms that have been run, we took those and analyzed the entire period of the storm. So, some of those are multi peak events that actually occurred historically, or they may be a one bump storm. But the idea is to try to capture the actual variability that's happened historically and in these extreme storms and represent that in the simulation. So, we have a suite of forty different storm templates or something that we can draw from to capture the variability that's happening.

Follow-up (Breithaupt):

Is this information going to be when this is going to be released? A website or something Response (Gabe Miller, TVA):

So, we have a lot of documentation. I don't think we're going to put it on a website, but we do have a lot of documentation and information that we can and are willing to share about it. So, if you want to contact or talk to me or one of us at TVA afterwards we'd be happy to share that information.

Question (Joe Wright, Reclamation):

This time I have a question for Ken, but it could be directed to all of you. This involves construction risks and it's something that we wrestle with that at Reclamation and I'm just curious how you 3-346

might deal with interim construction risks, in reference to some of the large floods that can occur while the construction site is, so to speak, vulnerable to the more frequent floods?

Response (Ken Fearon, FERC):

I found at Oroville that a lot of effort went into looking at start data for rainfall events and, pretty much, the target was June 1 to November 1. Because historically California doesnt get rain June 1st to November 1st. So, for this one, and, you know, like it or not, things had to be fixed. And so, we didn't have an option to spend a lot of time and say we're going to do analyses after analysis. I mean it was looked at. Everything was done fast. But that's why it was built the way it was. There was no way you're going to rebuild this entire structure in a couple months. So, you know you have sections that are final you have sections that are going to be replaced.

Response Question (Joe Wright, Reclamation):

Orville might be a bad example or an extreme example there. But what if you have something that spans a three-year or five-year construction period.

Response (John England, USACE/ Risk Management Center (RMC)):

Now we have those in the Corps of Engineers and, as Joe knows, in Reclamation so I will point to interested folks to the joint publication by Reclamation and the Corps of Engineers Best Practices in Dam and Levee Safety Risk analysis. We have a course that's usually every year with documentation that's periodically updated and one of the sections in that is on construction risk.

So, you'll find out if you look on the web and do some searching that one of our facilities in Houston had an embankment exposed; working on essentially a filter blanket during [Hurricane]

Harvey. So, the choice in the risk analysis is focusing on what level of cofferdams do you design to protect the particular area of interest. So, there are procedures to do construction risks when you're doing structural modifications for existing dams. It's not a straightforward and there's lots of areas or improvements.

Question (Tom Nicholson , U.S. NRC):

I have a quick question for Victor. I'm very fascinated. You were talking about the probabilistic coastal hazard assessment and you said that you had looked because of Hurricane Harvey at the Galveston district. One of the questions we had of the weather district (we went and actually met with them about a month before that) and the things we were concerned about is that not only do you worry about storm surge, but the accumulation of rainfall. So, the areas in the interior: they referred to it as fresh water flooding as opposed to saltwater flooding. I'm interested in what kind of insights you got before and after that storm with regard to rainfall amounts and flooding due to that, especially the long duration of that hurricane over Houston. What kind of information you have for us on that?

Response (Victor Gonzalez, USACE/CHL):

Well, for the lab it was a big eye-opener and, I know for a fact, that now other branches in the lab, the ones involved in hydrology, are actively engaging in research in this area. That's one of the research areas we want to expand because at some point we need to somehow include precipitation on our hurricane models, but as of now, it it's not included.

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Question:

Not to pick on you, Victor, but I think you're talking about synthetic storm generation as part of this East Coast risk assessment / risk management process. My recollection is a few years ago people were talking about East Coast tsunamis and I was wondering how tsunamis fit into this risk management picture, if at all.

Response (Victor Gonzalez, USACE/CHL):

No, its separate. I mean, the forcing, the response that we're looking at is completely different.

Response Question:

I understand the different mechanism. Im talking more programmatically: who's got the ball? I Response (Joseph Kanney, U.S. NRC):

NOAA and USGS collaborate to have a tsunami warning system. And then NOAA has developed several tsunami modeling systems.

Question (Joseph Kanney, U.S. NRC):

I have a question for Sharon. You mentioned that your guidance has developed over several years: you're in version 5. As that handbook has evolved and, you group has several projects, like the waste treatment plant, which has been under construction for many years. What happens when you revise your guide to facilities like that that are they're still under construction? At NRC we have this concept of back fit with our licensees: we have to do a cost-benefit analysis to show that changing the standard will have a significant safety benefit before one would go back and look at older facilities or facility you've already made a decision about. How do you handle that?

Response (Sharon Jasim-Hanif, DOE):

So, we have a similar process except that it depends on the contract. So, if a site has a contract: it depends on the year of the design. For example, with this 2016 update, most of our sites have not incorporated the 2016 and they still they have to work within the policy document that are within their contract. So, it is in their interest to use the most updated information out there and I get calls about that. And I provide training on the differences between the previous version of the standard and the updated: what changed. But if it's in their contract to use a 2012 version of the standard they are about to use that.

Question (Joseph Kanney, U.S. NRC):

John one question for you. In the paleo-flood studies that the Corps has done have you guys looked at any other methods other than slack water deposits to determine paleo-stages?

Response (John England, USACE/RMC):

So, the questions are on slackwater. I neglected to highlight that issue. I was relying on some of the talks from yesterday. So, we actually are focusing on non-exceedance: both [non-exceedance and slack water). The same thing that would happen at Reclamation. Where if you have a strap terrace in Vermont you have a nice stable surface that's of a certain age and you can tell that it 3-348

hasn't been exceeded. And then the record between that and, say, some positive evidence of flood is incomplete. So, we try to use both pieces of information. So, it's not just the slack water, but it's those evidence of terraces and longer-term features, maybe eight thousand years in the Ball Mountain case and in the Garrison case a couple thousand years, that the floods havent reach that. So, there is a limit on the magnitude-frequency relationship.

Question (Joseph Kanney, U.S. NRC):

Victor. One question I had for you is with regard to the storm sim tool. Have you thought about getting results from other people's models to enhance what you all have done (thousands of simulations)? But there's also been collectively thousands of simulations done for FEMA studies, for example. Have you contemplated actually going out and getting other peoples results to add to yours to give it more statistical power?

Response (Victor Gonzalez, USACE/CHL):

Yes, and the coastal hazard system has some results from FEMA studies and wherever we can get those results for further modeling we incorporate them in our database. So, yes, we do actively search for that information for other studies.

Question:

Is it available?

Response (Victor Gonzalez, USACE/CHL):

It's online so you're free to access the webpage of the coastal hazard systems and you can download the information.

Comment (Joseph Kanney, U.S. NRC):

Yes, actually here at NRC we've leveraged some of the information from that database to help in some of some of our reviews.

Question (Tom Nicholson, U.S. NRC):

John you had mentioned the extreme storm database collection. Could you tell us the status of that? We know something about it - we actually had a demo about a year ago. The question is what are you doing now with regard to creating the portal and the database so that we can interact with it and then look at a variety of storms? As I understand it, you look at some severe storms and then you ask the question: what is the precipitation distribution associated with that storm and then what was the river responds to that storm? So, if you could give us some insights of where that project is now and how soon that could become available to other federal agencies?

Response (John England, USACE/RMC):

The status is easy. The schedule is a challenge. For background I think on my website if you go search there's probably a presentation I gave last year on this. Chuck McWilliams in Omaha district is a meteorologist in charge of our extreme storm database project with folks spread throughout the Corps and they're working now on upload capability. The database has been restructured to look like the National Levee Database. So, for those of you who are familiar with 3-349

that you can go online and look at essentially static Maps and download some information.

They're essentially grid points - so we'll have essentially this space-time of individual events - like what Gabe was talking about - we're doing the same sort of things and I believe Reclamation has done some similar things with storm patterns and since you store those in a library of the largest ones. Currently, though, the limitation is that its internal only, so we've just got the protocol and CAC authentication, so people have the capability to upload that information. I would hazard a guess maybe in 18 months we could hopefully open it up to sharing for others to contribute. So hopefully between then, now and then, we could hopefully have it as an open website, just like the National Levee Database, so people can get the information. Resource issues are always a challenge with that one, sorry to say.

Question (Ray Schneider, Westinghouse):

I have a question for Victor. Given the amount of synthetic storms that you can create or have created and the large expanse of areas that you have the ability to basically model storm surge and the various tracking and your ability to do this: Is the intent to develop statistical models to basically come up with the probability of flood surge at various point locations if you need to? To just basically sample all your data - could you put your data with, basically, some kind of a sampling structure with reasonable paths and reasonable locations and at various probabilities of getting certain storm sizes of various types to create something that resembles a synthetic way of creating a probabilistic hazard curve for the coast?

Response (Victor Gonzalez, USACE/CHL):

Yes, the answer is just that the selection of storms is done per study and typically those studies get output set at save-points. So, obviously, as we do more areas of the coast we will get coverage over most of the coast of the United States. We will have the ability to do that throughout the Coastline. But, right now, yes you have the ability to create a probabilistic hazard curve based on the probabilities of the synthetic storms for a particular location.

Follow-up Question:

And that could just be done basically by sampling using your data in your systems and basically moving stuff around?

Response (Victor Gonzalez, USACE/CHL):

And you have to download the data. We're working towards making this a little bit more user friendly and in order to better distribute this. But, yes, you can download the storm and you can download the relative probabilities of the storms and you can download the corresponding response for these storms. So, you have what you need to do sampling.

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3.3.10.2 External Flooding Probabilistic Risk Assessment (PRA): Perspectives on Gaps and Challenges (Session 2C-2, ADAMS Accession No. ML17355A077)

Session Chair: Fernando Ferrante, EPRI 3.3.10.2.1 US NRC, Office of New Reactors, Division of Site Safety & Environmental Analysis, Chief, Hydrology and Meteorology, Christopher Cook, P.E.

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3-352 3-353 3.3.10.2.2 Nuclear Energy Institute(NEI), Frances Pimentel and Victoria Anderson 3-354

3-355 3-356 3.3.10.2.3 EPRI, John Weglian 3-357

3-358 3-359 3.3.10.2.4 Idaho National Laboratory, Zhegang Ma, Ph.D.

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3-361 3.3.10.2.5 Westinghouse, Ray Schneider 3-362

3-363 3-364 3-365 3-366 3.3.10.2.6 U.S. NRC, Office of Nuclear Regulatory Research, Nathan Siu, Ph.D.

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3-368 3.3.10.2.7 Discussion Question (Tom Nicholson, U.S. NRC):

I'm fascinated to hear from NEI that you are welcoming the concept of a pilot study: you want more than one. So, the question really comes down to: what would be the attributes of the pilot studies chosen? What are you looking specifically for? What would be the objective? For instance, would you want to do a PRA in which you looked at combined events, that Ray brought up and others have brought up? The argument is that it's a systematic disciplined way of looking at events combined events and what is risk significant and what isn't was significant? So, if you can answer that, Victoria and Frances, and other people might want to chime in as well.

Response

Sure. I think what we want to be doing is doing a pilot with a plant that could represent the full spectrum of events and I think also part of the methodology that we would pilot would need to be some sort of screening process and I think that has to be part of any kind of PRA methodology process and so we'd have to pilot that screening process and make sure that that was robust and practical as well.

Response (John Weglian, EPRI)

I think from my perspective even though we're here at the NRC, we would probably want international participation in pilots to make sure that it's not US-centric. Any guidance that WPRI produces: we are going to be focused internationally as well as domestically.

Question (Joseph Kanney, U.S. NRC):

Since you mentioned the phased mission concept: what I was wondering is how does - when you mentioned that I immediately thought back to Victoria's comment about sort of doing things piecemeal and I was wondering - how do we do phased mission approaches without stepping into the trap of having conservatisms built into each of the different phases that we didn't really notice until we put everything back together?

Response (Nathan Siu, U.S. NRC):

I think those are kind of different issues but the issue with piecemeal had to do with specific phenomena. So, for example heat release rate for a fire or let's say the treatment of detection of fires. There are different pieces that are all the things that need to go into a PRA. And, yes, if you are conservative in each of these pieces you will get a conservative answer unless you've missed something that you haven't included in your model at all. Phased mission simply says I'm going to discretize the scenario into pieces (and of course not to pay attention to the handoffs) so I lead from this part of the scenario to this part of the scenario. There's nothing that forces me to be conservative about that. I may, as a business decision, choose to do that if I don't want to take the time to analyze things to the level of detail that maybe could be done and maybe I want to take a conservative shortcut. But these are things that are done in regular PRAs. Its I think the question of degree and extent, but I really think those are two separate issues. And, Victoria, of course 3-369

Response (Victoria Anderson, NEI):

I think he may be a little closer than that. I mean, when we talked about in fire PRA and the piecemeal approach and the different phenomena: the issue was that they all stacked up on each other as different steps and I think if you take the phased approach you could run into the same kind of danger.

Response: (Nathan Siu, U.S. NRC):

Obviously, your intent would be a realistic analysis and given the resource constraints that youve got. But, yes, if you have conservatism at one level of the one phase, it could propagate. In fact, this is one of the problems with the dynamic analyses is when you start getting into the details. If you start making simplifications in part of the analysis you can end up with things that just make no sense whatsoever by the time you get to the end of the analysis. You have operators responding to cues that they aren't going to see in practice because of the simplifications made earlier. So, there's a certain degree of detail that has to be taken care of if you're going to do that kind of analysis.

Question (Moderator Fernando Ferrante, EPRI):

Let me ask the question to Chris. You had an interesting bullet where you said, from the civil engineering community, what does the nuclear engineering community expect to get? And so, we had discussions during this workshop on essentially paleo-flood, how to do coastal surge, what are the different numerical models - L-moments, Bayesian Inference let me ask, maybe Ray or John or anybody else the question that Chris posed. I mean, we are looking at a different world and different communities here. I think we all do risk assessment, at the end of the day. I haven't seen anything new under the sun in risk since we said hazard: risk triplets hazards, impacts, consequences and so forth. And so, from somebody who is doing plant analysis and we have several that have done this before, what have you guys heard and what you are looking to see forward to be able to have something useful. You know I always was the person, at some point, until I got more educated, to say just give me that number and tell me what the flood frequency is until I realized its not that easy. But what do you guys see moving forward? Ray John, do you want to take a stab at that?

Response (John Weglian, EPRI)

Can you rephrase that in a simple question?

Follow-up:

If you had all the money in the world and all the time in the world and you were going to do a pilot and you have a plan and he said, to the community, to the hydrologic community, tell me something I can use? Given your experience in having seen some of this, what would you hope to see; more than that, are you going to use?

Response (John Weglian, EPRI)

I see multiple parts that need to feed in to get to the end goal and the end goal is to assess the risk. The end goal is not to get a CDF number - right. It's to evaluate a plant and understand the risks to the plant so pieces of that are the hazard and different hazards have different 3-370

uncertainties. They have different modeling techniques. Simulation may be extremely useful for some and may be less useful for others. So that's the piece: you've got how do you do the assessment of the plant responses? And so, I mentioned the plant walk down. But that's one way to assess that. Right now, for barrier fragilities for example, typically we assume kind of a go/no-go - the water level gets to this high on the door and we assume the door fails. There's a there's a more nuanced approach to that that may or may not change your risk insights. So, you don't want to just put in a fragility which greatly complicates your analysis just to have it in there when the risk insights would be the same otherwise. Then once you've got your model for all your different hazards - I envision if you're at a site and you have three applicable hazards that didn't screen, you would effectively have three separate external flooding models that you could run independently of each other because they may have completely different site impacts. That's how I see it being put together. You might get risk insights from one floating mechanism and not another. So, at the end of the day you have to look at your total package and what do you learn from it? One thing I want to address: so, I mentioned before - do the risk insights change and one of the concerns that that I have with the SHAC-F process is - is it going to give you a different answer? If it cost five million dollars to do a site-specific SHAC-F and it costs a hundred thousand dollars to do the equivalent of a level one that you just do in-house with you someone else doing a peer review for you and you don't get a different risk insight at the end of the day. If it doesn't change any of your decisions that you would make - you wasted the money if you did the five million dollars thing. So, we need to understand what's the benefit of doing all these extra things?

And that might also be site-specific. It might be that at this site it's not going to change the answer enough to make further refinement of the analysis worthwhile and at another site it might be completely worthwhile because cliff-edge effects, or whatever. That it's extremely important that we understand the cascade of events that happen there.

Response (Ray Schneider, Westinghouse):

I'd like to take it from you. I think the question you were kind of asking is: what are we getting from the hydrologic community in terms of what we what we need? I think that the thing is that when you develop the hazard curves in a number of instances you're actually developing a series of curves that you basically are putting this on one another which have different characteristics. And so, for the events I have, again, different levels of run-up because you have different wind velocities or wind speeds. You may have different accumulation rates or information like that that may be relevant in, basically, dealing with downstream work and PRA, like human factors or something. So, if there's assumptions you're making that that would likely affect how that event is actually proceeding, not so much just this total elevation. And, say, well, I have a 10-5 110 foot flood, or something like that, but there's information that goes along with that that created that that may be made up of five or six different types of floods and have different characteristics. That would be of used in developing the initiating event that basically drives the rest of the model because without that it's really hard to really understand the pieces that are going to follow. And this mainly comes, in my instance, from dam breaks, dam events: you can have early releases, you could have random failures of the dam , you could have catastrophic failures: I mean with the full-on, totally catastrophic overtopping kind of failures and as a result of that they all go into the same curve: but every one of those is a different event. And so, we just have to be careful that if you want us to view it as one event, where one kind of event with all of the same characteristics, then that's fine. But if there really are multiple different events in it: somehow you have to figure out a way of communicating that information to the people downstream.

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Response (Nathan Siu, U.S. NRC):

I know John has a question but I'm going to jump in. The part about insights - of course who would argue about the importance of insights. But I also want to say that insights aren't necessary the only metric and the only reason why one does something. You obviously have to have confidence in the results of the study. You have to believe the insights have sufficient basis and if this expensive process gives you that increased confidence, and that's an if, then you could say that might be worth doing even if in the end you don't get the a very different answer. And of course, the classic example people have these jokes about HRA and the various calculator used for HRA which are totally random. But would you indeed base a safety decision on that - even if you had some belief that you were going to end up with a number that kind of was similar to what the HRA might produce - I don't think so. The other thing of course is that insights are very dependent on the purpose of the analysis. So, a level one study you get certain kind of insights, a level two study may give different insights. I think a lot of times in PRA we do these game-over modeling assumptions and say, look, I know this is going to failure so I'm not going to model it anymore and that's fine for the purpose of analysis. But you're not going to gain any insights, perhaps, about what people might do after you reach that particular point. So, helping operators, perhaps, respond to the event in this extreme situation- youre not going to get that insight. So, it might be worthwhile pursuing with a more detailed model. But again, it depends on the purpose of the study.

Comments (John England, USACE):

I don't have a question. I just have a whole page full of comments, so I'll keep it brief. For those who haven't - who missed my presentation 2013, we started flood hazard analysis at Reclamation when I walked in there in 1997 and we've gone through an inventory of 350 dams and made major decisions on those facilities. So that one key difference I came up with earlier was Reclamation, which I can't speak for, but I can speak for the Corps of Engineers: we're self-regulating. So, we use risk to prioritize limited funds across portfolios. So, you can rack and stack when you only have 40 plants, all potential failure modes (PFMs), for those individual plants which may add up to our matrix of the number of facilities we have. But we've made structural decision modifications on those facilities based on these hazard curves, so I encourage you to look at some of those reports and whats behind them because besides water levels we have durations, which are the key triggers for things like Orville, which Ken Fearon presented. I think one of the barriers: two of them. One is the industry's lack of moving toward PRA in the first place for floods -

which I think Reclamation led 20 years ago - is now upon us. You've seen all the things talking about that. But the pieces youve been missing with this panel and that we've experienced over the years is the integration of the hazard folks with response folks - so you can target specific hazard curves for those things like duration for the spillage of the cracks in that joint in Orville which drives - that is totally duration driven issue - and you can quantify that. It's very easily done.

And as far as uncertainty is concerned we've provided - I think Joe Wright mentioned this earlier -

what uncertainties do you or what level of confidence do you want? So, for various water levels we have full uncertainty but they're full uncertainty. So, if you want to choose conditional non-exceedance probability at 97 a half % or 84% we have that information in there. So, I think there's a disconnect in terms of at least what I'm hearing here is the usage of that information in the PRA process and structuring of individual decisions on event trees.

Comments (Moderator Fernando Ferrante, EPRI):

John can I comment on that before you continue. So, I think one of the things that it's interesting that you bring up is and this is the kind of the different communities coming together. I mean I 3-372

can't speak for the NRC, of course, anymore, but risk is tremendously used within the NRC or risk management. So, I understand your point. I mean it is used right now in terms of if a piece of equipment is out of service, how you rank whatever actions you might need to take with that. It's used for risk ranking on all the important components. It is used for risk ranking on fire protection.

So, I think there is a framework in the nuclear world. When it comes to flooding is where we're kind of I can speak because I took the training at the Bureau when you were there, and I was very impressed with a framework insight. I can see putting aside the two frameworks and then saying okay how do we come together when flooding is the topic? And what is it that can be cross-pollinated given the requirements that you need to have for a nuclear grade PRA with flooding hazard within that. So, I fully hear you. I think nuclear risk has gone very far. I think the consequence is there. I think there's a gap presented here. I mean we do go to consequence analysis, radiological consequence analysis. Our models are - they tend to become surrogates because they stop a core damage. NRC is doing a level three which is when you take it all the way from the beginning to the end. I think your point is fully well taken. I think the question is for flooding is where some gaps are. And then how do we bring this community. For example, if somebody were to do a very detailed NPRA and they get all the sequences, now how do we give it to Ray or somebody within the utility who have millions of cutsets coming up of other things and now they have to prioritize that information and put it so I ask the question because I had a question in hidden in my mind which was the narrowing of the information so somebody within the nuclear plant response (let me put it that way so we don't divide people in PRA bends) uses that in some manner. I don't know if that aligns with your comment in a way.

Comments (John England, USACE):

I'm looking at my former coworker here. If the integration, so when the report is producing you get that report. It's like back to Chris's point about visualization - we completely agree on that. I don't have anything profound standard for visualization which we need more of. Because we have found over the years that we produce a report. Then engineers in response get it two years or five years later and they don't have questions about it. So, then it's an integration and part of the PRA process, as you probably already have facilitated, to integrate that information collectively and then ask for additional key information when you need it because there's gaps all along the way.

The other thing I could comment on - back to case histories - I'll put Joe Wright on the spot here because, at least when I was at Reclamation, we modified facilities. Meaning we use taxpayer money to improve them and reduce risk (structural). Some cases in the Corps now we're doing non-structural solutions. So, having a library of which facilities you've gone through on fire hazard protection or maybe some seismic retrofit and gone through, so you can holistically start to use that as a screening process to gain information might be useful.

Question (Joe Wright, Reclamation):

My question is a little unrelated, a little different. My question kind of goes back to John Weglian. I heard you mentioned a couple of times dealing with non-stationarity and this was something that we wrestled with quite a bit in Reclamation. I just thought since everyone's here I might open this up for some discussion on how we might deal with non-stationarity. One method that we have at Reclamation and are comfortable using is the fact that we review our facilities every eight years, we see the non-stationarity problem kind of taking care of itself within that process. But that doesn't address any kind of final design or corrective action type fixes where we're going to make a decision based on the non-stationarity of a project. I'm just worried that sometimes when I hear of these decisions. Are we taking a step backwards from risk-based decision making and going back to the old deterministic PMF type problem? Any thoughts?

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Response (Victoria Anderson, NEI):

I'm actually going to break in before John. We don't we don't do risk-based regulation at all. It's all risk-informed meaning that we're never abandoning the first deterministic principles in the first place so that's something that shouldn't be an issue Response (John Weglian, EPRI);

In terms of how you update there's kind of two pieces of that. So, PRA models typically get updated every two fuel cycles. So, every three to four years you do a re-evaluation of your data and I would imagine with external flooding you would be looking at that. I know EPRI has activity underway that looks at new information for external hazards and see does that fundamentally challenge the existing base of information. Hurricane Harvey was an example. They looked at that event and said does this challenge our PMP analyses and things like that right. So, there's this process in place that looks to see is there new information that we need to consider right now, is it information that we add to the database that you include over time. As an industry we are looking for those kinds of things. I would say the potential problem with relying on the recent change in data: you're getting what is the non-stationarity of the one in ten-year type events. Maybe you have no idea how that's affecting the one in a million which you really had no idea anyway because your uncertainties were so large. I think when you're looking at that range the only way to really handle it, with these uncertainties, is with sensitivity studies. You do a sensitivity study and say: what if I was wrong by a lot? I don't know what a lot is: is a lot 20%, is a lot 50%? I dont know. But you do an assessment there and you know if I was off by a lot does the answer fundamentally change or not and if it's effectively the same risk insights. Then you feel confident that your model is giving you good risk insights. If it is fundamentally different now you might want to look at why is it different. Where does that come from? Is it a particular cliff edge effect and if it is maybe I want to focus on that and try to understand what I can do to reduce that impact on my client.

Question:

If you have limited resources, what problem will you tackle first? Which problems are really bugging you as other industry, NRC or NEI or EPRI?

Response (John Weglian, EPRI):

Everybody's got limited resources right, so everybody has that problem. I think it's probably site-specific. I think we have in most cases a decent path forward on the hazard risk assessment. We saw a lot of work today on that, so I think we have a good path forward there. I would focus the efforts on how to build the PRA model with what we have and identify is there something out there that's missing that that we need to invest in more research to address that issue.

Response (Christopher Cook, U.S. NRC)

I guess from my standpoint at the NRC about 10 years and I was working for about five years before as a contractor. I think if I look at that 15-year period and I look at where we are today both of my gaps are more: how do we bring these roles together? That's what John was getting at with Joe; in some ways, as Victoria mentioned, getting on with pilots. It's taking what we have: its what this workshop is about, what everyone has been doing for the past several years on this and now looking to the future. We're looking at how we direct it going forward taking what we have and then actually using it to keep it going then those studies.

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3.3.11 Day 2: Session 2D - Future Work in PFHA Session Chair: Mark Fuhrmann, NRC/RES/DRA/FXHAB 3.3.11.1 Future Work in PFHA at EPRI John Weglian*, EPRI (Session 2D-1; ADAMS Accession No. ML17355A078) 3.3.11.1.1 Presentation 3-375

3-376 3-377 3-378 3.3.11.1.2 Questions and Answers Question:

I think a side benefit of doing a fragility analysis will be bringing this into the PRA model and as a parameter that can then be addressed with importance measures.

Response

Therere additional benefits to including that: you can make it an element in your online risk assessment and when you do maintenance and you've got the door propped open you can actually fail that directly and see exactly what that impact is. If you do that for every door in the plant, you've greatly complicated the model, and you may have made it unquantifiable in a reasonable amount of time. So, you need to you need to look at the balance: where does it make sense to do that where does it not make sense. And I would imagine that you probably wind up with a mixture. Here are my most important doors and I'm going to treat those with the fragility and the others I'm going to treat deterministically.

Question:

You mentioned tsunamis. This workshop didn't focus on tsunamis but I'm sure you're aware the NRC's produced some recent NUREG reports on tsunamis especially for the East Coast of the United States. Are you guys reviewing those?

Response

I'll have to look at that. I've been a little busy.

Question:

On your storm surge research, is the work that you're doing now for a single site or is it regional?

Response

It's based on work that was done for a particular site, but the guidance should be general.

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3.3.11.2 Future Work in PFHA at NRC Joseph Kanney, Ph.D., Meredith Carr*, Ph.D., P.E.,

Thomas Aird, Elena Yegorova, Ph.D., and Mark Fuhrmann, Ph.D., Fire and External Hazards Analysis Branch, Division of Risk Analysis; and Jacob Philip, P.E, Division of Engineering, Structural, Geotechnical and Seismic Engineering Branch, Office of Regulatory Research, U.S.

NRC (Session 2D-2; ADAMS Accession No. ML17355A079) 3.3.11.2.1 Presentation 3-380

3-381 3-382 3-383 3-384 3-385 3-386 3-387 3.3.11.2.2 Questions and Answers Question:

The interface between the hazard and the PRA model are you working on this in 2017-2018 or I couldn't catch?

Response

We've started internal discussions within our office to try to get an idea what's going on and we're starting to spread that out to other offices. We'd like to get collaboration because it's such an issue that we want to make sure people from all the different fields get involved before we come out with some sort of hazard guidance that doesn't fill the needs of PRA.

Question:

So, this is budgeted basically?

Response

Yes. But there's no specific external funding 3.3.12 Day 2: Final Wrap-up Session / Public Comment Question:

I've mentioned to a couple people this idea: the nuclear industry has a database of component failures and I've noticed the dam industry does not have such a system but a lot of dams, independently, keep track of component issues that they have is there a way we could leverage the nuclear industrys set-up for the dam industry as well?

Response

(Joe Kanney, NRC): I actually had an inquiry from Marty McCann about this probably at least two years ago. I told him about what the structure is in the nuclear industry, how their organizations, like INPO, collect this information and have databases. I'm not sure where it went after that.

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3.4 Summary This report documents the 3rd Annual NRC Probabilistic Flood Hazard Assessment Research Workshop held at NRC Headquarters in Rockville, MD, on December 4-5, 2017. These proceedings included the following:

• Section 4.2: Workshop Agenda (in the program (ADAMS Accession No. ML17355A081)

• Section 4.3: Proceedings (abstracts at ADAMS Accession No. ML17355A081 and complete workshop presentation package including slides and questions and answers at ADAMS Accession No. ML17355A071)

• Section 4.4: Summary

• Section 4.5: Workshop Participants 3-389

3.5 Workshop Participants Thomas AirdSC, C Michelle Bensi General Engineer Assistant Professor U.S. NRC/RES/DRA/FXHAB University of Maryland thomas.aird@nrc.gov mbensi@umd.edu Victoria AndersonP Paul BouldenR Technical Advisor President NEI Appendix R Solutions, Inc .

vka@nei.org paulboulden@ars-corp.net John AntignanoR David Bowles Managing Director Managing Principal & Professor Emeritus Nuvia USA RAC Engineers and Economists, LLC &

john.antignano@nuvia-usa.com Utah State University David_S_Bowles@hotmail.com William AsquithR Research Hydrologist Stephen Breithaupt U.S. Geological Survey Hydrologist wasquith@usgs.gov U.S. NRC/NRO/DSEA Stephen.Breithaupt@nrc.gov Greg Baecher Professor of Engineering Robert BudnitzR University of Maryland Scientist gbaecher@mac.com Lawrence Berkeley National Laboratory RJBudnitz@lbl.gov Victoria Sankovich BahlsR Project Manager and Senior Andy Campbell Hydrometeorologist Deputy Director, DSEA MetStat, Inc. U.S. NRC/NRO/DSEA vbahls@metstat.com Andy.Campbell@nrc.gov James Barbis Karen CarboniR Senior Water Resource Engineer Program Manager Wood Tennessee Valley Authority James.barbis@woodplc.com kcarboni@tva.gov Joseph BelliniR Shaun Carney Vice President - Principal Water Resources Senior Water Resources Engineer Eng. RTI International Aterra Solutions scarney@rti.org joe.bellini@aterrasolutions.com Meredith CarrS, SC, C Christopher Bender Hydrologist Senior Coastal Engineer U.S. NRC/RES/DRA/FXHAB Taylor Engineering meredith.carr@nrc.gov cbender@taylorengineering.com S - Speaker; P - Panelist, SC - Session Chair, C - Organizing Committee, R -Remote, RS - Remote Speaker 3-391

Yung Hsien Chang William CummingsS Human Reliability Engineer Principal Engineer U.S. NRC/RES/DRA/HFRB Fire Risk Management James.Chang@nrc.gov wmark@fireriskmgt.com Laura ChapR Biswajit DasguptaR Senior Engineer Staff Engineer Atkins Center for Nuclear Waste Regulatory laura.chap@atkinsglobal.com Analyses / SwRI biswajit.dagupta@swri.org Hasan Charkas Senior Technical Leader Mary Alice Lisa Davis EPRI Associate Professor hcharkas@epri.com University of Alabama, Dept. of Geography lisa.davis@ua.edu Yuan Cheng Hydrologist Gerald (Jay) DayR U.S. NRC/NRO/DSEA/RHM Senior Director yuan.cheng@nrc.gov Water Resources Management Division, RTI International Nilesh Chokshi gday@rti.org Consultant U.S. NRC/NRO/DSEA Scott DeNeale nilesh.chokshi@nrc.gov Water Resources Engineer Oak Ridge National Laboratory Young-Sun Choun denealest@ornl.gov Principal Research Engineer Korea Atomic Energy Research Institute Stephanie Devlin-Gill sunchun@kaeri.re.kr DSEA Techincal Assistant U.S. NRC/NRO/DSEA Jon ClarkR Stephanie.Devlin-Gill@nrc.gov Project Manager Amec Foster Wheeler Alain DibS jon.clark@amecfw.com Postdoctoral Scholar University of California, Davis Leremy ColfR aedib@ucdavis.edu Director of Disaster Science Department of Health and Human Services Kevin Dobbs Assistant Secretary for Preparedness Visiting Scientist and Response NGA leremy.colf@hhs.gov kevindobbsfr@gmail.com Christopher CookP Adrienne Driver Chief, Hydrology and Meteorology Branch Reliability and Risk Analyst U.S. NRC/NRO/DSEA/RHM U.S. NRC/NRR/DRA christopher.cook@nrc.gov Adrienne.Driver@nrc.gov Kevin CoppersmithR President Coppersmith Consulting kevin@coppersmithconsulting.com 3-392

John England, Jr.P Victor GonzalezS Lead Civil Engineer Research Civil Engineer U.S. Army Corps of Engineers, Risk U.S. Army Engineer Research and Management Center Development Center Coastal and john.f.england@usace.army.mil Hydraulics Laboratory victor.m.gonzalez@usace.army.mil J. Christopher EyR Senior Professional Associate Orli GottliebR HDR Engineer II chris.ey@hdrinc.com Alden Research Laboratory ogottlieb@aldenlab.com Kenneth FearonP Deputy Director, Division of Dam Safety and Kevin Griebenow Inspections Civil Engineer Federal Energy Regulatory Commission FERC kenneth.fearon@ferc.gov kevin.griebenow@ferc.gov Fernando FerrantePC Eric GrossR Principal Technical Leader Senior Civil Engineer Electric Power Research Institure FERC FFerrante@EPRI.com eric.gross@ferc.gov Justin Habit Mark FuhrmannSC, C Intern Geochemist USDA - NRCS U.S. NRC/RES/DRA/FXHAB justin.habit@wdc.usda.gov mark.fuhrmann@nrc.gov Kenneth HamburgerF,C Amitava Ghosh Fire Engineer Geotechnical Engineer U.S. NRC/RES/DRA/FXHAB U.S. NRC/NRO/DSEA/RPAC Kenneth.Hamburger@nrc.gov amitava.ghosh@nrc.gov Tess HardenS R

Joseph Giacinto Hydrologist Hydrologist U.S. Geological Survey U.S. NRC.NRO tharden@usgs.gov joseph.giacinto@nrc.gov Donnie Harrison Jason Giovannettone Senior Level Advisor for Risk Assessment President U.S. NRC/NMSS HydroMetriks Donnie.Harrison@nrc.gov jpgiovannettone@hydrometriks.com Brad Harvey Jeanne Godaire Team Lead, Meteorology Team Supervisory Geologist U.S. NRC/NRO/DSEA/RHM/RMET U.S. Bureau of Reclamation brad.harvey@nrc.gov jgodaire@usbr.gov Lyle Hibler Felix GonzalezR Hydrologist Reliability and Risk Engineer U.S. NRC/NRO/DSEA U.S. NRC/RES/DRA/FXHAB lyle.hibler@nrc.gov felix.gonzalez@nrc.gov 3-393

Claudia Hoeft Shih-Chieh Kao National HydraulicError! Bookmark not Senior Research Scientist defined. Engineer Oak Ridge National Laboratory Error! Bookmark not defined. - NRCS kaos@ornl.gov claudia.hoeft@wdc.usda.gov Bill KappelR RS, R Kathleen Holman President/Chief Meteorologist Meteorologist Applied Weather Associates Bureau of Reclamation billkappel@appliedweatherassociates.com kholman@usbr.gov Keith KelsonR R

Georgette Holmes Paleoflood Group Lead, Hydrologic Hazard Chief, Geospatial Analysis Branch Team Department of Homeland Security U.S. Army Corps of Engineers, Sacramento georgette.holmes@hq.dhs.gov Dam Safety Protection Center keith.i.kelson@usace.army.mil Robert HolmesR National Flood Hazard Coordinator Julie KiangR U.S. Geological Survey Chief, Analysis and Prediction Branch bholmes@usgs.gov USGS jkiang@usgs.gov Douglas HultstrandR Senior Hydrometeorologist Minkyu Kim Applied Weather Associates Principal Researcher dhultstrand@appliedweatherassociates.com Korea Atomic Energy Research Institute Matt Humberstone minkyu@kaeri.re.kr Reliability and Risk Analyst U.S. NRC Kellie KvarfordtS matt.humberstone@nrc.gov Software Engineer Idaho National Laboratory Jennifer Irish kellie.kvarfordt@gmail.com Owner Coastal Resilience Innovations Mike Lee jirish@taylorengineering.com Senior Hydrologist U.S. NRC Sharon Jasim-HanifP Mike.Lee@nrc.gov Natural Phenomena Hazards Policy Manager Shizhong LeiR U.S. Department of Energy Geoscience Technical Specialist sharon.jasim-hanif@hq.doe.gov Canadian Nuclear Safety Commission shizhong.lei@canada.ca Andrew KalukinR Senior Lead Scientist Dave LeoneR National Geospatial-Intelligence Agency Associate Principal / Hydraulic Engineer kalukin_andrew@bah.com GZA.

davidm.leone@gza.com Joseph KanneyS, SC, PC, C Hydrologist U.S. NRC/RES/DRA/FXHAB joseph.kanney@nrc.gov 3-394

L. Ruby LeungS Stephen McDuffie Battelle Fellow Seismic Engineer Pacific Northwest Nationa Laboratory U.S. Department of Energy ruby.leung@pnnl.gov stephen.mcduffie@rl.doe.gov Marc LevitanR Fehmida MesaniaR Acting Director, NWIRP Flooding Engineer NIST Duke Energy marc.levitan@nist.gov Fehmidakhatun.Mesania@duke-energy.com Rachel Lombardi Andrew Miller Graduate Research Assistant Senior Engineer University of Alabama Jensen Hughes rlombardi@crimson.ua.edu amiller@jensenhughes.com David LordR Gabriel MillerP Senior Civil Engineer Hydrologist FERC Tennessee Valley Authority dwlord101@gmail.com gamiller0@tva.gov Zhegang MaP Jeffery Mitman Lead Risk Analysis Engineer Senior Reliability and Risk Analyst Idaho National Laboratory U.S. NRC/NRR/DRA/APOB zhegang.ma@inl.gov Jeffrey.Mitman@nrc.gov Pathmathevan Mahadevan Michael MobileR Civil Engineer Senior Technical Specialist FERC - Dam Safety Division GZA.

devan.mahadevan@ferc.gov michael.mobile@gza.com Kelly MahoneyR Mathieu Mure-RavaudS Meteorologist PhD student NOAA Earth System Research Lab Physical UC-Davis Sciences Division mmureravaud@ucdavis.edu kelly.mahoney@noaa.gov Debbie MartinR Norberto Nadal-CaraballoS, P, R Project Manager and Senior Leader, Coastal Hazards Group Hydrometeorologist U.S. Army Engineer R&D Center, Coastal MetStat, Inc. and Hydraulics Laboratory dmartin@metstat.com Norberto.C.Nadal-Robert MasonR Caraballo@usace.army.mil Extreme Hydrologive Events Coordinator U.S. Geological Survey Keil NeffS rrmason@usgs.gov Hydrologic Engineer Bureau of Reclamation, Technical Service Michael MazaikaR Center, Flood Hydrology & Meteorology Physical Scientist (Meteorologist) kneff@usbr.gov U.S. NRC/NRO/DSEA/RHM/RMET michael.mazaika@nrc.gov 3-395

Kit Yin Ng Steven Prescott Chief Engineer PRA Software Engineer Bechtel Infrastructure and Power LLC Idaho National Lab kyng@bechtel.com Steven.Prescott@inl.gov Thomas NicholsonC Kevin QuinlanR Senior Technical Advisor Physical Scientist U.S. NRC/RES/DRA U.S. NRC/NRO/DSEA thomas.nicholson@nrc.gov kevin.quinlan@nrc.gov Lauren Ning John RandallR Reliability And Risk Engineer U.S. NRC/RES Retired U.S. NRC/RES/DRA/PRAB JohnRandall2@comcast.net lauren.ning@nrc.gov Mehdi Reisi-Fard R

Nicole Novembre Reliability and Risk Analyst Chief Hydrologic Engineer U.S. NRC/NRR/DRA/APLB/RILI MetStat, Inc. mehdi.reisifard@nrc.gov nnovembre@metstat.com Tammie Rivera Desta O'ConnorR Reliability And Risk Engineer U.S. Department of Homeland Security U.S. NRC/RES/DRA/FXHAB Desta.OConnor@HQ.DHS.GOV Tammie.Rivera@nrc.gov Tye ParzybokR Christina RoyR President/CEO and Chief Meteorologist NGA Liaison to DHS - FEMA MetStat, Inc. National Geospatial-Intellligence Agency tyep@metstat.com christina.c.roy.us@gmail.com Sanja PericaR Karen RybergS HDSC Chief Research Statistician NOAA/NWS/OWP U.S. Geological Survey sanja.perica@noaa.gov kryberg@usgs.gov Jacob PhilipC MarkHenry SalleyC Sr. Geotechnical Engineer Branch Chief U.S. NRC/RES/DE/SGSEB U.S. NRC/RES/DRA/FXHAB jacob.philip@nrc.gov MarkHenry.Salley@nrc.gov Frances PimentelP Periandros Samothrakis Sr. Project Manager, Risk and Technical Engineering Specialist - Hydraulics &

Support Hydrology NEI Bechtel Corporation fap@nei.org psamothr@bechtel.com Rajiv PrasadS Selim Sancaktar Senior Research Scientist Reliability and Risk Analyst Pacific Northwest National Laboratory U.S. NRC/RES rajiv.prasad@pnnl.gov selim.sancaktar@nrc.gov 3-396

Raymond SchneiderP C. Lance Stewart Fellow Graduate Assistant Westinghouse Electric Coporation Murray State University Dept. of schneire@westinghouse.com Geosciences cstewart19@murraystate.edu Penny SelmanR Sr. Program Manager, Seismic Gary Stinchcomb Tennessee Valley Authority Assistant Professor pbselman@tva.gov Murray State University Dept. of Geosciences Ken ShelleyR gstinchcomb@murraystate.edu Southern Nuclear klshelly@southernco.com Craig Talbot Principal Engineering Specialist Nathan SiuP Bechtel Corporation Sr Technical Adviser in PRA ctalbot@bechtel.com U.S. NRC/RES/DRA Nathan.Siu@nrc.gov Robert Taylor Director, DSEA Brian SkahillS U.S. NRC/NRO/DSEA Engineer Robert.Taylor@nrc.gov USACE Coastal and Hydraulics Laboratory Hydrologic Systems Branch Stewart Taylor Brian.E.Skahill@usace.army.mil Bechtel Fellow Bechtel Global Corporation Brennan Smith swtaylor@bechtel.com Senior Research Scientist/Group Leader Oak Ridge National Laboratory /Energy- Keith TetterR Water Resources Systems Group Reliability and Risk Engineer smithbt@ornl.gov U.S. NRC Keith.Tetter@nrc.gov Curtis Smith Division Director Jenise ThompsonR Idaho National Laboratory Geologist Curtis.Smith@inl.gov U.S. NRC/NRO/DSEA/RGS jenise.thompson@nrc.gov Ellen SmithR Oak Ridge National Laboratory Nebiyu TirunehSC smithed@ornl.gov Hydrologist U.S. NRC/NRO Thomas SpinkR nebiyu.tiruneh@nrc.gov Project Manager TVA Nicholas ValosR tespink@tva.gov Region 3 Senior Reactor Analyst U.S. NRC/R-III Mathini Sreetharan nicholas.valos@nrc.gov Senior Engineer Dewberry Andrew VerdinRS, R msreetharan@dewberry.com Hydrologic Engineer Bureau of Reclamation averdin@usbr.gov 3-397

Carrie VuyovichS Gordon WittmeyerR Engineer Director of Technical Resources Error! Bookmark not defined.Cold Regions Center for Nuclear Waste Regulatory Research and Engineering Laboratory Analyses, Southwest Research Institute Carrie.m.vuyovich@usace.army.mil gordon.wittmeyer@swri.org Tony WahlRS, R Jeff WoodR Hydraulic Engineer Reliability and Risk Analyst USBR U.S. NRC/RES/DRA/PRAB twahl@usbr.gov jeffery.wood@nrc.gov Bin WangR Joseph Wright Senior Technical Specialist Supervisory Hydraulic Engineer GZA GeoEnvironmental, Inc. U.S. Bureau of Reclamation bin.wang@gza.com jmwright@usbr.gov Weijun WangR Geotechnical Engineer David ZiebellR U.S. NRC/NRO Senior Technical Leader weijun.wang@nrc.gov EPRI dziebell@epri.com Z. Gary Wang Reliability and Risk Engineer U.S. NRC/RES/DRA/PRB Zeechung.Wang@nrc.gov David Watson Senior Research Scientist Oak Ridge National Laboratory watsondb@ornl.gov David Watson Project Manager/Flood Engineer Duke Energy David-Watson3@duke-energy.com Michael WeberS Director, Office of Nuclear Regulatory Research U.S. NRC/RES Michael.Weber@nrc.gov John WeglianP Senior Technical Leader EPRI jweglian@epri.com Jason WhiteR Physical Scientist U.S. NRC/NRO/DSEA/RHM/RMET Jason.White@nrc.gov 3-398

5

SUMMARY

AND CONCLUSIONS 5.1 Summary This report has presented agendas, presentations and discussion summaries for the first four NRC Annual PFHA Research Workshops (2015-2019). These proceedings include presentation abstracts and slides and a summary of the question and answer sessions. The first workshop was limited to NRC technical staff and management, NRC contractors, and staff from other Federal agencies. The three workshops that followed were meetings attended by members of the public; NRC technical staff, management, and contractors; and staff from other Federal agencies. Public attendees over the course of the workshops included industry groups, industry members, consultants, independent laboratories, academic institutions, and the press. Members of the public were invited to speak at the workshops. The fourth workshop included more invited speakers from the public than from the NRC and the NRCs contractors.

The proceedings for the second through fourth workshops include all presentation abstracts and slides and submitted posters and panelists slides. Workshop organizers took notes and audio recorded the question and answer sessions following each talk, during group panels, and during end of day question and answer session. Responses are not reproduced here verbatim and were generally from the presenter or co authors. Descriptions of the panel discussions identify the speaker when possible. Questions were taken orally from attendees, on question cards, and over the telephone.

5.2 Conclusions As reflected in these proceedings PFHA is a very active area of research at NRC and its international counterparts, as well as other Federal agencies, industry and academia. Readers of this report will have been exposed to current technical issues, research efforts, and accomplishments in this area within the NRC and the wider research community.

The NRC projects discussed in these proceedings represent the main efforts in the first phase (technical-basis phase) of NRCs PFHA Research Program. This technical-basis phase is nearly complete, and the NRC has initiated a second phase (pilot project phase) that is a syntheses of various technical basis results and lessons learned to demonstrate development of realistic flood hazard curves for several key flooding phenomena scenarios (site-scale, riverine and coastal flooding). The third phase (development of selected guidance documents) is an area of active discussion between RES and NRC User Offices. NRC staff looks forward to further public engagement regarding the second and third phases of the PFHA research program in future PFHA Research Workshops.

5-489

ACKNOWLEDGEMENTS These workshops were planned and executed by an organizing committee in the U.S. Nuclear Regulatory Commissions (NRCs) Office of Nuclear Regulatory Research (RES), Division of Risk Analysis, Fire and External Hazards Analysis Branch, and with the assistance of many NRC staff.

Organizing Committees 1st Workshop, October 14-15, 2015: Joseph Kanney and William Ott.

2nd Workshop, January 23-25, 2017: Co-Chairs: Meredith Carr, Joseph Kanney; Members:

Thomas Aird, Thomas Nicholson, MarkHenry Salley; Workshop Facilitator: Kenneth Hamburger 3rd Workshop, December 4-5, 2017: Chair: Joseph Kanney, Members: Thomas Aird, Meredith Carr, Thomas Nicholson, MarkHenry Salley; Workshop Facilitator: Kenneth Hamburger 4th Workshop, April 30-May 2, 2019: Co-Chairs: Meredith Carr, Elena Yegorova; Members:

Joseph Kanney, Thomas Aird, Mark Fuhrmann, MarkHenry Salley; Workshop Facilitator:

Kenneth Hamburger Many NRC support offices contributed to all of the workshops and these proceedings. The organizing committee would like to highlight the efforts of the RES administrative staff; the RES Program Management, Policy Development and Analysis Branch; and the audiovisual, security, print shop, and editorial staff. The organizers appreciated office and division direction and support from Jennene Littlejohn, William Ott, MarkHenry Salley, Mark Thaggard, Michael Cheok, Richard Correia, Mike Weber, and Ray Furstenau. Michelle Bensi, Mehdi Reisi-Fard, Christopher Cook, and Andrew Campbell provided guidance and support from the NRC Office of New Reactors and the Office of Nuclear Reactor Regulation. The organizers thank the Electric Power Research Institute (EPRI) for assisting with planning, contributions, and organizing several speakers. EPRI personnel who participated in the organization of the workshops include John Weglian, Hasan Charkas, and Marko Randelovic.

During the workshops, Tammie Rivera assisted with planning and organized the registration area during the conference. David Stroup and Don Algama assisted with room organization.

Notes were studiously scribed by Mark Fuhrmann, David Stroup, Nebiyu Tiruneh, Michelle Bensi, Hosung Ahn, Gabriel Taylor, Brad Harvey, Kevin Quinlan, Steve Breithaupt, Mike Lee, Jeff Wood, and organizing committee members. The organizers appreciate the assistance during the conference of audiovisual, security, and other support staff. The organizers thank the panelists, the technical presenters, and poster presenters for their contributions; Thomas Aird and Mark Fuhrmann for performing a colleague review of this document; and the Probabilistic Flood Hazard Assessment Research Group for transcript reviews.

Members of the Probabilistic Flood Hazard Assessment Research Group:

MarkHenry Salley (Branch Chief), Joseph Kanney (Technical Lead), Thomas Aird, Meredith Carr, Mark Fuhrmann, Jacob Philip, Elena Yegorova, and Thomas Nicholson (Senior Technical Advisor) 5-490

APPENDIX A: SUBJECT INDEX 17B, Bulletin, 1-48, 1-178, 1-189, 2-36, 2- annual maximum series, 1-72, 1-165, 2-155, 187, 2-200, 4-215, 4-262, 4-265 2-201, 2-373, 3-75 17C, Bulletin, 2-36, 2-187, 2-194, 2-244, 3- searching, 4-149 121, 3-332, 4-163, 4-208, 4-214, 4-220, ANS, 3-377, 4-442, 4-452, 4-461, 4-471 4-230, 4-232, 4-236, 4-252, 4-257, 4- areal reduction factor. See ARF 261, 4-265, 4-289 ARF, 1-84, 2-374, 2-383, 2-417, 3-224, 3-2D, 1-34, 3-385, 4-314 401, 4-18, 4-120, 4-133, 4-142, 4-144, model, 1-183, 1-186, 2-52, 2-211, 2-362, 4-149, 4-152, 4-162 2-367, 2-377, 3-367, 4-202, 4-313, 4- averaging, temporal and spatial, 4-148 326 dynamic scaling model, 4-151 CASC2D, 1-151 methods, 4-147 HEC-RAS. See HEC-RAS empirical, 4-151 TELEMAC, 4-203, 4-206, 4-328 test cases, 4-147 3D, 4-314 arid, 1-61, 2-217, 2-223, 3-163, 3-200, 4-132 coastal, 4-123 semi-, 4-131 model, 1-252, 1-261, 2-288, 2-295, 2-302, ARR. See rainfall-runoff: model: Austrailian 2-306, 2-393, 3-22, 3-25, 3-199, 3-378, Rainfall and Runoff Model 4-24, 4-126, 4-291 ASME, American Society of Mechanical terrain mapping, 1-252 Engineers, 4-442 accumulated cyclone energy, ACE, 4-372 associated effects, 1-12, 1-31, 1-34, 2-43, 3-ADCIRC, 1-196, 2-78, 2-334, 2-379, 2-403, 15, 4-15, 4-31 4-57, 4-94 atmospheric AEP, xxxvii, 1-12, 1-17, 1-36, 1-50, 1-54, 1- conditions, 1-90 69, 1-149, 1-166, 1-191, 1-198, 2-22, 2- dispersion, 2-16 43, 2-54, 2-154, 2-187, 2-201, 2-204, 2- environment, 2-71 219, 2-225, 2-270, 2-307, 2-340, 3-15, instability, 4-377 3-21, 3-74, 3-97, 3-116, 3-117, 3-132, 3- interactions, 4-98 135, 3-138, 3-337, 3-355, 4-15, 4-60, 4- moisture, 3-28, 4-125, 4-346, 4-353 74, 4-94, 4-120, 4-127, 4-132, 4-194, 4- parameters, 2-81, 3-111 209, 4-214, 4-253, 4-286, 4-381 patterns, 2-85 drainage area based estimate, 1-69 processes, 3-310 low AEP, 2-187, 3-117, 3-135 rivers, 1-56, 1-59, 1-162 neutral, 1-185 stability, 4-163 return periods, 1-51 variables, 4-122 very low AEP, 1-166, 2-187, 3-117, 4-158 at-site, 1-84, 1-180, 2-31, 2-152, 2-155, 2-AEP4. See distribution:Asymmetric 160, 2-163, 2-188, 2-206, 2-209, 3-70, Exponential Power 3-75, 3-79, 3-84, 3-132, 3-139, 3-310, 3-aleatory uncertainty. See uncertainty, 315, 4-125, 4-137, 4-208, 4-214, 4-264 aleatory Austrailian Rainfall and Runoff Model. See American Nuclear Society. See ANS rainfall-runoff: model: Austrailian Rainfall AMM. See Multi-decadal:Atlantic Meridional and Runoff Model Mode autocorrelation, 3-126 AMO. See Multi-decadal:Atlantic Multi- BATEA. See error:Bayesian Total Error Decadal Oscillation Analysis AMS. See annual maximum series Bayesian, 2-151, 2-162, 2-165, 2-313, 2-400, ANalysis Of VAriance, ANOVA, 4-201 2-402, 3-70, 3-88, 3-93, 3-140, 3-304, 4-annual exceedance probability. See AEP 163, 4-220, 4-223, 4-229, 4-257, 4-294 A-1

analysis, 1-171, 4-308 mass wasting, 4-419 approach, 1-167, 2-168, 2-308, 4-257, 4- models, 3-301, 4-425 366 tests, 3-269, 4-406 BHM, 1-86, 1-175, 2-338, 2-345, 3-304 Bulletin 17B. See 17B, Bulletin estimation, 1-156 Bulletin 17C. See 17C, Bulletin framework, 1-161, 1-163, 2-321, 2-369, 2- calibration, 1-89, 1-90, 1-101, 1-123, 1-158, 400, 4-257 1-161, 1-177, 2-207, 2-312, 2-317, 3-67, gridded, 3-90 3-70, 3-144, 3-146, 3-202, 4-25, 4-75, 4-hazard curve combination, 4-220 105, 4-217, 4-227, 4-313, 4-332, 4-369 inference, 2-338, 2-342, 2-347, 3-70, 3-78, CAPE, 1-60, 1-139, 2-96, 2-381, 4-136, 4-3-93, 3-304, 3-313, 3-387, 4-223 144, 4-161, 4-218 maximum likelihood, 1-186 CASC2D. See 2D:model CASC2D model, 2-321, 2-345, 2-353, 2-402, 3-307, CDB. See current design basis:

3-326 CDF, 1-152, 1-164, 4-66 posterior distribution, 1-161, 1-163, 1-171, center, body, and range, 1-136, 1-207, 2-2-163, 2-321, 2-338, 2-342, 3-78, 3-79, 354, 2-359, 3-94, 3-314, 3-320, 4-266, 3-88, 3-93, 4-223 4-313 prior distribution, 1-161, 1-171, 2-163, 3-78 CFHA. See flood hazard:flood hazard Quadrature, 1-196, 2-68, 4-69 assessment:comprehensive regional, 2-163, 3-79 CFHA. See coastal flood hazard assessment Bayesian Hierarchical Model. See CFSR. See reanalysis:Climate Forecast Bayesian:BHM System Reanalysis best practice, 1-15, 1-151, 2-34, 2-45, 2-248, CHS. See Coastal Hazard System 2-259, 2-405, 3-17, 3-22, 3-25, 3-242, 3- Clausius-Clapeyron, 1-58, 2-89, 4-353, 4-246, 3-301, 3-361, 4-18, 4-24, 4-254, 4- 384 318 cliff-edge effects, 1-12, 1-31, 2-43, 3-15, 3-Blayais, 2-9, 2-266, 3-27, 3-240, 4-390, 4- 373, 3-382, 4-15, 4-474 472 climate, 1-51, 1-54, 1-98, 1-151, 1-196, 1-bootstrap 209, 1-267, 2-16, 2-77, 2-88, 2-223, 2-1000 year simulation, 3-359 372, 2-402, 3-29, 3-81, 3-120, 3-133, 3-resampling, 4-64 136, 3-179, 3-189, 3-208, 4-11, 4-105, boundary condition, 1-90, 1-95, 1-196, 2- 4-113, 4-119, 4-125, 4-132, 4-137, 4-102, 2-113, 2-150, 2-312, 2-320, 2-326, 335, 4-354, 4-369, 4-379, 4-380, 4-383 2-354, 2-366, 2-413, 3-43, 3-47, 3-68, 4- anomalies, 1-61, 3-196 30, 4-39, 4-203, 4-266, 4-271, 4-298 hydroclimatic extremes, 4-335 bounding, 2-323, 2-337, 3-28, 4-457, 4-470, index, 2-338, 2-345, 3-304, 3-310, 3-313 4-478 mean precipitation projections, 4-341 analyses, 2-268, 2-322, 3-28, 4-470 mean precipitation trends, 4-339 assessments, 3-370 models, 1-58, 1-63, 1-95, 2-97, 2-100, 2-assumptions, 2-322 112 estimates, 2-37 downscaling, 4-341 tests, 2-268 patterns, 1-56, 2-88, 3-29, 3-192 BQ. See Bayesian:Quadrature predictions, 1-96 breach, dam/levee, 1-21, 1-148, 1-209, 1- projections, 1-22, 1-51, 1-55, 1-96, 2-48, 214, 1-220, 2-34, 2-322, 2-325, 2-329, 2-89, 2-112, 2-373, 3-19, 3-30, 3-47, 3-3-267, 3-268, 3-314, 4-198, 4-204, 4- 67, 3-162, 4-335, 4-356, 4-369 262, 4-312, 4-404, 4-405, 4-425 precipitation, 4-344 computational model, 4-415, 4-417 regional, 1-74, 1-123 development, 3-267 scenarios, 4-341 initiation, 3-198 science, 1-22, 1-52, 2-90, 2-405, 3-193, 4-location, 4-262, 4-313 381 A-2

temperature changes, 3-32 design basis flood, 4-454 trends, 4-335 event, 3-245 variability, 2-100, 4-137, 4-225, 4-371, 4- return period, 3-352 377 flood walkdown, 2-254 climate change, 1-22, 1-51, 1-63, 1-95, 1- dam, 1-210, 2-201, 2-244, 2-307, 2-329, 2-162, 1-188, 2-48, 2-77, 2-88, 2-98, 2- 338, 2-400, 3-15, 3-136, 3-149, 3-194, 102, 2-114, 2-168, 2-199, 2-307, 2-366, 3-197, 3-267, 3-314, 3-338, 3-405, 4-14, 3-19, 3-29, 3-35, 3-38, 3-115, 3-195, 3- 4-130, 4-208, 4-224, 4-228, 4-253, 4-398, 4-20, 4-30, 4-33, 4-98, 4-260, 4- 257, 4-278, 4-281, 4-312, 4-404, 4-425, 355, 4-364, 4-370, 4-378, 4-380, 4-383, 4-451, 4-476 4-454 assessments, 4-196 high temperature event frequency breach. See breach, dam/levee increase, 2-94 case study, 1-65, 1-74, 2-348, 2-378, 3-hydrologic implacts, 2-99 143, 3-333, 3-336, 3-355, 3-358, 4-125, mean changes, 2-99 4-213, 4-218, 4-238, 4-298, 4-329 precipitation changes, 2-91 computational model, 4-405 scenarios, 2-93 embankment. See embankment dam streamflow change, 2-98 erosion. See erosion: dam coastal, 1-148, 1-267, 4-34, 4-93, 4-317 failure, 1-6, 1-11, 1-37, 1-172, 1-227, 2-12, CSTORM, 2-379 2-34, 2-52, 2-276, 2-288, 2-322, 2-325, StormSim, 2-379 2-329, 2-340, 2-353, 2-409, 3-22, 3-26, coastal flood hazard assessment, 1-194 3-136, 3-197, 3-217, 3-266, 3-353, 3-Coastal Hazard System, 2-379, 3-328 371, 3-374, 3-378, 3-388, 3-395, 4-14, coincident and correlated flooding, 2-40, 3- 4-228, 4-295, 4-318, 4-322, 4-455, 4-10, 3-15, 3-395, 3-403, 4-15, 4-19, 4- 476 318, 4-448 failure analysis, 4-324 coincident events, 1-12, 2-43, 2-332, 3-15, models, 1-159, 3-191 4-15, 4-86 operations, 2-384 combined effects, 1-12, 1-30, 2-43, 4-432, Oroville, 3-339, 3-361, 3-389, 4-258 4-440 overtopping, 3-277, 3-303, 3-367, 4-330, combined events, 1-25, 1-31, 1-37, 1-133, 4-333, 4-407 2-89, 2-356, 2-419, 3-318, 3-380, 3-386, physical model, 1-209, 1-216, 3-268, 4-4-95, 4-440, 4-451, 4-454, 4-456, 4-477 405 combined processes, 1-25 potential failure modes, 2-340 compound event framework, 4-320 regulation, 1-155, 1-188, 4-289 concurrent hazards, 1-228, 2-276, 3-374, releases, 2-97, 3-37, 4-287, 4-318, 4-363 3-377 risk, 1-24, 2-378, 2-416, 3-138, 3-197, 3-correlated hazards, 2-52, 2-410, 3-26 369, 3-400, 4-20, 4-287, 4-320, 4-334 confidence interval, 1-72, 1-157, 3-15, 3-139, risk assessment, 4-321 4-14, 4-199, 4-214 safety, 1-151, 1-211, 2-203, 2-400, 2-404, confidence limits, 1-178, 1-194, 1-199, 2-36, 3-135, 3-202, 3-331, 3-353, 4-114, 4-2-196, 3-94, 3-108, 4-57, 4-69, 4-232, 4- 124, 4-130, 4-158, 4-161, 4-163, 4-209, 253 4-217, 4-224, 4-227, 4-229, 4-231, 4-NOAA Atlas 14, 2-373 279, 4-323, 4-369 convective potential energy. See CAPE system of reservoirs, 3-334 correlation system response, 3-354 spatial and temporal, 2-340, 3-307 data cumulative distribution function. See CDF collection, 4-458 current design basis, 1-10, 1-23, 1-247, 2-21, regional information, 1-154 2-42, 2-202, 2-255, 3-12, 3-154, 4-381, transposition, 4-123 4-480 A-3

data, models and methods, 1-136, 1-197, 1- distribution, 1-71, 1-153, 2-151, 2-179, 2-207, 2-53, 2-57, 2-62, 3-94, 3-96, 3-99, 187, 2-245, 2-270, 2-307, 2-369, 3-70, 3-104, 3-320, 4-57, 4-59, 4-268 3-96, 3-143, 3-315, 4-81, 4-125, 4-159, model choice, 3-312 4-163, 4-256, 4-260, 4-275, 4-315 model selection, 3-312 Asymmetric Exponential Power (AEP4), 2-DDF. See depth-duration-frequency 193, 2-197, 2-200 decision-making, 1-23, 1-32, 1-36, 2-30, 2- empirical, 4-64 246, 2-271, 2-395, 3-136, 3-248, 3-337, exponential, 1-165, 1-208, 2-63, 2-207 3-400, 4-31, 4-34, 4-117, 4-129, 4-243, extreme value, 2-151, 2-155, 3-70, 3-74 4-276, 4-465, 4-476 flood frequency, 2-207, 2-246, 3-117, 3-dendrochronology, 2-220, 2-222, 3-124, 3- 126, 4-208 190, 4-229 full, 2-205 botanical information, 4-216 Gamma, 2-63, 2-347 tree ring estimate, 3-123 generalized skew normal (GNO), 1-80, 1-tree rings, 3-124, 3-183 83, 2-159, 2-187, 2-193, 2-200, 2-373, deposits, 2-216, 2-244, 3-116, 3-182, 3-188, 3-77 3-190, 3-212, 3-234, 4-241, 4-243, 4- generalized extreme value (GEV), 1-80, 1-259 83, 1-175, 1-207, 1-258, 2-63, 2-159, 2-alluvial, 2-245 163, 2-174, 2-179, 2-187, 2-193, 2-197, bluff, 3-187 2-200, 2-207, 2-318, 2-346, 2-373, 3-70, boulder-sheltered, 2-239, 3-188, 4-250 3-77, 4-111, 4-119, 4-149, 4-157, 4-224, cave, 2-220, 2-222, 2-240, 3-187, 4-229 4-261, 4-343, 4-360 flood, 2-223, 2-225, 2-227, 2-241, 2-242, generalized logistic (GLO), 1-83, 1-84, 2-2-245, 3-163, 3-171, 3-173, 3-185, 3- 159, 2-193, 2-197, 2-373, 3-77 190, 3-196, 3-200, 3-213, 4-238, 4-243 generalized Pareto (GPA or GPD), 1-83, paleoflood characterization, 4-239 1-155, 1-196, 1-207, 2-63, 2-159, 2-187, slackwater, 2-220, 3-124, 3-186, 3-362, 4- 2-193, 2-197, 3-77, 4-224 229, 4-230 GNO (generalized skew normal), 2-197 surge, 4-259 Gumbel, 1-155, 1-196, 1-207, 2-63, 2-346, terrace, 2-220, 2-245, 3-124, 3-183, 3-184 4-205, 4-328 depth-duration-frequency, 2-372, 4-330 Kappa (KAP), 2-174, 2-177, 2-193, 2-200, deterministic, 1-30, 1-35, 1-149, 1-151, 1- 2-373, 3-358, 4-218, 4-307, 4-332 257, 2-8, 2-38, 2-71, 2-83, 2-179, 2-205, log Pearson Type III (LP-III), 1-155, 1-178, 2-260, 2-286, 2-323, 2-337, 2-408, 2- 2-36, 2-187, 2-194, 2-199, 4-208, 4-214, 410, 3-10, 3-22, 3-28, 3-103, 3-140, 3- 4-257, 4-261 246, 3-259, 3-262, 3-374, 3-391, 3-393, lognormal, 1-155, 1-207, 2-63, 2-66, 2-3-395, 4-13, 4-27, 4-31, 4-56, 4-122, 4- 207, 3-100, 4-229 126, 4-130, 4-158, 4-175, 4-293, 4-383, lognormal 3, 2-200 4-386, 4-454, 4-475, 4-477, 4-481 low frequency tails, 2-65 analysis, 2-179, 2-246, 2-322, 2-337, 3- marginal, 4-60, 4-70 390, 4-85, 4-382 multiple, 2-53, 2-187, 2-403, 3-117, 4-257 approaches, 1-6, 1-28, 1-73, 2-26, 2-50, 2- mutltivariate Gaussian, 3-102 154, 2-322, 2-337, 2-409, 3-24, 4-24, 4- normal, 1-207, 2-63, 2-171, 4-49, 4-52, 4-199, 4-470 69, 4-205, 4-229 criteria, 2-168, 2-400 parameters, 2-179, 2-188 focused evaluations, 2-21 Pearson Type III (PE3), 1-83, 2-159, 2-Hydrometerological Reports, HMR, 1-185 193, 2-197, 2-373, 3-77, 4-224 increasing realism, 2-332 Poisson, 1-165, 1-198 methods, xxxviii, 1-29, 2-25, 2-202, 4-472 posterior. See Bayesian: posterior model, 1-151, 1-243, 2-88, 3-29, 3-304, 4- distribution 330, 4-355, 4-382 precipitation. See precipitation:distribution A-4

prior. See Bayesian: prior distribution Environmental Factors, 1-19, 1-21, 1-223, 1-probability, 3-99, 4-89 238, 2-31, 2-47, 2-271, 2-276, 2-415, 3-quantiles, 2-155 19, 3-250, 3-398, 4-20, 4-441 tails, 2-207 epistemic uncertainty. See uncertainty, temporal, 1-160, 2-179, 4-121, 4-290 epistemic triangle, 4-205, 4-208, 4-229, 4-328 erosion, 1-11, 1-153, 1-222, 2-245, 3-15, 3-type, 3-101 261, 4-14, 4-81, 4-96, 4-230, 4-330, 4-uniform, 4-205, 4-208, 4-257, 4-328 334, 4-404, 4-417 Wakeby (WAK), 1-83, 2-159, 2-193, 2- dam, 3-271, 3-284, 3-292, 3-302, 3-303, 4-197, 2-373, 3-77 407, 4-414, 4-424 Weibull (WEI), 1-155, 1-196, 1-207, 2-63, embankment, 1-19, 1-21, 2-47, 3-19, 3-2-69, 2-187, 2-193, 2-197, 2-200, 3-100, 277, 3-292, 3-301, 4-19, 4-407 3-103, 4-328 rockfill, 1-209, 4-404, 4-424 Weibull plotting position, 4-64 zoned, 3-267, 4-422, 4-424 Weibull type, 4-68 zoned rockfill, 3-267, 4-404 EC. See Environmental Conditions equations, 4-420 EHCOE. See External Hazard Center of erodibility parameters, 3-273, 3-303, 4-Expertise 404, 4-415, 4-422 EHID. See Hazard Information Digest headcut, 3-267, 4-414, 4-416, 4-418 EMA. See expected moments algorithm internal, 1-213, 3-136, 3-267, 3-272, 3-embankment dam, 1-21, 1-148, 1-209, 2-47, 290, 3-292, 3-300, 3-302, 3-303, 4-416 3-19, 3-267, 3-269, 3-272, 3-276, 3-336, parameters, 1-221, 3-285 4-19, 4-424 processes, 1-21, 1-148, 1-221, 3-270, 4-erosion. See erosion: embankment 407, 4-425 rockfill, 1-216, 3-273, 4-330, 4-404 rates, 1-221, 3-267, 3-285, 4-404, 4-415 zoned rockfill, 3-274 resistance, 3-267, 3-270, 4-407, 4-417 ensemble, 1-85, 1-124, 1-144, 2-100, 2-152, spillway, 3-136, 3-343, 4-211 2-161, 3-81, 3-86, 4-41, 4-52, 4-56, 4- surface, 2-330, 3-267, 3-284, 4-414, 4-97, 4-114, 4-117, 4-123, 4-381 416, 4-418, 4-422, 4-424 approaches, 4-123 tests, 1-209, 1-215, 1-217, 3-267, 3-286, Global Ensemble Forecasting System, 4-404, 4-405 GEFS, 4-35, 4-56 error, 1-35, 1-125, 1-166, 1-195, 2-56, 2-200, gridded precipitation, 2-152, 2-160, 3-71, 2-317, 3-67, 3-105, 4-34, 4-41, 4-57, 4-3-81, 3-86, 3-89 76, 4-87, 4-90, 4-95, 4-102, 4-228, 4-models, 4-55, 4-56 262, 4-468 real-time, 4-49 Bayesian Total Error Analysis, BATEA, 1-storm surge, 4-34, 4-35, 4-36 161 ENSO. See Multi-decadal:El Nino-Southern bounds, 3-116, 3-117 Oscillation defined space, 4-35 Environmental Conditions, 1-21, 1-224, 2- distribution, 2-56, 4-49 271, 3-248 epistemic uncertainty, 3-94 impact quantification, 3-257 estimation, 4-108 impacts on performance, 2-280 forecasting, 4-35 insights, 3-256 instrument characteristic, 4-102 literature, 2-278, 3-252, 3-257 mean absolute, 4-62 method limitations, 2-284 mean square, 3-130 multiple, simultaneously occuring, 3-257 measurement, 1-161, 1-164, 4-262 performance demands, 2-275, 3-251 model, 1-162, 2-193, 2-403, 4-57, 4-69, 4-proof-of-concept, 2-273, 2-281, 3-251 79 standing and moving water, 2-279 operator, 2-284, 3-247, 3-257 quantification, 2-189, 4-59 A-5

random, 4-105, 4-107 extreme precipitation, 1-58, 1-90, 1-100, 2-relative, 3-48 88, 2-89, 2-104, 2-105, 2-153, 2-167, 3-root mean square, RMSE, 4-151, 4-306 33, 3-35, 3-40, 3-45, 3-70, 3-398, 4-101, sampling, 1-71, 2-192, 3-332, 4-79 4-110, 4-347, 4-354 seal installation, 2-267 change, 2-91 simulation, 1-197, 2-57, 2-102, 3-42, 3-67, classification, 1-92, 2-105, 3-44 3-97, 3-105 climate projections, 4-342 space, 4-35, 4-52 climate trends, 4-339 term, 2-53, 2-57, 2-73, 3-94, 3-96, 4-57, 4- Colorado/New Mexico study, 4-144, 4-159, 60, 4-228 4-383 unbiased, 3-97, 4-60 event, 1-91 undefined space, 4-35 increases, 2-94 EVA. See extreme value analysis spatial coherence, 4-337 evapotranspiration, 3-40 temporal coherence, 4-337 event tree, 1-22, 1-46, 1-260, 2-28, 2-288, 2- variability, 4-337 297, 2-300, 2-401, 2-405, 2-417, 3-301, extreme storm data, 3-334 3-303, 3-389, 4-324, 4-440 extreme storm database, 2-377 analysis, 4-313, 4-477 increase, 4-359 EVT. See extreme value theory frequency, 4-364 ex-control room actions, 4-474, 4-475 intensity, 4-364 expected moments algorithm, 1-156, 1-186, model, 1-65, 2-153, 3-72 1-188, 2-187, 2-194, 2-199, 2-207, 2- advances, 2-341 212, 2-214, 3-117, 3-122, 3-139, 3-141, risk, 4-337 3-149, 4-208, 4-214, 4-252, 4-257 extreme value analysis, 1-194, 3-328 expert elicitation, 1-135, 2-338, 2-343, 2-347, extreme value theory, 3-304, 3-313, 4-114, 3-326, 4-220, 4-226, 4-229, 4-313 4-151 external flood, 2-247, 2-259, 2-288, 3-22, 3- fault tree, 1-46, 1-260, 4-324 198, 4-385, 4-429 FHRR. See Near Term Task Force: Flooding equipment list, 3-262, 3-264, 4-435 Hazard Re-Evaluations operator actions list, 3-262, 3-264 FLEX, 2-24, 2-288, 2-304, 3-199, 3-248, 3-human action feasibility, 3-264 258, 3-263, 4-314, 4-381, 4-440 warning time, 3-264 flood, 2-415, 3-31 risks, 3-260 causing mechanisms, 4-318 scenarios, 3-132, 3-261 complex event, 4-449 external flood hazard, 2-290, 4-455 depths, 1-34 frequency, 2-79 design criteria, 3-352 model validation, 2-394 duration, 1-31, 1-34, 1-255, 2-30, 2-291 external flooding PRA. See XFPRA dynamic modeling, 1-255, 2-291, 2-304 External Hazard Center of Expertise, 2-15 elevations, 1-51 extratropical cyclone, 1-11, 1-17, 1-18, 1-58, event, 1-253, 2-289 1-91, 1-196, 2-77, 2-89, 2-97, 4-55, 4- extreme events, 1-172, 2-207, 4-466 98, 4-346, 4-355 gates, 4-473 reduced winter frequency, 4-362 hazard, 1-12, 1-153, 2-44, 3-16, 4-15 extreme event, 4-290 diverse, 4-447 extreme events, xxxvii, 1-56, 2-30, 2-88, 2- increase, 4-364 101, 2-168, 2-201, 2-307, 2-400, 3-29, mechanisms, 1-31, 1-132, 2-309, 2-325, 2-3-42, 3-140, 3-181, 3-193, 3-304, 3-313, 356, 4-432 3-371, 4-281, 4-315, 4-349, 4-381, 4- mitigation, 2-30 475 operating experience, 4-11 external events, 4-29 organizational procedure, 3-245 meteorology, 4-352 response, 3-245 A-6

risk, 1-177 comprehensive, CFHA, 1-152 riverine, 1-6, 1-16, 1-133, 1-148, 1-150, 1- influencing parameters, 4-202 168, 1-175, 1-267, 2-46, 2-202, 2-227, probabilistic analysis, 1-30 2-288, 2-338, 2-353, 2-355, 3-15, 3-18, re-evaluated, 1-248 3-22, 3-27, 3-115, 3-198, 3-246, 3-314, riverine, 2-307 4-11, 4-14, 4-24, 4-31, 4-164, 4-197, 4- scenarios, 4-458 228, 4-255, 4-265, 4-295, 4-311, 4-455 static vs. dynamic, 3-368 routing, 1-11 Flood Hazard Re-Evaluations. See Near runoff-induced riverine, 4-318 Term Task Force: Flooding Hazard Re-SDP example, 1-43 Evaluations simulation, 2-52 flood mitigation, 4-20, 4-472 situation, 4-202 actions, 3-379 sources, 4-456 approaches, 4-449 sparse data, 4-30 fragility, 3-381 stage, 4-480 proceduralized response, 3-245 warning time, 1-34, 2-30 procedures, 4-473, 4-475 flood events strategies, 2-254 Blayais, 4-465 flood protection, 1-255, 2-51, 2-248, 2-250, Cruas, 4-466 2-291, 3-22, 3-25, 3-242, 4-21, 4-24, 4-Dresden, 4-466 33, 4-472 Hinkley Point, 4-466 barrier fragility, 2-52, 2-410, 3-26, 3-395 St. Lucie, 4-466 criteria, 2-250 flood frequency, 2-30, 3-118, 3-398, 4-252, failure modes, 3-374 4-330, 4-473 features, 2-250, 3-245, 3-262, 3-265, 4-27, analysis, 1-13, 1-148, 1-150, 1-153, 1-172, 4-435 1-176, 1-180, 2-45, 2-81, 2-187, 2-190, fragility, 3-377, 3-379 2-202, 2-227, 2-244, 3-17, 3-116, 3-119, inspection, 2-250 3-126, 3-129, 3-135, 3-137, 3-142, 3- maintenance, 2-254 163, 3-199, 3-234, 3-325, 4-18, 4-246, oversight, 3-246 4-265, 4-474 reliability, 1-37 gridded, 3-92 survey, 2-257 methods, 1-13, 2-45, 3-17 testing methods, 2-250 benchmark, 4-33 training, 2-254 curve, 3-112, 3-355, 4-176, 4-253 work control, 3-245 extrapolation, 2-218 flood protection and mitigation, 1-11, 1-21, 2-extrapolation, 3-139 21, 2-43, 2-180, 2-271, 2-415, 3-13, 3-limits, 2-170 16, 3-150, 3-250, 4-11, 4-14 methods, 1-191 training, 3-245 flood hazard, 1-10, 1-27, 1-30, 2-16, 2-42, 2- flood seals, 1-19, 1-44, 1-223, 1-265, 2-19, 43, 2-182, 2-309, 3-12, 3-151, 3-371, 4- 2-47, 2-247, 2-251, 2-260, 2-265, 3-19, 14, 4-327, 4-473 3-235, 3-240, 4-20, 4-384, 4-392, 4-393, curves, 4-266 4-402, 4-403, 4-426, 4-473 combining, 4-219 characeristic types and uses, 1-266, 2-family of, 2-54, 3-108, 3-380, 4-71, 4- 262, 3-237, 4-386, 4-394, 4-397 267, 4-475 condition, 4-387, 4-435 dynamics, 3-385 critical height, 4-435 flood hazard analysis, 3-354 failure mode, 4-387 case study, 4-191 fragility, 3-381 riverine pilot, 2-50 historic testing, 2-251 flood hazard assessment, 1-29, 3-328, 3- impact assessment, 4-387 336, 4-318 A-7

performance, 1-19, 2-47, 2-261, 3-19, 3- GLO. See distribution:generalized logistic 235, 4-393 Global Climate Model, 1-128, 1-162, 2-53, 2-ranking process, 4-388 55, 2-63, 2-67, 2-71, 2-77, 2-96, 2-99, 2-risk significance, 4-386 403, 3-41, 3-47, 3-94, 3-100, 3-103, 4-tests, 1-20, 1-265, 2-262, 3-236, 4-394 99, 4-114, 4-163, 4-260, 4-360 criteria development, 2-251 downscaling, 2-55, 3-102 plan, 2-264, 3-238, 4-395 model forcing, 2-71 procedure, 1-265, 3-239, 4-396 Global Precipitation Measurement, GPM, 4-results, 4-400, 4-401 100, 4-117 series, 4-397 global regression model, 4-61 Focused Evaluations. See Fukushima Near global sensitivity analysis, 4-198, 4-327 Term Task Force: Focused Evaluations case studies, 4-202 FPM. See flood protection and mitigation simple case, 4-205 fragility, 1-11, 3-13, 4-14 GNO. See distribution:generalized skew analysis, 1-259 normal curve, 4-324 goodness-of-fit, 2-102, 2-187, 2-194 flood barrier. See flood protection: barrier tests, 1-71 fragility GPA. See distribution: generalized Pareto framework GPD. See distribution:generalized Pareto NARSIS, 4-327 GPM. See Gaussian process metamodeling simulation based dynamic flood anlaysis Great Lakes, 3-31 (SBDFA), 1-253, 1-256, 2-292 water levels, 4-366 TVA Probabilistic Flood Hazard decreases, 4-368 Assessment, 2-320, 2-404, 4-277 lowered, 3-40 scenarios, 4-282 GSA. See global sensitivity analysis Fukushima Near Term Task Force, 1-9, 1- hazard 23, 1-27, 1-32, 2-17, 2-20, 3-263, 4-11, analysis, 3-349, 4-450 4-386 assessment, 3-22 Flooding Hazard Re-Evaluations, 1-23, 4- hydrologic, 3-136, 3-195, 4-115 440, 4-471, 4-480 identification, 2-82 Fukushima Flooding Reports, 4-471 probabilistic approach, 4-471 re-evaluated flooding hazard, 4-480 quantification, 2-315 Focused Evaluations, 3-263, 4-471 hazard curves, 1-11, 1-51, 1-164, 2-43, 2-68, Integrated Assessment, 2-21, 3-263, 4- 2-84, 2-218, 3-13, 3-100, 3-104, 3-332, 386 4-14, 4-90, 4-474, 4-477 Mitigating Strategies Assessments, 3-263, comparison, 4-281 4-440, 4-475 full, 1-12, 2-43, 3-15, 4-15 post Fukushima process, 4-472 full range, 2-30 Recommendation 2.1, 4-480 integration, 4-60, 4-70 Recommendation 2.3, 4-435, 4-479 MCI, 2-70 Gaussian, 2-67 MCLC, 2-69 Gaussian process metamodeling, 3-102, 4- weight and combine methods, 4-210 59, 4-61 Hazard Information Digest local correction, 4-61 External, 3-149, 3-399 uncertainty, 4-61 Flood, 1-13, 1-223, 1-241, 2-45, 2-180, 2-GCM. See Global Climate Model, See Global 181, 2-186, 2-413, 3-17, 3-149, 3-161, Climate Model 4-18 GEFS. See ensemble:Global Ensemble flood beta, 2-183, 3-152 Forecasting System flood workshop, 1-252, 2-183, 3-152 GEV. See distribution:generalized extreme Natural, 3-151 value population, 2-183, 3-152 A-8

hazardous convective weather, 1-57, 1-60, organizational behavior, 3-379, 3-382, 3-3-31, 3-36, 3-40, 4-368 385, 4-473 NDSEV, 3-35 organizational response, 4-473, 4-479 NDSEV increase, 4-361 humidity, 1-53, 4-358 severe weather, 4-30 HURDAT, 1-207 monitoring, 3-245 hurricane, 1-57, 1-95, 2-51, 2-53, 2-77, 2-81, HCW. See hazardous convective weather 2-89, 2-105, 2-407, 3-26, 3-37, 3-43, 3-headcut. See erosion: headcut 111, 3-247, 3-393, 4-25, 4-34, 4-35, 4-HEC, 3-195, 3-201 73, 4-98, 4-113, 4-259, 4-326, 4-370, 4-

-FIA, 4-261 380, 4-480

-HMS, 2-376, 3-202, 4-166, 4-263 2017 season, 4-371 MCMC optimization, 2-376 Andrew, 4-474

-LifeSim, 4-261 Category, 4-41, 4-98

-MetVue, 2-377 Florence, 4-481 models, 4-312 Frances, 1-101

-RAS, 4-166, 4-207, 4-230, 4-244 Harvey, 3-180, 3-329, 3-361, 3-367, 3-391,

-RAS 2D hydraulics, 2-377 4-95, 4-114, 4-124, 4-160, 4-259

-ResSim, 4-166, 4-258 Ike, 4-56

-SSP, 4-262 Isaac, 3-53, 3-69

-SSP, flood frequency curves, 3-334 Katrina, 1-194, 2-53, 4-263

-WAT, 2-378, 4-161, 4-165, 4-166, 4-256, Maria, 4-211 4-261, 4-263, 4-313, 4-316 Sandy, 4-259 FRA, 4-196 hydraulic, 2-226, 2-266, 2-288, 2-307, 2-354, hydrologic sampler, 4-191 2-400, 3-198, 3-199, 3-234, 3-315, 4-MCRAM runs, 2-378 144, 4-170, 4-230, 4-254, 4-257, 4-262, HEC-RAS, 4-191, 4-236 4-326 historical detailed channel, 1-11 data, 1-96, 3-117, 3-120, 3-122, 3-131, 4- models, 1-133, 1-158, 1-186, 2-311, 2-30, 4-215, 4-269 420, 3-195, 4-60, 4-70, 4-198, 4-326 flood information, 1-154 dependent inputs, 4-326 floods, 1-187 hydraulic hazard analysis, 2-324 intervals, 3-131 hydrologic observations, 1-55, 3-80 loading, 4-232 peak, 1-155, 3-123 models, 1-63, 1-133, 1-158, 2-311, 2-376, perception thresholds, 3-131 4-123, 4-282, 4-331, 4-381 records, 2-62, 3-21, 3-183 risk, 1-15, 2-46, 3-18, 4-329 records extrapolation, 2-80 routing, 2-387 spatial patterns, 4-141 runoff units (HRUs), 3-143 streamflow, 1-183 simplified model, 3-337 water levels, 2-50, 3-24, 3-113 simulation, 4-279 homogeneous region, HR, 1-71, 1-77, 2-151, hydrologic hazard, 2-378, 3-331, 4-211 2-155, 2-159, 2-167, 3-70, 3-75, 3-83 analysis, 3-334, 4-115 human factors, 3-388, 4-471 analysis, HHA, 1-85, 2-207, 3-136, 4-114, HRA, 2-30, 4-475 4-125 HRA/HF, 1-24 curve, 1-15, 1-170, 2-45, 2-204, 2-340, 3-human actions, 2-19, 3-385, 4-446, 4-473 17, 4-130, 4-219, 4-329 Human Error Probabilities, 2-280 stage frequency curve, 4-213 human errors, 2-293 Hydrologic Unit Code, HUC, 4-149 human performance, 2-273, 3-251 watershed searching, 4-150 human reliability, 4-474 hydrology, 2-151, 2-202, 2-226, 2-307, 2-operator actions, 4-474 338, 2-354, 2-369, 2-400, 2-411, 3-70, A-9

3-135, 3-195, 3-304, 3-315, 3-325, 3- 2-287, 2-291, 2-297, 2-322, 2-326, 2-366, 3-387, 4-114, 4-122, 4-127, 4-144, 337, 2-341, 2-353, 2-370, 2-421, 3-19, 4-161, 4-170, 4-211, 4-229, 4-244, 4- 3-22, 3-42, 3-47, 3-198, 3-246, 3-314, 3-276, 4-313, 4-381 315, 4-19, 4-24, 4-264, 4-295, 4-311, 4-initial condition, 1-90, 1-95, 2-104, 3-44 455 Integrated Assessments. See Fukushima analysis, 4-480 Near Term Task Force:Integrated framework, 1-17, 2-46, 2-104, 3-18 Assessment screening, 3-369 internal flooding, 3-25, 4-386 severe storm, 1-90, 3-46, 4-361 scenarios, 3-25 numerical simulation, 1-90, 1-95 inundation logic tree, 2-56, 2-63, 2-85, 2-369, 3-94, 3-mapping, 3-367, 3-368 97, 3-107, 3-114, 4-57, 4-81, 4-86, 4-93 dyanamic, 3-368 branch weights, 4-91 modeling, 4-176 LP-III. See distribution:log Pearson Type III period of, 3-261 manual actions, 1-21, 1-31, 2-272, 2-415, 3-river flood anlysis, 4-327 245, 3-250, 3-398, 4-449, 4-473 JPM, joint probability method, 1-35, 1-195, 1- decomposing, 2-275 199, 1-209, 2-34, 2-53, 2-56, 2-74, 2-77, modeling time, 3-257 3-94, 3-99, 3-112, 4-25, 4-57, 4-64, 4- reasonable simulation timeline, 3-246 73, 4-77, 4-88, 4-228, 4-318 timeline example, 3-256 integral, 1-199, 2-56, 3-97, 4-60 maximum likelihood, 1-156 parameter choice, 2-62 Bayesian, 1-186 storm parameters, 1-197, 1-207, 2-57, 3- estimation, 1-70, 2-404 97, 3-100, 4-68, 4-76 MCMC. See Monte Carlo:Markov Chain surge response function, 4-78 MCS. See mesoscale convective system JPM-OS, joint probability method, with MEC. See mesoscale storm with embedded optimal sampling, 1-194, 1-196, 2-53, 2- convection 55, 2-73, 2-77, 3-94, 3-102, 4-81 mesoscale convective system, 1-18, 1-57, 1-hybrid methodlogy, 2-68 59, 1-64, 1-91, 1-97, 1-100, 1-111, 1-KAP. See distribution:Kappa 123, 2-101, 2-104, 2-112, 2-150, 3-29, kernel function, 2-56, 3-99, 4-68 3-31, 3-33, 3-42, 3-47, 3-49, 3-52, 3-67, Epanechnikov, EKF, 2-58, 2-65, 3-98 4-133, 4-355 Gaussian, GKF, 1-200, 1-202, 2-58, 2-60, intense rainfall increase, 4-361 3-98, 4-99 precipitation increase, 3-40, 4-368 normal, 2-65 rainfall, 4-360 triangular, 2-65 reduced speed, 4-361 uniform, UKF, 2-60, 2-65, 3-98 simulations, 2-144 land use, 1-24, 2-420 mesoscale storm with embedded convection, urbanization, 2-98 2-381, 3-357, 4-128, 4-135, 4-142, 4-land-atmosphere interactions, 1-57 159, 4-161, 4-218 levee Meta-models, 4-61, 4-206 breach. See breach, dam/levee Meta-Gaussian Distribution, 4-59, 4-64, 4-likelihood, 3-78 69 functions, 1-166 example, 4-67 LIP. See local intense precipitation meteorological L-moment ratio, 2-194, 3-77 inputs, 4-132 diagram, 2-174 model, 1-133, 1-158, 2-311 local intense precipitation, 1-6, 1-17, 1-22, 1- MGD. See Meta-models:Meta-Gaussian 34, 1-54, 1-64, 1-76, 1-88, 1-100, 1-130, Distribution 1-133, 1-144, 1-223, 1-255, 2-34, 2-47, mid-latitude cyclone, 2-382, 4-120, 4-128, 4-2-50, 2-97, 2-101, 2-103, 2-168, 2-175, 133 A-10

Midwest, 4-357, 4-368 NACCS. See North Atlantic Coast floods, 4-363 Comprehensive Study intense snowpack, 4-363 NAO. See Multi-decadal:North Atlantic Region, 3-31 Oscillation MLC. See mid-latitude cyclone National Climate Assessment, 4th, 3-42, 4-model, 1-90 335 alternative conceptual, 4-470 NCA4. See National Climate Assessment, averaging, 2-352 4th dependence, 3-310 NEB. See non-exceeedence bound improved, 1-12, 2-44, 3-16, 4-15 NEUTRINO, 4-291, 4-297, 4-314, See also nested domain, 3-53 smoothed particle hydrodynamics, SPH nested grids, 4-55 NOAA Atlas 14, 1-72, 1-185, 2-158, 2-168, numerical modeling, 1-97, 4-327 2-171, 2-179, 2-181, 2-201, 3-87, 4-127, nested domain, 1-101 4-144 parameter estimation, 2-313 future needs, 2-372 parameters, 4-176 gridded, 1-73 selection, 2-346 tests, 2-373 warm-up, 2-385 non-exceedance bound, 4-229, 4-230, 4-moisture 236, 4-238 maximization, 3-45 nonstatitionarity/nonstationary, 1-37, 1-155, saturation deficit, 1-61 1-162, 1-177, 1-188, 1-191, 3-117, 3-saturation specific humiity profile, 1-58 133, 3-315, 4-264 sources, 1-76 change points, 3-125, 3-127 water vapor, 1-61, 4-347 model, 2-373 Monte Carlo, 1-163, 1-185, 2-77, 2-187, 2- processes, 1-12, 1-55, 2-44, 3-16, 4-15 286, 2-411, 3-23, 3-79, 3-93, 3-94, 3- trends, 3-125, 3-128 199, 4-57, 4-162, 4-175, 4-257, 4-330 North Atlantic Coast Comprehensive Study, analysis, 3-21, 3-111 1-196, 2-53, 3-102, 4-94, 4-99 Integration, 2-70, 3-103 numerical weather models, 1-18, 1-89, 1-95, Life-Cycle Simulation, 2-69, 3-103, 4-64 2-104, 3-44, 3-103, 4-55 Markov Chain, 1-161, 1-171, 2-402 regional, 2-104, 3-45 sampling, 4-201 observations, 1-71 simulation, 2-55, 2-74, 2-81, 2-85, 3-102, based, 3-81 3-111, 3-113, 3-328, 4-59 data, 1-95 MSA. See Fukushima Near Term Task record, 3-121 Force: Mitigating Strategies satellite Assessments combination algorithms, 4-105, 4-108, 4-Multi-decadal 112 Atlantic Meridional Mode (AMM), 4-370, 4- combinations, 4-104 373, 4-376, 4-379 mutli-satellite issues, 4-108 Atlantic Multi-Decadal Oscillation (AMO), operating experience, 1-31, 4-447, 4-473 4-373 data sources, 4-465 El Nino-Southern Oscillation (ENSO), 1- operational event, 4-464 206, 4-370, 4-373, 4-376, 4-379 chronology review, 4-466 North Atlantic Oscillation (NAO), 4-370, 4- orographic precipitation. See precipitation, 374, 4-376, 4-379 orographic Pacific Decadal Oscillation (PDO), 4-354 paleoflood, 1-24, 1-154, 1-181, 2-87, 2-216, persistence, 4-113, 4-354 2-217, 2-225, 2-369, 2-400, 2-407, 2-multivariate Gaussian copula, 3-104, 4-59 416, 3-21, 3-26, 3-116, 3-117, 3-136, 3-MVGC. See multivariate Gaussian copula 140, 3-163, 3-179, 3-181, 3-195, 3-207, A-11

3-325, 3-393, 4-18, 4-208, 4-228, 4-244, high level requirements, 4-459 4-253, 4-259, 4-290 paleoflood based, 4-289 analytical framework, 4-233 results, 4-459 analytical techniques, 4-242 river, 4-207 benchmark, 4-252 statistical case study, 4-234, 4-236 model, 2-84 data, 1-181, 1-186, 2-51, 2-81, 2-206, 2- team, 4-458 219, 3-113, 3-117, 3-120, 3-123, 3-141, PFSS 3-179, 3-333, 3-394, 4-30, 4-215, 4-221, historic water levels, 2-81, 3-111 4-246, 4-269 pilot studies, 3-70, 3-386, 3-404, 4-11, 4-16, database, 3-208, 3-213 4-22, 4-312, 4-440 deposits. See deposits pilot studies, 2-418 event, 3-139 plant response, 1-255, 2-20, 2-289, 2-291, 3-hydrology, 2-229, 3-164, 4-247 261, 3-398, 4-20 ice jams, 4-235 model, 1-260, 3-377 indicators, 3-181 proof of concept, 1-255 interpretation, 3-394 scenarios, 1-260 reconnaissance, 2-235, 3-168, 4-233, 4- simulation, 1-22 237 state-based PRA, 1-260 record length, 4-247 total, 1-253, 2-304, 2-415 screening, 4-242 PMF, 1-150, 2-25, 2-80, 2-202, 2-205, 2-400, studies, 3-333 3-21, 3-141, 3-149, 3-266, 3-355, 3-390, humid environment, 2-228, 3-163 4-230, 4-454, 4-474 suitability, 2-235, 3-167, 3-394 PMP, 1-50, 1-56, 1-66, 1-69, 1-73, 2-25, 2-terrace, 4-236, 4-242 153, 2-168, 2-169, 2-179, 2-405, 3-69, viability, 4-234 3-149, 3-391, 4-114, 4-117, 4-120, 4-partial-duration series, 1-165, 2-201, 2-373 158, 4-160, 4-383 PCHA. See Probabilistic Coastal Hazard State SSPMP Studies, 3-338 Assessment traditional manual approaches, 2-104 PDF. See probability density function PRA, 1-11, 1-42, 1-256, 2-24, 2-28, 2-43, 2-PDO. See Multi-decadal:Pacific Decadal 79, 2-168, 2-179, 2-202, 2-216, 2-268, Oscillation 2-287, 2-289, 2-337, 2-370, 2-401, 2-PDS. See partial-duration series 417, 2-421, 3-1, 3-13, 3-21, 3-25, 3-199, PFA. See precipitation frequency: analysis 3-259, 3-266, 3-315, 3-365, 3-368, 3-PFHA, 1-257, 2-79, 2-218, 3-307, 3-353, 4- 386, 3-390, 3-396, 3-405, 4-14, 4-264, 10, 4-453, 4-477 4-312, 4-323, 4-385, 4-391, 4-403, 4-case study, 2-380 429, 4-461, 4-462, 4-463, 4-469, 4-471, combining hazards, 4-207 4-474 documentation, 4-460 bounding analysis, 4-468 framework, xxxviii, 1-12, 1-16, 1-148, 1- dams, 1-24 157, 1-163, 1-166, 1-175, 2-44, 2-46, 2- dynamic, 1-22 307, 2-311, 2-322, 2-338, 2-345, 2-353, external flood. See XFPRA 2-401, 3-16, 3-18, 3-304, 3-359, 3-398, initiating event frequency, 1-47, 2-79 4-11, 4-15, 4-19, 4-455 inputs, 1-132 aleatory, 1-163 insights, 4-476 peer review, 2-87 internal flooding, 3-262, 4-440 regional analysis, 2-342, 2-348 LOOP, 4-469, 4-474 riverine, 1-16, 2-46, 2-308, 2-312, 2-413, peer review, 4-461 3-18 performance-based approach, 4-451 site-specific, 2-309 plant fragility curve, 4-476 hierarchical approach, 4-458 quantitative insights, 4-464 A-12

recovery times, 4-469 4-146, 4-158, 4-161, 4-218, 4-228, 4-risk 282, 4-290, 4-312, 4-315 information, 4-464 analysis, 1-66, 1-73, 1-175, 3-74, 4-128, 4-insights, 4-478 138 safety challenge indications, 4-465 curve, 3-75 Standard, 3-377 estimates, 4-144 precipitation, 1-11, 1-53, 1-64, 1-160, 1-267, exceedance, 2-95 2-88, 2-168, 2-179, 2-181, 2-201, 2-226, large watershed, 3-359 2-260, 2-270, 2-288, 2-307, 2-353, 2- regional analysis, 4-133 369, 2-381, 2-402, 3-15, 3-27, 3-31, 3- relationship, 1-67, 1-85, 1-87, 3-73, 4-129 38, 3-40, 3-42, 3-52, 3-56, 3-67, 3-115, precipitation, orographic 3-134, 3-136, 3-150, 3-162, 3-198, 3- linear model, 1-86 248, 4-11, 4-14, 4-56, 4-100, 4-113, 4- methodology, 1-66 127, 4-144, 4-158, 4-210, 4-218, 4-228, regions, 1-17, 1-65, 2-153, 2-156, 2-167, 4-315, 4-326, 4-335, 4-353, 4-359, 4- 2-414, 3-72, 3-398, 4-18 380 pressure setup, 4-36, 4-37 classification, 2-105, 3-45 Probabilistic Coastal Hazard Assessment, 3-cool season, 3-307 328 distribution, 3-363, 4-114 Probabilistic Flood Hazard Assessment. See duration, 2-155, 2-179, 3-74 PFHA field area ratio, 3-48 Probabilistic Risk Assessment. See PRA gridded, 2-161, 3-81 probabilistic safety assessments, 4-472, 4-historical analysis, 1-19 474 increases, 3-40, 4-359, 4-364, 4-368 probabilistic seismic hazard assessment, 1-instrumentation, 4-102 30, 2-58, 3-94, 4-57, 4-59, 4-477 modeling framework, 3-46 probabilistic storm surge hazard near-record spring, 3-37 assessment, 2-53, 2-78, 4-81 numerical modeling, 1-17 probability density function, 1-57, 1-133, 1-patterns, 4-120, 4-140 152, 1-163, 1-164, 1-201, 2-79, 2-85, 3-point, 2-382, 2-417, 3-359, 4-18, 4-101, 4- 113, 4-205, 4-207, 4-316 146 probable maximum flood. See PMF processes, 1-90 probable maximum preciptiationrecipitation.

quantile, 3-74 See PMP regional models, 4-117 PSHA. See probabilistic seismic hazard seasonality, 1-72, 2-171, 2-382, 3-32 assessment simulation, 1-89, 2-103, 3-48 PSSHA. See probabilistic storm surge warm season, 2-340, 3-33, 3-38 hazard assessment precipitation data, 3-156, 4-147 rainfall. See precipitation/rainfall fields, 1-125 rainfall-runoff, 4-210 gage, 1-79, 2-156, 3-83, 4-117 methods, 1-15, 2-46, 3-18 geo0IR, 4-102 model, 1-11, 1-152, 1-157, 1-183, 2-211, Liveneh, 3-308, 4-119, 4-143 2-384, 2-386, 2-398, 3-15, 3-143, 4-14, microwave imagers, 4-102 4-134, 4-217 observed, 1-96, 1-181, 2-154, 3-48, 3-140 Austrailian Rainfall and Runoff Model, 1-regional, 1-181 70, 1-73, 1-150, 1-185, 2-212 satellite, 4-101, 4-104, 4-112 SEFM, 1-151, 2-213, 2-216, 3-23, 3-28, precipitation frequency, 1-19, 1-64, 1-185, 2- 3-149, 4-276, 4-316, 4-329 151, 2-154, 2-168, 2-181, 2-211, 2-270, stochastic, 1-151 2-372, 3-70, 3-72, 3-81, 3-150, 3-198, 3- stochastic, HEC-WAT, 3-334 224, 4-119, 4-127, 4-132, 4-141, 4-144, VIC, 4-119, 4-369 A-13

reanalysis, 2-56, 2-151, 4-114, 4-122, 4-125, screening, 4-124, 4-233, 4-268, 4-471, 4-4-143, 4-160, 4-269 473, 4-477 Climate Forecast System Reanalysis external flood hazard, 4-31 (CFSR), 1-95, 2-102, 2-113, 2-150, 3- Farmer, 1967, 4-477 47, 4-118 flood, 4-456 PRISM, 4-117, 4-163, 4-370 hazard, 2-82 Stage IV, 1-96, 1-100, 2-113 methods, 4-328 record length non-conservative, 4-477 effective, 3-126 Probabilistic Flood Hazard Assessment, 3-equivalent independent, ERIL, 2-175 369 equivalent, ERL, 4-159, 4-221, 4-230 SDP, 1-10, 1-41, 1-51, 1-248, 2-28, 2-42, 2-historical, 2-66 180, 3-12, 3-116, 3-149, 3-325 period of record, 2-53, 2-151, 2-373, 3-70, floods, 2-30 3-83, 3-136, 4-113 Seals, 1-44 regional growth curve, RGC, 1-77, 1-80, 1- sea level rise, 1-53, 2-89, 2-97, 4-86, 4-92, 4-84, 2-151, 2-155, 2-166, 3-75, 3-85, 3- 355, 4-381 89, 3-91 nuisance tidal floods, 2-93 uncertainty, 1-82 projections, 2-100 regional L-moments method, 1-71, 1-73, 1- SLR, 1-57 87, 1-185, 2-151, 2-154, 2-159, 2-161, sea surface temperature, SST, 4-370, 4-373 2-165, 2-167, 2-174, 2-179, 2-187, 2- anomalies, 4-374, 4-377, 4-378 201, 2-404, 3-70, 3-72, 3-77, 3-85, 3-93, SEFM. See rainfall-runoff:model:SEFM 3-143, 3-387, 4-127, 4-332 seiche, 1-6, 2-52, 2-409, 3-395, 4-318, 4-455 regional precipitation frequency analysis, 2- seismic, 1-6, 4-451 151, 2-154, 2-167, 3-70, 3-71, 3-72, 3- self-organizing maps, SOM, 1-77, 2-151, 2-75, 3-93, 3-144, 3-334, 4-218 157, 2-167, 3-70, 3-83, 3-93 reservoir, 4-170 Senior Seismic Hazard Assessment operational simulation, 4-279 Committee. See SSHAC rule-based model, 4-281 sensitivity, 4-76 system, 4-287 analysis, 4-326 RFA. See regional precipitation frequency analysis ranking, 4-200 analysis quantification, 4-476 RIDM. See Risk-Informed Decision-Making to hazard, 4-476 risk, 1-39, 1-50, 2-20, 2-154, 2-340, 2-380, 3- SHAC-F, 1-16, 1-64, 1-130, 2-46, 2-353, 3-21, 3-138, 4-166 18, 3-314, 3-325, 3-388, 4-264, 4-290, analysis, 1-51, 1-177, 2-203, 2-205, 2-401, 4-311 3-136, 3-149, 3-197, 3-217, 3-361, 4- Alternative Models, 1-142, 4-266 175, 4-462 coastal, 2-419, 3-403, 4-19 assessment, 4-92, 4-196, 4-233, 4-473 framework, 1-132, 1-133 computational analysis, 3-378 highly site specific, 3-319 qualitative information, 3-385 key roles, 2-360 risk informed, 1-6, 1-10, 1-29, 1-40, 1-149, 2- Levels, 4-268, 4-269, 4-271 42, 2-182, 2-392, 3-12, 3-151, 3-202, 4- LIDAR data, 4-271 10, 4-14, 4-129, 4-322, 4-451 LIP, 1-138, 1-142, 4-19 approaches, 2-26 LIP Project Structure Workflow, 3-318 oversight, 2-28 participatory peer review, 4-266 use of paleoflood data, 2-51 project structure, 2-360 Risk-Informed Decision-Making, 1-151, 2-24, LIP, 2-363 2-246, 2-288, 3-135, 3-198, 3-332, 3- riverine, 2-367, 3-323 337, 4-127, 4-210, 4-229, 4-279, 4-323, redefined levels, 3-322, 3-324 4-330 riverine, 2-366, 4-19 A-14

site-specific, 3-324 approach, 3-332 Work Plan, 1-135 inputs, 4-119 significance determination process. See SDP storm parameters, 4-74 skew simulation, 3-103, 3-328, 4-279, 4-281, 4-at-site, 4-214 320 regional, 4-214 storm generation, 4-140 SLOSH, Sea Lake and Overland Surges storm template, 3-145 from Hurricanes, 4-38 storm transposition, SST, 4-120 smoothed particle hydrodynamics, SPH, 1- weather generation, 3-334 263, 3-25, 3-378, 4-291, 4-296, See also Stochastic Event-Based Rainfall-Runoff NEUTRINO Model. See rainfall-runoff:model:SEFM validation, 4-306 storm snowmelt, 1-133, 2-340, 3-307, 4-217 local scale, 4-133 energy balance, 2-376 maximization, 4-120 extreme snowfall, 1-60 parameters, 4-41 flood, 1-183 patterns, 3-144, 3-364, 4-120, 4-257, 4-rain on snow, 2-97 276, 4-286, 4-332 site, 3-308 precipitation templates, 2-383 snow water equivalent, SWE, 3-306, 4- seasonality, 4-134, 4-331 224, 4-332 synoptic scale, 4-133 snowpack increased, 3-37 storm recurrence rate. See SRR VIC, snow algrorithm, 3-308 storm surge, 1-6, 1-17, 1-35, 1-57, 1-192, 1-soil moisture, 3-40 193, 2-34, 2-47, 2-53, 2-78, 2-87, 2-97, reduction, 1-57 2-259, 2-288, 2-322, 2-337, 2-369, 2-space for time, 1-77, 2-207 411, 3-19, 3-22, 3-24, 3-26, 3-29, 3-94, spillway. See erosion: spillway 3-109, 3-110, 3-112, 3-115, 3-198, 3-SRR, 1-196, 1-202, 2-57, 2-59, 3-96, 4-60, 4- 229, 3-328, 3-361, 3-364, 3-396, 4-25, 70, 4-86 4-30, 4-34, 4-35, 4-57, 4-70, 4-73, 4-81, models, 2-58, 3-98, 3-99 4-93, 4-228, 4-259, 4-295, 4-311, 4-317, rate models, 2-60 4-355, 4-382, 4-451, 4-455 sensitivity, 4-88 case study, 2-84 variability, 2-59 data partition, 4-70 SSCs, xxxviii, 1-152, 1-260, 1-265, 2-288, 2- deterministic, 2-331 307, 2-309, 2-353, 3-198, 3-262, 3-264, wind-generated wave and runup, 2-333 4-264, 4-429, 4-435, 4-440, 4-445 hazard, 2-54, 2-55, 4-84 flood significant components, FSC, 4-387 hurricane driven, 3-394 fragility, 3-371, 3-381, 4-32 model, 1-194, 4-75 safety, 4-472 numerical surge simulation, 3-105 SSHAC, 1-30, 1-64, 1-132, 2-85, 2-354, 3- PCHA Studies, 2-379 317, 4-93, 4-229, 4-264, 4-274, 4-313 probabilistic approaches, 2-50 Project Workflow, 3-321 Probabilistic Flood Hazard Assessment, 2-state-of-practice, 1-176, 4-61, 4-321, 4-444, 407, 3-393, 4-24 4-447 probabilistic model, 3-97, 4-60 statistical approaches, 1-179, 4-320 P-Surge model, 4-53 copula-based methods, 4-320 tidal height, 3-111 extreme value analysis, 4-320 total water level, 2-86 statistical models, 4-268, 4-269 uncertainty, 3-398, 4-19 streamflow based, 1-15, 2-46, 3-18 storm transposition, 2-81, 2-377, 3-21, 3-47, stochastic, 1-185, 1-257, 3-143 3-54, 3-357, 4-133, 4-281 flood modeling, 4-129, 4-132 storm typing, 2-381, 3-334, 3-356, 4-119, 4-model, 3-100, 4-458 133, 4-138, 4-217, 4-282, 4-286 A-15

large winter frontal storms, MLC, 3-357 3-67, 3-99, 3-101, 3-193, 4-14, 4-35, 4-scaling and placement, 3-359 51, 4-57, 4-61, 4-68, 4-73, 4-98, 4-125, seperation, 3-359 4-138, 4-346, 4-355, 4-370, 4-380 summer thunderstorm complexes, MEC, parameters, 2-65 3-357 P-Surge, 4-49 tropical storm remants variable cross track, 4-51 TSR, 3-357, 4-134 tropical storm remnant, 3-357 stratified sampling, 4-282 TSR, 2-382, 4-127 stratiform tsunami, 1-6, 2-52, 2-409, 2-420, 3-395, 4-leading, 1-93, 1-94 318, 4-455 parallel, 1-93, 1-94 model, 1-25 trailing, 1-93, 1-94 uncertainty, 1-36, 1-72, 1-125, 1-148, 1-167, stratigraphy, 3-163, 3-183, 3-199, 3-200, 3- 1-178, 1-187, 1-197, 2-30, 2-53, 2-74, 2-234, 4-18, 4-250 78, 2-87, 2-152, 2-165, 2-177, 2-179, 2-analysis, 2-227 187, 2-219, 2-270, 2-320, 2-338, 2-340, record, 4-251 2-377, 2-400, 2-403, 3-21, 3-29, 3-40, 3-streamflow 67, 3-71, 3-90, 3-94, 3-105, 3-119, 3-data, 3-157 126, 3-136, 3-138, 3-149, 3-163, 3-194, gage regional data, 1-181 3-202, 3-246, 3-304, 3-315, 3-326, 3-historical, 3-38 334, 3-389, 4-30, 4-34, 4-35, 4-57, 4-81, Structured Hazard Assessment Committee 4-88, 4-95, 4-114, 4-163, 4-196, 4-197, Process for Flooding. See SHAC-F 4-207, 4-228, 4-244, 4-254, 4-256, 4-structures, systems, and components. See 264, 4-275, 4-282, 4-291, 4-313, 4-355, SSCs 4-381, 4-426, 4-450, 4-462, 4-477 synoptic storms, 1-91, 2-105, 3-45 analytical, 4-242 synthetic Bayesian, 1-86 datasets, 2-62, 4-269 bounds, 1-89 storm, 2-67, 2-81, 2-386, 3-21, 3-96, 3- discretized, 4-64 102, 4-60, 4-62, 4-70, 4-78, 4-279, 4- distribution choice, 2-187, 2-193, 2-197, 3-282 70 storm simulations sets, 2-73 full, 1-15, 2-45, 3-17 storms, 2-57 hazard curve evaluation, 2-317 systematic data hydrologic, 2-99, 3-338, 4-233 gage record, 1-177, 2-206, 3-119, 3-123, integration results, 2-76 3-130, 3-183, 4-252 joint probability analysist, 2-47, 3-19 TC. See tropical cyclone knowledge, 2-356, 3-317, 4-175, 4-233 TELEMAC. See 2D:model:TELEMAC PRA, 3-373 temperature, 1-53 reduced, 2-219, 3-357 change, 2-91 SLR projections, 2-100 high, 1-57 sources, 1-42 profiles, 4-122 SRR, 2-60 trends, 4-357 storm surge, 1-17, 1-193, 2-47, 2-54, 3-19, Tennessee River 3-95, 4-58 Valley, 2-153, 2-156, 3-83, 3-182 temporal, 1-257 Watershed, 4-246 tolerance, 4-215 TRMM,Tropical Rainfall Measuring Mission, uncertainty analysis, 2-87, 4-326, 4-476 4-100, 4-111 UA, 4-198 tropical cyclone, 1-11, 1-17, 1-64, 1-67, 1-91, uncertainty characterization, 1-15, 2-46, 2-1-100, 1-123, 1-194, 1-198, 1-204, 2-53, 74, 2-81, 2-341, 3-18, 3-105, 4-233 2-55, 2-59, 2-71, 2-89, 2-95, 2-101, 2-105, 2-112, 3-15, 3-29, 3-42, 3-47, 3-53, A-16

uncertainty propagation, 1-83, 1-87, 1-193, XFEL. See external flood equipment list 2-54, 2-58, 2-73, 2-398, 3-15, 3-95, 3- XFOAL. See external flood operator actions 102, 3-106, 4-14, 4-58, 4-60, 4-200 list uncertainty quantification, 1-161, 1-193, 1- XFPRA, 3-259, 3-370, 3-372, 3-377, 3-379, 200, 2-54, 2-189, 2-206, 2-420, 3-95, 4- 3-384, 3-402, 4-429, 4-441, 4-475, 4-30, 4-58, 4-60, 4-71, 4-206, 4-215, 4- 479 298 capability categories, 4-443 input parameter, 4-201 documentation, 4-438 river flood models, 3-404 flood event oriented review, 4-467 sources, 4-205, 4-327 flood progression, 4-433 uncertainty, aleatory, 1-12, 1-42, 2-43, 2-57, fragility, 4-30, 4-444, 4-445 2-192, 2-313, 3-15, 3-96, 3-106, 4-15, 4- guidance development, 4-27 60, 4-79, 4-267, 4-268, 4-269, 4-271 hazard analysis, 4-444, 4-445 natural variability, 4-86, 4-175 HRA, 3-265, 3-374 variability, 1-194, 2-54, 4-458 initial plant state, 3-379, 3-382 uncertainty, epistemic, 1-12, 1-42, 1-163, 1- initiating event, 4-446 194, 1-197, 1-202, 2-43, 2-54, 2-57, 2- key flood parameters, 4-433 62, 2-193, 2-313, 3-15, 3-93, 3-96, 3-98, multiple end states, 3-382 3-106, 4-15, 4-57, 4-71, 4-79, 4-81, 4- operating experience, 3-371 86, 4-92, 4-267, 4-458, 4-475 period of inundation, 4-433 knowledge, 4-86 period of recession, 4-433 SRR models, 4-68 physical margin assessment, 4-435 validation, 1-90, 1-95, 1-125, 2-312, 3-48, 4- pilots, 3-371 62, 4-76, 4-293, 4-298 plant response, 3-373, 4-444 warming, 1-60, 4-337, 4-368 preferred equipment position, 3-264 increased rates, 4-357 propagation pathways, 4-433 increased saturation water vapor, 4-346 requirements, 4-443 surface, 3-34 scenarios, 3-265, 3-373, 3-385, 4-433, 4-warning, 2-259, 3-362, 4-35, 4-314, 4-479 446, 4-464 time, 1-34, 1-153, 3-261, 3-371, 4-450 screening, 4-445 triggers and cues, 3-382, 4-473, 4-479 sources, 4-433 watershed, 1-157, 3-56 uncertainty, 3-385 model, 1-158 vulnerabilities, 3-265, 4-473 Watershed Level Risk Analysis, 4-166 walkdown, 2-51, 3-26, 3-260, 3-393, 3-wave, 4-295 395, 4-26, 4-437, 4-440, 4-445, 4-475 impacts, 4-299 walkdown guidance, 2-408, 3-259, 4-440 physical modeling, 4-300 warning time, 4-433 setup, 4-36 wind, 1-53 setup, 4-36 stress formulation, 4-76 tornado frequency increasing, 2-92 locations, 2-92 warning, 2-259 waves, 1-11 WRF, Weather Research and Forecasting model, 1-18, 1-85, 1-90, 1-95, 1-97, 1-185, 2-102, 2-114, 3-28, 3-42, 3-47, 3-52, 3-69, 4-160 parameterization, 1-123, 2-114, 3-47 A-17

APPENDIX B: INDEX OF CONTRIBUTORS This index includes authors, co-authors, panelists, poster authors and self-identified participants from the audience who spoke in question and answer or panel discussions.

Adams, Lea, 4-162 Craven, Owen, 3-5, 3-195, 3-209 Ahn, Hosung, 5-490 Cummings, William (Mark), 2-256, 3-227, 4-Aird, Thomas, 2-38, 2-407, 3-11, 3-195, 3- 386, 4-419, 4-420, 4-421, 4-422 380, 4-12, 4-378, 4-419, 5-490 Dalton, Angela, 1-220, 2-267, 3-240 Al Kajbaf, Azin, 4-312 Daoued, A. Ben, 4-315 Allen, Blake, 4-323 Davis, Lisa, 3-5, 3-179, 3-195, 3-209 Anderson, Victoria, 3-354, 3-370, 3-374 DeNeale, Scott, 3-197, 3-198, 3-213, 3-219, Andre, M.A., 4-287 4-111, 4-142, 4-312, 4-315, 4-320 Archfield, Stacey A., 4-206 Denis, Suzanne, 4-464, 4-467, 4-468, 4-469, Asquith, William, 2-184 4-472, 4-473 Bacchi, Vito, 4-195, 4-320 Dib, Alain, 3-42 Baecher, Gregory, 3-197, 3-213, 4-315 Dinh, N., 4-287 Bardet, Philippe M., 4-287, 4-306, 4-309 Dong, John, 4-323 Barker, Bruce, 4-323 DuLuc, Claire-Marie, 2-391, 4-195, 4-252, 4-Bellini, Joe, 2-30 253 Bender, Chris, 4-91, 4-92, 4-94, 4-97 Dunn, Christopher, 2-370, 2-398, 4-162 Bensi, Michelle, 1-24, 4-312, 4-435, 4-464, England, John, 2-370, 2-396, 2-400, 2-401, 4-465, 4-466, 4-469, 4-471, 4-473, 5- 3-68, 3-319, 3-347, 3-348, 3-349, 3-372, 490 3-373, 4-112, 4-156, 4-157, 4-159, 4-Bertrand, Nathalie, 4-195, 4-320 160, 4-161, 4-206, 4-252, 4-253, 4-254, Bittner, Alvah, 1-220, 2-267, 3-240 4-255, 4-256, 4-258, 4-259, 4-260, 4-Blackaby, Emily, 3-5, 3-195, 3-209 307, 4-311, 4-363 Bowles, David, 2-396, 3-40 Fearon, Kenneth, 3-322, 3-347, 3-372 Branch, Kristi, 1-220, 2-267, 3-240 Ferrante, Fernando, 3-315, 3-351, 3-370, 3-Breithaupt, Steve, 3-346, 5-490 372 Bryce, Robert, 1-129, 2-349 Fuhrmann, Mark, 2-38, 2-407, 3-11, 3-163, Byrd, Aaron, 1-166 3-375, 3-380, 4-12, 4-162, 4-252, 5-490 Caldwell, Jason, 4-112, 4-323 Furstenau, Raymond, 4-1, 4-9, 5-490 Campbell, Andrew, 2-12, 4-375, 4-422, 4- Gage, Matthew, 3-209 455, 4-470, 4-473, 5-490 Gaudron, Jeremy, 4-464, 4-465, 4-467, 4-Carney, Shaun, 3-346, 4-272, 4-306, 4-307, 472 4-308, 4-310 Gifford, Ian, 4-456, 4-464, 4-467 Carr, Meredith, 2-38, 2-407, 3-9, 3-11, 3-380, Godaire, Jeanne, 3-195, 3-205 4-9, 4-12, 4-162, 4-252, 4-311, 4-456, 4- Gonzalez, Victor M., 1-190, 2-50, 3-94, 3-472, 4-474, 5-490 198, 3-223, 3-316, 3-347, 3-348, 3-349, Charkas, Hasan, 5-490 3-350, 4-56, 4-91, 4-95, 4-97 Cheok, Michael, 5-490 Gupta, A., 4-287 Cohn, Timothy, 1-174, 4-250 Hall, Brian, 4-227 Coles, Garill, 1-220, 2-267, 3-240 Hamburger, Kenneth, 5-490 Cook, Christopher, 1-24, 3-351, 3-374, 5-490 Hamdi. Y, 4-315 Coppersmith, Kevin, 1-129, 2-349, 3-304, 4- Han, Kun-Yeun, 4-328 261 Correia, Richard, 1-5, 5-490 B-1

Harden, Tessa, 2-224, 3-163, 3-194, 3-199, Miller, Andrew, 4-423, 4-464, 4-467, 4-468, 3-226, 4-242, 4-243, 4-252, 4-253, 4- 4-469, 4-471, 4-472, 4-474 255, 4-256, 4-258 Miller, Gabriel, 3-339, 3-345, 3-346 Hartford, Des, 4-470 Mitman, Jeffrey, 1-36 Hockaday, William, 3-5, 3-195, 3-209 Mohammadi, Somayeh, 4-312 Holman, Katie, 1-63, 2-148, 3-70 Molod, Andrea, 4-364 Huffman, George J., 4-98, 4-156, 4-158, 4- Montanari, N, 4-287 160, 4-161 Mouhous-Voyneau, N., 4-315 Ishida, Kei, 1-86, 2-98 Mure-Ravaud, Mathieu, 1-86, 2-98, 3-42 Jasim-Hanif, Sharon, 3-335, 3-348 Muto, Matthew, 4-323 Jawdy, Curt, 2-375, 2-396, 2-400, 4-272 Nadal-Caraballo, Norberto, 1-190, 2-50, 2-Kanney, Joseph, 1-7, 2-38, 2-266, 2-367, 2- 370, 2-399, 3-94, 3-198, 3-223, 3-316, 407, 3-11, 3-94, 3-193, 3-316, 3-348, 3- 4-56, 4-91, 4-94, 4-95, 4-96, 4-97 349, 3-369, 3-380, 4-12, 4-33, 4-91, 4- Nakoski, John, 4-1, 4-28 242, 4-256, 4-306, 4-307, 4-309, 4-310, Neff, Keil, 2-199, 3-135 4-329, 4-363, 4-374, 4-421, 4-423, 4- Nicholson, Thomas, 3-347, 3-349, 3-369, 4-455, 4-456, 4-464, 4-465, 4-473, 5-490 261, 4-306, 5-490 Kao, Shih-Chieh, 3-197, 3-198, 3-213, 3-219, Novembre, Nicole, 4-323 4-111, 4-142, 4-156, 4-157, 4-160, 4- OConnor, Jim, 2-224, 3-163, 4-242, 4-243 312, 4-320 Ott, William, 1-5, 5-490 Kappel, Bill, 3-41, 3-69 Pawson, Steven, 4-364 Kavvas, M. Levent, 1-86, 2-98, 3-42, 3-69 Pearce, Justin, 4-227 Keeney, David, 1-63, 2-148, 3-70 Perica, Sanja, 2-367, 2-399, 2-400 Keith, Mackenzie, 3-163, 4-243 Pheulpin, Lucie, 4-195, 4-320 Kelson, Keith, 3-192, 4-208, 4-227, 4-252, 4- Philip, Jacob, 1-261, 2-38, 2-407, 3-11, 3-253, 4-255, 4-256, 4-257, 4-259 380, 4-12, 4-419, 4-421, 4-422, 5-490 Kiang, Julie, 2-184, 3-116 Pimentel, Frances, 3-354 Kim, Beomjin, 4-328 Prasad, Rajiv, 1-50, 1-129, 1-147, 1-220, 2-Kim, Minkyu, 4-328 85, 2-303, 2-349, 2-365, 3-29, 3-192, 3-Klinger, Ralph, 3-195, 3-205 193, 3-240, 3-304, 3-315, 4-261, 4-306, Kohn, Nancy, 1-220 4-307, 4-349, 4-363 Kolars, Kelsey, 3-116 Prasad, Rajiv, 2-267 Kovach, Robin, 4-364 Prescott, Steven, 2-284, 3-194, 3-199, 3-Kunkel, Kenneth, 4-329, 4-376, 4-378 223, 4-287 Kvarfordt, Kellie, 1-238, 2-177, 3-149 Quinlan, Kevin, 4-156, 4-162, 4-374, 4-377, Lehman, Will, 4-162, 4-252, 4-253, 4-254, 4- 5-490 255, 4-257, 4-258, 4-260, 4-306, 4-307, Ramos-Santiago, Efrain, 3-198, 3-223 4-308, 4-309, 4-311 Randelovic, Marko, 4-23, 4-72, 4-384, 4-386, Leone, David, 4-80 4-423, 5-490 Leung, Ruby, 1-50, 2-85, 3-29, 3-115, 4-349, Randelovic, Marko, 4-378 4-363, 4-374, 4-375 Rebour, Vincent, 2-391, 2-399, 4-195 Lim, Young-Kwon, 4-364, 4-374 Reisi-Fard, Mehdi, 2-22, 3-227, 5-490 Lin, L., 4-287 Ryan. E., 4-287 Littlejohn, Jennene, 5-490 Ryberg, Karen, 3-116, 3-192, 3-194 Lombardi, Rachel, 3-209 Salisbury, Michael, 4-72, 4-91, 4-96 Ma, Zhegang, 1-250, 2-284, 3-199, 3-223, 3- Salley, MarkHenry, 5-490 360 Sampath, Ramprasad, 2-284, 3-199, 3-223, Mahoney, Kelly, 3-68, 3-69 4-287 McCann, Marty, 3-40, 3-388 Schaefer, Mel, 4-114, 4-117, 4-125, 4-156, Melby, Jeffrey, 1-190, 2-50 4-158, 4-159, 4-160, 4-161, 4-286 Meyer, Philip, 1-129, 2-303, 4-261 B-2

Schneider, Ray, 2-30, 3-350, 3-362, 3-371, Therrell, Matthew, 3-209 4-374, 4-375, 4-377, 4-378, 4-384, 4- Tiruneh, Nebiyu, 3-116, 5-490 385, 4-386, 4-419, 4-446, 4-464, 4-466, Vail, Lance, 1-50, 1-129, 2-85 4-469, 4-471, 4-472 Verdin, Andrew, 2-148, 3-70 Schubert, Sigfried, 4-364 Vuyovich, Carrie, 3-295 Sergent, P., 4-315 Wahl, Tony, 1-206, 3-258, 4-398, 4-419 Shaun Carney, 4-310 Wang, Bin, 4-80, 4-91, 4-94, 4-96, 4-97 Siu, Nathan, 3-257, 3-367, 3-369, 3-370, 3- Wang, Zeechung (Gary), 4-456 372, 4-456 Ward, Katie, 4-323 Skahill, Brian, 1-166, 2-334, 2-396, 2-397, 2- Watson, David, 3-197, 3-213, 4-111, 4-320 399, 2-400, 3-195, 3-200, 3-295, 4-206 Weber, Mike, 2-1, 2-7, 3-1, 3-9, 5-490 Smith, Brennan, 3-197, 3-213 Weglian, John, 2-46, 2-75, 2-165, 2-213, 2-Smith, Curtis, 1-238, 1-250, 2-177, 2-284, 2- 243, 2-318, 2-402, 3-20, 3-109, 3-191, 387, 2-397, 2-398, 3-149, 3-199, 3-223 3-192, 3-193, 3-234, 3-250, 3-295, 3-Stapleton, Daniel, 4-80 357, 3-369, 3-370, 3-373, 3-374, 3-375, Stewart, Kevin, 4-315 5-490 Stewart, Lance, 3-5, 3-195, 3-209 Wille, Kurt, 3-195, 3-205 Stinchcomb, Gary, 3-5, 3-179, 3-195, 3-209 Wright, Joseph, 1-174, 2-199, 3-135, 3-345, Taflanidis, Alexandros, 4-56 3-346, 3-347, 3-372, 3-373 Taylor, Arthur, 4-33, 4-91, 4-93, 4-95, 4-96, Yegorova, Elena, 2-38, 2-407, 3-11, 3-29, 3-4-97 380, 4-12, 4-98, 4-156, 5-490 Taylor, Scott, 2-267, 3-240 Ziebell, David, 2-243, 3-234 Thaggard, Mark, 5-490

SUMMARY

AND CONCLUSIONS B-3

APPENDIX C: INDEX OF PARTICIPATING AGENCIES AND ORGANIZATIONS AECOM, 4-485, 4-486 Department of Energy, xv, 2-6, 2-387, 3-7, 3-Agricultural Research Service - USDA, xxxiv 335, 3-394, 3-395, 4-483 ARS, xxxi, xxxiv DOE, x, xv, xvii, xxii, xxvi, 2-397, 2-398, 3-Alden Research Laboratory, 3-393, 4-480 348, 4-306, 4-309, 4-454, 4-481 Amec Foster Wheeler, 2-419, 3-392 Department of Health and Human Services, American Polywater Corporation, 4-479, 4- 3-392 484 Department of Homeland Security, 3-394, 3-Appendix R Solutions, Inc., 3-391 396 Applied Weather Associates, 3-41, 3-345, 3- Dewberry, 2-424, 3-397, 4-480, 4-485, 4-486 394, 4-481, 4-482 Dominion Energy, 4-486 Aterra Solutions, 2-3, 2-30, 2-419, 2-422, 3- Duke Energy, 2-422, 2-424, 3-395, 3-398, 4-391, 4-478, 4-483 487 Atkins, 2-420, 3-392, 4-2, 4-3, 4-72, 4-91, 4- Electric Power Research Institute, iii, xvi, 2-1, 479, 4-485 2-425, 3-393, 4-1, 4-479 Battelle, Columbus, Ohio, 1-220, 2-5, 2-267, EPRI, iii, xvi, xxi, xxxii, xxxvii, 2-1, 2-3, 2-4, 3-6, 3-240, 3-395, 4-482 2-5, 2-6, 2-37, 2-46, 2-75, 2-165, 2-213, BCO, 1-4, 1-220 2-223, 2-243, 2-318, 2-333, 2-402, 2-Baylor University, 3-5, 3-195, 3-209 407, 2-421, 3-1, 3-3, 3-4, 3-6, 3-7, 3-20, BC Hydro, 4-481 3-27, 3-28, 3-109, 3-115, 3-191, 3-193, Bechtel Corporation, 3-396, 3-397, 4-478, 4- 3-234, 3-238, 3-250, 3-257, 3-295, 3-482, 4-483, 4-485, 4-486 315, 3-351, 3-357, 3-369, 3-370, 3-372, Bittner and Associates, 2-5, 2-267, 2-419, 3- 3-374, 3-375, 3-392, 3-398, 4-2, 4-7, 4-6, 3-240 8, 4-23, 4-72, 4-378, 4-379, 4-384, 4-B&A, xii, 1-4, 1-220 423, 4-462, 4-484, 5-490 Booz Allen Hamilton, 4-481 Électricité de France, xvi, xxxiii, 2-262, 3-232 Brava Engineering, Inc., 4-6, 4-323 EDF, xvi, 3-232, 3-233, 4-8, 4-226, 4-384, Canadian Nuclear Safety Commission, xiii, 4-385, 4-434, 4-464, 4-465, 4-477, 4-3-394, 4-482 481 Center for Nuclear Waste Regulatory Enercon Services, Inc., 2-422, 4-480 Analyses Engineer Research and Development SwRI, 3-392, 3-398 Center, xvi, 2-3, 2-6, 2-50, 2-334, 2-421, Centroid PIC, 2-5, 2-284, 3-5, 3-199, 3-223, 2-423, 2-424, 3-5, 3-6, 3-7, 3-94, 3-195, 4-5, 4-287 3-198, 3-200, 3-223, 3-295, 3-316, 3-Cerema, 4-6 393, 4-56 Coastal and Hydraulics Laboratory, xiii, 2-3, ERDC, xvi, 3-94, 4-56, 4-478, 4-480, 4-2-6, 2-50, 2-334, 2-421, 2-423, 2-424, 3- 483, 4-484 4, 3-5, 3-94, 3-195, 3-198, 3-223, 3-393, Environment Canada and Climate Change, 3-395, 3-397, 4-2, 4-3, 4-4, 4-56, 4-91, 4-483 4-206 Environmental Protection Agency, xvi, xxxii Coppersmith Consulting, Inc, xii, 2-6, 2-349, EPA, xvi, 4-260 2-420, 3-6, 3-304, 3-392, 4-5, 4-261 Environmentalists Incorporated, 2-422, 2-424 CCI, xii, 1-3, 1-63, 1-129 Exelon, 4-477 Curtiss-Wright, 4-479 Federal Emergency Management Agency, Defense Nuclear Facilities Safety Board, 2- xvii, 2-50 420 FEMA, xvii, xxii, 2-50, 2-399, 3-349, 3-396, DNFSB, 4-485 4-91, 4-259, 4-260 DEHC Ingenieros Consultores, 4-483 Federal Energy Regulatory Commission, xvii, Department of Defense, 2-302 2-420, 2-421, 2-422, 3-7, 3-322, 3-393 C-1

FERC, xvii, 2-424, 3-347, 3-393, 3-395, 4- Institute for Water Resources - USACE, xx, 122, 4-480, 4-483 xxii, 4-4, 4-162 Finland Radiation and Nuclear Safety IWR, xxii, 4-4, 4-5, 4-252, 4-306, 4-482 Authority, xxxii Instituto de Ingeniería, UNAM, 4-479, 4-482 STUK, xxxii INTERA Inc., 4-479, 4-481 Fire Risk Management, xviii, 2-5, 2-256, 2- International Atomic Energy Agency, xxi 420, 3-6, 3-227, 3-392 IAEA, xxi FRM, xviii Jensen Hughes, 2-422, 3-395, 4-8, 4-423, 4-First Energy Solutions, 4-478 464, 4-483 Fisher Engineering, Inc., 4-7, 4-386, 4-419, Korea Atomic Energy Research Institute, 4-477, 4-479 xxii, 3-392, 3-394, 4-6, 4-328, 4-482 Framatome, Inc., 4-485 KAERI, xxii French Nuclear Safety Authority, xii, 4-482 Korean Institute of Nuclear Safety, 4-481 George Mason University, 4-480 Kyungpook National University, 4-6, 4-328, George Washington University, 4-5, 4-287, 4-481, 4-482 4-306, 4-477 Lawrence Berkeley National Laboratory, 3-Global Modeling and Assimilation Office, xix, 391 4-7, 4-364, 4-482 Lynker Technologies, 4-487 Global Research for Safety, xix Meteorological Development Lab, xxiv, 4-33 GRS, xix, 4-29, 4-486 MDL, xxiv, 4-33, 4-480, 4-486 Goddard Space Flight Center, xix, 4-7, 4- MetStat, Inc., xxxi, 2-419, 2-421, 2-423, 3-364, 4-481, 4-482 391, 3-395, 3-396, 4-6, 4-323, 4-477, 4-Earth Sciences Division, 4-7, 4-364 484, 4-487 GSFC, xix, 4-3, 4-7, 4-98, 4-156, 4-374 MGS Engineering Consultants, 2-401, 2-424, GZA GeoEnvironmental Inc., xix, 2-422, 2- 4-3, 4-6, 4-125, 4-156, 4-323, 4-477, 4-423, 2-424, 3-394, 3-395, 3-398, 4-3, 4- 485 80, 4-91, 4-92, 4-482, 4-486 Michael Baker International, 2-424, 4-486 HDR, 3-393 Murray State University, 3-4, 3-5, 3-179, 3-Hydrologic Engineering Center, xv, xx, 2- 195, 3-196, 3-209, 3-397 399, 2-420, 3-5, 3-195, 3-200, 4-4, 4- National Aeronautics and Space 252 Administration, xxv HEC, xviii, xx, 4-4, 4-5, 4-162, 4-208, 4- NASA, xviii, xix, xxv, 4-3, 4-7, 4-98, 4-156, 306, 4-482 4-374, 4-481, 4-482 HydroMetriks, 3-393 National Environmental Satellite, Data, and I&C Engineering Associates, 4-477 Information Service Idaho National Laboratory, xxi, 1-220, 2-4, 2- NESDIS, xxvi, 4-485 5, 2-6, 2-177, 2-284, 2-387, 2-422, 2- National Geospatial-Intelligence Agency, 3-424, 3-4, 3-5, 3-7, 3-149, 3-199, 3-223, 394, 3-396 3-360, 3-394, 3-395, 3-396, 3-397, 4-5, NGA, 3-392, 3-396 4-287, 4-482, 4-484 National Oceanic and Atmospheric INL, xxi, 1-4, 1-220, 1-238, 1-250, 2-177, 2- Administration, xxvi, 2-6, 2-165, 2-367, 4-178, 2-284, 2-397, 2-398, 3-149, 3-150, 142 3-193, 3-198, 3-315, 4-384 NOAA, xiv, xvi, xviii, xx, xxi, xxv, xxvi, xxvii, Idaho State University, 4-5, 4-287 xxix, 2-165, 2-176, 2-178, 2-198, 2-399, IIHR-Hydroscience & Engineering, 4-486 2-400, 2-401, 2-421, 2-423, 3-150, 3-Institut de Radioprotection et de Sûreté 348, 3-395, 3-396, 4-125, 4-142, 4-158, Nucléaire, xxii, 2-6, 2-391, 2-420, 4-6, 4- 4-311, 4-376, 4-480, 4-481, 4-483, 4-315, 4-320 485, 4-486 IRSN, xxii, xxviii, 2-6, 2-391, 2-397, 2-399, National Weather Service, xiv, xv, xvii, xxvi, 2-420, 2-423, 4-4, 4-195, 4-252, 4-479, 2-6, 2-99, 2-367, 3-42, 3-239, 4-2, 4-3, 4-484 4-33, 4-91, 4-92, 4-472 C-2

NWS, xiii, xx, xxiv, xxv, xxvi, xxvii, xxxi, 2- SEPI, Inc., 4-487 99, 2-165, 2-256, 2-399, 2-400, 2-421, 2- Sorbonne UniversityUniversité de 423, 3-396, 4-2, 4-33, 4-34, 4-480, 4- Technologie de Compigne, 4-6, 4-315 481, 4-486 Southern California Edison, 4-6, 4-323 Natural Resources Conservation Service Southern Nuclear, 3-397, 4-485 NRCS, xxvi, xxviii, xxxv, 3-393, 3-394 Southwest Research Institute, 2-420, 2-425, Naval Postgraduate School, 4-480 3-398, 4-479 NIST, 3-395 Taylor Engineering, 2-419, 3-391, 4-3, 4-91, North Carolina State University, 4-5, 4-7, 4- 4-478 287, 4-329, 4-482 Technical Services Center - USBR, 2-4, 2-Nuclear Energy Agency, xxv, 4-1, 4-2, 4-28 148, 2-199, 2-423, 2-424, 2-425, 3-3, 3-NEA, xxv 4, 3-5, 3-70, 3-135, 3-195, 3-395 Nuclear Energy Institute, xxvi, 3-7 Tennessee Valley Authority, xxxiii, 2-6, 2-NEI, xxvi, 2-333, 3-354, 3-369, 3-370, 3- 375, 2-419, 2-421, 2-422, 3-339, 3-391, 374, 3-391, 3-396, 4-464, 4-473, 4-484 3-395, 3-397, 4-5, 4-272, 4-478 NuScale Power, 4-487 TVA, xxxiii, 2-223, 2-316, 2-396, 2-400, 2-Nuvia USA, 3-391 401, 3-191, 3-345, 3-346, 3-397, 4-5, 4-Oak Ridge National Laboratory, xxvii, 2-424, 121, 4-125, 4-142, 4-156, 4-157, 4-159, 3-5, 3-198, 3-219, 3-392, 3-394, 3-397, 4-251, 4-252, 4-272, 4-286, 4-307, 4-3-398, 4-6, 4-312, 4-315, 4-320, 4-479, 308, 4-310 4-482 U.S. Army Corps of Engineers, xiii, xvi, xxxiv, ORNL, xxvii, 3-5, 3-197, 3-213, 4-3, 4-111, 1-147, 2-3, 2-6, 2-420, 2-421, 2-422, 2-4-142, 4-156, 4-160 423, 2-424, 3-5, 3-6, 3-7, 3-195, 3-198, Oklo Inc., 4-484 3-200, 3-223, 3-295, 3-316, 3-319, 3-Oregon Water Science Center - USGS, 2- 393, 4-2, 4-56, 4-113, 4-307, 4-482, 4-224, 2-421, 3-5, 3-199, 3-226 483, 4-484 Pacific Northwest National Laboratory, xxviii, COE, xiii, xxxiv 2-4, 2-5, 2-6, 2-85, 2-267, 2-303, 2-349, Corps, xiii, xxxiv, 2-50, 2-334, 2-370, 3-2-419, 2-420, 2-422, 2-423, 3-3, 3-6, 3- 347, 3-348, 3-349, 3-372, 3-373, 4-91, 4-29, 3-240, 3-304, 3-395, 3-396, 4-5, 4-7, 156, 4-159, 4-160, 4-259, 4-260, 4-307, 4-261, 4-306, 4-349, 4-374, 4-478, 4- 4-309, 4-311, 4-470, 4-482, 4-483, 4-482, 4-484 484 PNNL, xxviii, 1-3, 1-4, 1-50, 1-63, 1-129, Dam Safety Production Center, 4-208 1-147, 1-220, 3-192, 3-193, 3-240, 4- Galveston District, 4-3, 4-112, 4-478 307 RMC, Risk Management Center, xxx, 2-Parsons, 4-480, 4-485 420, 3-7, 3-319, 3-347, 3-348, 3-349, 3-Penn State University, 4-483 393, 4-3, 4-4, 4-112, 4-156, 4-206, 4-PG&E, 4-484 208, 4-227, 4-252, 4-308, 4-479 PRISM Climate Group at Oregon State Sacramento Dam Safety Protection University, xxviii Center, xv, 3-394, 4-4, 4-227, 4-252 RAC Engineers and Economists, LLC, 3-391 USACE, xiii, xvi, xvii, xx, xxii, xxv, xxx, River Engineering & Urban Drainage xxxiii, xxxiv, 1-4, 1-147, 1-166, 1-190, 2-Research Centre, 4-482 50, 2-199, 2-396, 2-397, 2-398, 2-399, 2-RTI International, 3-346, 3-391, 3-392, 4-5, 4- 400, 2-401, 3-68, 3-347, 3-348, 3-349, 3-272, 4-306, 4-478 350, 3-372, 3-373, 3-397, 4-3, 4-4, 4-5, Sargent & Lundy, 2-423, 4-485 4-91, 4-97, 4-112, 4-125, 4-156, 4-162, Schnabel Engineering, 4-480 4-206, 4-208, 4-227, 4-228, 4-252, 4-Science Systems and Applications, Inc., 4-7, 306, 4-478, 4-479, 4-480, 4-482, 4-483, 4-364 4-484 Secretariat of Nuclear Regulation Authority, U.S. Bureau of Reclamation, xii, xvii, xxxiii, 4-481 xxxiv, 1-3, 1-63, 2-4, 2-148, 2-199, 2-C-3

421, 2-423, 2-424, 2-425, 3-3, 3-4, 3-5, 3-6, 3-70, 3-135, 3-136, 3-149, 3-192, 3-195, 3-205, 3-258, 3-345, 3-346, 3-347, 3-348, 3-350, 3-372, 3-373, 3-393, 3-394, 3-395, 3-397, 3-398, 4-7, 4-114, 4-117, 4-242, 4-254, 4-259, 4-363, 4-398, 4-419, 4-470, 4-483, 4-486 USBR, xvii, xxv, xxxii, xxxiv, 1-3, 1-4, 1-63, 1-147, 1-174, 1-206, 2-213, 2-241, 2-396, 2-400, 3-192, 3-398, 4-125 U.S. Department of Agriculture, xxxiv USDA, xxxi, xxxiv, xxxv, 3-393 U.S. Fish and Wildlife Service, xxxiv USFWS, xxxiv U.S. Geological Survey, xxxiv, 2-4, 2-178, 2-184, 2-419, 2-421, 2-423, 3-4, 3-5, 3-116, 3-117, 3-163, 3-199, 3-226, 3-391, 3-393, 3-394, 3-395, 3-396, 4-4, 4-206, 4-243, 4-252, 4-259, 4-477, 4-481, 4-482, 4-483 USGS, xxi, xxvii, xxviii, xxxiv, xxxv, 1-4, 1-147, 1-174, 2-5, 2-178, 2-184, 2-198, 2-224, 3-150, 3-162, 3-192, 3-194, 3-196, 3-348, 3-394, 4-242, 4-256, 4-258, 4-259 UNC Chapel Hill, 4-477 University of Alabama, 3-4, 3-5, 3-179, 3-190, 3-195, 3-196, 3-209, 3-392, 3-395 University of California U.C. Davis, xxi, 1-3, 1-63, 1-86, 2-4, 2-98, 2-422, 2-423, 3-3, 3-42, 3-392, 3-395 University of Costa Rica, 4-483 University of Maryland, xxxiv, 3-5, 3-197, 3-226, 3-391, 4-6, 4-8, 4-312, 4-315, 4-435, 4-464, 4-477, 4-478, 4-483 US Global Change Research Program, 4-477 Utah State University, 2-396, 3-391 Virginia Tech, 2-422 Weather & Water, Inc., 4-6, 4-323 WEST Consultants, 4-479 Western Univerisity, 4-486 Westinghouse, 2-3, 2-30, 2-424, 3-7, 3-350, 3-362, 3-371, 3-397, 4-7, 4-8, 4-378, 4-419, 4-446, 4-464, 4-485 Wood, 2-149, 3-391, 5-490 World Meteorological Organization WMO, xxxv, 4-376 Zachry Nuclear Engineering, 4-484 C-4