ML20045F283

RIL-2001, Part 2, - Proceedings of NRC Annual Probabilistic Flood Hazard Assessment Research Workshops I-IV

ML20045F283
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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. 2
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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 3DUW6HFRQG$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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2 SECOND ANNUAL NRC PROBABILISTIC FLOOD HAZARD ASSESSMENT RESEARCH WORKSHOP 2.1 Introduction This chapter details the 2nd Annual NRC Probabilistic Flood Hazard Assessment (PFHA)

Research Workshop held at the U.S. Nuclear Regulatory Commission (NRC) Headquarters in Rockville, MD, on January 23-25, 2017.

The workshop began with an introduction from Mike Weber, Director, NRC Office of Nuclear Regulatory Research (RES). Following the introduction, NRC licensing staff and industry representatives presented their perspectives on PFHA research needs and priorities. Finally, 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 a panel discussion on their PFHA research and applications. 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.

2.1.1 Organization of Conference Proceedings Section 2.2 provides the agenda for this workshop. The program is also located at ADAMS Accession No. ML17054C495.

Section 2.3 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. ML17054C495. The complete workshop presentation package is available at ADAMS Accession No. ML17040A626.

Section 2.4 provides a summary of the workshop and section 1.1 provides a list of the workshop attendees, including remote participants.

2-1

2.2 Workshop Agenda 2nd Annual NRC Probabilistic Flood Hazard Assessment Research Workshop at NRC headquarters in Rockville, Maryland AGENDA: DAY 1: JANUARY 23, 2017 Session 1A - Introduction 13:00-13:10 Welcome 13:10-13:25 Introduction 1A-1 Mike Weber, Director, NRC Office of Nuclear Regulatory Research 1325-13:45 PFHA Research Needs for New and Operating Reactors 1A-2 NRC/NRO/DSEA 13:45-14:05 Use of Flooding Hazard Information in Risk-Informed Decision-making 1A-3 Mehdi Reisi-Fard, NRC/NRR/DRA 14:05-14:40 Flooding Research Needs: Industry Perspectives on Development of 1A-4 External Flood Frequency Methods Ray Schneider*, Westinghouse Electric Corporation, and Joe Bellini*,

Aterra Solutions 14:40-14:55 NRC Flooding Research Program Overview 1A-5 Joseph Kanney*, Meredith Carr, Tom Aird, Elena Yegorova, Mark Fuhrmann, and Jacob Philip, NRC/RES 14:55-15:10 EPRI Flooding Research Program Overview 1A-6 John Weglian, EPRI 15:10-15:25 BREAK Session 1B - Storm Surge Research 15:25-16:05 Quantification of Uncertainty in Probabilistic Storm Surge Models 1B-1 Norberto C. Nadal-Caraballo*, Victor Gonzalez and Jeffrey A. Melby, U.S. Army Corps of Engineers, Engineer Research and Development Center , Coastal and Hydraulics Laboratory 16:05-16:45 Probabilistic Flood Hazard AssessmentStorm Surge 1B-2 John Weglian, EPRI 16:45-17:05 Daily Wrap-Up and Public Comments/Questions

• indicates speaker, ^ indicates remote speaker 2-3

AGENDA: DAY 2, JANUARY 24, 2017 08:00-08:05 Welcome, Day 2 Session 2A - Climate and Precipitation 08:05-08:40 Regional Climate Change Projections: Potential Impacts to Nuclear 2A-1 Facilities L. Ruby Leung^, Rajiv Prasad*, and Lance Vail, Pacific Northwest National Laboratory 08:40-09:20 Numerical Modeling of Local Intense Precipitation Processes 2A-2 M. Lev Kavvas*, Kei Ishida*, and Mathieu Mure-Ravaud*, Hydrologic Research Laboratory, Department of Civil and Environmental Engineering, University of California, Davis 09:20-09:55 Extreme Precipitation Frequency Estimates for Orographic Regions 2A-3 Andrew Verdin*, Kathleen Holman, and David Keeney, Flood Hydrology and Meteorology Group, Technical Services Center, U.S. Bureau of Reclamation 09:55-10:10 BREAK 10:10-10:50 Local Intense Precipitation Frequency Studies 2A-4 John Weglian, EPRI Session 2B - Leveraging Available Flood Information I 10:50-11:20 Development of Flood Hazard Information Digests for Operating NPP 2B-1 Sites Curtis Smith* and Kellie Kvarfordt, Idaho National Laboratory 11:20-12:00 At-Streamgage Flood Frequency Analyses for Very Low Annual 2B-2 Exceedance Probabilities from a Perspective of Multiple Distributions and Parameter Estimation Methods William H. Asquith^, U.S. Geological Survey, Lubbock, TX; and Julie Kiang, U.S. Geological Survey, Reston, VA 12:00-12:30 Extending Frequency Analysis Beyond Current Consensus Limits 2B-3 Keil Neff* and Joseph Wright, U.S. Bureau of Reclamation, Technical Service Center, Flood Hydrology and Meteorology 12:30-13:45 LUNCH 2-4

Session 2C - Leveraging Available Flood Information II 13:45-14:25 Collection of Paleoflood Evidence 2C-1 John Weglian, EPRI 14:25-15:05 Paleofloods on the Tennessee RiverAssessing the Feasibility of 2C-2 Employing Geologic Records of Past Floods for Improved Flood Frequency Analysis Tessa Harden*, USGS Oregon Water Science Center; and Jim OConnor*, USGS Geology, Minerals, Energy, and Geophysics Science Center, Portland, OR 15:05-15:20 BREAK Session 2D - Reliability of Flood Protection and Plant Response to Flooding Events I 15:20-16:00 EPRI Flood Protection Project Status 2D-1 David Ziebell and John Weglian*, EPRI 16:00-16:40 Performance of Flood-Rated Penetration Seals 2D-2 William (Mark) Cummings*, Fire Risk Management , Inc.

16:40-17:00 Comments/Questions from Public 17:00-17:10 Daily Wrap-Up AGENDA: DAY 3, JANUARY 25, 2017 08:00-08:05 Welcome, Day 3 Session 3A - Reliability of Flood Protection and Plant Response to Flooding Events II 08:05-08:45 Effects of Environmental Factors on Manual Actions for Flood 3A-1 Protection and Mitigation at Nuclear Power Plants Rajiv Prasad*, Garill Coles^, and Angie Dalton^, Pacific Northwest National Laboratory; Kristi Branch and Alvah Bittner, Bittner and Associates; and Scott Taylor, Battelle Columbus 08:45-09:25 Modeling Total Plant Response to Flooding Events 3A-2 Zhegang Ma*, Curtis L. Smith, Steven R. Prescott, Idaho National Laboratory, Risk Assessment and Management Services, and Ramprasad Sampath, Centroid PIC, Research and Development Session 3B - Frameworks I 09:25-10:05 Technical Basis for Probabilistic Flood Hazard Assessment 3B-1 Rajiv Prasad* and Philip Meyer, Pacific Northwest National Laboratory 10:05-10:20 BREAK 2-5

Session 3C - Frameworks II 10:20-11:00 Evaluation of Deterministic Approaches to Characterizing Flood 3C-1 Hazards John Weglian, EPRI 11:00-11:40 Probabilistic Flood Hazard Assessment Framework Development 3C-2 Brian Skahill*, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Hydrologic Systems Branch, Watershed Systems Group 11:40-12:20 Riverine Flooding and Structured Hazard Assessment Committee 3C-3 Process for Flooding (SHAC-F)

Rajiv Prasad* and Robert Bryce, Pacific Northwest National Laboratory; and Kevin Coppersmith*, Coppersmith Consulting 12:20-13:35 LUNCH Session 3D - Panel Discussion 13:35-15:05 Probabilistic Flood Hazard Assessment Research Activities in Partner 3D Agencies, Panel Chair: Joseph Kanney, U.S. NRC National Oceanic and Atmospheric Administration/National Weather Service Sanja Perica U.S. Army Corps of Engineers Christopher Dunn, Norberto Nadal-Caraballo, John England Tennessee Valley Authority Curt Jawdy U.S. Department of Energy Curtis Smith, Idaho National Laboratory Institut de Radioprotection et de Sûreté Nucléaire (Frances Radioprotection and Nuclear Safety Institute (IRSN))

Vincent Rebour 15:05-15:20 BREAK Session 3E - Future Work in PFHA 15:20-15:50 Future Work in PFHA at EPRI 3E-1 John Weglian*, EPRI 15:50-16:20 Future Work in PFHA at NRC 3E-2 Joseph Kanney, Meredith Carr*, Tom Aird, Elena Yegorova, Mark Fuhrmann, and Jacob Philip, NRC/RES 16:20-16:40 Public Comments/Questions 16:40-16:55 Final Wrap-Up 2-6

2.3 Proceedings 2.3.1 Day 1: Session 1A - Introduction There are no abstracts for this introductory session.

2.3.1.1 Welcome, Michael Weber, Director, Office of Nuclear Regulatory Research, U.S. NRC (Session 1A-1; ADAMS Accession No. ML17054C496) 2.3.1.1.1 Presentation 2-7

2-8 2-9 2-10 2-11 2.3.1.2 PFHA Research Needs for New and Operating Reactors, Andrew C. Campbell, Ph.D.,

Deputy Director, Division of Site Safety & Environmental Analysis, Office of New Reactors, U.S. NRC (Session 1A-2; ADAMS Accession No. ML17054C497) 2.3.1.2.1 Presentation 2-12

2-13 2-14 2-15 2-16 2-17 2-18 2-19 2-20 2-21 2.3.1.2.2 Questions and Answers Question:

NUREG-2150, A Proposed Risk Management Regulatory Framework, issued April 2012, recommends the risk-informed-based approach and defense in depth. How will this approach involve defense in depth?

Response

This is not a risk-based approach but a risk-informed approach. The agency has adhered to that perspective since the policy was developed in the 1990s. In 2006, the NRC developed the probabilistic risk assessment (PRA) policy. It is important that defense in depth be part of that.

Recently I was at a plant with a couple of inspectors and staff from the Office of Nuclear Reactor Regulation (NRR), and we looked at all of the plants FLEX equipment and the plant has defense in depth. It has multiple ways of pumping water to where it is needed. The facility has multiple ways of providing power to the plant. This is an example of defense in depth. Even if it were possible, on a probability basis, to determine that the likelihood of an event occurring is miniscule, from the plants perspective and the NRCs regulatory perspective defense in depth is not quantifiable directly but it is something that makes a great deal of sense not only in the history of the NRCs approach to regulation but going forward. The concern with moving to only a probability basis is understandable, but that is not what the NRC is doing. Instead, this is using a risk-informed approach. The NRC believes it is beneficial to have many ways to solve a problem.

2.3.1.3 Use of Flooding Hazard Information in Risk-Informed Decision-making, Mehdi Reisi-Fard, Ph.D., Reliability and Risk Analyst, PRA Licensing Branch, Division of Risk Assessment, Office of Nuclear Reactor Regulation, U.S. NRC (Session 1A-3; ADAMS Accession No. ML17054C498) 2.3.1.3.1 Presentation 2-22

2-23 2-24 2-25 2-26 2-27 2-28 2-29 2.3.1.4 Flooding Research Needs: Industry Perspectives on Development of External Flood Frequency Methods, Ray Schneider*, Westinghouse Electric Corporation; and Joe Bellini*, P.E., P.H., D.WRE, C.F.M., Aterra Solutions (Session 1A-4; ADAMS Accession No. ML17054C499) 2.3.1.4.1 Presentation 2-30

2-31 2-32 2-33 2-34 2-35 2-36 2.3.1.4.2 Questions and Answers Question:

How big of a watershed or subwatershed can be modeled successfully using the stochastic event-based rainfall runoff model?

Response

As the watershed increases in size, it becomes more complex because it includes both moving and nonstationary fronts. The only point of reference that I have is that EPRI performed some work primarily considering stationary storms for a power plant in an 8,000-square-mile watershed.

That work considered all the different initiating precipitation/snow melt processes but only stationary storms. With only that single example, it is possible that any larger area would be difficult to model in that way.

Question:

What are the most difficult problems with developing the American Nuclear Society flooding standard?

Response

The biggest problem at present is ensuring consistency among the various sections of the standard. The models and processes that we considered work and the technology that exists is acceptable, so the issue is being consistent through the standard as it is such a wide change.

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2.3.1.5 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 Nuclear Regulatory Research, U.S. NRC (Session 1A-5; ADAMS Accession No. ML17054C500) 2.3.1.5.1 Presentation 2-38

2-39 2-40 2-41 2-42 2-43 2-44 2.3.1.5.2 Questions and Answers Comment:

This area is very important with regard to waste management and decommissioning. Would it be possible to work cooperatively with the Office of Nuclear Material Safety and Safeguards (NMSS) by adding NMSS to the review of the documentation?

Response

We would certainly like to work with you on this issue.

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2.3.1.6 EPRI Flooding Research Program Overview, John Weglian, EPRI (Session 1A-6; ADAMS Accession No. ML17054C501) 2.3.1.6.1 Presentation 2-46

2-47 2-48 2-49 2.3.2 Day 1: Session 1B - Storm Surge Research This session covered the development of guidance for the application of improved mechanistic and probabilistic modeling techniques for key flood-generating processes and flooding scenarios.

2.3.2.1 Quantification of Uncertainty in Probabilistic Storm Surge Models, Norberto C.

Nadal-Caraballo*, Ph.D., Victor Gonzalez, P.E., and Jeffrey A. Melby, Ph.D., U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory (Session 1B-1; ADAMS Accession No. ML17054C502) 2.3.2.1.1 Abstract Quantification of the storm surge hazard is an integral part of the PFHA of structures and facilities located in coastal zones. The U.S. Army Corps of Engineers Engineer Research and Development Centers Coastal and Hydraulics Laboratory is performing a comprehensive assessment of uncertainties in probabilistic storm surge models in support of the NRCs efforts to develop a framework for probabilistic storm surge hazard assessment (PSSHA) for nuclear power plants (NPPs). Modern stochastic assessment of coastal storm hazards in hurricane-prone coastal regions of the United States requires the development of a joint probability analysis model of tropical cyclone forcing parameters. The joint probability method (JPM) with optimal sampling (JPM-OS) has become the standard probabilistic model used to assess coastal storm hazard in these areas, having been adopted by the Federal Emergency Management Agency (FEMA) and USACE in most post-Katrina coastal hazard studies. Different JPM-OS approaches have been developed, but they typically follow a common general methodology. Nevertheless, the details in the application of these approaches can vary significantly by study, depending on the adopted solution strategies. Variations between studies, for example, can be found in the computation of storm recurrence rate (SRR), definition of univariate distributions and joint probability of storm parameters, and development of the synthetic storm suite (e.g., different optimization methods).

The treatment of uncertainties in the JPM -OS methodology also varies by study and is typically limited to the quantification and inclusion of uncertainty as an error term in the JPM integral.

An alternative for the treatment and quantification of uncertainty is derived from probabilistic seismic hazard assessment guidance, where the epistemic uncertainty arises from the application of different, technically defensible data, methods, and models relevant to hazard assessment and proposed by the larger technical community. This allows for the computation of a family of hazard curves, with associated weights, that represents each of the alternate modeling approaches. The present study has the objective of assessing the technically defensible data, models, and methods that have been applied to individual components of the JPM-OS methodology, along with the characterization of their respective uncertainties. The quantification of uncertainty associated with the SRR, for example, focused on the characterization of the SRR variability due to the selection of computational approach, optimal kernel size, tropical cyclone intensity, period of record, observational data, and data resampling. The development of univariate probability distributions of storm parameters was evaluated by fitting multiple distributions to each relevant tropical cyclone parameter, focusing on three different datasets, including observational data from the National Hurricane Center and synthetic data from a global climate model (GCM). The uncertainty related to optimal sampling techniques was examined by constructing a reference storm set using a Gaussian process metamodel that was trained with data from the North Atlantic Coast Comprehensive Study recently performed by USACE. Numerical experiments were also designed for the assessment of methods typically used for the discretization of and incorporation of uncertainty.

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2.3.2.1.2 Presentation 2-51

2-52 2-53 2-54 2-55 2-56 2-57 2-58 2-59 2-60 2-61 2-62 2-63 2-64 2-65 2-66 2-67 2-68 2-69 2-70 2-71 2-72 2-73 2.3.2.1.3 Questions and Answers Question:

When you performed GCM downscaling, what grid resolution did you consider?

Response

The set of results that we were given did not directly characterize the extratropical transition storms. The results were from a hurricane model that focused exclusively on tropical cyclones.

Follow-up Question:

Without the proper grid resolution, such storms would not be captured. The presentation mentioned 1,470 storms; from how many GCM projections did this result?

Response

The researchers simulated 15,000 years and produced 5,000 storms. The reference for this joint study is as follows:

• Lin, Ning, K. Emanuel, M. Oppenhemier, and E. Vanmarcke. 2012. Physically Based Assessment of Hurricane Surge Threat under Climate Change. Nature Climate Change 2 (6): 462-467.

Follow-up Question:

There are about 70 GCM projections and if you use each with 100 years, the results would cover about 7,000 years. You had looked at these Monte Carlo simulations and reconciled them with the downscaled data; how do you reconcile them with climate change?

Response

This study did not specifically consider climate change.

Response from NRC Project Manager:

The focus of this project is not specifically to look at climate change or to look at the change in recurrence rate or change in landfall 1.

Response

With regard to downscaling, this is a very valid method. This method can be used with JPM-OS to assess tropical cycles, assuming that some issues can be fixed.

Question:

Your presentation alluded to transitioning from a tropical cyclone to an extratropical cyclone and how you condition your model based on the source, for example considering whether it is in the Gulf of Mexico, the South Atlantic, mid-Atlantic, or the North Atlantic and the complications that 1

NRC Program Manager indicated that he would get back to the questioner with a more complete response.

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arise as you go further north with regard to the synoptic weather. It seems likely that issues would arise with the model as a storm moved from the Gulf to the north, especially over the Atlantic.

Response

The set of models used for this approach (i.e., the meteorological model and the hydrodynamic model, the Advanced CIRCulation model (ADCIRC)in this case) only see tropical cyclones.

Therefore, we need to characterize the extratropical transition by reflecting that in our synthetic storm surge. For example, when we move to the north and storms go through the extratropical transition, they tend to increase in their translational speed and size, so we need to make sure that the synthetic storms that we are generating are also comparable with those changes. If we develop a set for the Gulf of Mexico versus for the North Atlantic, the parameters will reflect those differences. The historical occurrences in those individual seasons inform the individual characteristics that we have those storms carry.

2.3.2.2 Probabilistic Flood Hazard AssessmentStorm Surge, John Weglian, EPRI (Session 1B-2; ADAMS Accession No. ML17054C503) 2.3.2.2.1 Abstract It is important to evaluate risks to NPPs and other vital structures from external hazards that could simultaneously impact multiple, diverse equipment relied upon for accident mitigation. External flooding hazards can lead to floodwaters, which overwhelm a sites response, especially when the flood levels exceed the plants design basis. A PFHA provides a mechanism to determine the risk to a site from an external flooding hazard, including from extremely rare, beyond-design-basis events. One of the external flooding hazards that can impact a site is a storm surgethe elevation in water level at the shore due to the atmospheric effects of a large storm.

Many storm surge methods and analyses are focused on assessing the flooding impacts from a tropical storm making landfall; however, other types of storms can also cause storm surges, and these events can occur on large lakes as well as oceans. EPRI has published a technical report, on the subject, Probabilistic Flooding Hazard Assessment for Storm Surge with an Example Based on Historical Water Levels, EPRI ID 3002008111, dated August 31, 2016 (http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002008111).

The report describes multiple methods for performing a PSSHA ; however, the detailed example is based on the assessment of a storm surge at an inland lake site based on historical water levels and wave heights.

The process of performing a PSSHA begins with identification that a site is potentially subject to a storm surge. The PSSHA then utilizes a qualitative or quantitative screening approach to determine if the hazard can be screened out form further consideration. If the hazard cannot be screened, a probabilistic approach is used to determine the frequency of the storm surge flooding parameters (e.g., water level). At each step in the process, the uncertainty in the analysis is considered and characterized. The PSSHA process includes the use of a peer review to provide an independent assessment of the process and decisions made in the analysis.

The report includes an example that uses historical information to assess the probability that a storm surge on one of the Great Lakes could impact a particular site. The historical data were used to determine the lake level, surge level, and wave heights. Additional evidence from paleo 2-75

data was used to extend the historical record for lake level. This information was used to determine probabilistic distribution functions (PDFs) for the parameters of interest. These PDFs were used in a Monte Carlo simulation to estimate the storm surge-frequency hazard curve for the site. This hazard curve provides the likelihood that a particular flood level at the site would be exceeded by a storm surge per year. This information can then be used to develop a PRA model to determine the core damage frequency, large early release frequency, or other metrics.

2.3.2.2.2 Presentation 2-76

2-77 2-78 2-79 2-80 2-81 2-82 2-83 2.3.2.2.3 Questions and Answers Question:

How did you use the paleoflood data to extrapolate for 4,000 years? The timeframe for glaciation is much longer, and the performance period for waste management ranges from 10,000 to 20,000 years. Would you be able to extrapolate further given the large amount of data?

Response

The paleo data available did go back beyond 4,000 years; however, the data before 4,000 years was judged not to be applicable to the current time. The lake levels were significantly different than those currently observed. Therefore, the researchers limited the analysis to 4,000 years.

Question:

The paleoflood data were used only for the lake level. There is an assumption that, given the paleo elevation 4,000 years ago, this would still be a potential initiating level for the lake for the next 60 years of operation.

Response

The lake level is different from paleoflood because it considered the average lake level during that timeframe rather than surge levels. Two different reports cover that topic in different ways. The assumption is that weather patterns can add more or less water to the lakes. It is possible that a storm event in the last 4,000 years added significantly more water to the lakes than what we have seen in our historical measurement, which would be reflected in the higher lake level. This is an attempt to capture that portion of the uncertainty based on the starting point for the storm surge itself.

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Follow-up Question:

When testing the resulting lake level statistically, was the level 10 or 20 or 30 feet higher? Was it in a reasonable range that you could expect?

Response

Although paleo data were available beyond 4,000 years, the researchers did not deem them to be applicable for the current effort.

Question:

What input did the peer reviewers provide?

Response

I was not involved in that activity and do not know the answer.

2.3.3 Day 2: Session 2A - Climate and Precipitation This session continued to consider the development of guidance for the application of improved mechanistic and probabilistic modeling techniques for key flood-generating processes and flooding scenarios.

It also included an assessment of the potential impacts of dynamic and nonstationary processes on flood hazard assessments and flood protection at nuclear facilities.

2.3.3.1 Regional Climate Change Projections: Potential Impacts to Nuclear Facilities, L. Ruby Leung^, Ph.D., Rajiv Prasad*, Ph.D., and Lance Vail, Pacific Northwest National Laboratory (Session 2A-1; ADAMS Accession No. ML17054C504) 2.3.3.1.1 Abstract This research project is part of the NRCs 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 with regard to 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 the NRCs safety 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. The current focus is on the southeast region, consisting of 11 southeastern States in the conterminous United States. Except for Kentucky, all these States have currently operating NPPs. New nuclear power reactor permit and license 2-85

applications submitted to the NRC in the recent past were for sites located in several of the southeastern States (Virginia, North Carolina, South Carolina, and Florida).

The literature review includes an overview of the climate of the southeastern United States, focusing on temperature and precipitation extremes, floods and droughts, strong winds (hurricanes and tornadoes), sea level rise, and storm surge. The southeast region occasionally experiences extreme heat during summer and extreme cold during winter. Floods can be produced by several mechanisms, including locally heavy precipitation, slow-moving extratropical cyclones during the cool season, tropical cyclones during summer and fall, late spring rainfall on snowpack, storm surge near coastal areas from hurricanes, and occasional large releases from upstream dams. Hurricanes cause major economic loss but also contribute significantly to the regions rainfall. Combined with sea level rise, hurricanes pose significant threats from storm surge and inland inundation. The overview is followed by discussions of projected changes in the aforementioned climatic aspects. For example, depending on the future emission scenarios, seasonal precipitation shows moderate increases to significant decreases in magnitude. Very heavy precipitation events are projected to increase in frequency, while annual maximum precipitation is expected to increase in magnitude. Although precipitation intensity generally scales with the Clausius-Clapeyron rate of 7 percent per degree of warming, precipitation intensity decreases at higher temperatures because of the transition to a moisture-limited environment.

Besides climate change, urbanization and changing land use may result in changes in runoff and flooding. However, both short-term and longer-term droughts are expected to intensify in the Southeast. Streamflow is expected to decline as evapotranspiration generally increases with warmer temperatures. Urbanization and population growth may increase stress on water supplies.

As sea surface temperatures increase in the future, hurricanes are projected to intensify as the thermodynamic environments for major hurricanes become more favorable. With sea level projected to rise and hurricanes to become more intense, there is increased probability for storm surge along the southeastern Coastline. Lastly, the researchers made a current assessment of climate modeling and Federal agency activities related to climate change.

2.3.3.1.2 Presentation 2-86

2-87 2-88 2-89 2-90 2-91 2-92 2-93 2-94 2-95 2-96 2-97 2.3.3.1.3 Questions and Answers Comment:

Fort Calhoun and St. Lucie experienced profound effects from flood, but neither were extreme events. Its important to emphasize that in addition to considering extreme events, those with 50-100-year return periods also need to be taken into account.

Comment:

Be cautious as the term extreme event has a different meaning for hydrologists than for climatologists.

2.3.3.2 Numerical Modeling of Local Intense Precipitation Processes, M. Lev Kavvas*,

Ph.D., Kei Ishida*, Ph.D., and Mathieu Mure-Ravaud*, Hydrologic Research Laboratory, Department of Civil and Environmental Engineering, University of California, Davis (Session 2A-2; ADAMS Accession No. ML17054C505) 2.3.3.2.1 Abstract As population and infrastructure continue to increase, our society has become more vulnerable to extreme events. A flood is an example of a hydrometeorological disaster that has a strong societal impact. Tropical cyclones and mesoscale convective systems are recognized for their ability to generate intense precipitation that may in turn create disastrous floods. Tropical cyclones are intense atmospheric vortices that form over the warm tropical oceans, while mesoscale convective systems are organized collections of several cumulonimbus clouds that interact at the mesoscale (regional scale) to form an extensive and nearly contiguous region of precipitation.

This study assessed the suitability of a regional numerical weather model to simulate local intense precipitation processes within intense tropical cyclones and mesoscale convective systems. More specifically, the study used the Weather Research and Forecasting (WRF) model at 5-kilometer 2-98

resolution in order to reconstruct the intense precipitation fields associated with several historical tropical cyclones and mesoscale convective systems that affected the United States. The WRF model was run in the simulation mode, which means that it was only subject to the influence of its initial and boundary conditions, and no observation was used to improve the simulations through nudging or other data assimilation techniques.

Numerous studies have shown that regional numerical weather models perform relatively well in reconstructing such storms in the forecasting mode where such techniques are used to improve the models performances. However, in the context of climate change where one may be interested in simulating the storms of the future, it is important to evaluate the performances of regional numerical weather models in the simulation mode, since no observation is available for the future that would allow using nudging or data assimilation. The storm systems that were simulated were selected within the time period from 2002 to present, based on the National Centers for Environmental Prediction (NCEP) Stage IV precipitation dataset, which is a mosaic of regional multisensor analysis generated by National Weather Service (NWS) River Forecast Centers 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 initial and boundary conditions for the simulations were obtained from the Climate Forecast System Reanalysis (CFSR) dataset, which is provided by NCEP at 0.5 x 0.5 degree spatial resolution and 6-hour temporal resolution. For the simulations of the mesoscale convective systems, 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.

However, several sets of simulation nested domains were prepared for the simulations of the tropical cyclones because of the diversity in the paths of these systems. More precisely, while the outer domain was the same for all cases and was chosen so as to cover the paths of all the identified severe tropical cyclones, different inner domains were set up so as to include the severe precipitation areas caused by each individual tropical cyclone. With these sets of simulation nested domains, the WRF model was configured to obtain the best results for the simulation of each of the selected severe mesoscale convective system and tropical cyclone storm events with respect to the simulated and observed precipitation fields.

The study compared the simulation results with observations from the Stage IV precipitation dataset. More precisely, on the one hand, the simulation results were evaluated by means of several goodness-of-fit statistics: the relative error for the simulation inner-domain total precipitation, and the percentage of overlapping between the simulated and observed fields for several precipitation thresholds. On the other hand, the simulated and observed precipitation fields were plotted so as to visually appreciate the similarities and differences in the fields texture and structure. The study showed that under an appropriate choice of the models options and boundary conditions, the WRF model provided satisfactory results in reproducing the location, intensity, and texture of the intense precipitation fields in the historical tropical cyclones and mesoscale convective systems. The models options that were investigated include the parameterization schemes such as microphysics, cumulus parameterization, planetary boundary layer physics, and long-wave and short-wave radiation physics; the vertical resolution (number of layers); the initial date for the simulation; the time step; and other options related to the physics and dynamics. Although certain combinations of the parameterization schemes provided in each case realistic results in terms of the precipitation fields textures and structures, placing these fields in the correct spatial locations required additional efforts, so that the best set of the models options varies from one storm system to the other.

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2.3.3.2.2 Presentation 2-100

2-101 2-102 2-103 2-104 2-105 2-106 2-107 2-108 2-109 2-110 2-111 2-112 2-113 2-114 2-115 2-116 2-117 2-118 2-119 2-120 2-121 2-122 2-123 2-124 2-125 2-126 2-127 2-128 2-129 2-130 2-131 2-132 2-133 2-134 2-135 2-136 2-137 2-138 2-139 2-140 2-141 2-142 2-143 2-144 2-145 2-146 2.3.3.2.3 Questions and Answers Question:

The models seem to reproduce the storms well. When the storm is moved to a different location, what assumptions do you make in order to conclude that the initial and boundary conditions that occurred in the Midwest could happen in the Southeast? How do we know when we have transposed a storm to a region that is not realistic, that we have done something that is not meteorologically possible? Are there limits on where we can transpose storms and in what situations?

Response

The modeling is not creating new artificial initial or boundary conditions. We performed a similar exercise for atmospheric rivers, in California and in the West. This modeling involved a shift in the boundary conditions in the zonal direction, with respect to the meridional direction, or with respect to the latitudinal direction, and as such there must be realism. However, in this case, we are using historically observed conditions and shifting them either in the south-north direction or in the east-west direction. These storms are historical cases that have set initial and boundary conditions in the CFSR reanalysis data.

Follow-up Question:

Could those initial and boundary conditions occur in a different location?

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Response

Yes; before starting a transposition exercise, analysts need to justify how much shifting is realistic.

The new work is investigating synoptic conditions and will help analysts decide what level of shifting is realistic. In this case, we are simply shifting historically observed boundary conditions and not creating any. The storms initial conditions are just moved.

Question:

Have you considered the Goddard database on intense storms?

Response

No, we did not.

Comment:

The Goddard database provides satellite data with a much lower resolution of storms as Stage IV reanalysis data.

Response

The reanalysis data set is good for assessing the performance of these models.

2.3.3.3 Extreme Precipitation Frequency Estimates for Orographic Regions, Andrew Verdin*, Kathleen Holman, and David Keeney, Flood Hydrology and Meteorology Group, Technical Services Center, U.S. Bureau of Reclamation (Session 2A-3; ADAMS Accession No. ML17054C506) 2.3.3.3.1 Abstract This presentation gave an update to the research project Phase II: Research to Develop Guidance on Extreme Precipitation Frequency Estimates for the Tennessee Valley. The focus of this presentation was the use of sophisticated statistical techniques for identifying homogeneous regions within greater orographic domains and the subsequent fitting of extreme value distributions for point-scale return-level estimates of precipitation within each homogeneous region. Identification of homogeneous regions is essential for regional frequency analysis.

Regional analyses are based on the assumption that data from stations within each homogeneous region come from the same theoretical distribution, which is a common method of extending environmental datasets. Parameter estimation is sensitive to a number of influential factors, the period of record being one of the most important. It is essential, then, to strengthen the parameter estimates by substituting space for time. The presentation discussed the Self-Organizing Maps (SOM) algorithm, a widely used method of identifying homogeneous regions, and the application of the SOM algorithm to the Tennessee River Valley. Results from the SOM algorithm are consistent with subjective methods of regionalization. For each homogeneous region, the study applied two distinct methods of regional frequency analysis for estimating the extreme value distribution parameters of the regional growth curve: L-moments and Bayesian .

The regional growth curve for each homogeneous region is produced using scaled annual maximum precipitation data. Subsequently, a point-scale return level is estimated by scaling the 2-148

regional growth curve by the at-site mean of the location of interest. However, it may be of interest to estimate precipitation magnitudes at locations where no historical observations exist. To this end, gridded reanalysis may be used as input to regional frequency analysis. Specifically, the Newman et al. (2015) 2 dataset offers an ensemble of gridded daily precipitation for 33 years. The ensemble contains 100 members, each of which is an equally plausible precipitation total for the grid cell of interest. Similar to the identification of homogeneous regions, the study assumed that all ensemble members come from the same theoretical distribution, which extends the period of record by two orders of magnitude. The ensemble members may be collapsed into a single dataset, and the extreme value distribution parameters are estimated independently at each grid cell. The presentation discussed differences in the inherent assumptions and resulting differences in the two methods. The presentation ended with an illustration of the two methods abilities in quantifying small exceedance probability precipitation events with associated uncertainty .

2.3.3.3.2 Presentation 2

Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., Mizukami, N.,

Brekke, L. & Arnold, J. R. (2015). Gridded ensemble precipitation and temperature estimates for the contiguous United States. Journal of Hydrometeorology, 16(6), 2481-2500.

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2-150 2-151 2-152 2-153 2-154 2-155 2-156 2-157 2-158 2-159 2-160 2-161 2-162 2-163 2-164 2.3.3.3.3 Questions and Answers Comment:

The methods that were used in National Oceanic and Atmospheric Administration (NOAA) Atlas, Precipitation-Frequency Atlas of the United States, Volume 2, issued 2004 and revised 2006, were changed, in large part because of regionalization. Regionalization is an approach that combines a lot of stations together in a way that is actually very subjective, and the results vary significantly depending on how many clusters are chosen and the parameters chosen for the kinds of clusters. In addition, when defining clusters that have many stations, the data end up being smoothed because you are averaging too much, especially when the method is combined with the L-moments approach. The resulting estimates hardly represent the local estimates that are needed in engineering design, which is the primary purpose for producing precipitation frequency estimates. Because of this, NOAA has changed these results and will also change the first volumes of the series if funds are available. It is surprising that the L-moments ended up being higher than in the Bayesian approach, because they tend to be very low for extreme frequencies. This is why NOAA is moving away from their use and toward a maximum likelihood approach. A climate change analysis that is based on annual maxima series cannot take into account changes in frequencies of extreme events. For this reason, we should not do further analysis on the annual maximum series, methods used in the first few volumes of NOAA Atlas 14 and proposed to use here but that were developed in the 1990s. Instead, we need to move toward a more advanced methodology. To best apply NOAA Atlas 14, use the methodology in more recent volumes.

2.3.3.4 Local Intense Precipitation Frequency Studies, John Weglian, EPRI (Session 2A-4; ADAMS Accession No. ML17054C507) 2.3.3.4.1 Abstract To ensure that NPPs are adequately protected against extreme rainfall, plant design has traditionally relied on deterministic requirements to define the extent of flooding that might need to be accommodated. For purposes of PRA, a more comprehensive understanding of the relationship between the frequency and amount of extreme rainfall is necessary. Such an understanding is also needed to provide further perspective on the challenges posed by precipitation corresponding to the deterministic criteria.

To explore the state of the technology and data available to support a more comprehensive probabilistic evaluation, EPRI undertook an evaluation of the precipitation-frequency relationship for two sites in the United States, one an inland site and the other an Atlantic Ocean coastal site.

The study was primarily based on regional precipitation-frequency relationships that embody NWS data from a large number of precipitation measurement stations in the vicinity of the plant sites. The study was published as Local Precipitation -Frequency Studies: Development of 1-Hour/1-Square Mile PrecipitationFrequency Relationships for Two Example Nuclear Power Plant Sites, EPRI ID 3002004400, dated October 2, 2014 (http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002004400).

Plants in the United States are designed to be protected against flooding that could result from local intense precipitation. For design purposes, local intense precipitation is defined based on precipitation associated with a 1-hour/1-square-mile probable maximum precipitation (PMP) 2-165

event. The method described in this report was applied to calculate the probability of the PMP occurring for the two example sites as well.

The approach employed in this report successfully demonstrated the feasibility of a probabilistic technique for establishing precipitation-frequency relationships for local precipitation events. The regional analyses also found that an event corresponding to the 1-hour/1-square-mile PMP would result in an extremely large amount of precipitation and would be extremely rare.

2.3.3.4.2 Presentation 2-166

2-167 2-168 2-169 2-170 2-171 2-172 2-173 2-174 2-175 2.3.3.4.3 Questions and Answers Comment:

Because of the dependence in the data, when nearby stations are used, you are basically creating a sample from the same store. As a result, when using the regional approach, the actual record is much shorter than perceived. It is not the sum of the station years from all the stations in the region. That sometimes leads to overconfidence when estimating precipitation frequency at extreme frequencies. This was a mistake; the first few volumes of NOAA Atlas 14 did not account for the spatial correlation and independence of observations at nearby stations, and therefore the confidence intervals in those volumes were very narrow. This approach gives the appearance of high levels of confidence in our estimates even for a return period of 1,000 years. One slide of the presentation showed a 1,000- or 2,000-year rare event, not even an extreme. I may be one of a few people who feels that we should not extrapolate to those return periods. One slide in a presentation for tomorrow will address this issue. There is lots of disagreement over this issue and feel that some type of approach must be taken, as some designs need those numbers. Although we are assuming a distribution, such as generalized extreme value (GEV) distribution, in many cases there are many other distributions that pass the statistical test. We use L-moments to calculate distribution parameters but there are other ways to fit distribution parameters. Because of this, when all these uncertainties are put together at the 1,000- and 2,000-year return periods, the range of estimates is so wide, practically between zero and infinity. We should be very careful when selecting those numbers and using them, especially in a deterministic mode as it is currently done in engineering design.

Response

I agree that it is extremely dangerous to extrapolate to very long levels. For example, from my aerospace background, in the failure of the space shuttle Columbia, when analysts saw on the camera that foam had hit the leading edge of a wing on takeoff, they took the existing foam data and extrapolated three orders of magnitude to conclude that it was not an issue. That extrapolation was inaccurate and in fact there was a very serious issue. It is extremely difficult to rely on extrapolation to those extreme levels. Because of this, our industry relies both on deterministic, with a PMP approach, and risk-based approaches to help inform decisions. A PRA practitioner who wishes to assess vulnerabilities at a site to a level commensurate with other hazards faced there needs to have an estimate that can be used down to low frequencies (e.g.,

10-6), but also needs to keep in mind at the same time the wide range of uncertainty associated with such an estimate. The PRA can be used to show vulnerabilities to try to make the plant safer, but it cannot indicate that a plant is safe to a certain level and nothing can go wrong.

Comment:

The assignment of frequencies to deterministic stylized events is of concern (e.g., the frequency of exceedance of the PMP for a stylized 1-hour event), even understanding the deterministic concept and the assignment of assumptions. Ponding elevation on the ground is an important example. The value for this can come from a 1-hour event, 6-hour events, or any other durations, as well as different temporal distributions and other characteristics. As a result, we report this one number on this stylized event, and we underestimate the frequency of, for example, experiencing inconsequential flooding. There is not a one-to-one mapping between the concepts. Analysts should be cautious when using a frequency of one duration of precipitation from one type of event when the relevant consideration is the frequency of exceeding ponding elevations on the ground as a result from many different types of events.

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Response

This is a good point. The PRAs in other areas show something similar. For example, the risk of core damage from a large-break loss-of-coolant accident (LOCA) is extremely small because the probability of a large-break LOCA is extremely small. The probability of a small-break LOCA leading to core damage is usually significantly higher just because the event happens more often.

Similarly, a lesser but longer duration flood may result in more water intrusion into a plant. Even if the flood does not result in a higher water level in the plant, it may still have some impact because it occurs more often and thus may challenge the plant enough to be more risk significant than the isolated worst-case scenario.

2.3.4 Day 2: Session 2B - Leveraging Available Flood Information I This session covered research to develop the means by which the staff can leverage available frequency information on flooding hazards.

2.3.4.1 Development of Flood Hazard Information Digest for Operating NPP Sites, Curtis Smith*, Ph.D. and Kellie Kvarfordt, Idaho National Laboratory (Session 2B-1; ADAMS Accession No. ML17054C508) 2.3.4.1.1 Abstract The objective of this project is for Idaho National Laboratory (INL) to develop and demonstrate a database architecture for a flood hazard information digest to facilitate gathering, organizing, and presenting a variety of flood hazard data sources. Additionally, INL is assisting in the population of the digest.

The goal of the project is to provide information and tools to support external flooding-related activities, particularly the risk-informed aspects of the Significance Determination Process (SDP ).

Under the SDP, the use of probabilistic flood hazard information and insights is an important input in the determination for follow-up inspection actions and resource allocation, and for risk-informing licensing actions. However, the NRC staff has had to improvise and only use probabilistic flooding hazard estimates on an ad hoc basis, in a limited manner, with acknowledged limitations with respect to the technical defensibility of the resulting estimates.

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

The following major data sources have been identified and targeted for inclusion in the Flood Hazard Information Digest:

• flood hazard information, including flood protection and mitigation strategies, available from sources that include NUREGs, final safety analysis reports, individual plant examination for external events submittals, and SDP analyses

• recommendations of the Fukushima Near-Term Task Force (ML111861807) 2-177

o Recommendation 2.1: Flood hazard reevaluation submittals o Recommendation 2.3: Walkdown submittals

• available precipitation frequency information from the NOAA Atlas 14 database

• available flood frequency information from U.S. Geological Survey (USGS) databases

• available information for hurricane landfall and intensity along U.S. coastal areas In addition to providing access to these and other data sources, the flood digest must provide, where needed, guidance for using the available information.

The Flood Hazard Information Digest has been implemented as a cloud-based Web application.

The digest utilizes 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 emphasis of the Safety Portal is to serve as a resource to promote collaboration between producers and users of information. The flood digest shares available services such as user account management, file sharing, and a publications/permissions/

subscriptions model.

The Flood Hazard Information Digest application is available to eligible users at https://safety.inl.gov/flooddigest. New users will be prompted to register for access. Sample data for selected plants are currently available, and data population efforts for remaining operating NPP sites are underway. The bulk of data population is targeted for completion by end of this fiscal year. The flood digest application has been implemented in such a way as to facilitate the inclusion of additional external event hazards if needed.

2.3.4.1.2 Presentation 2-178

2-179 2-180 2-181 2-182 2-183 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, William H. Asquith^, Ph.D., P.G., U.S. Geological Survey, Lubbock, TX; and Julie Kiang, Ph.D., U.S. Geological Survey, Reston, VA (Session 2B-2; ADAMS Accession No. ML17054C509) 2.3.4.2.1 Abstract USGS , in cooperation with the NRC, is investigating statistical methods for flood hazard analyses. One task is to provide guidance on very low annual exceedance probability (AEP) estimation and the quantification of corresponding uncertainties using streamgage-specific data.

The term very low AEP implies exceptionally rare events, defined as those having AEPs less than about 0.001 (or 10-3 in scientific notation). Such low AEPs are of great interest for flood frequency analyses for critical infrastructure such as NPPs. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to small AEPs . Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS -

PeakFQ software implementing well-known guidelines of USGSError! Bookmark not defined.,

Bulletin 17B Guidelines for Determining Flood Flow Frequency, issued 1982 (method of moments), and pending updates (Bulletin 17C , the expected moments algorithm using the Pearson type III) was specially adapted for an Extended Output user option to provide estimates at selected AEPs from 10-3 to 10-6. Parameter estimation methods, in addition to the product moments and expected moments algorithm, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This project comprehensively studies multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, NJ, and 01638500 Potomac River at Point of Rocks, MD).

This task involved the four techniques of parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates related to AEP are depicted and quantified as two primary forms: quantile (aleatoric (random sampling) uncertainty )and distribution-choice (epistemic (model) uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches.

Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated to hone distribution choice. The results in a generalized sense show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values. Future work includes consideration of nonstandard flood data at streamgage locations, regional information, and nonstationarity in flood frequency analyses.

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2.3.4.2.2 Presentation 2-185

2-186 2-187 2-188 2-189 2-190 2-191 2-192 2-193 2-194 2-195 2-196 2-197 2.3.4.2.3 Questions and Answers Question:

Have you considered other examples in the United States besides the Northeast, such as the Pecos River?

Response

This study is limited to the Potomac and Raritan Rivers. For this project, the criteria required a very long record period and a fair amount of skew in the data, free from regulation; therefore, it was limited to this area. The continuing work in this project will review data from a host of additional gauges across the United States, from west to east.

Comment:

One of the future tasks in the project appears to be to consider nonstationarity by urban effects or climate change. The experience with NOAA Atlas 14 leads to some suggestions. First, when discussing nonstationarity, you should distinguish between a change in frequency or in magnitude, or whether there is a change in both of them. If there is a change in magnitude only, approaches based on annual maxima series could be applicable. However, if there is a change in the frequency of extreme events, either streamflow or precipitation, you should change the series used in the analysis. That is, you should go from annual maxima series to partial duration or peaks over threshold, because extremes are more common in recent periods than they were before. Applying this suggestion does pose some issues. Methods currently being used for streamflow and precipitation, in USGS Bulletin 17B and NOAA Atlas 14, are based either on conventional moments or L-moments and cannot be adjusted easily to include nonstationarity.

However, the maximum likelihood approach can be adjusted. L-moments were suggested for frequency in 1990s when sample sizes were relatively small, and relations showed that they were more reliable than maximum likelihood. However, with 20 more years of data available for the analysis of extreme events, there may no longer be a reason to use L-moments. Recent studies show that we should abandon L-moments in the analysis of extreme events and instead move to maximum likelihood. There are a number of other conflicts like this. For example, Federal agencies have agreed to use the term AEP. If we change from annual maximum series (AMS) to partial duration series (PDS), we will need to go back to using the terms return period or recurrence interval, because AEP does not go with the partial duration series. In general, care must be taken with terminology because we use different terms to define the same thing. For example, the term extreme event means different things to different professions and different people. An event for some is something that has a beginning and an end. We perform frequency analysis, which is very important for precipitation. We are not analyzing events, but rather the amounts per duration, which can be from a single event or multiple events. It is therefore necessary to distinguish between the two [events and partial duration series], because there are a lot of differences in methodologies that are used to analyze these two different things.

Response from NRC Project Manager:

Another example of the difference is considering the frequency of a particular volume for a dam on a reservoir. The number of events contributing to that volume is not important, but rather the frequency of getting a particular volume.

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2.3.4.3 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 Mete orology (Session 2B-3; ADAMS Accession No. ML17054C510) 2.3.4.3.1 Abstract Traditionally, deterministic methods have been used to determine inflow design floods based on a particular loading event to meet regulatory criteria. For infrastructure with high hazard potential, including nuclear facilities and many large dams, the probable maximum flood (PMF) has often been used as the inflow design flood. Risk-informed decision-making is currently used by the U.S. Bureau of Reclamation (USBR), USACE, and other agencies to assess the safety of dams, recommend safety improvements, and prioritize expenditures. This involves developing estimates of hydrologic hazards to perform PRAs . Hydrologic hazard curves provide magnitudes and probabilities for the entire ranges of peak flow, flood volume, and water surface elevations. There are multiple methods available to estimate magnitudes and probabilities of extreme flood events; these methods can be generally classified as streamflow-based statistical analyses or rainfall-based with statistical analyses of the modeled runoff. Method selection is based on the level of detail necessary and site-specific consideration, including data availability, hydrologic complexity, and required level of confidence. This presentation focused on describing recommended methods and approaches for extending frequency analysis methods beyond current consensus limits (AEPs greater than 1:105) for both rainfall and riverine flooding applications.

2.3.4.3.2 Presentation 2-199

2-200 2-201 2-202 2-203 2-204 2-205 2-206 2-207 2-208 2-209 2-210 g4-13 2-211

2-212 2.3.4.3.3 Questions and Answers Question:

Are any other similar projects underway, and what did you learn from their application?

Response

Another current project uses a SEFM [Stochastic Event Flood Model] to estimate frequencies, which gives a much better understanding of the process that is involved, both for infrequent events and more frequent events.

Question:

The presentation mentioned that a team of hydrologists came to a consensus decision. Do you have any more details about that?

Response

USBR has an internal review process and works in a team approach for more complicated studies.

2.3.5 Day 2: Session 2C - Leveraging Available Flood Information II This session presented research to develop the means by which the staff can leverage available frequency information on flooding hazards.

2.3.5.1 Collection of Paleoflood Evidence, John Weglian, EPRI (Session 2C-1; ADAMS Accession No. ML17054C511) 2.3.5.1.1 Abstract In a PRA, it is important to estimate the frequency of initiating events (events that can cause or demand an immediate trip of the reactor). The estimation of this frequency is challenging for rare events and particularly so for external hazards like external flooding, for which the historical record is limited to about 100 to 200 years. An external flooding PRA would use a flood hazard frequency curve that plots at much rarer return periods.

Various techniques are available to extend the data at a particular site, including the use of storm transposition and numerical generation of synthetic storms, but these are still based on data collected in the recent past. The investigation of paleoflood evidence (evidence of flooding that occurred outside of the observed record) has the ability to inform the record of actual past flooding events in the region of interest.

In major flooding events, debris and sediment can be suspended and transported long distances in the fast-moving water. When the water enters a low-flow region, some of the suspended material will sink and become deposits on the surrounding floor. If these deposits are preserved in the environment, they can be used to estimate the time of the event and the flood discharge.

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Paleoflood evidence can be found in terrace or overbank deposits when the water exceeds the riverbank and leaves the deposits on the surrounding land. These deposits may be good for estimating the frequency of flooding events that exceed that particular height, but they may not be good at estimating the flood stage for any particular event. Paleoflood evidence may also be deposited in caves or canyon walls, which could provide a good estimate for the flood stage, but the topography may be more prone to have one flooding event wash away the evidence of previous flooding events.

Paleoflood evidence has been used in arid climates with great success, but it was not clear if the same evidence would be preserved in humid climates. Initial research indicates that paleoflood evidence is preserved in humid environments but extracting the data may be more challenging than in arid environments.

2.3.5.1.2 Presentation 2-214

2-215 2-216 2-217 2-218 2-219 2-220 2-221 2.3.5.1.3 Questions and Answers Question:

The presentation stated that partial information is better than looking for perfect information and as a result having no information. How do you understand the environmental setting at the time of the flood and whether there is collaborating evidence? That is, how can you tell if the information available is reliable and how should it be used in a flood assessment? This is particularly important with regard to frequency and the development of hazard curves.

Response

The answer is very site specific. If a site appeared to be a meandering river that has changed flow paths you would consider results differently than at a site that is a canyon that has remained stable for 10,000 years. Therefore, the analyst has to consider the site environment and river basin and how they many have changed over time. Sensitivity studies provide on was to deal with this. For example, if you want to study how a river would change in depth or base height, you could consider depositional effects that lift up the riverbed and scouring effects that wear it down.

However, if you are looking at paleoflood evidence, the river may have moved up or down a little bit but may have roughly maintained the same width It takes a significant amount of water to make a difference in height at high elevations. However, in a sensitivity analysis, one can consider an area that is 10 feet deeper and determine whether that change has an impact on the results.

One concern is that though flooding may have occurred at some point in history, evidence of it cannot be found and so it would be missing from the data. However, this concern can be minimized by gathering information from multiple different locations. For example, data from the different universities that are working in different, yet nearby, locations could be combined. If there is evidence of floods around Knoxville, TN, that do not have corresponding evidence [from the sites studied by researchers working in the same area, but from] Alabama, that could indicate a problem and the need to gather data from many more places to have a good estimate of the flood history Question:

How is carbon dating applied to flooding studies for NPPs?

Response

Radiocarbon dating can be used only over a certain period back in time and requires something in the flood deposit that has enough carbon to analyze. For example, a flood deposit may contain a twig, but to use radiocarbon dating the analyst would have to know whether that twig would have the same date as that flood, or whether that twig had been present for long time before the flood occurred and picked it up and carried it away. Another sampling technique called optically stimulated luminescence (OSL) looks at the effect of exposure to the sunlight of particular crystals. A sample that has been buried in the dark for a number of years, then collected and kept in the dark before analysis, would generate an output that can be correlated to much longer timeframes than radiocarbon samples. Other techniques may be able to date samples that are much older, but paleoflood evidence from a million years ago would not be applicable because a sample from that time would have been under the ocean. Paleoflood evidence is applicable for flooding events with AEPs of about 10-2 to 10-4 but becomes less credible beyond that.

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Question:

The presence of freshwater clam shells and other evidence of reptiles and amphibians in these flood deposits can also provide some insight.

Response

Depending on the finding, the finding could have been affected by humans because humans have been present during that time period. For example, some dirt next to a canyon wall was sunken.

The geologists thought that it could have been the result of rainfall washing soil away. The archaeologist pointed out that looters digging for artifacts were a more likely cause. It takes a range of expertise to fully understand what the field evidence shows. You have to look in multiple places and see what all the evidence tells you to form a complete picture.

Question:

A researcher may find evidence in a particular location, but the site with the relevant frequency analysis is 10 miles downstream. What method would be used to try to bring that information together? Locations may have radically different responses in terms of the water surface elevation.

Response

EPRI has partnered with the Tennessee Valley Authority (TVA), which has two different models.

One includes all of the existing dams, but the other is a naturals model that assumes that none of the dams have been built. The naturals model can be run with a water level at a particular site, downstream or upstream, to determine water level at another site. The model has been calibrated with water flows. Models probably already exist for large rivers, but a hydraulic /hydrologic model could be built for the watershed of interest to determine the water height at the site of interest based on the water height at another location along the river. If the difference between the sites is significant, then the analysis would need to consider where the water came from in the first place.

For example, if the cause was some kind of precipitation event that added water to the watershed, the water could have fallen in one part of the watershed and not the other. As these rivers filter into each other and combine, a flooding event may affect one part of the watershed and not another.

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2.3.5.2 Paleofloods on the Tennessee RiverAssessing the Feasibility of Employing Geologic Records of Past Floods for Improved Flood Frequency Analysis , or Eastern US Riverine Flood Geomorphology Feasibility Study - Are Paleoflood Studies Possible in the midst of the tress and ticks? Tessa Harden*, Ph.D., USGS, Oregon Water Science Center; and Jim OConnor*, Ph.D., USGS, Geology, Minerals, Energy, and Geophysics Science Center, Portland, OR (Session 2C-2; ADAMS Accession No. ML17054C513) 2.3.5.2.1 Abstract A 2015 field survey and stratigraphic analysis, coupled with geochronologic techniques, indicate that a rich history of large Tennessee River floods is preserved in the Tennessee River Gorge area. Deposits of flood sediment from the 1867 peak discharge of record (460,000 cubic feet per second at Chattanooga, TN) appear to be preserved at many locations throughout the study area.

Small exposures at two boulder overhangs reveal evidence of three to four earlier floods similar in size to or larger than the 1867 flood in the last 3,000 years, one possibly more than 50 percent larger. Flood deposits are also preserved in stratigraphic sections at the mouth of the gorge at Williams Island and near Eaves Ferry about 70 miles upstream from the gorge. These stratigraphic records may extend as far back as about 9,000 years, preserving a long history of Tennessee River floods. Although more evidence is needed to confirm these findings, it is clear that a more in-depth, comprehensive paleoflood study is feasible for the Tennessee River. This study also lends confidence to the feasibility of successful comprehensive paleoflood studies in other basins in the eastern United States.

2.3.5.2.2 Presentation 2-224

2-225 2-226 2-227 2-228 2-229 2-230 2-231 2-232 2-233 2-234 2-235 2-236 2-237 2-238 2-239 2.3.5.2.3 Questions and Answers Question:

Among other things, the project looked at mica grains to help understand the energy of the flood and its deposit. Could you describe the energy regime and how you try to understand how big the flood is based upon the evidence of these so-called marker layers, as well as the scenario of the flood that might have deposited them? For example, could there have been ice jams or some earthquake that caused debris to create a dam that then caused the water levels to rise? What kinds of analysis have you done to think about the nature of the flood itself?

Response

The first question relates to the method of extrapolation or taking the information of the deposit to determine how big the flood was. The approach is actually simple. The flood had to have a stage that was at least as high as a deposit. However, that is all that is known. For big floods or thick deposits, attempts are made to trace deposits up as high as possible in the existing records, but it is still not possible to know how much bigger the flood was above the deposit. One positive aspect of these new approaches is that they can accommodate that type of data (i.e., data that quantify the presence of a certain number of floods above a certain level in a given time period, but that do not indicate how much more above that level the floods were). However, that type of data is efficiently incorporated using these maximum likelihood techniques and the estimator approaches into the flood frequency analyses.

The second question relates to context issues. Historic information is valuable, in that if you find a deposit at the level of a known flood, it is certainly plausible that the found deposit was from an event similar to the known flood. However, if you find a deposit that is significantly higher or coarser, then you would need to consider what other types of mechanisms could generate higher stages. This is another advantage of these field studies in that they reveal considerations that are outside of those you were visualizing as the potential range of hazards affecting a site. For example, there could have been some sort of landslide in the Tennessee River Gorge that blocked the valley. Although that changes your flood frequency analysis, it is important to know for the plants upstream or downstream. As a result, these kinds of geological approaches are doubly valuable because they can tell you something about the problem of interest, but they might also tell you about problems that you should be interested in but did not know about.

Question:

Has Bulletin 17C been published yet?

Response

Bulletin 17C is out for peer review, with plans to publish it by summer 2017 3.

3 Bulletin 17C was published March 29, 2018 as England, J.F., Jr., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Jr., Veilleux, A.G., Kiang, J.E., and Mason, R.R., Jr., 2018, Guidelines for determining flood flow frequency Bulletin 17C: U.S. Geological Survey Techniques and Methods, book 4, chap. B5, 148 p.,

https://pubs.usgs.gov/tm/04/b05/tm4b5.pdf 2-240

Question:

Have you used heavy mineral analysis? This could be an indicator for the movement based on the gravitational forces and the flood forces. It could also help in considering the particulate size distribution and determining how particles are distributed in the floods in different periods of time.

Such information may indicate the dynamics for the flood forces taking place. The particulate size and shape of the particulate could be useful in determining the erosional forces that took place and thus the size of the flood. It may also be useful to conduct a heavy metal analysis for all of the sediments. It could be useful to separate the heavy minerals in the sediments of different layers and try to analyze them, including how they are distributed among the layers.

Response

This studys stratigraphic approach is very simple, but there are many more complicated approaches that could be taken involving the techniques mentioned that would likely require expensive equipment. USBR does use the particle size, although it is not quantified in a rigorous way. Rather, it is used in a qualitative way in that, if one deposit is coarser or thicker than the other, it would hint that it was from a bigger flood. Although such an observation does not indicate flood size for certain, it inspires us to look higher. The minerology is also considered informally, mainly to be secure about the source of the sediment. For example, in the Tennessee River sites, one question is whether those deposits could have resulted from water coming down the hillslopes and somehow reaching underneath and into those caves. In that example, we know that could not have been the case because there is no source of mica in the rocks on the hill slopes.

By contrast, the Tennessee River sediment is full of mica, which is easy to recognize in the sediments. Therefore, if mica is present, it indicates that this is Tennessee River sediment. This approach is simple, straightforward stratigraphy, although there are certainly ways to make it much more complicated and spend a lot more money doing it.

Question:

Did you observe any terrace deposits either in the Gorge or upstream that you could use to bracket the flood stages?

Response

The Gorge itself does not contain much in the way of terrace deposits or alluvial deposits. At the upstream entrance to the Gorge, Williams Island has about a 10,000-year record of stratigraphy.

We did study that; however, because that island was inundated by the 1867 flood and other historic floods, it does not reveal much about the full size of the floods. The stratigraphic record of thick and thin deposits could be correlated with the better record of high floods along the canyon margins. Further work at Williams Island will be done to determine whether that floodplain stratigraphy can be linked with what is seen up higher. The floodplain stratigraphy upstream could also be considered in terms of trying to evaluate flood history. In addition, although the valley was quite wide upstream, in some places the river banks up against bedrock, and that bedrock itself also contained higher flood deposits that could be evaluated. In addition to the Tennessee Gorge, other places on that river corridor are also worth investigating.

Question:

When the 1-in-500-year flood essentially turns out to be in the 60-year systematic data range, and research for the history of the flood reveals other floods, how can you be confident that you have 2-241

not missed information in between the big floods, or on a lot of other smaller floods (but that are bigger than the regular-sized flood), affecting the statistical tails because you do not have the precisions that are available from observing floods (i.e., those 60 years of systematic data)?

Response

One way to address this issue is by conducting a sensitivity test. For example, if the data are missing three floods in a particular timeframe between given sizes, how big a difference does it make to the results? It turns out that if the biggest flood in the last certain number of years can be determined, the other ones do not matter as much. This results in an interpretative conclusion, such as We know that we have had at least one flood of 50,000 cubic feet per second in the last 1,000 years. To help constrain the statistical tail, you would need to draw a conclusion about what has not happened. For example, if you can say that, because of the stratigraphy here, there was no flood this high in the last 10,000 years that helps constrain the flood frequency distribution.

This type of information is also now much more efficiently employed in these newer flood frequency estimation techniques. In the end, you do need to have some confidence in some aspect of the record. This is one reason why both these studies are being done in parallel, to identify what happens when two different groups are doing the same work on the same river. Do the interpretive aspects work out to the same results in the end?

Comment:

In licensing, the approach is to base everything on procedures. The standard is presented, and peer reviews are conducted to determine whether the standard is met. The goal is to remove judgment from the process. In this discussion, there is concern with the extent that extrapolation can be done. These studies involve the professional judgment of expert geologists, statisticians, and others and provide good information to help improve decision-making. How does the NRC anticipate applying the information from these studies and this discussion into the nuclear power plant licensing process?

Response: NRC Hydrologist The NRC will need to use multiple lines of evidence, multiple methods to increase our confidence in decision-making. This information is very good input to the risk-informed decision-making process, even though the answers may not be as crisp as we are used to obtaining in deterministic analysis.

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2.3.6 Day 2: Session 2D - Reliability of Flood Protection and Plant Response I This session considered the development of guidance for assessing the reliability of flood protection and plant response to flooding events.

2.3.6.1 EPRI Flood Protection Project Status, David Ziebell and John Weglian*, EPRI (Session 2D-1; ADAMS Accession No. ML17054C515) 2.3.6.1.1 Abstract EPRI is actively helping nuclear electric generating companies manage the risk of external flooding by providing good technical practices where needed. The Flood Protection Systems Guide was published in November 2015 (EPRI ID 3002005423, available at cost to non-members) and describes flood-protection components at NPPs and the design, testing, inspection, and maintenance of these components. This presentation highlighted some of the information provided in that EPRI guide and describes a follow-on research and development effort to identify and communicate good practices in maintaining an external flooding design/

licensing basis. These guides are based on information collected from a consensus of industry peers. EPRIs members have asked for information to assist in the development and management of their flood-protection basis requirements in regard to external flooding-related events.

The published guideline gives specific attention to flood barrier penetration seals because of the relative complexity, varying designs, and lack of existing codes and standards for these components. Although the focus of the guide is on external flooding-related events, this guide provides descriptions of components, design considerations, maintenance activities, and other topics that can apply to both external and internal flood-protection requirements. Additional sections within the guide address recent industry events and major considerations for establishing and managing flood-basis requirements at the site level.

The design/licensing basis guide being developed is based on a detailed survey of design and management practices regarding maintaining adequate basis for operability of external flood -protection components at NPPs. This presentation described the survey approach and summarized the current status of the results being analyzed. In addition, this presentation described the planned report outline, which constitutes current views as to the kinds of management elements needed for an NPP owner to effectively manage the risk of external flooding.

Examples of key elements to be described in the guide include the following:

• design

• qualification

• maintenance

• design change process

• inspection

• periodic surveillance of flood protection features

• mitigating strategies for off-normal conditions

• training

• reevaluations of the adequacy of management methods

• integrated assessment

• documentation and reporting 2-243

2.3.6.1.2 Presentation 2-244

2-245 2-246 2-247 2-248 2-249 2-250 2-251 2-252 2-253 2-254 2.3.6.1.3 Questions and Answers Question:

You mention triggers and the advanced warning aspect of this effort. Because a warning affects the time available to potentially perform some actions, how was this aspect considered?

Response

I will note that I do not have personal insight into this project and am just presenting it. External flooding is the only hazard that we consider that might provide time for a warning. The approach of a forest fire may also permit such a warning. Other hazards do not give that flexibility. Certain flooding events may allow between hours and days of forewarning that the event is coming, and temporary barriers such as sandbags could be included. Considerations would include how long it takes to put up those barriers, the training requirements, and best practices, both in actions and the triggers to use. Guidance provided should be clear-cut and unambiguous so that users will not get it wrong. Timing and training should be addressed to make sure that users implement those actions correctly.

Follow-up Question:

Many of these cases happened very recently because of the reassessment and the walkdowns.

To what extent does this survey map or provide an image of a situation that is in flux? How much variability was evident between people who did have a well-established external flood program and those that did not, and between people who had well-established triggers and those that did not.

Response

The goal is to find and report on the best practices of each particular aspect that are identified by industry respondents. The report will not consider the variability in the answers but provide the best practices and not the worst.

Comment:

With regard to external hazards, certain high-wind scenarios would also come with some sort of a warning time. In some cases, that will actually be correlated to the warning time for flooding. For example, in a hurricane, wind and storm surge are predicted. To a lesser extent, the convective environment that would be prone to hazards such as tornadic outbreaks might be known.

Response

Utilities have high-wind procedures and actions that they would implement knowing that such a hazard was coming. This may include a tornado warning and additional steps, such as not sending people outside anymore.

Comment:

With regard to trigger points, some organizations have strived to identify actions that are easy to reverse and not too expensive and that can be based on the forecast. Actions that are hard to reverse and very big decisions should be based on rain on the ground. Quantitative precipitation 2-255

forecasts (QPFs) can predict a large amount of rainfall but only a small amount falls, and organizations do not want to recommend difficult actions and be seen to cry wolf.

Response

Weather forecasters probably do not forecast the absolute extreme. For a rain forecast of 4 to 6 inches, the 90-percent probability may be 12 inches, but they are not forecasting 12 inches.

They likely have particular wording to use when the model actually shows that 12 inches will fall.

The approach also probably differs between an NWS forecast and one from the local news station.

Question:

Given that one of the future elements of the flood protection status will be the periodic surveillance of flood protection features, will flood risk significance be a criterion as to what, when, and how to inspect that flood protection feature? For example, because a plant may have 1,000 seals, external flooding would be a hazard. Would you consider all 1,000 of the seals or can only those seals that may lead to the greatest flood possibility be the focus, to best use limited resources?

How will this periodic surveillance be accomplished?

Response

This activity will not incorporate risk-based methodologies to try to consider that aspect. Instead, it covers more of a deterministic side of the equation that looks for best practices in the industry and actions people take. If a reasonable approach to prioritization is made available, that would be communicated.

2.3.6.2 Performance of Flood-Rated Penetration Seals, William (Mark) Cummings*, P.E., Fire Risk Management, Inc. (Session 2D-2; ADAMS Accession No. ML17054C516) 2.3.6.2.1 Abstract Overall risk analyses of NPPs include the need for protection against potential flooding events, both internal and external events. Typically, a primary method used to mitigate the effects of a flooding event is the implementation of flood-rated barriers that isolate areas of the plant from the intrusion or spread of flood waters. Any penetrations through flood-rated barriers to facilitate piping, cabling, or other components 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. The NRC has 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 provided a status of that research project and outlined plans to perform flood testing on candidate seal assemblies. This testing will evaluate the ability of the procedures to adequately address and record the various performance parameters of individual seal assemblies/materials. The results of this research program may be used in the evaluation of a seal assembly/material and whether it is acceptable for protecting penetrations in flood-rated barriers.

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2.3.6.2.2 Presentation 2-257

2-258 2-259 2-260 2.3.6.2.3 Questions and Answers Question:

Companies such as Nuvia may test seals for NPPs, maybe in France or other parts of Europe.

Have you considered whether there are protocols outside of the United States that can inform this work?

Response

We are unaware of any other company in the world besides NUVIA that is making or testing actual flood seals. To consider seals associated with maritime uses, we looked at the standards available through Lloyds, the American Bureau of Shipping, and the military, well as non-U.S. entities, in terms of the authorities with jurisdiction. However, none of those organizations appear to be doing this work using a standard format.

Question:

This research appears to focus on new, manufactured seals produced by different manufacturers and testing them in a defined facility. How will this research translate to the plants? Will they have portable options to test seals? Will those be recommended in the study? How are you considering installed seals?

Response

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Ideally, many of the plants are using defined configurations, especially for the mechanical seals.

Using the data provided by the plants on the exact nature of the seals, specific assemblies can be replicated in a test. The assembly may not be one that is currently marketed as a flood penetration or flood-rated seal assembly. This type of testing is planned for the second part of the study Follow-up Question:

The task will then be to provide recommendations on how to test the seals in place, and the pass criteria will be no more than a certain number of cubic centimeters of leakage.

Response

The determination of the metrics is the question. The easiest approach, used in NUVIAs testing, is to state that the passing criterion is zero leakage. It will be more challenging to develop metrics that are performance based or risk informed (i.e., a little leakage may not lead to fail, if other mechanisms are in place to ensure that this leakage will not impact plant safety). For example, leakage of 0.6 liters per hour for one particular seal under 40 feet of head pressure may not pose a risk for plant A, but may for Plant B. Or 15 such seals replicated in a wall may pose a risk collectively. Therefore, we are trying to develop a protocol that gives a standardized way of looking at seals, testing those seals, and capturing whether there is leakage and if so what that leakage rate is. This information would return to the manufacturer to adjust designs for new seals.

Existing plants would use the results as a basis to determine whether or not to replace a particular seal.

Question:

A previous presentation discussed the failure of some seals at the Blayais site in France. Did

Électricité de France perform any failure mode analyses that could be taken into account in this study and the resulting recommendations? For example, you have spoken about hydraulic tests.

What about thermal, chemical, biological, and longevity effects on the seals, or does that add too much complexity? Is the study only considering whether the seal will hold water regardless of the environment it will be put in?

Response

The development and use of seals does need to take many other variables into account, such as chemical (i.e., material interactions). Many considerations are important to the adherence of a material, such as whether a penetration is sleeved or just core drilled. Manufacturers need to indicate what their material can or cannot do and then put restrictions on the use of the seals, such as that the penetration has to be sleeved or it has to be core drilled to get the proper adhesion properties to allow the seal to work.

A fire penetration seal for an application where seismic factors are a consideration, such as for a seismic rated wall or barrier, would have to be designed so that the penetrant is braced and moves as the wall moves. A significant amount of flex would not be expected in the penetration itself. The significance of such properties would need to be considered. The potential for an external seal to be exposed to impact damage during a flood would need to be considered.

However, such analyses involving different variables can become very complicated, and the limits of the proposed protocol need to be established. From a thermal perspective, materials will experience some expansion, but a fire test would not consider this. For the study of seals and 2-262

flooding, some questions will require interaction with manufacturers about the limitations of a given material. For example, can it support expansion and contraction? Does the material shrink?

Are small gaps a problem? Over time, minor leakage around the seal may begin to occuris this a separation of the material from the wall, whether it is a sleeve and concrete, or is it separation from the penetrant? Although such questions rely on the manufacturer as the one with knowledge of its chemical formulas, many of these data are proprietary and will not be available to researchers. Manufacturers should ideally perform the testing or installing, where appropriate, so that they cannot blame performance issues on incorrect testing or installation. A risk-informed approach needs to take such factors into consideration, as well as plant-specific sensitivity analysis (i.e., what makes a big difference for a particular plant?).

Question:

When modeling the degradation processes, is it possible to speed up the degradation? Is it possible to have a seal in the test apparatus that performs more like what is actually out there now, which could be very old?

Response

This is less of a problem for mechanical seals, but boot seals can crack. In the field, a visual inspection would reveal a condition such as rust on a mechanical seal that would indicate that it would not perform as well. Other materials might show surface cracking. There are ways of age-accelerating such conditions, but these may or may not be appropriate or representative. For example, exposing a seal to higher heat or higher levels of ultraviolet light for short periods of time may make it age faster. The level of accuracy of these methods is not known in terms of replicating how a seal would perform after 20 years.

Follow-up Question:

Errors of installation, such as a failure to comply with the development length given in the manufacturers specifications, could occur. Do you plan to look at seals that are outside of the specifications for installation to determine how that does or does not affect their performance?

Response

The human element is certainly a consideration. If the manufacturers specifications are not followed, a seal may not adhere or will pop out with a higher pressure. However, the assumption is that when an installation of a seal is signed off, that means that the seal was installed in accordance with the manufacturers requirements and the manufacturer or certified representative is liable for that assertion. If the seal fails, then an investigation would consider the reason for failure and whether it was a materials or an installation issue. In-service and other nondestructive testing cannot always tell whether installation was performed properly. Destructive testing is performed in some plants for some applications. If any were not installed properly, further investigation would be needed on others.

Comment:

Questions have arisen on aging and the fact that the performance or future performance of both new material/seals and existing seals needs to be considered, in both new and existing plants. In addition, once there is an accepted testing protocol, plants that are decommissioning will provide an opportunity to harvest and then test various types of seals that have been in service for various 2-263

periods of time. This could provide an opportunity to apply the protocol and gather a lot of very significant data that could be put to use in plant PRAs.

Response

The engineering of the seals is a factor. Evaluations are trying to extrapolate some of the test data to existing scenarios. For example, some seals use low-density forms. Testing has shown that in some cases, putting pressure around a low-density foam will cause it to shrink and can allow a greater flow of water around the seal. Evidence of that kind of performance may force the industry to investigate further and make some more broad-based decisions on the types of seals that may not be appropriate for use in a flood-rated barrier (as opposed to a fire-rated barrier).

It is a great suggestion to take advantage of opportunities to extract some seal materials from existing plants, because in some real-world applications, such as in a cable spreading room, accessing the penetrations is difficult, even for visual inspection.

Question:

Will the product provide guidance on selecting bounding tests for other seal assemblies that might have varying geometric properties (e.g., annular spaces, penetrant size/number)?

Response

Such tests would need to be done on many different configurations to bound those materials and how they perform. For example, a 24-inch-diameter penetration has a 2-inch conduit running through it, which means that there is a lot of free surface area available to pressure. Such a configuration may react significantly differently when 10 conduits are running through that same penetration, with less free area to be subject to pressure. However, this configuration has more surface area, depending on the adhesive property of the materials, for the seal to adhere to that will keep the water from pushing through. Ultimately, this requires considering the types of materials that are used as the seal material and their individual properties to guide some of those bounding evaluations. If a material is tested with a fairly broad disparity between the configurations, it may be possible to extrapolate to determine how configurations in the middle of tested extremes will work as well. However, this will likely require a material assembly-type specific evaluation each time.

Question:

Are the tests under discussion laboratory testing or is situ testing or both?

Response

At present, tests require an appropriate test apparatus, seals are not tested on a wall where they have been installed. This would not be feasible as a plant will not permit flooding of a compartment to test the seals there. Testing requires having a laboratory with the appropriate apparatus that can appropriately run the test using a standard methodology. The testing can involve changes to the protocol and a sensitivity analysis. For example, should the seal be hit immediately with a full range of pressure or should that pressure build up? There is a wide range of variables to address, depending on the flooding scenario.

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Question:

There is a distinction between qualification and acceptance testing versus in-service testing. In-service testing may not be feasible for items such as flood seals. Are there examples of in-service testing for fire seals?

Response

A fire seal would be examined visually using that manufacturers recommendation for what it should look like when new. If some cracking is observed, look to the manufacturers specifications. However, the only way to verify that a seal is still good is to perform destructive testing. To do this, a certain percentage of the seals would be removed and examined to determine whether the seal was still good. If those seals pass, there is a higher level of confidence in the installation of other seals with the same type of installation and installed during roughly the same time period.

Question:

Could an acoustic technique be used, either in the laboratory or in situ, in order to measure performance? Acoustic techniques are very effective in doing that. Long-term performance of the material and any kind of structural deformation could be examined. Even if it is not clear whether the material is degraded, you could see a lot about how the structure changes on an atomic scale.

Response

This is outside my area of expertise. Acoustic testing may not be able to measure minor shrinkage that is not even visible to the naked eye. However, such testing may be able to provide information on some properties, such as the density and whether the material has any open areas or pores. Whether such testing is appropriate would likely depend on the specific material to be tested.

Question:

Some dams are solid concrete and might have 30 or 40 feet or more of head. Are you are assuming that the walls themselves are leak free?

Response

This protocol is not meant to assess the actual wall leakage.

Follow-up Question:

If a plant has a concrete wall 2-feet thick, what would have more leakage, the wall or some kind of penetration?

Response

Are you considering whether the concrete is water resistant or cracks over time? If there is a crack through all 2 feet of a wall, there are likely other issues from a structural standpoint.

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Follow-up Question:

We have a seminar in about 2 weeks on the alkali-silica reaction. That may be a case that results in a lot of leakage through the concrete.

Response

As part of their installation for a core drill, some manufacturers specify that a sealant needs to be applied to the concrete because there was some reaction between the material and the concrete that would not allow it to adhere properly.

2.3.7 Day 2: Daily Wrap-Up Question and Answer Period Question:

Speakers were talking earlier about using fitting or uncertainty in frequency analysis and the apparently wide uncertainties in the AEP curve, especially for the very rare event. Is it necessary to examine so many different probability distributions that do not appear to vary among themselves within the uncertainty limits?

Response: Joseph Kanney, Hydrologist, NRC This behavior is only evident once the analysis has been done and the different distributions evaluated. The point is to characterize and quantify the uncertainties. One way to do this is to run through the different factors that are contributing to the epistemic uncertainties. In the case of the flood frequency or precipitation frequency analysis, the different distributions are a key contributor to the uncertainties. It may turn out that, for some of the examples, the analysis shows that that there was not a lot of difference between certain distributions. The problem is that this is not known until the analysis is done.

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2.3.8 Day 3: Session 3A - Reliability of Flood Protection and Plant Response II This session considered the development of guidance for assessing the reliability of flood protection and plant response to flooding events.

2.3.8.1 Effects of Environmental Factors 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; and Scott Taylor, Ph.D., Battelle Columbus (Session 3A-1; ADAMS Accession No. ML17054C517) 2.3.8.1.1 Abstract Following the Fukushima nuclear accident, the NRC identified the need to ensure the manual actions for flood protection and mitigation(FPM) at NPPs are both feasible and reliable.

Environmental factors and conditions associated with floods that trigger manual actions for FPM can adversely affect the operators ability to perform these actions. In 1994, a study (NUREG/CR-5680, The Impacts of Environmental Conditions on Human Performance, issued September 1994) reviewed available research on the impacts of environmental conditions (ECs) on human performance. The current research is part of the NRCs PFHA research plan in support of developing a risk-informed licensing framework. It aims to apply the lessons learned from NUREG/CR-5680 and more recent research on how ECs affect human performance for actions similar to NPP FPM manual actions. The first year of the project focused on characterizing manual actions from available NPP FPMs, developing a conceptual framework for assessment of impacts of ECs on human performance, characterizing ECs that are expected to be associated with floods that may trigger NPP FPM procedures, and reviewing the research literature related to effects of ECs on human performance. The second year of the current research has continued to refine the conceptual framework, complete the review of more recently available literature, and propose a proof-of-concept method for application of the available information within the conceptual framework.

The conceptual framework represents an FPM procedure as a set of manual actions, tasks and subtasks, generic actions (Gas), and performance demands (PDs). A manual action is a distinct group of interrelated tasks that are performed outside the main control room to achieve an operational goal. A task is one step of a manual action that has a distinct outcome or predetermined objective contributing to accomplishment of the manual action. A task generally requires both motor and cognitive abilities. Several subtasks may comprise a task. A GA is an individual component of a task or subtask that is sufficiently simple to evaluate the impact of ECs on human performance. Successful completion of a GA may require several PDs, which are human abilities including cognitive, motor, and communication. The PDs were developed from three sources: (1) NUREG/CR-5680 performance abilities, (2) OBrien et al. (1992) 4 task taxonomy, and (3) cognitive functions from NUREG-2114, Cognitive Basis for Human Reliability Analysis, issued January 2016. The proposed PDs include (1) detection and noticing, (2) understanding, (3) decision-making, (4) action, and (5) teamwork. The PD action is further subdivided into fine motor and coarse motor skills, and the PD teamwork is further subdivided into (1) reading and writing, (2) oral communication, and (3) crew interaction.

4 OBrien, L.H., Simon, R., and H. Swaminathan, Development of the Personnel-Based System Evaluation Aid (PER-SEVAL) Performance Shaping Functions, United States Army Research Institute for the Behavioral and Social Sciences, 1992.

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The literature review was structured to integrate the most recent research information with that assembled in NUREG/CR-5680, address ECs that had not been covered in that review and present the findings in a format that is most useful for those reviewing and assessing performance impacts from the range and combinations of tasks, Gas, and PDs pertinent to outdoor work in varying weather conditions. Because the literature reviewed represented a wide range of methods, objectives, variables, and rigor, the presentation also provided an overview of the state of the literature on performance effects on a range of ECs that include those associated with extreme weather conditions.

The presentation used an example to describe a proof-of-concept method to demonstrate how impacts can be assessed on a task that is part of an FPM procedure taken from a real NPP.

Research on ECs impacts is available in four categories: (1) quantitative information that is directly applicable, (2) quantitative information that is less directly applicable, (3) qualitative information that may be used to inform expert judgments or sensitivity analyses, and (4) no information (i.e., a research gap). The proof-of-concept method as illustrated by the example has limitations that need to be addressed. Finally, potential future research topics were presented that will further improve upon the conceptual framework and facilitate application of the framework to evaluation of FPM manual actions at operating NPPs.

2.3.8.1.2 Presentation 2-268

2-269 2-270 2-271 2-272 2-273 2-274 2-275 2-276 2-277 2-278 2-279 2-280 2-281 2.3.8.1.3 Questions and Answers Question:

IMPRINT is not available to the public. The framework seems to lead to a deterministic yes/no answer rather than trying to establish the probability that an action is successful. However, the probabilistic information is important, and definitive answers on the time required are not needed on a generic basis because of the site-specific nature of actions. We developed a simple model and structure that cover some of these.

Response

IMPRINT, which is a stochastic tool, provides statistics, and the Monte Carlo simulation can give probability information. The result is site specific and condition specific depending on the site (access road, obstacle, etc.). Lead time is important.

Question:

It would be a valuable tool. How would it handle the intersection between an emergency condition (or isolated condition) and a sunny-day version (nonemergency condition)? It seems that key assumptions for emergency conditions are more or less vulnerable.

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Response

We did not look at emergency conditions in the current scope of work (e.g., stress or perception of fear); we focused on what is in the literature and conceptual framework; those issues would be part of the next steps. We needed the simple example to show things could be done.

Question:

How do emergency conditions effect the key assumptions?

Response

Those are some things that need to be worked through [in another scope] (e.g., when crew members are not available to go out and perform tasks). This might actually be best to look at from a design point of view and assess feasibility to make procedures work.

Comment:

If the control room personnel believe the water is dangerous, they will not send people out (or vice versa). Perception may be more important than actual conditions.

Also, there is a hierarchy, in that the top three adverse conditions might be the most controlling, so all factors do not need to be considered.

Question:

Secondary effects are also important. For example, even a small elevation of water (from a local intense precipitation event) in a switch gear room with energized equipment would be an issue.

Although there are not a lot of forces from the water, the energized equipment poses a larger risk. How can this be translated into probabilities of basic events?

Response

Many of the secondary effects are very site specific, along with the perception of fear. This framework can allow analysts to plug in to a human reliability analysis or PRA framework that allows for the determination of when actions are not feasible, and mitigation is required.

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2.3.8.2 Modeling Total Plant Response to Flooding Events, Zhegang Ma*, Ph.D., P.E., Curtis L. Smith, Ph.D. and Steven R. Prescott, Idaho National Laboratory, Risk Assessment and Management Services; and Ramprasad Sampath, Centroid PIC, Research and Development (Session 3-A2; ADAMS Accession No. ML17054C518) 2.3.8.2.1 Abstract All NPPs 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 decisionmakers. Many unique challenges exist in modeling the complete plant response to the flooding event. Structures, systems, and components (SSCs); flood protection features; 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 SSCs and operator actions during an external flooding event. In support of the NRC PFHA research plan, INL was tasked to develop such new approaches and demonstrate a proof of concept for the advanced representation of external flooding analysis. This project developed a work plan and framework to perform a simulation-based dynamic flooding analysis. This framework was applied to a LIP event as a case study. A three-dimensional (3D) plant model for a typical pressurized-water reactor and 3D flood simulation models for the LIP event were developed. A state-based PRA modeling tool, Event Model Risk Assessment using Linked Diagrams (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 state-based PRA model was developed to represent two accident sequences in a simplified traditional general transient event tree, along with incorporating 3D simulation elements into the logic so that the PRA model could communicate with the 3D simulation models. This integrated EMRALD model was run with 34 3D dynamic 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. They can provide visual demonstration of component or system behavior during a highly spatial- and time-dependent flood event. They could provide additional important insights to risk-informed decisionmakers. 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 by performing a standalone analysis that focuses on specific issues with limited sequences and components (e.g., FLEX).

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2.3.8.2.2 Presentation 2-285

2-286 2-287 2-288 2-289 2-290 2-291 2-292 2-293 2-294 2-295 2-296 2-297 2-298 2-299 2-300 2-301 2.3.8.2.3 Questions and Answers Question:

Can the 3D modeling approach be applicable to seismic?

Response

This is not part of the scope of this project, but there are some examples. It was done in a U.S. Department of Defense project, looking at a detailed piping network and internal flooding from an earthquake. It can also be applied to a high-wind case and atmospheric modeling, particle tracking, and other situations.

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2.3.9 Day 3: Session 3B - Frameworks I This session considered the development and demonstration of a PFHA framework for flood hazard curve estimation.

2.3.9.1 Technical Basis for Probabilistic Flood Hazard Assessment, Rajiv Prasad*, Ph.D.,

and Philip Meyer, Ph.D., Pacific Northwest National Laboratory (Session 3B-1; ADAMS Accession No. ML17054C482) 2.3.9.1.1 Abstract The purpose of this project was to develop technical bases for incorporating probabilistic assessment of riverine flood hazards into NRC guidance related to permitting, licensing, and oversight activities. Characterization and estimation of floods with return periods significantly greater than those for which statistical approaches are currently established are needed for the NRCs purposes.

PFHA is defined as a site-specific, systematic evaluation of the probabilities and frequencies of exceedance of hazards generated by applicable flood mechanisms to which SSCs could be exposed during specified exposure times at an NPP site. Flood mechanisms are those hydrometeorological, geoseismic, or structural failure phenomena that may produce a flood at or near an NPP site. Flood flows are characterized by several parameters, such as flood discharge, flood velocity, flood water-surface elevation, flood depth, flood duration, and hydrostatic and hydrodynamic forces. Flood hazards are those flood parameters that directly or indirectly affect the safety of NPP SSCs. All flood hazards may vary spatially and temporally during a flood event.

To adequately estimate the potential for failure of and access to the SSCs during a flood, both the spatial and temporal variation in flood hazards should be estimated.

Traditionally, probabilistic flood analysis has focused on estimation of the return period (the inverse of the AEP of the annual maximum discharge using observations). These analyses are also called flood-frequency analyses. A nonmechanistic model, typically a parametric probability distribution, is used to represent the frequency of occurrence of observed peak flows. To estimate the complete flood hydrograph and other hydrodynamic flood parameters, a more mechanistic approach is required. A simulation-based framework using precipitation-runoff and hydraulic models with appropriate hydrometeorologic, topographic, bathymetric, and geomorphologic data can be used to provide a more comprehensive estimate of flood hazards. In addition, a simulation-based approach allows for the explicit representation of nonstationary behavior in riverine floods, such as changes in the river basin (e.g., localized changes, including installation or removal of a dam; or distributed changes, including gradual clearing of forests) and climate change effects (e.g., changes in magnitude and frequency of extreme events).

The project proposes a PFHA framework that is simulation based and includes a comprehensive evaluation of uncertainties. The framework uses three components: (1) a meteorological component that provides hydrometeorologic input data, (2) a hydrologic component that estimates runoff discharges from precipitation events given hydrometeorologic input data, watershed initial conditions, and physical watershed data, and (3) a hydraulic component that estimates hydraulic flood parameters, including floodwater-surface elevations and flood velocities given runoff discharges and physical river network properties. In addition, there may be another component to transform the watershed model outputs into the required flood parameters for which hazard curves are required. Aleatory uncertainties are associated with the hydrometeorologic inputs and with the watershed initial and boundary conditions. These quantities describe the primary 2-303

irreducible uncertainties affecting the occurrence of future flooding at a site: the depth and intensity of rainfall events in the future, and the watershed conditions at the time of those events.

Epistemic uncertainties are associated with the parameters of the watershed model and describe the lack of knowledge in modeling the precipitation-runoff processes, in characterizing the watershed, and in determining appropriate parameter values for the models. These are the primary uncertainties that could be reduced by collecting additional data. By incorporating available data, a Bayesian approach is used to reduce the epistemic uncertainties. Watershed model outputs either directly represent the flood hazards of interest or may be transformed to them (e.g., hydrostatic and hydrodynamic loads, scour potential). The aleatory uncertainties result in a distribution of each flood hazard, which constitutes a hazard curve. Epistemic uncertainties contribute to the uncertainty in the quantiles of the distribution representing the hazard curve (e.g., the uncertainty in the exceedance probability of a given flood hazard value). The team expects to address issues related to implementing the proposed framework in the near future.

2.3.9.1.2 Presentation 2-304

2-305 2-306 2-307 2-308 2-309 2-310 2-311 2-312 2-313 2-314 2-315 2.3.9.1.3 Questions and Answers Comment:

TVA recently finished a framework very similar to that described here. With regard to uncertainty, we recently decided that we are really mostly interested in the extremely rare end of the spectrum.

At that end of the spectrum, for a 15-inch rainfall, a watershed model will probably give a result of 12 inches plus-or-minus a half an inch or similar. On the more frequent side of that band, 4 inches of rainfall might give a half an inch of runoff, or it might give 3.8 inches of runoff. The way we run our reservoirs on a given day (the normal operating day), we could make almost any decision on the river, so the uncertainty is huge. However, at the really rare end of the spectrum, we would spill 100 percent. The structure of your uncertainty very much changes depending on what part of the probability spectrum you are looking at. We felt confident with that approach: looking primarily at the uncertainty in the rainfall and, at least for now, glossing over some of the rainfall runoff model uncertainty and routing uncertainty, because at the extreme end they are comparatively small, while the rainfall uncertainty is extremely large at the rare end.

Response

Those traits argue the site-specific nature of this analysis with regard to developing generalizations. Some of those concepts you expressed may or may not be generalized for the kind of reservoir TVA has. The type of evaluation you performed is what I meant when I talked earlier about applying expert judgment to simplify the problem and trying to establish generalizations.

Question:

This is a very impressive watershed modeling, including uncertainty analysis. How do you specify the initial and boundary conditions of the watershed?

Response

In determining the initial and boundary conditions, we considered what data might be available and, because that may be model specific, what model will be used and what output is desired. In general, watershed modeling involves conditions such as soil moisture, initial streamflow, and baseflow; the conditions in a winter or springtime situation; and the amount of the snow on the ground. Datasets are available for different places in the country that could supply that information to the model, taking uncertainties into account. For example, these data might be measured infrequently or only available in certain locations and require you to extrapolate. This returns to the same issue of what data you have and how much characterization do they afford you in terms of that particular model. This project used spatially distributed datasets that have been maintained and that could be used at least initially to look at those conditions.

Question:

Listening to the descriptions of all these studies, it is clear that inferences are being made. Do todays statistical methods have credibility for statistical inference? This whole process of very challenging inferences really needs to have statistical validation because it involves many assumptions concerning many different aspects of the uncertainty analysis, not only in the models, but also in the way in that uncertainty is defined. How are those models and uncertainties validated? Many different models are available. My own experience with many years of modeling 2-316

is that you can fit just about anything to anything. You do not need to have very detailed models.

You can take any polynomial with enough degrees of freedom and you can get a perfect fit. The issue really is how can you actually validate, with completely independent data, the particular model that you are using or the particular structure you are assuming for quantifying your uncertainties?

Response

Uncertainty is a big issue for us. We acknowledge that there are two parts to it. One could come from variabilities: things could be measured with certain degrees of uncertainty. The second relates to these model structures. We treat the model structures as the epistemic part of the analysis, which leads to model validation and to how much weight should be given to a model or to a parameter structure that leads to a different conceptual model that is faithful to what you have observed. It goes back to the quality of your conditioning based on observed data, which is all we have. Based on the observed data, how can you condition the model parameter set as well as these models? We are trying to put this whole framework into a Bayesian model, where the parameter estimation is constrained by all the observations that we can find for that particular analysis. That would allow us to build a posterior distribution that can have not only model parameters but also weighting for different model structures. As more and more data become available, the Bayesian framework allows you to include that dataset and try to update the model structure. We do not yet know how much difference it would make. However, we need to do some computations with this framework to determine whether we can actually reach a practical solution where we can address some of these issues.

Response

Validation is always a difficult topic. In any case, this issue will involve extrapolation because we are moving beyond the data to exceedance probabilities that are extreme. In terms of extrapolation, in order to validate your inferences, you are at the extreme end and you get data that are mostly less extreme. Therefore, you rely on expert knowledge and a physically based process that relies on the less extreme more than the more extreme parts of the process. You use what you have, and the more information, the better the approximation. We are relying on the knowledge of the process.

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2.3.10 Day 3: Session 3C - Frameworks II This session considers the development and demonstration of a PFHA framework for flood hazard curve estimation.

2.3.10.1 Evaluation of Deterministic Approaches to Characterizing Flood Hazards, John Weglian, EPRI (Session 3C-1; ADAMS Accession No. ML17054C483) 2.3.10.1.1 Abstract Following the earthquake and tsunami that struck Japan in 2011 and led to core damage at three units at the Fukushima Dai-ichi Nuclear Power Plant, the NPPs in the United States were required to reexamine their risk to flooding from external sources using the current regulatory guidance for new reactor sites. In many cases, these reexamined flood hazards exceeded the plants original design basis. Many NPPs outside of the United States have also reevaluated their sites for external flooding hazards.

Deterministic, bounding analyses are used to ensure that NPPs are protected from what is expected to be the worst-case flooding events that could impact a site. Utilities will typically use the most conservative and bounding assumptions when initially assessing the flood hazard to a site. If the site is not able to withstand the flood using those bounding assumptions, the analysis is refined using more realistic, but still bounding, assumptions. This process is known as the hierarchical hazard assessment. EPRI published the technical report, Evaluation of Deterministic Approaches to Characterizing Flood Hazards, EPRI ID 3002008113, dated November 29, 2016 (http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002008113).

The report examines the assumptions, inputs, and methods used for assessing the external flooding hazards for the following flooding mechanisms: local intense precipitation, flooding of streams and rivers, dam breaches and failures, storm surge, wind-generated wave and runup, and hydrodynamic and debris loads. For each of these flood mechanisms, the report provides several areas where the analysis can be improved to provide a more realistic characterization of the flood hazard. Some examples are provided to describe some of these improvement opportunities. Utilities can use the report to identify opportunities to improve their bounding flood hazard analyses for existing or new plants.

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2.3.10.1.2 Presentation 2-319

2-320 2-321 2-322 2-323 2-324 2-325 2-326 2-327 2-328 2-329 2-330 2-331 2-332 2.3.10.1.3 Questions and Answers Question:

Is this approach really deterministic in the sense that many of the models that are deployed in this application setting are calibrated?

Response

This is not a probabilistic approach, with the exception of storm surge, which most likely will have some probabilistic aspects. It would still be considered a deterministic analysis. In terms of the Manning roughness coefficient, it is not sufficient to look in a book and decide that while I am in this kind of area, this is what I am going to use. You have to look at the area upstream of your particular plant and identify the kinds of flow restrictions that are there. It is still a deterministic type of approach because you have to make sure that you are being bounding. Some of this could be accomplished with sensitivity studies. You would identify a site-specific refinement and run the model again with that piece a little higher or lower and see if it changes your results.

Question:

In terms of project scope, are you looking at some of these conservatisms and analyzing what value they add, because introducing more realism can also increase the extent of cost-benefit thinking on what to target. We acknowledge that every site is different, and hazards are different.

Second question, the PRA models related to National Fire Protection Association Standard 805, Performance-Based Standard for Fire Protection for Light Water Reactor Electric Generating Plants, have brought debate on conservatisms. Sometimes a deterministic aspect being debated is what is ultimately influencing, or even distorting, the realism in the PRA. This work, even though you are calling it deterministic, could ultimately also translate into conservatism in a more risk-informed approach.

Response

Some of these approaches could also be used to obtain the most realistic result possible for use in the PRA model. However, that was not the intent of this approach or this paper. The genesis of this effort is responding to 10 CFR 50.54(f) letters on the flooding hazards. The Nuclear Energy Institute (NEI) is having a workshop on the approach for addressing those in NEI-16-05, External Flooding Assessment Guidelines, Revision 1, issued June 2016. This involves the option to revise your flood hazard as your starting point. Where do you start in your analysis to show whether you are protected? Do you need to rely on your mitigating strategy? EPRI did not begin with the assumption that everything that we might do will result in a reduction in the flood hazard.

Some things in the approach can be considered conservative or nonconservative. Vehicle barriers are a great example. During a local intense precipitation event, the giant cement wall put in for security purposes may hold the water in and result in higher water levels that might have otherwise occurred. The project did not begin with that assumption that we are only reducing conservatism, but instead sought to say objectively what can we do to improve the realism but still maintain it as bounding? We received great feedback from the NRC, and I made some significant adjustment to some of the wording to account for some of the concerns that the agency had on the draft version. It should now be a much better product from both the NRC and industry standpoints.

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2.3.10.2 Probabilistic Flood Hazard Assessment Framework Development, Brian Skahill*,

Ph.D.; U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Hydrologic Systems Branch, Watershed Systems Group (Session 3C-2; ADAMS Accession No. ML17054C484) 2.3.10.2.1 Abstract This research project is part of the NRCs PFHA research plan. Its objective is to develop and demonstrate a framework for PFHA for inland nuclear facility sites that will facilitate construction of site-specific flood hazard curves and support full characterization of uncertainties in site-specific storm flood hazard estimates for the full range of return periods of interest for NPPs. A PFHA must be able to incorporate probabilistic models for a variety of flood-related processes, allow for characterization and quantification of aleatory and epistemic sources of uncertainty, and facilitate not only propagation of uncertainties but also sensitivity analyses. The research project tasks are defined by focus areas, and in each case the objective is to develop and demonstrate a conceptual, mathematical, and logical framework for the probabilistic modeling of the given task specific flooding process. The focus areas include the following:

• literature review

• warm season rainfall and local intense precipitation

• cool season rainfall, snow, and snowpack

• site-scale flooding from local intense precipitation

• riverine flooding rainfall or rainfall and snowmelt

• riverine flooding hydrologic dam/levee failure

• knowledge transfer This presentation summarized features of a current draft, proposed PFHA framework for warm season rainfall, which outlines the use of a spatiotemporal Bayesian Hierarchical Model (BHM) embedded within a multimodel averaging technique to leverage the capacities of Bayesian inference while generalizing the problem of extreme rainfall model selection. The Bayesian inference methodology was selected not only because it supports a probabilistic analysis of extreme rainfall, but also because it is a flexible means by which to combine all available and relevant complementary data. These characteristics of Bayesian inference are either required or highly desirable for extreme rainfall analysis, particularly given the application focus wherein quantile estimates are necessary for low exceedance probabilities. For example, additional data that could be combined with a given stations systematic record for a local or regional analysis of extreme rainfall are data from surrounding stations, information derived from expert elicitation, or included in a nonstationary climate index. An additional attractive feature of the Bayesian inference methodology is that it supports the capacity to compute the predictive posterior distribution for a future observation. Several demonstrations of the proposed PFHA framework for warm season rainfall not only reinforced various aspects of the key framework elements, but they also underscored the flexibility of the framework to accommodate different data scenarios. The first four demonstrations in aggregate emphasized the importance of data analysis, model selection, and inference methodology for the evaluation of extreme rainfall risk at a given location.

The fifth demonstration emphasized the flexibility of the Bayesian inference methodology to accommodate treatment of nonstationarity in an analysis of extreme rainfall. The sixth demonstration profiled application of a BHM for the analysis of extreme daily rainfall using annual maxima data from 68 stations located within and surrounding the 11,478-square-mile Willamette River Basin in northwestern Oregon. The final demonstration briefly profiled two multimodel averaging techniques to generalize the problem of extreme rainfall model selection. The 2-334

presentation concluded with a brief summary of ongoing framework development for the probabilistic modeling of cool season rainfall processes.

2.3.10.2.2 Presentation 2-335

2-336 2-337 2-338 2-339 2-340 2-341 2-342 2-343 2-344 2-345 2-346 2-347 2-348 2.3.10.2.3 Questions and Answers Questions were postponed until the end of the day.

2.3.10.3 Riverine Flooding and Structured Hazard Assessment Committee Process for Flooding (SHAC-F), Rajiv Prasad*, Ph.D., and Robert Bryce, Ph.D., Pacific Northwest National Laboratory; Kevin Coppersmith*, Ph.D., Coppersmith Consulting (Session 3C-3; ADAMS Accession No. ML17054C487) 2.3.10.3.1 Abstract This research project is part of the NRCs PFHA research plan in support of development of a risk-informed analytical approach for flood hazards. The approach is expected to support estimation of flood hazards at new and existing facilities and enhance the NRCs capacity to support reviews of license applications, license amendment requests, and reactor oversight activities. Flood hazards at NPPs result from various flooding mechanisms, including local intense precipitation (LIP), precipitation and snowmelt in a river basin, dam failures, and storm surges and tsunamis. These flood events have the potential to challenge offsite power, threaten many onsite NPP SSCs, challenge the integrity of plant structures, and limit plant access. However, there is no widely accepted framework for performing a PFHA, and there are large uncertainties involved with estimating floods of magnitudes and frequencies of occurrence of interest for safety evaluations at NPPs. In 2013 and 2014, NRC-sponsored workshops discussed the available methods for conducting PFHAs and the development of a structured hazard assessment committee process 2-349

for flooding (SHAC-F). The need to develop implementation details of SHAC-F methodology was also recognized.

The objective of this project is to develop and apply the SHAC-F process 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. The research team started with the overarching guidance from the Senior Seismic Hazard Analysis Committee (SSHAC) process (NUREG/CR-6372, Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of Experts, issued April 1997; and NUREG-2117, Practical Implementation Guidelines for SSHAC Level 3 and 4 Hazard Studies, Revision 1, issued April 2012) used in probabilistic seismic hazard assessments and adapting them to the needs of flood hazard assessments. The SSHAC process is particularly well suited for structuring hazard assessments for purposes of risk analyses. For SHAC-F, the project adapted four levels, similar to SSHAC Levels 1-4. The virtual studies in the current project are carried out to simulate the full scope and activities that would accompany a full SHAC-F Level 3 PFHA. The project is investigating these aspects using virtual studies for LIP floods and riverine floods, excluding dam failures.

The research team will conduct the riverine PFHA SHAC-F virtual study using the same virtual site as for the LIP PFHA SHAC-F virtual study. It anticipates that several of the issues identified and solutions proposed during the LIP PFHA SHAC-F virtual study will inform the riverine PFHA SHAC-F virtual study. These issues include 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.

For the riverine flood PFHA, the team initially expected to perform two separate Level 3 PFHA virtual studies: (1) riverine flood from precipitation in the river basin and (2) riverine flood from precipitation and snowmelt in the river basin. Because the only difference between the two is the snowmelt component and the expected seasonality, the team decided to combine the two virtual studies. The riverine Level 3 PFHA virtual study will have three technical integration teams: (1) the meteorological model characterization team, (2) the hydrologic model characterization team, and (3) the hydraulic model characterization team. For a riverine flood, hydrologic and hydraulic modeling are best handled by separate teams because of the spatially and temporally varied nature of runoff generation and flood routing in streams and rivers. A site visit may not be critical for a riverine SHAC-F study, but the technical integration teams should be familiar with the specific hydrologic and hydraulic characteristics of the river basin. This objective can be accomplished by selecting the members of the technical integration team who have extensive experience conducting flood studies in the river basin and by encouraging others familiar with technical and policy matters for the river basin to join the study on the Participatory Peer Review Panel.

Compared to the LIP PFHA SHAC-F virtual study, the team expects that a significantly larger amount of observed flood data will be available. At the same time, the team expects to face new issues related to characterizing the variability of inputs, parameters, and initial and boundary conditions over space, time, and seasons. One additional issue to be addressed is the need for characterizing flood hazards at the local NPP scaleriverine flood models typically use a lumped or semidistributed hydrologic model and a one-dimensional hydraulic stream reach model. A two-dimensional hydrodynamic model may be necessary to evaluate the effects of the riverine flood overtopping the banks and spreading on the NPP site. Characterization of flood hazards may be needed at a finer spatial scale sufficient to adequately resolve the locations of safety-related SSCs and doors.

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2.3.10.3.2 Presentation 2-351

2-352 2-353 2-354 2-355 2-356 2-357 2-358 2-359 2-360 2-361 2-362 2-363 2-364 2.3.10.3.3 Questions and Answers Comment:

I have no issue with your analysis or the approach, but I have a fundamental issue with expending the amount of resources that are required to do a SHAC Level 3 type of study for a site-specific implementation. Would you actually obtain new risk insights by going to that level of detail? We have talked earlier about which distribution to use when you find out at the end that all of them were within the 5th and 95th percentiles. Something similar can happen here, where many experts are spending a lot of time performing this analysis. Because the SHAC process does not lend itself to being amended, what happens if in the future you perform a paleoflood study and one of those pieces does not fit what the new data indicate. The present SHAC process, at least for seismic, would require you to go through the whole process again with these new data, again expending potential millions of dollars to get to this point. My storm surge study included an approach that was similar to a SHAC process and used logic trees. I would make that akin to a SHAC Level 1, but in my opinion, in a case that is very site specific, utilities will find it untenable to perform an analysis at a SHAC Level 3.

Response: NRC Program Manager We are not recommending a specific level of SHAC, but, for the purposes of working through the ideas, we felt that at a SHAC Level 3 we can gain the insights and downselect to identify what would be needed for a SHAC Level 2 or Level 1. If we had done this at a SHAC Level 2, we would not have gleaned any information about what a Level 3 might require. The purpose of this project was not to decide what level of SHAC is needed for any particular analysis, whether site specific or not. We chose SHAC Level 3 because we thought that was the right level to gain the desired insights.

Response: Rajiv Prasad:

With regard to the new data, that is one of the reasons we want to use a Bayesian framework, under which you do not need to perform the whole study again. You can basically say that I already have my prior historical inferencing and I need to update that. The updating could be done at a lower level than the SHAC Level 3.

Question:

Does a SHAC Level 3 for flood really need to mirror the same level of effort and time required for a SHAC Level 3 for seismic? Looking at the big picture and fully recognizing the projects purpose of adding structure, how does this apply in the sense that hydrology and meteorology are imbedded as part of the analysis? A seismic analysis is easier in that you can do a SHAC at one site and that will require considering the full gamut of issues, including the hydrologic response, versus asking whether we perform a SHAC at the level of a localized area? Do we develop a hazard aspect only on precipitation or a very specific subset of that?

Response

In a seismic analysis, you have the advantage that you have a source and it could include an entire region. Flooding is rather site specific. Even in a large watershed, you have to make sure that watershed analysis is appropriate for each particular site of interest, which makes it more complicated. Your point is well taken in asking whether we need to follow all of those steps that 2-365

the seismic community does. Apart from the issue of data versus model, one challenge is whether the hydrologists understand what they need to do. The nuclear community has performed such analyses for a while, but the hydrologists do not quite know what we mean, for example, for local intense precipitation, in some cases. We also note the lack of a framework. On the seismic side, over the years, experts have come to an agreement on what the framework looks like for performing the probabilistic seismic hazard assessments. We do not have such an agreed-upon framework for flooding, and that needs to be worked through with all of the experts to give the context for the analysis. In addition, such a framework needs to represent the community, not the personal bias of a particular hydrologist. In SHAC, we want to represent the full body and range of the technically informed community. We might come to the conclusion that we will have to tailor SHAC even more for flooding than we already have. The project document will include some of the lessons learned, and this will be an ongoing process. When we perform a SHAC in a real situation, we will work through the practical nature of the computations. These include how many models and how many people should be involved, what resources should be devoted? Is SHAC Level 1 sufficient, or is Level 2 needed? Do we need to go to SHAC Level 3? What is the risk significance in the first place? All of these need to be worked through as we continue the project, develop these terminologies and this whole approach, and try to apply it.

Question:

My question relates to the variation in the data and how they fit, as well as the data quality objectives. As presented, the SHAC process lacks explicit actual review of the data quality objectives and whether the data fit. You mentioned that if the data do not fit the model, the data could be rejected. However, this means that you are relying on data and that you try to strive to obtain more data to compare to the model rather than relying on the specific analysis. Ultimately, you need to consider the data quality and the data quality objectives and how everything fits your objectives for the analysis. I agree that you are looking for a risk, but now you are dealing with hazard, which is different from risk. The risk includes different types of uncertainties and impacts, and this could be specific to the actual conditions that you are addressing. I also suggest that you include the data quality objectives process in order to accept or reject the data. It is possible that you may reject data that are very important and significant, and you may call certain data outliers that are actually important.

Response

Quality assurance, which relates not only to data, takes place throughout the process. We will apply the process from the seismic side to the flood side. Sometimes we obtain Federal agency data from the agencys Web site, and the agency indicates that it has already applied a data quality control process and only publishes processed data. We sometimes rely on such assurances, but, in addition, whenever there is a project activity that transforms the data and tries to use them in the analysis, we will subject the data to quality control. With regard to your statement that we are only dealing with that hazard and not the risk, I completely agree that this is a hazard analysis. We are trying to determine a probabilistic description of the hazard. The framework that I presented this morning tries to do this and we are trying to use the best information and tools available to try to build a sound foundational basis in probability and statistics. We will then go from there to say that we have come up with uncertainties that in the SHAC process allow you to say that you have considered all sources of uncertainties that can arise, not only from variabilities, but also from people's personal opinions and the way they do modeling. The question still remains on how to take these hazard assessments and then interface with the risk community. What products do we need to give you to be able to use them in a human reliability analysis or PRA, and where are those interfaces? We need to have further conversation between the risk community and the hazard community.

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2.3.11 Day 3: Session 3D - Panel Discussion This session included panel presentations and discussion on Probabilistic Flood Hazard Assessment Research Activities in Partner Agencies, chaired by Joseph Kanney of the NRC.

2.3.11.1 National Oceanic and Atmospheric Administration/National Weather Service (NOAA/NWS), Sanja Perica, Ph.D. (Session 3D-1-A; ADAMS Accession No. ML17054C488) 2-367

2-368 2-369 2.3.11.2 U.S. Army Corps of Engineers, Christopher Dunn, P.E., D.WRE.,

Norberto Nadal-Caraballo, Ph.D., John England, Ph.D., P.E., P.H., D.WRE. (Session 3D-1-B; ADAMS Accession No. ML17054C489) 2-370

2-371 2-372 2-373 2-374 2.3.11.3 Tennessee Valley Authority (TVA), Curt Jawdy, P.E. (Session 3D-1-C; ADAMS Accession No. ML17054C490) 2-375

2-376 2-377 2-378 2-379 2-380 2-381 2-382 2-383 2-384 2-385 2-386 2.3.11.4 U.S. Department of Energy (DOE), Curtis Smith, Ph.D., Idaho National Laboratory (Session 3D-1-D; ADAMS Accession No. ML17054C491) 2-387

2-388 2-389 2-390 2.3.11.5 Institut de Radioprotection et de Sûreté Nucléaire (Frances Radioprotection and Nuclear Safety Institute (IRSN)), Vincent Rebour*, Claire-Marie Duluc (Session 3D-1-E; ADAMS Accession No. ML17054C492) 2-391

2-392 2-393 2-394 2-395 2.3.11.6 Discussion Question:

The USBR report on hydrologic hazard estimation discussed the Logan Workshop 1999 effort that led to that table that gives the limits of credible extrapolation with outside data, regional data, paleoflood data, and others. Where are we today within that scope? That report talks about Bayesian and other methods that have been presented in the last few days. Are we trying to establish better understanding of the credibility of these more rare or extreme eventsare we trying to narrow down better on those limits?

Curt Jawdy, TVA From TVAs perspective as a dam safety owner, TVA has a portfolio of 49 dams to keep as safe as possible. As a result, we have to prioritize our portfolio of projects in the best way. Therefore, we want to obtain the absolute value of the flood loading that is as accurate as we possibly can.

For this reason, we put all this work into storm typing and understanding our uncertainties and breaking the system down as finely as possible. Ultimately, we have a decision to make, whether we have great data or not. For us, it is more about the relative value of dam A investment or dam B investment or dam C investment. While the absolute value is important, from a portfolio perspective, it is just as important to compare them to help make decisions.

John England, USACE:

Data on Australian rainfall and runoff are available on the Web, including a new 2016 version that has the same table I have worked on over the years with [Rory] Nathan [University of Melbourne]

and David Bowles [Utah State University], who were both at the 2013 NRC PFHA workshop. We are working on implementing tools to make those probabilities as credible as possible with full uncertainty and then propagate that to decisions. The first step is to achieve credible estimates and quantify that uncertainty, which is large, and then second to roll it into a decision framework that USACE and USBR are managing. The framework is an f-n chart [estimated life loss vs.

annualized failure probability plot]. As a result, in a dam safety construct decision framework, given some fixed set of consequences, you can state that the consequence estimate is 1 and you use lives lost as the surrogate for consequences. You do not need to go to 10-5 to 10-6 to use the process to focus on other locations in your inventory of dams. The key is combining information to obtain the best estimate you can with the information you have to help make a decision for the portfolio across an inventory. When considering specific dams or levees in USACE , you need to take a harder look, and sometimes you just have to make a call to do what is best for the decision of that organization. This may include collecting additional paleoflood information or performing additional rainfall studies such as storm typing, or you can decide that, given other factors, we can assess them in almost a deterministic standard, such as the probable maximum flood(PMF) for overtopping in the case of a dam, and take some sort of action based on that. In my opinion, we have not really made very particular progress on the tables, which have been criticized a lot over the years. We have not made much particular progress in refining those numbers. Instead, we have focused on the tools and data that go into making those numbers and quantifying that uncertainty. As Brian Skahill (USACE) mentioned earlier on combining disparate data types. The hydraulic hazard community is trying to grow PFHA skills. USBR has focused on trying to include site data with regional information, expand the information in space and time, and bring in causative mechanisms. This is within a Bayesian framework in the research area that is used within USACE. But holistically, those are the pieces of information the community is still grappling 2-396

with how to apply practically. In the meantime, we are making decisions with the best information we have at the time.

Brian Skahill, USACE:

The spatial statistics of extremes is an active area research. Some papers in the past 5 years are pivotal for how we will look at the statistics of hydrometeorological extremes. Academia and government agencies are involved in active research to advance tool development and increase capacity to use those tools. The capability to combine all that information is being worked on within the PFHA framework development activity for the NRC.

Question:

The last two presentations tried to address the actual risk impacts to NPPs, which is of primary importance. My question is related to the integration of uncertainties from the hazard event. The hazard relates to analysis of the flood event itself, including when and how it will take place.

Unfortunately, we do not have the qualifications to do that, so how do we determine the kind of consequences? The consequences of the hazard are really the risk. When we talk about risk at the NRC, we know about the risk triplet: the hazard itself, the probability of the hazard, and the consequences. Consequence is not dealt with much. This can be called the scenario or the impact scenario. Could you elaborate on the consequence analysis and how the uncertainties from the event itself (flood, in this case) and those of the consequence (scenario uncertainty) can be integrated together in order to achieve the results of the risk analysis we are trying to achieve?

Second, how will we deal with the data? Although we did not talk much about the independence of the data, our colleague from IRSN did address the independence of the parameters in the uncertainty analysis. Our discussion of risk also did not give much consideration to data independence. These are important factors in a risk analysis that pertains to flood events.

Curtis Smith, INL for DOE Once we get to the risk analysis part, it will be building on accepted practices, including industry and NRC PRA models. Some of the uncertainties for factors such as the hazard curves for a specific magnitude of floods are not really much different than some of the initiating events we already have in the models that we use for decisions every day. For example, the medium- and large-break LOCAs have frequencies down to 10-4, 10-5, and 10-6, with a fair amount of uncertainties. However, we do not really question that those came from work in the 1980s and 1990s, and we just continue to use them with those large uncertainties. This is a similar case, although a flood has some unique features in that it is the kind of a failure that might knock out many components that a LOCA may not. However, the models being produced, whether an event tree/fault tree or a more dynamic kind of a model, are equipped to handle the dependency specific to external hazards. That element sometimes is a challenge. This is just another tool in a scenario that is in a larger kind of model.

Question:

Is MetVue publicly available?

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Christopher Dunn, USACE:

MetVue is not yet publicly available but will be in the future. The goal was to release it by the end of 2016, so that could take place sometime in 2017 5.

Follow-up Question:

You talked about transposing and moving storms in MetVue. Are there any adjustments made to the precipitation amount as that is done, such as to take into account storms passing over changes in elevation or orographics? Or is it just strictly moving that spatial pattern to a new spot?

Christopher Dunn, USACE:

Currently, MetVue does not do that. Users will have a lot of discretion in how to use it. You could potentially move a storm to another area where it does not make physical sense. Users will need to be careful when manipulating events, moving them around, and transposing them to ensure that they are doing something that is physically possible.

Question:

When will USACE release Hydrologic Modeling System (HMS) 4.3, including the Markov chain Monte Carlo method (MCMC) capability?

Christopher Dunn, USACE:

This is planned for September 2017 6.

Christopher Dunn, USACE:

The NRCs research themes have focused in part on epistemic uncertainties and understanding them from a framework of different interpretations of data models and methods, which has not been a big focus in the flooding field. Will the work the NRC is funding improve the state of practice with respect to the treatment of epistemic uncertainties? How that will help in applications that are not related to NPPs?

Curtis Smith, INL for DOE:

People appear to be agreeing on the different kinds and drivers of uncertainty, and the community of practice is moving forward. I would encourage it to keep moving forward and also consider what the NRC does for the other hazards. For example, we have a Bayesian distribution for the frequency of a large-break LOCA as an initiating event. If we want to use something like flooding hazards as an initiating event or a probabilistic PRA-type of initiating event, we would want to have an apples to apples kind of model. This appears to be an issue of concern among participants. We have that issue with a loss of offsite power, in terms of how far back in history we should consider, given that the practices of the grid have changed over last 20 to 30 years. This is the challenging question, as we are discussing climate and floods from 500 years ago, so going far back in history may be necessary. But the idea of having Bayesian distribution and Bayesian 5

HEC MetVue was released publicly in summer 2019 and can be accessed at https://www.hec.usace.army.mil/software/hec-metvue/

6 HEC-HMS 4.3 was released in September 2018 and includes the Monte Carlo Uncertainty tool https://www.hec.usace.army.mil/software/hec-hms/documentation.aspx 2-398

models and classifying uncertainty (aleatory, epistemic) very much fits into the NRC way of thinking. This is the case for many synergies with other activities in risk assessment.

Norberto Nadal-Caraballo, USACE:

The collaboration with the NRC has given us the chance to evaluate many methods and models that we had not even considered in the past, and we are applying a lot of the lessons learned. We are still learning a lot doing this study, and we are in the process of applying some of those lessons learned to USACE products. For example, before this study, we did not even contemplate considering some of the epistemic uncertainties whose importance we are now realizing. In previous studies by FEMA and USACE, we just computed, for example, the modeling errors. We compared high-water marks with the results from ADCIRC, and, although we incorporated those uncertainties in the hazard curve, we basically ignored everything else. We have seen that the uncertainty in the SRR models is significant and can impact the final hazard curve. This has been a very good opportunity for us to see that and to give us the chance to improve our products moving forward.

Question:

In terms of both the statistical analysis and the uncertainty in the riverine modeling, are you considering multiple models, multiple distributions in the statistical part of it or different physical mechanisms or models that contain different physical mechanisms in modeling uncertainty portions?

Vincent Rebour , IRSN:

Our first objective is to identify the set of tools. The second step will be testing different methods of modeling.

Question:

What resolution are the researchers at the University of Illinois using in those climate models to make climate projections? Until you reach a very fine resolution, essentially going to a convection permitting climate model, there will be certain precipitation events that just cannot be modeled, for example.

Sanja Perica, NOAA/NWS: Response unclear and not recorded.

Brian Skahill, USACE:

Based on the ongoing work on Error! Bookmark not defined.development for the NRC and then the related work with HMS at the Hydrologic Engineering Center, I proposed to the HMS team and the supervisory chain at the Hydrologic Engineering Center that as we now have that capability encased in HMS, and given that the HMS tool has a lot of flexibility and an adaptable, user-friendly interface, it would not be too difficult to transition to looking at different loss mechanisms for basin modeling and different transformation methods. We could definitely leverage the sampler and MCMC sampler we now have in HMS to support treatment of the model generalization problem that was brought up in the last two questions.

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Question:

What are the limitations of using maximum likelihood estimation for probabilities of less than 10-4 for a dam safety application?

Sanja Perica, NOAA/NWS:

Although I do not know the answer, under the maximum likelihood approach, L-moments approach, or whichever approach you choose, once you are at frequencies of 10-4, there are of course many uncertainties. It is difficult to identify which approach will result in smaller uncertainties.

Follow-up Question:

Can others address the question in terms of dam safety assessment?

Curt Jawdy, TVA:

We are just starting to look into the uncertainties outside of rainfall, but we are seeing that most TVA dams are fairly well in control out to the 10,000-year event. When you are looking at a time out that far, the soil is full of moisture and it is all going to runoff no matter what model you use.

The rainfall uncertainty is so high, the further out you go. Much of the operational uncertainty in the reservoir system and the runoff uncertainty in the hydrological model will likely be swamped by the rainfall uncertainty. As a result, we are taking the approach of tackling the uncertainties that seem to be the biggest first and working down.

John England, USACE:

USBR did this in practice in about 2002 when we were working with FloodFreq3. The code is available, and Brian Skahills research now is moving in a more modern framework with R (a statistical package). We looked at model uncertainty. Some of the issues that William Asquith touched on (see Section 3.4.2) in a likelihood framework you could do a little more conveniently to exploit LP-III versus GEV in terms of the peak flow frequency. As a result, you can directly account for which models fit well and then include those and weight them to inflate the uncertainty to include some model uncertainty. A couple of journal articles were published on that, and USBR published a report for Folsom Dam in 2002. This gives a place for likelihood and makes it a little more convenient to include covariates. However, it is challenging for practitioners to understand, and so we are still trying to include long data sets, as well as the biggest rainfalls and the biggest floods in the analysis. That is really the first order problem on the data side, rather than arguing between L-moments and likelihood. It is important to include the really big events and know the physics you are trying to mimic with the statistical models.

Question:

Has the work on automated storm typing been published?

Curt Jawdy , TVA:

It has not been published in a journal yet. We are reviewing our entire framework in April and need to determine what is proprietary and what can be published.

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Question:

Could you provide more detail on how you are tackling the debris loading and gate failure scenarios for the Willamette River Project?

John England, USACE:

With regard to the previous question, there is extensive information in the academic literature, namely hydrology system science, on patterning and typing in climate. From a research perspective, MGS Engineering Consultants work for TVA in terms of patterning and using typing to do storms is not unique but well founded in other academic literature, including SOMs. It is actually in the roots of NOAA/NWS Hydrometeorological Report No. 55a, Probable Maximum Error! Bookmark not defined. EstimatesUnited States Between the Continental Divide and the 103rd Meridian, issued June 1988, with the storm classification system in its PMP .

With regard to the question in the probabilistic world about the debris and gates on the Willamette River, this is a new part of project, so we do not yet have clear documentation. We are essentially adopting a gate scenario, based on the fault tree work that was shared through USACE and Error! Bookmark not defined.s Best Practices in Dam Safety Risk Analysis. That training course on mechanical reliability, offered for the past 5 or 6 years, contains a whole gate module, and we are now trying to take the step of moving those pieces into the hydrologic hazard analysis.

We have not yet come to consensus on how to do it. We know gate reliability and debris are huge issues, and therefore we are sort performing the scenario analysis and looking at initiation nodes in an event tree.

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2.3.12 Day 3: Session 3E - Future Work in PFHA 2.3.12.1 Future Work in PFHA at EPRI, John Weglian*, EPRI(Session 3E-1; ADAMS Accession No. ML17054C493) 2.3.12.1.1 Presentation 2-402

2-403 2-404 2-405 2-406 2.3.12.1.2 Questions and Answers Question:

How are you planning to model the storm surges for the Monte Carlo simulations?

Response

We will use probability density functions for the storm surge parameters that will be used. The actual hydrologic model (developed by the contractor) has not been chosen yet.

Question:

What EPRI exists for NPP sites located on the Great Lakes?

Response

If the meteorological parameters can be successfully estimated, then this modeling may be applicable to both coastal and Great Lakes sites.

2.3.12.2 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 Nuclear Regulatory Research, U.S. NRC (Session 3E-2; ADAMS Accession No. ML17054C494) 2.3.12.2.1 Presentation 2-407

2-408 2-409 2-410 2-411 2-412 2-413 2-414 2-415 2.3.12.2.2 Questions and Answers Question:

You alluded to combining different flood mechanisms in a single hazard curve. I would think of treating them differently in building the PRA and considering the importance of other factors besides the water level. Note that the hazard curve may be discontinuous (including step changes).

Response

Realistically, different portions of the hazard curves will come from different places. Contributions to the total hazard may come from different processes.

Question:

What is the peer review process for your model and its application/performance demands?

Response

The model will be reviewed internally at the NRC. This report can be shared with other Federal agencies as well.

Comment:

Validation should reflect the predictive power of the model.

Question:

With respect to the utility of the models, how would you use surrogate models?

Response

With local intense precipitation, for example, we will use anecdotal data to deal with the limited data case. These data can be used to constrain the model in certain situations. Other situations include ungauged catchment areas.

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2.4 Summary This report documents the 2nd Annual NRC Probabilistic Flood Hazard Assessment Research Workshop held at NRC Headquarters in Rockville, MD, on January 23-25, 2017. These proceedings included the following:

• Section 3.2: Workshop Agenda (in the program (ADAMS Accession No. ML17054C495)

• Section 3.3: Proceedings (abstracts in the program at ADAMS Accession No. ML17054C495 and complete workshop presentation package including slides and questions and answers at ADAMS Accession No. ML17040A626)

• Section 3.4: Summary

• Section 3.5 Workshop Participants 2-417

2.5 Participants

• indicates speaker, ^ indicates remote participant Boby Abu-Eid Michelle Bensi Senior (Senior Level Service) Advisor Civil Engineer U.S. NRC/NMSS/DUWP U.S. NRC/NRO/DSEA/RHM1 e-mail: boby.abu-eid@nrc.gov e-mail: michelle.bensi@nrc.gov Hosung Ahn Don Bentley^

Hydrologist Entergy U.S. NRC/NRO/DSEA/RHM1 e-mail: dbentley@entergy.com e-mail: hosung.ahn@nrc.gov Kristi BranchError! Bookmark not defined.^

Tom Aird Senior Scientist General Engineer Bittner & Associates U.S. NRC/RES/DRA/FXHAB in collaboration with the Pacific Northwest e-mail: thomas.aird@nrc.gov National Laboratory e-mail: kristi.branch@gmail.com William H. Asquith*^ Stephen Breithaupt Research Hydrologist Hydrologist U.S. Geological Survey U.S. NRC/NRO/DSEA e-mail: wasquith@usgs.gov e-mail: stephen.breithaupt@nrc.gov Victoria Bahls Chris Cahill Hydrometeorologist Senior Reactor Analyst MetStat, Inc. U.S. NRC/R-I/DRS/EB3 e-mail: vbahls@metstat.com e-mail: christopher.cahill@nrc.gov James Barbis R. Jason Caldwell^

Senior Water Resource Engineer Chief Hydrometeorologist Amec Foster Wheeler MetStat, Inc.

e-mail: james.barbis@amecfw.com e-mail: jcaldwell@metstat.com Valerie Barnes^ Andrew Campbell*

Senior Human Factors Analyst Deputy Director U.S. NRC/RES/DRA/HFRB Division of Site, Safety and Environmental e-mail: valerie.barnes@nrc.gov Analysis U.S. NRC/NRO/DSEA Joseph V. Bellini* e-mail: andy.campbell@nrc.gov Vice President/

Principal Water Resources Engineer Karen Carboni Aterra Solutions Program Manager, Hydrology and e-mail: joe.bellini@aterrasolutions.com Groundwater Tennessee Valley Authority Christopher Bender^ Nuclear Design Engineering Senior Engineer, Coastal Engineering e-mail: kcarboni@tva.gov Taylor Engineering, Inc.

e-mail: cbender@taylorengineering.com 2-419

Meredith Carr* Angela Dalton^

Hydrologist Engineer U.S. NRC/RES/DRA/FXHAB Pacific Northwest National Error! Bookmark e-mail: meredith.carr@nrc.gov not defined.

e-mail: angela.dalton@pnnl.gov Laura Chap Senior Engineer, Water Resources Biswajit Dasgupta^

Atkins Southwest Research Institute e-mail: laura.chap@atkinsglobal.com e-mail: bdasgupta@swri.org Yuan Cheng Jonathan DeJesus^

Hydrologist Reliability and Risk Analyst U.S. NRC/NRO/DSEA/RHM2 U.S. NRC/RES/DRA/HFRB e-mail: yuan.cheng@nrc.gov e-mail: jonathan.dejesus@nrc.gov Michael Cheok Suzanne Dennis^

Division of Construction Inspection and Risk and Reliability Engineer Operational Programs U.S. NRC/RES/DRA/PRAB U.S. NRC/NRO/DCIP e-mail: michael.cheok@nrc.gov Claire-Marie Duluc Head of Flood Hazards Assessment Section Garill Coles^ IRSN: Institut de Radioprotection et de Principal Engineer Sûreté Nucléaire Pacific Northwest National Laboratory (French Radioprotection and Nuclear Safety e-mail: garill.coles@pnnl.gov Institute) e-mail: claire-marie.duluc@irsn.fr Christopher Cook Chief, Hydrology & Met Branch 1 Christopher Dunn*^

U.S. NRC/NRO/DSEA/RHM1 Director e-mail: christopher.cook@nrc.gov U.S. Army Corps of Engineers Hydrologic Engineering Center Susan Cooper e-mail: christopher.n.dunn@usace.army.mil Senior Reliability and Risk Analyst U.S. NRC/RES/DRA/HFRB John F. England, Jr.*^

e-mail: susan.cooper@nrc.gov Hydrologic Hazards Lead Civil Engineer U.S. Army Corps of Engineers Kevin Coppersmith*^ Risk Management Center Principal e-mail: john.f.england@usace.army.mil Coppersmith Consulting, Inc.

e-mail: kevin@coppersmithconsulting.com Ken Fearon Senior Civil Engineer Ryan Coppersmith Federal Energy Regulatory Commission Coppersmith Consulting, Inc. e-mail: kenneth.fearon@ferc.gov e-mail: ryan@coppersmithconsulting.com Fernando Ferrante William (Mark) Cummings* Senior Engineer Principal Engineer Defense Nuclear Facilities Safety Board Fire Risk Management, Inc. e-mail: fernandof@dnfsb.gov e-mail: wmark@fireriskmgt.com 2-420

CJ Fong Tessa Harden*

Team Leader Hydrologist U.S. NRC/NRR/DRA/APLA/RILIT U.S. Geological Survey e-mail: cj.fong@nrc.gov Oregon Water Science Center e-mail: tharden@usgs.gov Mark Fuhrmann Geochemist Brad Harvey U.S. NRC/RES/DRA/FXHAB Team Lead e-mail: mark.fuhrmann@nrc.gov U.S. NRC/NRO/DSEA/RHM1/RMOT e-mail: brad.harvey@nrc.gov Raymond Gallucci Senior Engineer Jimmy Harvey^

U.S. NRC/NRR/DRA/APLA Tennessee Valley Authority e-mail: ray.gallucci@nrc.gov e-mail: jharvey@tva.gov Mirela Gavrilas Barb Hayes Deputy Director, Division of Risk Project Manager Assessment U.S. NRC/NRO/DEIA/NRCB U.S. NRC/NRR/DRA e-mail: barbara.hayes@nrc.gov e-mail: mirela.gavrilas@nrc.gov Alyssa Hendricks Joseph Giacinto^ Hydrometeorologist Hydrologist MetStat, Inc.

U.S. NRC/NRO/DSEA/RHM2 e-mail: ahendricks@metstat.com e-mail: joseph.giacinto@nrc.gov Dan V. Hoang Structural Engineer Victor Gonzalez U.S. NRC/NRR/DE/ESEB Research Engineer e-mail: dan.hoang@nrc.gov U.S. Army Corps of Engineers Engineer Research and Development Center Kathleen Holman^

Coastal and Hydraulics Laboratory Meteorologist e-mail: victor.m.gonzalez@usace.army.mil U.S. Bureau of Reclamation e-mail: kholman@usbr.gov Kevin Griebenow Civil Engineer Victor Hom^

Federal Energy Regulatory Commission Hydrologist email: kevin.griebenow@ferc.gov NOAA/NWS e-mail: victor.hom@noaa.gov Kenneth Hamburger*

Fire Protection Engineer Ken Huffman^

U.S. NRC/RES/DRA/FXHAB Technical Executive e-mail: kenneth.hamburger@nrc.gov EPRI e-mail: khuffman@epri.com Mohammad Haque Senior Hydrologist Matthew Humberstone U.S. NRC/NRO/DSEA/RENV Reliability and Risk Analyst e-mail: mohammad.haque@nrc.gov U.S. NRC/NRR/DRA/APHB e-mail: matthew.humberstone@nrc.gov 2-421

Jennifer Irish^ Marvin Lewis^

Professor Environmentalists Incorporated Virginia Tech e-mail: marvlewis@juno.com e-mail: jirish@vt.edu Samuel Lin Kei Ishida* Civil Engineer Assistant Project Scientist Federal Energy Regulatory Commission University of California, Davis e-mail: shyangchin.lin@FERC.gov e-mail: kishida@ucdavis.edu David Lord Curtis Jawdy* Senior Civil Engineer Lead Hydrologist Federal Energy Regulatory Commission Tennessee Valley Authority Dam Safety and Inspections e-mail: cmjawdy@tva.gov e-mail: david.lord@ferc.gov Andrea Judge^ Zhegang Ma

• e-mail: judge.andrea.c@gmail.com Lead Risk Assessment Engineer Idaho National Laboratory Joe Kanney* e-mail: zhegang.ma@INL.gov Hydrologist U.S. NRC/RES/DRA/FXHAB Petr Masopust^

e-mail: joseph.kanney@nrc.gov Principal Water Resources Engineer Aterra Solutions, LLC Levent Kavvas* e-mail: petr.masopust@aterrasolutions.com Professor University of California, Davis Marty McCann^

e-mail: mlkavvas@ucdavis.edu e-mail: mccann@riosca.biz Shaun W. Kline Fehmida Mesania^

Engineering Lead Flood Engineer Enercon Services, Inc. Duke Energy e-mail: skline@enercon.com e-mail: fehmidakhatun.mesania@duke-energy.com David Leone^

Associate Principal/Hydraulic Engineer Bob Meyer^

GZA GeoEnvironmental Inc. e-mail: nucbiz@hotmail.com e-mail: davidm.leone@gza.com Philip Meyer^

L. Ruby Leung*^ Senior Research Engineer Laboratory Fellow Pacific Northwest National Laboratory Pacific Northwest National Laboratory e-mail: philip.meyer@pnnl.gov e-mail: ruby.leung@pnnl.gov Drew Miller^

Amanda Lewis^ Principal Engineer U.S. Army Corps of Engineers Jensen Hughes e-mail: amanda.b.lewis@usace.army.mil e-mail: amiller@jensenhughes.com Jeffrey T. Mitman Senior Reliability and Risk Analyst U.S. NRC/NRR/DRA/APHB e-mail: jtm1@nrc.gov 2-422

Sean Peters Michael Mobile Branch Chief Senior Technical Specialist U.S. NRC/RES/DRA/HFRB GZA GeoEnvironmental Inc. e-mail: sean.peters@nrc.gov e-mail: michael.mobile@gza.com Jacob Philip Mathieu Mure-Ravaud* Senior Geotechnical Engineer Graduate Student U.S. NRC/RES/DE/SGSEB University of California, Davis e-mail: jacob.philip@nrc.gov e-mail: mmureravaud@ucdavis.edu Rajiv Prasad*

Norberto C. Nadal-Caraballo* Senior Research Scientist Leader, Coastal Hazards Group Pacific Northwest National Laboratory U.S. Army Corps of Engineers e-mail: rajiv.prasad@pnnl.gov Engineer Research and Development Center Coastal and Hydraulics Error! Bookmark Efrain Ramos-Santiago^

not defined. U.S. Army Corps of Engineers e-mail: norberto.c.nadal- Engineer Research and Development Center caraballo@usace.army.mil Coastal and Hydraulics Laboratory e-mail: efrain.ramos-santiago@erdc.dren.mil Keil Neff*

Hydrologic Engineer Vincent Rebour*

U.S. Bureau of Reclamation Head of Site and Natural Hazards Dept.

Technical Service Center IRSN: Institut de Radioprotection et de Flood Hydrology and Meteorology Sûreté Nucléaire e-mail: kneff@usbr.gov (French Radioprotection and Nuclear Safety Thomas Nicholson Institute)

Senior Technical Advisor e-mail: vincent.rebour@irsn.fr U.S. NRC/RES/DRA e-mail: thomas.nicholson@nrc.gov Mehdi Reisi-Fard*

Reliability and Risk Analyst Nicole Novembre U.S. NRC/NRR/DRA/APLA/RILIT Hydrologic Engineer e-mail: mehdi.reisifard@nrc.gov MetStat, Inc.

e-mail: nnovembre@metstat.com Mehrdad Salehi^

Senior Coastal & Hydraulic Specialist Jim O'Connor* Sargent & Lundy Research Geologist e-mail: mehrdad.salehi@sargentlundy.com U.S. Geological Survey e-mail: oconnor@usgs.gov Mark Henry Salley Branch Chief Tye Parzybok^ U.S. NRC/RES/DRA/FXHAB MetStat, Inc. e-mail: markhenry.salley@nrc.gov e-mail: tyep@metstat.com Selim Sancaktar Sanja Perica* Senior Reliability and Risk Engineer Chief, Hydrometeorological Design Studies U.S. NRC/RES/DRA/PRAB Center e-mail: selim.sancaktar@nrc.gov NOAA/NWS e-mail: sanja.perica@noaa.gov 2-423

Mel Schaefer^ Nebiyu Tiruneh President, Hydrologic Risk Hydrologist MGS Engineering Consultants, Inc. U.S. NRC/NRO/DSEA/RHM2 e-mail: mgschaefer@mgsengr.com e-mail: nebiyu.tiruneh@nrc.gov Raymond Schneider* Ruth Thomas^

Fellow Coordinator Westinghouse Environmentalists Incorporated e-mail: schneire@westinghouse.com e-mail: marvlewis@juno.com Paul Shannon Wilbert O. Thomas, Jr.^

Branch Chief Senior Technical Consultant FERC Michael Baker International e-mail: paul.shannon@ferc.gov e-mail: wthomas@mbakerintl.com Brian Skahill* Juan Uribe Engineer Project Manager U.S. Army Corps of Engineers U.S. NRC/NRR/JLD/HMB Engineer Research and Development Center e-mail: juan.uribe@nrc.gov Coastal and Hydraulics Laboratory e-mail: brian.e.skahill@usace.army.mil Nicholas Valos^

Senior Reactor Analyst Curtis Smith U.S. NRC, Region 3 R-III/DRP Directorate Fellow e-mail: nicholas.valos@nrc.gov Idaho National Laboratory e-mail: curtis.smith@inl.gov Andrew Verdin*

Hydrologic Engineer Ellen Smith^ U.S. Bureau of Reclamation Oak Ridge National Laboratory Technical Services Center e-mail: smithed@ornl.gov Flood Hydrology and Meteorology Group e-mail: averdin@usbr.gov Mathini Sreetharan Senior Engineer, Associate Jessica Voveris Dewberry Meteorologist e-mail: msreetharan@dewberry.com U.S. NRC/NRO/DSEA/RHM1/RMOT e-mail: jessica.voveris@nrc.gov Winston Stewart^

Duke Energy Bin Wang^

e-mail: winston.stewart@duke-energy.com Technical Specialist GZA GeoEnvironmental Inc.

Alice Stieve^ e-mail: bin.wang@gza.com Geologist U.S. NRC/NRO/DSEA/RGS Weijun Wang e-mail: alice.stieve@nrc.gov Senior Geotechnical Engineer U.S. NRC/NRO/DSEA/RGS Mark Thaggard e-mail: weijun.wang@nrc.gov Acting Director, Division of Risk Analysis U.S. NRC/RES/DRA e-mail: mark.thaggard@nrc.gov 2-424

Mike Weber*

Director, Office of Nuclear Regulatory Research U.S. NRC/RES e-mail: michael.weber@nrc.gov Sunil Weerakkody Branch Chief U.S. NRC/NRR/DRA/APHB e-mail: sunil.weerakkody@nrc.gov John Weglian*

Senior Technical Leader Electric Power Research Institute e-mail: jweglian@epri.com Gordon Wittmeyer^

Southwest Research Institute e-mail: gordon.wittmeyer@swri.org Jason White Physical Scientist U.S. NRC/NRO/DSEA/RHM1 e-mail: jason.white@nrc.gov Joseph Wright^

Supervisory Civil Engineer U.S. Bureau of Reclamation Technical Services Center e-mail: jmwright@usbr.gov Elena Yegorova^

Meteorologist U.S. NRC/RES/DRA/FXHAB e-mail: elena.yegorova@nrc.gov 2-425

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