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NUREG/KM-0015 Considerations for Estimating Site-Specific Probable Maximum Precipitation at Nuclear Power Plants in the United States of America Final Report
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Issue date:	09/30/2021
From:	Deneale S, Kao S, Kevin Quinlan, Watson D; Office of Nuclear Reactor Regulation, Oak Ridge
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	Malone, Tina
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NUREG/KM-0015 ORNL/SPR-2021/1375

Considerations for Estimating Site-Specific Probable Maximum Precipitation at Nuclear Power Plants in the United States of America

Final Report

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NUREG/KM - 0015 ORNL/SPR-2021/1375

Considerations for Estimating Site-S pecific Probable Maximum Precipitation at Nuclear Power Plants in the United States of America

Final Report

Manuscript Completed: September 2021 Date Published: September 2021

Prepared by:

S.T. DeNeale, S.-C. Kao, and D.B. Watson Oak Ridge National Laboratory Oak Ridge, TN 37831-6 038

K.R. Quinlan U.S. Nuclear Regulatory Commission Washington, DC 20555 -0001

Office of Nuclear Reactor Regulation

ABSTRACT

As a result of advances in data and methodology, the U.S. Nuclear Regulatory Commission (NRC) has reviewed many studies pertaining to site-specific probable maximum precipitation (SSPMP) calculated at U.S. nuclear power plants. As described in NRC guidance and hazard assessment-related documents (e.g., Regulatory Guide 1.59; 1 NUREG-0800, 2 NUREG/CR-7046 3), the NRC guides its licensees to use the National Oceanic and Atmospheric Administrations (NOAAs) hydrometeorological reports as an acceptable resource for the probable maximum precipitation estimates used to evaluate the design-basis flood for nuclear power plants. This NUREG/KM summarizes the knowledge the NRC staff has developed over the course of the reviews based on the similarities and differences between the methodologies.

The SSPMP estimates resulting from these studies were used as a critical hydrologic modeling input in multiple submittals by licensees, such as those responding to the NRCs letter of March 12, 2012, under Title 10 of the Code of Federal Regulations 50.54(f), requesting updated flooding hazard analyses for nuclear power plants. 4 The estimates were also used in topical reports. Although the licensees development and estimation of SSPMP studies generally followed processes similar to those described in the existing guidance, several different methods, data sources, assumptions, and procedures were used to obtain site-specific results other than those found us ing the NOAA hydrometeorological report methodology.

The purpose of this document is to help fulfill the NRCs goal of maintaining and preserving knowledge and deriving lessons learned from the recent flood hazard reevaluations at nuclear power plant sites performed in connection with the accident at the Fukushima Dai-ichi nuclear power plant in Japan. Specifically, this document (1) identifies terminologies, theories, methods, and lessons learned for SSPMP studies submitted to the NRC for review and (2) presents key considerations for developing and reviewing potential future SSPMP studies.

Although the NRC staff may suggest a course of action in this NUREG/KM publication, these suggestions are not legally binding, and the regulated community may use other approaches to satisfy regulatory requirements.

1 Regulatory Guide 1.59, Design Basis Floods for Nuclear Power Plants, Revision 2, August 1977 (Agencywide Documents Access and Management System (ADAMS) Accession No. ML003740388).

2 NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants :

LWR Edition, March 2007 (ADAMS Accession No. ML070810350).

3 NUREG/CR-7046, Design-Basis Flood Estimation for Site Characterization at Nuclear Power Plants in the United States of America, November 2011 (ADAMS Accession No. ML11321A195).

4 ADAMS Accession No. ML12053A340.

iii

TABLE OF CONTENTS

ABSTRACT............................................................................................................................... iii

LIST OF FIGURES................................................................................................................... vii

ABBREVIATIONS AND ACRONYMS....................................................................................... ix

ACKNOWLEDGMENTS............................................................................................................ xi

1 I NTRODUCTION...............................................................................................................1-1 1.1 Background..............................................................................................................1-1 1.2 Purpose and Objective of t his Document..................................................................1-6 1.3 Regulatory Context...................................................................................................1-6 1.4 Probable Maximum Precipitation Definition and Existing Regulatory Guidance........1-7 1.5 Summary of Conventional Probable Maximum Precipitation Met hods......................1-8 1.6 Summary of Methods Used in Licensee Site -Specific Probable Maximum Precipitation Submittals.........................................................................................1-10

2 S TORM SELECTION........................................................................................................2-1 2.1 Terminology, General Methodology, and Lessons Learned......................................2-1 2.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development............................................................................................................2-2

3 S TORM RECONSTRUCTION...........................................................................................3-1 3.1 Terminology, General Methodology, and Lessons Learned......................................3-1 3.2 Key Considerations for Site -Specific Probable Maximum Precipitation Development............................................................................................................3-3

4 S TORM TRANSPOSITION................................................................................................4-1 4.1 Terminology, General Methodology, and Lessons Learned.....................................4-1 4.2 Key Considerations for Site -Specific Probable Maximum Precipitation Development............................................................................................................4-3

5 S TORM REPRESENTIVE D EWPOINT SELECTION AND P RECIPITABLE WATER E STIMATION....................................................................................................................5-1 5.1 Terminology, General Methodology, and Lessons Learned.....................................5-1 5.1.1 Moisture Source Identification.....................................................................5-1 5.1.2 Storm Representative Dewpoint Data and Selection..................................5-2 5.1.3 Historical Dewpoint Conversion..................................................................5-5 5.1.4 Precipitable Water Estimation.....................................................................5-6 5.2 Key Considerations for Site -Specific Probable Maximum Precipitation Development............................................................................................................5-7

6 D EWPOINT CLIMATOLOGGY, MOISTURE MAZIMIZATION, and MOISTURE TRANSPOSITION.............................................................................................................6-1 6.1 Terminology, General Methodology, and Lessons Learned.....................................6-1 6.1.1 Dewpoint Climatology.................................................................................6-1 6.1.2 Moisture Maximization................................................................................6-3

v 6.1.3 Moisture Transposition Adjustment.............................................................6 -3 6.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development............................................................................................................6 -4

7 TERRAIN ADJUSTMENT................................................................................................. 7-1 7.1 Terminology, General Methodology, and Lessons Learned.....................................7 -1 7.1.1 Barrier Adjustment Factor...........................................................................7 -1 7.1.2 Orographic Transposition Factor................................................................7 -2 7.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development............................................................................................................7 -5

8 E NVELOPMENT AND PROBABLE MAXIMUM PRECIPITATION DETERMINATION............................................................................................................8 -1 8.1 Terminology, General Methodology, and Lessons Learned.....................................8 -1 8.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development............................................................................................................8 -2

9 SPATIAL AND TEMPORAL DISTRIBUTIONS FOR SSPMP APPLICATIONS................9 -1 9.1 Terminology, General Methodology, and Lessons Learned.....................................9 -1 9.1.1 Spatial Distribution......................................................................................9 -1 9.1.2 Temporal Distribution..................................................................................9 -3 9.2 Key Considerations for SSPMP Development..........................................................9 -5

10 P OTENTIAL EFFECTS OF L ONG-TERM CLIMATIC CHANGE..................................... 10- 1

11 S UMMARY...................................................................................................................... 11-1

12 R EFERENCES................................................................................................................ 12-1

APPENDIX A DISPOSITION OF PUBLIC COMMENTS................................................... A-1

vi LIST OF FIGURES

Figure 1-1 Map of regions covered by NOAA PMP documents (as of 2015)

(https://www.nws.noaa.gov/oh/hdsc/studies/images/pmp_figure.jpg)................1-5 Figure 1-2 Map of NPP locations for which the NRC reviewed a detailed SSPMP.............1-5 Figure 2-1 Map of USACE Black Book storm locations (data from USACE 1973)..............2-2 Figure 3-1 Map of precipitation gauge data for a June 1948 Golden, CO, storm (from HMR No. 55A).........................................................................................3-2 Figure 3-2 DAD table for a July 1942 Smethport, PA, storm (from HMR No. 51)................3-3 Figure 4-1 HMR transposition limit map for a May 1943 Warner, OK, storm (from HMR No. 51).....................................................................................................4-3 Figure 5-1 Example weather map showing moisture inflow and dewpoint observations (adapted from WMO 2009)..........................................................5-2 Figure 5-2 Example dewpoint temperature data with the maximum 24-h average dewpoint and maximum 12-h persisting dewpoint values identified...................5-3 Figure 5-3 Example of improper representative dewpoint timing selection based on theoretical moisture travel time for precipitation event.......................................5-4 Figure 5-4 Map of storms used to evaluate alternative storm representative dewpoint conversion factors.............................................................................................5-6 Figure 6-1 Example maximum 12-h persisting dewpoint map for July (EDS 1968).............6-2 Figure 8-1 Example DAD curves (including envelopment) for a 72-h PMP (Note:

Values do not represent actual storms or PMPs)..............................................8-2 Figure 9-1 Standard isohyetal spatial distribution pattern recommended in HMR No. 52 (left; as included in WMO 2009) and example probable maximum March isohyets provided in HMR No. 41 (right).................................9-2 Figure 9-2 Example temporal distribution for a watershed-scale 72-h PMP event provided in HMR No. 52 (left) and example temporal distributions for middle-loaded, local-scale storms provided in HMR No. 57 (right)....................9-4

vii

ABBREVIATIONS AND ACRONYMS

ACWI Advisory Committee on Water Information ADAMS Agencywide Documents Access and Management System AEP annual exceedance probability AWA Applied Weather Associates, LLC BAF barrier adjustment factor C Celsius CFR Code of Federal Regulations DAD depth-area-duration EPRI Electric Power Research Institute F Fahrenheit FEMA Federal Emergency Management Agency FERC Federal Energy Regulatory Commission FHRR flood hazard reevaluation r eport GTF geographic transposition factor HMR hydrometeorological report HYSPLIT Hybrid Single-Particle Lagrangian Integrated Trajectory IPMF in-place maximization factor KM knowledge management LIP local intense precipitation MCC mesoscale convective complex MTF moisture transposition factor NCDC National Climatic Data Center NCEI National Center for Environmental Information NEI Nuclear Energy Institute NEXRAD Next-Generation Radar NOAA National Oceanic and Atmospheric Administration NPP nuclear power plant NRC U.S. Nuclear Regulatory Commission ORNL Oak Ridge National Laboratory OTF orographic transposition factor PMF probable maximum flood PMP probable maximum precipitation PW precipitable water SC storm center grid point SE storm elevation SRep storm representative dewpoint location SRP Standard Review Plan (NUREG-0800)

SSM storm separation method

ix SSPMP site-specific probable maximum precipitation SST sea surface temperature ST storm transposition location TAF total adjustment factor TDL Techniques Development Laboratory (NOAA)

TP technical paper TR topical report TVA Tennessee Valley Authority USACE U.S. Army Corps of Engineers USGCRP U.S. Global Change Research Program WMO World Meteorological Organization WS watershed-scale

x ACKNOWLEDGMENTS

The authors wish to acknowledge and extend sincere appreciation to the many individuals who contributed to the development of this document and to expanding the knowledge base of the U.S. Nuclear Regulatory Commission (NRC) staff concerning research development and review of site-specific probable maximum precipitation (SSPMP). The author team would also like to extend its gratitude to Kevin Griebenow and Kenneth Fearon of the Federal Energy Regulatory Commission (FERC) for introducing the NRC staff to the FERC Board of Consultants process,

as well as for the lengthy discussions that furthered the teams understanding of SSPMP. The authors received thoughtful feedback in the form of public comments from Applied Weather Associates, the Nuclear Energy Institute, FERC, and Mr. Steve Weinbeck. We would like to thank the public commenters for taking the time to provide their thoughts and make this NUREG/KM a more robust and useful document.

xi

1 INTRODUCTION

This report su mmarizes m ethods, data so urces, and procedures used in the development o f site-specific pr obable maximum pr ecipitation (SSPMP) e stimates. To dat e, the U.S. N uclear Regulatory C ommission ( NRC) staff, with assistance from Oak R idge National Laboratory (ORNL) st aff, has r eceived and review ed multipl e submittals i n response to the letter dated March 12, 2012 ( Agencywide Documents A ccess and Management S ystem (ADAMS)

Accession No. ML12053A340), requesting, unde r Title 10 of t he Code of F ederal R egulations (10 CFR) 50.54(f), updated flooding hazard analyses for nu clear pow er pl ants (NPPs) and topical r eports (TRs) that contain SSPM P evaluations. T he objective of t his docu ment is t o summarize the NRC staff s cu rrent understanding to su pport the development and r eview of SSPMP estimates f or NRC -regulated NPPs i n the United States for long-term kn owledge management (KM).

Specifically, this do cument ( 1) identifies terminologies, theories, methods, and lessons l earned for SSPM P studies submitted to t he NRC f or review and ( 2) suggests key c onsiderations fo r the NRC staff when developi ng or r eviewing any futur e SSPM P studies.

1.1 Background

The NRC has long recognized the importance of protecti ng NPPs against natural pheno mena as a means t o pr event reactor core da mage, t o en sure containment, and to preserve spent f uel pool i ntegrity. The NRC established several r equirements add ressing natural phenom ena i n 1971, which are described in G eneral D esign C riterion ( GDC) 2, Design b ases f or p rotection against n atural p henomena, of A ppendix A, General Design Cr iteria for N uclear P ower P lants, to 10 CFR Part 50, Domestic l icensing of p roduction and u tilization f acilities. GDC 2 states, in part, that st ructures, systems, and components important t o sa fety be designed to withstand t he effects of nat ural pheno mena such as floods, tsunami, and se iches w ithout l oss of ca pability to perform t heir i ntended sa fety f unctions. GDC 2 also states that design bas es f or these structures, systems, and components reflect (1) appropriate consideration of t he most sever e of the natural pheno mena t hat have been historically reported for t he site and surrounding region, with sufficient m argin for t he limited accu racy and quantity o f the hi storical data and t he per iod of time in w hich the dat a have been accumulated, ( 2) appropriate co mbinations of t he effects of normal and acci dent co nditions w ith the effects of t he nat ural pheno mena, and ( 3) the importance of the safety functions t o be performed.

On March 11, 2011, a 9. 0- magnitude earthquake struck t he Japanese mainland, triggering a 14-meter ( 45-foot) tsunami. The co mbination of events resulted i n extensive damage t o the Fukushima Dai-ichi NPP site overlooking the P acific O cean. (Additional i nformation about t he accident appea rs i n NRC R esponse to Lessons Lear ned from Fuku shima, i ssued September 2018 ( ADAMS Acce ssion No. ML 112660383). )

In response to these events, the NRC developed a comprehensive set of recommendations (documented in Recommendations f or E nhancing Reactor S afety i n the 2 1st Century, dat ed July 12, 2011 ( ADAMS A ccession No. ML111861807)) and identified ensu ing actions fo r current owners and oper ators (licensees) t o co nsider an d address. These actions w ere intended to allow the NRC staff t o verify c ompliance against approved seismic and ext ernal f looding design bases, and t o d etermine whether a pl ants l icense should be suspended, revoked, or m odified based on any addi tional safety enhance ments ne eded. Specific t o external flooding events, the NRC staff issued a request for informati on letter on March 12, 2012 ( ADAMS Accession

1-1 No. ML12053A340), under 10 CFR 50.54(f) (herein referred to as the §50.54(f) letter ) to all power reactor licensees and holders of construction permits in active or deferred status. to the §50.54(f) letter requested that licensees confirm the appropriateness of the reevaluated beyond-design-basis flooding events assumed for their plants (and their ability to protect against them) using current guidance and methodologies. Those reevaluations were also to rely on analytical approaches and methods consistent with current engineering practices.

This information would allow the NRC staff to assess individual plant responses and determine whether any additional regulatory actions were needed for a particular site.

The current guidance and methodologies ment ioned in this NUREG/KM are the regulatory guidance and methodologies typically used for early site permit and combined license reviews, including NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition (SRP), Section 2.4.2, Floods, Revision 4, issued March 2007 (ADAMS Accession No. ML070100647), and NUREG/CR-7046, Design-Basis Flood Estimation for Site Characterization of Nuclear Power Plants in the United States of America, issued November 2011 (ADAMS Accession No. ML11321A195).

In response to the §50.54(f) letter, current owners and operators submitted 61 flood hazard reevaluation reports (FHRRs) corresponding to the fleet of operating domestic nuclear power reactor sites. Many of the sites had reevaluated hazards exceeding the design basis, particularly since the local intense precipitation (LIP) hazard was not part of the original design basis for several of the sites. For some licensees, changes in modeling data, assumptions, and techniques made during these reevaluations resulted in vari ous changes in flood estimates. The following flood-causing mechanisms of interest were the subject of the §50.54(f) letter:

LIPa measure of the anticipated extreme precipitation over a specific site location that is reported as a site characteristic

riverine floodingassociated with some stream or river and considers the characteristics of the watershed, extreme rainfall and runoff on a regional basis, and accounts for land cover, topography, flood control features, and dams

dam failureclosely related to riverine flooding and considers the effect of seismic, overtopping, and sunny-day dam failures on the site location (this NUREG/KM addresses riverine flooding and dam failure analyses as one topic)

storm surgeassociated with flooding that arises due to relatively la rge, intense (typically coastal) storm systems and includes the effects of tides, winds, and storm characteristics (e.g., storm track, radius of maximum winds, and meteorological pressure differentials)

seicheassociated with waves similar in motion to a s eesaw and a temporary disturbance in the water level of an enclosed (e.g., lake) or partially enclosed body of water that may be caused by changes in atmospheric pressure or ground motion

tsunamis typically caused by earthquakes, subaerial or submarine landslides, or volcanic eruptions displacing large volumes of water

ice-induced floodingtypically caused by the release of upstream ice dams or the formation of downstream ice dams on rivers

1-2

channel diversions/migrationsassociated with changing riverine channel flowpaths

Based on the analysis results provided in the FHRRs, the respondents to the §50.54(f) letter reported that most of the power plant sites had reevaluated flood hazard elevations exceeding the design-basis flood level elevations previously used in licensing. Most of the exceedances were associated with LIP, an external flooding hazard generally not explicitly considered as part of the original design basis for several of the sites. Computer modeling results performed by the respondents indicated that LIP, in concert with power plant grading, would likely lead to external flooding within the reactor power block at most operating reactor locations. Exceedances were also reported for some of the other external flood-causing mechanisms listed in the SRP for many of the power reactor sites. In many instances, improvements in modeling data and techniques, and changes in assumptions, made before the 2012 reevaluations resulted in modifications to previous external flood estimates.

In terms of the source of the precipitation values used in the LIP and riverine-based flood analyses, current NRC guidance for the evaluation of e arly site permits and combined operating license applications is to select the appropriate probable maximum precipitation (PMP) event reported in the National Weather Services hydrometeorological reports (HMRs) regionally applicable to the power reactor site under review.

A common change in many of the FHRR submittals was the use of SSPMP estimates in place of conventional HMR PMP estimates. 5 PMP estimates (see Section 1.4) serve as a critical hydrologic modeling input for assessing flood hazards, and SRP Sections 2.4.2 and 2.4.3, Probable Maximum Flood (PMF) on Streams and Rivers, Revision 4, issued March 2007 (ADAMS Accession No. ML070730405), identify the HMR PMP estimates for estimating LIP flooding and riverine flooding. While SSPMP estimates employ a storm-based maximization and transposition process similar to the HMRs, they ultimately use different and updated methods, data sources, and procedures than those found in the HMRs. Because of these differences and uncertainties, the review may be more complex.

At the time of the 2012 reevaluation, the SRP did not describe SSPMP estimates, as they were a relatively recent approach to estimating extreme precipitation at nuclear sites. For the reevaluations, as is stated in SRP Section 2.4.3, the SSPMP estimates required licensees to evaluate how the proposed alternatives to the SRP acceptance criteria provide an acceptable method of complying with the NRC regulations.

The National Oceanic and Atmospheric Administration (NOAA) developed conventional PMP estimates and published them in a series of HMRs. In addition to the HMRs, NOAA provided PMP guidance through two technical memoranda (HYDRO 39, Probable Maximum Precipitation for the Upper Deerfield River Drainage Massachusetts/Vermont, issued June 1984 ( Miller et al. 1984), and HYDRO 41, Probable Maximum Precipitation Estimates for the Drainage above Dewey Dam, Johns Creek, Kentucky, issued August 1985 ( Fenn 1985))

and two technical papers (Technical Paper No. 42, Generalized Estimates of Prob able Maximum Precipitation and Rainfall-Frequency Data for Puerto Rico and Virgin Islands (U.S. Weather Bureau 1961), and Technical Paper No. 47, Probable Maximum Precipitation and Rainfall-Frequency Data for Alaska ( Miller 1963) ). Figure 1-1 illustrates the area of applicability associated with each NOAA HMR document.

5 During the period covered in this NUREG/KM, Applied Weather Associates, LLC, performed all of the SSPMP studies reviewed by the NR C.

1-3 Of the 61 FHRRs submitted, 26 included an evaluation of SSPMP for estimating LIP or riverine flooding. When it first received an SSPMP submittal, the NRC conducted an initial screening assessment to determine whether detailed review was required. Many of the s ubmittals did not require an in-depth NRC examination because of small differences (generally less than 6 inches (in.)) in the resulting water surface elevation when comparing the SSPMP-driven simulation to the NOAA HMR PMP-driven simulation. For 12 sites with notable relative water surface elevation changes, the NRC and ORNL staff performed a detailed review of the SSPMP calculation and implementation. The remaining sites that used an SSPMP did not result in a notable difference in water surface elevation (e.g., 6 inches) when compared with the NOAA HMRs. 6 Figure 1-2 shows the geographic locations of the sites for which the NRC has reviewed detailed SSPMP information as part of a submittal. As indicated in Figure 1-2, all these NPP sites are in the eastern United States, east of the Mississippi River, and within the regions covered by HMR No. 51, Probable Maximum Precipitation Estimates, United States East of the 105th Meridian (Schreiner and Riedel 1978) ; HMR No. 52, Application of Probable Maximum Precipitation EstimatesUnited States East of the 105th Meridian (Hansen et al. 1982); and HMR No. 56, Probable Maximum and TVA Precipitation Estimates with Areal Distribution for Tennessee River Drainages Less Than 3,000 Mi2 in Area (Zurndorfer et al. 1986), as shown in Figure 1-1.

To date, most of the SSPMP studies submitted to the NRC have been primarily used for evaluations in response to the §50.54 (f) letter. With the exception of one study submitted as part of a TR, these evaluations were mostly completed outside of the framework of a quality assurance program. However, this methodology is also likely to be increasingly applied to licensing actions that should be completed under a quality assurance program in compliance with Appendix B, Quality Assurance Criteria for Nuclear Power Plants and Fuel Reprocessing Plants, to 10 CFR Part 50.

6 The staff used a metric of 6 inches to identify notable differences between the NOAA HMR and SSPMP water surface elevati ons at each site. This was used as a first screening analysis, but the final determination on whether to perform a detailed revi ew was dependent on the conditions of the unique site.

1-4 Figure 1-1 Map of regions covered by NOAA PMP documents (as of 2015)

(https://www.nws.noaa.gov/oh/hdsc/studies/images/pmp_figure.jpg)

Figure 1-2 Map of NPP locations for which the NRC reviewed a detailed SSPMP7

7 Markers in Figure 1.2 indicate NPPs at Beaver Valley Power Station, Browns Ferry Nuclear Plant, Calvert Cliffs Nuclear Power Plant, Catawba Nuclear Station, Duane Arnold Energy Center, Indian Point Energy Center, Millstone Nuclear Station, Perry Nuclear Power Plant, Quad Cities Generating Station, R.E. Ginna Nuclear Power Plant, Sequoyah Nuclear Plant, and Watts Bar Nuclear Plant.

1-5 1.2 Purpose and Objective of t his Document

The purpose of this document is to maintain and preserve the knowledge gained and document the lessons learned from the § 50.54(f) FHRR reviews, specifically related to the development and review of SSPMP studies. Since its inception, the Atomic Energy Commission and its successor, the NRC, have focused on preserving the decision-making record using documents such as NUREGs, SECY papers, and r egulatory guides. However, in 2006, the agency recognized a need to engage in a more formal program of KM that also reflects the less tangible human capital aspect of the agencys knowledge base. This feature was considered to be particularly important as the agency enter ed its fifth decade of operation a period characterized by the increasing number of retirements of long-serving NRC staff involved in many of the agencys early licensing programs.8 The staff describes other aspects of the agencys KM program in SECY- 06- 0164, The NRC Knowledge Management Program, dated July 25, 2006 (ADAMS Accession No. ML061550002).

The NRC is enhancing its regulatory processes by developing and implementing a framework for the ongoing assessment of natural hazards information, including information related to climate change, such as increased storm intensities. The NRCs enhancements to the existing regulatory processes, described in SECY-16-0144, Proposed Resolution of Remaining Tier 2 and 3 Recommendations Resulting from the Fukushima Dai -ichi Accident, dated December 29, 2016 (ADAMS Accession No. ML16286A552), are referred to as the Process for the Ongoing Assessment of Natural Hazards Information. The Commission approved this process, which uses a graded approach to proactively, routinely, and systematically seek, evaluate, and respond to new information on natural hazards, including climate change impacts on storm intensity, using the approved framework for the ongoing assessment of natural hazard information. This applies to all phases of an NPPs operational timeline.

This document summarizes the terminologies, theories, general methods, data sources, and procedures used in SSPMP development. This document also identifies key considerations in developing and reviewing SSPMP estimates. The staff issues reports such as this to describe, and make available to the public, methods acceptable to the NRC staff for submitting specific analyses to the NRC. This NUREG/KM is not a substitute for the regulations, and compliance with it is not required. Ultimately, this NUREG /KM aims to inform and support possible future estimates related to NPP SSPMP development and associated NRC review efforts.

1.3 Regulatory Context

GDC 2 states, in part, the most severe of the natural phenomena that have been historically reported for the site and surrounding area, with sufficient margin for the limited accuracy, quantity, and period of time in which the historical data have been accumulated.

Regulations in 10 CFR Part 100, R eactor site criteria, state that factors to be considered when evaluating sites include the disposition and proximity of dams and other hu man-related hazards (10 CFR 100.20(b)) and the physical characteristics of the site, including the meteorology and hydrology (10 CFR 100.21(d)).

8 NRC staff efforts to preserve this legacy of experience that descr ibes historical events, facts, and research that were instrumental in shaping the NRCs regulatory programs can be found at http://www.nrc.gov/reading-rm/doc -collections/nuregs/knowledge/.

1-6 In 10 CFR 50.54(f), the NRC states that a licensee shall at any time before expiration of its license, upon request of the Commission, submit written statements, signed under oath or affirmation, to enable the Commission to determine whether the license should be modified, suspended, or revoked.

, Enclosure 2, to the §50.54(f) letter identified the flood-causing mechanisms that licensees were to address in their FHRRs. These flood-causing mechanisms correspond to major sections currently found in SRP Section 2.4.2 (ADAMS Accession No. ML070100647) for license applications to construct NPPs. Those flood hazard mechanisms include the following:

LIP and a ssociated drainage

streams and rivers

failure of dams and onsite water control and storage structures

storm surge

seiche

tsunami

ice-induced flooding

channel migrations or diversions

combined-effect flood

Under Regulatory Guide 1.59, Design-Basis Floods for Nuclear Power Plants, Revision 2, issued August 1977 (ADAMS Accession No. ML003740388), and SRP Section 2.4.2 (ADAMS Accession No. ML070100647), PMP estimates are necessary inputs to evaluate LIP and s tream and river flooding.

1.4 Probable Maximum Precipitation Definition and Existing Regulatory Guidance

The World Meteorological Organization (WMO) defines probable maximum precipitation as the greatest depth of precipitation for a given duration meteorologically possible for a design watershed or a given storm area at a particular time of year (WMO 2009). Operationally, when sufficient historical extreme rainfall observations are available, PMP is estimated based on a widely used method of combining storm moisture maximization, transposition (i.e., relocating patterns of storm precipitation to other areas), and envelopment (i.e., identifying maximum storm precipitation values), known as the storm-based approach (Schreiner and Riedel 1978).

PMP has been used as input to simulate the probable maximum flood (PMF), which is a conservative design criterion for evaluating the safety for NPPs, according to RG 1.59. The NRC staff considers conservative design criteria to satisfy the requirements of GDC 2, which states that applications for construction permits and operating licenses should consider the most severe of the natural phenomena that have been historically reported for the site and surrounding area, with sufficient margin for the limited accuracy, quantity, and period of time in which the historical data have been accumulated. As the theoretical upper bound of rainfall depth that could occur under a series of severe hydrometeorological conditions, PMP is quantified in terms of a precipitation depth for a given duration and area. For example, a PMP for a particular location may be expressed as the 24-hour (h), 100-square-mile (mi2) PMP, or the theoretical maximum amount of rainfall that would occur over a 24-h period for a 100- mi2 area of interest.

Historically, PMP values for locations across the United States have been estimated primarily through a series of HMRs issu ed by NOAA and predecessor agencies. These estimates were

1-7 based on data collected over decades and for a variety of extreme rainfall events. SRP Sections 2.4.2 and 2.4.3 document the use of the HMRs to estimate PMP when assessing LIP flooding and riverine flooding. However, NOAA stopped updating the HMRs in 1999, and the associated storm catalogs became dated. While some agencies analyze and develop depth-area-duration (DAD) curves, there are no curren t procedures to update storm data sets (U.S. Bureau of Reclamation 2011).

More recently, licensees, assisted by private entities (e.g., companies, contractors, and consultants), have conducted SSPMP studies using methods similar to those used by the HMRs, including SSPMP estimates, for a number of NPPs in the United States. In response to the §50.54(f) letter, approximately 26 of the 61 FHRR submittals used SSPMP estimates provided by private entities.

Sections 1.5 and 1.6 of this report provide overviews of conventional PMP and SSPMP methods, respectively.

1.5 Summary of Conventional Probable Maximum Precipitation Methods

Fundamentally, PMP estimates can be classi fied as either theoretical or operational. Although the formal definition of PMP assumes a theoretical upper limit for precipitation, in practice, a theoretical PMP cannot be directly computed or verified. Instead, most conventional PMP estimates follow an operational approach in which historical data and subjective professional judgment may result in PMP estimates that are typically lower than the theoretical upper limit (Micovic et al. 2015).

Many NOAA HMRs have described the basic approach and detailed m ethods used in developing operational PMP estimates. 9 For example, HMR No. 51 (Schreiner and Riedel 1978) provides generalized all-season PMP estimates for the United States east of the 105th meridian for drainage areas from 10 to 20,000 mi2 and for durations from 6 to 72 h. The NOAA HMRs identify two types of PMP estimates: generalized PMPs and individual drainage PMPs. The PMP estimates provided in most HMRs (e.g., HMR No. 51) are termed generalized estimates.

With these HMRs, PMP isolines are overlain on a basin map to determine the basin-average PMP for a drainage basin. Typically, simplifying assumptions regarding the influence of topography and orographic processes were used in lieu of a detailed analysis. Other HMRs and studies produced by NOAA (e.g., HMR No. 41, Probable Maximum and TVA Precipitation over the Tennessee River Basin above Chattanooga (Schwarz 1965) ; HMR No. 46, Probable Maximum Precipitation, Mekong River Basin (U.S. Weather Bureau 1970 ); and HMR No. 56) provide PMP estimates for individual drainage basins that are specifically adjusted for the area and physical influences of the drainage basin under consideration. R easons for analyzing individual drainage basins include (1) generalized PMP studies wer e not available, (2) the watershed was larger than those covered by available generalized PMP studies, or (3) detailed studies indicated that orographic effects would yield PMP estimates significantly different from those based on available generalized PMP charts (e.g., watersheds in the Appalachian Mountains).

To estimate PMP for a specific region, the HMR methods are initiated by identifying all historical extreme storms for the region (or for regions with similar meteorological and topographical settings that allow transposition of storms). This process is referred to as storm selection. For each storm, multiple rain gauge records are jointly analyzed to construct a rainfall DAD

9 http://www.nws.noaa.gov/oh/hdsc/studies/pmp.html

1-8 relationship; more recent PMP studies may use other precipitation products, such as a combination of rain gauges and gauge-adjusted radar data. The total moisture air mass that supplied each storm (referred to as the total storm precipitable water ) is estimated using representative surface dewpoint observations or sea surface temperatures as a surrogate for atmospheric humidity readings. A measure of historical maximum dewpoint (often estimated from dewpoint climatology products, such as 100- year dewpoint maps) is used to estimate the climatically maximum total precipitable water (PW) that could occur during a similar annual timeframe and at the general location of the storm being analyzed. A ratio between observed and maximized PW is then calculated and multiplied with observed DAD to estimate the moisture-maximized DAD through a moisture maximization process. In practice, the identification and use of dewpoint values are selected as a proxy for PW content, which is computed following the pseudo-adiabatic assumption (Reitan 1963). Using similar calculation concepts, maximum dewpoint is analyzed at the PMP location of interest, with the storms rainfall depth further adjusted through a storm transposition process for moisture adjustment.

Other precipitation effects, such as barrier-induced moisture depletion or orographic enhancement, may be captured through further storm transposition calculations accounting for terrain adjustment. In the last step, envelopment (i.e., identifying maximum values) of all moisture-maximized storms across various durations and areas, a PMP DAD relationship is constructed. Proper spatial and temporal distributions are then used to disaggregate the derived total PMP depth for further modeling applications. To date, this data-driven storm moisture maximization, transposition, and envelopment method remains the most commonly used approach in engineering practice, though some new methods based on numerical weather simulation models have been proposed and developed in the past decade (e.g., Ohara et al. 2011; Ishida et al. 2014, 2015; Rastogi et al. 2017).

Conventional PMP methods have been criticized for the validity of the method and concerns about the sufficiency of data to implement this deterministic approach (e.g., Papalexiou and Koutsoyiannis 2006), as well as the uncertainty of the PMP estimates (Micovic et al. 2015). An intrinsic assumption of the conventional PMP method is that the most significant storm that could lead to PMP (with maxim ized moisture) has occurred during the observation period. Such an assumption is difficult to test and may even be invalid in a nonstationary climatic condition.

From the engineering and regulatory perspectives, perhaps the most concerning component of the PMP methodology is the significant professional judgment required at various steps during the development of conventional PMP estimates. Such professional judgment s could be specific to a particular site or expert and hence limit the reproducibility of PMP estimates by an independent third party. To gain acceptance by both the engineering and regulatory communities, the uncertainty and sensitivity involved in these professional judgment s must be further understood to ensure the proper use of PMP for protecting critical infrastructure.

Highlighting the need for informed PMP estimation, the August 2017 landfall of Hurricane Harvey along the U.S. Gulf Coast produced historically high rainfall totals in the continental United States and approached the PMP estimates in HMR No. 51. Using the radar-driven National Center for Environmental Prediction Stage IV Quantitative Precipitation Estimates (Lin 2011), Kao et al. (2019) reported that some Hurricane Harvey precipitation depths (72 -h 5,000- mi2 and 10,000- mi2) in the Houston, TX, area exceeded HMR No. 51 values. This extreme event demonstrates the uncertainty with extreme precipitation given the limited historical record (for example, Hurricane Harvey exceeded Houstons previous 1-day rainfall observation by more than 50 percent) and emphasizes the importance of critical infrastructure management.

1-9 1.6 Summary of Methods U sed in Licensee Site-Specific Probable Maximum Precipitation Submittals

The SSPMP methods submitted by licensees to date largely follow the conventional HMR methods but also include some updated procedures. In some cases, these updates are related to new meteorological knowledge and may require use of professional judgment. D eviations from HMR methods are reasonable when they are justified by advances in meteorological analysis techniques and data. Specific aspects of the SSPMP calculation process require varying levels of professional judgment, as described in the remainder of this report.

Fundamentally, HMRs and SSPMP estimates associat ed with the 2012 §50.54(f) letter followed a storm-based PMP approach, which is based on historical storm observations. T he NOAA HMRs used a storm-based PMP approach.

As included in multiple SSPMP submittals to the NRC, PMP estimates can be provided either for a shorter duration local PMP (i.e., LIP-PMP) or for a longer duration, watershed-scale (WS)

PMP (i.e., WS-PMP). From an NPP perspective, LIP-PMP may drive flooding in the immediate vicinity of the powerblock and may affect onsite plant facilities, while WS -PMP may drive flooding in streams and rivers near NPP s as a result of heavy precipitation in the upstream watershed or flood -induced dam failure. NUREG/CR-7046 contains additional information on LIP-PMP and WS-PMP design-basis flood estimation.

An LIP-PMP estimate is developed largely based on historical local storms, which WMO (2009) defines as follows:

Local stormA storm ev ent that occurs over a small area in a short time period.

Precipitation rarely exceeds 6 [h] in duration, and the area covered by precipitation is less than 1,300 km2 [500 mi2]. Frequently, local storms will last only 1 or 2 [h], and precipitation will occur over area sizes of up to 500 km2

[193 mi2]. Precipitation in local storms will be isolated from general storm rainfall.

However, the staff notes that for estimating the period of inundation, longer duration (up to 12 hours1.388889e-4 days 0.00333 hours 1.984127e-5 weeks 4.566e-6 months or more) regional storms may be used to develop a LIP-PMP estimate.

A WS-PMP estimate is developed largely based on historical general storms, which WMO (2009) defines as follows:

General stormA storm event that produces precipitation over areas in excess of around 1,300 km2 [500 mi2] and for durations longer than 6 [ h] and is associated with a major synoptic [ or large-scale] weather feature.

Depending on the watershed location, the WS-PMP may be controlled by general, synoptic storms, or by tropical storms, or by a combination of storm types. In addition, licensees have provided LIP-PMP and WS-PMP estimates in terms of all-season or cool-season bases, or both, to assess conservative ness and bounding scenarios, as described in NUREG/CR-7046. While an all-season PMP estimate considers all PMP storm types, regardless of when they occur throughout the year, a cool-season PMP estimate limits storm evaluation to historical storms that occurred during the cool season. Where the regional climate may support heavy rain-on-snow events with large snow melt and associated runoff, a separate cool-season PMP analysis is performed. The beginning and ending dates of a cool season differ depending on location.

1-10 To produce a n SSPMP estimate for a given location, historical storm event data are collected and modified through a series of analytical procedures. Based on the NRCs participation in interagency research (ACWI 2018) 10 as well as the NRCs flood hazard reevaluation reviews, the NRC staff identified certain primary steps in the SSPMP estimate methodology. Those steps form the balance of this NUREG/KM and include the following actions:

storm selection (Section 2 )

storm reconstruction (Section 3)

storm transposition (Section 4)

storm representative dewpoint selection and PW estimation (Section 5)

dewpoint climatology, moisture maximization, and moisture transposition (Section 6)

terrain adjustment (Section 7 )

envelopment and PMP determination (Section 8)

special and temporal distributions for SSPMP applications (Section 9)

The steps outlined above are based on findings from the NRC staffs flood hazard reevaluation reviews, a detailed TR review for a watershed, and experience gained from participating in or observing Federal Energy Regulatory Commission (FERC) Board of Consultant reviews.

Depending on the objective and location of an SSPMP study, more or fewer steps may be appropriate. Consequently, it is premature for the staff to issue guidance on this particular topic at this time as the state-of-the-art continues to evolve. Thus, the SSPMP process steps identified in this NUREG/KM should be viewed as the staffs assessment of the current state of science on this specific topic. Lastly, Section 10 presents an additional discussion of potential effects of long-term climatic change before the overall report summary in Section 11.

Appendix A provides all of the public comments received, as well as the staff responses.

10 The NRC actively participates in interagency research with the Advisory Committee on Water Information (ACWI) related to the evolving field of SSPMP study methodologies (ACWI 2018).

1-11

2 STORM SELECTION

This s ection discusses the storm se lection pr ocess that co uld be used i n the development of SSPMP estimates, including descriptions of t he t erminology, general methodology, and key considerations. It ends w ith a summary of l essons learned from t he recent r eviews of the FHRRs i n response to t he 2012 §50.54(f ) letter (i.e., t he 2012 §50.54(f) FH RRs).

2.1 Terminology, General M ethodology, an d L essons L earned

Storm se lection is the process of identifying and selecting historical st orm eve nts t hat ar e appropriate f or i nclusion in SSPMP deve lopment for a t argeted si te. T o evaluat e an SSPMP across various dur ations and ar eas following a storm -based approach, the storm se lection process should incorporat e into th e SSPM P analysis a ll major historical st orm eve nts that ca n be collected and technically justified. Exampl e resources co ntaining historical storm dat a includ e observations f rom t he N ational C enter f or E nvironmental Informations (NCEI s)11 Cooperative Observer Program Network 12 gauges, U.S. Geological S urvey flood reports, journal ar ticles, books, I nternet publications, storm dat a f rom H MRs, U.S. Army Corps of Engineers (USACE) storm r eports, U.S. Bureau of R eclamation st orm databases, previously submitted S SPMP studies, commercial dat abases, and o ther so urces.

High-quality, reliable d ata on historical ex treme precipitation events ca n be difficult to obtain, though several resources ar e ava ilable. I n pa rticular, t he USACE Black Book ( USACE 1973) provides a comprehensive collection of obs erved storm e vents, with dat a o n more than 550 historical storms occ urring from 1875 t o 1969 (see Figure 2-1 ). This d atabase provides a valuable resource when reviewing SSPMP s torm selection and should be leveraged during SSPMP deve lopment and reviewed to ensure tha t storm se lection reasonably considered major historical storms.

Once an in itial list is assembled, t he storms are further evaluated to determine which storms to examine in the full analysis. To begin, a storm se arch domain should be identified. A s torm search domain represents t he geographic regi on around a l ocation of interest used to identify and select storms. Storms occu rring within t he storm se arch domain are as sumed to have occurred because of regional hydrometeorological and topographic characteristics that are similar t o ( or co uld r esult in similar precipitation o ver) t he l ocation of i nterest. The storm search domain may i nclude longitudinal ( east-west) ext ents bounded by t opography ( e.g., the Appalachian or R ocky Mountains) or reasonabl e distance (e.g., a certain l ongitudinal ext ent away f rom t he poi nt of i nterest). Latitudinal ( north-so uth) ext ents m ay be l imited to approximately 6 degrees north or so uth of the st udy ar ea. This latitudinal constraint is applied because of ch anges i n storm dyn amics (e.g., vorticity) that oc cur across relatively large changes in latitude (HM R No. 5 7, Probable Maximum PrecipitationP acific Northwest States: Columbia River ( Including P ortions of C anada), Snake River and P acific Coastal D rainages (Hansen et al. 1994)).

Seasonal and regional cl imatology and m eteorology m ay al so be co nsidered to det ermine whether an historical storm eve nt could have occurred ove r t he ar ea of i nterest. An SSPMP study may pr ovide bot h all-season and cool -season estimates. A ll-season est imates may occur at any t ime of the year and are t ypically appl ied as pur e r ainfall events, as t he resulting runoff

11 Formerly the National Climatic Data Center (NCDC) before dissolving in 2015.

12 https://www.ncdc.noaa.gov/data-access/land-based-station -data/land-based -datasets/cooperative-observer -

network-coop

2-1 would be purely from rainfall. Cool-season estimates are intended to capture potential rain-on-snow events that may produce different flood hydrograph characteristics (e.g., snowmelt effects) than a rain-only event, as the resulting runoff would combine rainfall with snowmelt; such cool-season estimates may be needed in colder climates. The HMRs (e.g., HMR No. 57; HMR No. 59, Probable Maximum Precipitation for California ( Corrigan et al. 1999) ) discussed cool -season PMP in general terms; however, cool -season PMP estimates were only provided for limited cases (e.g., HMR No. 48, Probable Maximum Precipitation and Snowmelt Criteria for Red River of the North Above Pembina, and Souris River Above Minot, North Dakota (Riedel 1973), and HMR No. 53, Seasonal Variation of 10- square-mile Probable Maximum Precipitation Estimates, United States East of the 105th Meridian (Ho and Riedel 1980) presented estimates for the 10- mi2 PMP only).

The staff notes that the sufficiency of historic al storms included in the catalog heavily influences the reliability of a n SSPMP estimate. While no specific research exists to suggest a minimum number of required storms for SSPMP development, if there are sufficient samples to support the identification of a statistical maximum, removing any single sample should not result in a major change in the PMP estimate. If the intent is to derive SSPMP for applications across a wide range of areas and durations, a large collection of historical extreme storms with varying sizes and durations will be needed.

Figure 2-1 Map of USACE Black Book storm locations (data from USACE 1973)

2.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 §50.54 (f) FHRRs, the NRC staff identified several key considerations in the development of an SSPMP. These considerations build on the previously desc ribed terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development. The NRC staff has identified the following key considerations r elated to storm selection:

2-2

The comprehensive list of storms and their characteristics considered during the storm selection process should be clearly documented. If a major historical storm is purposely excluded during the storm selection process, justification should be provided for further review and evaluation.

Extreme precipitation data that are both reliable and publicly available should be leveraged whenever possible. While publicly available storm data are available from the USACE Black Book (USACE 1973) and other data sources, there is currently no comprehensive inventory of major historical storms, and there is a lack of public data on more recent storm events. In its 2018 Extreme Rainfall Product Needs proposal (ACWI 2018), the E xtreme Storm Events Working Group outlined its recommendation to create an archive of extreme precipitation events across the United States for use in developing PMP estimates. This archive would provide a digital repository of storm event data and be updated as new storm analyses are performed. If such an archive becomes publicly available, it should be considered for use in storm selection.

2-3

3 STORM RECONSTRUCTION

This s ection discusses the storm r econstruction process that co uld be used i n the dev elopment of S SPMP est imates, including descriptions o f the t erminology, general methodology, and key considerations. It ends w ith a summary of l essons lear ned from the r ecent reviews of t he 2012

§50.54(f) F HRRs.

3.1 Terminology, G eneral Methodology, an d L essons L earned

Storm r econstruction involves deve lopment of DAD cu rves, which summarize the highest observed rainfall dept h a t various combinations o f storm areas and dur ations. D uring later phases of S SPMP ca lculation, t he DAD o f each storm i s adjusted to estimate the theorized maximum p recipitation of that storm under m ore critical meteorological conditions, and the collection of ad justed storm D AD values ar e enveloped to e stimate P MP.

A D AD va lue is exp ressed as a maximum depth of p recipitation occurring over a gi ven area for a specified duration. For a single storm eve nt, DAD va lues ar e co mputed b y anal yzing rain gauge, g ridded precipitation, radar da ta, or a combination of al l three over sp ace and time.

USACE and N OAA h istorical DAD st orm r econstruction involved manual c alculation of mapped rain gau ge data. Figure 3-1 is a n exa mple precipitation map. Collectively, multiple DA D values form D AD cu rves and tables ( Figure 3-2 ), which ar e used t o summarize storm p recipitation data.

DAD curves f or m ultiple storms m ay be compared and play a ke y r ole i n PMP deve lopment through st orm enve lopment (Section 8 ). DAD cu rves r equire extensive data collection and processing efforts. General pr ocedures f or D AD a nalysis ar e provided in WMO (1969) and U.S. Weather Bureau (1946).

With the advancement o f automated r ain gauges and improve d radar r ainfall estimates in recent years, extreme storm ev ents a re now m easured with greater pr ecision, r esolution, and co verage compared w ith conventional m ethods used i n past dec ades. To m atch improvements i n data quality, improved anal ysis t ools have also become available to quickly ass ess and quantif y extreme storm events. Storms w ith existing DAD c urves de veloped by NOAA, USACE, Environment C anada, an d other o rganizations should be considered f or i nclusion, when available. S ince some of these DAD cu rves w ere developed manually using undocumented information sources, it may be difficult to ve rify them. I n t hese cases, SSPMP studies r equire professional, or engi neering, judgment relat ed t o the potential use of the data.

Certain PMP appl ications ( e.g., LIP -SSPMP or s mall basin WS-PMP studies f or NPPs ) require subhourly P MP va lues to ca pture rapidly occu rring peak f lood effects. W hile some Next Generation Radar ( NEXRAD) databases enabl e precipitation estimation for periods a s short as 5 minutes, many s torms are analyzed onl y i n hourly i ncrements. However, since many databases d o not provide subhourly precipitati on data, standard ratios availabl e fro m the HM Rs have been use d in practice to co nvert hourly pr ecipitation values to subhourly pr ecipitation values ( see Section 9).

The accuracy o f DAD va lues, which serve as the storm -specific base line before adjustment to the study ar ea, is a major co nsideration for S SPMP r eviews. Variabilities and uncertainties in observed rainfall values ar e propagated t hroughout the PMP deve lopment pr ocess an d can have significant i mpacts on th e flood elevations e stimated. Typ ically, for r iverine flooding, long-duration and l arge-area DAD va lues w ill have the m ost impact on f lood magnitude since total p recipitation volume w ill often drive riverine flood impac ts. LIP eva luations ar e typically

3-1 affected by short-duration DAD values because peak flooding conditions are most directly tied to peak rainfall intensity. Both riverine and LIP flooding assessments warrant a robust analysis of PMP scenarios and hydrologic response.

While the accuracy of DAD values mainly depends on the quality of input rainfall gauge data and the computational algorithm used to calculate DAD, other significant technical considerations are also involved. For instance, a major multiday and m ulticenter storm event may be analyzed as separate storm events (because of changing moisture sources and mechanisms) and characterized by DAD values. Such actions could reduce PMP values for larger areas (while having limited impacts on PMP values in smaller areas when considering the separate storm centers). As an example, the 1972 Hurricane Agnes storm event produced significant rainfall across the U.S. Atlantic Coast and inland portions of the Appalachian range.

During review of one SSPMP submittal, the NRC found that the licensee had split the precipitation event into multiple storm centers, thereby separating and excluding the most intense portion of the precipitation event. This method resulted in a large decrease in the adjusted storm rainfall compared to a scenario that includes the full storm. Given its significant impacts on larger-area DAD values, decomposition of multicenter storm events should be avoided or, if used, accompanied by substantive justifications. Such justification could include storm timing, interactions with topography, or a combination of both. Ultimately, sensitivity analysis may be needed to assess the impact of evaluating a storm event as a whole or using multiple storm centers.

Figure 3-1 Map of precipitation gauge data for a June 1948 Golden, CO, storm (from HMR No. 55A)

3-2 Figure 3-2 DAD table for a July 1942 Smethport, PA, storm (from HMR No. 51)

3.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 §50.54 (f) FHRRs, the NRC staff identified several key considerations for the development of an SSPMP. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development. The NRC staff identified the following key considerations related to storm reconstruction:

The source(s) of all relevant data used in storm reconstruction should be documented and provided for the NRC staffs review, including data related to storm center location, elevation, observed DAD data/chart, cumulative mass curve(s), isohyetal precipitation maps, and any supporting information from historical analyses.

Reliable, publicly available storm reconstruction data should be leveraged whenever possible. For DAD data, the USACE Black Book (USACE 1973) and other data sources provide information, but the USACE Black Book is outdated and does not include storms since the 1970s. If the studies and databases proposed in the 2018 Extreme Rainfall Product Needs proposal (ACWI 2018) are developed, the corresponding information would provide a key additional source of storm reconstruction data.

When subhourly precipitation estimates are not available for a particular storm but are needed for hydrologic modeling, standard HMR conversion ratios can be used.

Alternative approaches to estimating subhourly precipitation accumulation may also be considered.

When available, DAD variations of a common storm analyzed due to different studies and software can be compared to evaluate potential differences and areas of uncertainty. When significant deviations exist among datasets (especially when lower DAD values are used), the selection should be justified.

Documentation of the software used to derive DAD values and of the detailed analytical steps (i.e., algorithm) is relevant when reviewing SSPMP submittals associated with the

§50.54(f) letter. The softwares ability to reconstruct storm DAD relationships can be documented or demonstrated through controlled test cases. Any software used for NRC licensing actions for NPPs must meet the quality assurance requirements of 10 CFR Part 50, Appendix B. Licensees that have submitted a license amendment

3-3 request or topical report applications to the NRC and have included SSPMP need to justify the software under the quality assurance and quality control requirements of 10 CFR Part 50, Appendix B.

Decomposition of multicenter storm events involves significant professional judgment and is generally discouraged unless substantial justification can be provided.

3-4 4 STORM TRANSPOSITION

This s ection discusse s stor m transposition considerations that c ould be used in SSPM P development, including descriptions o f the t erminology, general m ethodology, and other key considerations. It ends w ith a summary of l essons learned from t he recent r eviews of t he 2012

§50.54(f) FHRR s.

Subsequent p recipitation adj ustments ar e m ade t o account for m oisture a nd terrain differences when relocating storms v ia transposition. S ections 6 and 7, respectively, describe t hose moisture and t errain transposition adjustments.

4.1 Terminology, General M ethodology, an d L essons L earned

The collection of hi storical extreme precipitation events t hat have occurred in or near t he r egion of i nterest represents a v ery i mportant asp ect of storm-based P MP anal ysis. Given the limited historically reported/instrumentally recorde d precipitation data available for u se, PMP anal yses typically require t ransfer of multiple storms and their asso ciated pr ecipitation characteristics to other r egions w here they co uld occur through a process known as storm t ransposition. As documented i n HMR No. 51, storm t ransposition is def ined as relocating i sohyetal pa tterns of storm p recipitation w ithin a region that i s hom ogenous relative to t errain a nd meteorological features i mportant t o t he particular storm r ainfall under c oncern. Section 2.5 of W MO (2009) gives det ails o f this pr ocess, which is standard pr actice for t raditional st orm-based P MP methods.

HMR No. 55A, Probable M aximum P recipitation Estimates U nited States B etween the Continental D ivide and the 103rd M eridian (Hansen et al. 1988), defines t ransposition limits as the out er boundaries o f t he region where a particular storm co uld occur. Figure 4-1 shows an example of a n HMR-analyzed storm t ransposition limit map. Affected by t he storm f eatures (e.g., location, dur ation, seasonality), t he transposition limit can be unique f or each storm.

When considering an SSPMP site o f interest, t he transposition limits and whether a storm can be transpositioned t o the l ocation nee d evaluat ion. To i nform these decisions, p rofessional judgment is use d in assessi ng precipitation location, moisture source location, s torm dyn amics, seasonal influences, orographic ef fects, and other co nsiderations. In so me cases, these judgments ca n be fairly s ubjective, and t he basis for se lection should be de scribed. Ther efore, the main focus during the development and r eview of S SPMP usually is whether a cr itical storm can be allowed to be t ranspositioned to a specific S SPMP si te.

Storm transposition determination involves co nsiderable professional judgment, and t he resulting application of transposition limits ca n greatly i nfluence PMP v alues. Common transposition limit cr iteria ( NRC 2015) applied include the f ollowing:

Storms should not be transpositioned across significant barriers (e.g., the Appalachian and Rocky Mountain ranges).

Storms should not be transpositioned more than 5 or 6 degrees of latitude (Hansen et al. 1994) because of changes in storm dynamics (e.g., vorticity).

4-1

Storms should not be transpositioned over unreasonable distances from moisture sources, and the closest quality data should be used (e.g., storms associated with coastal hurricane flooding may be restricted from inland transpositioning).

Storm types that would not occur in the area of interest may not be applicable (e.g., an extreme rainfall event occurring in March in the South-Central Plains states may not be appropriate for consideration in the Upper Midwest).

Another common transposition limit consideration is the elevation difference between an original storm center and the study area. This consideration may exclude storms that occurred at elevations more than about 300 meters (1,000 feet) above or below the study area elevation because of potential differences in moisture availability and other meteorological factors. As an example, HMR No. 51 includes so-called stippled regions along the Appalachian and Rocky Mountains, where the effects of topography create additional challenges in estimating PMP.

In addition to considering whether a single storm event is transpositionable to a location of interest, SSPMP development may involve storm center separation in which individual storm centers resulting from the same storm may be justifiably di vided into different sets of DAD data, each with potentially unique transposition limits. When and how to separate storms into multiple storm centers involves considerable professional judgment and can result in significant changes to PMP results.

As mentioned in the prior sections, the reliability of SSPMP estimates is heavily influenced by the sufficiency of the included historically reported storms, while storm transposition is a necessary method to increase the number of selected storms for SSPMP development (i.e., given the limited historical observations). If adequate historical observations are available for a given site, storm transposition can be reduced to using strict transposition criteria.

However, if the historical storm observations are limited for an SSPMP site, more general transposition criteria must be used to avoid under estimates due to insufficient sampling. Each SSPMP study should clearly justify and evaluate the tradeoff between sample sufficiency and allowable storm transpositionability.

4-2 Figure 4-1 HMR transposition limit map for a May 1943 Warner, OK, storm (from HMR No. 51)

4.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 §50.54 (f) FHRRs, the NRC staff identified several key considerations in the development of an SSPMP. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development. Related to storm transposition, the NRC staff identified the follo wing key considerations:

When defining transposition limits, clear justification and supporting evidence should be provided. D ocuments should adequately explain where the analyzed storms were transpositioned to within the study domain.

For studies that include dynamic transposition zones in which some storms are transpositioned to only part of the basin or region of interest, summary information should be provided to document which storms are analyzed in each zone. This helps identify regions where a small number of storms may be used for SSPMP development.

Since some major historical storms tend to control SSPMP wherever they are transpositioned, DAD curves (or data) showing individual storm DAD and the SSPMP within each subregion should be provided to help identify cases where a single storm event may greatly affect SSPMP results or where the PMP relies on a small number of storms.

4-3

5 STORM REPRESENTATIVE DEWPO INT SELECTION AND PRECIPITABLE WATER ESTIMATION

This section discusses considerations in the storm representative dewpoint selection and storm PW estimation processes used in SSPMP development, including descriptions of the terminology, general methodology, and other key considerations. It ends with a summary of lessons learned from the recent reviews o f the 2012 §50.54(f) FHRRs.

5.1 Terminology, General Methodology, and Lessons Learned

As with storm selection, reconstruction, and transposition, the identification of storm representative dewpoint is a significant step in the PMP development process. Conventionally, based on the pseudo-adiabatic assumption, surface dewpoint observation is used as a surrogate to estimate the theoretical moisture air mass supply (i.e., PW) for each storm. Used for the calculation of moisture maximization ( see Section 6), storm representative dewpoint selection requires evaluation of observed windspeed, dewpoint, and other meteorological information to identify the most representative moisture air mass that supplies each selected storm. This determination often involves significant professional judgment.

5.1.1 Moisture Source Identification

To identify the moisture air mass trajectory of a storm, some modern storm assessments use NOAAs Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) 13 in conjunction with daily weather maps (Figure 5-1) to analyze a storms moisture travel path based on a selected windspeed dataset. HYSPLIT returns a trajectory shapefile that can be overlapped with dewpoint observation data to identify an approximate moisture source location (or source region) and set of suitable weather stations from which storm representative dewpoint is analyzed. While the variable-height HYSPLIT trajectories are useful in determining moisture inflow paths, some historical storm events may suffer from large uncertainties with respect to moisture source timing and location. In such cases, professional, or engineering, judgment may be required to determine appropriate locations of the storm representative dewpoint. In general, storm representative dewpoint timing is selected for a period before the rainfall event begins.

Section 5.1.2 presents additional information on the storm representative dewpoint location and timing.

Despite some limitations, the use of HYSPLIT is a significant improvement over the conventional manual approach to identifying storm moisture source. The trajectories calculated by HYSPLIT can increase the clarity of moisture inflow and help reduce the subjectivity in the conventional approach. Nevertheless, determining the most suitable storm representative location based on HYSPLIT trajectories still requires careful judgment from experienced analysts and careful examination during the SSPMP review.

When the main moisture mass is judged to come from a lake or ocean, sea surface temperature (SST) data are used as a surrogate for surface dewpoint. SST data measurements are mainly taken from ships and, consequently, have coarser temporal resolution (reported daily instead of hourly), spatially sparse and nonstationary coverage, and higher data uncertainty. Therefore, when available, land-based dewpoint measurements are preferred to SST measurements to determine atmospheric moisture. Given the poorer temporal and spatial coverage and

13 http://ready.arl.noaa.gov/HYSPLIT.php

5-1 uncertainty, significant differences between land-based dewpoint and SST measurements can be seen in many SSPMP studies. Nevertheless, SST is still considered an acceptable surrogate measure of moisture over the ocean.

Figure 5-1 Example weather map showing moisture inflow and dewpoint observations (adapted from WMO 2009)

5.1.2 Storm Representative Dewpoint Data and Selection

While submittals to the NRC have used various sources of data, the surface dewpoint data were mainly collected from the NOAA Techniques Development Laboratory14 (TDL) U.S. and Canada Surface Hourly Observations dataset. 15 However, the recommended dataset to collect and process hourly dewpoint observations is the National Climatic Data Center (NCDC) 16 TD3505 Integrated Surface Hourly Data released by NOAA. Both the TD3505 and TDL15 datasets are official NCDC/NCEI data products; the TDL dataset is a collection of as-recorded weather station observations, whereas TD3505 is subjected to additional quality control and processing by NOAA. Although the two dataset s are largely similar, differences do exist that could affect the storm representative dewpoint and subsequent adjustment of dewpoint values.

The representative dewpoint values for storms used in analysis should be obtained from HMRs and mostly based on the highest persisting 12-h dewpoint. In contrast, SSPMP studies have often used maximum 6-, 12-, or 24-h average dewpoints to define the storm representative dewpoint. The use of multiple durations is intended to improve the representativeness of the analysis to match the observed storm duration more closely. Local storms, which typically produce intense precipitation over short durations, are typically analyzed using 6-or 12-h durations, while larger scale general or tropical storms are analyzed using 12-or 24-h durations.

The selection of a maximum average dewpoint is consistent with the hypothesis that extreme precipitation events require high moisture supply for extended periods.

Based on the type of storm (e.g., shorter duration mesoscale convective or longer duration frontal), licensees calculated the maximum 6 -,12-, or 24-h average dewpoint of each station and

14 Now named the Meteorolo gical Development Laboratory: http://www.nws.noaa.gov/mdl/

15 ds472.0, http://rda.ucar.edu/data sets/ds472.0/#!description 16 Now the NCEI: https://www.ncdc.noaa.gov/

5-2 determined the region with the highest moisture mass (dewpoint) in HYSPLITs trajectory zones.

To determine the maximum 6-, 12-, or 24-h average dewpoint, time series of dewpoint data must be collected. A series of 6-, 12-, or 24-h average dewpoint values is computed (typically using instantaneous, hourly measurements, though less frequent measurements may be available), and the maximum average value is selected.

To illustrate the differences between approaches using the maximum average dewpoint and the conventional 12-h persisting dewpoint (used in HMRs), Figure 5-2 provides a comparison. The histogram depicted as the background in Figure 5-2 shows the hourly dewpoint observations, and the thick black line represents the running 24-h average dewpoint with a maximum of 74.6 degrees Fahrenheit (F) (as the representative dewpoint). Such running maximum average dewpoint can be calculated only when hourly (or every 3-h) observations are available.

However, when the conventional 12-h persisting dewpoints were derived for HMRs, most dewpoint observations were often made only at three fixed times per day (e.g., at 0700, 1200, and 1900 hours0.022 days 0.528 hours 0.00314 weeks 7.2295e-4 months local time). Following a similar HMR process and assuming the same data limitation, the 12-h persisting dewpoint values, with a maximum of 74.5 degrees F, are also provided in Figure 5-2 for comparison. In this particular example, both approaches may yield similar storm representative dewpoints. Nevertheless, the maximum average dewpoint approach can leverage more dewpoint observations and should be more reliable than the conventional approach.

Figure 5-2 Example dewpoint temperature data with the maximum 24-h average dewpoint and maximum 12-h persisting dewpoint values identified

Section 5.2 of HMR No. 55A provides recommended criteria for selecting storm representative dewpoints. In general, these same criteria are applicable to SSPMP studies, though the use of

5-3 maximum average dewpoint temperatures is also considered reasonable. In particular, HMR No. 55A requires the use of data from at least two stations located along the inflow trajectory but outside the rain area for a timeframe that generally allows transport of the moisture to the precipitation site during a reasonable interval compatible with observed winds in the storm.

The timing of storm representative dewpoint selection was identified as a common area of concern during several SSPMP reviews. Figure 5-3 illustrates data for an example storm and shows that the dewpoint selection timeframe used to compute the storm representative dewpoint did not adequately represent the rainfall event. Since dewpoint observations used for this step of the SSPMP calculation are typically located hundreds of miles away from the precipitation location, it would be inconsistent to use dewpoint data that could not have influenced the precipitation observations based on meteorological conditions. As shown by the red arrow in the figure, a travel time is required for the moisture (denoted as PW for precipitable water and described in more detail in Section 5.1.4) to move from the moisture source location (the timing of which is represented by the gray box) to the storm center location (the timing of which is represented by the red box). For this example, the theoretical moisture arrival at the storm center location coincides with a timeframe after most rainfall had already occurred; therefore, the dewpoint has been improperly selected.

Figure 5-3 Example of improper representative dewpoint timing selection based on theoretical moisture travel time for precipitation event

5-4 5.1.3 Historical Dewpoint Conversion

While evaluations of dewpoint climatology and precipitation recurrence intervals typically benefit from the fact that adequate periods of record exist and, therefore, are generally reliable, observed dewpoint values for historical storms depend heavily on station-specific or local observations. Though improvements in technology have enabled higher measurement frequency and density, much historic al meteorological data suffer from less frequent, sparser measurements. Consequently, a direct reevaluation of older storms is often not possible. In fact, many of the 12-h persisting dewpoint values documented by NOAA relied on data measurements taken 2-3 times daily rather than the hourly observations available for more recent storms.

Since many historical HMR and USACE storm analyses produced maximum 12-h persisting dewpoint values for which supporting data are not available, the Electric Power Research Institute (EPRI)-sponsored PMP study for Wisconsin and Michigan (EPRI 1993) developed an approach for converting maximum 12-h persisting dewpoint s to maximum average dewpoints.

To quantify the conversion factors, the EPRI study analyzed seven historical storms occurring in the Midwest. These storms were analyzed as a part of the PMP study in Michigan and Wisconsin that had sufficient data available to compute and directly compare the 6 -, 12-, and 24-h average dewpoint values with the 12-h persisting dewpoint value. Of these seven storms, three were classified as mesoscale convective complexes (MCCs ; i.e., local storms) and four were classified as synoptic systems (i.e., general storms).

For the three MCC (local) storms analyzed in EPRI (1993), the maximum 6-h average dewpoint was found to be 7-8 degrees F higher than the maximum 12-h persisting dewpoint. To provide a more conservative adjustment (lower storm representative dewpoint values are more conservative), the EPRI study recommended a conversion factor of +5 degrees F (i.e., maximum average dewpoint = maximum 12-h persisting dewpoint + 5 degrees F).

However, subsequent evaluations by licensees resulted in modification of this recommendation to a value of +7 degrees F for MCC storms. For the four synoptic (general) storms analyzed in EPRI (1993), the maximum 24-h average dewpoint was 2-3 degrees F higher than the maximum 12-h persisting dewpoint. The EPRI study recommended a conversion factor of

+2 degrees F for synoptic (general) storms. These EPRI conversion factors are used throughout recent SSPMP studies when converting maximum 12-h persisting dewpoint values to maximum 6-, 12-, or 24-h average dewpoint values.

To support SSPMP review and evaluate the EPRI conversion factors, the ORNL staff conducted limited independent analysis using multiple storms from SSPMP submittals from the 2012

§50.54(f) FHRR request in the Midwest (see Figure 5-4). Results demonstrate that some large differences exist, and alternative conversion factors may be reasonably estimated as

+3 to +4 degrees F for local storms and +1 to +2 degrees F for general storms. The differences between the EPRI study and the ORNL staffs independent assessment demonstrate the need to closely evaluate historical storms to which the conversion factors were applied, especially considering the proximity of the original EPRI storms to those analyzed.

To summarize, based on limited independent analysis of multiple storms used in licensee SSPMP submittals from the 2012 § 50.54(f) FHRR request, the NRC staff makes following recommendations:

an alternative conversion factor for MCC (local) storms of +3 to +4 degrees F (i.e., increase the 12-h persisting dewpoint by 4 degrees F)

5-5

an alternative conversion factor for synoptic (general) storms of +1 to +2 degrees F (i.e., increase the 12-h persisting dewpoint by 2 degrees F)

Since these alternative conversion factors were developed based on storms occurring exclusively in the Midwest, they may not apply in other regions. Future analyses in other regions should be conducted to better assess the reliability of the results presented here and evaluate whether alternative conversion factors are needed for storms in other regions.

Figure 5-4 Map of storms used to evaluate alternative storm representative dewpoint conversion factors

5.1.4 Precipitable Water Estimation

To numerically estimate storm precipitable water (PWStorm) through the entire vertical atmospheric column, based on the surface observed dewpoint, a lookup table published in Appendix C to HMR No. 55A and based on pseudo-adiabatic assumption may be used. For instance, for a 22-degree Celsius (C) (71-degree F) surface dewpoint, the total PW from sea level to the top of the atmosphere (i.e., 0- 9,150 meters (0- 30,000 feet )) is estimated to be 6 centi meters (2.36 inches). Overall, the PWStorm value is considered to be one of the most significant variables affecting the calculation of SSPMP. Although the three-dimensional PW values are now available in modern meteorological reanalysis datasets, all current SSPMP studies to date have used the conventional way to estimate PW by surface dewpoint since such mixed approaches would yield inconsistencies between old and new storm PW estimation and storm maximization calculations.

In reality, the value of PW Storm would also change dynamically as the storm progressed, but the current approach provides only a snapshot of total PW right before a storm occurs. In addition, the current moisture maximization process assumes that the amount of PW is linearly proportional to rainfall depth. This critical assumption has yet to be examined as part of the previously reviewed PMP calculations; however, some recent studies have started to further

5-6 explore the relationship between precipitable water and rainfall rate (e.g., Chen and Bradley 2007; Mahoney et al. 2013; Mahoney 2016).

In terms of sensitivity, a lower storm representative dewpoint will lead to a more conservative PMP estimate. In general, a 1-degree F difference in dewpoint contributes to an approximate 4-5 percent difference in PMP. This sensitivity stems from the moisture maximization process in which moisture availability is estimated based on the relationship between dewpoint and PW provided in the HMR No. 55A PW tables. Various storm-specific factors, including dewpoint temperature and elevation, affect t he calculation of moisture availability.

5.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 § 50.54(f) FHRRs, the NRC staff identified several key considerations in the development of an SSPMP. These considerations build on the terminology, general methodology, and lessons learned described previously and highlight issues associated with some of the more subjective areas of SSPMP development. The NRC and ORNL staff identified the following key considerations related to storm representative dewpoint selection and PW estimation:

For storms that require conversion of persisting dewpoint temperature data to maximum averaged dewpoint temperature, the adjustment should be carefully considered. Several licensee submittals have used adjustment values that have been shown to be potentially nonconservative based on limited independent ORNL staff sensitivity analysis.

All relevant data used in storm representative dewpoint selection should be used for storm representative dewpoint analysis at the moisture source location. These data may include the storm moisture inflow maps (e.g., HYSPLIT trajectory maps, weather maps) and the observed in-place dewpoint temperature data and maps (or SST data and maps). For storms for which hourly dewpoint data were unavailable or not used, t he relevant data or source information used to determine the storm representative dewpoint should be documented and provided.

If the selection of the storm representative dewpoint location deviated significantly from the HYSPLIT trajectories, detailed meteorological reasoning should be documented and provided.

The selection of the storm representative dewpoint timeline should be clearly compatible with moisture transport characteristics (i.e., the location, timing, and travel time of dewpoint observations relative to the precipitation location and timing). If the compatibility is not clear, detailed meteorological reasoning should be documented and provided.

PW estimation involves high uncertainty given the typical lack of robust analysis. While the conventional HMR procedures for estimating PW (i.e., using surface dewpoint before the storm occurs) remain an acceptable approach, the use of modern meteorological tools and products that can yield more methodologically defensible PW estimates is highly encouraged.

5-7

6 DEWPOINT CLIMATOLOGY, MOISTURE MAXIMIZATION, AND MOISTURE TRANSPOSITION

This section discusses considerations in the dewpoint climatology, moisture maximization, and moisture transposition selection processes used in SSPMP development, including descriptions of the terminology, general methodology, and other key considerations. It ends with a summary of lessons learned from the recent reviews of the 2012 §50.54(f) FHRRs.

6.1 Terminology, General Methodology, and Lessons Learned

After a storm representative dewpoint and the corresponding PWStorm are identified for each selected storm (see Section 5), the next step in SSPMP development is to estimate the probable maximum total precipitable water (PWMax) for moisture maximization, which WMO (2009) defines as the process of adjusting observed precipitation amounts upward based on the hypothesis of increased moisture inflow to the storm.

To estimate PW Max, a maximum dewpoint is determined using dewpoint climatology maps derived from historical dewpoint observations. The resulting ratio (i.e., adjustment factor) between the PW Max and PWStorm is then used to rescale the observed storm rainfall depth to calculate SSPMP with the assumption that PW Max sufficiently estimates the theoretical maximum water content that could have been available to the storm. This process of identifying a storm representative dewpoint and performing moisture maximization using dewpoint climatology is consistent with the process employed in the HMRs, with some methodological nuances as described below.

6.1.1 Dewpoint Climatology

To estimate the PW Max, HMR No. 51 used the highest dewpoint s observed for a given location and time of year from the Climatic Atlas of the United States (EDS 1968). In particular, the HMR used 12-h persisting dewpoint, defined as the dewpoint value at a station that has been equaled or exceeded throughout a period of 12 consecutive hours (WMO 2009). The use of the 12-h persisting dewpoint partially reflected the state of dewpoint observations in the 1960s when instantaneous dewpoint measurements were generally made only twice a day (e.g., 7 a.m./7 p.m. or 8 a.m./8 p.m.) and could not provide temporal resolution finer than 12 h.

With only morning and evening observations available for many locations, calculations of persisting dewpoint could not capture the intermediate changes in dewpoint temperatures occurring throughout the day. The maximum 12-h persisting dewpoint maps (6-1) in the Climatic Atlas of the Unite d States were constructed by summarizing all available dewpoint observations and directly constructing contour maps covering the entire United States for each calendar month.

6-1 Figure 6-1 Example maximum 12-h persisting dewpoint map for July (EDS 1968)

Perhaps the most significant difference between the HMRs and SSPMP stud y methods submitted by licensees in response to the 2012 § 50.54(f) letter is the use of different dewpoint climatology values. Instead of using 12-h persisting dewpoint s for all storm types, SSPMP studies used maximum 6-, 12-, or 24-h average dewpoint s for different storm types (in general, 6-h for mesoscale convective systems and 24-h for large-scale frontal systems). This refinement has become possible through the increased availability of hourly (and 3-h) dewpoint observations since the publication of the Climatic Atlas of the United States in 1968.

Nevertheless, because climatology maps using maximum 6-, 12-, or 24-h average dewpoints were not otherwise available, SSPMP studies have relied on proprietary, privately developed dewpoint climatology datasets. SSPMP submittals have used the 100-year return period dewpoint climatology with the assumption that a 1-percent annual exceedance probability (AEP) represents a reasonable maximum moisture level for moisture maximization.

As mentioned in Section 5.1.2, many SSPMP estimates associated with the 2012 § 50.54(f) information request submittals included storm representat ive dew point estimates and dew point climatology datasets developed using land-based surface dewpoint data from the NOAA TDL dataset rather than the higher quality-controlled TD3505 dataset. This lower quality -controlled TDL dataset has been used for both storm representative dewpoint temperature determination and dewpoint climatology estimation. Although the two dataset s are largely similar, differences in the annual maximum dewpoint values caused by the presence of missing or erroneous values in the TDL dataset may result in different annual maximum series fitting results and dewpoint climatology estimates as compared to a TD3505-based analysis. Differences in dewpoint climatology estimates can lead to differences in moisture maximization and moisture transposition adjustments, as described in Sections 6.1.2 and 6.1.3, thereby affecting SSPMP estimates.

These independently created dewpoint climatology maps have been used when the storm moisture mass is located on land. When the moisture mass is located over the ocean, SSPMP studies have used an approach similar to what is used in the HMRs. As a surrogate for the land-based dewpoint, an SST value that is two standard deviations (i.e., +2) warmer than the

6-2 mean SST (approximately equivalent to a 40- year return level (AEP: 2.3 percent) based on a normal distribution) is used. Licensees have used t he 1981-2010 NOAA Optimum Interpolation Sea Surface Temperature Analysis 17 to generate the +2 maps for each calendar month. Given the inconsistencies in return periods (i.e., 100 years versus approximately 40 years), as well as the very different data types (i.e., land-based gauge dewpoint measurement versus ocean-based gridded SST product), significant discontinuity of dewpoint climatology can be seen in the land and ocean interface in multiple SSPMP studies. Section 6.2 presents additional information on how this discontinuity should be addressed.

6.1.2 Moisture Maximization

After a land -based, 100- year dewpoint value (or a sea-based +2 SST) is identified, it is converted to a corresponding PWMax value, and an in -place maximization factor (IPMF) is calculated using Equation (1), as explained below. The IPMF adjustment attempts to maximize an historical storm event by increasing the events PW content to an historical maximum value through use of a ratio (i.e., adjustment factor):

IPMF = PWMax,Srep,SE, (1)

PWStorm,Srep,SE where PWMax,Srep,SE = the PW calculated using the 100- year dewpoint (or +2 SST) at the storm representative dewpoint location from the storm elevation to the top of atmosphere PWStorm,Srep,SE = the PW calculated using the storm representative dewpoint at the storm representative dewpoint location from the storm elevation to the top of atmosphere

To avoid over-adjustment and unreasonable PMP estimation, HMR authors suggested an upper bound of 1.5 in computing this factor (i.e., if it is calculated to be above 1.5, the IPMF value is set to 1.5). The IPMF adjustment factor is then multiplied with two additional adjustment factors.

The justification for limiting the IPMF to a value of 1.5 remains unclear, as large adjustments may be indicative of changing storm dynamics and storm efficiency that are no longer meteorologically reasonable. A recent study based on the numerical weather simulation model suggested that the change of PMP depth can be even larger than the change of PW during moisture maximization (Rastogi et al. 2017). On the other hand, various reports in the literature (e.g., Chen and Bradley 2006, 2007) have questioned the assumptions and process used to perform moisture maximization.

6.1.3 Moisture Transposition Adjustment

For an historical storm event considered transpositionable to a study area, the moisture transposition process accounts for differences in maximum available moisture (i.e., PW) associated with relocating the storm to the study area. To determine the PW available under the original and transpositioned scenarios, the calculation relies on various data, including the maximum dewpoint value (e.g., from dewpoint climatology) and elevation associated with the storm representative dewpoint location and the transpositioned location. This same general procedure for converting from dewpoint to PW (i.e., using Appendix C to HMR No. 55A) may be used.

17 http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/

6-3 Once the PW depths are identified, the ratio between the transpositioned locations maximum PW and the original locations maximum called PW (the moisture transposition factor (MTF))

can be computed using Equation (2):

MTF = PWMax,ST,SE (2)

PWMax,SRep,SE

where PWMax,ST,SE = the PW calculated using the 100- year dewpoint (or +2 SST) at the storm transposition location from the storm elevation to the top of atmosphere PWMax,SRep,SE = the PW calculated using the 100- year dewpoint (or +2 SST) at the storm representative dewpoint location from the storm elevation to the top of atmosphere

To estimate PWMax,ST,SE and PWMax,SRep,SE, the PW associated with the maximum dewpoint is identified for the surface elevation of interest (i.e., the PW that would otherwise occur between the surface elevation and sea level) and subtracted from the total PW available above sea level.

The resulting difference provides the amount of PW available at the location of interest. Similar to the HMRs (e.g., Schreiner and Riedel 1978), the SSPMP studies reviewed included a 2 -week adjustment toward the warm season when identifying the maximum dewpoint.

6.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 § 50.54(f) FHRRs, the NRC staff identified several key considerations in the development of an SSPMP. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development. The NRC and ORNL staff identified the following key considerations related to dewpoint climatology, moisture maximization, and moisture transposition:

If not publicly available, all relevant data used to develop dewpoint climatology datasets should be documented and provided, including the station-based annual maximum series data used for fitting, the monthly 100- year dewpoint climatology values at each station, and the monthly 100- year dewpoint climatology maps. T he 100- year threshold used for moisture maximization and transposition is only an example threshold; other approaches could reasonably be used with adequate justification.

When performing storm moisture maximization, the type of data used to estimate observed moisture and maximum moisture should be compatible. For instance, if SST data are used to estimate observed storm moisture, SST data (and not dewpoint temperature data) should be used to estimate maximum moisture. In addition, the use of analysis technique (e.g., persisting dewpoint temperatures versus maximum average temperatures) should be consistent.

The maximum thresholds used to analyze land-based dewpoint temperature data and ocean-based SST are different (100- year dewpoint versus +2 SST, respectively). Wh ile land-based thresholds are associated with an AEP of 1 percent, ocean-based thresholds are associated with a less extreme value with an approximate AEP of 2.3 percent. This

6-4 difference can impart high sensitivity, depending on which data source is used, and affect SSPMP estimates for both coastal regions and noncoastal regions that include storms with ocean moisture sources (i.e., near-coast storms). Since near-coast storms are often transpositioned inland for SSPMP development, land/ocean data differences may affect SSPMP estimates across a large region and not just coastal NPP sites.

Given the potentially high sensitivity between land-and ocean-based PW estimation and moisture adjustments for near-coast storms and in coastal regions, the NRC staff recommends using the approach that provides a more conservative SSPMP value.

As mentioned in Section 5, PW estimation involves high uncertainty given the typical lack of robust analysis. Further research should be conducted to examine the reasonableness of the PMP maximization factor (i.e., assuming the change of total PW is linear to the change of rainfall depth). Previous studies (e.g., Rastogi et al. 2017 ;

Abbs 1999) have questioned the reasonableness of the linear relationship assumed when applying PW adjustments for PMP maximization.

During the development of dewpoint climatology, annual maximum dewpoint data should be tested for potential long-term trends through commonly used trend detection methods (e.g., Mann-Kendall test). If a significant trend is found, then a nonstationary statistical fitting method should be used to avoid biased dewpoint climatology estimation.

Maximum moisture levels are currently assumed to be stationary, without consideration of historical data trends (e.g., changing 100- year dewpoint temperature estimates).

Based on published literature, this assumption is questionable and potentially nonconservative in a changing climate. Section 10 includes more discussion of the potential effects of climate change.

6-5

7 TERRAIN ADJUSTMENT

This se ction discusse s considerations in th e terrain adjustment processes used in SSPMP development, including descriptions o f the t erminology, general m ethodology, and other key considerations for S SPMP deve lopment. It ends with a summary of l essons learned from the recent r eviews of t he 20 12 §50.54(f) FH RRs.

7.1 Termi nology, General Methodology, and Lessons Le arned

Historically, simplifying assumptions about the influence of t opography and or ographic processes w ere use d instead of a detailed analysis of P MP est imates. However, w hen a storm event i s bei ng transpositioned from i ts or iginal l ocation to an SSPMP s tudy ar ea with dissimilar underlying topographic features, the influences o f new terrain shoul d be considered. Fo r instance, elevated t errain located between t he moistur e source and t he study ar ea m ay present physical barriers t o moisture inflow and decr ease the amount of r ainfall that can occu r i n the study area. O n the other hand, t errain-induced lifting could increase the amount of r ainfall. In t he conventional H MR f ramework, the t errain-related adjustments a re introduced as ano ther adjustment multiplier after the steps of st orm i n-place moisture m aximization and transposition (discussed in S ection 6). To account f or the impacts of terrai n o n moisture r eduction, one of two different approaches have been used: a barrier adjustment f actor ( BAF) or a n orographic transposition factor (OTF), also referred to as a g eographic t ransposition f actor (GTF) in some recent st udies.

These approaches differ from p revious terrain adjustment methods u sed in the H MRs, including the storm separation method (SSM). The researc h community has not dev eloped a physically based alternative t o replace the S SM, which has not been use d in r ecent SSPMP st udies because of its co mplexity and subjectivity. SSMs have been described in the literature, i ncluding Hansen et al. (1994).

The SSM, BAF, and OTF can be ca tegorized as types of ter rain adjustment f actors. In general, an enhancement of storm s due to changing topography ( e.g., terrain -induced lifting) require a terrain adjustment factor greater than 1.0, w hereas a reduction of st orms due to m oisture blockage requires a terrai n adjustm ent of l ess than 1.0.

7.1.1 Barrier A djustment Factor

Various l icensee SSPMP su bmittals have simulated the r eduction of moisture using a BAF. One BAF calculation approac h involves usi ng digital el evation data ( e.g., National E levation Dataset18 or G lobal M ulti-resolution Terrain Elevation Data19) t o i dentify bar riers for each storm based on the inflow di rection. U sing t he study ar ea location as a starting point, a ve ctor i s plotted upwind for each inflow di rection co nsidered, and a cone of i nfluence (a t riangle with a length 1.5 times the base) i s e xtended beyo nd the most significant barrier. The co ne of influence is t hen r epositioned to enco mpass t he entire basin, and t he m aximum upw ind barrier across t he w idth of the cone base is i dentified. Th e average bar rier height i s t hen det ermined.

An effective ba rrier hei ght may al so be computed to acco unt for i rregularities i n t he maximum barrier pr ofile through which more or l ess m oisture may pass. This ef fective barrier hei ght adjustment is made on a storm -by-storm basis and can be subjective, though the se nsitivity i s usually m inor. HMR No. 55A, Section 3.3, documents a similar appr oach to account for

18 https://catalog.data.gov/dataset/usgs -national -elevation -dataset -ned 19 https://www.usgs.gov/land-resources/eros/coastal -changes -and-impacts/gmted2010

7-1 orographic blockage of storm moisture inflow; however, the development of geographic information system methods has greatly enhanced SSPMP practi tioners ability to identify and quantify barriers. The BAF is also similar to various procedures described in WMO (2009).

A consideration in the BAF method that neither licensee-nor HMR-based barrier analyses take into account is whether the original storm track crossed physical barriers that resulted in less total PW downwind of the barrier because of moisture depletion. While moisture depletion due to a physical barrier is considered explicitly for the transpositioned storm track when the BAF method is applied, moisture depletion that may have occurred when the original storm crossed a barrier is not considered. For example, for the original storm track, if a moisture center moves over a barrier and results in rainfall on the downwind side of the barrier, the available moisture would (theoretically) be reduced through the same mechanisms that reduce PW content that are applied to the transpositioned storm track thr ough the BAF method. However, under previously discussed methodologies, no accommodation is made for moisture depletion that reduced the observed rainfall because of BAF effects. The potential result is reduced conservatism in transpositioning storms that moved over a barrier.

7.1.2 Orographic Transposition Factor

Various licensee SSPMP submittals have used the OTF terrain adjustment approach rather than the BAF. The OTF is a grid-based adjustment factor computed using precipitation frequency data (e.g., NOAA Atlas 14 20) and is calculated as the ratio of a climatological precipitation parameter at a target location (i.e., any grid point within the target basin) to the same parameter at the storm source location. To use this terrain adjustment approach, licensees assume that spatial variation in climatological precipitation depth ( e.g., 100-year rainfall) can be used to predict spatial rainfall patterns for extreme events. Typically, this approach has used a 24-h duration for synoptic storms (i.e., general and tropical storms) and a 6-h duration for MCC storms (i.e., local storms). The OTF calculation approach represents a significant departure from conventional HMR approaches. The OTF was originally developed to quantify the effects of topography on rainfall in mountainous terrain. However, in more recent submittals, licensees have applied the OTF procedure throughout regions where reliable precipitation frequency data are available, regardless of terrain. Therefore, the OTF procedure has been used to quantify the effects of topography and elevation differences between any two locations. Given the large adjustment that OTF can bring in non-orographic regions and its large deviation from HMR methods, the use of OTF in non-orographic regions is discouraged, unless it results in no significant difference compared to the more conventional BAF approach.

In some licensee studies, the terrain adjustments are computed following a linear best-fit approach. Following this approach, NOAA Atlas 14 precipitation frequency depths with recurrence intervals of 10 to 1,000 years are collected for the storm center and target locations.

The identified values are fit into a linear regression line, shown in Equation (3), to estimate m and b:

20 http://hdsc.nws.noaa.gov/hdsc/pfds/

7-2 PAtlas14,Site = m*PAtlas14,SC+b (3)

where PAtlas14,Site = target grid point rainfall frequency depth (in inches (in.)) across various selected durations and return periods from NOAA Atlas 14 PAtlas14,SC = storm center grid point rainfall frequency depth (in.) across various selected durations and return periods from NOAA Atlas 14 m = slope b = intercept (in.)

The linear relationship determined through the target-source regression fit above is then used to determine the orographically adjusted rainfall for all grid points based on the in-place rainfall using Equation (4):

PSite = m*PSC+b (4)

where PSite = orographically adjusted rainfall (in.) at the targeted grid point PSC = in-place rainfall (in.)

m = slope from Equation (3) b = intercept (in.) from Equation (3)

Rearranging Equation (4) yields the OTF, as shown in Equation (5):

OTF = PSite = m + b (5)

PSC PSC

More recent SSPMP studies have computed terrain adjustments using different calculation methodologies. Rather than using the linear best-fit approach, the adjustment can also be computed by using the ratio between the 100- year precipitation frequency depths at two locations. Ratios using alternative recurrence intervals (e.g., 1,000 years) may also be used; however, licensees have suggested that the NOAA Atlas 14 100- year precipitation frequency depths offer a preferred balance between the rarity and uncertainty of the estimate.

Equation (6) shows the adjustment calculation using the 100- year ratio:

OTF = PAtlas14,100-y,Site (6)

PAtlas14,100-y,SC

7.1.2.1 Calculation Methodology for Orographic Transposition Factor

Preliminary comparison between Equations (5) and (6) has show n nonnegligible differences.

While both equations are conceptually similar, the staff has noticed more stable and intuitive results from Equation (6). From an end-use standpoint, calculating the OTF using the ratio of the 100-year precipitation frequencies may offer a more sound approach because the spatial variation in OTF values is consistent with the spatial variation in NOAA Atlas 14 precipitation frequencies. Use of a linear best-fit approach introduces variability in OTF values without clear justification. Therefore, using a 100- year ratio approach, rather than the linear best-fit approach, is preferred when calculating the OTF.

7-3 7.1.2.2 Orographic Transposition Factor Lower and U pper Limits

When applying the OTF, licensees have typically enforced an upper limit (usually 1.50) but no lower limit. While the selection of upper and lower limits for terrain or geographic adjustment values is subjective, when terrain or geographic adjustment limits are used, the NRC staff recommends that the bounds should be inversely related (i.e., an upper limit of 1.50 should be used with a lower limit of 0.67, or 1/1.50). This ensures consistency in applying this methodology between two locations, regardless of the source and target locations.

Consequently, any studies that use terrain or geographic adjustment should include consistent lower limits or provide a defensible rationale for not doing so.

7.1.2.3 Use of O rographic Transposition Factor in Non-Orographic Regions

The OTF was originally developed to capture the orographic ef fects that drive extreme precipitation production in complex terrain with the assumption that such effects may be represented in precipitation frequency estimates. However, some licensee s have subsequently extended OTF to non-orographic regions where precipitation frequency data are available. In some cases, in which OTF was used in non-orographic regions, the NRC staff found that OTF use may result in significant precipitation reductions that do not have a clear physical explanation. Hence, using OTF in non-orographic regions represents a relatively large deviation from the conventional HMR-based PMP approach. As stated previously, t he use of OTF in non-orographic regions is generally discouraged. However, w hen the OTF is used in non-orographic regions, detailed NRC staff review is justified to ensure that the SSPMP adjustments are reasonable; more conventional BAF-based adjustments could provide reasonable comparison.

7.1.2.4 Uncertainty and U se of NOAA Atlas 14 to C ompute Orographic Transposition Factor

While statistical parameters are used for other PMP-related calculations (e.g., the use of 100- year dewpoint climatology for moisture maximization), the use of NOAA Atlas 14 to calculate terrain adjustments diverges from standard PMP calculation practices. The lack of clear evidence that precipitation-producing processes are appropriately captured in NOAA Atlas 14 and unexpected spatial variations in precipitation frequency depths have been raised as important indicators of sensitivity and uncertainty. The ORNL staff has observed the following:

NOAA Atlas 14 has been developed in stages, and multiple volumes have been produced. Each volume provides precipitation frequency data for specific regions that are considered meteorologically similar. Different regional probability distributions may exist for each region and can contribute to large differences across regions.

NOAA Atlas 14 precipitation frequencies are computed based on annual maximum precipitation and do not distinguish among various storm types. Therefore, storm -type mixing presents an issue when precipitation frequencies are applied in a storm-based PMP approach. In an attempt to address this issue, SSPMP studies have used 6-h precipitation frequency data for local storm types, and 12-or 24-h data for general or tropical storm types.

Since NOAA Atlas 14 precipitation depths are derived based on point precipitation observations, it is unlikely that the spatial patterns and magnitude of variation represent precipitation frequencies for large-area storms (NUREG/CR-7271, Application of Point

7-4 Precipitation Frequency Estimates to Watersheds, issued February 2021 (ADAMS Accession No. ML21039A503). Since multiple areas of uncertainty and a lack of a clear physical basis a re associated with the OTF calculations, future PMP reviews should carefully examine the use of this or similar methodologies.

7.1.2.5 Double Counting of Orographic Transposition Factor and M oisture Transposition Factor

When using a terrain adjustment method (e.g., the OTF method), there is a potential for double counting moisture adjustment because the influence of climatology is retained when precipitation frequency analysis is used. SSPMP developers should evaluate and discuss terrain adjustments and the MTF because the degree to which the atmospheric component of the MTF is also accounted for in the OTF is unknown. Until the relationship between terrain adjustments and the MTF is determined, the MTF adjustment should continue to be used regardless of whether the OTF is used.

The NRC staff notes that while MTF values have a clear physical basis, OTF values do not and introduce high uncertainty in the SSPMP calculation. The refore, the reasonableness and conservativeness of the OTF for controlling and near-controlling storms should be carefull y evaluated when assessing SSPMP estimates and consequential flood hazard determinations.

7.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 § 50.54(f) FHRRs, the NRC staff has identified several key considerations for SSPMP development. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development.

The staff identified the following key considerations related to terrain adjustment using the BAF:

All relevant data used to develop terrain adjustment should be provided, including barrier profile elevation and effective barrier height data.

While the BAF has known limitations and does not capture orographic enhancement effects, its use can be physically justified in regions with high terrain influence. Where alternative methods are being considered, preference should be given to the more conservative and physically justifiable approach to improve the reasonableness of the flood hazard assessment.

When calculated average barrier heights are adjusted to effective barrier heights, clear justification should be provided and explained. The ORNL staff has found that the sensitivity associated with this adjustment is usually minor.

When using the BAF, analysts should consider an observed precipitation events potential loss of moisture over high terrain based on the original storm track.

Related to terrain adjustment using the OTF, the staff identified the following key considerations:

All relevant data used to develop terrain adjustment should be provided, including all precipitation frequency data used for OTF calculation across the study area.

7-5

The OTF should be used with caution, with application limited to regions where orographic influences are clear drivers of extreme precipitation. Geographic patterns in OTF values derived from NOAA Atlas 14 do not have a clear physical basis, and some of the characteristics may introduce double-counting effects overlapping with the MTF.

At a more fundamental level, subject matter experts or academic peer reviewers have not thoroughly vetted the assumption that variation in NOAA Atlas 14 point-based precipitation frequency depths provides a reasonable approximation of PMP-scale precipitation. The use of point-based precipitation frequency depths for OTF calculation may also be problematic when used to estimate precipitation over large areas, al though datasets for areal precipitation frequency depth are not currently publicly available.

Where alternative methods are being considered, preference should be given to the more conservative approach to improve the reasonableness of the flood hazard assessment (e.g., PMF estimate).

When used, the OTF should be calculated based on the best available techniques. For example, calculating the OTF using a ratio of precipitation depths of the same return period is considered superior to using regression techniques, which exhibit high uncertainty and can bias results.

A reasonable lower limit should be used for the OTF method.

Given the large adjustment that OTF can bring in non-orographic regions and its large deviation from HMR methods, the use of OTF in non-orographic regions is discouraged, unless its use results in no significant difference compared to the more conventional BAF approach.

7-6 8 ENVELOPMENT AND PROBABLE MAXIMUM PRECIPITATION DETERMINATION

This section discusses considerations in the PMP determination and envelopment processes used in SSPMP development, including descriptions of the terminology, general methodology, and other key considerations. It ends with a summary of lessons learned from the recent reviews of the 2012 §50.54(f) FHRRs.

8.1 Terminology, General Methodology, and Lessons Learned

Once all adjustment factors (i.e., IPMF, MTF, and OTF or BAF) have been determined for a storm, the total adjustment factor (TAF) can be determined. Since both the OTF and BAF provide a means of capturing terrain adjustment, only one of the two factors may be used for any given storm. Note that when using the OTF instead of the BAF, licensees have typically modified the MTF calculation to exclude elevation adjustment (i.e., they modify the calculation in Equation (2) ). Equation (7) shows the calculation of TAF:

= xx or

= xx (7)

where TAF = the total adjustment factor IPMF = the in-place maximization factor MTF = the moisture transposition factor OTF = the orographic transposition factor BAF = the barrier adjustment factor

Once all selected storms have been analyzed and subjected to TAF adjustment, the final adjusted DAD values used to determine PMP values are determined using envelopment. As documented in HMR No. 51, envelopment is smoothly interpolating between the maxima from a group of values for different durations and/or areas. Envelopment represents the final step in the SSPMP development process and uses the maximized, transpositioned DAD values computed using the methods described in Sections 5-7. It is worth noting that for the gridded PMP products reviewed by the staff (where the OTF is used), the final spatial patterns reveal similarities to NOAA Atlas 14 precipitation frequency estimates; this is likely due to the influence of the OTF on final, estimated PMP depths.

Since most LIP-PMP evaluations for SSPMP submittals associated with the 2012 § 50.54(f) letter relied on a 6-h, 10- mi2 PMP value or a 1 -h, 1-mi2 PMP value (ADAMS Accession No. ML11321A195), the maximum final adjusted DAD value among all storms is selected. This same approach is used for all area -duration combinations required for a study; however, storm-specific DAD curves typically are plotted for a particular duration to produce an appropriately smoothed envelopment curve. Since most WS-PMP evaluations for licensee submittals use a 72-h precipitation hyetograph, WS-PMP studies typically plot area versus

8-1 depth for 6-, 12-, 24-, 48-, and 72-h durations. The resulting smoothed envelopment curve provides the final PMP values used for simulating flooding hazards.

Figure 8-1 provides an example of an envelopment curve, where the curve equals or exceeds the maximum depth among all storms for each area size. For some areas (e.g., 2,000 mi2), the example envelopment curve exceeds the maximum value by a larger margin because of the desire to produce a smoothed curve. Professional judgment also ensures that the envelopment curves across all durations exhibit similar patterns, and further smoothing may be performed as needed. As stated in HMR No. 51, Section 2.1, such smoothing compensates for the random occurrence of large rainfalls, in that a drainage may not have experienced equally efficient precipitation mechanisms for all pertinent durations and sizes of areas.

Figure 8-1 Example DAD curves (including envelopment) for a 72-h PMP (Note: Values do not represent actual storms or PMPs)

8.2 Key Considerations for Site-Specific Probable Maximum Precipitation Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 § 50.54(f) FHRRs, the NRC staff has identified several key considerations for SSPMP development. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development. The staff identified the following key considerations related to temporal and spatial envelopment:

8-2

All relevant data used in PMP determination and storm envelopment should be provided, including all storm-based adjustment factors, adjusted storm DAD data, and final PMP data.

SSPMP estimates should ensure that all DAD values are properly enveloped to ensure no loss of PMP values for the simulated flooding hazard.

The final enveloped DAD curve should produce a concave down shape, with minima l gaps compared to the maximized DAD storm curves.

During the envelopment process, a developer should verif y that sufficient storms have been captured in each combination of durations and areas. While further research is needed to quantify a minimum number of storms to support the SSPMP development, a preliminary suggestion is to include at least the 10 largest moisture-maximized storms (after exclusion considering transpositionability) in the envelopment of SSPMP at each duration, area, and subarea. Multiple storms are needed since no single historic storm should (theoretically) control PMP values across all durations and areas simultaneously.

If a minimum storm count cannot be reasonably identified, the storm transposition limit criteria should be revisited to increase the representativeness of the storms.

8-3

9 SPATIAL AND TEMPORAL DISTRIBUTIONS FOR SSPMP AP PLICATIONS

This section discusses considerations in the use of spatial and temporal distributions for SSPMP applications, including descriptions of the terminology, general methodology, and lessons learned, as well as other key considerations. It ends with a summary of lessons learned from the recent reviews of the 2012 §50.54(f) FHRRs.

9.1 Terminology, General Methodology, and Lessons Learned

While PMP values are provided in terms of a DAD relationship, that relationship lacks specificity as to where, when, and how the precipitation is distributed. For hydrologic and hydraulic modeling applications, the concentration of precipitation in space and time is determined by using spatial and temporal distributions ( defined below). The spatial and temporal distributions can impart significant changes to a basins hydrologic response to PMP. As with the development of SSPMP, the application of spatial and temporal distributions can be highly subjective and require careful review.

WMO (2009) defines spatial distribution as the geographic distribution of precipitation over a drainage [basin] according to an idealized pattern storm of the PMP for the storm area and temporal distribution as the time order in which incremental PMP amounts are arranged within the PMP storm.

Conventional PMP documentation found in the HMRs specifies procedures for determining and applying spatial and temporal distributions. For example, HMR No. 52 documents a procedure for temporally and spatially distributing the PMP values found in HMR No. 51. The procedure is stepwise and involves several key steps, including i dentifying a suitable temporal distribution, isohyetal pattern, and isohyetal orientation. To simplify the implementation of HMR No. 52 procedures, the USACE developed the HMR52 computer program (USACE 1984). Procedures vary among HMRs, and some SSPMP submittals associated with the 2012 §50.54(f) letter used alternative approaches.

The SSPMP submittals generally did not consider spatial and temporal distribution. Instead, many submittals included the SSPMP application of both spatial and temporal distribution as a part of the drainage basin hydrologic modeling analysis rather than as a part of the meteorological precipitation analysis, with many applications simply applying standard HMR procedures. Both HMR and SSPMP methods are discussed below.

9.1.1 Spatial Distribution

Precipitation intensity is often visualized spatially in the form of an isohyetal map, which shows equivalent precipitation depths in a storm (similar to how an elevation contour map shows lines of equal elevation across an area). Conventional HMR procedures for developing a spatial distribution rely on standard, idealized isohyetal patterns. Figure 9-1 (left) shows an example standard isohyetal map from HMR No. 52 in which an elliptical pattern is used with a major axis 2.5 times longer than the minor axis (a generalized pattern that was informed by analyzing actual storm patterns). The total area and average rainfall within each layer of isohyetal would correspond to the same storm area and PMP depth summarized in the DAD table. Through this approach, the spatial distribution can capture PMP across various storm areas to help effectively identify the most critical peak flow condition. In more complex terrain, precipitation

9-1 tends to display less symmetric spatial patterns; thus, alternative isohyetal maps have been used in highly topographic regions. For example, the map shown in Figure 9-1 (right) represents a PMP spatial distribution from HMR No. 41 intended to provide a reasonably conservative PMP spatial pattern over the upper portion of the Tennessee Valley Authority ( TVA) watershed. HMR No. 41 also provides other spatial distributions, including a topographically adjusted pattern to concentrate precipitation over the lower watershed and an elliptical pattern similar to the one found in HMR No. 52.

While HMR No. 52 recommends using a single-centered isohyetal pattern, it notes that multicentered rainfall patterns occur and tend to be more common for large -area events. Such multicentered analyses can produce less conservative stream flow estimates because, as noted in HMR No. 52, all else being equal, the more centers used, the lower the peak discharge. The HMR states that when using custom patterns, the arrangement should not violate the basic elliptical shape of the total isohyetal pattern. HMR No. 52 discusses this further.

Figure 9-1 Standard isohyetal spatial distribution pattern recommended in HMR No. 52 (left; as included in WMO 2009) and example probable maximum March isohyets provided in HMR No. 41 (right)

Following HMR No. 52 procedures, once a standard isohyetal pattern is developed, its placement and orientation over the drainage basin are determined with a goal of maximizing precipitation volume over the basin and thereby maximizing peak stream flow. The volume will vary based on the pattern centering, basin shape, and area of PMP distributed over the basin.

Given this complexity, HMR N o. 52 employs a series of trials and provides suggestions. Note that where patterns have been adjusted to account for topographic influences or, as stated in HMR No. 52, major storm patterns that have been observed on the drainage, reorientation may not be needed. While HMR No. 52 recommends use of the standard elliptical pattern for all drainages covered by HMR No. 51, the staff acknowledges that the stippled regions covered by either HMR should be an exception.

Among the considerations for storm orientation is whether a particular orientation is of typical significance in the region or is constrained by terrain or other factors. HMR No. 52 documents its analysis of precipitation storm orientations and develops a procedure for quantifying geographically varying preferred orientations and adjustments when an orientation i s notably different (e.g., larger than 40 degrees).

9-2 While the HMR No. 52 procedures focus on an idealized elliptical pattern, later HMRs (e.g., HMR No. 57 and HMR No. 59) use spatial distributions based on 100 -year precipitation frequency climatology from NOAA Atlas 2 (Miller et al. 1973), the predecessor of NOAA Atlas 14. Thos e reports found that NOAA Atlas 2 correlated well with underlying topography, which was useful for evaluation in complex terrain, and noted that actual storms may have quite different spatial distributions (Hansen et al. 1995). HMR No. 57 suggests another approach of using an observed storms pattern to develop an isopercental distribution; however, it notes that data for such storms are limited and, ultimately, the report does not provide specific spatial distribution procedures.

The SSPMP submittals to the NRC have generally not considered spatial distribution. I nstead, many submittals include the SSPMP application of both spatial and temporal distribution as a part of the drainage basin hydrologic modeling analysis, with many applications simply applying standard HMR procedures. However, few SSPMP submittals included spatial distribution analysis. To date, such analyses have been based on storm-specific analysis of multiple major precipitation events that occurred over the watershed, similar to the suggested isopercental approach described in HMR No. 57. Instead of using a synthetic structure, such as shown in Figure 9-1, to spatially distribute SSPMP depths across different storm areas to each layer of the isohyetal, selected historical extreme rainfall events were rescaled to meet the SSPMP depth respective to the watershed size, and then to support the hydrologic and hydraulic modeling. In other words, while the average rainfall depth for the entire watershed is the same in both approaches, the alternative approach uses the historically observed rainfall records to spatially distribute the total rainfall depth within the watershed and leads to different storm structures.

An important factor in this storm-based spatial distribution approach is whether the selected spatial distributions (historic al storms) are sufficiently different and produce a conservative result. As mentioned previously, the HMR No. 52 trial-based approach seeks to maximize precipitation volume over the basin as a way of maximizing peak stream flow. By using multiple storm-based spatial distributions of sufficient variability, a similar effect can be achieved. It is also worth noting that such storm-based distributions may exhibit multicenter characteristics as artifacts of the original event. In some cases, the most intensive portion of the constructed storm can be even larger than the smaller area SSPMP depth in the DAD table. These potential issues need to be considered when selecting and applying the spatial distributions.

9.1.2 Temporal Distribution

Precipitation intensity may be visualized temporally in the form of a hyetograph, which WMO (2009) defines as a graph displaying the intensity of precipitation versus time. For watershed-scale PMP application, the HMR No. 52 procedure for hyetograph development involves dissecting PMP values into 6 -h increments and arranging them in a sequence to form a 72-h event. The sequencing suggested in HMR No. 52 involves ranking the 6-h precipitation increments, positioning the peak 6-h incremental precipitation, and arranging progressively decreasing 6-h increments on either side of the peak; Figure 9-2 (left) provides an example.

HMR No. 52 notes that based on study of major storms, maximum rainfall rarely occurs at the beginning of an event sequence; hence, the gradual increase exemplified in Figure 9-2 (left).

For local-scale PMP application, while HMR No. 52 does not provide explicit hyetograph examples, maps are provided for converting 1-h PMP values to subhourly PMP values (5, 15, and 30 minutes) using ratios that vary geographically. These maps were produced using maximum annual precipitation values for each duration since none of the available storm

9-3 studies contained subhourly precipitation data and were suggested for use for areas of 517 square kilometers (200 mi2) or less. In contrast, HMR No. 57 illustrates representative front-and middle-loaded hyetograph curves based on storm analyses; Figure 9-2 (right) provides an example for middle-loaded storms. HMR No. 57 does not provide curves for end-loaded storms because of their rarity in the Pacific Northwest region covered by HMR No. 57.

Figure 9-2 Example temporal distribution for a watershed-scale 72-h PMP event provided in HMR No. 52 (left) and example temporal distributions for middle-loaded, local-scale storms provided in HMR No. 57 (right)

Various studies in the literature suggest that temporal rainfall characteristics vary regionally based on the prevalence of assorted driving factors (e.g., convective storm patterns, climate, topography).

As described in WMO (2009), another temporal distribution method involves simulating observed temporal storm patterns. Regardless of the method selected, WMO (2009) notes that it is the responsibility of the meteorologist and hydrologist to determine which arrangement is appropriate for a particular region and will result in the critical design storm for a basin.

Given its goal of providing generalized PMP estimates, the HMR No. 52 procedure recommends using the same isohyetal spatial distribution and orientation throughout the PMP event (i.e., fixed spatial pattern), though th e report acknowledges that it is meteorologically reasonable for the rainfall center to travel across the drainage with time during the storm. This consideration may be especially important for application over large drainage basins, and application of a moving storm could result in a higher peak if the direction and speed of movement coincides with downstream progression of the flood crest (Hansen et al. 1982).

Given the important role of PMP application in dam safety, a review of relevant dam safety literature offers additional insight into the application of PMP temporal distribution practices.

According to the Federal Emergency Management Agency (FEMA), 57 percent of States do not provide temporal distribution guidance, with about half of the existing guidance indicating that the HMRs should be used (FEMA 2012). Other approaches mentioned include distributions

9-4 developed by the Natural Resources Conservation Service and those developed by the States for regional or custom applications.

It is also important to note that while the HMRs address PMP durations for up to 72 h, longer precipitation events have been observed and have contributed to major flood events historically.

Evaluation of precipitation events exceeding 72 h may be warranted. FEMA (2012) indicates that 37 percent of States do not specify design storm duration in their hydrologic design guidelines; the 63 percent that do provide guidance typically base the design duration on a watersheds time of concentration, with durations ranging from 6 to 72 h.

9.2 Key Considerations for SSPMP Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in connection with the 2012 § 50.54(f) FHRRs, the NRC staff has identified several key considerations for SSPMP development. These considerations build on the previously described terminology, general methodology, and lessons learned and highlight issues associated with some of the more subjective areas of SSPMP development.

The NRC staff identified the following key considerations for the SSPMP application using spatial distributions:

One main objective of spatial distribution (following the conventional HMR No. 52 approach) is to apply the SSPMP DAD information using a spatial pattern that produces the hydrologically most critical runoff scenario. Since no single distribution is likely to maximize flooding across different storm sizes, this process calls for the consideration of alternative distributions (e.g., historical rainfall events or different isohyetal patterns or gridded subbasin combinations) to assess hydrologic impacts. The set of alternative distributions should contain multiple distinct spatial patterns to identify the most critical scenario for flood risk evaluation.

All relevant data used to develop spatial distribution, such as precipitation hyetograph shapefiles or spatially gridded precipitation event data, should be provided for review and evaluation.

The ORNL staff identified the following key consideration for PMP application using temporal distributions:

The timing of peak precipitation intensity within a temporal distribution can significantly affect the resulting flood hydrograph. Analysis using alternative temporal distributions can provide insight into the relative sensitivity of using different timing.

9-5

10 POTENTIAL EFFECTS OF LONG-TE RM CLIMATIC CHANGE

None of the S SPMP s tudies su bmitted by ow ners and operators in response to t he st affs 2012

§50.54(f) i nformation r equest captured o r add ressed t he pot ential ef fects o f long-term climatic change. I n t he context o f climate ch ange, the issue is w hether de terministic P MP va lues will demonstrate a trend or w ill remain unchanged under the projected f uture c limatic co nditions.

Until now, t he magnitude of extreme storms r elevant t o PMP development has been considered stationary, meaning that the mechanism producing PMP-relevant storm s will not ch ange significantly i n the f uture. If there ar e significant changes and climate variability, it is assumed that t hese changes w ould be captured w hen in tegrating recent s torm ev ents into t he analysis, such that t he impact s of gradual climate ch ange on PM P will be addressed, at least in part, by incorporating storm list s that are su fficiently detailed and up to date to represent t he impacts o f climate change on PMP.

Over t he l ast few deca des, studies have produced evidence that the cl imate at gl obal t o l ocal scales has become nons tationary, with t he cl imate si gnals cl early showing an increase in ambient t emperature. T he U.S. Global C hange R esearch Program (USGCRP ) Climate S cience Special R eport (USGCRP 2017) state s the following:

The global, long-term, and unambiguous warming trend has continued during recent years. Since the last National Climate Assessment was published, 2014 became the warmest year on record globally; 2015 surpassed 2014 by a wide margin; and 2016 surpassed 2015. Sixteen of the warmest years on record for the globe occurred in the last 17 years.

Increasing air temperature has a direct implication for the air moisture holding capacity and the occurrence of extreme precipitation events. The USGCRP report (2017) states that extreme precipitation events are generally observed to increase in intensity by about 6% to 7% for each degree Celsius of temperature, as dictated by the Clausius-Clapeyron relation. The USGCRP report also states that [t]he frequency and intensity of heavy precipitation events are pro jected to continue to increase over the 21st century (high confidence). The findings remained unchanged in the fourth National Climate Assessment (USGCRP 2018) that based on evidence from climate model simulations and our fundamental understanding of the relationship of water vapor to temperature, confidence is high that extreme precipitation will increase in all regions of the United States, and extreme precipitation events are projected to increase in a warming climate and may lead to more severe floods and greater risk of infrastructure failure in some regions.

The WMO (2009) defines PMP as being calculated under modern meteorological conditions and with no allowance made for long-term climatic trends. Nevertheless, C hapter 1.8 of WMO (2009) indicates that extreme rainfall events would likely increase in the 21st century (because of the overall increase in available moisture in a warming climate) and highlighted the need for careful examination of potential climate-change effects on major PMP driving mechanisms such as moisture availability, depth-area curves, storm types, storm efficiency, and generalized rainfall depths.

The conventional PMP theory used in the HMRs and in SSPMP studies depends on the physical relationship between the amount of moisture in the atmosphere available to a storm and the efficiency of the storm to turn that moisture into rainfall. It stands to reason that a warming atmosphere may lead to increased moisture ( PW). Through numerical modeling, some

10-1 recent studies suggest the likely change of PMP in the future climatic conditions (e.g., Kunkel et al. 2013; Stratz and Hossain 2014; Klein et al. 2016; Rastogi et al. 2017). Statistically significant increasing trends were also found in some surface dewpoint observations (e.g., Kao et al. 2019) that would gradually change the estimate of dewpoint climatology and dir ectly affect SSPMP estimation.

As suggested by various studies and evidence, future SSPMP studies should account for the effects of climate change, especially when cons idering PW. Studies should provide details of how the effects of climate change are incorporated into the study or, alternatively, provide a justification as to why this information is not necessary for a specific site or watershed. In view of the long lifespan of NPPs and other critical infrastructure (including dams) and the current use of PMP in establishing design criteria, future SSPMP studies should address PMP changes related to projected climate change.

The NRC is enhancing its regulatory processes by developing and implementing a framework for ongoing assessment of natural hazards information, including information related to climate change, such as increased storm intensities. SECY 0144 describes the NRCs enhancements to the existing regulatory processes; these enhancements are referred to as the Process for the Ongoing Assessment of Natural Hazards Information. The Commission approved this process, which uses a graded approach to proactively, routinely, and systematically seek, evaluate, and respond to new information on natural hazards, including climate change impacts on storm intensity, using the approved framework for the ongoing assessment of natural hazard information.

10- 2 11

SUMMARY

This NURE G/KM report presents t he NRCs lessons learned fro m t he r ecent flood hazard reevaluations at NPP sites per formed in co nnection with the reviews of S SPMP e stimates submitted in connection with the 2012 §50.54(f ) FHRRs and a recent TR review. Specifically, this report identif ies and explains the terminologi es, theories, methods, and data so urces us ed in historical and m odern PMP s tudies. It al so includes ke y considerations f or developi ng and reviewing potential f uture SSPMP studies.

Since the development o f the NOAA H MR-based PMP estimates f rom t he 1950s t hrough the 1990s, technological adv ances have enabled more efficient da ta collection and processing, while improved meteorological under standing has refined how su ch data are used. While the HMRs per iodically i ntroduced new methods o r p rocedures, much of the framework r emained unchanged. SSPMP s tudies have maintained the use of a similar f ramework t hough the introduction of some new dat a so urces, calculation procedures, and m ethods. T his r eport documents the current state of p ractice in SSPMP st udies and t he NRC s taff knowledge gained through r eview. The approaches and methods de scribed will continue to evolve as technological and meteorological advances ar e made. C onsequently, professional judgment w ill continue playing a key role in SSPMP deve lopment.

As not ed i n this r eport, several ar eas o f the SSPMP deve lopment pr ocess i nvolve considerable uncertainty and r equire s ome subjective professional j udgment. T he most significant areas of uncertainty include the following:

storm DAD data and multicenter storm analysis

sto rm transposition limit determination

PW estimation and maximization based on dewpoint temperature

terrain adjustment

While not discussed in detail in this report, precipitation measurement data are also subject to error and uncertainty. With precipitation measurement representing the base input used for SSPMP estimation, any error and uncertainty in such data propagate throughout the calculation process and directly affect SSPMP estimates. In addition, the observation longevity for storm-based PMP studies presents a key uncertainty because historical rainfall records are limited to approximately 150 years or (in many cases) less. The storm transposition, moisture maximization, and envelopment processes attempt to overcome this challenge, yet uncertainty remains as to how accurately SSPMP estimates meet the definition of a theoretical upper precipitation limit. In short, the SSPMP estimation process involves considerable uncertainty, yet the methods, data sources, and procedures used provide a defined framewor k for quantifying SSPMP.

For the purpose of long-term KM, the objective of this report is to summarize the NRC staffs current understanding of SSPMP estimation. By documenting this information and identifying key considerations, this NUREG/KM repo rt aims to support the development and review of SSPMP estimates for NRC-regulated NPPs in the United States. This NUREG/KM report does not constitute guidance or invalidate any prior guidance documents, or the studies conducted in accordance with the prior guidance.

11-1 While the NRCs SSPMP reviews to date have not related to NPP license amendment requests, such licensing actions are likely to include SSPMP studies in the future. Consequently, this report may inform future NRC guidance on SSPMP development.

11-2 12 REFERENCES

U.S. Code of Federal Regulations (CFR), Domestic licensing of p roduction and u tilization facilities, Part 50, Chapter I, Title 10, Energy.

CFR, Reactor site criteria, Part 100, Chapter I, Title 10, Energy.

Abbs, D.J. 1999. A numerical modeling study to investigate the assumptions used in the calculation of probable maximum precipitation. Water Resour. Res. 35(3), 785-796, doi:10.1029/1998WR900013.

ACWI (Advisory Committee on Water Information). 2018. Extreme Rainfall Product Needs.

Prepared by the Extreme Storm Events Working Group of the Subcommittee on Hydrology of the ACWI). June 2018. https://acwi.gov/.

U.S. Bureau of Reclamation. 2011. Extreme Storm Data Catalog Development.

Report DSO-11-07. Denver, CO.

Chen, L.-C., and A.A. Bradley. 2006. Adequacy of using surface humidity to estimate atmospheric moisture availability for probable maximum precipitation. Water Resour.

Res., 42, W09410.

Chen, L.-C., and A.A. Bradley. 2007. How does the record July 1996 Illinois rainstorm affect probable maximum precipitation estimates? J. Hydrol. Eng., 12(3), 327-335, 10.1061/(ASCE)1084- 0699(2007)12:3(327).

Corrigan, P., D.D. Fenn, D.R. Kluck, and J.L. Vogel. 1999. Probable Maximum Precipitation for California. HMR No. 59. Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

EPRI (Electric Power Research Institute). 1993. Probable Maximum Precipitation Study for Wisconsin and Michigan: Volumes 1 and 2. Report Nos. TR-101554-V1 and TR-101554-V2. North American Weather Consultants.

http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=TR -101554-V1.

EDS (Environmental Data Service). 1968. Climate Atlas of the United States, Washington, DC:

U.S. Department of Commerce, Environmental Science Services Administration.

FEMA (Federal Emergency Management Agency). 2012. Summary of Existing Guidelines for Hydrologic Safety of Dams. FEMA P-919. Washington, DC.

Fenn, D.D. 1985. Probable Maximum Precipitation Estimates for the Drainage above Dewey Dam, Johns Creek, Kentucky. HYDRO 41. Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Hansen, E.M., L.C. Schreiner, and J.F. Miller. 1982. Application of Probable Maximum Precipitation EstimatesUnited States East of the 105th Meridian. HMR No. 52.

Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

12-1 Hansen, E.M., D.D. Fenn, L.C. Schreiner, R.W. Stodt, and J.F. Miller. 1988. Probable Maximum Precipitation EstimatesUnited States Between the Continental Divide and the 103rd Meridian. HMR No. 55A. Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Hansen, E.M., D.D. Fenn, R. Corrigan, and J.L. Vogel. 1994. Probable Maximum PrecipitationPacific Northwest States: Columbia River (including portions of Canada),

Snake River and Pacific Coastal Drainages. HMR No. 57. Silver Spring, MD:

U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Ho, F.P. and J.T. Riedel. 1980. Seasonal Variation of 10- square-mile Probable Maximum Precipitation Estimates, United States East of the 105th Meridian. HMR No. 53. Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Ishida, K., M. Kavvas, S. Jang, Z. Chen, N. Ohara, and M. Anderson. 2014. Physically based estimation of maximum precipitation over three watersheds in northern California:

Atmospheric boundary condition shifting. J. Hydrol. Eng. 20(4).

doi:10.1061/(ASCE)HE.1943-5584.0001026.

Ishida, K., M. Kavvas, S. Jang, Z. Chen, N. Ohara, and M. Anderson. 2015. Physically based estimation of maximum precipitation over three watersheds in northern California:

Relative humidity maximization method. J. Hydrol. Eng. 20(10).

doi:10.1061/(ASCE)HE.1943-5584.0001175.

Kao, S.-C., S.T. DeNeale, and D.B. Watson. 2019. Hurricane Harvey highlights: Need to assess the adequacy of probable maximum precipitation estimation methods. J. Hydrol.

Eng. 24(4), 05019005, doi:10.1061/(ASCE)HE.1943-5584.0001768.

Klein, I.M., A.N. Rousseau, A. Frigon, D. Freudiger, and P. Gagnon. 2016. Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies. J. Hydrol., 537, 74-85, doi:10.1016/j.jhydrol.2016.03.031.

Kunkel, K.E., T.R. Karl, D.R. Easterling, K. Redmond, J. Young, X. Yin, and P. Hennon. 2013.

Probable maximum precipitation and climate change. Geophys. Res. Lett., 40, 1402-1408, doi:10.1002/grl.50334.

Lin, Y. 2011. GCIP/EOP Surface: Precipitation NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data, Version 1.0. University Corporation for Atmospheric Research/National Center for Atmospheric Research Earth Observing Laboratory.

http://data.eol.ucar.edu/dataset/21.093.

Mahoney, K., J. Caldwell, and V. Sankovish. 2013. High -resolution numerical modeling as a tool to assess extreme precipitation events. Workshop on Probabilistic Flood Hazard Assessment, January 29, 2013. Agencywide Documents Access and Management System (ADAMS) Accession No. ML13057A727.

12-2 Mahoney, K. 2016. Examining terrain elevation assumptions used in current extreme precipitation estimation practices: A modeling study of the 2013 Colorado Front Range floods. American Meteorological Society 30th Conference on Hydrology, January 2016, New Orleans, LA.

Micovic, Z., M.G. Schaefer, and G.H. Taylor. 2015. Uncertainty Analysis for Probable Maximum Precipitation Estimates. J. Hydrol. 521:360-373. doi:10.1016/ j.jhydrol.2014.12.033.

Miller, J.F. 1963. Probable Maximum Precipitation and Rainfall-Frequency Data for Alaska.

Technical Paper No. 47. Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Miller, J.F., R.H. Frederick, and R.J. Tracey. 1973. Precipitation frequency atlas of the western United States. NOAA Atlas 2, Vol. I, Montana; Vol. V, Idaho; Vol. IX, Washington; and Vol. X, Oregon. Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Miller, J.F., E.M. Hansen, and D.D. Fenn. 1984. Probable Maximum Precipitation for the Upper Deerfield River Drainage Massachusetts/Vermont. (HYDRO 39. Washington, DC:

U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

NRC. 1977. Design Basis Floods for Nuclear Power Plants. Regulatory G uide 1.59, Revision 2, August 1977. ADAMS Accession No. ML003740388.

NRC. 2006. The NRC Knowle dge Management Program. SECY- 06- 0164. July 25, 2006.

ADAMS Accession No. ML061550002.

NRC. 2007. Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition, NUREG-0800, Section 2.4.2, Floods. Revision 4.

March 2007. ADAMS Accession No. ML070100647.

NRC. 2007. Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition, NUREG-0800, Section 2.4.3, Probable Maximum Flood (PMF) on Streams and Rivers, Revision 4, March 2007. ADAMS Accession No. ML070730405.

NRC. 2007. Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition NUREG-0800. March 2007. ADAMS Accession No. ML070810350.

NRC. 2011. Recommendations for Enhancing Reactor Safety in the 21st Century.

July 12, 2011. ADAMS Accession No. ML111861807.

NRC. 2011. Design-Basis Flood Estimation for Site Characterization at Nuclear Power Plants in the United States of America. NUREG/CR-7046. November 2011. ADAMS Accession No. ML11321A195.

NRC. 2012. Request for Information Pursuant to Title 10 of the Code of Federal Regulations 50.54(f) Regarding Recommendations 2.1, 2.3, and 9.3, of the Near-Term Task Force Review of Insights from the Fukushima Dai-ichi Accident, March 12, 2012. ADAMS Accession No. ML12053A340.

12-3 NRC. 2015. Report for the Audit of Applied Weather Associates, LLC, Regarding Site Specific Probable Maximum Precipitation Development in Support of Near-Term Task Force Recommendation 2.1 Flood Hazard Reevaluations. May 19, 2015. ADAMS Accession No. ML15113A029.

NRC. 2016. P roposed Resolution of Remaining Tier 2 and 3 Recommendations Resulting from the Fukushima Dai-ichi Accident, SECY-16- 0144, December 29, 2016. ADAMS Accession No. ML16286A552.

NRC. 2018. NRC Response to Lessons Learned from Fukushima. Backgrounder, NRC Office of Public Affairs. September 2018. ADAMS Accession No. ML112660383.

NRC. 2021. Application of Point Precipitation Frequency Estimates to Watersheds.

NUREG/CR-7271, ORNL/SPR-2019/1323. February 2021. ADAMS Accession No. ML21039A503.

Ohara, N., M. Kavvas, S. Kure, Z. Chen, S. Jang, and E. Tan. 2011. Physically Based Estimation of Maximum Precipitation over American River Watershed, California.

J. Hydrol. Eng. 16(4):351-361. Doi:10.1061/(ASCE)HE.1943-5584.0000324.

Papalexiou, S.M. and D. Koutsoyiannis. 2006. A probabilistic approach to the concept of Probable Maximum Precipitation. Adv. Geosci. 7:51-54. Doi:10.5194/adgeo 51-2006.

Rastogi, D., S.-C. Kao, M. Ashfaq, R. Mei, E.D. Kabela, S. Gangrade, B.S. Naz, B.L. Preston, N. Singh, and V.G. Anantharaj. 2017. Effects of climate change on probable maximum precipitation: A sensitivity study over the Alabama-Coosa-Tallapoosa River Basin.

J. Geophys. Res. 122: 4808-4828. Doi: 10.1002/2016JD026001.

Reitan, C.H. 1963. Surface Dew Point and Water Vapor Aloft. J. Applied Meteorology, 2, 776-779. Doi: 10.1175/1520- 0450(1963)002<0776:SDPAWV>2.0.CO;2.

Riedel, J.T. 1973. Probable Maximum Precipitation and Snowmelt Criteria for Red River of the North Above Pembina, and Souris River Above Minot, North Dakota. HMR No. 48.

Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Schreiner, L.C. and J.T. Riedel. 1978. Probable Maximum Precipitation Estimates, United States East of the 105th Meridian. HMR No. 51. Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Schwarz, F.K. 1965. Probable Maximum and TVA Precipitation over the Tennessee River Basin above Chattanooga. HMR No. 41. Washington, DC: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

Stratz, S.A. and F. Hossain. 2014. Probable maximum precipitation in a changing climate:

Implications for dam design. J. Hydrol. Eng., 19(12), 06014006.

Doi:10.1061/(ASCE)HE.1943-5584.0001021.

12-4 U.S. Army Corps of Engineers (USACE). 1973. Storm Rainfall in the United States 1945-1973. Washington, DC.

USACE. 1984. HMR52 Probable Maximum Storm (Eastern United States), Users Manual.

Washington, DC.

U.S. Global Change Research Program (USGCRP). 2017. Climate Science Special Report:

Fourth National Climate Assessment, Volume I. Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.). Washington, DC. doi:

10.7930/J0J964J6.

USGCRP. 2018. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart (eds.). Washington, DC. doi:

10.7930/NCA4.2018.

U.S. Weather Bureau. 1946. Manual for Depth-Area-Duration Analysis of Storm Precipitation.

Cooperative Studies Technical Paper No. 1. Washington, DC: U.S. Department of Commerce.

U.S. Weather Bureau. 1961. Generalized Estimates of Probable Maximum Precipitation and Rainfall-Frequency Data for Puerto Rico and Virgin Islands. Technical Paper No. 42.

Washington, DC: U.S. Department of Commerce.

U.S. Weather Bureau. 1970. Probable Maximum Precipitation, Mekong River Basin. HMR No. 46. Washington, DC: U.S. Department of Commerce.

WMO (World Meteorological Organization). 1969. Manual for Depth-Area-Duration Analysis of Storm Precipitation. WMO No. 129, Geneva, Switzerland.

WMO. 2009. Manual for Estimation of Probable Maximum Precipitation. WMO No. 1045, Geneva, Switzerland.

Zurndorfer, E.A., F.K. Schwarz, E.M. Hansen, D.D. Fenn, and J.F. Miller. 1986. Probable Maximum and TVA Precipitation Estimates with Areal Distribution for Tennessee River Drainages Less Than 3,000 Mi2 in Area. HMR No. 56. Silver Spring, MD:

U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.

12-5

APPENDIX A DISPOSITION OF PUBLIC COMMENTS

A.1 List of Commenters

Table A-1 lists the names (and organizational affiliate, where applicable) of each commenter and the U.S. Nuclear Regulatory Commission (NRC) A gencywide Documents Access and Management System (ADAMS) accession number for the comments received.

Table A-1 Individuals and Organizations Providing Comments during the Public Comment Period Commenter ADAMS Accession Number Bill Kappel on behalf of Applied Weather ML21075A007 Associates, LLC (AWA)

Frances A. Pimentel on behalf of Nuclear ML21075A008 Energy Institute (NEI)

Kenneth B. Fearon on behalf of Federal ML21075A009 Energy Regulatory Commission (FERC)

Steve Weinbeck ML21075A010

A.2 Comments and Responses

This section contains the NRC staffs summary responses to public comments. Comments a re categorized by NUREG/KM section. If the comment did not explicitly cite a section, then the NRC staff applied a section based on the context of the comment.

A.2.1 General and Introduction Comments

Comment 1-1 (NEI): The document provides guidance on how to use existing information to estimate Site-Specific Probable Maximum Precipitation at NPPs in the US. In many places, it talks about professional judgement, justification and documentation.

It would be useful to have a section on Gathering New Information to fill in gaps.

Response: The existing report covers storm selection and reconstruction of both historical and new storm data. The same general procedures for collecting storm-related or climatological data are r elevant as further discussed in the response to Comment 1-11.

Comment 1-2 (Weinbeck): I find this guidance as a useful addition to the material available on extreme precipitation estimates for design and regulatory purposes. In particular, I find the details that are included on the techniques an improvement over the material presented in the WMO-1045 document in the reference list, which is very general (in the WMO document). I also prefer the approach used in the NUREG/KM-0015 as a superior approach to t hat given in documents such as NOAA Atlas 14. The NOAA Atlas 14 gives the appearance of a more objective method of estimating extreme precipitation, but the unevenness of data period of records used mask the uncertainty inherent. The NUREG/KM-0015 does an excellent job of outlining the subjective portions of the latest PMP techniques and address the uncertainties for applicants. It is well written and precise.

Response: The authors thank Mr. Weinbeck for his comment.

A-1 Comment 1-3 (NEI): The doc ument is basically a Knowledge Summary of work that has been performed in the US. It would be useful to have a section if similar work has been performed in Europe.

Response: This report documents procedures and information related to probable maximum precipitation ( PMP) estimation within the United States. Where appropriate, the report discusses other global procedures, such as reference to World Meteorological Organization ( WMO)

(2009).

Comment 1-4 (NEI): It would be useful to have a section on the most common gaps seen in the 50.54(f) submittals. What were the most common NRC Requests for Additional Information?

Response: This report documents procedures and information related to PMP estimation within the United States rather than lessons learned from the recent flood hazard reevaluations at current power plant sites performed most recently in connection with the reviews performed in response to the information request letter sent in 2012 under Title 10 of the Code of Federal Regulations (10 CFR) 50.54(f) (2012 §50.54(f) letter). With regard to PMP estimation, a significant focus of the 2012 §50.54(f) reviews was related to local intense precipitation (LIP).

Review of flood hazard reevaluation reports revealed that the external flood analysis for LIP was not consistently addressed when the safety analysis reports for the operating plants were initially prepared. The s taff noted that LIP analyses went frequently undocumented in a large percentage of the final safety analysis reports for power plants that were subject to the staffs 2012 information request; an informal staff review of the final safety analysis reports for those power plant sites indicated that fewer than half described some type of explicit LIP analysis.

Additional information related to the 50.54(f) submittals and plant-specific actions can be found by visiting https://www.nrc.gov/reactors/operating/ops -experience/japan-dashboard.html.

Comment 1-5 (NEI): This sections states, GDC 2 requires, []. GDC 2 also requires [.].

Since GDC can only be described as requirements for post-GDC plants and under particular circumstances (see COM-SECY-16- 0020) change this wording to be, GDC 2 states that.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-6 (AWA): Should reference be made to AWA and any other consultants who performed this work and/or examples provided?

Response: The authors have added a footnote stating that AWA perform ed all the studies reviewed during the time period covered in this NUREG/KM.

Comment 1-7 (AWA): In the 3rd paragraph, 3rd sentence, propose revising to say "Whe reas SSPMP estimates employ a storm based maximization and transposition process similar to the HMRs, they ultimately use different and updated methods, data sources and procedures than those found in the HMRs"

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-8 (AWA): SSPMP is not new, private consultants have perf orming SSPMP for more than 50 years, with the first accepted by FERC in the US in 1993 (EPRI Michigan/Wisconsin). Suggest adding "for Nuclear sites".

Response: The authors have made the recommended change in the NUREG/KM.

A-2 Comment 1-9 (AWA): define notable, higher/lower and by how much and why this is important.

Response: The authors have revised the texted and added a footnote to better define notable.

Comment 1-10 (AWA): In Note 5, consider adding that these SSPMP studies were all performed by Applied Weather Associates.

Response: Based on the resolution of a previous comment, the authors determined that no change is necessary.

Comment 1-11 (FERC): No discussions found on new storm screening or frequency. The SSPMP depths are estimated based on recorded extreme storm events up to the time of the study. The draft report (NUREG/KM-0015) does not provide guidance on how future extreme storms should be incorporated into the reassessment of the SSPMP studies. Any new, significant storm event that occurs in the studys storm search area represents an opportunity to add to the available dataset used to develop PMP values. Another benefit of evaluating new storm data is to avoid the possibility of hydrometeorological and hydrologic studies becoming outdated, such as the current suite of HMRs. It is prudent to maintain these studies including evaluating any new storm data and comparing to/or including the information into existing studies.

Response: The authors agree that newly available storm data and other relevant information should be leveraged to maintain up-to-date, reliable PMP estimates. Storm selection and reconstruction as well as relevant climatological data should be reasonably up to date, as described in this NUREG/KM. Although the authors support them, Federal initiatives to produce updated information are outside the purview of this NUREG/KM.

Comment 1-12 (FERC): No discussions found on validating the PMP depths. The NRC draft report does not provide guidance on validating the new PMP depths. The PMP depth should be compared to the HMR developed PMP value, NOAA Atlas 14, or other local rainfall frequency studies to ensure the developed value is reasonable. It is understood that reasonable is a subjective estimate, however, the SSPMP estimate should at a minimum exceed historic records. If it doesnt, it shouldnt be considered the PMP.

Response: By following the methods outlined in this NUREG/KM, particularly by selecting and reconstructing the appropriate body of historical storms, resulting PMP estimates would necessarily produce precipitation depths exceeding historical records.

Comment 1-13 (AWA): Consider adding known as the storm based approach at the end of the sentence before the (Schreiner and Riedel 1978) reference.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-14 (AWA): In the first paragraph and last sentence of Section 1.5, insert or higher after in PMP estimates that are lower

Response: The authors have added the word typically. This better conveys the intended use of the cited paper, Micovic et al. 2015.

A-3 Comment 1-15 (AWA): In the 2nd paragraph, 1st sentence on page 1.9, add and topographical after with similar meteorological

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-16 (AWA): In the 2nd paragraph, 3rd sentence on page 1.9, the storm analysis process is more detailed than stated. Consider referencing the SPAS [Storm Precipitation Analysis System] process and outputs that have been used. Also, suggest updating the wording at the end of that sentence from such as radar-driven precipitation to such as a combination of rain gauges and gauge adjusted radar data.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-17 (AWA): After surface dewpoint observations add or sea surface temperatures

Response: The authors have made the recommended change in the NUREG/KM.

Comment 1-18 (AWA) In the 1st paragraph, 3rd sentence on Page 1-10, the statement Such an assumption has never been tested is debatable. PMP level storms have occurred and been applied. Examples include Smethport, PA July 1942; Cherry Creek, CO May 1935, Thrall, TX September 1921, and Harvey August 2017. Whether these represent PMP before maximizations is of course unknown, but given the amount of rainfall and how these compare to other extreme storms and how these storms control the PMP envelop curves, it is likely these storms do represent the upper limit of rainfall for those locations.

Response: The authors have modified the text of the NUREG/KM to characterize this assumption more clearly.

Comment 1-19 (AWA): In the 1st paragraph, 4th sentence on Page 1 -10, consider using storm transposition decisions as an example of profession judgement rather than storm representative dewpoint. A likely bigger assumption is whether that storm representative dew point actually represents the moisture producing the rainfall and whether it can be maximized and still produce the same storm dynamics. Storm transposition decisions would be a much better example of professional judgment. Storm representative dew point selection actually is very straightforward. The bigger assumption is whether that storm representative dew point actually represents the moisture producing the rainfall and whether it can be maximized and still produce the same storm dynamics.

Response: The authors have removed the parenthetical phrase in question. This example is discussed in later sections of the NUREG/KM.

Comment 1-20 (AWA): cool-season is hyphenated in one sentence then not hyphenated in the next

Response: The authors have made the recommended change in the NUREG/KM when appropriate.

A.2.2 Storm Selection

A-4 Comment 2-1 (AWA): On page 2.1, 2nd paragraph, it is noted that the USACE Black Book includes storm through 1972 (Hurricane Agnes). Consider updating the 1969 to 1972. It is also noted that the Bureau of Reclamation has a similar, extensive storm database that covers much of the western US from the Rocky Mountains westward and should be utilized. These storm data were utilized in HMR 49, 55A, 57, and 59. Also, it may be worth noting that AWA has utilized SPAS to update the old storm databases with storms analyzed through 2020 and added hundreds of storms to the overall database which have been used in SSPMP development in NRC studies and throughout the US.

Response: The authors have revised the discussion to include references to the U.S. Bureau of Reclamation (USBR) and commercial databases.

Comment 2-2 (AWA): In Section 2.1, 2nd paragraph, last sentence, change review to reviewed.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 2-3 (AWA): In Section 2.1, 3rd paragraph, 2nd sentence, recommend augmenting the storm search domain description to include regional hydrometeorological and topographic characteristics

Response: The authors have made the recommended change in the NUREG/KM.

Comment 2-4 (AWA): The discussion on the latitudinal constraint should reference HMR 57, page 69 note as that is where this originates from.

Response: The authors have implemented the recommended change.

Comment 2-5 (AWA): I n regard to the discussion/description of all-seasonal and cool-season, reword this to say, In regions where the greatest rainfall occurs without snow on the ground, are applied as rainfall only. However, the all-season can also occur in regions where the greatest rainfalls accumulate with an antecedent/concurrent snowpack. Examples would be the West Coast of the United States (e.g., Sierra Nevada and Cascade Ranges). In regions where the all-season rainfall is greater than the rainfall that could occur with an antecedent/concurrent significant snow pack, a separate cool-season PMP is developed to apply as a rain-on-snow event. In these situations the cool-season rainfall will produce lower rainfall depths, but the additional concurrent snowmelt combined with the cool-season PMP rainfall may produce a total runoff amount greater than the rain only all-season PMP runoff. Example locations would be large basins in the Upper Midwest and New England regions."

Response: The authors have updated the NUREG/KM to provide some additional clarifying text and example locales that would require seasonal consideration.

Comment 2-6 (AWA): Add HMR 48 in parenthesis as an example cool-season PMP estimates for limited cases.

Response: The authors have added HMR No. 48, Probable Maximum Precipitation and Snowmelt Criteria for Red River of the North Above Pembina, and Souris River Above Minot, North Dakota (Riedel 1973), as a reference in this section.

A-5 Comment 2-7 (AWA): Note that in Figure 2-1, some storm locations are not plotted (e.g., Rattlesnake, ID Nov 1909). For completeness, USBR storms should be plotted, as they will fill in much of the western US. Also, consider plotting SPAS [Storm Precipitation Analysis System] storm locations. In addition, consider adding HMR 55A, 57, and 59 storm locations to fill in the western US.

Response: After a second check of the map in Figure 2-1, the staff determined that the map of U.S. Army Corps of Engineers (USACE) Black Book storms is correct and no modification is required.

Comment 2-8 (NEI): Recommend that the NUREG provide discussion on the benefits/reasons of developing and utilizing site-specific PMP analyses in lieu of generalized event derivations from sources such as Hydrometeorological Reports (HMRs).

Response: The authors determined that adding a discussion on this topic would be outside the scope of this NUREG/KM. Each nuclear power plant was required to provide an updated flooding analysis in response to the 2012 §50.54(f) letter (as discussed in Section 1.1). Each plant made the decision to use site -specific probable maximum precipitation (SSPMP) or the National Oceanic and Atmospheric Administ ration (NOAA) HMRs for the analysis; this was not at the NRCs direction.

A.2.3 Storm Reconstruction

Comment 3-1 (AWA): Recommend that SPAS be discussed in Section 3.1, 2nd paragraph.

Also, use of climatological base maps (e.g., PRISM) and remote sensing output provides another advancement/improvement to develop DADs.

Response: The authors note that remote sensing output is discussed in Section 3.1; however, the authors decided to not discuss SPAS and its use of PRISM [Parameter-elevation Regressions on Independent Slopes Model ], directly in this document.

Comment 3-2 (AWA): On page 3-2, 1st paragraph, i f this is referring to SPAS, this is not accurate. The most intense portion of the storm (and any SPAS storm) is NEVER excluded. If this referring to separating the storm into 2 (or more) DAD zones, they each contain the most extreme rainfall but do not combine them into one center because that is not meteorologically accurate. The HMRs also separated numerous storms into individual DAD zones (e.g., HMR 55A, 57 and 59).

Response: The authors note that the document is referring to separating storm centers and not the SPAS analysis process. The paragraphs main conclusion on decomposition of multicenter storm events remains unchanged.

Comment 3-3 (AWA): The last sentence of the 1st paragraph, page 3.2 does not appear to be justified by the preceding discussion. Recommend revising the last sentence to just state:

...decomposition of multicenter storm events should be accompanied by substantive justifications.[] One incorrect example (1972 Hurricane Agnes) does not invalidate the process.

It would be helpful for this discussion to detail the reasoning for separating storms into distinct DAD zones. The reasons for this are related to storm timing, storm interactions with topography, or a combination of both as they occur within an overall larger storm domain. It should also be noted that USACE Black Book storms and storms in HMR 55A, 57, and 59 utilized separate/multiple DAD zones.

A-6 Response: The authors have added clarifying text on the type of justification that may be appropriate.

Comment 3-4 (FERC): In regard to the method of splitting storm centers and developing multiple DADs, the report states Given its significant impacts on larger area DAD values, decomposition of multicenter storm events should be avoided or, if used, accompanied by substantive justifications. Recommend that the guidance further clarifies that the justification may include performing a sensitivity analysis to check the effect of applying this method to develop the PMP with respect to the storm area, duration and storm type for the basin in question. Developing a single DAD for a multicenter storm may have the most impact for larger basins that are controlled by long duration, larger area storms. However, developing DAD for individual storm centers may have the most impact for smaller basins that may be controlled by short duration, smaller area storms. A sensitivity analysis should be performed to evaluate these potential impacts that are specific to the basin of interest.

Response: The authors have added an additional sentence pointing to the use of a sensitivity analysis.

Comment 3-5 (AWA): In Section 3.2, 3rd bullet, the HMR conversion ratios are severely outdated and produced using a lack of data. Suggest noting that significant recent work has been completed to investigate actual storm accumulation patterns at sub hourly intervals to develop site-specific, storm specific ratios. Consider utilizing the updated information available from the numerous storm analyses that include actual sub-hourly data

Response: The authors note that the HMR conversion ratios were not used in any of the studies reviewed or summarized in this NUREG/KM. However, a sentence was added to the bullet stating that alternative approaches to estimating subhourly precipitation accumulation may also be considered.

Comment 3-6 (AWA): Suggest replacing is generally discouraged and replace with requires substantial justification. The process is meteorologically valid as long as it is properly justified.

Response: The authors have revised the NUREG/KM to address this comment.

A.2.4 Storm Transposition

Comment 4-1 (AWA): This sentence should be re-worded to more clearly state the intent, which I think is, if a given location has adequate storm data, there is no need to transposition in more storms than necessary.

Response: The authors have made the recommended change in the NUREG/KM.

A.2.5 Storm Representative Dewpoint Selection and Precipitable Water Estimation

Comment 5-1 (AWA): Recommend including a discussion on the use of sea surface temperatures as a surrogate for dewpoint. This was utilized in many SSPMP submissions and is also described in HMRs 57 and 59.

Response: The authors note that Paragraph 3 of Section 5.1.1 states that SST data are used in cases of lake or ocean moisture sources. No change was made as a result of this comment.

A-7 Comment 5-2 (AWA): In Section 5.1.2, 2nd paragraph, note that HMR 51 used 12 -hr persisting, but HMRS 55A, 57, and 59 used 3-hr as well. In additional, all AWA PMP studies have utilized 3-, 6-, 12-, and/or 24-hr climatologies. This is because those data sets are now available (they weren't for HMR 51) and they better represent the intent of the process.

Response: The authors have updated the description to clarify that HMRs mostly used persisting 12-hour (h) dewpoint. SSPMP studies have often used 6-, 12-, or 24-h, as stated.

Comment 5-3 (AWA): In Section 5.1.4, 1st paragraph, 1st sentence, recommend referencing the WMO Manual for PMP and the precipitable water table contained in that document as it provides a more complete table.

Response: The studies reviewed and summarized in this NUREG/KM reference HMR No. 55A, Probable Maximum Precipitation EstimatesUnited States Between the Continental Divide and the 103rd Meridian (Hansen et al. 1988), rather than the WMO manual, which is the reason HMR No. 55A is included as the publication in this section.

Comment 5-4 (AWA): Remove extra space after 71-F.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 5-5 (AWA) In Section 5.1.4, 1st paragraph, last sentence, clarify by adding to the end of the sentence: since 3 dimensional PW datasets do not exist for older storms and therefore would produce an inconsistent data set between old and new storms for PW estimation and storm maximization calculations.

Response: The authors have made the recommended change in the NUREG/KM.

Comment 5-6 (AWA): In Section 5.1.4, 2nd paragraph, last sentence, it is noted that this critical assumption has been evaluated for individual storms and through numerical modeling. The assumption is true in some cases but not in others. References (Chen and Bradley 2007; Mahney et al., 2013; Mahoney 2016, etc.).

Response: The authors agree with the suggestion to add the referenc es to Section 5.1.4 and have made the necessary change to the text.

A.2.6 Dewpoint Climatology, Moisture Maximization, and Moisture Transposition

Comment 6-1 (AWA): Suggest referencing AWA studies (TVA TR for example) where the dew point climatologies were developed and updated.

Response: The authors referenced AWA and AWA studies in other locations of the NUREG/KM and therefore decided to exclude this particular suggestion from updates.

Comment 6-2 (AWA): In Section 6.1.2, 2nd paragraph, 2nd sentence in regard to the statement that the...limiting the IPMF to a value of 1.5 remains unclear, the 1.50 value is somewhat arbitrary but is likely overly conservative. The assumption is that significantly increasing the moisture to a given storm would alter the storm dynamics and therefore produce a storm that is no longer the same as observed from a storm dynamic/storm efficiency perspective. Modeling of storm adjustments with different levels of RH/moisture applied have shown that storms do

A-8 change significantly with far less moisture variation. Instead, the issue is within the underlying assumption in the PMP process, that simply adding more moisture can produce the same storm with more rainfall and that the proc ess of IPMF is valid.

Response: The authors have updated the text to provide clarification.

Comment 6-3 (AWA): In Section 6.1.2, 2nd paragraph, consider adding additional references like Chen and Bradey, Mahoney, etc.

Response: The authors have updated the text to include reference to Chen and Bradley.

Comment 6-4 (FERC): The draft report does not provide guidance on the appropriate range of values for the moisture transposition factor. In general, the lower limit is set as 1.0 because of the potential exists for double counting of moisture adjustment. Per HMR 55A, MTF upper level adjustment is limited to an increase of 20% or a factor of 1.2. This limitation was adopted to avoid the unduly increasing of storm moisture beyond to reasonable limits.

Response: The authors note that the staffs review discussed consideration of moisture transposition factor (MTF) limits. Throughout all the reviews conducted by the staff, no upper or lower limit was used for the MTF. In particular, the topic of potential double counting between the MTF and orographic transposition factor ( OTF) was discussed. As described in Section 7.1.2.5 of the NUREG/KM, Until the relationship between terrain adjustments and the MTF is determined, the MTF adjustment should continue to be used regardless of whether the OTF is used.

Comment 6-5 (AWA): In the discussion at the top of page 6 -4, recommend adding the need to move a given storm 2 weeks towards the warm season during the maximization process.

Reference can be made to HMR 51 discussion.

Response: The authors have updated the text to provide clarification.

Comment 6-6 (AWA): P eriod missing after SSPMP and before These

Response: The authors have made the recommended change in the NUREG/KM.

A.2.7 Terrain Adjustment

Comment 7-1 (AWA): In Section 7.1, 1st paragraph, last sentence, note the orographic transposition factor (OTF) is now called the Geographic Transposition Factor (GTF). All studies utilizing this process since 2018 refer to the GTF. Suggest a clarification note or stating "OTF/GTF".

Response: The authors added clarification to the end of the first paragraph in Section 7.1, noting that the OTF is also referred to as a GTF in some recent studies.

Comment 7-2 (AWA): In Section 7.1.2, 3rd sentence, it is noted t hat the OTF (GTF) is not for spatial pattern development as seemingly inferred. It is recommended that this sentence needs to be clarified to note that the OTF (GTF) is used to calculate the difference in recurrence interval depths at one location vs another with the assumption that these reflect the expected difference in precipitation that would occur between the observed storm location and the basin grid location. And that those differences are inherently captured in the precipitation frequency

A-9 depths and therefore capture all the precipitation process that vary between the locations. This includes the effect of topography in-place and upwind of each location. The spatial component comes back into play when calculating the PMP depths and redistributing them by following the precipitation frequency spatial patterns or other spatial patterns over the basin.

Response: These topics are already discussed; therefore, no change is warranted.

Comment 7-3 (AWA): In Section 7.1.2, 1st paragraph, 5th sentence, the statement that the OTF (GTF) is a significant departure from HMR approaches" [sic] should be clarified. The Storm Separation Method utilized in HMRs 55A, 57, and 59 applies the 24-hr 100- yr precipitation frequency in its calculations and the HMRs utilize precipitation frequency patterns as a recommended spatial pattern application. Th erefore, the intent of the process and use of precipitation frequency estimates for PMP calculation and spatial distribution is not a signi ficant depar ture.

Response: The authors found that the information included in this section of the NUREG/KM is correct and that no change is warranted.

Comment 7-4 (AWA): In Section 7.1.2.4, 2nd bullet, consider adding a note that the storm type mixing is assumed to be addressed by use of the various durations (6 and 24-hr to represent given storm types[)]. The assumption is that 100-yr recurrence intervals would be driven by local storms at the 6-hr duration and by tropical or general storms at the 24-hr duration.

Response: The authors added c larifying text to the second bullet in Section 7.1.2.4 to address this comment.

Comment 7-5 (AWA): In Section 7. 1.2.4, 3rd bullet, suggest additional wording noting that the use of PRISM and other climatological basemaps accounts for the spatial variations that occur in large area storms. These are applied in the regionalization of NOAA Atlas 14. The GTF process simply calculated the difference in precipitation frequency depths at the storm center location and each grid location. This ratio is then applied to the entire DAD matrix of values and therefore the rarity of the overall spatial variations associated with individual events is captured.

Response: Since PRISM is used as a basemap for NOAA Atlas 14, the authors do not see a need to update the discussion as a result of this comment.

Comment 7-6 (AWA): In Section 7.1.2.5, in regard to double counting of OTF and MTF, it is noted that several recent studies completed subsequent to the studies reviewed in this NUREG/KM have explicitly evaluated this aspect and demonstrated that the MTF is captured within the GTF process.

Response: This information is outside the purview of the NRCs reviews; therefore, the authors determined that this level of information is not appropriate to include in the NUREG/KM.

A.2.8 Envelopment and Probable Maximum Precipitation Determination

Comment 8-1 (FERC): The draft guidelines notes that the envelopment curve may provide a value at a given storm area by a larger margin than the storm DADs, owing to the desire to produce a smoothed curve. However, the guidelines do not discuss what to look for in the envelopment curve. Typically, it is preferred for the envelopment curve to be concaved down.

Additionally, we have found that when the envelopment is significantly greater than the storm

A-10 DADs at a particular storm area (or range of areas), this identifies a potential need to reevaluate and include additional storms. However, it should also be noted that the development of the envelopment curve is critical only for the range of storm areas that are likely to control for the subject basin. In the case were there is uncertainty for storm areas not applicable to the study basin, the study should clearly identify any limitations in applicability to adjacent watersheds.

For example, using the "Example 72-Hour Depth-Area Curve" in the guidelines, the envelopment would appear to be appropriate for a 500 square miles basin. However, if the guidelines were for a 2,000 square miles basin, additional review/sensitivity of envelopment curve and potentially inclusion of additional storms to attempt to fill in the "gap" would be warranted.

Response: The authors have added a new third bullet to Section 8.2 to address this comment and provide further clarification.

Comment 8-2 (AWA): On top of page 8 -3, 2nd bullet, since the 10 storms threshold is arbitrary and would vary by location, basin size, storm type(s), etc., it is suggested that if at least 10 storms are not used, a detailed justification for a more limited number of storms should be included in the development of the SSPMP.

Response: The authors determined that the text already discusses how a study should be conducted if a minimum number of storms cannot be reasonably identified. Therefore, no update is needed.

A.2.9 Spatial and Temporal Distributions for SSPMP Applications

Comment 9-1 (NEI): Insert new paragraph:

In a submitted LAR, artificial isohyetal storm patterns, as discussed in HMR No. 52, were not used. PMP values from an NRC-approved TP [technical paper], adjusted to account for topographic influences, was generated for each grid point on a gridded network covering the entire drainage basin for given storm areas and durations. The gridded PMP approach produces a default spatial pattern that closely follows NOAA Atlas 14 precipitation frequency patterns following the approach in HMRs 55A, 57, and 59. Since the total drainage basin consisted of numerous sub-basins and dams, a large number of spatial distributions over sub-basins and combinations of sub-basins were evaluated in the hydraulic modeling to maximize volume over various portions of the watershed to test for dam failures and to maximize stream -flow and flooding levels at plant sites.

Response: The authors note that this information is correct. However, since the submitted license application renewal falls outside the scope of the staffs reviews completed to date, the authors decided to exclude this discussion from the current NUREG/KM.

Comment 9-2 (AWA): On Page 9-3, 2nd paragraph, recommend adding to the paragraph that the gridded PMP approach produces a default spatial pattern that closely follows precipitation frequency patterns thereby following the approaches recommended in HMRs 55A, 57, and 59.

Response: The authors note that this comment was deemed most relevant to Section 8 of the NUREG/KM since it relates to the spatial pattern of final PMP values rather than the subsequent spatial distribution used for hydrologic modeling. Therefore, the authors added a sentence to Section 8.1 of the NUREG/KM in response to this comment.

A-11 Comment 9-3 (AWA): In the Section 9.1.1 spatial distribu tion discussion, it is important to take into consideration the effects of topography on rainfall accumulation patterns. HMR 52 patterns are not meteorological possible in regions effected by terrain and should not be used in those regions. This was recognized by the authors of HMR 52 and noted in the stippled region discussions, TVA HMRs, and HMRs west of 105th meridian.

Response: The authors agree with the statements and have added a sentence on the stippled regions and effects of topography to Sections 4 and 9.1.1 of the NUREG/KM.

Comment 9-4 (AWA): Many recent studies have utilized storm specific patterns, controlling storm patterns, synthetic patterns, and Huff curve methods for 10th, 50th, and 90th percentiles.

Response: The authors find that it is outside the scope of this NUREG/KM and determined that no update is needed.

Comment 9-5 (NEI): Recommend providing examples of any concerns/challenges that were encountered during review of licensee site-specific PMP estimates. Specifically, any analytical techniques or inputs that resulted in licensees revising and resubmitting site-specific PMP calculations.

Response: The authors determined that much of t he requested information is included in the text and references in this NUREG/KM and that no update is needed.

Comment 9-6 (NEI): After the words, historical rainfall events or different isohyetal patterns recommend adding the words or gridded sub-basin combinations

Response: The authors have made the recommended change in the NUREG/KM.

A.2.10 Potential Effects of Long-Term Climatic Change

Comment 10- 1 (AWA): General Comment on Chapter 10: Research regarding extreme precipitation varies significantly depending on the duration, recurrence interval, storm type, and geographic location. Several studies have shown that PMP level rainfall may not increase or even decrease depending on the combination of atmospheric dynamics, moisture inflow, and thermodynamic gradients that all combine to produce PMP type rainfalls. The simple assumption that warmer temperatures produce a higher holding capacity of moisture in the air does is correct, but does not mean that the most extreme rainfall events will increase in number of occurrence and/or amounts. It may mean that more rainfall accumulates overall o r in more frequent lesser intensity events but that the atmospheric dynamics that must combine to produce the most extreme events are lessened. Precipitation projections in the suite of GCM and regional downscaled models is the most variable and unpredictable of all the parameters.

Site-specific climate change evaluations and application of climate change projections completed by AWA have resulted in both increases and decreases in PMP estimates. In addition, it is also important to note the changing seasonality in relation to potential timing of occurrence of PMP type events, amount of snowpack accumulation, and timing of runoff and how each of those are combined may change.

Response: The authors have determined that no update is needed.

Comment 10- 2 (AWA): On page 10- 1, 3rd paragraph, 3rd sentence, the reference to heavy precipitation events is not specific to PMP, but to 10-, 25-, 50-, and 100- year level recurrence

A-12 intervals. The term heavy should be explicitly defined as PMP is very different and will likely have very different outcomes versus other recurrence intervals and precipitation types.

Response: The text in question is a direct quote from the U.S. Global Change Research Program report and is presented as such; therefore, no change to the NUREG/KM is warranted at this time.

A.2.11 Summary

Comment 11-1 (NEI): Recommend including a discussion on how site -specific PMP estimates relate to the current licensee guidance documents. The currently approved revision of Regulatory Guide 1.59 significantly pre-dates many of the scientific advancements, research and source documents that have made site-specific estimations a viable meteorological option.

Response: At this time, it would be inappropriate to comment on, or include a discussion of, any guidance documents under revision. The authors agree with the NEI that much of the current guidance predates the state-of-the-art science in this area. This lack of guidance was part of the motivation for creating this NUREG/KM.

A-13

NUREG/KM-0015

Considerations for Estimating Site-Specific Probable Maximum Precipitation at Nuclear Power Plants in the United States of America September 2021

S.T. DeNeale, S.-C. Kao, K.R. Quinlan, D.B. Watson Technical

U.S. Nuclear Regulatory Commission Oak Ridge National Laboratory Washington, DC 20555- 0001 Oak Ridge, TN 37831-6038

Office of Nuclear Reactor Regulation U.S. Nuclear Regulatory Commission Washington, DC 20555- 0001

The purpose of this document is to help fulfill the NRCs goal of maintaining and preserving knowledge and deriving lessons learned from the recent flood hazard reevaluations at nuclear power plant sites performed in connection with the accident at the Fukushima Dai-ichi nuclear power plant in Japan.

Specifically, this document (1) identifies terminologies, theories, methods, and lessons learned for site-specific probable maximum precipitation (SSPMP) studies submitted to the NRC for review and (2) presents key considerations for developing and reviewing potential future SSPMP studies.

ML21245A418

Text

1.1 Background

SUMMARY

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