NUREG/KM-0015

ORNL/SPR-2019/1375

Considerations for Estimating

Site-Specific Probable 

Maximum Precipitation at

Nuclear Power Plants in the 

United States of America 

Draft for Public Comment 

Office of Nuclear Reactor Regulation
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NUREG/KM-0015

ORNL/SPR-2019/1375

Manuscript Completed: December 2020

Date Published: December 2020

Prepared by: 

K.R. Quinlan

U.S. Nuclear Regulatory Commission

Washington, DC 20555-0001

S.T. DeNeale, S.-C. Kao, and D.B. Watson

Oak Ridge National Laboratory

Oak Ridge, TN 37831-6038

Considerations for Estimating

Site-Specific Probable 

Maximum Precipitation at

Nuclear Power Plants in the 

United States of America 

Draft for Public Comment 

Office of Nuclear Reactor Regulation
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iii

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 1.591

; NUREG-1800,

2 Section 2; 

NUREG/CR-70463

), the NRC guides its licensees to use the National Oceanic and Atmospheric 

Administration’s 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 NRC’s 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. The estimates were also used in topical 

reports. Although the licensee’s 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 using the NOAA hydrometeorological report methodology. 

The purpose of this document is to help fulfill the NRC’s goal of maintaining and preserving 

knowledge and deriving lessons learned from the recent flood hazard re-evaluations at nuclear 

power plant sites performed in connection with the Fukushima Dai-ichi accident. 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” (Agencywide Documents Access 

and Management System (ADAMS) Accession No. ML003740388). 2 NUREG-1800, “Standard Review Plan for Review of License Renewal Applications for Nuclear Power 

Plants—Final Report,” Revision 2, issued December 2010 (ADAMS Accession No. ML103490036). 3 NUREG/CR-7046, “Design-Basis Flood Estimation for Site Characterization at Nuclear Power Plants in the 

United States of America,” issued November 2011 (ADAMS Accession No. ML11321A195).

v 

TABLE OF CONTENTS

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

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

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

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

1 INTRODUCTION.................................................................................................................1-1

1.1 Background ..................................................................................................................1-1

1.2 Purpose and Objective of this Document......................................................................1-6

1.3 Regulatory Context.......................................................................................................1-7

1.4 Probable Maximum Precipitation Definition and Existing Regulatory Guidance ............1-8

1.5 Summary of Conventional Probable Maximum Precipitation Methods ..........................1-8

1.6 Summary of Methods Used in Licensee SSPMP Submittals....................................... 1-10

2 STORM SELECTION..........................................................................................................2-1

2.1 Terminology, General Methodology, and Lessons Learned..........................................2-1

2.2 Key Considerations for SSPMP Development ..............................................................2-2

3 STORM RECONSTRUCTION.............................................................................................3-1

3.1 Terminology, General Methodology, and Lessons Learned..........................................3-1

3.2 Key Considerations for SSPMP Development ..............................................................3-3

4 STORM TRANSPOSITION.................................................................................................4-1

4.1 Terminology, General Methodology, and Lessons Learned..........................................4-1

4.2 Key Considerations for SSPMP Development ..............................................................4-3

5 STORM REPRESENTATIVE DEWPOINT SELECTION AND PRECIPITABLE WATER

ESTIMATION........................................................................................................................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 SSPMP Development ..............................................................5-7

6 DEWPOINT CLIMATOLOGY, MOISTURE MAXIMIZATION, 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

6.1.3 Moisture Transposition Adjustment.................................................................6-3

6.2 Key Considerations for SSPMP Development ..............................................................6-4

7 TERRAIN ADJUSTMENT....................................................................................................7-1

7.1 Terminology, General Methodology, and Lessons Learned..........................................7-1
vi 

7.1.1 Barrier Adjustment Factor...............................................................................7-1

7.1.2 Orographic Transposition Factor.....................................................................7-2

7.2 Key Considerations for SSPMP Development ..............................................................7-5

8 ENVELOPMENT AND PROBABLE MAXIMUM PRECIPITATION DETERMINATION.......8-1

8.1 Terminology, General Methodology, and Lessons Learned..........................................8-1

8.2 Key Considerations for SSPMP 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 POTENTIAL EFFECTS OF LONG-TERM CLIMATIC CHANGE........................................10-1

11 SUMMARY........................................................................................................................11-1

12 REFERENCES..................................................................................................................12-1
vii

LIST OF FIGURES

Figure 1-1 Map of Regions Covered by NOAA PMP Documents (as of 2015)...................1-5

Figure 1-2 Map of NPP Locations for which the NRC Reviewed a Detailed SSPMP..........1-6

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 June 1948 Golden, CO, Storm (from 

HMR No. 55A)...................................................................................................3-2

Figure 3-2 DAD Table for July 1942 Smethport, PA, Storm (from HMR No. 51)..................3-3

Figure 4-1 HMR Transposition Limit Map for May 1943 Warner, OK, Storm (from HMR 

No. 51)..............................................................................................................4-2

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; Hansen et al. 1982 as Included in WMO 2009) and 

Example Probable Maximum March Isohyets Provided in HMR No. 41 

(Right; Schwarz 1965).......................................................................................9-2

Figure 9-2 Example Temporal Distribution for a Watershed-Scale 72-h PMP Event 

Provided in HMR No. 52 (Left; Hansen et al. 1982) and Example Temporal 

Distributions for Middle-Loaded Local-Scale Storms Provided in HMR No. 57 

(Right; Hansen et al. 1994)................................................................................9-4

ix

ABBREVIATIONS AND ACRONYMS

ACWI Advisory Committee on Water Information

ADAMS Agencywide Documents Access and Management System

AEC Atomic Energy Commission

AEP annual exceedance probability

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 re-evaluation report

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

NEXRAD Next-Generation Radar

NOAA National Oceanic and Atmospheric Administration

NPP nuclear power plant

NRC U.S. Nuclear Regulatory Commission

OTF orographic transposition factor

PMP probable maximum precipitation

POANHI Process for the Ongoing Assessment of Natural Hazards Information

PW precipitable water

SRep storm representative dewpoint location

SSCs structures, systems, and components

SSM storm separation method

SSPMP site-specific probable maximum precipitation

SST sea surface temperature

TAF total adjustment factor

TDL Techniques Development Laboratory

TP technical paper

TR topical report

USACE United States Army Corps of Engineers
x 

USGCRP United States Global Change Research Program

WMO World Meteorological Organization

WS watershed-scale 
xi 

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 team’s understanding of SSPMP.

1-1

1 INTRODUCTION

This report summarizes methods, data sources, and procedures used in the development of 

site-specific probable maximum precipitation (SSPMP) estimates. To date, the U.S. Nuclear 

Regulatory Commission (NRC) staff, with assistance from Oak Ridge National Lab (ORNL) 

staff, has received and reviewed multiple submittals in response to the March 12, 2012, letter 

(Agencywide Documents Access and Management System (ADAMS) Accession 

No. ML12053A340) requesting, under Title 10 of the Code of Federal Regulations

(10 CFR) 50.54(f), updated flooding hazard analyses for nuclear power plants (NPPs) and 

topical reports (TRs) that contain SSPMP evaluations. The objective of this document is to 

summarize the NRC staff’s current understanding to support the development and review of

SSPMP estimates for NRC-regulated NPPs in the United States for long-term knowledge 

management (KM). 

Specifically, this document (1) identifies terminologies, theories, methods, and lessons learned 

for SSPMP studies submitted to the NRC for review and (2) suggests key considerations for the 

NRC staff when developing or reviewing any future SSPMP studies. 

1.1 Background

The NRC has long recognized the importance of protecting NPPs against natural phenomena 

as a means to prevent reactor core damage, to ensure containment, and to preserve spent fuel 

pool integrity. The NRC established several requirements addressing natural phenomena in 

1971, which are described in General Design Criterion (GDC) 2, “Design Bases for Protection 

Against Natural Phenomena,” of Appendix A, “General Design Criteria for Nuclear Power 

Plants,” under Title 10 of the Code of Federal Regulations (10 CFR) of Part 50, “Domestic 

Licensing of Production and Utilization Facilities.” GDC 2 requires, in part, that structures, 

systems, and components (SSCs) important to safety be designed to withstand the effects of 

natural phenomena such as floods, tsunami, and seiches without loss of capability to perform 

their intended safety functions. GDC 2 also requires that design bases for these SSCs reflect 

(a) appropriate consideration of the most severe of the natural phenomena that have been

historically reported for the site and surrounding region, with sufficient margin for the limited

accuracy and quantity of the historical data and the period of time in which the data have been

accumulated, (b) appropriate combinations of the effects of normal and accident conditions with

the effects of the natural phenomena, and (c) the importance of the safety functions to be

performed.

On March 11, 2011, a 9.0-magnitude earthquake struck the Japanese mainland triggering a

14-meter (m) (45-foot (ft)) tsunami. The combination of events resulted in extensive damage to

the Fukushima Dai-ichi NPP site overlooking the Pacific Ocean. (See ADAMS Accession

No. ML112660383 for additional information about the accident).

In response to these events, the NRC developed a comprehensive set of recommendations

(documented in the Near-Term Task Force report prepared by the staff entitled, 

“Recommendations for Enhancing Reactor Safety in the 21st Century,” dated July 12, 2011
1-2

(ADAMS Accession No. ML111861807) and identified ensuing actions for current owners and 

operators (licensees) to consider and address. These actions were intended to allow the NRC 

staff to verify compliance against approved seismic and external flooding design bases, and to 

determine whether a plant’s license should be suspended, revoked, or modified based on any 

additional safety enhancements needed. Specific to external flooding events, the NRC staff 

issued a request for information letter on March 12, 2012 under 10 CFR 50.54(f) (hereafter 

referred to as the “§50.54(f) letter”) to all power reactor licensees and holders of construction 

permits in active or deferred status. Enclosure 2 of the §50.54(f) letter requested that licensees

confirm the appropriateness of the re-evaluated beyond-design-basis flooding events assumed 

for their plants (and their ability to protect against them) using current guidance and 

methodologies. Those re-evaluations 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 if any additional regulatory actions were 

needed for a particular site.

The current guidance and methodologies mentioned 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,” Section 2.4.2, “Floods,” 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. 

In response to the 2012 §50.54(f) letter requesting updates of the flood hazard assessments, 

current owners and operators submitted 61 flood hazard re-evaluation reports (FHRRs) 

corresponding to the fleet of operating domestic nuclear power reactor sites. Many of the sites 

had re-evaluated 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 various changes in flood estimates. The flood-causing mechanisms of 

interest that were the subject of the staff’s 2012 information request included

• Local Intense Precipitation (LIP), a measure of the anticipated extreme precipitation over a

specific site location which is reported as a site characteristic.

• Riverine Flooding, associated with some stream or river considering 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 Failure, closely related to riverine flooding and considers the effect of seismic,

overtopping and sunny day dam failures on the site location. Riverine flooding and dam

failure analyses are addressed as one topic in this report.

• Storm Surge, associated with flooding that arises due to relatively large, 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).
1-3 

• Seiche, associated with waves similar in motion to a seesaw and is a temporary disturbance 

in the water level of an enclosed (e.g., lake) or partially enclosed body of water which 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 Flooding, typically caused by the release of upstream ice dams or the formation 

of downstream ice dams on rivers. 

• Channel Diversions/Migrations is associated with changing riverine channel flow paths. 

Based on the analysis results provided in the FHRRs, the 2012 §50.54(f) respondents reported 

that most of the power plant sites had re-evaluated 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 prior to the 2012 re-evaluations resulted in 

modifications to previous external flood estimates. 

In the matter of the source of the precipitation values used in the LIP and riverine-based flood 

analyses, current NRC guidance for the evaluation of Early Site Permits and Combined 

Operating License Applications is to select the appropriate probable maximum precipitation 

(PMP) event reported in the National Weather Service’s Hydrometeorological Reports (or 

HMRs) regionally-applicable to the power reactor site under review. 

A common change among many of the FHRR submittals was the use of SSPMP estimates in 

place of conventional HMR PMP estimates. PMP estimates (discussed in detail in Section 1.4) 

serve as a critical hydrologic modeling input for assessing flood hazards, and the NRC’s 

Standard Review Plan (SRP), NUREG-0800, Sections 2.4.2 and 2.4.3, “Probable Maximum 

Flood (PMF) on Streams and Rivers,” issued March 2007 (ADAMS Accession 

No. ML070730405), identify the HMR PMP estimates for estimating LIP flooding and riverine 

flooding. Whereas SSPMP estimates employ a process similar to the HMRs, they ultimately use 

different methods, data sources, and procedures than those found in the HMRs. Because of 

these differences and uncertainties, the review may be more complex. 

The NRC’s SRP at the time of the re-evaluations did not describe SSPMP estimates, as they 

were a relatively recent approach to estimating extreme precipitation. For the re-evaluations, as 

is stated in NUREG-0800, 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.” 
1-4

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” (Miller et al. 1984) 

and HYDRO 41, “Probable Maximum Precipitation Estimates for the Drainage above Dewey 

Dam, Johns Creek, Kentucky” (Fenn 1985), and two technical papers (TP No. 42, “Generalized 

Estimates of Probable Maximum Precipitation and Rainfall-Frequency Data for Puerto Rico and 

Virgin Islands” (U.S. Weather Bureau 1961), and TP 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.

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 submittals did not 

require an in-depth NRC examination because of small differences (generally less than 

6 inches) in the resulting water surface elevation when comparing the SSPMP-driven simulation

to the NOAA HMR PMP-driven simulation. For 12 sites where the relative water surface

elevation changes were notable, 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 when compared against the NOAA HMRs.

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 of these NPP sites are 

in the eastern United States, east of the Mississippi River, and within the regions covered by 

HMR No. 51/52/56 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. 
1-5

Figure 1-1 Map of Regions Covered by NOAA PMP Documents (as of 2015)4

4 https://www.nws.noaa.gov/oh/hdsc/studies/images/pmp_figure.jpg
1-6

Figure 1-2 Map of NPP Locations for which the NRC Reviewed a Detailed SSPMP5

1.2 Purpose and Objective of this 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 (AEC) and its 

successor, the NRC, have focused on preserving the decision-making record through the use of 

documents such as NUREGs, SECY papers, and regulatory guides. However, in 2006, the 

agency recognized that there was a need to engage in a more formal program of KM that also

reflects the less tangible human capital aspect of the agency’s knowledge base. This feature 

was considered to be particularly important as the agency enters its fifth decade of operation—a 

period characterized by the increasing number of retirements of long-serving NRC staff involved 

in many of the agency’s early licensing programs.6 Other aspects of the agency’s KM program 

are described 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 ongoing assessment of natural hazards information, including information related to climate 

change, such as increased storm intensities. The NRC’s enhancements to the existing 

regulatory processes are 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), and are referred to as the Process

5 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.

6 NRC staff efforts to preserve this legacy of experience that describes historical events, facts, and research that 

were instrumental in shaping the NRC’s regulatory programs can be found at http://www.nrc.gov/reading-rm/doccollections/nuregs/knowledge/. 
1-7 

for the Ongoing Assessment of Natural Hazards Information (POANHI). 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 a nuclear plant’s 

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. Reports such as this are issued 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

Current NRC regulations for licenses, certifications, and regulatory approvals (Appendix A, 

General Design Criteria [GDC] 2, “Design Bases for Protection Against Natural Phenomena” of 

10 CFR Part 50, “Domestic Licensing of Production and Utilization Facilities”) state, 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. 10 CFR Part 100, also states that 

factors to be considered when evaluating sites include the disposition and proximity of dams 

and other human-related hazards (10 CFR 100.20(b)) and the physical characteristics of the 

site, including the meteorology and hydrology (10 CFR 100.21(d)). 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 if the license should be modified, suspended, or revoked.

Attachment 1, 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 associated 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 the current NRC regulatory guidance (Regulatory Guide 1.59, “Design-Basis Floods for 

Nuclear Power Plants,” (ADAMS Accession No. ML003740388), and SRP Section 2.4.2

(ADAMS Accession No. ML070100647), PMP estimates are necessary inputs to evaluate LIP 

and stream and river flooding. 
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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) (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 Regulatory 

Guide 1.59 (ADAMS Accession No. ML003740388). 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 issued by NOAA and predecessor agencies. These estimates were 

based on data collected over decades and for a variety of extreme rainfall events.

Sections 2.4.2 and 2.4.3 of the NRC’s SRP 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 current 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 NRC’s §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 classified 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 lower than the theoretical upper limit (Micovic 

et al. 2015).
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Many NOAA HMRs have described the basic approach and detailed methods used in 

developing operational PMP estimates.

7 For example, HMR No. 51, “Probable Maximum 

Precipitation Estimates, United States East of the 105th Meridian” (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 of 6–72 hr. 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, “Probable 

Maximum and TVA Precipitation Estimates with Areal Distribution for Tennessee River 

Drainages Less Than 3,000 Mi2 in Area” (Zurndorfer et al. 1986)) provide PMP estimates for 

individual drainage basins that are specifically adjusted for the area and physical influences of 

the drainage basin under consideration. Reasons for analyzing individual drainage basins 

include: (1) generalized PMP studies were 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 Appalachians).

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 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 relationship; more 

recent PMP studies may use other precipitation products, such as radar-driven precipitation

data to derive DAD. The total moisture air mass that supplied each storm (referred to as the 

“total storm precipitable water”) is estimated using representative surface dewpoint observations 

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 storm’s 

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 

7 http://www.nws.noaa.gov/oh/hdsc/studies/pmp.html
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simulation models have been proposed and developed in the past decade (e.g., Ohara

et al. 2011, Ishida et al. 2014 and 2015, Rastogi et al. 2017).

Conventional PMP methods have been criticized on the basis of 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 associated with 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 maximized moisture) has occurred 

during the observation period. Such an assumption has never been tested and may even be

invalid in a nonstationary climatic condition. From an engineering and regulatory perspective,

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 (e.g., determination of the storm-representative dewpoint). Such professional

judgments 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 scientific and

regulatory communities, the uncertainty and sensitivity involved in these professional judgments 

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 HMR No. 51 PMP estimates. 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

) exceeded HMR No. 51 values in the Houston, TX, area. This extreme event

demonstrates the uncertainty associated with extreme precipitation given the limited historical 

record (for example, Hurricane Harvey exceeded Houston’s previous 1-day rainfall observation

by more than 50 percent) and emphasizes the importance of critical infrastructure management.

1.6 Summary of Methods Used in Licensee SSPMP 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. Deviations

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 the report.

Fundamentally, HMRs and SSPMP estimates associated with the 2012 §50.54(f) information

request followed a storm-based PMP approach, which is an approach to estimating PMP based 

on historical storm observations. The 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 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 NPPs 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 (ADAMS Accession No. ML11321A195). 
1-11

Local storm—A storm event 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 hours 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 storm—A 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 conservativeness and bounding scenarios, as described in NUREG/CR-7046

(ADAMS Accession No. ML11321A195). 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.

To produce a SSPMP estimate for a given location, historical storm event data are collected and 

modified through a series of analytical procedures. Based on the NRC’s participation in 

interagency research (ACWI 2018)8 as well as NRC’s flood hazard re-evaluation 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)

• PMP determination and envelopment (Section 8)

• applying SSPMP using spatial and temporal distributions (Section 9)

The steps outlined above are based on findings from the NRC staff’s flood hazard re-evaluation

reviews, a detailed TR review for a watershed, and experience gained from participating in or 

observing Board of Consultant reviews by the Federal Energy Regulatory Commission (FERC). 

8 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).

An LIP-PMP estimate is developed largely based on historical local storms, which WMO 

(2009) defines as follows: 
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Depending on the objective and location of an SSPMP study, more or fewer steps than those 

described in Sections 2 through 9 of this NUREG/KM may be appropriate. Consequently, it is 

premature for the staff to issue guidance on this particular topic at this time as the state-ofthe art continues to evolve. Thus, the SSPMP process steps identified in this NUREG/KM 

should be viewed as the staff’s assessment of the 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. 
2-1

2 STORM SELECTION

This section of the Knowledge Management NUREG discusses the storm selection process that 

could be used in the development of SSPMP estimates, including descriptions of the

terminology, general methodology, and key considerations. This chapter ends with a summary

of lessons-learned from the recent reviews of the 2012 §50.54(f) FHRRs.

2.1 Terminology, General Methodology, and Lessons Learned

Storm selection is the process of identifying and selecting historical storm events that are

appropriate for inclusion in SSPMP development for a targeted site. To evaluate a SSPMP 

across various durations and areas following a storm-based approach, the storm selection 

process should incorporate into the SSPMP analysis all major historical storm events that can 

be collected and technically-justified. Example resources containing historical storm data 

include observations from the National Center for Environmental Information’s (NCEI)9

Cooperative Observer Program Network (COOP)10 gauges, U.S. Geological Survey flood 

reports, journal articles, books, and Internet publications, as well as storm data from HMRs,

U.S. Army Corps of Engineers (USACE) storm reports, previously submitted SSPMP studies,

and other sources.

High-quality, reliable data on historical extreme precipitation events can be difficult to obtain,

though several resources are available. In particular, the USACE Black Book (USACE 1973)

provides a comprehensive collection of observed storm events, with data on over 550 historical 

storms occurring from 1875 to 1969 (see map in Figure 2-1). This database provides a valuable 

resource when reviewing SSPMP storm selection and should be leveraged during SSPMP 

development and review to ensure that storm selection reasonably considered major historical

storms.

Once an initial list is assembled, the storms are further evaluated to determine which storms to

examine in the full analysis. To begin, a storm search domain should be identified. A storm

search domain represents the geographic region around a location of interest used to identify

and select storms. Storms occurring within the storm search domain are assumed to have 

occurred because of regional hydrometeorological characteristics that are similar to (or could

result in similar precipitation over) the location of interest. The storm search domain may include 

longitudinal (east–west) extents bounded by topography (e.g., the Appalachian or Rocky

Mountains) or reasonable distance (e.g., a certain longitudinal extent away from the point of

interest). Latitudinal (north–south) extents may be limited to approximately 6° north or south of

the study area. This latitudinal constraint is applied because of changes in storm dynamics

(e.g., vorticity) that occur across relatively large changes in latitude.

Seasonal and regional climatology and meteorology may also be considered to determine if a

historical storm event could have occurred over the area of interest. A SSPMP study may 

provide both all-season and cool-season estimates. All-season estimates may occur at any time

of the year and are typically applied as pure rainfall events. 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. The HMRs (e.g., Hansen et al.

1984; Corrigan et al. 1999) discussed cool-season PMP in general terms; however, cool-season

9 Formerly the National Climatic Data Center (NCDC) before dissolving in 2015. 10 https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/cooperative-observernetwork-coop
2-2

PMP estimates were only provided for limited cases (e.g., 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 historical storms included in the catalog heavily influences 

the reliability of a 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 SSPMP Development

Based on experience and knowledge gained through reviews of SSPMP estimates submitted in 

connection with the 2012 §50.54(f) FHRR submittals, the NRC staff identified several key 

considerations in the development of a 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 has

identified the following key considerations related to storm selection:

• 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 is 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
2-3

comprehensive inventory of major historical storms as well as a lack of public data on 

more recent storm events. In its 2018 Extreme Rainfall Product Needs proposal, the 

Extreme Storm Events Working Group (ACWI 2018) 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. 

3-1

3 STORM RECONSTRUCTION

This section of the Knowledge Management NUREG discusses the storm reconstruction 

process that could be used in the development of SSPMP estimates, including descriptions of

the terminology, general methodology, and key considerations. This chapter ends with a

summary of lessons-learned from the recent reviews of the 2012 §50.54(f) FHRRs.

3.1 Terminology, General Methodology, and Lessons Learned

Storm reconstruction involves development of DAD curves, which summarize the highest

observed rainfall depth at various combinations of storm areas and durations. During later

phases of SSPMP calculation, the DAD of each storm is adjusted to estimate the theorized

maximum precipitation of that storm under more critical meteorological conditions, and the 

collection of adjusted storm DAD values are enveloped to estimate PMP. 

A DAD value is expressed as a maximum depth of precipitation occurring over a given area for

a specified duration. For a single storm event, DAD values are computed by analyzing rain 

gauge or radar data over space and time. Historical DAD storm reconstruction by the USACE

and NOAA involved manual calculation of mapped rain gauge data. Figure 3-1 provides an 

example precipitation map. Collectively, multiple DAD values form DAD curves and tables 

(Figure 3-2), which are used to summarize storm precipitation data. DAD curves for multiple 

storms may be compared and play a key role in PMP development through storm envelopment

(Section 8). DAD curves require extensive data collection and processing efforts. General

procedures for DAD analysis are provide in WMO (1969) and the U.S. Weather Bureau (1946).

With the advancement of automated rain gauges and improved radar rainfall estimates in recent

years, extreme storm events are now measured with greater precision, resolution, and coverage 

compared with conventional methods used in past decades. To match improvements in data 

quality, improved analysis tools have also become available to quickly assess and quantify 

extreme storm events. Storms with existing DAD curves developed by NOAA, USACE,

Environment Canada, and other organizations should be considered for inclusion, when 

available. Since some of these DAD curves were developed manually using undocumented

information sources, it may be difficult to verify them. In these cases, SSPMP studies require 

professional, or engineering, judgment related to the potential use of the data.

For certain PMP applications (e.g., LIP-SSPMP or small basin WS-PMP studies for NPPs),

subhourly PMP values are required to capture rapidly occurring peak flood effects. While some 

Next Generation Radar (NEXRAD) databases enable precipitation estimation for periods as

short as 5 minutes, many storms are analyzed only in hourly increments. However, since many

databases do not provide subhourly precipitation data, standard ratios available from the HMRs

have been used in practice to convert hourly precipitation values to subhourly precipitation 

values (Section 9). 

The accuracy of DAD values, which serve as the storm-specific baseline before adjustment to 

the study area, is a major consideration for SSPMP reviews. Variabilities and uncertainties in

observed rainfall values are propagated throughout the PMP development process and can 

have significant impacts on the flood elevations estimated. Typically, for riverine flooding, longduration and large-area DAD values will have the most impact on flood magnitude, since total

precipitation volume will often drive riverine flood impacts. LIP evaluations are typically affected 

by short-duration DAD values because peak flooding conditions are most directly tied to peak
3-2

rainfall intensity. However, for both riverine and LIP flooding assessments, a robust assessment 

of PMP scenarios and hydrologic response is warranted. 

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 multicenter 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 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 in which the full storm is included. Given its significant impacts on larger 

area DAD values, decomposition of multicenter storm events should be avoided or, if used, 

accompanied by substantive justifications. 

Figure 3-1 Map of Precipitation Gauge Data for June 1948 Golden, CO, Storm (from 

HMR No. 55A) 
3-3

Figure 3-2 DAD Table for July 1942 Smethport, PA, Storm (from HMR No. 51) 

3.2 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) FHRR submittals, the NRC staff identified several key

considerations in the development of a 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 staff’s 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 as it 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.

• 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

2012 §50.54(f) information request. The software’s 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 (QA)

requirements of 10 CFR Part 50, Appendix B. Licensee’s that have submitted license

amendment request or topical report applications to the NRC, and have included

SSPMP have needed to justify the software under the QA/QC requirements of

Appendix B.
3-4

• Decomposition of multicenter storm events involves significant professional judgment

and is generally discouraged.
4-1

4 STORM TRANSPOSITION

This section of the Knowledge Management NUREG discusses storm transposition 

considerations that could be used in SSPMP development, including descriptions of the 

terminology, general methodology, and other key considerations. This chapter ends with a 

summary of lessons-learned from the recent reviews of the 2012 §50.54(f) FHRRs.

Subsequent precipitation adjustments are made to account for moisture and terrain differences

when relocating storms via transposition. Sections 6 and 7, respectively, describe those 

moisture and terrain transposition adjustments. 

4.1 Terminology, General Methodology, and Lessons Learned

The collection of historical extreme precipitation events that have occurred in or near the region

of interest represents a very important aspect of storm-based PMP analysis. Given the limited

historically-reported/instrumentally-recorded precipitation data available for use, PMP analyses

typically require transfer of multiple storms and their associated precipitation characteristics to 

other regions where they could occur through a process known as storm transposition. As 

documented in HMR No. 51 (Schreiner and Riedel 1978), storm transposition is defined as “... 

relocating isohyetal patterns of storm precipitation within a region that is homogenous relative to

terrain and meteorological features important to the particular storm rainfall under concern...” 

Section 2.5 of WMO (2009) gives details of this process, standard practice for traditional

storm-based PMP methods.

Transposition limits have been defined as “... the outer boundaries of the region where a

particular storm could occur...” (Hansen et al. 1988) Figure 4-1 shows an example of an 

HMR-analyzed storm transposition limit map. Affected by the storm features (e.g., location,

duration, seasonality), the transposition limit can be unique for each storm.

When considering an SSPMP site of interest, the transposition limits and whether a storm can 

be transpositioned to the location need to be evaluated. To inform these decisions, professional

judgment is used in assessing precipitation location, moisture source location, storm dynamics,

seasonal influences, orographic effects, and other considerations. In some cases, these 

judgments can be fairly subjective and the basis for selection should be described. Therefore,

whether a critical storm can be allowed to be transpositioned to a specific SSPMP site is usually

the main focus during the development and review of SSPMP. 

Storm transposition determination involves considerable professional judgment, and the

resulting application of transposition limits can greatly influence PMP values. Common 

transposition limit criteria (NRC, 2015) applied include the following: 

• 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° of latitude (Hansen et al. 1994)

owing to changes in storm dynamics (e.g., vorticity).

• 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).
4-2

• 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 plain 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 (m) (1,000 feet (ft)) above or below the study area 

elevation because of potential differences in moisture availability and other meteorological 

factors. 

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 separated into separate 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). With enough historic observations for any given site, 

an objective is to avoid storm transposition or use strict transposition criteria for a more 

meteorologically rigorous analysis. However, if the historical storm observations are limited for 

an SSPMP site, then more general transposition criteria must be used to avoid underestimates 

due to insufficient sampling. Each SSPMP study should clearly justify and evaluate the tradeoff 

between sample sufficiency and allowable storm transpositionability.

Figure 4-1 HMR Transposition Limit Map for May 1943 Warner, OK, Storm (from HMR 

No. 51) 
4-3 

4.2 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) FHRR submittals, the NRC staff identified several key 

considerations in the development of a 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 following key considerations:

• When defining transposition limits, clear justification and supporting evidence should be 

provided. Documents 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 sub-region 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.

5-1

5 STORM REPRESENTATIVE DEWPOINT SELECTION AND

PRECIPITABLE WATER ESTIMATION

This section of the Knowledge Management NUREG 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. This chapter ends with a summary of lessons-learned from the recent reviews of 

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 (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 

NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)11 in conjunction 

with daily weather maps (Figure 5-1) to analyze a storm’s 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 instead. 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 

11 http://ready.arl.noaa.gov/HYSPLIT.php
5-2

atmospheric moisture. Given the poorer temporal and spatial coverage and 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 various sources of data have been used in submittals to the NRC, the surface dewpoint

data were mainly collected from the NOAA Techniques Development Laboratory12 (TDL) U.S. 

and Canada Surface Hourly Observations dataset.

13 However, the recommended dataset to 

collect and process hourly dewpoint observations is the National Climatic Data Center (NCDC)14

TD3505 Integrated Surface Hourly Data released by NOAA. Both TD3505 and TDL13 datasets 

are official NCDC/NCEI data products; the TDL dataset is a collection of as-recorded weather 

station observations, whereas the TD3505 is subjected to additional quality control (QC) and 

processing by NOAA. Although the two datasets 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 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 more 

closely match the observed storm duration. 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.

12 Now named the Meteorological Development Laboratory (MDL): http://www.nws.noaa.gov/mdl/ 13 ds472.0, http://rda.ucar.edu/data sets/ds472.0/#!description 14 Now named the National Centers for Environmental Information (NCEI): https://www.ncdc.noaa.gov/
5-3

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 

determined the region with the highest moisture mass (dewpoint) in HYSPLIT’s 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 the maximum average dewpoint and the conventional 12-h 

persisting dewpoint (used in HMRs) approaches, Figure 5-2 provides a comparison. The 

histogram depicted as the background in Figure 5-2 show 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 hours 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 
5-4

Section 5.2 of HMR No. 55A, “Probable Maximum Precipitation Estimates—United States 

between the Continental Divide and the 103rd Meridian” (Hansen et al. 1988), provides 

recommended criteria for selecting storm representative dewpoints. In general, these same 

criteria are considered applicable to SSPMP studies, though the use of 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.” (Hanson et al. 1998)

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 
5-5 

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 historical meteorological data suffer from less frequent, sparser 

measurements. Consequently, a direct re-evaluation 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 dewpoints 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 for which sufficient data were 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 to 8 ◦

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 ◦

F (i.e., maximum average dewpoint = 

maximum 12-h persisting dewpoint + 5 ◦

F). However, subsequent evaluations by licensees 

resulted in modification of this recommendation to a value of +7 ◦

F for MCC storms. For the four 

synoptic (general) storms analyzed in EPRI (1993), the maximum 24-h average dewpoint was 

found to be 2 to 3 ◦

F higher than the maximum 12-h persisting dewpoint. The EPRI study 

recommended a conversion factor of +2 ◦

 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, ORNL staff conducted 

limited independent analysis using multiple storms from SSPMP submittals associated with the 

2012 §§50.54(f) information 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 ◦

F for local storms and +1 to +2 ◦F for general storms. The differences found 

between the EPRI study and the ORNL staff’s 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 associated with the 2012 §50.54(f) information request, the following 

recommendations are made:

• an alternative conversion factor for MCC (local) storms of +3 to +4 ◦

F (i.e., increase the 

12-h persisting dewpoint by 4 ◦

 F) 
5-6

• an alternative conversion factor for synoptic (general) storms of

+1 to +2 ◦

F (i.e., increase the 12-h persisting dewpoint by 2 ◦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 (Hansen et al. 1988) and based on pseudo-adiabatic assumption 

may be used. For instance, for a 22-degree Celsius (◦

C) (71- ◦F) surface dewpoint, the total PW

from sea level to the top of the atmosphere (i.e., 0–9,150 m (0–30,000 ft)) is estimated to be

6 centimeters (2.36 in.). 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. 

In reality, the value of PWStorm 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. 
5-7 

In terms of sensitivity, a lower storm representative dewpoint will lead to a more conservative 

PMP estimate. In general, a 1-◦

F difference in dewpoint contributes to an approximate 

4 to 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 (Hansen et al. 1988). Various storm-specific factors, 

including dewpoint temperature and elevation, affect the calculation of moisture availability.

5.2 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) FHRR submittals, the NRC staff identified several key 

considerations in the development of a 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 staffs 

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, 

etc.) 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, the 

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, modern meteorological tools and 

products that can yield more methodologically defensible PW estimates are highly 

encouraged. 

6-1

6 DEWPOINT CLIMATOLOGY, MOISTURE MAXIMIZATION, AND

MOISTURE TRANSPOSITION

This section of the Knowledge Management NUREG 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. This chapter 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 (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 PWMax, a maximum dewpoint is determined using dewpoint climatology maps 

derived from historical dewpoint observations. The resulting ratio (i.e., adjustment factor) 

between the PWMax and PWStorm is then used to rescale the observed storm rainfall depth to 

calculate SSPMP with the assumption that PWMax 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 in the sections below.

6.1.1 Dewpoint Climatology

To estimate the PWMax, HMR No. 51 used “the highest dewpoints observed for a given location 

and time of year” from the Climatic Atlas of the United States (EDS 1968). In particular, it 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 (Figure 6-1) in the

Climatic Atlas of the United States were constructed by summarizing all available dewpoint

observations and directly constructing contour maps covering the entire United States for each

calendar month.
6-2

Figure 6-1 Example Maximum 12-h Persisting Dewpoint Map for July (EDS 1968) 

Perhaps the most significant difference between the HMRs and SSPMP study methods

submitted by licensees in response to the 2012 §50.54(f) information request is the use of

different dewpoint climatology values. Instead of using 12-h persisting dewpoints for all storm

types, SSPMP studies used maximum 6-, 12-, or 24-h average dewpoints 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 representative 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 datasets 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., +2σ) warmer than the
6-3

mean SST (approximately equivalent to a 40-year return level (AEP: 2.3 percent) based on a 

normal distribution) is used. Licensees have used the 1981–2010 NOAA Optimum Interpolation 

Sea Surface Temperature Analysis15 to generate the +2σ 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 +2σ 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 

a historical storm event by increasing the event’s PW content to a historical maximum value 

through use of a ratio (i.e., adjustment factor):

IPMF = PWMax,Srep,SE

PWStorm,Srep,SE

 , (1)

where

PWMax,Srep,SE = the PW calculated using the 100-year dewpoint (or +2σ 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. 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).

6.1.3 Moisture Transposition Adjustment

For a 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. 

15 http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/
6-4

MTF = 

PWMax,ST,SE

PWMax,SRep,SE

(2)

where

PWMax,ST,SE = the PW calculated using the 100-year dewpoint (or +2σ 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 +2σ 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.

6.2 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) FHRR submittals, the NRC staff identified several key 

considerations in the development of a 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. 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. The 100-year threshold

used for moisture maximization and transposition is only an example threshold, and

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 +2σ SST, respectively).

Whereas 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 difference can impart high sensitivity depending on which data source

is used and affect SSPMP estimates for both coastal regions and noncoastal regions

Once the PW depths are identified, the ratio between the transpositioned location’s maximum 

PW and the original location’s maximum called PW (the moisture transposition factor (MTF)) 

can be computed by Equation (2): 
6-5

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 only affect 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 (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. See Section 10 for more discussion of the

potential effects of climate change.

7-1

7 TERRAIN ADJUSTMENT

This section of the Knowledge Management NUREG discusses considerations in the terrain 

adjustment processes used in SSPMP development, including descriptions of the terminology,

general methodology, and other key considerations for SSPMP development. This chapter ends

with a summary of lessons-learned from the recent reviews of the 2012 §50.54(f) FHRRs are

also discussed. 

7.1 Terminology, General Methodology, and Lessons Learned

Historically, simplifying assumptions regarding the influence of topography and orographic

processes were used in lieu of a detailed analysis of PMP estimates. However, when a storm

event is being transpositioned from its original location to an SSPMP study area with dissimilar

underlying topographic features, the influences of new terrain should be considered. For

instance, elevated terrain located between the moisture source and the study area may present

physical barriers to moisture inflow and decrease the amount of rainfall that can occur in the 

study area. On the other hand, terrain-induced lifting could increase the amount of rainfall. In the

conventional HMR framework, the terrain-related adjustments are introduced as another

adjustment multiplier after the steps of storm in-place moisture maximization and transposition 

(discussed in Section 6). To account for the impacts of terrain on moisture reduction, one of two

different approaches have been used: a barrier adjustment factor (BAF) or an orographic

transposition factor (OTF). 

These approaches differ from previous terrain adjustment methods used in the HMRs, including 

the storm separation method (SSM). The research community has not developed a physically

based alternative to replace the SSM, which has not been used in recent SSPMP studies

because of its complexity and subjectivity. SSMs have been described in the literature, including

Hansen et al. (1994). 

The SSM, BAF, and OTF can be categorized as types of terrain adjustment factors. In general,

for enhancement of storms due to changing topography (e.g., terrain-induced lifting), a terrain

adjustment factor of greater than 1.0 is needed, whereas for reduction of storms due to moisture

blockage, a terrain adjustment of less than 1.0 is needed. 

7.1.1 Barrier Adjustment Factor

Various licensee SSPMP submittals have simulated the reduction of moisture using a BAF. One 

BAF calculation approach involves using digital elevation data (e.g., National Elevation Dataset

(NED16)) or Global Multi-resolution Terrain Elevation Data (GMTED201017)) to identify barriers

for each storm based on the inflow direction. Using the study area location as a starting point, a

vector is plotted upwind for each inflow direction considered, and a cone of influence (a triangle 

with a length 1.5 times the base) is extended beyond the most significant barrier. The cone of

influence is then repositioned to encompass the entire basin, and the maximum upwind barrier

across the width of the cone base is identified. The average barrier height is then determined.

An “effective” barrier height may also be computed to account for irregularities in the maximum

barrier profile through which more or less moisture may pass. This effective barrier height

adjustment is made on a storm-by-storm basis and can be subjective, though the sensitivity is

usually minor. HMR No. 55A, Section 3.3 (Hansen et al. 1988), documents a similar approach to 

16 https://catalog.data.gov/dataset/usgs-national-elevation-dataset-ned 17 https://www.usgs.gov/land-resources/eros/coastal-changes-and-impacts/gmted2010
7-2

account for orographic blockage of storm moisture inflow; however, the development of 

geographic information system methods has greatly enhanced SSPMP practitioners’ 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 

account for 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 via 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 another terrain adjustment approach, the OTF, 

in place of the BAF. The OTF is a grid-based adjustment factor computed using precipitation 

frequency data (e.g., NOAA Atlas 1418) 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 convective 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: 

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

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

where

PAtlas14,Site = target grid point rainfall frequency depth (in inches (in.)) across 

various selected durations and return periods from Atlas 14

PAtlas14,SC = storm center grid point rainfall frequency depth (in.) across various 

selected durations and return periods from 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. Eq. (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

PSC

= m + b

PSC

(5) 

In more recent SSPMP studies, terrain adjustments have been computed 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. Eq. (6)

shows the adjustment calculation using the 100-year ratio:

OTF = PAtlas14,100-y,Site

PAtlas14,100-y,SC (6) 

7.1.2.1 Calculation methodology for orographic transposition factor 

Preliminary comparison between Equations (5) and (6) has shown nonnegligible differences 

resulting from the use of these two equations. 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, since 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, is preferred when calculating the OTF.
7-4

7.1.2.2 Orographic transposition factor lower and upper limits

When applying the OTF, licensees have typically enforced an upper limit (usually 1.50) but no 

lower limit. While the selection of an upper and lower limit 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 orographic transposition factor in non-orographic regions

The OTF was originally developed to capture the orographic effects that drive extreme 

precipitation production in complex terrain with the assumption that such effects may be 

represented in precipitation frequency estimates. However, some licensees 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, the use of OTF in nonorographic regions is generally discouraged. However, when 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 use of NOAA Atlas 14 to compute 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.

• Since NOAA Atlas 14 precipitation depths are derived based on point precipitation

observations, it is unlikely that the spatial patterns and magnitude of variation are

representative of precipitation frequencies for large-area storms. Since there are multiple

areas of uncertainty and a lack of a clear physical basis associated with the OTF

calculations, future PMP reviews should carefully examine the use of this or similar

methodologies.
7-5

7.1.2.5 Double counting of orographic transposition factor and moisture transposition 

factor 

When using a terrain adjustment method (e.g., the OTF method), the potential exists for double 

counting of moisture adjustment, since the influence of climatology is retained when 

precipitation frequency analysis is used. SSPMP developers should evaluate and discuss 

terrain adjustments and MTF since 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 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. Therefore, the reasonableness and 

conservativeness of OTF for controlling and near-controlling storms should be carefully 

evaluated when assessing SSPMP estimates and consequential flood hazard determinations. 

7.2 Key Considerations for SSPMP Development

Based on experience and knowledge gained through the earlier reviews of SSPMP submittals

associated with the 2012 §50.54(f) information request, 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 event’s

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.

• 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.
7-6

At a more fundamental level, subject matter experts or academic peer review has 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, although 

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.
8-1

8 ENVELOPMENT AND PROBABLE MAXIMUM PRECIPITATION

DETERMINATION

This section of this Knowledge Management NUREG discusses considerations in the PMP 

determination and envelopment processes used in SSPMP development, including descriptions 

of the terminology, general methodology, and other key considerations. This chapter 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., the calculation in Equation

(2) is modified). Equation (7) shows the calculation of TAF:

TTTTTT = IIIIII × × BB

or

TTTTTT = IIIIII × × OO (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 (Schreiner and Riedel 1978), envelopment is defined as “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. 

Since most LIP-PMP evaluations for SSPMP submittals associated with the 2012 §50.54(f) 

information request 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 are typically 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 

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.
8-2

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 owing to the desire 

to produce a smoothed curve. Professional judgment is also used to ensure 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 (Schreiner and Riedel 1978), “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 SSPMP Development

Based on experience and knowledge gained through reviews of SSPMP submittals associated 

with the 2012 §50.54(f) information request, 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:

• 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.
8-3

• SSPMP estimates should ensure that all DAD values are properly enveloped to ensure

no loss of PMP values for the simulated flooding hazard.

• During the envelopment process, a developer should verify 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 that at least the 10 largest moisture-maximized storms (after

exclusion considering transpositionability) should be included 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.

9-1

9 SPATIAL AND TEMPORAL DISTRIBUTIONS FOR SSPMP

APPLICATIONS

This section of this Knowledge Management NUREG 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. 

This chapter 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 basin’s 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. 

Spatial distribution is defined as “the geographic distribution of precipitation over a drainage 

[basin] according to an idealized pattern storm of the PMP for the storm area” (WMO 2009). 

Temporal distribution is defined as “the time order in which incremental PMP amounts are 

arranged within the PMP storm” (WMO 2009). 

Conventional PMP documentation found in the HMRs specify procedures for determining and 

applying spatial and temporal distributions. For example, HMR No. 52, “Application of Probable 

Maximum Precipitation Estimates—United States East of the 105th Meridian” (Hansen 

et al. 1982), documents a procedure for temporally and spatially distributing the PMP values 

found in HMR No. 51 (Schreiner and Riedel 1978). The procedure is stepwise and involves 

several key steps, including identifying a suitable temporal distribution, isohyetal pattern, and 

isohyetal orientation. To simplify the implementation of HMR No. 52 procedures, the USACE 

developed the computer program HMR52 (USACE 1984). Procedures vary among HMRs, and 

some SSPMP submittals associated with the 2012 §50.54(f) information request used 

alternative approaches. 

The SSPMP submittals generally excluded consideration of spatial and temporal distribution. 

Instead, many submittals include 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 (Hansen et al. 1982) in which an elliptical pattern is 

used with a major axis 2.5 times longer than the minor axis (a generalized pattern which 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 
9-2

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 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 (Schwarz 1965) 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 since, as noted in 

HMR No. 52, “all else being equal, the more centers used, the lower the peak discharge” 

(Hansen et al. 1982). The report 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; Hansen et al. 1982 as Included in WMO 2009) and Example 

Probable Maximum March Isohyets Provided in HMR No. 41 (right; 

Schwarz 1965) 

Following HMR No. 52 (Hansen et al. 1982) 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 No. 52 employs a series of trials and provides 

suggestions. Note that where patterns have been adjusted to account for topographic influences 

or, per HMR No. 52, “major storm patterns that have been observed on the drainage,” 

reorientation may not be needed.

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 (Hansen 

et al. 1982) documents its analysis of precipitation storm orientations and develops a procedure 

for quantifying geographically varying preferred orientations and adjustments when an 

orientation is notably different (e.g., larger than 40 degrees).
9-3

While the HMR No. 52 procedures focus on an idealized elliptical pattern, later HMRs 

(e.g., HMR No. 57, “Probable Maximum Precipitation—Pacific Northwest States: Columbia 

River (Including Portions of Canada), Snake River and Pacific Coastal Drainages” (Hansen 

et al. 1994), and HMR No. 59, “Probable Maximum Precipitation for California” (Corrigan et al. 

1999)) use spatial distributions based on 100-year precipitation frequency climatology from 

NOAA Atlas 2 (Miller et al. 1973), the predecessor of NOAA Atlas 14. The authors of those 

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). The HMR No. 57 authors suggest another approach of using 

an observed storm’s pattern to develop an isopercental distribution; however, they note that 

data for such storms are limited, and ultimately, no specific spatial distribution procedures are 

provided. 

The SSPMP submittals to the NRC have generally excluded consideration of spatial distribution. 

Instead, 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 historic 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 (historical 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, defined by

WMO (2009) as “a graph displaying the intensity of precipitation versus time.” For 

watershed-scale PMP application, the HMR No. 52 (Hansen et al. 1982) 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. The authors of that report note 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). 
9-4

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 

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 (Hansen et al. 1994) illustrates

representative front- and middle-loaded hyetograph curves based on storm analyses; Figure 9-2

(right) provides an example for middle-loaded storms. The authors do 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; Hansen et al. 1982) and Example Temporal Distributions 

for Middle-Loaded Local-Scale Storms Provided in HMR No. 57 (Right; Hansen 

et al. 1994) 

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 the report acknowledges that “it is meteorologically 

reasonable for the rainfall center to travel across the drainage with time during the storm”

(Hansen et al. 1982). 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).
9-5

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 

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 

watershed’s time of concentration, with durations ranging from 6–72 h.

9.2 Key Considerations for SSPMP Development

Based on experience and knowledge gained through reviews of SSPMP submittals associated 

with the 2012 §50.54(f) information request, 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 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 requires that alternative

distributions (e.g., historical rainfall events or different isohyetal patterns) be considered

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, are needed for review and

evaluation.

Related to PMP application using temporal distributions, the ORNL staff identified the following 

key consideration:

• 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.

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10 POTENTIAL EFFECTS OF LONG-TERM CLIMATIC CHANGE

None of the SSPMP studies submitted by owners and operators in response to the staff’s 2012 

§50.54(f) information request captured or addressed the potential effects of long-term climatic

change. In the context of climate change, the issue is whether deterministic PMP values will

demonstrate a trend or will remain unchanged under the projected future climatic conditions.

Until now, the magnitude of extreme storms relevant to PMP development has been considered

stationary, meaning that the mechanism producing PMP-relevant storms will not change

significantly in the future. If there are significant changes and climate variability, it is assumed

that these changes would be captured when integrating recent storm events into the analysis,

such that the impacts of gradual climate change on PMP will be addressed, at least in part, by

incorporating storm lists that are sufficiently detailed and up to date to represent the impacts of

climate change on PMP.

Over the last few decades, studies have produced evidence that the climate at global to local

scales has become nonstationary with the climate signals clearly showing an increase in 

ambient temperature. The U.S. Global Climate Change Research Program (USGCRP) Climate

Science Special Report (USGCRP 2017) stated 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 stated 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 stated that “[t]he frequency and intensity of heavy precipitation events are projected 

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 defines PMP as being calculated “under modern meteorological conditions” and “with 

no allowance made for long-term climatic trends” (WMO 2009). Nevertheless, Chapter 1.8 of 

WMO (2009) indicated that extreme rainfall events would likely increase in the 21st century 

(owing to 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 
10-2

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 directly affect

SSPMP estimation. 

As suggested by various studies and evidence, future SSPMP studies should account for the 

effects of climate change, especially in the consideration of 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-16-0144 describes the NRC’s 

enhancements to the existing regulatory processes; these enhancements are referred to as the 

Process for the Ongoing Assessment of Natural Hazards Information (POANHI). 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. 
11-1

11 SUMMARY

This NUREG/KM report presents the NRC’s lessons learned from the recent flood hazard reevaluations at NPP sites performed in connection with the 2012 §50.54(f) reviews and a recent

TR review. Specifically, this Knowledge Management NUREG identifies and explains the 

terminologies, theories, methods, and data sources used in historical and modern PMP studies.

It also includes key considerations for developing and reviewing potential future SSPMP 

studies. 

Since the development of the NOAA HMR-based PMP estimates from the 1950s to 1990s,

technological advances have enabled more efficient data collection and processing, while 

improved meteorological understanding has refined how such data are used. While the HMRs

periodically introduced new methods or procedures, much of the framework remained

unchanged. SSPMP studies have maintained the use of a similar framework though introduction 

of some new data sources, calculation procedures, and methods. This report documents the 

current state of practice in SSPMP studies and the NRC staff knowledge gained through review.

The approaches and methods described will continue to evolve in the future as technological

and meteorological advances are made. Consequently, professional judgment will continue 

playing a key role in SSPMP development.

As noted in this report, several areas of the SSPMP development process involve considerable

uncertainty and require some subjective professional judgment. The most significant areas of

uncertainty include the following: 

• storm DAD data and multicenter storm analysis

• storm 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 framework for 

quantifying SSPMP.

For the purpose of long-term KM, the objective of this report is to summarize the NRC staff’s 

current understanding of SSPMP estimation. By documenting this information and identifying 

key considerations, this NUREG/KM report aims to support the development and review of 

SSPMP estimates for NRC-regulated NPPs in the United States. This Knowledge Management

NUREG report does not constitute guidance or invalidate any prior guidance documents or the 

studies conducted in accordance with the prior guidance. 
11-2

While the NRC’s SSPMP reviews to date have excluded NPP license amendment requests, 

SSPMP studies are likely to be included as a part of such licensing actions in the future. 

Consequently, this report may inform future NRC guidance regarding SSPMP development.
12-1

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NUREG/KM-0015

Draft

Considerations for Estimating Site-Specific Probable Maximum Precipitation at 

Nuclear Power Plants in the United States of America 

December 2020