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Modernizing Probable Maximum Precipitation Estimation Committee on Modernizing Probable Maximum Precipitation Estimation Board on Atmospheric Sciences and Climate Water Science and Technology Board Division on Earth and Life Studies Consensus Study Report PREPUBLICATION COPYâUncorrected Proofs
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COMMITTEE ON MODERNIZING PROBABLE MAXIMUM PRECIPITATION ESTIMATION JAMES SMITH (Chair), Senior Scientist and Professor Emeritus, Princeton University DANIEL COOLEY, Professor, Colorado State University JOHN ENGLAND, JR., Lead Civil Engineer, U.S. Army Corps of Engineers EFI FOUFOULA-GEORGIOU, Distinguished Professor and Samueli Endowed Chair, University of California, Irvine KATHLEEN D. HOLMAN, Meteorologist, Bureau of Reclamation SHIH-CHIEH KAO, Senior Research Staff, Oak Ridge National Laboratory RUBY LEUNG, Battelle Fellow, Pacific Northwest National Laboratory ROBERT MASON, Extreme Hydrologic Events Coordinator and Senior Science Advisor for Surface Water, U.S. Geological Survey (Retired as of December 31, 2022) JOHN NIELSEN-GAMMON, Regents Professor and Texas State Climatologist, Texas A&M University JAYANTHA OBEYSEKERA, Research Professor, Institute of Environment, Florida International University CHRISTOPHER PACIOREK, Adjunct Professor, University of California, Berkeley RUSS SCHUMACHER, Professor and Colorado State Climatologist, Colorado State University Study Staff STEVEN STICHTER, Study Director, Senior Program Officer, BASC JONATHAN M. TUCKER, Program Officer, WSTB KATRINA HUI, Associate Program Officer, BASC (until June 2023) HUGH WALPOLE, Associate Program Officer, BASC (until March 2024) KYLE ALDRIDGE, Senior Program Assistant, BASC (until February 2024) ANNE MANVILLE, Program Assistant, BASC (February 2024 to present) Prepublication copy v
Reviewers This Consensus Study Report was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published report as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We thank the following individuals for their review of this report: FAISAL HOSSAIN, University of Washington KENNETH KUNKEL, North Carolina State University VENKATARAMAN LAKSHMI, University of Virginia BILL MCCORMICK, Black & Veatch and ASDSO EPIC Task Group ANGELINE PENDERGRASS, Cornell University ANDREAS F. PREIN, National Center for Atmospheric Research MELVIN SCHAEFER, MGS Engineering Consultants RICHARD SMITH, University of North Carolina JEFFREY ULLMAN (NAS, NAE), Stanford University DANIEL WRIGHT, University of Wisconsin Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations of this report nor did they see the final draft before its release. The review of this report was overseen by GEORGE M. HORNBERGER (NAE), Vanderbilt University, and ANA P. BARROS (NAE), University of Illinois. They were responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authoring committee and the National Academies. Prepublication copy vii
Acknowledgments Many individuals assisted the committee in creating this report. The committee would like to thank the following people who gave presentations, participated in panel discussions, or provided some analysis on the National Inventory of Dams. Kelcy Adamec, Federal Energy Regulatory Commission Michael Anderson, California Department of Water Resources Keith Banachowski, Ohio Department of Natural Resources David Bascom, Federal Emergency Management Agency Chris Bretherton, University of Washington, Allen Institute for AI William Collins, Lawrence Berkeley National Laboratory Pierre Gentine, Columbia University Kevin Griebenow, Federal Energy Regulatory Commission Joseph Kanney, U.S. Nuclear Regulatory Commission Bill Kappel, Applied Weather Associates Kenneth Kunkel, North Carolina State University Gary Lackmann, North Carolina State University Kelly Mahoney, NOAA Physical Sciences Laboratory David Margo, U.S. Army Corps of Engineers Bill McCormick, Black & Veatch and ASDSO EPIC Task Group Daniel McGraw, U.S. Army Corps of Engineers William McKercher, Mississippi Department of Environmental Quality Zoran Micovic, BC Hydro Mark Perry, Colorado Dam Safety Andreas F. Prein, National Center for Atmospheric Research Michael Pritchard, Nvidia, jointly at University of California, Irvine Kevin Quinlan, U.S. Nuclear Regulatory Commission Kristen Lani Rasmussen, Colorado State University Kevin A. Reed, Stony Brook University Alexander Ryzhkov, National Oceanic and Atmospheric Administration, University of Oklahoma Melvin Schaefer, MGS Engineering Consultants Christoph Schär, Atmospheric and Climate Science, ETH Zürich, Switzerland Laura Slivinski, National Oceanic and Atmospheric Administration Amanda Stone, U.S. Bureau of Reclamation Paul Ullrich, Lawrence Livermore National Laboratory Michael Wehner, Lawrence Berkeley National Laboratory Daniel Wright, University of Wisconsin Prepublication copy ix
Contents SUMMARY ................................................................................................................................................. 1 1 NEED AND OPPORTUNITY FOR A MODERNIZED PMP APPROACH .......................................... 11 Committee Charge and Statement of Task, 11 Roadmap for Report, 13 2 COMMON UNDERSTANDING OF PMP ............................................................................................ 14 Definition, 14 Fundamental Components of PMP, 15 PMP Estimates in the United States, 18 Uses and Users of PMP, 19 Spatial and Temporal Scales for PMP Estimates, 29 PMP and Probable Maximum Floods, 32 3 STATE OF THE SCIENCE AND RECENT ADVANCES IN UNDERSTANDING EXTREME PRECIPITATION ..............................................................................................................36 Scientific Advances: Meteorology of Extreme Rainfall, 36 Scientific Advances: Rainfall Data, 43 Numerical Modeling and Computing, 45 Scientific Advances: Climate Change and Extreme Rainfall, 49 Advances: Statistical Methods, 55 PMP as an Upper Bound?, 59 4 CRITICAL ASSESSMENT OF CURRENT PMP METHODS ...............................................................64 Overview, 64 PMP Definitions, 64 PMP Data and Methods, 66 Numerical Modeling and PMP, 86 Implications of Climate Change for PMP, 87 Criteria for a Modern PMP Estimation Process, 90 Critical Assessment of Current PMP Methods: Summary, 92 5 RECOMMENDED APPROACH ............................................................................................................93 Overview of a Phased Approach, 93 Core Principles, 94 PMP Definition, 95 Near-Term Enhancements to PMP Estimation, 98 Model-Based PMP Estimation, 106 Model Evaluation Project, 114 Bridging Near-Term and Long-Term Strategies, 115 User Needs, 117 Criteria For Valid/Useful PMP Estimates and Estimation Process, 119 Summary, 120 REFERENCES ........................................................................................................................................... 122 Prepublication copy xi
xii Contents APPENDICES A COMMITTEE MEMBER AND STAFF BIOGRAPHICAL SKETCHES............................................. 148 B HISTORY OF PMP.............................................................................................................................. 153 C DAM CHARACTERISTICS ................................................................................................................ 180 D CRITERIA FOR A MODERN PMP ESTIMATION PROCESS .......................................................... 188 E R CODE USED IN REPORT FIGURES 3-5 AND 5-3 .......................................................................... 193 BOXES, FIGURES, AND TABLES BOXES 1-1 Statement of Task, 12 2-1 Precipitation Frequency Analysis, 17 2-2 Dam Rehabilitation, Expansion, and Construction, 21 2-3 Risk-Informed Decision Making, 28 2-4 Atmospheric Variables for Estimating Extreme Floods and Probable Maximum Floods, 34 3-1 Storm Types, 38 3-2 Generalized Extreme Value and Generalized Pareto Distributions, 56 4-1 Trading Space for Time, 70 4-2 Annual Exceedance Probability of PMP, 81 TABLES 5-1 Summary of Model Simulation Types, Characteristics, and Purpose to Support the Recommended Approach (Near-Term Enhancements, Model Evaluation Project, and Long-Term Approach), 117 B-1 Average Percent Change in 10 mi2 PMP from HMR 55A over Colorado and New Mexico for Various Locations and Durations, 158 B-2 Summary of Percent Changes in PMP Estimates at 47 Watersheds from HMR 43 to HMR 57, 158 B-3 Summary of Percent Changes in PMP Estimates at 38 Watersheds from HMR 36 to HMR 59, 158 D-1 User Criteria for Valid/Useful PMP Estimates and Estimation Process, 188 FIGURES S-1 Overview of modernized PMP estimation, 7 2-1 Fundamental components of PMP, including storm catalog, transposition, maximization, and orographic adjustment, 14 2-2 Statewide PMP and precipitation frequency studies for dam safety, 20 2-3 Example dam projects that use PMP for rehabilitations, expansions, and new designs (clockwise from upper left): North Fork Dam, Prado Dam, Gross Dam, Chimney Hollow Dam, 22 2-4 South Carolina rainfall totals for 2â4 October 2015, 23 2-5 Old Mill Pond Dam failure in Lexington, South Carolina, October 2015, 23 2-6 Locations of high-hazard dams, 25 2-7 Locations of currently operable and proposed nuclear reactors, 26 2-8 Example flood hazard curve (maximum reservoir stages) for Lake Okeechobee, Florida, 28 2-9 Empirical cumulative distributions of drainage areas, shown by primary owner type, for high-hazard dams, 29 2-10 (a) Isohyetal (lines of equal rainfall) map and mass curves of the 6â12 May 1943 storm (top) and (b) the storm transposed and rotated to the critical location for the design rainfall of Keystone Dam on the Arkansas River near Tulsa, Oklahoma, 31 Prepublication copy
Contents xiii 2-11 (a) Isohyetal map of the intense 4-hour rainfall and (b) mass curves for the 31 July 1976 Big Thompson, Colorado, storm, showing that the local storm rainfall decreases rapidly over a short distance (in this case 2 mi2), 32 2-12 Hypothetical example of a 1-mi2 nuclear reactor site (not a watershed) to apply locally intense precipitation, 33 2-13 Example spatial distributions of extreme storm rainfall over a watershed (a) 72-hour PMP over the Santa Ana River watershed (Southern California) for the Prado Dam spillway rehabilitation design and (b) spatially distributed extreme rainfall and flood runoff depths, Arkansas River watershed upstream of Pueblo, Colorado, 34 2-14 Diagram of a section showing typical paleoflood features used as paleostage indicators, 35 3-1 Precipitation magnitudes and meteorological causes for the 30 largest 4-day events for an area size of ~50,000 km2, 41 3-2 Left panel: Examples of clouds simulated by SCREAM, a global CPM, at 3.25 km grid spacing and comparison with satellite data. Right panel: Throughput of SCREAM in Simulated Days per day of wall clock time (SDYD) vs. node count on the Frontier (AMD GPUs) and Summit (Nvidia GPUs) demonstrating a throughput of more than 1 SYPD on the exascale Frontier machine, 48 3-3 Observed changes in three measures of extreme precipitation: (a) total precipitation falling on the heaviest 1 percent of days, (b) daily maximum precipitation in a 5-year period, and (c) the annual heaviest daily precipitation amount over 1958â2021, 52 3-4 Illustration of the possible change in intensity of PMP due to climate change, expressed as a percent change per degree of increase of global mean surface temperatures, 54 3-5 Relationships of the upper bound (black curve) and of precipitation depths corresponding to extreme AEPs (green, blue, and red curves for return periods of 104, 105, and 106 years, respectively) to the shape parameter of the extreme value distribution, 57 3-6 Envelope curves (linear and log scales), with world record point rainfall measurements with respect to duration, 61 3-7 Distribution of shape parameter estimates from fitting individual station- and season-specific GEV distributions to GHCN daily precipitation data from stations in the contiguous United States, 63 4-1 Importance of storm transposition and subjectivity: Smethport, 71 4-2 Example basin-average (555 mi2) precipitation frequency curve with uncertainty and design rainfall estimates (horizontal lines) for Whittier Narrows Dam, California, 81 4-3 Example dam safety tolerable risk guideline used in RIDM (FEMA, 2015) illustrating risk estimates for four dams, with different overtopping failure probabilities and consequences, 82 4-4 Examples of envelopment of generalized PMP estimates in time (across durations) and in space (across drainage areas), 83 5-1 Overview of modernized PMP estimation, 94 5-2 PMP precipitation depth that reflects the new definition, 96 5-3 Sample size needed to achieve reasonable statistical uncertainty (in terms of the standard error) for an AEP depth or the upper bound as a function of the shape parameter value, under the assumptions of extreme value analysis, 113 5-4 Example spatial and temporal scales desired for PMP products at kilometer-scale resolution: (a) mean annual precipitation for a specified climate period over CONUS (4 km), illustrating the scale and coverage desired for PMP estimates; (b) event-scale (24-hour accumulation) spatial distribution of an extreme storm (3 km); (c) maximum precipitation in each grid cell (3 km) at 1-, 2-, and 3-hour durations over New Mexico, Colorado, and Wyoming; and (d) spatial distributions of event precipitation over a watershed (shown as black lines) for a 72-hour accumulation (3 km), 118 B-1 Conceptual model for PMP based on a convective cell, 154 B-2 Conceptual orographic model for PMP based flow over a ridge, 155 B-3 Conceptual orographic model for PMP based flow over a ridge with discretized pressure layers, 156 B-4 Percent change in 1-hour, 10 mi2 PMP from HMR 55 to HMR 55A at high elevations, 157 C-1 Number of dams listed within each hazard potential classification, 181 C-2 Number of high-hazard potential dams within each state, 181 C-3 High-hazard potential dams by owner type, 182 C-4 Regulators of high-hazard potential dams, 183 C-5 Empirical cumulative distributions of drainage areas, shown by hazard classification, 183 C-6 Median drainage area of high-hazard potential dams for each state, 184 Prepublication copy
xiv Contents C-7 Smoothed density estimates of drainage areas, shown by primary owner type, for high-hazard potential dams, 184 C-8 Drainage areas for four classes of dam heightsâhigh-hazard potential dams, 185 C-9 Primary dam type of high-hazard potential dams, 185 C-10 Median height of high-hazard potential dams in each state, 186 C-11 Dam height and storage relations, shown by primary owner type, for high-hazard potential dams, 186 C-12 Dam height and storage relations, shown by primary dam type, for high-hazard potential dams, 187 Prepublication copy
Acronyms and Abbreviations AEP Annual Exceedance Probability AMS American Meteorological Society AR atmospheric river ASDSO Association of State Dam Safety Officials BAF Barrier Adjustment Factor CC Clausius-Clapeyron CONUS Continental United States CPM convection-permitting model CRM cloud-resolving model DAD Depth-Area-Duration DDF Depth-Duration-Frequency DYAMOND Dynamics of the Atmospheric General Circulation Modeled on Nonhydrostatic Domains EVA extreme value analysis FEMA Federal Emergency Management Agency FERC Federal Energy Regulatory Commission GCM General Circulation Model GEV Generalized Extreme Value GIS Geographic Information System HMR Hydrometeorological Report IDF Intensity-Duration-Frequency LES large-eddy simulation MCS mesoscale convective system MEP Model Evaluation Project MPP Maximum Possible Precipitation MRMS Multi-Radar Multi-Sensor MTF Moisture Transposition Factor NEXRAD Next Generation Weather Radar NID National Inventory of Dams NOAA National Oceanic and Atmospheric Administration Prepublication copy xv
xvi Contents NRC National Research Council NWP Numerical Weather Prediction NWS National Weather Service OTF Orographic Transposition Factor PFA Precipitation Frequency Analysis PGW pseudo-global warming PMF Probable Maximum Flood PMP Probable Maximum Precipitation PMS Probable Maximum Storm PW precipitable water RIDM Risk-Informed Decision Making SSM Storm Separation Method SST Stochastic Storm Transposition TC tropical cyclone TVA Tennessee Valley Authority USACE U.S. Army Corps of Engineers USBR U.S. Bureau of Reclamation USGS U.S. Geological Survey USWB U.S. Weather Bureau WMO World Meteorological Organization Prepublication copy
