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EXTREME STORM RAINFALL NEEDS FOR CRITICAL INFRASTRUCTURE��CLIMATE COMPUTER SUMMIT - NSF�SEPTEMBER 2024

John England, Ph.D., P.E., F.ASCE

Lead Civil Engineer

Risk Management Center

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CORPS OF ENGINEERS FLOOD PROTECTION

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740+ Dams

15,000 miles of Levees

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EXTREME STORM RAINFALL FOR FLOOD HAZARDS – CRITICAL INFRASTRUCTURE

Smallwood,�Sanford Dams,

Michigan (Private – regulated by FERC)

Wautauga Dam,

Tennessee (TVA)

Watt Barr Plant,

Tennessee (NRC)

Addicks &�Barker Dams,�Texas (USACE)

Folsom Dam,

California�(USBR/USACE)

USACE National Inventory of Dams https://nid.sec.usace.army.mil/

U.S. Army Corps of Engineers

U.S. Bureau of Reclamation

Federal Energy�Regulatory Commission

Tennessee Valley Authority

Nuclear Regulatory Commission

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HIGH-HAZARD DAMS – STATES

Figure 2-6: 16,564 high-hazard dams in the U.S.

Figure 2-4: South Carolina rainfall totals for 2–4 Oct 2015

Figure 2-5: Old Mill Pond Dam failure in Lexington, South Carolina, October 2015 [NASEM, 2024]

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Events in 2015 and 2016 in the Carolinas illustrate the need to modernize the storm catalog, PMP estimates, and precipitation frequency estimates. The historic 1–5 October 2015

rainfall and flooding across South Carolina resulted in 50 dam failures in that state (FEMA, 2016). During this event, point rainfall depths at many locations exceeded 20 inches in 3 days

(Figure 2-4) and exceeded the NOAA Atlas 14 10-3 AEP depth for the 2-day through 7-day durations (FEMA, 2016). The 50 dam failures, including Old Mill Pond (Figure 2-5), were

located across the state, with most on small watersheds less than 30 mi2. One year later, Hurricane Matthew resulted in the failure of 12 state-regulated dams in North Carolina and 20 in

South Carolina (FEMA, 2017). Rainfall totals again were extreme, exceeding the 10-3 24-hour AEP depth on 9 October 2016.

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GUAJATACA DAM – PUERTO RICO, SEPT 2017

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MICHIGAN DAM FAILURES – MAY 2020

9/27/2024

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RISK-INFORMED DECISION MAKING (RIDM)

Dam and Levee Safety - Quantitative Risk Estimates - 10^-3 to 10^-7

Extreme Storm Rainfall Studies supports RIDM and Design Storm Rainfall - Probable Maximum Precipitation (PMP)

USACE ER-1110-2-1156

P_l = Probability of Load

Hydrologic Hazard Curve with Extreme Rainfall Data

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EXTREME RAINFALL ESTIMATES FOR RIDM

Watershed-Average Frequency Curves with Uncertainty

Based on Regional Precipitation Frequency Analysis – input to Rainfall-Runoff Models

Trinity River at Dallas, TX 6,100 sq mi (Kappa Distribution)

Address Risk Questions at Specific Facilities; Need for All Dams and Levees Across US/Territories

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EXTREME RAINFALL FOR FLOOD RISK ESTIMATES

Precipitation for flood rainfall-runoff model (HEC-HMS) calibration and validation

Temporal: 15-minute to hourly; Spatial: ~ 1km

April 25 – May 5, 1990 event, Grapevine Dam, TX

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MODERNIZING PROBABLE MAXIMUM PRECIPITATION

PMP is important and major changes are needed to assure the safety of high-hazard infrastructure.

The committee recommends a model-based approach to estimate PMP in the long term.

Model-based PMP estimation has advantages for computing Probable Maximum Flood (PMF).

Recommended new PMP definition �is based on extremely low exceedance probabilities, not upper bounds on rainfall.

The committee recommends a phased approach to modernizing PMP with near-term enhancements leading to model-based PMP estimation.

Download the report

Committee Website with links to Public meeting videos, agendas

www.nationalacademies.org/PMP

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ROADMAP FOR MODERNIZING PMP

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Figure 5-1

Recommendation 5-1: NOAA should pursue a phased approach to modernizing PMP estimation, with the near-term approach building on enhancements to conventional PMP procedures and leading to a long-term model-based framework that can provide uncertainty characterization of PMP estimates, fully incorporating the effects of climate change.

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MODEL-BASED PMP ESTIMATION

Recommendation 5-10: In the long term, NOAA should adopt a model-based approach to PMP estimation that aligns with the revised PMP definition, consisting of multi-model large ensemble kilometer-scale or finer-resolution modeling to construct the probability distribution of precipitation for PMP estimation under different climates.

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GETTING FROM HERE TO THERE: MODEL EVALUATION PROJECT

Recommendation 5-12: NOAA should embark on a Model Evaluation Project to assess model skill, identify strengths and limitations relevant to PMP estimation in current and future climate states, and achieve fitness for purpose, which is necessary for community confidence in models for estimating PMP.

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EXAMPLE MODEL-BASED PMP STUDIES

New Methods [see Toride et al., 2019, Hiraga et al., 2021]

Studies utilizing numerical models (WRF) with moisture maximization at boundary and shifting of individual storms
Direct account for moisture, transport, and orographics
Focus is Atmospheric Rivers in Pacific Northwest (Willamette and Columbia River Basins) - long-duration winter maximum precipitation over large areas

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We are moving beyond the traditional NWS/WMO/HMR PMO procedures to utilize WRF. A physically-based, modern approach to PMP estimation…..

Fig 7 from Hiraga et al 2021

The spatial distribution of the time-averaged Integrated water vapor transport (IVT; kg m􀀀 1 s􀀀 1) (a–c) and accumulated precipitation depths (d-f) during the atmospheric river event 12-b in the 1974 water year (1200 UTC 12 Jan to 1200 UTC 17 Jan 1974): (a), (d) the control (CTL); (b), (e) the maximum case obtained by the simulation with the optimal combination for Bonneville Dam’s drainage area (5.0◦ south latitudinal shifting + RHP150-IVT); (c), (f) the maximum case obtained by the simulation with the optimal combination for Libby Dam’s drainage area (4.9◦ south latitudinal shifting + RHP100-IVT). “Bonneville” and “Libby” represent the drainage areas of Bonneville Dam and Libby Dam, respectively

CRB Bonneville/Libby technical report currently in external peer review through USACE NWD.

PMP articles listed here https://sites.google.com/a/alumni.colostate.edu/jengland/publications

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Example Products for Users/Decision Makers

Durations

6-hour
24-hour
72-hour
and longer

Watershed Areas

1 sq mi
10 sq mi
100 sq mi
1,000 sq mi
10,000 sq mi
and larger

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NASEM MODERNIZING PROBABLE MAXIMUM PRECIPITATION – CLIMATE MODELING SIMULATIONS

Model-based probabilistic estimates of extremely low exceedance probability precipitation depths under current and future climates will be attainable at space and time scales relevant for design and safety analysis of critical infrastructure within the next decade.

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Achieving the Vision requires significant research and modeling advances, and collaboration between federal agencies, academia, and the private sector.

Undertaking this effort would benefit not just PMP but myriad applications in climate research.

Challenges and opportunities

VISION

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