Riverine PFHA for NRC Safety 

Reviews – why and how?

RAJIV PRASAD

Pacific Northwest National Laboratory

Richland, WA

(509) 375-2096 rajiv.prasad@pnnl.gov
Overview

Probabilistic Flood Hazard Assessment (PFHA)

What it is, in the NRC context

A tool for site characterization and selection of design bases that uses 

probabilistic approaches

A tool to determine exceedance probabilities of riverine flood hazards

A tool to evaluate potential changes to flood hazards in the future

What it is not, in the NRC context

A probabilistic risk assessment tool

A systems design tool

A licensing basis tool

During this presentation

The term PFHA is used for Riverine PFHA

The terms PFHA methods is used for methodologies to carry out Riverine 

PFHA

January 30, 2013 2
Background

Current NRC approach to hydrology safety reviews – regulatory bases

10 CFR 50

Appendix A, General Design Criteria, Criterion 2 (GDC 2)

Criterion 2—Design bases for protection against natural phenomena.

Structures, systems, and components important to safety shall be 

designed to withstand the effects of natural phenomena such as 

earthquakes, tornadoes, hurricanes, floods, tsunami, and seiches

without loss of capability to perform their safety functions. The design 

bases for these structures, systems, and components shall reflect: (1)

Appropriate consideration of 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, (2) appropriate 

combinations of the effects of normal and accident conditions with the 

effects of the natural phenomena and (3) the importance of the safety 

functions to be performed.

10 CFR 52

10 CFR 100

January 30, 2013 3
Background

Current NRC approach to hydrology safety reviews

Deterministic

Relies on “probable maximum” events

Relies on “bounding” assumptions

Relies on “reasonable and conservative” design bases with “margins”

Philosophy of “defense-in-depth”

Hierarchical Hazard Assessment

Guidance for Applicants

Regulatory Guides 1.27, 1.29, 1.59, 1.102, 1.113, 1.125

Guidance for NRC Staff

Standard Review Plan NUREG-0800, Section 2.4

PNNL’s role during the last ~10 years

Assisted NRC in performing ESP and COL safety reviews (since 2003-04)

Assisted NRC in updating Section 2.4 of NUREG-0800 (2007)

Assisted NRC in developing tsunami review guidance, NURG/CR-6966 (2009)

Assisted NRC in updating Regulatory Guide 1.59, NUREG/CR-7046 (2011)

January 30, 2013 4
PFHA – the Need

Why PFHA?

NRC’s 1995 Probabilistic Risk Assessment Policy Statement (60 FR 42622)

Current deterministic approach to flood site characterization

Expresses the hazard as a single number

Provides no exceedance probabilities

Provides little uncertainty information

Inconsistency in selection of design bases

Does not explicitly evaluate the consequences of design bases being 

exceeded or significant consequences of near-design bases events

Regulatory decisions increasingly need exceedance probabilities

Can a design basis be exceeded? How likely is it?

Beyond design-basis issues

Can a design basis not be exceeded yet result in significant damage 

and/or compromised operations?

Less than design-basis issues

To support performance-based, risk-informed approaches

January 30, 2013 5
PFHA – the Hazards

What are flood hazards?

Characteristics of floods that may adversely affect safety-related systems

Examples

Flood water surface elevation

Hydrodynamic load (velocity, momentum)

Areal extent and duration

Debris load (availability, velocity, momentum)

Scouring potential (velocity, momentum)

The hazards are not only site-specific, they are also extremely likely to be very 

sensitive to location of a safety-related system on/at the site

Examples

Flow velocity patterns can vary significantly with bathymetry, channel 

properties, obstructions, and such

Hydrodynamic loads, debris loads, and scouring will also vary 

significantly with flow velocity patterns, availability of debris, and 

substrate conditions

January 30, 2013 6
PFHA – Objectives and Methods

What do PFHA methods need to accomplish?

Estimate complete probability distributions of the flood hazards

Estimate the uncertainty associated with exceedance probabilities

Provide a way to update probability distributions of future flood hazards

How can we perform PFHA?

Two general approaches:

Data-centric approaches (e.g., flood frequency analysis)

Runoff modeling or simulation approach

Outcome:

For each flood hazard and for each safety-related system exposed to that 

flood hazard, an annual exceedance probability distribution (the Hazard 

Curves)

In NRC terminology, hazard curves can be thought of as characteristics

of the site

And these site characteristics can change with time

January 30, 2013 7
PFHA Methods – the Data-Centric Approach

January 30, 2013 8

Data-centric PFHA

Typically, a frequency analysis of observed floods

(some of this would have been talked about in Panels 1, 2, and 5)

Estimate a probability distribution of floods

Use the probability distribution to estimate floods of desired frequencies

Examples

Bulletin 17-B

Fits a log-Pearson Type III probability distribution to annual peak 

discharge data

GEV approaches

Used in UK and elsewhere

Non-parametric approaches

Kernel density estimators

For desired exceedance probability, obtain the flood magnitude
PFHA Methods – the Data-Centric Approach

January 30, 2013 9

Data-centric PFHA

Caveats

Limited length, sometimes even unavailability of historical flood record 

at/near location of interest

Supplemental data (paleo-flood data, tree rings data, ...), regional 

similarity

Non-stationarity

Choice of parametric or non-parametric probability distributions to “fit” 

observed (and extended) record

Extrapolation to very low exceedance probabilities

Quantification of uncertainties

Updating fitted probability distributions as more data becomes available

Need to estimate hazards other than just the flood discharge
January 30, 2013 10

PFHA using Runoff Modeling

Basically, uses a Monte Carlo-like simulation approach using a precipitationrunoff-routing model

Needs inputs: hydrometeorology, initial conditions, and watershed 

characteristics along with properly selected values of model parameters

Hydrometeorology, initial conditions, watershed characteristics, and 

model parameters can all have their own probability distributions

There could be some combinations of model parameters and/or 

initial and watershed conditions that are physically unrealistic

Construct the probability distribution of flood hazards predicted by the 

precipitation-runoff-routing model

PFHA Methods – the Runoff Modeling Approach

Input

AEP

Hazard

AEP

Precipitation-Runoff-Routing Model

φ

Probability

Density
PFHA Methods – the Runoff Modeling Approach

January 30, 2013 11

Runoff Modeling PFHA

Caveats

The model must be validated

Probability distributions of inputs, initial conditions, and model parameters 

must be specified

Multiple inputs, multiple initial conditions, and multiple model parameters 

quickly result in a need to run a large number of simulations to adequately 

cover the range of hazards

Need to keep number of simulations manageable

Uncertainty in hazard estimates

Contribution from input uncertainty

Contribution from model parameter uncertainty

Contribution from model inability to accurately represent river basin 

processes
PFHA Methods – the Runoff Modeling Approach

January 30, 2013 12

Runoff Modeling PFHA

Model validation

Needs to account for the fact that the model would be predicting extreme 

floods

Current practice is to validate against “floods of record”

Typically, discharge is used for validation

What to validate model predictions to?

Peak discharge

Complete hydrograph

Flow velocities

Probability distribution of inputs

Hydrometeorology

Precipitation, temperature, ...

Initial conditions

Baseflow, soil moisture, reservoir levels, snowpack, ...
PFHA Methods – the Runoff Modeling Approach

January 30, 2013 13

Runoff Modeling PFHA

Probability distribution of model parameters

Equifinality

GLUE, adaptive sampling of parameter “hyperspace”

Management of simulations

GLUE

Metropolis-like sampling algorithms
PFHA Methods – addressing Non-stationarity

January 30, 2013 14

Global Climate Change

“Climate change is real,” he said. “It is denial to say each of these situations is 

a once-in-a-lifetime. There is a 100-year flood every two years now. It is 

inarguable that the sea is warmer and there is a changing weather pattern, 

and the time to act is now.” Andrew Cuomo, Governor of New York State in 

his State of the State Address, as cited in the New York Times January 9th, 

2013.

Changes in precipitation

Amount, phase, and seasonality

Changes in temperature

Amount, and seasonality

Changes in storm patterns

Sea-level rise

Backwater issues related to near-coast riverine floods

Subsidence issues
PFHA Methods – addressing Non-stationarity

January 30, 2013 15

River Basin Changes

Development/urbanization/land use changes/water use and flood control

Basin flood management changes

Example: installation of new flood control reservoirs or changes in flood 

management rules of existing reservoirs

How do these changes affect PFHA?

Data-centric methods

Observed floods are already, at least to some extent, affected by past 

changes and will continue to be affected

Runoff-modeling methods

Need to account for the effects on probability distributions of model 

parameters and may also need to update the model structure
Needed 

Interface?

PFHA Methods – the Results

January 30, 2013 16

Results from PFHA

Presentation of hazard curves

As parametric or non-parametric distributions?

As look-up tables?

Other ways

Interfacing with plant PRA or risk-informed, performance-based evaluation

Role of Section 2.4 (FSAR/SER) in supporting PRAs

PFHA

Hazard

AEP

Hazard

AEP

PRA

or

RIPB

Hazard

AEP

Hazard

AEP
PFHA Methods – Gaps

January 30, 2013 17

Where do we need to focus?

Data-centric methods

Selection of probability distributions

Use of supplemental data (paleo-flood data, tree rings data, ...)

Regional flood frequency analysis

Treatment of non-stationarity

Extrapolation to very low exceedance probabilities

Validation

Uncertainty estimation

Ways to estimate hazards other than just flood discharge

Runoff-modeling methods

Estimation of probability distributions of inputs, initial conditions, and 

model parameters

Validation

Management of number of simulations

Uncertainty estimation