ML22223A116
| ML22223A116 | |
| Person / Time | |
|---|---|
| Issue date: | 04/21/2022 |
| From: | Joseph Kanney NRC/RES/DRA |
| To: | |
| Kanney, Joseph 301 4155 1920 | |
| Shared Package | |
| ML22223A112 | List: |
| References | |
| Download: ML22223A116 (18) | |
Text
1 Sources of Uncertainty in Probabilistic Flood Hazard Assessment (PFHA)
OECD/NEA/CSNI/WGEV Workshop on Uncertainties in the Assessment of Natural Hazards April 19-21, 2022 Joseph Kanney Fire and External Hazards Analysis Branch Division of Risk Analysis Office of Nuclear Regulatory Research
Outline
- Probabilistic Flood Hazard and Risk Assessment Overview
- Dimensions of Uncertainty
- Flooding Scenarios
- Site-scale Flooding
- Riverine Flooding
- Coastal Flooding
- Combined Flooding
- Summary 2
Protected?
Flood Protection Feature No Impact Mitigation Impacts Success?
No No Yes Yes Reliability of Procedures and Manual Actions 1,
2,
Magnitude Hazard Curves :
Quantitative probabilistic assessment of flood hazard(s)
Load Intensity Failure Probability 1
2
Fragility Curves :
Quantitative Reliability of Passive and Active Flood Protection Features Impact of Environmental Conditions on flood protection and/or mitigation procedures Human Reliability Assessment (HRA) and Human Factors (HF) insights Procedures and Manual Actions Plant PRA Model No Impact
4
PFHA Approaches
- Statistical Approach
- Fit probability distribution(s) to flood severity metric of interest
- Flood frequency analysis
- Precipitation frequency analysis
- (Monte Carlo) Simulation Approach
- (Mechanistic) simulation models to compute flood severity metric(s)
- Probability distributions for model parameters, BCs, etc.
5
Key Challenges
- Hazard Estimation
- Range of annual exceedance probabilities (AEPs)
- Moderately rare to extreme floods
- Multiple flooding mechanisms
- Coincident and correlated mechanisms
- Uncertainty characterization and estimation
- Aleatory (e.g., storm recurrence rates)
- Epistemic (e.g., model structure, parameters)
- Impacts
- Cliff-edge effects, nonlinearity
- Flood duration may be important
- Sparse structure/component reliability information
- Sparse human reliability information 6
Magnitude Load Intensity Failure Probability
Dimensions of Uncertainty
- Aleatory variability*
- Natural randomness in a process
- Irreducible uncertainty
- Epistemic Uncertainty*
- Scientific uncertainty in the model of the process
- Model structure
- Model parameters
- Reducible uncertainty
- *Characterization is a function of analytical approach
- An addressed uncertainty may be aleatory in one model while in another model it may be epistemic
- These concepts only make unambiguous sense if they are defined within the confines of a model of analysis.
7
Riverine Discharge Example
- (Statistical) Flood Frequency Model
- Aleatory variability reflected by the use of a probability distribution
- For a given set of data, sampling uncertainty is not reducible
- Epistemic uncertainty in choice of distribution
- (Simulation) Hydrologic Model
- Future is stochastic (aleatory)
- Timing and spatiotemporal distribution of precipitation
- Changes in regulation, land use/land cover
- Epistemic uncertainty in watershed model structure and parameters
- Unit hydrograph, infiltration, routing 8
Site-scale Flooding (LIP)
- Aleatory variability
- Precipitation: magnitude, duration, temporal distribution
- Precipitation frequency analysis (e.g., NOAA Atlas 14)
- Stochastic weather generators
- Numerical weather prediction models (e.g., WRF, HRRR)
- Initial/antecedent conditions: soil moisture content, surface storage/ponding, initial stormwater drainage system state
- Long-term temporal trends (e.g., climate change):
can affect precipitation, temperature, initial conditions, and boundary conditions 9
Site-scale Flooding (Cont.)
- Epistemic Uncertainty:
- Boundary conditions: upstream discharge, downstream water levels
- Process representation: runoff generation-related processes including infiltration and roof runoff; stormwater drainage; hydraulic routing (using one-,
two-, and three-dimensional [1-D, 2-D, and 3-D]
models); flow transitions (supercritical to subcritical and vice versa); surface roughness effects in shallow, turbulent flows
- Site configuration: aboveground features including buildings and vehicle barrier systems; status of temporary flood protection; blockage of roof drains; blockage of stormwater drains
- Model resolution: spatial and temporal 10
Riverine Flooding
- Flood Frequency Analysis Approach
- Aleatory: Sampling uncertainty (e.g., limited data)
- Short records (often only a few decades or less)
- Data gaps
- Epistemic: Uncertainty in choice of distribution
- Extreme flooding not represented in the data
- Possible mixed population
- Nonstationarity (climate, regulation, land use/land cover) 11
Riverine Flooding (Cont.)
- Simulation Approach
- Aleatory: Precipitation magnitude, duration, temporal and spatial distribution
- Discharge:
- Aleatory: Initial/Antecedent conditions (e.g., soil moisture, flows)
- Epistemic: Hydrologic model structure and parameters
- e.g., infiltration, unit hydrographs, and hydrologic routing
- Epistemic: Model resolution
- Stage and velocity:
- Epistemic: Hydraulic model structure and parameters
- 1D/2D, steady/unsteady
- Epistemic: Model resolution (e.g.,
channel/floodplain) 12
Coastal Flooding (TCs)
- Storm recurrence rate is key aleatory variability for tropical cyclones
- Key epistemic uncertainties:
- Distributions for storm parameters
- Track location
- Heading direction
- Central pressure deficit
- Radius of maximum winds
- Translational speed
- Hydrodynamic model structure and parameters
- Bottom friction, wind stress
- Resolution of bathymetry, topography 13
Combined Flooding
- Extreme flooding due to combined processes
- Associated effects (e.g., wave action)
- Multi-mechanism flooding (e.g., rainfall w/ snowmelt, rainfall w/ dam failure) 14
Combined Flooding (Cont.)
- Statistical Approach (Joint Probability)
- Direct Estimation of Joint Distributions
- Assumed functional form for the joint distribution
- Copula-Based Approaches Individual variables can have different distributions
- Observations used to separately estimate
- (1) the parameters of the marginal distributions and
- (2) the parameters of the copulas, which are typically related to the correlation between the quantities, as estimated from data
- Simulation Approach
- Fully-or semi-coupled simulation models
- Coupled hydrologic, reservoir, and hydraulic models to simulate riverine flooding w/ dam failure
- Coupled atmospheric boundary layer, hydrodynamic, and wave models to simulate coastal flooding
- Sparse data for calibrating models 15
Summary
- Separating uncertainty into aleatory variability and epistemic uncertainty is a useful exercise, but these concepts are unambiguous only within a given analytical/modeling framework
- Important sources of uncertainty vary with:
- Scale and setting (e.g., site-scale, watershed-scale)
- Analysis approach (e.g., statistical vs simulation)
- Flooding metric of interest
- Interaction of multiple flooding mechanisms introduces complexity and additional uncertainty 16
References Der Kiureghian, A. & Ditlevsen, O. (2008). Aleatory or epistemic? Does it matter? Structural Safety 31(2), 105-112, https://doi.org/10.1016/j.strusafe.2008.06.020 Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigations Report 2017-5038, 93 p. https://doi.org/10.3133/sir20175038 Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., (2020). Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020-5065, 89 p., https://doi.org/10.3133/sir20205065 Bensi, Michelle, Mohammadi, Somayeh, Kao, Shih-Chieh, and DeNeale, Scott T. (2020). Multi-Mechanism Flood Hazard Assessment: Critical Review of Current Practice and Approaches.
https://doi.org/10.2172/1637939 William Lehman,(2022). HEC-WAT As a Framework for PFHA, 7th Annual NRC PFHA Research Workshop, Feb 15-18, 2022, https://www.nrc.gov/docs/ML2106/ML21064A438.pdf Norberto C. Nadal-Caraballo, Jeffrey A. Melby, Victor M. Gonzalez, and Andrew T. Cox (2015).
North Atlantic Coast Comprehensive Study (NACCS), Coastal Storm Hazards from Virginia to Maine, ERDC/CHL TR-15-5, https://www.nad.usace.army.mil/Portals/40/docs/ComprehensiveStudy/Coastal_Storm_Hazard s_from_Virginia_to_Maine.pdf 17
18 Questions?
Contact Information Joseph.Kanney@nrc.gov