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1Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
USE OF USGS NATIONAL SEISMIC HAZARD MODEL (NSHM) IN CATASTROPHE MODELING: RMS PERSPECTIVE
Nilesh ShomeRisk Management Solutions22nd September, 2015
2Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
INTRODUCTION
• RMS uses USGS NSHM model for seismic risk assessment of insurance portfolios
• Don’t use the hazard results directly.
• Use the source model (input data)
• USGS model is the industry benchmark
• Additionally consider geotechnical updates and vulnerability updates
3Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
OUTLINE
• Model evaluation
• Model adjustment
• Time-dependent model
• Faster adoption of USGS update
• Uncertainty in hazard results
4Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
MODEL EVALUATION: UCERF3
% Change due to Source Model
% Change due toGMM
Average Annual Loss (AAL) for a Well Distributed Portfolio
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NSHM ADJUSTMENT
Large number of ruptures
– 254k for FM3.1
– 306k for FM3.2
– RMS set ~ 70k
UCERF3 Ruptures
6Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
NSHM ADJUSTMENT
Large number of ruptures
– UCERF3 ~ 500k
– RMS ~ 70k
Gro
und-
up lo
ss
UCERF3 Model RMS Set
M M
7Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
NSHM ADJUSTMENT
Large number of ruptures
– UCERF3 ~ 500k
– RMS ~ 70k
% difference
Difference in PGA at 2475 Year Return Period
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NSHM ADJUSTMENT
Less dense ruptures in less exposed areas
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TIME-DEPENDENT MODEL
• California – UCERF3
• Cascadia – BC Hydro
• Alaska – USGS
• Wasatch – WGUEP
Ratio of 2% in 50-year PGA
10Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
MODEL ADOPTION
• Standardize the hazard input files
• OpenSHA code
• Clean
• Improved documentation
• User group (?)
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EPISTEMIC UNCERTAINTY IN MEAN HAZARD RESULTS
Logic-tree framework
Sources
– Parameters (e.g., Mmax)
– Models (e.g., magnitude scaling relationship)
– Limited data (e.g., rate)
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UNCERTAINTY IN SA(0.2S) IN LOS ANGELES AT 2500-YEAR RP
Source: Shome, Power and Petersen (2014). Epistemic Uncertainty in 2014 US National Earthquake Hazard Maps. SSA Annual meeting, Anchorage.
RP = 1800-4000 Yr
13Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015 13September 29, 2015
QUESTIONS?
ABOUT RMSRMS is the world’s leading provider of products, services, and expertise for thequantification and management of catastrophe risk. More than 400 leadinginsurers, reinsurers, trading companies, and other financial institutions rely onRMS models to quantify, manage, and transfer risk. As an established provider ofrisk modeling to companies across all market segments, RMS provides solutionsthat can be trusted as reliable benchmarks for strategic pricing, risk management,and risk transfer decisions.
©2014 Risk Management Solutions, Inc. RMS and the RMS logo are registeredtrademarks of Risk Management Solutions,Inc. All other trademarks are propertyof their respective owners.
14Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015
ABOUT RMSRMS is the world’s leading provider of products, services, and expertise for thequantification and management of catastrophe risk. More than 400 leadinginsurers, reinsurers, trading companies, and other financial institutions rely onRMS models to quantify, manage, and transfer risk. As an established provider ofrisk modeling to companies across all market segments, RMS provides solutionsthat can be trusted as reliable benchmarks for strategic pricing, risk management,and risk transfer decisions.
©2014 Risk Management Solutions, Inc. RMS and the RMS logo are registeredtrademarks of Risk Management Solutions,Inc. All other trademarks are propertyof their respective owners.
14Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. September 29, 2015