Using ORNL Titan to Develop 50K Years of Flood Risk Scenarios (For FEMA / NFIP and Other Clients)

Using ORNL Titan to develop 50k years of Flood Risk Scenarios (for FEMA / NFIP and other clients)

Dag Lohmann, April 17th 2018

KatRisk LLC KatRisk Deutschland GmbH 752 Gilman St. Wilhelmstr. 6 Berkeley, CA 94710 79098 Freiburg, Germany 510-984-0056 0761-5146-7600 www.KatRisk.com KatRisk HPC User Forum Agenda

 KatRisk introduction  KatRisk cat response and flood maps: why new flood maps?  The core of catastrophe modeling  Using ORNL Titan to compute flood maps  From flood maps to risk models  Coupled tropical cyclone wind, flood and storm surge results for the US KatRisk Introduction – HPC User Forum

 Founded in 2012, global flood and wind catastrophe models Hurricane Wind Fields  Diverse customers – 3 of top 4 insurance brokers – 2 of top 3 reinsurance companies – ~ 30 primary insurers – FEMA (US Hurricane Wind, Storm Surge and Inland Flood) + others  Sea level rise and changing precipitation extremes sensitivity  Heavy compute power needed to compute US on 10m resolution New Orleans Storm Surge KatRisk utilized resources of the Oak Ridge Leadership Computing Facility at Detailed Flood (Harvey) the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

5 Million hours, 2-d GPU SWE code Detailed slides on sea level rise and US wide losses on http://www.katrisk.com KatRisk Introduction – HPC User Forum

5 Million hours, 2-d GPU SWE code Detailed slides on sea level rise and US wide losses on http://www.katrisk.com KatRisk Cat Response

 For the last three years KatRisk has released wind and flood footprints of major events within days of an event  Inform KatRisk models with observations (Data Assimilation)  Cat Response slides are on http://www.katrisk.com/recent-events Compare flood footprint with FEMA and point observations (Pensacola 2014) Harvey KatRisk Event Response

 http://www.katrisk.com/recent-events Pensacola Flooding April 2014

Flooded Downtown Area Outside of FEMA Hazard Zones

Blue Shading – KatRisk Flood Model Red Hatched – FEMA Zones A and V Pensacola Flooding April 2014

1 2 3

4 5 6 Photos Pensacola News Journal

7 8 9 10 Why new maps? Coverage and Extent of Modeling

Red outlines – FEMA 100 year flood zones

 FEMA FIRMs cover much but not all of the US  In many areas they cover the main rivers but not smaller streams and surface water flooding  Need to model the the water getting to the rivers as well as out of the rivers

Blue – high resolution model including pluvial (surface) and fluvial (riverine) flooding KatRisk Pluvial / Fluvial Modeling

USGS catchment 0101000906 Pluvial Flood Pattern Combined Flood Footprint Boundary conditions from storm water runoff input

Finite Volume Diffusive Wave

Fluvial Flood Pattern

Fluvial boundary conditions from upstream catchments

Finite Volume Navier-Stokes Equations 10 ORNL TITAN used to compute global pluvial and fluvial maps USA Flood Maps (NY)

NY State FEMA and KatRisk coverage Zoom into NY Flood Maps

KatRisk Red and White, FEMA Blue NYC Flood Map

KatRisk Red and White, FEMA Blue More NY State Flood Risk Map

KatRisk Red and White, FEMA Blue History of Catastrophe Models

 AIR (1987) and RMS (1988) founded – Build first EQ and hurricane models  1992: Hurricane Andrew – $16B insured losses – 11 insolvencies  1994: Northridge Earthquake – $12B insured losses  1996: First cat bonds, Rating Agencies require cat loss information  2001/2002: WTC and first terrorism model  2005: Hurricane Katrina – $40B insured losses – 0 insolvencies What is at the core of catastrophe models?

Answer: Quantifying the economic and insured losses of natural catastrophes KatRisk economic losses for US hurricane, storm surge and inland flood: AAL = $39 Bn

 Model run with economic exposure – About $80 Trillion insurable 16 Year RP Harvey ($80 Bn) was an event that – Three Lines of Business (RES, COM, IND) has a loss exceedance probability of 1/16 ~ 6% in any given year – Average Vulnerability – KatRisk Default Flood Defences – Ran every 10th location – 100 Samples (5 Million year EP) – Model IF/SS results sensitive to assumptions of BFE – SpatialKat run-time GU/GR ~ 80 min on 25 cores Xeon E5-2690

AAL = Average Annual Loss OEP = Occurrence Exceedance Probability Harvey = $80 Bn AEP = Aggregate Exceedance Probability KatRisk Simplified Global Workflow

Probabilistic VARMA based Ocean SST model

Probabilistic Tropical Cyclone Track Model Probabilistic Precipitation and Temperature Model, Surface Meteorology TC Precipitation Model

Tropical Cyclone Wind Model Land Surface Model River Routing Model Storm Surge Model, AWS Tidal Model 2-d hydraulic Hydraulic 2-d Flood Model TITAN

Probabilistically sampled vulnerability with correlated severity distributions

Exposure and GU Loss Model (API for third party data integration)

Insured Loss Model (Policies, Treaties)

Statistics, Analysis, Maps (WMS), Web Interface (GUI) and Web Service (API)

Probabilistic Deterministic Expensive Financial + Analysis + API 2017 KatRisk US Inland Flood, Storm Surge, Hurricane Model

Summary Highlights and some Cat Model Industry Firsts  Fully correlated multi-peril 50k year event set (up to 50 Million years sampled) with TC and non-TC flood events  Groundbreaking low run-times from laptop to server to cloud  2-d hydraulic modeling everywhere (storm surge and inland flood) with user defined inland flood defenses (with KatRisk defaults)  Actuarial coherent view of risk computations with repeatable location aware correlated uncertainty sampling (allows buildings as footprints)  Global correlations through teleconnections and climate change sensitivity  Transparent financial model with multi-peril contracts  Expose key model sensitivities to user (flood defences, correlation, etc.) KatRisk Hurricane and Storm Surge Model

 A climate conditioned hurricane track set developed for the Atlantic Basin (1km resolution, 10k * 5 years of events)  Combined with roughness, windfield, and vulnerability models, full wind loss modeling capabilities Sample Tracks 100 Year Windspeed Map Storm Surge and Inland Flood

 Storm surge (SS) has been simulated for the entire 50k year track set and output on a 10m resolution grid with parametric wave model  Inland flood simulation with TC and non-TC rainfall. 50k years of continuous simulation of pluvial and fluvial flooding (KatRisk US Flood Model 2017)

Correlated Wind fields, storm surge, and TC precipitation Make new stochastic monthly precipitation

 Recipe: create non-TC precipitation from global VARMAX model – Run global SST model – Condition prec on SST – Create 10k periods with 5 years each – Add other precipitation sources (TC, AR) daily and and sub-daily – Movie shows 50 years of stochastic precipitation Storm Surge Modeling

 Storm surge has been analyzed for 50,000 years of hurricane tracks Images show KatRisk Score (1-10) New York

Houston

New Orleans

Chesapeake Bay Peril – Peril Correlation: Harvey

 KatRisk released modeled footprint during event, and updated throughout event  Loss estimates based on KatRisk footprints – 8.8 million point IED in Texas – $40 - $50 Billion GU Texas Inland Flood Loss – Large demand surge (1.4?) + wind and storm surge (<$2 Billion) + other areas ~ $80 Billion Overview of US Economic Insurable Losses

 USA AAL All Perils (TC, IF, SS) Combined = $39 Billion +- $6 Billion  Model run with economic exposure – About $80 Trillion insurable 16 Year RP – Three LOBs – Average Vulnerability – Ran every 10th location – 100 Samples (5 Million year EP) – Model IF/SS results sensitive to assumptions of BFE – SpatialKat run-time GU/GR ~ 80 min on 25 cores Xeon E5-2690 $80 Billion AAL and EP all Perils (Flood, Storm Surge, Wind)  Combined OEP and AEP curves for all perils and combined – AAL TC Wind = $12 Billion - $15 Billion – AAL Inland Flood = $16 Billion - $22 Billion Wind drives tail risk Flood AEP highest in low return periods – AAL Storm Surge = $4 Billion - $7 Billion OEP Combined AEP Combined Wind Wind 16 Yr RP Inland Flood Inland Flood 77 Yr RP Storm Surge Storm Surge

$80$80 Billion Billion EP all Perils (Flood, Storm Surge, Wind)

 Zoom: all perils combined

OEP Combined Combined Wind AEP Wind Inland Flood Inland Flood 16 Yr RP Storm Surge Storm Surge

77 Yr RP

$80 Billion Deeper look into TC vs. non-TC losses  Just TC only, wind, inland flood, storm surge  How special was Harvey for just TC flood? Answer: very

OEP Combined AEP Combined Wind Wind 25 Yr RP Inland Flood Inland Flood Storm Surge Storm Surge

420 Yr RP

$80 Billion Deeper look into TC vs. non-TC losses

 Zoom: Just TC only, wind, inland flood, storm surge

OEP Combined AEP Combined Wind Wind Inland Flood Inland Flood Storm Surge Storm Surge

420 Yr RP

$80 Billion Overview of Losses – TC contribution to Flood

 AAL TC Flood = $3.5 Billion to $5 Billion (18% to 25%) – Contribution of Atlantic is about 17% to 23.5%, Pacific the rest Overview of Losses – TC contribution to Flood

 AAL TC Flood = $3.5 Billion to $5 Billion (18% to 25%) – Contribution of Pacific is about 1% to 1.5%, Atlantic the rest Effects of ENSO on Precipitation in Oct – March

https://www.climate.gov/news-features/featured-images/how-el-ni%C3%B1o-and-la-ni %C3%B1a-affect-winter-jet-stream-and-us-climate Flood AAL differrence El Nino

. Strongest Effect on Dry Precipitation is during Oct-Mar 1. Filter out non Oct-Mar Events (IF Only) 2. Compute State AAL 3. Filter Out Oct-Mar and Strong + ENSO Years Wet 4. Compute % Difference Flood AAL Difference La Nina

AAL by Atlantic SST Introduction of SST leads to clustering for TCs that cause losses in the USA # Atlantic TCs with SST Dispersion = 1.15 # Atlantic TCs Poisson

AAL by ENSO

Hurricane losses dependency on Atlantic SST Anomaly and ENSO SpatialKat Financial Model

Explicit inuring order Financial Model between perils Limits, Deductibles Choose wind or flood first and Blankets Location Coverage Choose how wind and Site flood losses should be Account executed within a contract Portfolio Facultative Choose how surge and Reinsurance inland flood should be Special Conditions

executed within a contract Comprehensive client survey to ensure contracts execute as they do in reality Climate Sensitivity: Short Story about Sea Level

11k to today rise in sea level

LIG 127k

Representative Concentration Pathways

Lohmann, AWI Sea Level Rise Puzzle

 During Last Inter-Glacial (LIG) exposed fossil reef indicate 5m - 9m higher sea level (Dutton & Lambeck, 2012, Dutton et al., 2015)  LIG with Sea Surface Temperature Southern Hemisphere + 1 - 3oC warmer (Capron et. al. 2014)

Lohmann, AWI Melting West Antarctic Ice Sheet from below

 Last Interglacial: Climate Models and paleo-climate data are consistent  Antarctic Ice Sheet: Marine ice sheet instability -> Sea level rise  Threshold ~2°C based on paleo-climate and climate model studies

Lohmann, AWI KatRisk Surge Climate Change Study

 Compares USA surge losses with current conditions, conditions around 1900, and a uniform 30 cm sea level rise (SLR) – Current speed of SLR is about 2.8 to 3.6 mm/year (currently accelerating), and was about 1.8 mm/year in the 20th century  Use high resolution exposure of $6.88 trillion along the coasts results summarized on 200m gridded resolution  Buildings, contents, time element and appurtenant structures modeled  Ground-up AAL increased from $5 billion to $6.9 billion, implying an increase of about $60 million per centimeter SLR, or currently about $20 million per year (although the increase is not linear), equal to 0.4% of the AAL – but also with potential to accelerate.  Ground-up AAL increased from $4 billion to $5 billion based on a 20cm sea level rise between 1900 and today. Simulations for 1900 assume the same sea defenses, bathymetry, and tropical cyclone frequency and severity as today. Number are slightly different compared to before – ran different BFE assumptions for this SS EP curves past, present and potential future

1900 Current Sea Level Rise 30cm

AAL = $4.0 Bn AAL = $5.0 Bn AAL = $6.9 Bn

Loss / RP 2 5 10 20 50 100 200 500 1000 1900 [$billion] 0.443 3.6 8.4 16.9 33.2 48.1 65.8 95.7 127.3 BASE [$billion] 0.65 4.8 10.8 20.9 39.5 56.4 75.9 108 141 SLR [$billion] 1.0 7.1 15.2 27.6 49.6 69.6 92.2 128 161 Southern Florida Exposure Southern Florida (AAL GU loss ratio 1900) Southern Florida (AAL GU loss ratio now) Southern Florida (AAL GU loss ratio SLR) Increase in GU loss AAL [$Bn] by State

 Increase in GU loss AAL between 1900 and today, as well as today to uniform 30cm SLR scenario.  Exposures are from current residential, commercial and industrial estimates  Risk increase is measured as increase in AAL by state KatRisk US results for wind, flood, storm surge

 Questions