Presentation about our probabilistic coupled inland flood, hurricane wind and storm surge model. We discuss Harvey losses, SpatialKat runtimes, and show for the first time industry wide EP curves for all perils combined and by themselves. Highlights also include our climate sensitivity and climate variability modeling, another industry first. Comments welcome.

1. US Coupled Inland Flood,
Storm Surge and Wind Modeling
Embracing Volatility
RAA, Orlando, February 2018
KatRisk LLC
752 Gilman St.
Berkeley, CA 94710
510-984-0056
www.KatRisk.com
KatRisk Deutschland GmbH
Wilhelmstr. 6
79098 Freiburg, Germany
0761-5146-7600

2. ARGO / Ariel Re US Model Intercomparison
 Compared all commercially available US flood models in November 2017
– KatRisk
– Impact Forecasting
– CoreLogic
– AIR
 Large range of modeled AAL
– Factor 3x between models
 Large differences in EP
– Number of events
– Correlations between regions
 Some models don’t model TCs explicitly
 Special thanks Federico Waisman (Ariel Re)
https://www.argolimited.com/flood-model-showcase/

3. 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.)

4. KatRisk Simplified Global Workflow
Probabilistic Deterministic Expensive Financial + Analysis + API
Probabilistic VARMA based
Ocean SST model
Probabilistic Tropical Cyclone Track Model Probabilistic Precipitation and
Temperature Model, Surface Meteorology
TC Precipitation Model
Land Surface Model
River Routing Model
Hydraulic 2-d Flood Model
Tropical Cyclone Wind Model
Storm Surge Model,
Tidal Model
2-d hydraulic
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)
Probabilistically sampled vulnerability with correlated severity distributions
Global Teleconnections
Climate Change SLR
Peril-Peril Correlation
Spatial - Temporal
Uncertainty Correlation
C
C
C C
C

5. 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

6. 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

7. Storm Surge Modeling
 Storm surge has been analyzed for 50,000 years of hurricane tracks
Houston
Chesapeake Bay
New Orleans
New York
Images show KatRisk Score (1-10)

8. Return Periods Atlantic TC [24h] Precipitation

9. Model Run-Times [1.2 Million Locations]
 KatRisk SpatialKat runs on most Windows or Linux computers with minimal
resources with 100% scalability with #samples
– 50MB / sec hard drive read speed per core, 1TB HD space for model data
– Less than 1GB / core memory for 1.2 Million locations for all perils, 10 samples
– Speed also depends on what level of detailed output is selected
– Software Requirement: Open Source R and packages, Open Source C++
Machine CPU Disk Memory TC wind Flood Storm
Surge
All Perils
Laptop 4
cores
500 MB/s
SSD drive
8 GB 25 min 28 min 7 min 72 min
High End
Desktop
6
cores
500 MB/s
SSD drive
32 GB 12 min 13 min 3 min 40 min
Server 25
cores
> 2 GB/s
PCIe drive
256 GB 189 sec 207 sec 41 sec 603 sec

10. 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

11. 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
– 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
16 Year RP

12. AAL and EP all Perils (Flood, Storm Surge, Wind)
Combined
Wind
Inland Flood
Storm Surge
Combined
Wind
Inland Flood
Storm Surge
OEP AEP
$80 Billion
16 Yr RP
77 Yr RP
$80 Billion
 Combined OEP and AEP curves for all perils and combined
– AAL TC Wind = $12 Billion - $15 Billion
– AAL Inland Flood = $16 Billion - $22 Billion
– AAL Storm Surge = $4 Billion - $7 Billion
Wind drives tail risk
Flood AEP highest in low return periods
OEP

13. EP all Perils (Flood, Storm Surge, Wind)
 Zoom: all perils combined
Combined
Wind
Inland Flood
Storm Surge
Combined
Wind
Inland Flood
Storm Surge
OEP AEP
$80 Billion
16 Yr RP
77 Yr RP

14. Deeper look into TC vs. non-TC losses
Combined
Wind
Inland Flood
Storm Surge
Combined
Wind
Inland Flood
Storm Surge
OEP AEP
$80 Billion
420 Yr RP
25 Yr RP
 Just TC only, wind, inland flood, storm surge
 How special was Harvey for just TC flood? Answer: very

15. Deeper look into TC vs. non-TC losses
 Zoom: Just TC only, wind, inland flood, storm surge
Combined
Wind
Inland Flood
Storm Surge
Combined
Wind
Inland Flood
Storm Surge
OEP AEP
420 Yr RP
$80 Billion

16.  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

17.  AAL TC Flood = $3.5 Billion to $5 Billion (18% to 25%)
– Contribution of Pacific is about 1% to 1.5%, Atlantic the rest
Overview of Losses – TC contribution to Flood

18. 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

19. Flood AAL differrence El Nino
Wet
Dry Strongest Effect on
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
4. Compute % Difference

20. Flood AAL Difference La Nina
Wet
Dry Strongest Effect on
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
4. Compute % Difference

21. Coastal AAL Difference El Nino (TC Wind)
High
Low Effects Hurricane
Generation (top 20% of
ENSO index)
1. Compute State AAL
2. Compute % Difference

22. AAL Difference El Nino (TC Surge)
High
Low Effects Hurricane
Generation
1. Compute State AAL (top
20% of ENSO index)
2. Compute % Difference

23. AAL Difference Flood Positive AMO
Wet
Dry
 Strongest Effect on
Precipitation is during Oct-
Mar
1. Compute State AAL (top
20% of AMO index)
2. Compute % Difference

24. AAL Difference Flood Negative AMO
Wet
Dry
 Strongest Effect on
Precipitation is during Oct-
Mar
1. Compute State AAL
(bottom 20% of AMO
index)
2. Compute % Difference

25. USA AAL by Atlantic SST and ENSO
Hurricane losses dependency on Atlantic SST Anomaly and ENSO
AAL by Atlantic SST
AAL by ENSO
Introduction of SST leads to clustering
for TCs that cause losses in the USA
# Atlantic TCs with SST
Dispersion = 1.15
# Atlantic TCs Poisson

26.  KatRisk has 22 failure modes by default
which define the probability of defense
failure vs return period up to 1 in 1000
year probability
 >1k years and above undefended
 User can add in any new curves
 By default, all locations have a defense
mode of 4 for pluvial and 5 for fluvial
 For the US, by default IF:
 1, fluvial defended at mode 20
 2, fluvial defended at mode 21
 3, fluvial defended at mode 22
 User-modifiable by imported location
or area
All Failure Modes Default Failure Modes plus a
low and a high
KatRisk SpatialKat Defense Failure Modes

27. KatRisk SpatialKat Defense Failure Modes

28.  SpatialKat also accepts building footprints instead of
latitude/longitude point locations
 For the example of the hospital in the image to the right,
using street level geocoding, it would get a location of the
red-star which has near zero loss
 KatRisk allows building discretization wherein value is
distributed over all the grid-cells over a footprint (blue dots)
 Allows for a coherent view of risk through location aware
sampling
SpatialKat Building Footprint Capability
Data from BuildingFootPrintUSA
Study to be published around March 2018

29. Catastrophe models need coherent measures of risk
 Many current catastrophe models do not support coherent measures
of risk
– Can you diversify your portfolio when hypothetically insuring the same building
twice and then take half the risk?
● Coherent risk measure ρ on measurable function Z
● Positive homogeneity: if α ≥ 0, then ρ(αZ) = αρ(Z)
– Can your risk measure go up when you diversify?
● Sub-additivity: ρ(Z1 + Z2) ≤ ρ(Z1) + ρ(Z2)
– Can EP losses go down anywhere on the EP curve when adding e.g. a
building?

30. SpatialKat Financial Model
Explicit inuring order
between perils
Choose wind or flood first
Choose how wind and
flood losses should be
executed within a contract
Choose how surge and
inland flood should be
executed within a contract
Please visit our booth for a live demo
Financial Model
Limits, Deductibles
and Blankets
Location
Coverage
Site
Account
Portfolio
Facultative
Reinsurance
Special Conditions
Comprehensive client survey to
ensure contracts execute as they
do in reality

31. Climate Sensitivity: Short Story about Sea Level
Representative
Concentration
Pathways
LIG
127k
11k to today rise in sea level
Lohmann, AWI

32. 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 - 3o
C warmer (Capron
et. al. 2014)
Lohmann, AWI

33. 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

34. 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

35. Sea Level Rise 30cm
SS EP curves past, present and potential future
Current
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
AAL = $5.0 Bn AAL = $6.9 BnAAL = $4.0 Bn
1900

36. Southern Florida Exposure

37. Southern Florida (AAL GU loss ratio 1900)

38. Southern Florida (AAL GU loss ratio now)

39. Southern Florida (AAL GU loss ratio SLR)

40. 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

41. 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)

42. Harvey KatRisk Event Response
 http://www.katrisk.com/recent-events

43. Summary
 Available Products KatRisk SpatialKat (laptop to server to cloud)
– US probabilistic TC (wind + storm surge + inland flood), non-TC inland flood
– Canada probabilistic inland flood (coupled to US)
 Available Products KatRisk SoloKat (laptop to server to cloud)
– Worldwide flood maps (US and UK 10m - Europe, Canada, Australia 30m)
– Worldwide location loss analytics
 KatRisk APIs and Third Party data integrators: SpatialKey, MapRisk, ...
 Please visit KatRisk Booth for a demo