NASSERI WED 230PM VEN.pdf

8/12/2019 NASSERI WED 230PM VEN.pdf

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If It Keeps on Rainin,evee s o n o rea

Kiran Chinnayakanahalli, PhD

Raulina Wojtkiewicz

1CONFIDENTIAL 2014 AIR WORLDWIDE


2/36

AGENDA

- Why is AIR developing a US Flood Model?

-

- Challenges of modeling flood- How do we do it?

- How do we model hazard?

- How do we estimate loss?



3/36

WHY IS AIR DEVELOPING A US FLOOD MODEL?

Presidential Declarations for Flood Disasters

(1964-2011)



4/36

FLOODING IS A MAJOR CONTRIBUTOR TO

ECONOMIC LOSSES

1) The Great Flood, 1993 ($25 B)

In the US, floods causes more than $5 billion in damageseach year

,

3) Gulf Coast Flooding, 1995 ($5.5 B)

4) Pacific Northwest, 1996-97 ($6 B)44 5

8 95) Red River Flood, 1997 ($2 B)

6) Texas Flood, 1998 ($1.3 B)

7) Northeast Flood, 2006 ($1 B)

12

108) Midwest Flooding, 2008 ($10 B)

9) Rhode Island Flooding, 2010 ($1.5 B)

10) Tennessee Flooding, 2010 ($2.3 B)3

6 11

11) Lower Mississippi River Flood, 2011($7.5 B)

Source: Economic Losses from


National Weather Service


5/36

ALL MODELING APPROACHES ARE NOT EQUAL

Modeling Approach Pros Cons

Historic event footprints - Understandable- Discrete

- Deterministic, does not produceEP curve

- -

Actuarial or based on trendinghistorical losses - Simple- Inexpensive - Deterministic, does not produceEP curve- NO off-plain component- Need lar e sam le of ast losses

Return period flood maps (e.g.,FEMA maps in U.S.)

- Estimates risk at location- Easy to apply to

underwriting andaccumulation


- NO off-plain component- Inconsistent mapping methods

Probabilistic models based onriver gauging station data

- Stream flow gauge data iseasily obtained

- Easily derived historicalevent foot rints

- Physical data requirements high- Incomplete view of runoff

generation- NO off- lain com onent

AIRs event-based, fullyprobabilistic model developedfrom precipitation and runoffmodels

- Scientifically mostadvanced

- Provides a full occurrenceand aggregate EP curve

- Length of time to develop model- Physical data requirements are

high- Computational requirements high


for portfolios


6/36

CHALLENGES OF MODELING FLOOD

- The hazard model should have realistic spatial and temporal patterns of precipitation at suitable scale

spatial distribution of soil saturation accounting of lakes and reservoir attenuation

- location of flood defense structures and their design capacities

effects of customized flood defense

spatial distribution of off-floodplain losses

- Data intensive

- Computationally demanding



7/36

MODEL FOR US FLOOD COVERS MORE THAN

2,000,000 KILOMETERS OF RIVER NETWORK

18 major river basins in theconti uous US

Every stream draining 10 km2

ormore

2.

2.2 M km of total streams

About 8,000 river gauges used forca ra on

Over 4 M river cross-sections,roughly spaced every 500 m

30-m NED DTM* used for flooddepth estimation


*NED DTM National Elevation Dataset - Digital Terrain Model


8/36

AIR USES SIMILAR FRAMEWORK ACROSS

DIFFERENT PERILS

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard

Assessment ENGINEERING

Event Definition

Estimation

Flood

amage

EstimationContract

LossCalculations

Flood Routing

Runoff Generation

Snowmelt Flood Mapping

Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy

ConditionsRegionalFF

X-sectional

Cutline


a a o g Analysis Generation


9/36

COMPONENTS OF A FLOOD MODEL

1. Hydrology

- Precipitation- Precipitation into

runoff components Storage in soil, snow pack etc

- Flows in river

2. Hydraulics

- Mechanics of flow- How water overflows banks

- Mapping of flood zone


Illustration adopted from http://www.metrofieldguide.com/the-hydrologic-cycle/


10/36

SIMULATING REALISTIC PRECIPITATION IS CENTRAL

TO GENERATING A STOCHASTIC CATALOG

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard


Event Definition

Estimation

Flood

amage

EstimationContract

LossCalculations

Flood Routing

Runoff Generation


Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy


X-sectional

Cutline




11/36

AIRS INNOVATIVE SOLUTION TO PRECIPITATION

SIMULATION: COUPLING GCM AND NWP MODELS

1. Couple Global Circulation Models (GCM) at globalscale with a mesoscale Numerical Weather Prediction

(NWP) models at regional scale to provide coherentlarge-scale patterns

.realistically simulate small scale features



12/36

AIRS DOWNSCALING APPROACH YIELDS REALISTIC

LOOKING PRECIPITATION PATTERNS



13/36

RUNOFF MODULE TRANSFORMS THE

PRECIPITATION INTO FLOWS

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard


Event Definition

Estimation

Flood

amage

EstimationContract

Loss

Calculations

Flood Routing

Runoff Generation


Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy


X-sectional

Cutline




14/36

AIR USES A WELL ESTABLISHED APPROACH

FOR ITS RUNOFF GENERATION MODULE

Nonlinear runoffeneration for realistic

Precipitation (P)

soil saturation Continuous water

balance tracksantecedent conditionsbefore a storm

Effects of snowmelt isaccounted



15/36

EVENTS ARE DEFINED BASED ON TEMPORAL

AND SPATIAL SIGNATURE OF HIGH FLOWS

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard


Event Definition

Estimation

Flood

amage

EstimationContract

Loss

Calculations

Flood Routing

Runoff Generation


Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy


X-sectional

Cutline




16/36

FLOWS ARE CLASSIFIED BASED ON PEAK

FLOW TIMING AND RELATIVE POSITION

Based on similarities between flow attributes (spatial andtemporal), they are grouped into different clusters

Each cluster is then named as an event


Each color represents an event


17/36

HYDRAULIC MODEL TRANSFORMS THE FLOWS

TO DEPTHS AND MAPS THE FLOOD ZONE

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard


Event Definition

Estimation

Flood

amage

EstimationContract

Loss

Calculations

Flood Routing

Runoff Generation


Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy


X-sectional

Cutline




18/36

THE HYDRAULIC MODEL DETERMINES THE

EXTENT OF FLOOD

Flows at each link is converted to depth at every cross section

2D mapping methodology than converts the depths into flood



19/36

AIR VALIDATES AND BENCHMARKS ITS

RESULTS AT EVERY STAGE

100-Year FEMA 100-Year AIR

AIR hazard ma s are

based on the latest flow andterrain data

produced using consistentmethods for the entire country



20/36

VULNERABILITY MODULE TRANSLATES

FLOODING INTO DAMAGE ESTIMATES

HAZARD

Model

Dama e

EVENT CATALOG

-

Hazard


Estimation

Runoff Generation

Flood

amage

EstimationContract

Loss

Calculations

Event Definition Flood Mapping

Exposure

Information

Large Scale

Precipitation

Downscaling

Model

Policy


X-sectional

Cutline




21/36

THE AIR INLAND FLOOD MODEL INCLUDES SEPARATE DAMAGE

FUNCTIONS FOR MODELING ON- AND OFF-FLOODPLAIN LOSSES

On-plain

damage



22/36

AIR USES COMPONENT-BASED APPROACH TO ESTIMATE

DAMAGEABILITY FOR ALL LINES OF BUSINESS

Building is divided into key flood vulnerable components

Each component vulnerability is aggregated using componentcost breakdown to determine the overall building vulnerability

Building and Component Damage Functions for a

80%

100%

ag

eRatio

Retail Shop

40%

60%

MeanDa

Building StructureServices

0%

20%

0.00 1.00 2.00 3.00Flood Depth

Fixtures and Fittings


Building Structure Services

Fixtures and Fittings Overall Building Coverage


23/36

THE AIR INLAND FLOOD MODEL INCLUDES SEPARATE DAMAGE

FUNCTIONS FOR MODELING ON- AND OFF-FLOODPLAIN LOSSES

On-plain Off-plain

damage damage



24/36

OFF-PLAIN FLOOD VULNERABILITY INCORPORATES SURFACE RUNOFF

AND PROXIMITY TO THE NEAREST DRAINAGE SYSTEM

Relative

Runoff

Surve of the 2011 Flood Event IL

50%

Type of Flooding f romExcessive Overland Flow

Relative

Elevation

10%

20%

30%


SanitaryBackup

Seepage SumpPump

failure

Streetand Yard

flooding

Streetflooding

Yardflooding


25/36

SECONDARY MODIFIERS PLAY A KEY ROLE IN

LOSS ESTIMATE ACCURACY IN THE MODEL



26/36

DAMAGE FUNCTIONS CREATED TO ACCOUNT FOR OCCUPANCY,

PRESENCE OF BASEMENTS, HEIGHT AND CONSTRUCTION MATERIAL

Basements are very-

vu nera e o oo ng

since they are closerto under round water

Building Damage Function

No Basement

sources

Basement

Unknown

cellar also increasesthe risk for contents

damage -2 -1 0 1



27/36

AIR SOFTWARE USERS CAN ENTER A SECONDARY

MODIFIER THAT SPECIFIES FOUNDATION TYPES

Build ing detail based Modifiers

Foundation 0 = Unknown/default= asonry asemen

2 = Concrete basement

4 = Crawl space cripple wall5 = Crawl space masonry6 = Post & pier

=

Crawlspace

Foundation

8 = Mat / slab9 = Pile10 = No basement

11 = Engineering foundation12 = Crawlspace - raised (wood)

Basement Levels 0 = Unknown/Default1,2,3= Any positive integral level of basement

Piles

Foundation

Basement FinishType

0 = Unknown1 = Unfinished basement used for storagepurposes2 = Finished basement used for dwelling

ur oses



28/36

FLOOD DESIGN REGULATIONS ARE CONSIDERED IN

ESTIMATING VULNERABILITY OF THE STRUCTURES

Without Flood Regulations

PRE-FIRMFlood Regulations

POST-FIRM



29/36

CUSTOMIZED SECONDARY MODIFIERS ALLOW

FOR IMPROVED LOSS ESTIMATES

Elevation/Hazard modif iers

Custom Elevation (CE) Elevation in feet0 = UnknownUser input value (positive/negative) to override AIR's DTM

Base Flood Elevation (BFE) BFE elevation pertinent to the location from FEMA's FIRM maps which is the "water surfaceelevation corresponding to a flood having a 1% probability of being equaled or exceeded ina given year"= n nown

First Floor Height (FFH) Height in feet above grade of the first floor in the building0 = Unknown

FFH

CE



30/36

COMMERCIAL BUILDING UTILITIES AND SERVICES TYPICALLY LOCATED

ON LOWER FLOORS ARE HIGHLY SUSCEPTIBLE TO FLOOD DAMAGE

Positioning of services such aselectrical equipment increase the

e oo o oss

Cleanup measures lead toncrease us ness n errup oncosts



31/36

THE MODEL WILL INCORPORATE A SECONDARY MODIFIER

THAT SPECIFIES SERVICES AND CONTENT LOCATION

Content and Building Based Modifiers

Service Equipment Protection 0 = Unknown flood protection1 = Protected for flood by being elevated and/or flood proofing2 = Unprotected for flood not elevated and not flood proofed

Content Vulnerability 0 = Unknown1 = Low (a large fraction of the contents are resistant to water damage)2 = Moderate (typical fraction of contents are resistant to water damage e.g. single familyhomes etc.3 = High (low fraction of contents are resistant to water damage e.g. grocery stores,restaurants etc.)4 = Very High (almost no contents are resistant to water damage)


Example of protected ground-level equipment


32/36

AIR TAKES A PROBABILISTIC APPROACH TO

MODELING FLOOD DEFENSES

Data sourcesFEMA Levee Accreditation Map

USACE National LeveeDatabase

USGS National HydrologicDataset

Available levee data 14,000miles

Total National estimate byUSACE > 100,000 miles



33/36

USERS CAN ENTER A SECONDARY MODIFIER THAT

SPECIFIES CUSTOMIZED FLOOD PROTECTION

The secondary modifier Custom Flood Protection enables users to setup site-specific custom flood protection

Without Custom Flood Protection: Levee Overflow

Custom Flood Protection (height of the structure)


S OS S ( ) S


34/36

INDUSTRY EXPOSURE DATABASE (IED) ENABLES AIR

TO VALIDATE THE MODEL AT AN INDUSTRY LEVEL

Data sources NFIP insured losses

nsure osses

National Weather Serviceeconomic losses

a ms a a rom ac ware

Low


NFIP PCSCopyright: 2013 EsriCopyright: 2013 Esri

High


35/36

CONCLUSIONS

AIR US Flood Model will be released in 2014 Event-based fully probabilistic model developed from precipitation

and runoff models

US Flood Model is suitable for underwriting and portfoliomanagement applications

State of the art precipitation model and non-linear runoffgeneration approach to simulate realistic flood events at a finer

resolution for more accurate loss estimates AIRs US flood model accounts for on- and off-floodplain losses to

yield the most realistic view of risk

Secondary modifiers, including custom flood protection features

allow users to correctly model exposures



36/36

Q&A

Modeling Approach Pros Cons

Historic event footprints - Understandable- Discrete


- -

Actuarial or based on trending

historical losses

- Simple

- Inexpensive

- Deterministic, does not produce

EP curve- NO off plain component- Need lar e sam le of ast losses

Return period flood maps (e.g.,FEMA maps in U.S.)

- Estimates risk at location- Easy to apply to

underwriting andaccumulation


- NO off plain component- Inconsistent mapping methods

Probabilistic models based onriver gauging station data

- Stream flow gauge data iseasily obtained

- Easily derived historicalevent foot rints

- Physical data requirements high- Incomplete view of runoff

generation- NO off lain com onent

AIRs event-based, fullyprobabilistic model developedfrom precipitation and runoffmodels

- Scientifically mostadvanced

- Provides a full occurrenceand aggregate EP curve

- Length of time to develop model- Physical data requirements are

high- Computational requirements high


for portfolios

Documents

NASSERI WED 230PM VEN.pdf