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Global Flood Risk Modeling

Dag Lohmann, Stefan Eppert, Guy MorrowKatRisk LL, !erkeley, "

http#$$www%katrisk%&om

Feb '()*, R"" onferen&e, +rlando, FL

http://www.katrisk.com/http://www.katrisk.com/

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Acknowledgements

This research used resources of the Oak RidgeLeadership Computing Facility at the OakRidge National Laboratory, which is supportedby the Office of cience of the !""

#epartment of $nergy under Contract No"#$%AC&'%&&OR(()('"

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!A Flood *aps

USA Flood Map on 10m resolution

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+oom into Florida

KatRisk and FEMA data shown. KatRisk red, FEMA blue

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omething about color scales

Blue FEMA ! KatRisk

Red KatRisk onl", no FEMA

#urple FEMA and KatRisk o$erlap

%hite Katrisk onl", low &lood depth

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omething about color scales

Blue FEMA ! KatRisk

Red KatRisk onl", no FEMA

#urple FEMA and KatRisk o$erlap

%hite Katrisk onl", low &lood depth

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omething about color scales

Blue FEMA ! KatRisk

Red KatRisk onl", no FEMA

#urple FEMA and KatRisk o$erlap

%hite Katrisk onl", low &lood depth

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Florida Flood *ap +oom

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Orlando FL- Flood *ap +oom

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Orlando FL- Flood *ap +oom

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.ortofino /otel Flood *ap +oom

Blue FEMA ! KatRisk#urple FEMA and KatRisk o$erlap Blue FEMA ! KatRisk#urple FEMA and KatRisk o$erlap

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.ortofino /otel Flood *ap +oom

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Agenda

The final result0 1lobal Flood *aps flood height, flood score, hot

spots-

1lobal correlated catastrophe model

1lobal data study

ea surface temperature

.recipitation

Risk score and more maps

Conclusions

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2&& yr Loss /otspots Asia

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Normali3ed urface 4ater monthly time step shown-

1lobal Flood 5 #rought *odel

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Considerations for Flood *aps

*odeling ri6erine and surface flooding

All areas co6ered with (%d modeling no threshold-

Flood depth pro6ided, not 7ust e8tent

*ultiple return periods '&, 2&&, (&&, '&&, others-

!se all obser6ation precipitation, near surfacemeteorology, ri6er flow- and fit model with 1enetic

Algorithm 1lobal approach with correlations between

countries

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Thoughts about Risk *odels

To represent correlations in space and time ofweather and climate e6ents

To ha6e a fle8ible modeling framework that

allows for the inclusion of climate changescenarios and forecasting

$6ent based models that are consistent withmaps

$8plicit modeling of wind, rain, storm surge

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Flood maps 6s" .robabilistic model

Flood Maps #robabilisti' Model

One or more return period maps e"g"2&&y, '&&y maps- for flood ha3ard

*any e6ents" Can be used to createrisk maps

mall data amount for results Large data amounts for results

No information about spatial andtemporal correlations

Correlation is inherently part of theprobabilistic model

Often good enough for underwriting,but can9t do portfolio management

ometimes too complicated forunderwriting" Can do portfoliomanagement"

Can be produced using simplifyingassumption : but can also be 6erytime consuming

Complicated, especially when createdfor large areas countries ; continents ;

whole earth-

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Two .robabilistic Flood *odeling Approaches

Runo&& Routin( )nundation

)nundation*au(e+bser$ations opula

#re'ipitation-Meteorolo("

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ome Obser6ed 1lobal Correlations

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1lobal 4eather ; Climate $8tremes

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Are weather and climate linked Trend > easonal > Anomalies

A6erage T has increasedabout &"?@ in the last & years

A6erage seasonal 6ariation isabout &"?@

Anomalies are Be8tremes

T trend by region

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T #ecomposition

4e can apply the same method of splitting upa temporal signal for each grid cell

how second mo6ie

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Analy3ing the .atterns of the Anomalies

4e can then analy3e the temporal anomaliesand e8press them in patterns that reduce datasi3e $OF ; .C analysis-

4e essentially separatespace and time with thismethod" $ach spatialpattern the $OF- has anassociated temporalcomponent the principalcomponent : see ne8tpage-

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4hat are these .rincipal Components