Upmanu Lall GLOBAL FLOOD RISK Columbia Water Center, IRIGlobal Flood Initiative Irish pork.

Upmanu LallGLOBAL FLOOD RISK

Columbia Water Center, IRI Global Flood Initiative

Irish pork

Hirschboeck, 1988

TRADITIONAL PERSPECTIVE• What is a flood? : river out of banks and inundates area for some duration

• Design/Insurance: Estimate T-year flood using at site runoff or rainfall-runoff data

• Annual Max data or Partial duration series

• Regional Flood – use multiple locations to improve at site T-year estimates

• Loss estimates – typically direct physical loss for flood impacted area

• Operation/Warning: Map QPF into event flood peak, volume, duration prediction using hydro-models

• Hydraulic analyses to map flood plain for zoning

• Retrospective analysis of Synoptic Meteorology/Climate state associated with floods

_______________________________________________________________________

Mixtures? Climate Mechanisms? Duration, intensity, recurrence attributes?

Hirschboeck, Paleo-floods work, ENSO/interannual variability in flood incidence

Flooding affects more people worldwide than any other form of natural disaster. And yet insurance cover against the risk of flooding is not widespread (locally correlated risk). - Swissre

Climate Characteristics

Water ResourceDevelopment + Use Socio-Economic

Values,Environmental

Values,RegulatoryProvisions,Community

Attitudes

Characteristics of Catchment/

Stream/Floodplain

SystemAgricultural

Land Use

Urban / IndustrialDevelopment

Flood Vulnerability

FloodHazard

Flood Probabilities

Other Flood Characteristics

FLOODRISK

FloodplainManagementStrategies,

Flood Design

FPM GoalsFLOOD LEVELESTIMATES

FLOOD FLOWESTIMATES

River Basin Flood Risk Analysis

A GLOBAL FLOOD PERSPECTIVEFlood: Atmospheric and terrestrial concentration of water flux into certain regions, that leads to multiple locations with inundation over a period of time?

• How do specific climate mechanisms lead to floods at different space-time scales across the world – conditional quantification using local, regional and global factors?

• IID: Fat tails or identifiable nonstationary, mixtures?

• Dynamics: Persistent climate state high frequency space-time precipitation dynamics with river basin topology and hydrologic dynamics: linked spatio-temporal stochastic models

• A dynamic risk rather than static risk paradigm, including its spatial implications

• Dynamic time scales, lead times, space scales

• Shift from purely watershed/river basin perspective to ocean-atmosphere pathways: Local correlation structure vs global or far field correlation structure– inferred from dynamical models?

Global Impacts and Decisions:

• Persistent and delayed socio-economic and health impacts in addition to direct physical loss

• Global Supply Chains

• Insurance, and infrastructure design/operation considering cumulative impacts and risk layering

• Disaster response

AUSTRALIAN FLOODS IMPACT GLOBAL SUPPLY CHAINS

• The impact of the devastating floods in Queensland will be felt through global supply chains for many months to come. Almost 70% of global steel production depends on metallurgical or coking coal. Australia produces two-thirds of global exports of coking coal, of which Queensland accounts for 35%.

• Fears over coal supplies as Australia floods worsen

• More heavy rainfall causes exports of coal, wheat and sugar to significantly decline as country left underwater

• Coal supplyAustralia is the world's largest exporter of coking coal, supplying half the global market. used to produce steel, and operators of around 40 mines have been affected by the floods.

• The supply of wheat, of which Australia is the world's fourth biggest exporter, has also been hit.

• Australia floods to squeeze India steel cost margins - CRISIL Reuters

PAKISTAN SUPPLY CHAIN UNDER STRAIN

• The floods have had a significant impact on Pakistan's nascent textile industry. Local business associations have estimated that the destruction has destroyed three million bales of cotton. As a consequence, the cost of clothes production within the country will rise by 20%. With apparel buyers seeking to stock inventories for the Christmas sales, companies are concerned over the viability of the Pakistan supply chain to deliver sufficient volume on time and on budget. Indeed, many orders have been re-directed to suppliers in Bangladesh and Sri Lanka. Already, export orders have declined by 7-10%, and this could fall by a further 30%.

• The FT reports that clothing companies such as Levi Strauss and UK-based Next have warned of inflating clothing prices.

Managing Climate Risk (Layering)

Climate Change

Anthropogenic

“Natural”

Abrupt

“Smooth”

Dynamic Risk

Predictable

Unpredictable

Long Term

Statistics

Near Term

Evolution

Infrastructure DesignAllocation/Operation

RulesResidual

Risk

Adaptive Operation & Allocation

Early Warning Systems

Financial Instrument

s:InsuranceCat Bonds

Relief

Pizarro, Lall and Atallah, Env Finance 10(10), 2009

EXPLORING THE CLIMATIC CONTEXT OF FLOODS• Floods associated with large scale circulation patterns

• Meridional and Zonal Moisture Transport and Convergence

• Spatial Incidence of Floods regions with high potential

• Identifiable low frequency forcing….ENSO etc

• Prediction? Hierarchical Bayesian Models of Floods

• Area Scaling

• Covariates

• Diagnosis of Large floods in a region

• Ohio River Basin

Global Flood incidence recent trends

Columbia Water Center Global Flood Initiative

Hypothesis: Meridional water vapor transport changes drive latitudinal shifts in flood incidence

JFM

2002 JJA

Year200

1200220032004200520062007200

8200

9201

0No. of floods 60 110 100 69 60 77 91 56 47 52

Latitude

Longitude

2003 JJA

Year200

1200220032004200520062007200

8200

9201

0No. of floods 60 110 100 69 60 77 91 56 47 52

Latitude

Longitude

2004 JJA

Year200

1200220032004200520062007200

8200

9201

0No. of floods 60 110 100 69 60 77 91 56 47 52

Latitude

Longitude

2009 JJA

Year200

1200220032004200520062007200

8200

9201

0No. of floods 60 110 100 69 60 77 91 56 47 52

Latitude

Longitude

2010 JJA

Year200

1200220032004200520062007200

8200

9201

0No. of floods 60 110 100 69 60 77 91 56 47 52

Latitude

Longitude

Latitude

200120052008

20022004

20062010 2009

2003

2007

JJA Flood Density by Latitude: Groups

CLIMATE INFORMED NON-STATIONARY, REGIONAL FLOOD PREDICTION

A Hierarchical Bayesian Model -- Lima and Lall, 2010

Flood Magnitude depends on Area (Scaling law)Flood magnitude may depend on a pre-season climate covariateCan we predict conditional flood distribution at gaged/ungaged locations?

Flood Data

Location of streamflow sites (red dots are testing sites)

Location of Basin in Brazil

• Daily naturalized series of 37 sites (Parana basin)• Provided by ONS – Period 1931-2001• Homogeneous sub-basins re climate (ENSO and SACZ)

• Simple Scaling Law: log(flow moments) ~ log(drainage

area)

• Hierarchical Bayesian Model: event based scaling

• Priors

• Hyperpriors (uniform)

][][ 1hhkh YEYE ][loglog][log 1

hh YEhkYE

)),((~)log( 210 jxNq iiij )log())(log()( AjAjx

Niiy

iyN

i

i ,,1 , ,)(

)(~

2120

1110

,1

,0

.1)( p

1),,,( 21201110 p .2 , 2/)1(

dpd

Climate index: NINO3 DEC(-1)

Hierarchical Bayesian Model

Flood Data – Drainage area pdf

Testing sites

Drainage areas varying from 2588 to 823555 km2

Results – non-stationary scaling parameters

Results – parameters vs pre-season NINO3 index

Slopes are statistically significant!

Results: predicting “ungaged” annual flood series

r=0.74

r=0.71

r=0.66

Dynamic Risk: 100 year event– site 1

Q* such that P(Q(t) > Q*) = 0.01

Dynamic 100 year flood – site 2

FLOODS AND LARGE SCALE MOISTURE TRANSPORT

Inverse Problem: I see a big flood….how did it get here

A very few selected examples out of many diagnostic ventures

Atmospheric Moisture Transport associated with one of the top 10 floods at different locations Source: Hyun-Han

Kwon



Nakamura et al, Dec 2010 AGU

Columbia Water Center Global Flood Project

DIRECTIONS……..• Invitation to develop global flood risk initiative

• An Open Source Risk Modeling & Mitigation Effort – Climate to Impacts to Response

• The design and exploration of a statistical-dynamical approach for the short (-5 to 10 days) and long lead (> 1 month) prediction, and for the conditional simulation of such events using climate (model) states.

• Inverse/Forward Modeling and Prediction at various lead times appears possible enabling dynamic risk management

• Spatio-temporal causal structure at large and fine scales needs to be identified and modeled (joint flood/drought incidence/extent)

• Integrating storm track dynamics and drainage network response including infrastructure

• Loss dynamics – composite events, delayed and far field losses

• Mitigation: Risk Layering, Response and Recovery Design


Upmanu Lall GLOBAL FLOOD RISK Columbia Water Center, IRIGlobal Flood Initiative Irish pork.

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