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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
Columbia Water Center Global Flood Initiative
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
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Initiative
Nakamura et al, Dec 2010 AGU
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Project
Columbia Water Center Global Flood Project
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Project
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Project
Columbia Water Center Global Flood Initiative
Columbia Water Center Global Flood Initiative
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
Columbia Water Center Global Flood Initiative