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

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Presentation on theme: "Upmanu Lall GLOBAL FLOOD RISK Columbia Water Center, IRIGlobal Flood Initiative Irish pork."— Presentation transcript:

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

2 Hirschboeck, 1988

3 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

4 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

5 Climate Characteristics Water Resource Development + Use Socio-Economic Values, Environmental Values, Regulatory Provisions, Community Attitudes Characteristics of Catchment/ Stream/ Floodplain System Agricultural Land Use Urban / Industrial Development Flood Vulnerability Flood Hazard Flood Probabilities Other Flood Characteristics FLOOD RISK Floodplain Management Strategies, Flood Design FPM Goals FLOOD LEVEL ESTIMATES FLOOD FLOW ESTIMATES River Basin Flood Risk Analysis

6 A GLOBAL FLOOD PERSPECTIVE Flood: 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

7 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 supply Australia 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

8 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.

9 Managing Climate Risk (Layering) Climate Change Anthropogenic “Natural” Abrupt “Smooth” Dynamic Risk PredictableUnpredictable Long Term Statistics Near Term Evolution Infrastructure Design Allocation/Operation Rules Residual Risk Adaptive Operation & Allocation Early Warning Systems Financial Instruments: Insurance Cat Bonds Relief Pizarro, Lall and Atallah, Env Finance 10(10), 2009

10 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

11 Global Flood incidence recent trends Columbia Water CenterGlobal Flood Initiative

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

13 Columbia Water CenterGlobal Flood Initiative

14 2002 JJA Year No. of floods LatitudeLatitude Longitude

15 2003 JJA Year No. of floods LatitudeLatitude Longitude

16 2004 JJA Year No. of floods LatitudeLatitude Longitude

17 2009 JJA Year No. of floods LatitudeLatitude Longitude

18 2010 JJA Year No. of floods LatitudeLatitude Longitude

19 Latitude JJA Flood Density by Latitude: Groups



22 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 covariate Can we predict conditional flood distribution at gaged/ungaged locations?

23 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 Homogeneous sub-basins re climate (ENSO and SACZ)

24 Simple Scaling Law: log(flow moments) ~ log(drainage area) Hierarchical Bayesian Model: event based scaling Priors Hyperpriors (uniform) Climate index: NINO3 DEC(-1) Hierarchical Bayesian Model

25 Flood Data – Drainage area pdf Testing sites Drainage areas varying from 2588 to km 2

26 Results – non-stationary scaling parameters

27 Results – parameters vs pre-season NINO3 index Slopes are statistically significant!

28 Results: predicting “ungaged” annual flood series r=0.74 r=0.71 r=0.66

29 Dynamic Risk: 100 year event– site 1 Q* such that P(Q(t) > Q*) = 0.01

30 Dynamic 100 year flood – site 2

31 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

32 Atmospheric Moisture Transport associated with one of the top 10 floods at different locations Source: Hyun-Han Kwon Columbia Water CenterGlobal Flood Initiative

33 Nakamura et al, Dec 2010 AGU

34 Columbia Water CenterGlobal Flood Initiative

35 Columbia Water CenterGlobal Flood Project


37 Columbia Water CenterGlobal Flood Initiative

38 Columbia Water CenterGlobal Flood Project

39 Columbia Water CenterGlobal Flood Initiative



42 Columbia Water CenterGlobal Flood Project

43 Columbia Water CenterGlobal Flood Initiative





48 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 CenterGlobal Flood Initiative

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