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Global flood risk

Global flood risk. Upmanu Lall. Irish pork. Columbia Water Center, IRI Global Flood Initiative. Hirschboeck , 1988. Traditional perspective. What is a flood? : river out of banks and inundates area for some duration

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Global flood risk

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  1. Global flood risk Upmanu Lall Irish pork Columbia Water Center, IRI Global Flood Initiative

  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 Characteristics of Catchment/ Stream/ Floodplain System Water Resource Development + Use Socio-Economic Values, Environmental Values, Regulatory Provisions, Community Attitudes Agricultural Land Use Urban / Industrial Development FLOOD FLOWESTIMATES Flood Vulnerability FPM Goals FLOOD LEVELESTIMATES Flood Hazard FloodplainManagement Strategies, Flood Design Other Flood Characteristics Flood Probabilities FLOOD RISK 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 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

  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) Abrupt Anthropogenic Climate Change “Natural” “Smooth” Predictable Dynamic Risk Unpredictable Long Term Statistics Near Term Evolution Financial Instruments: Insurance Cat Bonds Relief Adaptive Operation & Allocation Early Warning Systems Infrastructure Design Allocation/Operation Rules Residual Risk 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 Center Global Flood Initiative

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

  13. Columbia Water Center Global Flood Initiative

  14. 2002 JJA Latitude Longitude

  15. 2003 JJA Latitude Longitude

  16. 2004 JJA Latitude Longitude

  17. 2009 JJA Latitude Longitude

  18. 2010 JJA Latitude Longitude

  19. JJA Flood Density by Latitude: Groups 2001 2005 2008 2002 2004 2006 2010 2009 2003 2007 Latitude

  20. 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? A Hierarchical Bayesian Model -- Lima and Lall, 2010 Climate Informed non-stationary, Regional flood prediction

  21. Flood Data • Daily naturalized series of 37 sites (Parana basin) • Provided by ONS – Period 1931-2001 • Homogeneous sub-basins re climate (ENSO and SACZ) Location of Basin in Brazil Location of streamflow sites (red dots are testing sites)

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

  23. Flood Data – Drainage area pdf Drainage areas varying from 2588 to 823555 km2 Testing sites

  24. Results – non-stationary scaling parameters

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

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

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

  28. Dynamic 100 year flood – site 2

  29. Inverse Problem: I see a big flood….how did it get here A very few selected examples out of many diagnostic ventures Floods and large scale moisture transport

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

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

  32. Columbia Water Center Global Flood Initiative

  33. Columbia Water Center Global Flood Project

  34. Columbia Water Center Global Flood Project

  35. Columbia Water Center Global Flood Initiative

  36. Columbia Water Center Global Flood Project

  37. Columbia Water Center Global Flood Initiative

  38. Columbia Water Center Global Flood Initiative

  39. Columbia Water Center Global Flood Initiative

  40. Columbia Water Center Global Flood Project

  41. Columbia Water Center Global Flood Initiative

  42. Columbia Water Center Global Flood Initiative

  43. 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

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