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DSD-INT 2019 Flood damage modelling-Wagenaar

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Presentation by Dennis Wagenaar, Deltares, at the Delft3D - User Days (Day 1: Hydrology and hydrodynamics), during Delft Software Days - Edition 2019. Monday, 11 November 2019, Delft.

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DSD-INT 2019 Flood damage modelling-Wagenaar

  1. 1. Flood damage modelling using the Flood Impact Assessment Tool Dennis Wagenaar Delft3D User Days 2019
  2. 2. Why model flood impacts? 2 Intervention costs Reduction expected flood damages • Cost Benefit Analyses • Benefits of detailed measures • Optimal design • Spatial planning • Impact forecasting • Forecasting what the weather will do rather than what the weather will be • Insurance • Settings premiums
  3. 3. Application of impact modelling 3 CBA infrastructure investments Risk screening studies Adaptive delta management Setting insurance premiums Climate change impact Impact Based Forecasting: Warning information and preparation event. Where to send aid first How much money to free up for recovery. Initial distribution of recovery funds
  4. 4. Flood damage 4 Category Tangible Intangible Direct • Capital (houses, crops, cars, factory buildings) • Production losses, income losses • Casualties, injuries, ecosystems, monuments • Social disruption, emotional damage Indirect • Production losses / loss of utility services outside flooded area; • Unemployment, migration. • Cutting of infrastructure lines • Loss of potential for attracting investors • Reputation damage Use of multiplication factor for everything that is difficult to model! • Damage and loss • Modelling needs • Scope Delft-FIAT
  5. 5. Direct tangible - Business Interruption • A flood can last between hours up to sometimes 1 year (e.g. Zeeland 1953). • If water can flow away naturally it is short. • If water needs to be pumped and dikes need to be repaired this can be long. • Recovery time can also be long (easily 1 year) • Shortage of contractors, waiting for permits. • Experts need to check for mold. • Larger total disasters need more recovery time. • Recaptured value • Often a lot of interruption damage can be recaptured elsewhere (e.g. competitor does more). • Damage depends on definition. 5
  6. 6. Indirect tangible • Production losses outside flooded area • (e.g. production process halted because crucial component cannot be made). • Famous case of hard drives in Thailand • Part of the losses recaptured by competition • Modeled with several types of economic models, highly uncertain. • Cutting of infrastructure lines • E.g. Traffic problems, power outages, etc. New York Times – Nov 6, 2011
  7. 7. Direct intangible • Deadly casualties differ very strongly among floods. (often 0 sometimes 1000s). • Deadly casualties when: large water depths, rapid rise rate, unexpected and unprepared people. • Casualties are more often sick and elderly. • Poor people in developing countries might die from hunger or disease. • Poor people in developing countries may become homeless and get into major trouble. 7
  8. 8. Demand surge • After a flood there are often shortages in construction labour and expertise. • Shortages drive up prices as people compete for limited resources. • Especially important when a flood is focused on one densely populated area (e.g. dike breach near city). Including demand surge • Demand surge is a loss for some but an equal profit for others. Therefore, often not used in an economic analysis. • Insurance companies do take it into account. 8
  9. 9. Correcting for inequality in Cost Benefit Analyses 9 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 2 0 20 40 60 80 100 Utilityorwell-being Income Equal decrease in income/wealth Unequal decrease in well-being
  10. 10. 10 Hazard Vulnerability Delft-FIAT – Damage only Exposure
  11. 11. Inputs Delft-FIAT 11 Statistical models Hydrological models Hydrodynamic models Delft3D FM Suite: • D-Flow FM • D-Hydrology (wflow) Probabilistic Toolkit (PTK) Delft-FIAT ? - Mostly expert judgment - Only few techniques available
  12. 12. Available approaches damage functions Expert/synthetic approach • Expert or group of experts come together and estimate a damage function. • Elements of object of interest can be assessed individually. • Weakness is that experts typically have one setting in mind. 12 Data-driven approach (empirical) • Regression analysis on available data points of past flood damage. • Weakness: Data availability and bad fits.
  13. 13. Absolute and relative damage functions 0 20 40 60 80 100 120 -1 1 3 5 Damage(MEURO) Water depth (m) 0 0,2 0,4 0,6 0,8 1 -1 1 3 5 Damagefactor(-) Water depth (m) 0 0,2 0,4 0,6 0,8 1 -1 1 3 5 Damagefactor(-) Water depth (m) Max. damage = 240 k€ Max. damage = 120 k€ =
  14. 14. 14 Flood risk
  15. 15. Delft-FIAT - Risk 15
  16. 16. From damage to flood risk (EAD) • Flood damage can be calculated for an event. Yet many possible events might occur. • The flood damage of one event alone is therefore too little to get a complete picture and hence too little for rational decision making. Flood risk: Expected Annual Damage (EAD)/Annual Average Loss (ALL) • Summary statistic that combines all possible flood events, their probabilities and their damages into one figure. • The unit is: Euro/year • Very useful for decision making! 16
  17. 17. Calculating flood risk (EAD) • Combine many different flood events into maps (or aggregate damages) for different exceedance probabilities . • Take the integral to get the expected annual damage. • In practice calculate the area under the graph. 17 𝑅𝑖𝑠𝑘 = න 𝐷𝑎𝑚𝑎𝑔𝑒 𝑝 𝑑𝑝 0 20 40 60 80 100 0 1/20 1/10 3/20 1/5 1/4 Damage(M$) Exceedance probability (1/y) AAL
  18. 18. Future risks 18 • A risk reduction measure needs to function for a long time • A cost-benefit requires future risks as input and not just current • Hazard, Exposure and Vulnerability changes over time • Change needs to be predicted
  19. 19. Change in hazard 19 • Climate change • Sea level rise • More extremes (rain, droughts, wind) • Changes to the system: • Land subsidence • Erosion, sedimentation • Deforestation • Wetland encroachment • Change in impervious area Increasing hazard?
  20. 20. Change in exposure 20 • Extra buildings • Population growth • Fewer people per building • More value per building • GDP per capita growth
  21. 21. Change in vulnerability 21 • Often neglected, little research.. • Bangladesh example of reduction in vulnerability of loss of life Changing vulnerability? Mechler & Bouwer (2015) Climatic Change Bangladesh
  22. 22. Beyond Delft-FIAT: Machine Learning for better impact predictions From: Damage fraction = f(water depth) To: Damage fraction = f(water depth, warning time, wave height, …..) DF = f(water depth) DF = f(water depth, warning time, waves height, …..) Multi-variable damage models can be build from data with Machine learning methods!
  23. 23. • My PhD and project to prioritize humanitarian aid in the Philippines • Use of historical data on damages Machine Learning for macro level impact forecasting 2012 Now 2016 2013 RedCross data: 12 typhoons, 2012 - 2016 1600 damage data Response % Total damaged houses in a municipality Predictors (~40) Hazard : Average wind speed, rainfall Exposure : building, people (2010) Vulnerability : roof & wall type, GDP, slope (2008)
  24. 24. 24 Example project
  25. 25. Situation Flood risk Colombo • Recent floods • Combination river discharge, local rainfall and sea level • Wetland encroachment • Proposed interventions • WorldBank loan
  26. 26. Project Setup MIKE model 80 runs (30m) different boundary conditions Probabilistic part Return period maps per cell. Impact part FIAT model, projections and CBA. Delft-FEWS pilot Training 1 Training 2 Training 3 Ruben Dahm Local partners Ferdinand Diermanse Laurens Bouwer Dennis Wagenaar Local partners Marc van Dijk Simplified method outer areas carried out completely by local partners
  27. 27. Damage calculation Exposur e
  28. 28. Exposure and damage functions Exposure • Detailed data on building level • Collected for this project • Building type, number of floors, shanty. • 57 damage categories • Also vehicles, electricity and telecom. Damage functions • Created by experts • Workshop • Bills of quantities 0 0,2 0,4 0,6 0,8 1 1,2 0 5 10 Damageindex Inundation depth (m)
  29. 29. Expected Annual Damage Intervention package 1 (M$/y) Intervention package 2 (M$/y) Reference 45.9 45.9 New 43.8 42.5 Difference 2.1 3.4
  30. 30. Future damage projections - Damage assumed to increase with GDP per capita - Population growth not included because expected move to high rise buildings - Population growth in wetland areas considered separately - 5 growth scenarios
  31. 31. Cost Benefit Analysis • Sum of future risk reductions should be smaller than the investment costs of the intervention. • Risk reductions in the future count less (discount rate) • Discount rate Colombo difficult to estimate. • Internal Rate of Return is the discount rate for which the sum of future risk reductions is equal to the investment costs. • Indirect damage discussion
  32. 32. More information Details about this project and all additional assessments are available in article: “Evaluating adaptation measures for reducing flood risk”

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