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.

1. Flood damage modelling
using the Flood Impact Assessment Tool
Dennis Wagenaar
Delft3D User Days 2019

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. 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. 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. 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. 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. 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. 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. 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
Hazard Vulnerability
Delft-FIAT – Damage only
Exposure

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. 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. 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
Flood risk

15. Delft-FIAT - Risk
15

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. 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. 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. 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. Change in exposure
20
• Extra buildings
• Population growth
• Fewer people per building
• More value per building
• GDP per capita growth

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. 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. • 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
Example project

25. Situation Flood risk Colombo
• Recent floods
• Combination river
discharge, local rainfall
and sea level
• Wetland encroachment
• Proposed interventions
• WorldBank loan

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. Damage calculation
Exposur
e

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. 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. 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. 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. More information
Details about this project and
all additional assessments
are available in article:
“Evaluating adaptation
measures for reducing flood
risk”