1. FLOOD RISK MANAGEMENT
INCORPORATING
STAKEHOLDER PARTICIPATION
AND
CLIMATIC VARIABILITY
PhD Dissertation Defence Presentation
Hemalie Kalpalatha Nandalal
Supervised by
p y
Dr. U.R. Ratnayake
Department of Civil Engineering
University of Peradeniya
Peradeniya
d
Sri Lanka
24 th August 2011

2. Introduction
Objective
Study area and Data used
Methods used
R lt
Results
Conclusions

3. Problems related t fl di
P bl l t d to flooding h
have greatly i
tl increased
d
over recent decades because of
p p
population ggrowth
development of extensive infrastructures in close
proximity to rivers
increased frequency of extreme rainfall events
Governments all around the world spend millions of
funds to reduce flood risk by taking flood protective
measures; mainly in two different approaches
Structural measures (levees, flood walls, channel
improvements and storage reservoirs)
Non-structural measures (flood plain zoning, flood
proofing, land use conversion, warning and evacuation,
relief and rehabilitation and flood insurance
rehabilitation,

4. There has been a shift in paradigms from technical-
Th h b hift i di f t h i l
oriented flood protection measures towards non-
structural measures to reduce flood damage g
Flood risk management is not only assessment and
mitigation of flood risk, but also a continuous and
holistic
h li ti societal adaptation and mitigation
i t l d t ti d iti ti
There is a growing demand for better approaches
for risk identification and assessment particularly at
local level
Main scope of this research is to find non-structural
measures that can be taken to reduce flood risk
incorporating climate changes and stakeholders’
views

5. Investigate and i
I i d incorporate climatic
li i
variability in the process for managing flood
risk
i k
Evaluation of flood risk using conventional
method and investigating the application of
fuzzy logic in risk assessment
Inquire how to create a management process
with enhanced participation of stakeholders
p p
Development of an information system for
decision makers

6. Kalu-Ganga river b i i S i L k
K l G i basin in Sri Lanka
Population density varies from
100 to 1000 persons per sq. km
in the basin area
River basin is located in an
area that receives very high
rainfall where average annual
rainfall varies from 2000mm
to 5000mm

7. Kalu-Ganga river b i i S i L k
K l G i basin in Sri Lanka

8. Kalu-Ganga river b i i S i L k
K l G i basin in Sri Lanka
Administrative divisions of the Locations of rainfall and
Kalu-Ganga river basin discharge gauging stations

9. Topographical data
On-line accessible topographic data sets used in this study
Data set Link Coverage
g Horiz. Res. (m)
( )
SRTM http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp International ~ 90
USGS http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html International ~ 900
NGDC http://www.ngdc.noaa.gov/mgg/topo/globe.html
http://www ngdc noaa gov/mgg/topo/globe html International ~ 900
GIS data used in the study
Type
T Scale
S l Date f Production S
D t of P d ti Source
Contour Map, Land use Map, Spot heights, 1:10,000 2002 Survey Department, Sri Lanka
Administrative boundaries
LiDAR Data 2005 Survey Department, Sri Lanka
Cross section data of the Kalu-Ganga river at 2007 NBRO
1000 m interval

10. Hydro-meteorological data / Census data
Type Source Description
Daily Rainfall data Meteorological Daily rainfall during 1986 to 2009 at 14 gauging
Department, stations
Sri Lanka
Daily rainfall from 1901 to 2009 at rainfall gauging
station no. 14
Discharge data Irrigation Discharges at 3 gauging stations) from 1986 to
Department, 1996 and years 2003 and 2009
Sri Lanka
Census data from the Census and Statistic Department of Sri Lanka as of
2001

11. Satellite data
Satellite/Sensor Date Source Remarks
ALOS/PALSAR 3rd March 2008 JAXA/GIC Dry day
ALOS/PALSAR 3rd June 2008 JAXA/GIC Two days after a major flood

12. Field data
Fi ld d
Social survey
Based on a sample size calculation (WHO, 2005)
200 households in each district were surveyed
Flood depth records
p
At random points where flood depths could be
found either from people or marked surfaces
were recorded with corresponding GPS
coordinates

13. Estimation of climate variability
E i i f li i bili
Flood hazard, vulnerability and risk
assessment
Stakeholder participation in flood risk
management
Formulation of decision support system

14. Rainfall
R i f ll gauging stations were selected
i t ti l t d
Long term rainfall data were tested using
standard statistical test and tested to identify
any t
trends
d
Different approaches were tested to identify
any trend that exists in the data series to
predict rainfall with 0.01 probability (rainfall
ith 0 01 probabilit
with 100 year return period)
Using the parameters of the Gumbel distribution

16. Redistribution of rainfall among the available
R di ib i f i f ll h il bl
rainfall gauging stations

17. Estimation of flood hazard
E i i f fl d h d
Application of Rainfall-runoff model
Application of Inundation model
Two approaches were used to assess flood
risk
Crisp approach and
fuzzy approach
y pp

18. Application of Rainfall-runoff model
A li i f R i f ll ff d l

19. Application of inundation model
A li i fi d i d l

20. Hazard assessment (f
H d (for th
h
Depth
ND
∑ A(i, j ) ⋅HI ( j )
j =1
i
HFD (i ) = ND
∑ A(i, j )
Area j =1
Area under flood in land unit i
HFA (i ) = ×100
Total area of land unit i

21. Hazard assessment (f
H d (for th
h
Standardization
HF (i )
HF S (i ) =
HFmax
Hazard Factor
HFD (i ) + HFAS (i )
S
HF (i ) =
2

22. population d
l i density (f
i (for th
h
Poluation
VFD (i ) =
Land area
dependency ratio (for th
number of persons under age 20 + number of persons aged 55 or over
VFA (i ) = ×100
Total
T t l population
l ti

23. Similar
Si il to the Hazard factors, both of these
h H df b h f h
were standardized
VF (i )
VF (i ) =
S
VFmax
VF ( ) was taken as the hazard factor of the
land unit as given
VFP (i ) + VFA (i )
S S
VF (i ) =
(i
2

24. In
I general, risk i
l i k incorporates the concepts of
h f
hazard and vulnerability (for th
RF (i ) = HF (i ) × VF (i )

25. Basic architecture of fuzzy expert system

26. The membership functions
Population density of, 36 persons per ha need not be assigned
to either ‘low’ or ‘medium’ vulnerable category, but can be a
member of both categories, having a certain degree of
membership in each category (27% low as well as 68% medium
vulnerable).
)

27. Input f
I functions were identified
i id ifi d
For hazard identification the average flood
depth and fl d extent of each GND d to 100
d h d flood f h due
year rainfall were taken and fuzzy membership
functions were developed
Vulnerability was represented by the population
density and the dependency ratio, similar to
crisp risk evaluation

28. Fuzzy rule b
F l base
The f
fuzzified variables are related to each other
f
with a knowledge‐based rule system
The rules describing the system can be:
Rule 1: If population density is low and flood depth
is low, then the risk is low.
Rule 2: If population density is low and flood depth
is high, then the risk is medium.

29. Fuzzy model developed to estimate flood
risk depending on the hazard and
vulnerability levels

30. Adaptation i the only response available f the
Ad i is h l il bl for h
risk that will occur over the next several decades
before mitigation measures can have an effect
Increasing the adaptability of affected people to
floods or any natural disaster is a main objective
of allocating funds by governments
I thi research a model was d
In this h d l developed t
l d to
allocate available funds according to preferences
of flood affected people to improve their
adaptability to floods

31. Increasing the adaptability or adaptive
I i h d bili d i
capacity of the affected people will lead to
reduce the vulnerability to a fl d or any
d h l bili flood
natural disaster
Thus the adaptability incorporated to the
risk formula can be written as,
Risk = Hazard x Vulnerability x (1- Adaptability)
As indicated by United Nations publications.

32. Stakeholders i
S k h ld involved i fl d events i the
l d in flood in h
Kalu‐Ganga river basin were analysed to
identify the most contributing or the most
id if h ib i h
important stakeholders
They were queried to investigate their
preferences for non‐structural flood
alleviation measures to improve adaptability
Depending on the views of affected people
p g p p
the adaptability was formulated
Adaptability = f (View1, View2, ……….)
(View1 View2 )

33. Fuzzy model was d
F d l developed to assess
l d
adaptability depending on the views of the
stakeholders
k h ld
Membership function was selected such that
if 50% of the community prefer boats there is
no improvement in adaptability by spending
more than 50% of the available funds to
provide boats for flood affected people

34. Providing a website f
P idi b i for people to access
l
flood risk information is an effective way of
informing the public about the susceptibility
i f i h bli b h ibili
to flooding that they may otherwise not be
aware of f
The Adobe Dreamweaver software was used
y
to create flood information system

35. Estimation of climate variability
Flood hazard, vulnerability and risk
assessment
Stakeholder participation in flood risk
management
Formulation of the decision support system
pp y

36. Fitted
Fi d trends f
d found f l
d for long term d
data series
i
(all with increasing trends)
Linear y = 0.041x + 74.24
Exponential y = 217.2e-2E-0x
Logarithmic y = 84.07ln(x) - 481.1
Power y = 2721.x-0.38
Trend of parameters of Gumbel distribution
was found and that was used to determine
the rainfall at different return periods due to
climatic variation

37. Parameters of G
P f Gumbel di ib i
b l distribution for time periods of 30 years f
f i i d f from 1901
For Ratnapura g g g
p gauging 1901-1930 1931-1960 1961-1990 1991-2009
station (1) (2) (3) (4)
Average of the data
series 150.64 163.66 152.03 158.16
St dev. of the data
40.38 77.15 56.35 81.08441
series
Scale parameter (α) 0.031
0 031 0.016
0 016 0.0227
0 0227 0.015
0 015
Location parameter (m) 132.47 128.95 126.68 121.69

38. Plot of the trend of parameters of Gumbel distribution

39. Comparison of the expected and observed rainfall
C f h d d b d f ll
Periods of Predicted Gumbel parameters Expected 100 Maximum rainfall
years m Alpha year rainfall observed so far
1901-1930 133.10 0.02900 291.7 269.2
1931-1960 128.12 0.02206 336.5 394.4
96 990
1961-1990 125.21
5 0 0 80
0.01801 380 5
380.5 294.9
9 9
1991-2020 123.14 0.01513 427.0 392.5------
2021-2050
2021 2050 121.54
121 54 0.01290
0 01290 477.9
477 9
2051-2080 120.23 0.01108 535.3

40. Predicted Gumbel parameters Expected 100 year rainfall
Period of years (Basin average)
m Alpha Area ave /Arithmetic ave
ave./Arithmetic ave.
1901-1930 139.95 0.049626 220.1 232.6
1931-1960 134.97 0.042695 232.5 242.7
1961-1990 132.06 0.038640 245.8 251.1
1991-2020 129.99 0.035763 253.6 258.6
2021-2050 128.39 0.033532 259.8 265.6
2051-2081 127.08 0.031708 265.4 272.2

41. Gauge
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Stations
100yr
100 293 320 325 356 331 447 293 479 302 271 330 292 352 406
50yr 268 290 289 322 302 392 269 426 275 248 300 262 315 363
20yr 235 249 240 278 263 318 236 355 239 217 261 222 266 305
10yr 210 218 203 243 233 262 211 300 212 193 231 192 228 260
2yr 143 137 105 153 154 113 146 157 139 132 153 111 129 142

42. Comparison of the selected rainfall with rainfall at real
C f h l d f ll h f ll l
flood events

43. HEC HMS
Application of HEC-HMS
Rainfall at 14 gauging stations and runoff at 3 gauging stations
from 1984 to 2009 were used to calibrate the hydrologic model

44. HEC HMS
Application of HEC-HMS
Two sub-basin configurations developed with HEC-GeoHMS
4 sub-basin model 10 sub-basin model

45. HEC HMS
Application of HEC-HMS
Ten storm events were used for calibration and verification of
both models
Event Time period
1989 May-June 22 days
1992 N
November
b 13 d
days
1993 May 26 days
1993 October 17 days
1994 May 34 days
1996 June 14 days
2003 May 13 days
2003 July 14 days
2008 M J
May-June 15 d
days
2008 July 14 days

46. HEC HMS
Application of HEC-HMS
Hydrographs resulted from calibrated and verified HEC-HMS model for
Kalu-Ganga river
Rainfall runoff at Putupaula for Rainfall runoff at Putupaula for
1994 rainfall event for 4 basin 1994 rainfall event for 10 basin
model model

47. HEC HMS
Calibrated HEC-HMS model was used to derive discharges due to expected
100 year rainfall
River reach Flow data/(m3/s)
Kalu Ganga 403.2
Wey Ganga 465.90
Maha Ela 123.10
123 10
Hangamuwa 263.70
NiriElle 155.70
Yatipuwa Ela 106.40
Kuru Ganga 594.50
Galathure 147.00
Elagawa 2605.50
Mawakoya 245.50
Kuda Ganga 1260.70

48. HEC RAS
Application of HEC-RAS
Flood modelling was carried out in two sections
separately due to the difficulty in handing large data
p y y g g
files
River reach - downstream of Ellagawa River reach -upstream of Ellagawa
upstream

49. Flood
Fl d extent and d
d depth d i d f
h derived from HEC RAS
HEC-RAS
model
For Kalutara district For Ratnapura district

50. Model
M d l was verified using two approaches
ifi d i h
field survey
satellite SAR images

51. Flood depths d i
Fl d d h during the fl d on J
h flood June 2008 were
collected from flood affected people and recorded with
coordinates taken from GPS receivers during a field
survey

53. Verification of the flood depth and flood extent
V ifi i f h fl d d h d fl d
by satellite SAR images
The number of pixels rated as
wet b satellite i
by lli image and the
d h
HEC-RAS model were calculated
as 55%

58. Number of GNDs fall into each category of Risk:
Crisp approach
District Very low Low Medium High Very High
Kalutara 83 98 4 0 0
Ratnapura 33 26 7 0 1
Number of GNDs fall into each category of risk level:
Fuzzy approach
District Very low Low Medium High Very High
Kalutara 7 66 77 32 3
Ratnapura 8 12 29 13 5

59. Flood relief expenses for June 2008 flood and risk
levels obtained by the crisp and fuzzy approaches for
GNDs in Ratnapura District
p
GND Relief expense/ha Risk criteria
(LKR) Crisp Fuzzy
Ratnapura Rs.8,085.00 Very high risk Very high risk
Godigamuwa Rs.5,108.00 Medium risk Very high risk
Muwagama
g Rs.4,511.00
, Low risk High risk
g
Pallegedara Rs.2,547.00 Medium risk High risk
Angammana Rs.2,004.00 Very low risk Medium risk
Pahala
Pahala- Rs.1,260.00
Rs 1 260 00 Low risk Medium risk
Hakamuva
Mada Baddara Rs. 505.00 Very low risk Low risk
Withangagama Rs. 43 00
Rs 43.00 Very low risk Very low risk

60. A structured questionnaire survey was carried out
d i i i d
to gather views of flood affected people in 8
GNDs in the Ratnapura district and 12 GNDs in
the Kalutara district covering 400 families
Suggestions on possible solutions to reduce the
flood risk were obtained from them

61. Following suggestions were id
F ll i i identified as the
ifi d h
most preferred solutions
Improve infrastructure facilities
Installation of a better warning system
Improve river flow system
Release funds to improve individual dwellings
Supply of boats for flood affected people
Resettlement of the flood affected people

62. Preference for non-structural fl d alleviation
P f f l flood ll i i
measures of the residents
10%
10% 10%
River flow
Resettlement Boats
20%
Dwelling
10% 40%
Warning Infra structures

63. Preferences of a flood affected community
P f f fl d ff d i
were taken as fuzzy variables in the
development of the model
d l f h d l
The membership functions were developed
using the preferences of the flood affected
people

64. Fuzzy model developed to estimate final
adaptability depending on the % fund
allocation

65. Adaptability for different fund allocation combinations
Number
b % of f d provided f each proposed d l
f fund d d for h d developments
Boats Infrastructure Warning Dwelling Re settlement River flow Adaptability
1 5 50 20 15 5 5 0.630
2 10 60 10 20 0 0 0.731
3 20 60 10 10 0 0 0.725
4 40 20 10 10 10 10 0.533
5 50 10 0 20 10 10 0.470
6 10 10 20 20 20 20 0.599
7 10 20 20 10 20 20 0.607
8 10 30 20 20 20 10 0.623
9 0 30 20 30 10 10 0.580
10 0 10 10 10 50 20 0.584
11 10 40 10 20 10 10 0.710
12 5 33 3 30 14 15 0.584
13 10 33 12 23 11 11 0.609
0 609
14 13 41 10 28 3 5 0.773

66. Risk H
Ri k = Hazard x Vulnerability x (1 d
d V l bili (1-adaptability)
bili )

67. Providing a website f people to access fl d
P idi b i for l flood
risk information is an effective way of
informing the public about the susceptibility to
i f i h bli b h ibili
flooding that they may otherwise not be aware
off
Website

68. DATA
the topographical data taken from websites,
that is the SRTM DEM data are fairly acceptable
data,
the best representation of the topography is
achieved by 1:10,000 contour maps available at
y , p
the Department of Survey
Software used
HEC software series developed by US Army
Corps of Engineers of Hydrological Engineering
Centre can be used effectively in the data rich
Kalu-Ganga river basin for rainfall-runoff
modelling as well as for flood modelling

69. Investigation of climatic variation
I i i f li i i i
The analysis indicated that the Gumbel
parameters of the extreme rainfall intensity over
f h i f ll i i
the Kalu-Ganga river basin have an increasing
trend
The proposed method could be used to
determine extreme rainfalls expected to occur if
same trend in the climate change exists
The method used to redistribute return periods
p
among the rainfall gauging stations was very
much applicable in similar situations

70. Hydrological d hydraulic modelling
H d l i l and h d li d lli
The results confirmed the applicability of the
hydraulic model HEC RAS in the prediction of
h d li d l HEC-RAS i h di i f
flood inundation in the Kalu-Ganga river basin
fairly accurately
The results of this study indicate that the event
based semi distributed conceptual model HEC-HEC
HMS as suitable in modelling rainfall runoff of
the Kalu-Ganga river basin

71. Risk
Ri k analysis
l i
Two approaches were used to estimate the risk
The conventional crisp method based flood risk
levels did not capture the risk as expected
The fuzzy logic based approach has captured the
levels of indicator parameters, h
l l f i di hazard and
d d
vulnerability factors, effectively and resulted in a fair
risk distribution
The adaptability model proposed could be used for
fund allocation to reduce flood risk
The novel technique presented in this research is the
application of fuzzy inference systems which can be
recommended as a good method for the evaluation
of risk
f i k

72. The d
Th developed W b b
l d Web-based d i i
d decision support
system provides information regarding
floods
fl d to general public, d i i
l bli decision makers
k
and scientific community to make better
decisions i fl d risk reduction
d i i in flood i k d i

73. It i recommended th t l d use change also
is d d that land h l
incorporated in future flood predictions
It i b tt if unsteady flow conditions are
is better t d fl diti
applied in the flood modelling to capture the
duration of flooding flood wave velocity and
flooding,
rate of rise of water level
It is better if infrastructure vulnerability for
critical facilities are also included such as,
roads, railroads, hospitals, public buildings,
police stations, water treatment or sewage
p
plants, airports, etc
p

74. Instead of k
I d f keeping fl d related i f
i flood l d information i
in institutional environment it is
recommended to place them where anyone
d d l h h
can access and use them
Apart from informative web page if an
interactive graphical user interface using
web GIS system can be developed it will be
more useful for decision makers at each level

75. Papers presented at local conferences
1. Nandalal, H.K. and U. Ratnayake (2008), “Verification of a delineated stream network from a
DEM: Application to Kalu River in Sri Lanka”, Proceedings, The fifth National Symposium on
Geo-Informatics, Colombo, Sri Lanka, pp. 187.
2.
2 Nandalal, H.K.
Nandalal H K and U R Ratnayake (2008) “Comparison of a Digital Elevation Model with the
U.R. (2008), Comparison
heights extracted from the contour map”, Proceedings, Peradeniya University Research Sessions,
Vol 13,1, pp. 145-147.
3. Nandalal, H.K. and U.R. Ratnayake (2009), “Editing a Digital Elevation Model to Achieve a correct
Stream Network: An application to Kalu-Ganga river in Sri Lanka”, Proceedings, 4th Annual
Conference on Towards the Sustainable Management of Earth Resources-A Multi-disciplinary
Resources A Multi disciplinary
Approach, University of Moratuwa, Sri Lanka, pp. 9-12.
4. Nandalal, H.K. and U. R. Ratnayake (2009), “Effect of Different Rainfalls on Kalu-Ganga River
Runoff”, Abstracts, First National Symposium on Natural Resources Management (NRM2009),
Department of Natural Resources, Sabaragamuwa University of Sri Lanka, pp. 30.
5. Nandalal, H.K. and U. R. Ratnayake (2009), “Effect of Grid Size on Delineating River Network”,
Proceedings, The Sixth National Symposium on Geo-Informatics, Colombo, Sri Lanka, pp. 75-
80.
6. Nandalal, H.K. and U. R. Ratnayake (2009), ”Modeling Kalu-Ganga River Basin for Predicting
Runoff for Different Frequency Rainfalls , Proceeding, Peradeniya University Research Sessions,
Rainfalls”,
December 2009, pp. 486-488.
7. Nandalal, H.K. and U. R. Ratnayake (2009), “Use of HEC-GeoHMS and HEC-HMS to perform grid-
based hydrologic analysis of a watershed”, Proceedings, Annual Research Sessions, Sri Lanka
Association for the Advancement of Science , December 2009, In CD.
8.
8 Nandalal, H.K.
Nandalal H K and U Ratnayake (2010) “Prediction of Rainfall Incorporating Climatic
U. (2010), Prediction
Variability”, Proceeding, Peradeniya University Research Sessions, December 2010, pp. 546-548.

76. Papers presented at I t
P t d t International conferences
ti l f
1. Nandalal, H.K. (2008), “Global on-line GIS Data Availability for Hydrological
Modeling in SriLanka”, Proceedings, Second International Symposium,
University of Sabaragamuwa, Sri Lanka, pp. 95-100
2. Nandalal, H.K. and U.R. Ratnayake (2008), “Comparison of a river network
delineated from different digital elevation models available in public domain”,
Proceedings, 29th Asian Conference on Remote Sensing, CD_ROM, Colombo, Sri
Lanka.
3. Nandalal, H.K. (2009), “Stakeholder Analysis in Flood Risk Management at
Ratnapura”, Presentation made at International Conference on “Impacts of
Natural hazards and Disasters on Social and Economic” held at Ahungalla, Sri
Lanka.
4. Nandalal, H.K. and U. R. Ratnayake (2009), “Flood Plain Residents’ Preferences
for Non-Structural Flood Alleviation Measures in The Kalu-Ganga River,
Ratnapura, Sri Lanka”, Proceedings, International Exchange Symposium,
University of Ruhuna Sri Lanka, pp. 116-119.
5. Nandalal, H.K. and U. Ratnayake (2010), “Setting up of indices to measure
vulnerability of structures during a flood”, published at “International
Conference on Sustainable Built Environments – The state of the art”, 13-14
December 2010, Kandy, Sri Lanka, pp. 379-386.

77. Journal papers
J l
1. Nandalal, H.K.
1 Nandalal H K and U R Ratnayake (2010)
U.R (2010),
“Event Based Modelling of a Watershed using
HEC-HMS”. Engineer (Journal of Institution of
g
Engineers, Sri Lanka), 43(2), 28-37.
2. Nandalal, H. and Ratnayake, U. (2011), Flood
risk analysis using fuzzy models. Journal of
models
Flood Risk Management, 4: 128–139.
doi: 10.1111/j.1753-318X.2011.01097.x