This document summarizes Hemalie Kalpalatha Nandalal's PhD dissertation defence presentation on incorporating stakeholder participation and climatic variability into flood risk management. The presentation covered: introducing flood risk management issues; the objective to find non-structural flood risk reduction measures considering climate change and stakeholders; studying the Kalu-Ganga river basin in Sri Lanka using data, models, and field surveys; assessing flood hazard and risk both crisply and fuzzily; and formulating a decision support system.
The damaging effect of most common natural disaster flood can be minimized through the area risk assessment with the help of GIS technology and Remote Sensing techniques. With the help of Prayagraj district map and corresponding satellite images, some flood causing criteria raster layer, flood risk map can be obtained by multi-criteria evaluation approach AHP.
Aims at providing expertise for preparing flood mapping and estimating flood risks.
An integrated AHP and GIS analysis techniques are utilized for the case of Gujarat state.
Use of different flood causing elements like rainfall distribution, elevation, drainage network and density, land use and land cover, and
distance from the river stream.
The index developed is shown with a varying range from high to low with changing colours.
This presentations explains the main definitions related to flood risk management. and how to assess the Vulnerability of the society towards flood dangers. and flood risk analysis process. and gives some examples of flood risk assessment applications.
Groundwater models are simplified representation of large and real hydrogeologic systems like river basins or watersheds. GWM is attempted to analyse the mechanisms which control the occurrence and movement of groundwater and to evaluate the policies, actions and designs which may affect the systems. These models are less complex prototypes of complex hydrogeologic systems developed using spatially varying aquifer parameters, hydrologic properties, geologic boundary conditions and positions of withdrawal wells or recharging structures. These are designed to compute how pumping or recharge might affect the local or regional groundwater levels.
The damaging effect of most common natural disaster flood can be minimized through the area risk assessment with the help of GIS technology and Remote Sensing techniques. With the help of Prayagraj district map and corresponding satellite images, some flood causing criteria raster layer, flood risk map can be obtained by multi-criteria evaluation approach AHP.
Aims at providing expertise for preparing flood mapping and estimating flood risks.
An integrated AHP and GIS analysis techniques are utilized for the case of Gujarat state.
Use of different flood causing elements like rainfall distribution, elevation, drainage network and density, land use and land cover, and
distance from the river stream.
The index developed is shown with a varying range from high to low with changing colours.
This presentations explains the main definitions related to flood risk management. and how to assess the Vulnerability of the society towards flood dangers. and flood risk analysis process. and gives some examples of flood risk assessment applications.
Groundwater models are simplified representation of large and real hydrogeologic systems like river basins or watersheds. GWM is attempted to analyse the mechanisms which control the occurrence and movement of groundwater and to evaluate the policies, actions and designs which may affect the systems. These models are less complex prototypes of complex hydrogeologic systems developed using spatially varying aquifer parameters, hydrologic properties, geologic boundary conditions and positions of withdrawal wells or recharging structures. These are designed to compute how pumping or recharge might affect the local or regional groundwater levels.
Application of GIS and Remote Sensing in Flood Risk ManagementAmitSaha123
Introduction to catastrophic disaster flood. Its impact on environment and human lives. GIS and Remote Sensing based solutions that can provide key approaches to mitigate flood related hazard as well as vulnerablities.
Floods can be hugely destructive, but they also offer opportunities for farmers and fisherfolk. If their frequency and extent can be measured, then we will be better able to mitigate costs and maximise benefits. Digital geospatial flood inundation mapping is a powerful new approach for flood response that shows floodwater extent and depth on the land surface. IWMI research will evaluate this new technology and develop a prototype flood inundation map for South Asia. Also discussed is a project to flood map and model in a spate irrigation system in Sudan.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Kalu river basin is the 2nd largest river basin in Sri Lanka which covers 2766km2 of land. Much of the catchment area of the river is located in the highest rainfall area in the country. The average annual rainfall of the area is around 4000mm. The basin has steep gradient in upper part of the land and mild gradient in lower areas.The lower flood plains suffer from frequent floods during south-western monsoon season. The lower areas are densely populated and the land is a potential area for paddy cultivation.
Kalu river basin is a highest vulnerable location for floods. Frequent flooding along the Kalu Ganga is a common phenomenon that people live around the banks of Kalu Ganga and its tributaries experience every year.
The floods occur basically due to the high amount of rainfall in the catchment area and due to the gradient differences of the river. The gradient is very low in downstream of Rathnapura to upper stream of Kalutara town. This low gradient is inadequate to provide the sufficient velocity for the water to move downstream. The narrow gap or the bottleneck in Ellagawa area of the river retains the water for several days in the Ratnapura District before it releases to Kalutara District. Heavy extraction of river sand and the lack of proper constructions of infrastructures are also major causes for floods in Kalu river basin.
Program for Hydro-Meteorological Disaster Mitigation for Secondary Cities in South and South East Asia (PROMISE) recognized Kalutara as a secondary city that suffers annually due to floods and designed program to suit the needs of the city. Hazard mapping and vulnerability assessment, Kalu Ganga river flood forecasting and warning system, Demonstration activities as a measure for disaster preparedness and mitigation, Micro credit scheme as a measure of mitigation of flood impacts on the livelihood of urban poor, Activities to build the community awareness on flood risk management are some mitigation methods for floods.
Educating people more about the floods, having a proper flood alert system & Meteorological alert system, building a series of levees to prevent flood water spilling into low lying areas building a bypass canal which will take surplus water around Ratnapura town back to the river preventing the town from getting flooded, building a reservoir at Malwala several kilometers upstream from the town, Diversion of Kalu Ganga to Ruhuna, Having proper technological construction systems are some recommendations which can be dispensed for the current situation of floods in Kalu river basin.
Application of GIS and Remote Sensing in Flood Risk ManagementAmitSaha123
Introduction to catastrophic disaster flood. Its impact on environment and human lives. GIS and Remote Sensing based solutions that can provide key approaches to mitigate flood related hazard as well as vulnerablities.
Floods can be hugely destructive, but they also offer opportunities for farmers and fisherfolk. If their frequency and extent can be measured, then we will be better able to mitigate costs and maximise benefits. Digital geospatial flood inundation mapping is a powerful new approach for flood response that shows floodwater extent and depth on the land surface. IWMI research will evaluate this new technology and develop a prototype flood inundation map for South Asia. Also discussed is a project to flood map and model in a spate irrigation system in Sudan.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Kalu river basin is the 2nd largest river basin in Sri Lanka which covers 2766km2 of land. Much of the catchment area of the river is located in the highest rainfall area in the country. The average annual rainfall of the area is around 4000mm. The basin has steep gradient in upper part of the land and mild gradient in lower areas.The lower flood plains suffer from frequent floods during south-western monsoon season. The lower areas are densely populated and the land is a potential area for paddy cultivation.
Kalu river basin is a highest vulnerable location for floods. Frequent flooding along the Kalu Ganga is a common phenomenon that people live around the banks of Kalu Ganga and its tributaries experience every year.
The floods occur basically due to the high amount of rainfall in the catchment area and due to the gradient differences of the river. The gradient is very low in downstream of Rathnapura to upper stream of Kalutara town. This low gradient is inadequate to provide the sufficient velocity for the water to move downstream. The narrow gap or the bottleneck in Ellagawa area of the river retains the water for several days in the Ratnapura District before it releases to Kalutara District. Heavy extraction of river sand and the lack of proper constructions of infrastructures are also major causes for floods in Kalu river basin.
Program for Hydro-Meteorological Disaster Mitigation for Secondary Cities in South and South East Asia (PROMISE) recognized Kalutara as a secondary city that suffers annually due to floods and designed program to suit the needs of the city. Hazard mapping and vulnerability assessment, Kalu Ganga river flood forecasting and warning system, Demonstration activities as a measure for disaster preparedness and mitigation, Micro credit scheme as a measure of mitigation of flood impacts on the livelihood of urban poor, Activities to build the community awareness on flood risk management are some mitigation methods for floods.
Educating people more about the floods, having a proper flood alert system & Meteorological alert system, building a series of levees to prevent flood water spilling into low lying areas building a bypass canal which will take surplus water around Ratnapura town back to the river preventing the town from getting flooded, building a reservoir at Malwala several kilometers upstream from the town, Diversion of Kalu Ganga to Ruhuna, Having proper technological construction systems are some recommendations which can be dispensed for the current situation of floods in Kalu river basin.
Risk on the Rise: Understanding and Managing Coastal Flood RiskRMS
Coastal areas are some of the most developed or developing environments across the globe. These regions contain significant accumulations of exposure and are vulnerable to substantial flood hazard. A comprehensive understanding of those exposures, and the complex hazards that can impact them, is imperative to better manage your flood business.
In this webinar, RMS' Dr. Robert Muir-Wood and Juergen Grieser take an in-depth look at coastal flood risk.
Hazard Modelling and Risk Assessment for Urban Flood ScenarioMaryam Izadifar
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This research is part of an integrated study for the hydro-geological risk evaluation in a mountain environment, where an urban area is crossed by a mountain torrent in its downstream course and is thus prone to flash floods. The urban area considered here is the town of Sondrio in Northern Italy. The scope of this Master’s thesis is twofold. First, hydraulic modelling has been conducted for the urban area and has been complemented with sensitivity analyses in order to cope with uncertainties. Second, damage assessment has been made for buildings located in the area flooded according to the hazard scenario.
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Flood risk management
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
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
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
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
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 )
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
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
52.
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%
54.
55.
56.
57.
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