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FLOOD RISK MANAGEMENT INCORPORATING

STAKEHOLDER PARTICIPATION AND

CLIMATIC VARIABILITYPhD Dissertation Defence Presentation

Hemalie Kalpalatha NandalalSupervised by

Dr. U.R. RatnayakeDepartment of Civil Engineering

University of PeradeniyaPeradeniyaSri Lanka

24th August 2011

IntroductionObjectiveStudy area and Data usedStudy area and Data usedMethods usedResultsConclusions

Problems related to flooding have greatly increased over recent decades because of

population growth development of extensive infrastructures in close proximity to riversincreased frequency of extreme rainfall eventsincreased frequency of extreme rainfall events

Governments all around the world spend millions offunds to reduce flood risk by taking flood protectivemeasures; mainly in two different approaches

Structural measures (levees, flood walls, channelimprovements and storage reservoirs)Non-structural measures (flood plain zoning, floodproofing, land use conversion, warning and evacuation,relief and rehabilitation, and flood insurance

There has been a shift in paradigms from technical-oriented flood protection measures towards non-structural measures to reduce flood damageFlood risk management is not only assessment andmitigation of flood risk, but also a continuous andh li ti i t l d t ti d iti tiholistic societal adaptation and mitigationThere is a growing demand for better approachesfor risk identification and assessment particularly atlocal levelMain scope of this research is to find non-structuralmeasures that can be taken to reduce flood riskincorporating climate changes and stakeholders’views

Investigate and incorporate climatic variability in the process for managing flood riskEvaluation of flood risk using conventional method and investigating the application of fuzzy logic in risk assessmentInquire how to create a management process with enhanced participation of stakeholdersDevelopment of an information system for decision makers

Kalu-Ganga river 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

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Kalu-Ganga river basin in Sri Lanka Kalu-Ganga river basin in Sri Lanka

Locations of rainfall and discharge gauging stations

Administrative divisions of the Kalu-Ganga river basin

Topographical dataOn‐line accessible topographic data sets used in this study

Data set Link Coverage 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 International ~ 900

GIS data used in the study

Type Scale Date of Production Source

Contour Map, Land use Map, Spot heights,

Administrative boundaries

1:10,000 2002 Survey Department, Sri Lanka

LiDAR Data 2005 Survey Department, Sri Lanka

Cross section data of the Kalu‐Ganga river at

100 m interval

2007 NBRO

Hydro‐meteorological data / Census data

Type Source Description

Daily Rainfall data MeteorologicalDepartment,Sri Lanka

Daily rainfall during 1986 to 2009 at 14 gaugingstations

Sri LankaDaily rainfall from 1901 to 2009 at rainfall gaugingstation no. 14

Discharge data IrrigationDepartment,Sri Lanka

Discharges at 3 gauging stations) from 1986 to1996 and years 2003 and 2009

Census data from the Census and Statistic Department of Sri Lanka as of 2001

Satellite data

Satellite/Sensor Date Source RemarksSatellite/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

Field dataSocial survey

Based on a sample size calculation (WHO, 2005)200 households in each district were surveyed

Flood depth recordsAt random points where flood depths could befound from either from people or markedsurfaces were recorded with GPS coordinates

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Estimation of climate variability

Flood hazard, vulnerability and risk assessment

Stakeholder participation in flood risk management

Formulation of decision support system

Rainfall gauging stations were selected

Long term rainfall data were tested using standard tests

Different approaches were tested to identify any trend that exists in the data series to predict rainfall with 0.01 probability (rainfall with 100 year return period)

Using standard trends available in Microsoft ExelUsing the parameters of the Gumbel distribution

Redistribution of rainfall among the available rainfall gauging stations

Estimation of flood hazardApplication of Rainfall-runoff modelApplication of Inundation model

Two approaches were used to assess flood risk

Crisp approach andfuzzy approach

Application of Rainfall-runoff model

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Application of inundation model Hazard assessment (for th GN divisionDepth

∑

∑=

⋅=

D

D

N

i

N

jD

jiA

jHIjiAiHF 1

),(

)(),()(

Area∑=j

j1

)(

100)( ×=iunitlandofareaTotal

iunitlandinfloodunderAreaiHFA

Hazard assessment (for th GN divisionStandardization

max

)()(HF

iHFiHF S =

Hazard Factor

2)()()( iHFiHFiHF

SA

SD +

=

population density (for th GN division

areaLandPoluationiVFD =)(

dependency ratio (for th GN division

1005520)( ×+

=populationTotal

overoragedpersonsofnumberageunderpersonsofnumberiVFA

Similar to the Hazard factors, both of these were standardized

)()(VF

iVFiVF S =

VF ( ) was taken as the hazard factor of the land unit as given

maxVF

2)()()( iVFiVFiVF

SA

SP +

=

In general, risk incorporates the concepts of hazard and vulnerability (for th GN division

)()()( iVFiHFiRF ×=

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Basic architecture of fuzzy expert system

The membership functions

Population density of, 36 persons per ha need not be assignedto either ‘low’ or ‘medium’ vulnerable category, but can be amember of both categories, having a certain degree ofmembership in each category (27% low as well as 68% mediumvulnerable).

Input functions were identifiedFor hazard identification the average flood depth and flood extent of each GND due to 100 year rainfall were taken and fuzzy membership functions were developedfunctions were developedVulnerability was represented by the population density and the dependency ratio, similar to crisp risk evaluation

Fuzzy rule base

The fuzzified variables are related to each other with a knowledge‐based rule systemThe rules describing the system can be: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.

Adaptation is the only response available for therisk that will occur over the next several decadesbefore mitigation measures can have an effectIncreasing the adaptability of affected people tofloods or any natural disaster is a main objectivefloods or any natural disaster is a main objectiveof allocating funds by governmentsIn this research a model was developed toallocate available funds according to preferencesof flood affected people to improve theiradaptability to floods

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Increasing the adaptability or adaptivecapacity of the affected people will lead toreduce the vulnerability to a flood or anynatural disasterThus the adaptability incorporated to the risk formula can be written as,Risk = Hazard x Vulnerability x (1- Adaptability)

As indicated by United Nations publications.

Stakeholders involved in flood events in the Kalu‐Ganga river basin were analysed to identify the most contributing or the most important stakeholdersThey were queried to investigate their preferences for non‐structural flood alleviation measures to improve adaptabilityDepending on the views of affected people the adaptability was formulated

Adaptability = f (View1, View2, ……….)

Fuzzy model was developed to assess adaptability depending on the views of the stakeholdersMembership function was selected such that if 50% of the community prefer development of infrastructure there is no improvement in adaptability by spending more than 50% of the available funds

Providing a website for people to accessflood risk information is an effective way ofinforming the public about the susceptibilityto flooding that they may otherwise not be

faware of

The Adobe Dreamweaver software was usedto create flood information system

Estimation of climate variability

Flood hazard, vulnerability and risk assessment

Stakeholder participation in flood risk management

Formulation of the decision support system

Fitted trends found for long term data series (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

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For Ratnapura gauging station

1901‐1930(1)

1931‐1960(2)

1961‐1990(3)

1991‐2009(4)

Average of the data

Parameters of Gumbel distribution for time periods of 30 years from 1901

Average of the dataseries 150.64 163.66 152.03 158.16

St dev. of the data series 40.38 77.15 56.35 81.08441

Scale parameter (α) 0.031 0.016 0.0227 0.015

Location parameter (m) 132.47 128.95 126.68 121.69

Plot of the trend of parameters of Gumbel distribution

Periods of years

Predicted Gumbel parameters Expected 100 year rainfall

Maximum rainfall observed so farm Alpha

1901‐1930 133.10 0.02900 291.7 269.2

Comparison of the expected and observed rainfall

1901 1930 133.10 0.02900 291.7 269.2

1931‐1960 128.12 0.02206 336.5 394.4

1961‐1990 125.21 0.01801 380.5 294.9

1991‐2020 123.14 0.01513 427.0 392.5‐‐‐‐‐‐

2021‐2050 121.54 0.01290 477.9

2051‐2080 120.23 0.01108 535.3

Period of yearsPredicted Gumbel parameters Expected 100 year rainfall 

(Basin average)Area ave./Arithmetic ave.m Alpha

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

Gauge Stations

1 2 3 4 5 6 7 8 9 10 11 12 13 14

100yr 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 363y

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

Comparison of the selected rainfall with rainfall at real flood events

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Rainfall at 14 gauging stations and runoff at 3 gauging stations from 1984 to 2009 were used to calibrate the hydrologic model

Application of HEC-HMSTwo sub-basin configurations developed with HEC-GeoHMS

Application of HEC-HMS

4 sub-basin model 10 sub-basin model

Ten storm events were used for calibration and verification of both models

Event Time period

1989May‐June 22 days

1992 N b 13 d

Application of HEC-HMS

1992 November 13 days

1993May 26 days

1993 October 17 days

1994May 34 days

1996 June 14 days

2003May 13 days

2003 July 14 days

2008May‐June 15 days

2008 July 14 days

Hydrographs resulted from calibrated and verified HEC-HMS model for Kalu-Ganga river

Application of HEC-HMS

Rainfall runoff at Putupaula for 1994 rainfall event for 4 basin model

Rainfall runoff at Putupaula for 1994 rainfall event for 10 basin model

Calibrated HEC-HMS model was used to derive discharges due to expected 100 year rainfallRiver reach Flow data/(m3/s)

Kalu Ganga 403.2

Wey Ganga 465.90

Maha Ela 123 10Maha Ela 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

Flood modelling was carried out in two sections separately due to the difficulty in handing large data files

Application of HEC-RAS

River reach -upstream of EllagawaRiver reach - downstream of Ellagawa

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Flood extent and depth derived from HEC-RAS model

For Ratnapura districtFor Kalutara district

Model was verified using two approaches

field survey

satellite SAR images

Flood depths during the flood on June 2008 werecollected from flood affected people and recordedwith coordinates taken from GPS receivers during afield survey

Verification of the flood depth and flood extent by satellite SAR images

The number of pixels rated asb lli i d hwet by satellite image and the

HEC-RAS model were calculatedis 55%

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Number of GNDs fall into each category of Risk:Crisp approach

District Very low Low Medium High Very HighKalutara 83 98 4 0 0

Number of GNDs fall into each category of risk level:Fuzzy approach

District Very low Low Medium High Very HighKalutara 7 66 77 32 3Ratnapura 8 12 29 13 5

Ratnapura 33 26 7 0 1

GND Relief expense/ha (LKR)

Risk criteriaCrisp Fuzzy

Flood relief expenses for June 2008 flood and risklevels obtained by the crisp and fuzzy approaches forGNDs in Ratnapura District

Ratnapura Rs.8,085.00 Very high risk Very high riskGodigamuwa Rs.5,108.00 Medium risk Very high riskMuwagama Rs.4,511.00 Low risk High riskPallegedara Rs.2,547.00 Medium risk High riskAngammana Rs.2,004.00 Very low risk Medium riskPahala‐Hakamuva

Rs.1,260.00 Low risk Medium risk

Mada Baddara Rs.  505.00 Very low risk Low riskWithangagama Rs.    43.00 Very low risk Very low risk

A structured questionnaire survey was carried out 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

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Following suggestions were identified as the most preferred solutions

Improve infrastructure facilities Installation of a better warning systemImprove river flow systemRelease funds to improve individual dwellingsSupply of boats for flood affected peopleResettlement of the flood affected people

10% Boats

10%Resettlement

10%River flow

Preference for non-structural flood alleviation measures of the residents

40% Infra structures

10% Warning

20%Dwelling

Preferences of a flood affected community were taken as fuzzy variables in the development of the modelThe membership functions were developed using the preferences of the flood affected people

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

Adaptability for different fund allocation combinations

Number % of fund provided for each proposed developments

AdaptabilityBoats Infrastructure Warning Dwelling Re settlement River flow

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

14 13 41 10 28 3 5 0.773

Risk = Hazard x Vulnerability x (1-adaptability)

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Providing a website for people to access floodrisk information is an effective way ofinforming the public about the susceptibility toflooding that they may otherwise not be awarefof

Website

DATAthe topographical data taken from websites, that is the SRTM DEM data, are fairly acceptablethe best representation of the topography is achieved by 1:10,000 contour maps available at y , pthe Department of Survey

Software usedHEC 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

Investigation of climatic variationThe analysis indicated that the Gumbelparameters of the extreme rainfall intensity over the Kalu-Ganga river basin have an increasing trendtrendThe proposed method could be used to determine extreme rainfalls expected to occur if same trend in the climate change existsThe method used to redistribute return periods among the rainfall gauging stations was very much applicable in similar situations

Hydrological and hydraulic modellingThe results confirmed the applicability of the hydraulic model HEC-RAS in the prediction of flood inundation in the Kalu-Ganga river basin fairly accuratelyfairly accuratelyThe results of this study indicate that the event based semi distributed conceptual model HEC-HMS as suitable in modelling rainfall runoff of the Kalu-Ganga river basin

Risk analysisTwo approaches were used to estimate the riskThe conventional crisp method based flood risk levels did not capture the risk as expectedThe fuzzy logic based approach has captured the l l f i di h d dlevels of indicator parameters, hazard and vulnerability factors, effectively and resulted in a fair risk distributionThe adaptability model proposed could be used for fund allocation to reduce flood riskThe 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

The developed Web-based decision support system provides information regarding floods to general public, decision makers and scientific community to make better d i i i fl d i k d idecisions in flood risk reduction

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It is recommended that land use change also incorporated in future flood predictionsIt is better if unsteady flow conditions are applied in the flood modelling to capture the duration of flooding flood wave velocity andduration of flooding, flood wave velocity and rate of rise of water levelIt is better if infrastructure vulnerability for critical facilities are also included such as, roads, railroads, hospitals, public buildings, police stations, water treatment or sewage plants, airports, etc

Instead of keeping flood related information in institutional environment it is recommended to place them where anyone can access and use themApart 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

Papers presented at local conferences1. 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. Nandalal, H.K. and U.R. Ratnayake (2008), “Comparison of a Digital Elevation Model with the 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 disciplinaryConference on Towards the Sustainable Management of Earth 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, 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. Nandalal, H.K. and U. Ratnayake (2010), “Prediction of Rainfall Incorporating Climatic Variability”, Proceeding, Peradeniya University Research Sessions, December 2010, pp. 546-548.

Papers presented at International conferences1. 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.

Journal papers

1. Nandalal, H.K. and U.R Ratnayake (2010), “Event Based Modelling of a Watershed using HEC-HMS”. Engineer (Journal of Institution of Engineers, Sri Lanka), 43(2), 28-37.

2. Nandalal, H. and Ratnayake, U. (2011), Flood risk analysis using fuzzy models. Journal of Flood Risk Management, 4: 128–139. doi: 10.1111/j.1753-318X.2011.01097.x