participating agencies

Application of Remote Sensing and GIS for Flood Risk Analysis:

A Case Study at Kalu‐Ganga River, Sri Lanka

Participating Agencies

University of Peradeniya

Sri Lanka

-Hemali K. Nandalal

Survey Department

Sri Lanka

-Jayantha Samarasinghe

Arthur C Clarke Institute for Modern Technologies

Sri Lanka

-Dimuth Weliwitiya

Objectives

• The main objective of this Mini Project is to analyze the flood risk in lower Kalu-Ganga River Basin with respect to the Population and Physical vulnerability in support of flood relief and mitigation.

• Specific objectives are to :– Analyze and map the flood hazard in the Kalu-Ganga

River basin– Analyze the flood vulnerability in the area

Study Area

Data Collection

Field Visit

Data Processing……

Databasing

Hydro met Data

LiDAR

ALOS PALSAR

Buildings Hydro Land Use Terrain

Road Admin Utility

Topo 10 k sheet

Censes 2001 Demographic»Population»Age»Household

Data Processing……

LIDAR DATA Contour Data

TIN Model of the Study area

Methodology…….

Data Collection/ Data basing

Hazard Analysis Vulnerability Analysis

Risk Analysis

Contour Maps1:10,000 & Spot

Heights as of 2000

LiDAR Data as of2005

Landuse Map1: 10,000 as of 2000

Transportation1: 10,000 as of 2000

Buildings1: 10,000 as of 2000

Admin Boundaries(DS and GN )

1: 10,000 as of 2000

Rainfall For ReturnPeriods of 10, 20, 50

and 100

Daily Rainfall &Discharge since 1987 Census data

Questionnaire Survey

ALOS/PALSAR7 Scenes (Wet

Jun2008 & DryFeb,Mar 2008

Flood Area Extent &Depth survey

Flood FrequencyAnalysis

HEC/RAS

10, 20 50, 100 YrFlood Hazard Map

Modelcalibration

RS based FloodMapping

RS based floodMap

Data Synthesis and Analysis

Population VulnerabilityMap (10, 20, 50, 100 yr

RP Flood Hazad)

Physical VulnerabilityMap (10, 20, 50, 100 yr

RP Flood Hazad)

10, 20, 50, 100 Yr Flood RiskMap wrt Population Vulnerability

GIS Analysis

10, 20, 50, 100 Yr Flood Risk Mapwrt Physical Vulnerability

Hyd

ro/H

ydra

ulic

To

po

gra

ph

ica

l

Sa

telli

te D

ata

So

cia

l da

ta

conversion

DEM

HEC/HMS

Rainfall/Runoff

for 50 yrs

Web Casting: DRM Workers

Social Vulnerability Map(10, 20, 50, 100 yr RP

Flood Hazad)

Vulnerability =f(Capacity, Sensitivity,

Exposure)

Methodology…

Methodology : Hazard Analysis

Rainfall –Runoff Modeling

Rainfall-Runoff Modeling


Basin model developed using HEC-GeoHMS

Rainfall-Runoff Modeling


Results of the verification runs to compare the simulated flows and the observed flows of the calibrated HEC-HMS model

Flood Modeling

Flood Modeling

Results

HEC-RAS out put

Results…(Flood Hazard Maps)

100 yr

50 yr

20 yr

10 yr

Verification of model result by satellite data

Comparison: Flood Modeling and Satellite Data

Satellite Data

Raw Images (Dry Date, 03 March,2008) ALOS – PALSAR (HH)

Raw Images (Wet Date, 03 June,2008) ALOS – PALSAR(HH)

Satellite Data


Filtering – After Applying Lee 3 times with Threshold 0.29,0.14,0.07

Dry date image Wet date image

Model Result and Satellite Data


Comparison of the Flood extent derived from HEC-RAS model and Satellite image

b. Hazard AnalysisVerification of model result by satellite data

Comparison of Model Result and Satellite Data

Vulnerability Analysis

Household Vulnerability Analysis…

Design of Questionnaire

Household Vulnerability AnalysisHH Sampling Cluster Distribution

Sample cluster


Sensitivity

Adaptive Capacity

Exposure

Demographic Standing

Land Characteristics

Rural Standing

Water Resources

Educational Background

Economic Strength& Resilience

Position Relative to River Previous Flood Events

VULNERABILITY

Gender of Residents

Age Groups

Health Condition

Household Vulnerability Analysis

Flood Vulnerability analysis

Assets


Sub-Component Index calculation (e.g., Demographic Standing etc.)

Major-Component Index calculation (e.g., Sensitivity, Exposure, Adaptive Capacity)

Contributing Factor calculation


(Hahn a,Riederer & Foster ,2009)


Contributing Factor calculation

Flood vulnerability Index for Each Household


Major contributing factor Major Components Weighting Factor (Wi)

Sensitivity (S) 1. Demographic standing2. Land Characteristics3. Rural standing4. Water resources

4123

Adaptive Capacity (A) 1. Educational background2. Economic strength & resilience3. Assets

312

Exposure (E) 1.Position relative to River2.Previous flood events

12

(Hahn a,Riederer & Foster ,2009)



Flood Vulnerability Distribution of the Sample

0.05

>x

0.05

<x<0.

10

0.1<

x<0.

15

0.15

<x<0.

20

0.20

<x<0.

25

0.25

<x<0.

30

0.30

<x<0.

35

0.35

<x<0.

40

0.40

<x<0.

45

0.45

<x<0.

50

0.50

<x<0.

55

0.55

<x<0.

60

0.60

<x<0.

65

0.65

<x<0.

70

0.70

<x<0.

75

0.75

<x<0.

80

0.85

<x<0.

90

0.90

<x<0.

95

x>0.

950

5

10

15

20

25

30

35

40

1 03

8

7

12

14

21

3334

28

19

7

5

6

30 1 1

Flood Vulnerability for 100yr RP Event

Nu

mb

er

of

Ho

us

eh

old

s



Using the vulnerability index values households are categorized into 3 groups as follows

Vulnerability Category FVI Range

High 1-0.66

Moderate 0.66-0.33

Low 0.33-0.0



Vulnerability Category FVI Classes Number of Houses

High 1-0.66 38

Moderate 0.66-0.33 150

Low 0.33-0.0 15

High 19%

Moderate 74%

Low 7%

Flood Vulnerability for 100yr RP Event in Kaluthara District



Sensitivity

Adaptive CapacityExposure

0

0.5

1

FVI=0.1481FVI=0.5047FVI=0.8490

Flood Vulnerability Triangle

Flood Vulnerability Sensitivity Adaptive Capacity Exposure

FVI=0.1481 0.4575 0.5749 0.2402

FVI=0.5047 0.4711 0.3235 0.4355

FVI=0.8490 0.6222 0.3158 0.7201


Thank You

Your comments please…

participating agencies

Documents

flood risk analysis

flood extent

satellite image

physical vulnerability

satellite datacomparison

hecras model

satellite datafiltering

flood plains of kalu