DESIGN OF ADAPTATION MEASURES BASED ONTHE ASSESSMENT OF CLIMATE CHANGE IMPACTS
ON EXTREME FLOODS IN THE ATTANAGALU OYABASIN, SRI LANKASupervisor: Prof. RASMY Mohamed
Prof. KOIKE ToshioProf. MUROTA TetsuoDr. TAMAKAWA Katsunori
ABSTRACTThe Attanagalu Oya Basin experiences frequent flood hazards due to high-intensity rainfall events.
Complex flow behavior, rapid infrastructure development, and high population density increase basin
exposure, leading to severe damage. The proposed development activities are concentrated on low-lyingregions and floodplains, requiring comprehensive basin-wide risk evaluation to assess their potential
impact. To address these issues, this study presents a comprehensive approach for evaluating future
rainfall variations to determine flood exposure and probable risk by employing bias-corrected climateprojected outputs from general circulation models (GCMs) and simulating the rainfall-runoff-inundation(RRI) model. These results indicate an increase in future extreme and long-duration rain events. The
hydrological model results imply that inundation will increase, thereby increasing flood vulnerability.
The findings emphasize that the flood-exposed population could increase from 13.8–22.5% and building
exposure from 9.8–20% in the study area owing to climate change. The proposed risk matrix and landuse zoning are valuable for risk-based land-use planning, regulating urbanization in flood-prone areasand promoting resilient building practices. This study provides evidence-based information forpolicymaking, community awareness, and future flood exposure reduction, laying the groundwork forend-to-end approach to climate change adaptation.
Keywords: Climate change, RRI model, Inundation, Risk matrix, Urbanization
JAYATHILAKA Ralapahana Mudalige
Charya Prabodha Sri
MEE22721

INTRODUCTION
The Attanagalu Oya Basin is one of the major river basins in the wet zone of Sri Lanka, and a large
portion of it is located in the highly populated Western Province. The well-known Katunayake
International Airport is located in this basin, alongwith major cities such as Gampaha, Ja Ela, andNegombo. The basin features flat terrain, ameandering river network, and an interconnected
stream system. Figure 1 shows a map of theAttanagalu Oya Basin. The average annual rainfall is2685 mm, contributing to an annual water yield of1129 MCM, with 845 MCM discharged into
Negombo Lagoon. The basin had a population of 1.6
million in 2012, with a high population density of2000 people/km2, projected to reach 2.9 million by
2040 (Population and Housing Census, 2012).
Owing to its high population density and complex
flow behavior, the basin is vulnerable to flooding,which has occurred several times in recent years,Figure 1. Study area (Attanagalu Oya Basin)
Civil Engineer, Sri Lanka Land Development Corporation
Adjunct Professor GRIPS/ Senior Researcher, ICHARM
Adjunct Professor GRIPS/ Executive Director of ICHARM

Professor GRIPS/ Director of Disaster Risk Management ConcentrationResearch Specialist, PWRI, ICHARM1with severe floods occurring in 2006, 2007, 2010, 2012, 2016, 2018, and 2021. Flood damage has
increased owing to a lack of information regarding flood inundation and hazard risks in the basin. Many
developments have been proposed for floodplains, some of which are currently ongoing. These factors
must be reviewed while incorporating climate change.
Previous studies have not addressed climate change impact, as they were based on current climatology.
Additionally, these studies did not assess the basin's future land development impact concerning floodrisk (Perera et al., 2012). Owing to climate change impact on precipitation, extreme events are expected
to increase in magnitude and frequency, leading to severe floods and heightened basin vulnerability.
Therefore, there is a need for evidence-based information on future climate projections and their impact
on extreme floods to evaluate future probabilistic risk. This information is vital for sound policymakingregarding risk-based land-use management, aiming to regulate urbanization activities and reduce future
exposure to basin floods.
This study utilizes the climate-projected bias-corrected outputs of the general circulation model (GCM)
and rainfall–runoff–inundation (RRI) model to assess the potential impact of floods under present andfuture climatic conditions. The ultimate objective is to develop a risk-based land-use policy framework
to facilitate the effective regulation of future development in the basin.
THEORY AND METHODOLOGY
The methodology adopted in this study
consists of four components, as shownin the flow chart (Figure 2).
a.) Climate change analysis
Climate change analysis involves two
main components: past climatology
study and future projected climatology
analysis. Past recorded rainfall datafrom ground gauging stations wereused to analyze historical rainfall
trends. For future climatology, GCMs
were employed to project data for theperiod 2050–2075, and bias-corrected
Figure 2. Methodology flow chart.
GCM data were obtained from theCMIP-5 archive in the DIAS. The GCM selection was based on comparing key meteorological variables
with global reanalysis products in specific regional and local domains. A three-step statistical bias
correction was applied to correct the bias of GCM data using observed rainfall data (Nyunt et al.,2016).
Future climatological analyses involved innovative trend analysis, extreme rainfall variation
examination, and change comparison during long-spell rainfall events.
b.) RRI modeling
A basin-scale RRI model was developed to assess the hydrological response of the basin to past andfuture climatic conditions. The model theory is based on the mass balance and momentum equation for
gradually varying unsteady flows in two Cartesian directions. RRI employs the diffusion wave
approximation, neglecting the inertia terms in the momentum equations (Sayama, 2022). The model wascalibrated and validated using the 2021, 2018, and 2016 extreme events. The model performance wasevaluated using the Nash–Sutcliffe efficiency (NSE), mean bias error (MBE), and root mean squareerror (RMSE) for discharge comparison at the river gauging station, while inundation extent wascompared using the measure of fit criteria.
c.) Flood behavior, exposure and frequency analysis
The most extreme event in the last 10 years was compared with future expected events projected byapplying the incremental factors obtained from the GCM-projected annual rainfall maxima to the same,
where pessimistic average and optimistic factors were chosen. Changes in inundation extent, depth
variation, population exposure, and buildings were compared between the past and future climatic
scenarios. To obtain past and future rainfall intensities corresponding to 5-, 10-, 25-, 50, and 100-year
return periods, a basin-critical 4-day historical in situ and bias-corrected rainfall series from the GCMs
were fitted to the Gumbel distribution. Rainfall pattern analysis was performed to distribute return period
rainfall intensities according to past critical rainfall patterns. All policies and findings proposed in this2

study are based on a pessimistic scenario, representing the most adverse future conditions. Therefore,
the frequency analysis focused on comparing the inundation depth and extent variation for different
return periods under past and future pessimistic conditions.
d.) Risk-based land-use planning and policy framework
Finally, a risk-based land-use planning and policy framework is proposed to regulate future urbanization
activities and assess proposed and ongoing development work impact. A risk matrix was developed
based on flood occurrence probability and damage level potential magnitude (Ichidate et al., 2016).
Based on the risk matrix, cumulative probabilistic flood-risk maps were created for the basin to represent
the spatial risk distribution. Considering the inundation depth, occurrence probability, expected damagelevel, and country development strategy, two land-use zones (future urbanization restricted and floodrisk) were identified to regulate flood exposure.
DATA
Daily rainfall data from five gauging stations (1980–2020) were obtained from the MeteorologicalDepartment. Hourly rain and river discharge data for extreme events were obtained from theMeteorological and Irrigation Departments. The LiDAR digital elevation model (DEM) and groundsurvey data from the river network were used to create the RRI river and slope inputs. Building footprint
data were downloaded from the Microsoft Global Building Footprint Database, and population data
were acquired from the Department of Census and Statistics.
RESULTS AND DISCUSSIONa.) Climate change analysis
The innovative analysis examined past rainfall trends in the basin using historical in-situ rainfall eventsfrom 1980–2020, as shown in Figure 3. No discernible trend is demonstrated in low-intensity rainfall.
However, rainfall exceeding 150 mm/day
has shown an increasing tendency in therecent past (2001–2020) compared withthat of the distant past (1980–2000).
The basin average rainfall was considered
for future rainfall innovative trendanalysis, and the different GCM relevant
trends are shown in Figure 4. All themodels showed an increasing trend inFigure 3. Past trend.
Figure 4. Future trends.
intensities above 100 mm/day. The
ACCESS1.3 model indicates a minor
increasing trend in intensity ranging from
150–175 mm/day.
Significant increases in future extreme
precipitation were observed across allmodels upstream (Pasyala station) anddownstream (Katunayake station) of thebasin, as shown in Figure 5.
Figure 5. Future extreme rainfall.
Rainfall event analysis was conducted byconsidering rainy days as precipitation >1 mm/day, and the results are shown in
Figure 6. Three models indicated adecrease and two showed an increase in
future short-duration rainfall events (< 2days). However, for long-duration
rainfall events of > 4 days, all models
projected a future increase, with a 21.7%
increase in the average number of days
per year compared to that in the past. This
Figure 6. Rainfall event variation.
variation emphasizes that a greaternumber of long-duration rainfall events can be expected in future.
3Figure 7. Occurrence of extremes.

A comparison of the projected future extreme rainfall event
occurrence (intensity of more than 100 mm/day) is shown inFigure 7. All models indicated an increase in the occurrence ofextremes, with a 72% average increment compared to pastconditions. These results provide strong evidence of an increasein extreme rainfall events across both magnitude and frequency,indicating that the basin may experience more floods in the futurethan it has in the past.

b.) RRI model calibration and validation
The RRI model was calibrated for extreme events occurring between May and June 2021. The
calibration results showed good agreement between the observed and simulated discharge at the rivergauging station, with goodness of fit indices of NSE = 0.7, MBE = -8.61 m3/s, and RMSE = 20.0 m3/s.

Figure 8. Observed and simulated discharge at river gauge (Left – calibration, Right – validation).
Maximum observed inundation extent andTwo extreme events were selected for validation. Comparing theRRI model (2016 event)discharge for the extreme event in May 2018 resulted in an NSE of0.75, whereas inundation extent assessment for the extreme event in
May 2016 resulted in a measure of fit value of 67.3%, indicating
reasonable agreement between the observed and model-provided
flood extents. The corresponding hydrographs and inundation extent
maps are shown in Figures 8 and 9, respectively.
Figure 9. Inundation extent.
c.) Flood behavior, basin exposure, return period intensities and inundation
Incremental factors in annual maxima of selected GCM projected rainfall compared to those of the pastand considered factors for pessimistic, average, and optimistic scenarios are summarized in Table 1. The
May–June 2021 extreme event was found to be the most critical past event, and it was used for basin
flood behavior and exposure comparison with future climatic scenarios (Table 2).
Table 2. Discharge and inundation comparison

FactorScenario

CMCC-CESM
CNRM-CM5
GFDL-CM3
GFDL-ESM2M
Pessimistic
Optimistic
Average

1.05
1.35
1.37
1.01
1.37
1.01
1.20

Optimistic
Average
Pessimistic
Number of people

GCM /Climatic scenario

Inundation
extent(km2)
135
166
202

Inundation as %of the basin area
15.9
19.5
23.8

300,000
253622250,000204086
200,000 154752 166696
150,000
80460
100,000
36888
13962 19142
50,0006351
2507
0Increment factor compared to the 2021
Inundation
Flood
Peak
extentvolumedischarge
1.07
1.05
1.04
1.32
1.22
1.15
1.60
1.47
1.39
Number of buildings

Table 1. Annual maxima
factors.

75,00060,000

52509

73268

39562
45,000 35660
30,00016100

15,0001643
2721 6032
1095
Notably, the increase in393
0inundation extent was moresignificant than the increases inflood volume and peakh>0.6mh>3mh>5mh>0.6mh>3mh>5mdischarge.
Figure 10. Population exposure. Figure 11 Building exposure.
Flood exposure was evaluatedby assessing affected populations and buildings (Figures 10 and 11). The estimated percentages of theflood-affected population and buildings due to the 2021 event were 13.8% and 9.8%, respectively, which

4Factor

Future(mm/4-day)

Past(mm/4-day)

FactorFuture(mm/4-day)

Past(mm/4-day)

FactorFuture(mm/4-day)Past(mm/4-day)

can increase to 22.5% and 20% under pessimistic scenarios. These facts highlight the future flood
vulnerability of the basin and the importance of reducing flood exposure by incorporating the land-use
policy and enhancing community awareness of flood disasters.
The four-day rainfall duration was selected for the frequency analysis because it represents the basin's
critical rainfall duration. Table 3
Table 3. Return period and factors of future climatic scenarios.
summarizes the rainfall intensities
10-year return period
50-year return period
100-year return period
for different return periods,
GCM/
considering the past and future
ClimaticThiessen average rainfall series
scenariofrom the selected GCMs. Theincreasing (pessimistic) factors were
CMCC355.5
397.7
1.12
467.4
519.0
1.11
514.7
570.2
1.11
consistent across all return periods,CESM
and were applied to represent theCNRM310.4
392.9
1.27
401.5
518.4
1.29
440.0
571.5
1.30
CM5
climate change impact on future
rainfall at each gauging station. TheGFDL337.7
425.0
1.26
431.1
576.1
1.34
470.6
639.9
1.36
CM3
inundation depth variation andGFDLinundation increment compared
377.0
410.7
1.09
496.3
529.2
1.07
546.7
579.2
1.06
ESM2Mwith those of past conditions for thePessimistic
1.27
1.34
1.36
10-, 50-, and 100-year return periods
are shown in Figure 12.

Figure 12. Return period inundation depth variation.

Figure 13. Risk matrix and proposed land-use regulation.
5d.) Risk-basedland-useplanning and policy framework
The safety level against flooding isproposed to be determined using arisk matrix, which involves anassessment of the probability offlood occurrences and the potential
magnitude of damage they maycause. The proposed risk matrix forthe study area is shown in Figure 13.
A risk matrix was employed toprovide
acomprehensive
visualization of the varying levelsof probabilistic cumulative floodrisk in the study area, as shown inFigure 14. This study emphasizes
that an increase in flood risk isexpected in the future, even in areas
with no flood risk under the currentconditions.
The future urbanization-restrictedzone is defined as an area in which
the expected inundation is > 0.5 m
over a 10-year return period. Future
urbanization activities will not beallowed in this zone. A flood riskzone was categorized as an area inwhich the expected inundation is >3 m over a 100-year return period.
Development work is subject tobuilding regulations and current
residents will be promoted to buildsafe evacuation spaces above the estimated inundation level. These two zones are illustrated in Figure
15.

Figure 14. Probabilistic cumulative flood risk.
Figure 15. Proposed land use zones.
risk risk
The study indicated that the urbanization-restricted area would increase by 32.4% owing to climatechange, leading to the inclusion of 55,000 more people and 15,800 additional buildings in that zone. Inaddition, the ongoing central expressway project involving road embankment construction in low-lying
areas was assessed for induced probable flood risk. The findings indicated that road embankment
construction converted 0.5 km2 into an urbanization-restricted zone, and climate change impact
increased by 44%, exposing 600 buildings at risk.
CONCLUSIONS AND RECOMMENDATIONSThis study employed evidence-based information to formulate a risk-based land-use policy frameworkto address future potential flood impact. Climatological analysis revealed that all the selected GCMs
projected an increase in high-intensity rainfall events in the basin. Future event RRI simulations
indicated a significant increase in flood volume, peak discharge, and inundation extent. These findings
emphasize that the basin will experience future climate change impact through high-intensity, longduration rainfall events, frequently leading to more severe floods. Risk assessment indicated that thefuture flood-affected population is projected to rise from 13.8–22.5%, building exposure from 9.8–20%,
and flood risks are expected to rise, even in areas previously considered safe from floods. Therefore, itis essential to incorporate risk-based land-use policies to restrict urbanization in flood-prone areas andpromote resilient building practices.
The pressure and release model conceptualizes the flood disaster risk reduction framework through avulnerability analysis based on the study findings. The proposed risk matrix and land-use zoning arerecommended for implementation in risk-based land-use planning. The current practice of development
possibility assessment involves limited catchment assessment focusing on specific areas, whereas thisstudy enables a holistic approach by conducting a basin-wide probabilistic risk evaluation. This study
lays the foundation for a pathway towards climate change adaptation by emphasizing the need for an
end-to-end approach that integrates scientific, engineering, and socioeconomic approaches.
ACKNOWLEDGEMENTS
I express my profound gratitude to my supervisor, Professor Mohamed Rasmy, for continuous support,
instruction, and encouragement throughout the research. I am grateful to Professor Toshio Koike for hispatient guidance, valuable advice, and support during my studies. Special thanks are also extended toDr. Katsunori Tamakawa for his guidance and technical support during this study.
REFERENCES
Ichidate, S., Tsuji, M., Taki, K. and Nakamura, H. (2016). The Risk-Based Floodplain Regulation ofShiga Prefecture in Japan. E3S Web of Conferences, 7, p.13008.
Nyunt, C.T., Koike, T. and Yamamoto, A. (2016). Statistical bias correction for climate change impact
on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia. Hydrology andEarth System Sciences Discussions.
Perera, K.R.J. and Wijesekera, N.T.S. (2012). Potential on the Use of GIS Watershed Modeling forRiver Basin Planning - Case Study of Attanagalu Oya Basin, Sri Lanka. Engineer: Journal ofthe Institution of Engineers, Sri Lanka, 45(4), p.13.
Sayama, T. (2022). Rainfall-runoff-inundation (RRI) Model Technical Manual. International Centerfor Water Hazard Risk Management, PWRI.
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