River Modelling for Flood Risk Map Prediction: A Case ...ia-e.org/siteadmin/upload/1458IAE1214510.pdf · parameters associate with river flood hazard. In order to produce the flood

Abstract— This paper presents a simple methodology for river

flood risk map prediction in an urbanized area. River flood risk map

is a function of hazard, vulnerability and exposure. Hence, in this

case, water depth and flow velocity (river flood hazard), land-use

type, road accessibility and debris flow (vulnerability and exposure)

were incorporated. Furthermore, a systematic methodology was

proposed in order to develop and predict river flood risk map for a

range of defined scenarios. Therefore, a total of 6 scenarios were

identified including three rainfall magnitude (20 year, 50 year

and 100 year ARI) and two river basin development conditions

(existing and ultimate). The risk components were combined in

GIS interface and categorized based on a proposed risk value

classification. This case study confirms the efficiency of the

proposed method to some extent. However, more detailed analysis

need to be undertaken towards a well-developed and applicable

framework.

Index Terms— Flood hazard map, Flood risk map, HEC-RAS,

HMS-HMS, Kayu Ara River Basin.

I. INTRODUCTION

Starting in the year 2000s, extreme rainfall events with

high intensity is no longer a new issue in Malaysian urban

cities, especially in the West Coast area. This phenomenon is

formed mostly through convection process [1]. Hence,

flooding is one of the major natural hazards affecting

communities across Malaysia and has caused damages worth

millions of dollars every year. For instance, the required

allocation for flood mitigation projects has increased almost

600% (RM 6000 million) for the 8th

Malaysian Plan

compared to RM 1000 million during the 7th

Malaysian Plan

[2].

Natural risk can be defined as the probability of harmful

consequences or expected loss (of lives, people injured,

property, livelihoods, economic activity disrupted or

environment damaged) resulting from interactions between

natural or human-induced hazards and vulnerable conditions

[3]. Risk is sometimes taken as synonymous with hazard, but

risk has additional implication of the chance and probability a

particular hazard actually occurring. In fact, hazard refers to

the probability of a potentially dangerous phenomenon

occurring in a given location within a specified period of time

[4]. Therefore, risk does not exist if exposure to a harmful

situation does not or will not occur [5-6].

River flood mapping is the process of determining

inundation extents and depth by comparing river water levels

with ground elevation. The process requires the

understanding of flow dynamics over the river and the

1 Discipline of Civil Engineering, School of Engineering, Monash

University Malaysia, Bandar Sunway, Selangor, Malaysia, Email:

[email protected] 2 School of Civil Engineering, Universiti Sains Malaysia, Penang,

Malaysia

adjacent floodplain, topographic relationships and the sound

judgments of the modeler. In fact, river flood mapping is the

foundation of river flood risk prediction, which can be

produced using water depth, flood extent, flow velocity and

flood duration maps [7]. All the existing methods for flood

mapping can be grouped into three major categories namely

analytical, historical and physiographic methods [8]. These

three methods use same procedure to delineate floodplain

boundaries by determining the flood elevation at each river

cross section. The boundaries are then interpolated between

the cross section. The three methods differ only in the way

they determine the water surface profile. River flood

mapping involves three main components as follows [9]:

i. GIS interface as pre-processor (to extract geospatial

data) and post-processor (to visualize model outputs) (i.e.

HEC-GeoHMS and HEC-GeoRAS).

ii. Hydrological model, which develops rainfall-runoff

hydrograph from a design rainfall or historic rainfall event

(i.e. HEC-HMS).

iii. Hydraulic model, which routes the runoff through

river channel to determine water profiles and flow velocity

(i.e. HEC-RAS).

The increasing availability of powerful GIS software

packages offer new opportunities for engineers to perform

flood mapping incorporated with hydrological and hydraulic

models [7]. This is essential as flood modelling is inherently

spatial and hydrological and hydraulic models have large

spatially distributed data requirements [10]. In recent years,

efforts have been made to integrate hydrological and

hydraulic models and GIS to enhance the model outputs,

which led to the establishment of a new branch of hydraulics

and hydrology, namely, hydro-informatics. In general, there

are four methods for incorporating of river basin models into

GIS. They are classified as stand-alone system, loose

coupling system, tight coupling system and embedded

system. In this regard, HEC-GeoRAS and HEC-GeoHMS,

which were utilized in this study, are among tight coupling

system. This paper presents a simple methodology to

generate river flood risk map in an urban area. To this aim,

water depth and flow velocity (river flood hazard), land-use

type, road accessibility and debris flow (vulnerability and

exposure) were incorporated. Moreover, the proposed

methodology was utilized for a case study in Kuala Lumpur

for numbers of defined scenarios.

II. MATERIAL AND METHOD

A. Method

The proposed methodology in this research included five

main components; hydrological modelling, hydraulic

modelling, river flood visualization, river flood hazard

mapping and river flood risk mapping. Note that, in this

River Modelling for Flood Risk Map Prediction: A Case

Study of Kayu Ara River Basin, Malaysia

Sina Alaghmand*1, Rozi b. Abdullah

2, and Ismail Abustan

2

2014 International Conference on Chemical Processes & Environmental Engineering (ICCPEE’14) Dec. 30-31, 2014 Bangkok, Thailand

http://dx.doi.org/10.15242/IAE.IAE1214510 96

research, flood mapping, flood hazard mapping and flood

risk mapping were differentiated. Flood mapping consists of

visualization of hydraulic model results in forms of water

depth and flow velocity, whereas flood hazard mapping

represents combination of water depth and flow velocity to

define various level of flood hazard. Finally, flood risk map

was the resultant of hazard map, land-use, road accessibility

and debris flow risk.

This research involves integration of two numerical

models: HEC Hydrologic Modelling System (HEC-HMS) as

a hydrologic model to simulate rainfall-runoff process and

HEC River Analysis System (HEC-RAS) as a hydraulic

model to route the runoff through river to determine water

surface profiles and flow velocity. In order to assess the

effects of rainfall event magnitude (ARI) and also river basin

land-use development condition on the river flood hazard

maps, six scenarios were defined. These include three

different ARI (20, 50 and 100 years) and two land-use

development conditions (existing and ultimate). In all the

defined scenarios rainfall events with 60 minutes duration are

taken into account. Note that, in order to differentiate

between the various developments conditions, different

percentages of imperviousness were defined for each

development conditions (Table I).

TABLE I: PERCENTAGE OF IMPERVIOUSNESS AREA IN DIFFERENT

DEVELOPMENT CONDITIONS

Development Condition Existing Ultimate

Sub-river basin 1 26% 90%





B. Study area

Kayu Ara river basin is the case study in this research,

which is located in Kuala Lumpur, Malaysia. The study area

covers an area of 23.22 km2 and is geographically surrounded

within N 3° 6΄ to N 3° 11΄ and E 101° 35΄ to E 101° 39΄.

Kayu Ara river basin is a well-developed urban area with

different land-use and also high population density, and also,

10 rainfall stations and one water level station at the outlet

were available (Figure 1a).

(a)

(b)

Fig. 1. (a) Location of rainfall and water level stations in Kayu Ara river

basin, (b) Geospatial data extracted using HEC-GeoHMS.

III. RESULTS AND DISCUSSION

A. Hydrological modelling

HEC-HMS was used as the hydrological model in this

research, which was linked to GIS using HEC-GeoHMS

extension to extract geospatial input data (Figure 1b). To

develop a reliable numerical model establishing the

credibility of the model is essential. It includes sensitivity

analysis, calibration and validation processes. Sensitivity

analyses were applied to highlight the most sensitive

parameters in the hydrological model. The results of

sensitivity analysis showed that imperviousness, lag-time and

peaking coefficient were the most sensitive parameters.

Moreover, the recorded time series data with 10 minute

intervals was available since 1996. Among the recorded

rainfall and water level time series, 18 rainfall events were

selected for calibration and 18 rainfall events for validation.

The established hydrological model for Kayu Ara was used to

simulate the rainfall-runoff process based on rainfall design

hyetograph extracted from MSMA guideline [11]. Therefore,

IDF polynomial equation for, three ARI (20, 50 and 100

years) were used to derive the design rainfall for 60 minutes

events as an input to HEC-HMS hydrological model (Figure

2). The modeled runoff hydrograph for each scenario,

produced by the validated hydrological model, are shown in

Figure 3.

(a) ARI 20 year

(b) ARI 50 year



(c) ARI 100 year

Fig. 2. Hyetographs of the design rainfall for the defined scenarios.

(a)

(b)

Fig. 3. Simulated runoff hydrographs for rainfall events for the six defined scenario; (a) Existing development condition, (b) Ultimate development

condition.

B. Hydraulic modelling and flood visualization

The modeled runoff hydrographs were used as the main

input for hydraulic model. The hydraulic modelling in this

research was conducted for the last 5.1 km of Kayu Ara river.

The hydraulic model, HEC-RAS, were incorporated with

GIS using HEC-GeoRAS extension to prepare geospatial

data. Therefore, 25 surveyed cross-sections at 200 m interval

were used to create Digital Elevation Model (DEM) of the

main channel.

The calibration process of HEC-RAS consisted of a total of

20 events. The flood events for calibration were selected

from the historical data since 1996 at the water level station,

which was located at the outlet of Kayu Ara river basin. In the

validation process, HEC-RAS simulated the flood events

using the calibrated parameters. A total of 10 events, other

than calibration rainfall events, were employed to validate the

model. Furthermore, results of the hydraulic model were

visualized using HEC-GeoRAS. The generated flood extend

maps, flood water depth and flow velocity distribution for the

ultimate development condition scenarios are represented in

Figures 4 and 5.

(a) (b)

(c)

Fig. 4. Water depth and flood extend distribution for ultimate development

condition: (a) ARI 20 year, (b) ARI 50 year and (c) ARI 100 years

(a) (b)

(c)

Fig. 5. Flow velocity distribution for ultimate development condition: (a) ARI 20 year, (b) ARI 50 year and (c) ARI 100 years.

C. Flood hazard mapping

Flood hazard map covers the geographical areas which

could be flooded according to different scenarios [12]. The

magnitude of the damage depends on the flood characteristics

such as water depth and flow velocity [13]. In this study,

water depth and flow velocity are considered as two main



parameters associate with river flood hazard. In order to

produce the flood hazard maps for Kayu Ara river basin

NSW flood development manual [14] was applied. With

some modification, four river flood hazard categories were

determined consisting of low, medium, high and severe.

These are shown in Figure 7. Furthermore, the water depth

and flow velocity maps which were generated in the previous

section were overlaid and analyzed to classify flood hazard

for the defined scenarios. These are represented in Figures 7

and 8.

Fig. 6. Flood hazard Categories [14]

(a) (b)

(c)

Fig. 7. Flood hazard map for existing development condition at Kayu Ara river basin: (a) ARI 20 year, (b) ARI 50 year and (c) ARI 100 years.

(a) (b)

(c)

Fig. 8. Flood hazard map for ultimate development condition at Kayu Ara

river basin: (a) ARI 20 year, (b) ARI 50 year and (c) ARI 100 years.

D. Flood risk mapping

Risk can be defined as the probability of a loss, and this

depends on three elements including hazard, vulnerability,

and exposure. If any of these three elements in risk increases

or decreases, then the risk increases or decreases,

respectively. Exposure refers in the context of floods only to

the question whether people or assets are physically in the

path of flood waters or not, vulnerability may be defined as

the conditions determined by physical, social, economic, and

environmental factors or processes, which increase the

susceptibility of a community to the impact of hazards. This

concept is demonstrated in Figure 9.

Fig. 9. River flood risk definition [13]

Given Figure 9, to produce flood risk map for the study

area four main factors were included, those can fulfil three

components of risk definition. The first one was flood hazard

map which was created by combining water depth and flow

velocity. Then, the flood hazard map was classified in four



classes, low, medium, high and extreme hazard. For each

class one value was defined that shows its level in term of

flood risk (Table II).

TABLE II: RISK VALUE FOR FLOOD HAZARD CLASSES

Flood hazard class Low Medium High Extreme

Risk value 1 2 3 4

The second factor which involves in flood risk estimation

is land-use type, which contributes to the vulnerability factor.

For instance, in land-use map, residential and commercial

areas maintain higher risk in comparison with other land-uses

such as roads, water bodies, parks, forests and open areas.

The land-use map was classified in two classes, “residential

and commercial” and “non-residential and non-commercial”.

Therefore, in land-use map pixels with “residential and

commercial” class are assigned “4” and “non-residential and

commercial” are assigned “1” (figure 10b).

The third factor for preparation of flood risk map was

accessibility to main road. A vulnerability analysis considers

the population and structures at risk within the flood

inundated area. In term of vulnerability, the emergency

responses may be required, which includes the need for

evacuation and emergency services. In river flood event the

accessibility to main road is important at least for two

purposes; evacuation of people from inundated area and

emergency services. In fact, locating far from the main road

may lead to slower emergency response during flood

inundation and consequently higher risk (Figure 10c).

The fourth factor which is included in the proposed

methodology is debris flow risk. As the depth of floodwater

increases debris begin to float. For instance, if the flood

velocity is significant, buildings can be destroyed and cars

and caravans can be swept away. In certain areas, the buildup

of debris and the impact of floating objects can cause

significant structural damage to buildings and bridges at the

downstream. Hence, fast moving floodwaters carrying debris

expose a greater threat to both people and structures, than

those with no debris. In this case, debris risk was considered

as a function of distance from the source of the flood. In order

to classify the risk value for debris flows for Kayu Ara river

basin, the distance from the most upstream is considered and

assumed by moving towards downstream amount of debris,

and consequently, the debris risk is increasing (Figure 10d).

All the above mentioned maps were converted into raster

format with 1 m pixel size. In each map, proper values were

assigned for each pixel and combined. Then, the result map

(flood risk) was created based on the summarized risk values

of river flood hazard, land-use type, main road accessibility

and debris flow. For instance, minimum value for the flood

risk map is 4 which reflects the combination of the pixels

with “Low” class in flood hazard map, “non-residential and

commercial” class in land-use type map, “0-100 m” class in

main road accessibility and “0-1500 m” class in debris flow

hazard map. On the other hand, maximum is 16 represents

pixels with “Extreme” class in flood hazard map, “residential

and commercial” class in land-use type map, “> 300 m” class

in main road accessibility and “> 4500 m” class in debris

flow hazard map. Finally, the flood risk map was categorized

into four classes based on the value of each pixel; “Low”

(values 4,5 and 6), “Medium” (values 7, 8 and 9), “High”

(values 10, 11 and 12) and “Extreme” (values 13, 14, 15 and

16). Figure 12 shows summarized the proposed methodology

for flood risk map prediction. Figures 13 and 14 represent the

flood risk maps for Kayu Ara river basin.

(a) Flood hazard map

(b) Land-use map

(c) Main road accessibility map

(d) Debris flow hazard map

(e) Flood risk map

Fig. 10. Summary of proposed methodology for flood risk map prediction.

(a) (b)

(c)

Fig. 11. Flood risk map for existing development condition at Kayu Ara




(a) (b)

(c)

Fig. 12. Flood risk map for ultimate development condition at Kayu Ara


IV. CONCLUSION

The following conclusions can be considered according to

results of this research:

i. An increase in river basin land-use development

condition leads to increase of imperviousness of the river

basin and volume and peak discharge of the generated runoff

hydrograph.

ii. HEC-GeoRAS and HEC-GeoHMS are powerful tools

as pre-processor for preparation of geospatial input data and

also as a post-processor for visualization of the hydraulic

model results for HEC-RAS and HEC-HMS models,

respectively.

iii. The generated water level by hydraulic model is

significantly sensitive to river basin land-use development

condition and magnitude (ARI) of rainfall event.

iv. Flood water depth and flow velocity are the most

important elements of flood hazard mapping. However, the

generated flood hazard pattern distribution is more

influenced by water depth in comparison with flow velocity.

vi. The proposed methodology for flood risk map

prediction in an urban area is a simple technique which can

contribute to the research area. This case study confirms the

efficiency of the method to some extent. However, more

detailed analysis need to be undertaken towards a

well-developed and applicable framework.

ACKNOWLEDGMENT

This work was funded by Universiti Sains Malaysia under

Short-term Research Grant.

REFERENCES

[1] Embi, A.F. and D. N. Mohd, The Changing Microclimate in Urban Areas – The Urban Heat Island Connection. 2004.

[2] Abdullah, K., Flood Problem and Storm water Management Manual

for Malaysia (USMM). Keynote Address:Flood Problem and Storm water Management Manual for Malaysia (USMM), in Seminar on

Introduction to Storm water Management Manual for Malaysia. 2000:

Kelantan. Malaysia. [3] ACS, Understanding Risk Analysis. 1998, Washington, USA:

American Chemical Society.

[4] Alexander, D., Natural Disasters. 1993, New York, USA: Chapman & Hall.

[5] Omen, G.S., Kessler, A.C., Anderson, N.T., Chiu, P.Y., Doull, J., Goldstein, B., Lederberg, J., McGuire, S., Rall, D. and Weldon, V.V. ,

Framework for Environmental Health Risk Management. 1997, The

Presidential/Congressional Commission on Risk Assessment and Risk Management: Washington, USA.

[6] Sinnakaudan, S.K., A. Ab Ghani, M.S.S. Ahmad, and N.A. Zakaria,

Flood risk mapping for Pari River incorporating sediment transport. Environmental Modelling & Software, 2003. 18(2): p. 119-130.

http://dx.doi.org/10.1016/S1364-8152(02)00068-3

[7] Alaghmand, S., R. Abdullah, I. Abustan, M.A.M. Said, and B. Vosoogh, Gis-based river basin flood modelling using HEC-HMS and

MIKE11 - Kayu Ara river basin, Malaysia. Journal of Environmental

Hydrology, 2012. 20: p. 1-16. [8] Smith, K. and R. Ward, Floods: Physical Processes and Human

Impacts. 1998, Chichester, USA: John Wiley & Sons Ltd.

[9] Snead, D.B., Development and Application of Unsteady Flood Models Using Geographic Information Systems, in Civil Engineering. 2000,

The University of Texas at Austin: Austin, USA.

[10] Horritt, M.S. and P.D. Bates, Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology,

2002. 268(1-4): p. 87-99.

http://dx.doi.org/10.1016/S0022-1694(02)00121-X [11] DID, Urban Stormwater Management Manual for Malaysia. 2000,

Kuala Lumpur, Malaysia: Department of Irrigation and Drainage

Malaysia. [12] EXCIMAP, Atlas of Flood Maps. 2007, European Exchange Circle on

Flood Mapping.

[13] APFM, Urban Flood Risk Management, A Tool for Integrated Flood Management. 2008, The Associated Program on Flood Management:

Australia.

[14] NSW, Floodplain Development Manual: the management of flood liable land. 2005, Department of Infrastructure, Planning and Natural

Resources. New South Wales Government: Australia

Sina Alaghmand was born in July 1983 in Iran.

He received his BSc in irrigation engineering from Gorgan University of Agricultural Sciences and

Natural Resources in 2006. Then, he moved to

School of Civil Engineering of Universiti Sains Malaysia to study river engineering, where he

obtained his MSc in 2009. Sina received his PhD in

Civil (Water resources) Engineering at University of South Australia in 2014.

He has an extensive research experience in wide

range of water resources engineering. This includes hydrological and hydraulic modellings for flood map predictions, which he wrote a thesis

on this topic during his MSc study (the present paper). Moreover, he has

comprehensive research experience on surface-groundwater interactions and floodplain salinity interception, which was his PhD research topic.

He has published more than 15 peer-reviewed journal papers and

numbers of conference papers. Currently, he is a lecturer at Discipline of Civil Engineering at Monash University Malaysia. Also, he holds an

adjunct research fellow position at University of South Australia. Dr. Sina Alaghmand has been recipient of numbers of academic

honors and awards during his academic career. These include five

scholarships for his BSc, MSc and PhD, Gold medal for his MSc research (UNESCO-IHP Malaysia) and recognition for his PhD research (Goyder

Institute for Water Research and Australian Water Association).



http://dx.doi.org/10.1016/S1364-8152(02)00068-3

http://dx.doi.org/10.1016/S1364-8152(02)00068-3

http://dx.doi.org/10.1016/S1364-8152(02)00068-3

http://dx.doi.org/10.1016/S1364-8152(02)00068-3

http://dx.doi.org/10.1016/S0022-1694(02)00121-X

http://dx.doi.org/10.1016/S0022-1694(02)00121-X

http://dx.doi.org/10.1016/S0022-1694(02)00121-X

http://dx.doi.org/10.1016/S0022-1694(02)00121-X

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River Modelling for Flood Risk Map Prediction: A Case ...ia-e.org/siteadmin/upload/1458IAE1214510.pdf · parameters associate with river flood hazard. In order to produce the flood