IJERD – International Journal of Environmental and Rural Development (2015) 6-1

Research article
erdFloodplain Mapping Using HEC-RAS and GIS
in Nam Phong River Basin, Thailand
NARETH NUT*
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia
Email: nnareth@rua.edu.kh

VICHIAN PLERMKAMON
Department of Agricultural Engineering, Khon Kaen University, Khon Kaen, Thailand
Received 15 December 2014

Accepted 31 May 2015(*Corresponding Author)

Abstract Floodplain management and mapping is a new and applied method for the riverengineering and is essential for prediction of flood hazards. The lower Phong Basin area
regularly goes more or less under flooding every year in the monsoon season due to lack offlood protection and limited resources. Most of the flood protection works are carried out atthe local level without preplanning and considering the problems at the river basin scale.
Traditionally, individuals or communities have been trying to develop their own strategies
for minimizing the effects of floods. However, due to limitation of resources andknowledge, many householders are unable to protect their properties or possessions fromfloods. A methodology was applied to integrate hydraulic simulation model, HEC-RAS and
GIS analysis for delineation of flood extents and depths within a Nam Phong River innortheast of Thailand. It is necessary to simulate complicated hydraulic behaviour of theriver in a more simple way for the purpose of managing and performing all river training
works. In this research, the steady flow was used to simulate flood along 148 km end of
Nam Phong River starting at upstream from Ubolratana Dam to Chi River in Northeast of
Thailand. Floodplain mappings were derived using integrating of HEC-RAS and GIS
analysis. Delineation of flood extents and depths within the floodplain were conducted indifferent return periods. Critical flooding area along the river was distinguished based on
the grid layer of flood depths. The results indicated that hydraulic simulation by integratingwith GIS analysis could be effective for various kinds of floodplain management andmapping and give as different scenarios for river training works and flood mitigation
planning.
Keywords Nam Phong river, floodplain mapping, GIS, HEC-RAS model, Thailand

INTRODUCTION
The Nam Phong Watershed is the largest basin in the northeast of Thailand which provides animportant water resources for agriculture, electricity generation, aquaculture, domestic uses,
industrial and recreational purposes (KKU, 2003). In Nam Phong flood prone areas, flood causes
severe economic and social disruptions to many living households. Nevertheless, floodplain in
Lower Phong Basin is still in a very rudimentary stage and no serious concern on comprehensive
flood damages. In this area, most of the flood protection works are carried out at the local level
without preplanning and considering the problems at the river basin scale. Traditionally,
individuals or communities have been trying to develop their own strategies for minimizing theeffects of floods. However, due to limitation of resources and knowledge, many householders areunable to protect their properties or possessions from floods.
The most important factors affecting the intensity and flood return period in each region are:volume and time of upstream surface runoff and river or flood conditions, physical characteristics
of watershed (area, morphology), hydrological characteristics of the watershed (rainfall, storage,
evapotranspiration), and human activities (Noori, 2001). Flooding is a hazard with seriousⒸ ISERD

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socioeconomic consequences for all activities and infrastructure within an affected floodplain. Thus,
accurate delineation of flood extents and depths within the floodplain is essential for flood
management officials to make sensible and fair decisions regarding construction, insurance andother regulated practices on land and property potentially affected by flooding (Noman et al., 2003).
Computer models play a pivotal role in facilities such as storm drains, culverts, bridges, andwater quality as well as quantity-control structures which is an important design component ofthese facilities involving with hydraulic analysis to determine conveyance capacity. Recently,
computer models are developed and used for predicting and preventing natural disasters worldwide(Barbad et al., 2002; Earles et al., 2004; Abdalla et al., 2006; Yang et al., 2006).
Barbad et al. (2002) made flood zoning maps of the Sepid Rood River in Gilan Province, Iran,using Iranian cartographical maps of 1: 25000, cross sections measured by Iran Rasad Consulting
Engineers, and ArcView, HEC-RAS and HEC-GeoRAS software. They concluded that a
combination of GIS and the HEC software is feasible and makes the calculations easily. Earles et al.
(2004) demonstrated the utility of the HEC-GeoRAS model for floodplain delineation anddetermination of key hydraulic parameters and also, HEC-RAS capability of producing hydraulicresults in Los Alamos, New Mexico, USA. Abdalla et al. (2006) introduced hybrid approach forflood risk assessment through GIS. The results indicated that the developed methodology was
efficient in modeling and visualizing the spatial extent of different flood scenarios and in
determining flooded areas at risk. Yang et al. (2006) developed a direct-processing approach toriver system floodplain delineation by using GIS and HEC-RAS. Due to the efficiencies of thementioned methods, the purpose of this research was to identify the flood frequency analysis andflooded area and further created floodplain mappings based on the change of return periods or
flood events in Lower Phong Basin using HEC-RAS and GIS.
METHODOLOGY
Study area: This research was conducted in Lower Phong Basin located in the northeast of
Thailand bordering to Udon Thani, Mahasarakham, Chaiyaphum and Nong Bua Lamphu provinces.
The length of river is approximately 148 km that flows northwestward to southeastward to join Chi
River and ultimately draining into the Mekong River. The area of study was approximately 2993.95
km2 (Fig. 1). The lower part of the watershed consists mainly of paddy fields, agricultural land, andfactories and is heavily populated. The land surface in the watershed is generally undulating andsloping towards the east and southeast. The elevation of the relatively flat area around the
Ubolratana reservoir is about 190 m. The western watershed consists of many mountain ranges
with an average elevation of 900 m, and up to 1,300 m (FAO, 1985). Geographic location of thestudy area is in between Latitude N 160 23’ 39” to N 160 46’ 37” and Longitude E 1020 36’ 22” toE 1020 57’ 51”.
Datasets: The analyses of this research relied on two types of data; annual peak flow and GIS data
including cross-sections, elevation points and topographic map. Peak flow data of Nam Phongbasin was simulated from SWAT model with 12-year rainfall records from 2000-2012 water year,
and with the daily model calibration from 2004-2007 (R2=0.93). After evaluation of the accuracy ofthe data, Gumbel Distribution Method was employed for flood frequency analysis (only 14 major
sub-basins were calculated, whereas the small sub-basins were ignored). The analyzed data used as
input for the hydraulic simulation of the river reaches which divide into upper reach from
Ubolratana Dam to Nong Wai Weir and lower reach from Nong Wai Weir to Chi River.
Calibration for roughness coefficient: The roughness coefficients, which represent the surface’s
resistance to flow and are integral parameters for calculating water depth, were initially estimatedusing the Chow classification by Chow (1959). The Manning’s coefficients used for different zonesof the Nam Phong River varies between 0.025 and 0.075. Flood inundation results are derived
separately for each cross section in the main channel. Model parameters can largely be divided into
two categories (Gilard, 1996): (i) parameters that can be directly inferred from observation, such asarea, extent, depth, volume etc., and (ii) parameters that cannot be directly observed at the model
scale and will need to be estimated, such as roughness. Manning roughness coefficient n, togetherⒸ ISERD

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with the channel geometry is considered to have the most important impact on predicting
inundation extent and flow characteristics. Most of the methods from literature for estimating
roughness values are useful in establishing the range of roughness values for a river reach.
Calibrated roughness values are, however, effective at the reach scale (Beven and Carling, 1992).
Calibration is an inverse problem associated with identification, and is used to determine unknown
constants or parameters in a model. Calibrating the Nam Phong River model roughness values
involved running the hydraulic model several times and changing the Manning roughness
coefficients, first estimated from Chow tables, until the best fit between the simulated and observed
water level and water extend is found.
Floodplain mapping: For the floodplain delineation, the ArcView GIS software package,developed by the Environmental Systems Research Institute, was used as the computerdevelopment environment for this research. Topographic map of the study area with scale of
1:50,000 were applied for based map using 3D analyst capability of ArcView GIS. DEM is used
for preparation required data for hydraulic simulation in HEC-RAS, whereas the 113 surveyedcross-sections of the Nam Phong River have relative elevation which GPS have been used for
determining absolute and correct positioning of all cross-sections and elevation points. The HECGeoRAS extension is used in conjunction with 3D analyst for interpolation of digital terrain data
and Spatial Analyst for proper display of the output flow depth grids and velocity grids. HECGeoRAS had been used to import RAS geometry form HEC-RAS which represents stream
floodplains as a computed water surface elevation at each cross-section. During the data import
step, these elevations, along with the distance from the stream centerline to the left and rightfloodplain boundaries, are brought into ArcView GIS and stored in the cross-section parameter
table. As inputs, the script requires the cross-section line theme and the cross-section parameter
table. The output is a line theme that is identical to the cross-section theme in location andorientation. Using the water surface profile at each cross-section, DEM with resolution 5 mobtained from Water Resources and Environmental Institute (WREI, 2012) was represented theentire floodwater surface. The water surface lines were used as break-lines, and the cross-section
bounding polygon was used to bind the aerial extent of the water surface. When they were viewed
in conjunction with the terrain DEM or TIN, flooded areas can be seen. The three-dimensional
floodplain view is quite useful for floodplain visualization.
RESULTS AND DISCUSSION
In the present finding, flood frequency analyses were carried out with peak discharge data for 12
years (2000–2012) by the Gumbel’s Distribution method which it is a suitable method in thecondition like Thailand. The maximum annual water discharge from sub-watershed was calculated
for flooding occurrences for 5-year, 10-year, 25-year, 50-year and 100-year return periods (Table1). Flood frequency analysis was used as the steady flow data for simulation in order to further
analysis the floodplain area at the Nam Phong watershed. Critical depth for upstream and criticaldepth for downstream was considered as boundary conditions for this analysis. Other inputs such as
Manning’s n value, river system schematic, contraction and expansion coefficients, flow regime
entered to model and HEC-RAS model has run for steady flow regime. The result indicated only
three-major rivers: Huai Sai Bat, Huai Suea Ten, Huai Khum Mum could reach to the big
discharges based on changing of return period of flooding, whereas the other small sub-basins could
produce less discharge which would be caused less effect to the floodplain area. Fig. 2 show
floodplain mapping of the affected area and depth for the 10 and 100 years flooding events. Criticalflooding area along the river could be distinguished basing on the grid layer of flood depths, andthe flooding occurred mainly at the downstream of the basin which it is more populated,economical and industrious areas.

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Nong Wai
Weir

Chi River

Fig. 1 Location of the study area
Table 1 Maximum discharge (m3/s) of 14 sub-basins from the SWAT model based on return
periods (2000-2012)
Nam
Phong
River

Upper
Reach

Lower
Reach

Tributary
Name

Sub-basin
Name

Peak
Flow
(m3/s)

βuReturn Periods of Flood Frequency Analysis

Huai Chot
Huai Khum Mum

Sub-basin 08
Sub-basin 03

27.26
189.90

5.37
39.25

8.19
54.28

Huai Sai

Sub-basin 02

565.10

6.31

8.94

18.41

23.15

29.13

33.58

37.98

Huai Suea Ten

Sub-basin 04

169.7017.76

113.91140.54153.86

170.70183.19195.59Huai Yang 1

Sub-basin 09

84.67

16.30

27.37

51.82

64.05

79.51

90.97

102.35

Huai Hin Lat
Huai Kao Khot
Huai Nong Pla
Huai Pha Khue
Huai Plalai
Huai Sai Bat
Huai Siao
Huai Yai
Huai Yang 2

Sub-basin 12
Sub-basin 13
Sub-basin 16
Sub-basin 20
Sub-basin 18
Sub-basin 19
Sub-basin 06
Sub-basin 15
Sub-basin 10

9.09
48.03
68.36
40.37
36.26
478.00185.80
95.75
22.52

0.87
1.40
6.81
3.82
2.53
73.72
33.37
12.62
2.72

6.29
42.98
43.15
26.49
27.11
155.74
91.46
46.44
13.07

7.59
45.08
53.37
32.22
30.90
266.31141.51
65.37
17.16

8.24
46.13
58.48
35.09
32.79
321.63166.55
74.84
19.20

9.06
47.45
64.94
38.71
35.19
391.53198.19
86.81
21.78

9.67
48.44
69.73
41.40
36.96
443.38221.66
95.68
23.70

10.27
49.41
74.48
44.07
38.73
494.85244.96104.4925.60Ⓒ ISERD

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5 YR

10 YR

25 YR

50 YR

100 YR

16.24
113.15

20.27
142.6125.36
179.8329.14
207.4432.88
234.84

IJERD – International Journal of Environmental and Rural Development (2015) 6-1

a) 10-year-flood eventb) 100-year-flood event

Fig. 2 Flood-affected area of 10 and 100 years flood event
Table 2 shows the analysis of the flooded areas revealed that a larger percentage more than 76%
and 17% of paddy field and miscellaneous land lied in a floodplain area followed by urban,
cultivation and forest area comprising 2%, 1.35% and 1.15%, respectively in accordance with theflooded areas of 5-year, 10-year, 25-year, 50-year and 100-year return periods. Anyways, there was
not much (almost no flood) affected by the flood to the industrial land based on these various return
periods it was because the topography of this area was too high to be effected by the flooding. It also
illustrated that 2.56 km2, 2.85 km2, 0.10 km2, 36.87 km2, 154.89 km2, 1.93 km2, 3.59 km2 ofcultivation area, forest, industrial land, miscellaneous land, paddy field, uncommercial land and urban
are respectively inundated by 5-year flood. Similarly, 3.59 km2, 3.08 km2, 0.21 km2, 43.33 km2,
192.18 km2, 2.60 km2, 5.01 km2 of cultivation, forest, industrial land, miscellaneous land, paddy field,
uncommercial land and urban are respectively inundated by 100-year flood, which it is showed thatthe flooded area increased with flooding intensity, mostly paddy field was moderately inundated(0.49 % changes) by different year flooding from 5 - 100 years, and slightly followed by urban area,
and cultivation, whereas the uncommercial and industrial are the least effected.
Table 2 Flooded areas (%) according to the landuse types based on return periods
Total flood damage area (km2)
Landuse Types
Cultivation
ForestIndustrial land
Miscellaneous land
Paddy Field
Uncommercial land
Urban Area
Total

5 years
Flood
Area
%
2.56
1.26
2.85
1.41
0.10
0.05
36.87
18.18
154.89
76.38
1.93
0.95
3.59
1.77
202.78100.0010 years
Flood
Area
%
3.44
1.45
3.05
1.28
0.21
0.09
41.13
17.27
182.76
76.76
2.60
1.09
4.91
2.06
238.10100.00Ⓒ ISERD157

25 years
Flood
Area
%
3.47
1.44
3.07
1.28
0.21
0.09
41.65
17.33
184.36
76.72
2.60
1.08
4.93
2.05
240.28 100.00

50 years
Flood
Area
%
3.50
1.44
3.08
1.27
0.21
0.09
42.24
17.40
186.16
76.69
2.60
1.07
4.96
2.04
242.75 100.00

100 years Flood
Area
3.59
3.08
0.21
43.33
192.182.60
5.01
250.01

%
1.43
1.23
0.08
17.33
76.87
1.04
2.00
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CONCLUSION
This study focused on a systematic approach in the preparation of floodplain modeling integratingof hydraulic simulation with GIS analysis. The major tools/models used in this method were onedimensional numerical model HEC-RAS and ArcView GIS for spatial data processing and HECGeoRAS for interfacing between HEC-RAS and ArcView GIS. Results of this study can separate
high-hazard from low-hazard areas in the floodplain to minimize future flood losses. Hydraulics
simulation for floodplain mapping could be beneficiary in several aspects for land and water
resources management and also engineering purposes. It can be applied to prevent unwise land use
in flood prone areas and flood insurance studies, based on modeling of water surface elevations fordesign flood events. The evaluation of floodplain delineation are rather complex and demanding
activities, which require a comprehensive approach to hydraulic floodplain simulation and can belargely enhanced by using GIS capabilities. It can be concluded that the usage of GIS for theundertaking of a hydraulic simulation is as the potential to both of accuracy improvement and costsaving for floodplain and flood hazard mapping. Moreover, the combination of ArcView GIS andHEC-RAS provides powerful tools for planners and decision makers.
ACKNOWLEDGEMENTS
Authors would like to acknowledge for the financial support from The Greater Mekong Subregion(GMS) and also grateful thank to Assoc. Prof. Dr. Vichai Sriboonlue and staffs at Water Resources
and Environmental Institute (WREI) and Department of Agricultural Engineering, Faculty ofEngineering, Khon Kaen University (KKU) for their support and data collection for the research
process.
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