Flood hazard assessment under climate change scenarios … · 3 Flood hazard assessment under climate change scenarios in the ... P.O. Box 4, Klong Luang ... W. Flood hazard assessment

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International Journal of Sustainable Built Environment (2016) xxx, xxx–xxx

IJSBE 141 No. of Pages 14

3 October 2016

HO ST E D BYGulf Organisation for Research and Development

International Journal of Sustainable Built Environment

ScienceDirectwww.sciencedirect.com

Original Article/Research

Flood hazard assessment under climate change scenarios in theYang River Basin, Thailand

Sangam Shrestha ⇑, Worapong Lohpaisankrit

Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani

12120, Thailand

Received 27 May 2015; accepted 26 September 2016

Abstract

Climate change is expected to increase both the magnitude and frequency of extreme precipitation events, which may lead to moreintense and frequent river flooding. This study aims to assess the flood hazard potential under climate change scenarios in Yang RiverBasin of Thailand. A physically-based distributed hydrological model, Block-wise use of TOPMODEL using Muskingum-Cunge flowrouting (BTOPMC) and hydraulic model, HEC-RAS was used to simulate the floods under future climate scenarios. Future climate sce-narios were constructed from the bias corrected outputs of three General Circulation Models (GCMs) for 2020s, 2050s and 2080s.Results show that basin will get warmer and wetter in future. Both the minimum and maximum temperature of the basin is projectedto increase in future. Similarly average annual rainfall is also projected to increase in future, higher in near future and lower in far future.The extreme runoff pattern and synthetic inflow hydrographs for 25, 50 and 100 year return flood were derived from an extreme flood of2007 which were then fed into HEC-RAS model to generate the flood inundation maps in the basin. The intensity of annual floods isexpected increase for both RCP 4.5 and 8.5 scenarios. Compared to the baseline period, an additional 60 km2 area of basin is projectedto be flooded with the return period of 100 years. The results of this study will be helpful to formulate adaptation strategies to offset thenegative impacts of flooding on different land use activities in Yang River Basin.� 2016 The Gulf Organisation for Research and Development. Production and hosting by Elsevier B.V. All rights reserved.

Keywords: Climate change; RCP scenarios; Flood hazard; Thailand; Hydrological modeling; Hydraulic modeling

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1. Introduction

Yang River Basin is one of the most flood prone basinsin Northeast Thailand (Kuntiyawichai et al., 2011a,b). Sev-eral studies on climate change impact assessment and flood

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http://dx.doi.org/10.1016/j.ijsbe.2016.09.006

2212-6090/� 2016 The Gulf Organisation for Research and Development. Pro

⇑ Corresponding author. Fax: +66 2 524 6425.E-mail addresses: [email protected], [email protected]

(S. Shrestha).

Peer review under responsibility of The Gulf Organisation for Researchand Development.

Please cite this article in press as: Shrestha, S., Lohpaisankrit, W. Flood hazThailand. International Journal of Sustainable Built Environment (2016), h

management strategies have been conducted on its mainbasin, Chi River Basin, in recent years (Chaleeraktrakoonand Khwanket, 2013; Artlert et al., 2013; Kuntiyawichaiet al., 2011a,b). These studies reported that climate changeis consistent and it has strong implications on the basinscale hydrological cycle. Other studies done globally indi-cate the altercated meteorological variables have greatpotential to change the frequency and intensity of extremeevents specially floods (Dobler et al., 2012; Viviroli et al.,2011). The increase in temperature accelerates theevapotranspiration process which further influences the

duction and hosting by Elsevier B.V. All rights reserved.

ard assessment under climate change scenarios in the Yang River Basin,ttp://dx.doi.org/10.1016/j.ijsbe.2016.09.006


mailto:[email protected]

mailto:[email protected]



http://www.sciencedirect.com/science/journal/22126090


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2 S. Shrestha, W. Lohpaisankrit / International Journal of Sustainable Built Environment xxx (2016) xxx–xxx


3 October 2016

precipitation amount and ultimately contributes in modifi-cation of seasonal runoff. The present intra-annular vari-ability in the amount of runoff is expected to shift underclimate change scenarios at many regions of the worldincluding Thailand (Dobler et al., 2010).

In addition to the projected changes in the hydrologicalregime, the climate change will also have implications onthe extreme events. Studies have demonstrated that floodintensity is highly sensitive to temperature in many partsof the world (Prudhomme et al., 2013; Menzel et al.,2002; Panagoulia and Dimou, 1997). Several other studiesalso have argued that climate has been a contributing fac-tor to flood risk by raising the precipitation amount rela-tive to the average annual rainfall (Fleming et al., 2012;Hirabayashi et al., 2008). Therefore, basin scale assessmentof climate change impacts on flood plays a key role in for-mulation and evaluation of adaptation and mitigationstrategies for flood risk management.

Literature suggests that climate change impact assess-ment on extreme events has been less investigated and pos-sesses higher uncertainty (Dobler et al., 2012). In addition,whatsoever the research has been conducted, primary focusis on the basin of developed nations (Bauwens et al., 2011;Prudhomme et al., 2010; Steele-Dunne et al., 2008). Alsofocusing on Asian countries, many studies on floodsinduced by climate change have been conducted on severalbasins in China (Li et al., 2013; Zheng et al., 2012; Yanget al., 2012). This implies less focus on basins of developingcountries lying on the tropical regions which are evidentlymore susceptible to floods where the region has alreadyhigh precipitation and the hydrologic cycle is highly inter-linked and sensitive to its components (Kite, 2001).Although considerable studies on floods have been con-ducted in Northeast of Thailand yet only few of studieswere on the impact of climate change on extreme events(Jothityangkoon et al., 2013; Hunukumbura andThailand, 2012). Despite several flood events in Yang RiverBasin most of the studies focused on the management prac-tices and socio-economic impacts of floods(Keokhumcheng et al., 2012; Dutta, 2011; Hungspreuget al., 2000). Shrestha (2014) studied the climate changeimpact on flood hazard potential in Yang River Basin.However, the study used the climate change projectionsfrom Special Report on Emission Scenarios (SRES) andonly one Regional Climate Model, which poses greateruncertainty in flood hazard assessment. Hence the basinscale study of climate change impact on flood hazard usingfuture climate data from multiple climate models and newemission scenarios is important in Thailand.

Another important factor that has decisively influencedthe climate change impact studies is the use of Global Cir-culation Models (GCMs) and Regional Climate Models(RCMs) dataset for the future climate projection withoutbias correction (Cloke et al., 2013). Although RCMs per-form nested dynamic downscaling to the outputs of theGeneral Circulation Models (GCMs), yet the spatial reso-lution makes the data unreliable for basin scale impact


assessment studies and is necessary to be bias corrected(Muerth et al., 2013). A few studies have been conductedso far on analysis of different downscaling techniques withemphasis on extreme events. A comparison study of sixdownscaling technique with three RCMs suggests both sta-tistical and dynamic downscaling tends to have similarbias. However, the choice of method of downscalingdepends on variables to be downscaled (Schmidli et al.,2007). Leander and Buishand (2007) satisfactorily usedthe power law transformation method for RCM outputsat Western Europe for estimation of extreme events.

Many studies have adopted various climate change sce-narios to evaluate these effects. The scenarios presented inthe Special Report on Emission Scenarios (SRES) in theIntergovernmental Panel on Climate Change (IPCC)Fourth Assessment Report (AR4) (IPCC, 2007) have beenwidely applied to investigate hydrological responses to cli-mate change (Praskievicz and Chang, 2011; Moradkhaniet al.; 2010; Ficklin et al., 2009; Tu, 2009; Yoshimuraet al., 2009). The Fifth Assessment Report (AR5) of theIPCC published in 2014 includes new scenarios based onvarious technical developments. These new scenarios,called Representative Concentration Pathways (RCPs),are a set of greenhouse gas concentration and emissionpathways designed to support research on the impacts ofand potential policy responses to climate change (Riahiet al., 2011; Van Vuuren et al., 2011; Moss et al., 2010).The RCPs are also considered to include impacts causedby landuse and land cover (LULC) change.

The present study is conducted to assess the climatechange impact on flood hazard potential in Yang RiverBasin with the following objectives: (i) to developrainfall-runoff model of the Yang River Basin, (ii) to designsynthetic hydrographs with return periods of 25, 50 and100 years with regard to future climate conditions, and(iii) to simulate flood hazard potential representing returnperiods of 25, 50 and 100 years under future climate changescenarios. This study assumes that land use activities andpopulation remains the same in future. Although manyGCMs are available, only 3 GCMs and two RCPs wereselected to construct the future climate scenarios to addressthe uncertainty in climate change projections.

2. Materials and methods

2.1. Study area and data description

The Yang River Basin, a sub-basin of Chi River Basin,has a drainage area of approximately 4145 km2 whichreceives an average annual rainfall of 1390 mm (Fig. 1).The annual relative humidity and temperature are approx.71% and 26.7 �C respectively in the basin. The basin isinfluenced by two prominent wind systems, the northeastand southwest monsoons which are responsible for therainfall patterns and temperature variations. In the north-east monsoons, the dry cold wind picks up some moisturefrom the northeast, it takes place from mid-October to



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Figure 1. Location map of study area with hydro-meteorological stations in Yang River Basin, Thailand.

S. Shrestha, W. Lohpaisankrit / International Journal of Sustainable Built Environment xxx (2016) xxx–xxx 3


3 October 2016

early February. The southwest monsoons begin aroundmid-May and fade down by mid-September. In additionto monsoons, the Yang River Basin also faces tropicalstorms. The tropical depressions mainly come from theSouth China Sea. Consequently, the high moisture travel-ing over the water surface causes the heavy rain duringthe rainy season (Artlert et al., 2013).

Topographically, the basin is characterized by the PhuPhan mountain range at a relatively high elevation ofaround 600 masl, with the Yang River as the major riverthat flows through Kalasin province, and meets Chi Riverat Yasothon province. The landuse in this basin consists ofagriculture (70%), forest (25%), urban (2%), water bodies(1.2%) and others (1.8%).

The DEM, a digital representation of ground surfacetopography, was constructed from geometric data acquiredfrom Thailand Land Development Department (LDD) andRoyal Irrigation Department (RID). The geometric dataconsist of point elevation, 2-meter interval contours,


stream elevation, road elevation and Yang River bound-ary. Those geometric data were used to generate DEM of20-meter resolution. A land cover/land-use map for thisstudy area was obtained from Thailand Land DevelopmentDepartment (LDD) and classified based on the Interna-tional Geosphere Biosphere Programme (IGBP). Soil clas-sification map is acquired from Food and AgricultureOrganization (FAO) digital soil map of the world. A briefdescription of hydrological and meteorological data used inthis study is summarized in Table 1.

A three step modeling approach i.e. (i) correcting thebiasness of the large-scale atmospheric data, (ii) hydrolog-ical and hydraulic modeling for flood inundation and floodhazard analysis and (iii) change analysis was adopted inthis study. Fig. 2 gives an overview of the methodologyadopted in this study. Firstly, BTOPMC model andHEC-RAS model were calibrated based on the observedclimate variables; while in the second step, GCMs outputswere bias-corrected. Thereafter, the future precipitation



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Table 1Brief description of hydro-meteorological stations and data used in this study.

Code Lat Long Elevation (masl) Duration Source

Meteorological stations

A. Selaphum 49092 16�0105500 103�5601700 145 1974–2009 RIDKuchinarai 388002 16�3000000 104�0300000 155 1976–2006 TMDSomde 3888006 16�40000 103�4500000 187 1990–2009 TMDKuchinarai Self- help Settlement 3888009 16�39000 103�54000 183 1976–2011 TMDNamon Agriculture Office 3888011 16�34000 103�48000 177 1990–2009 TMD

Hydrological stations

Thung Kaho Luang E 18 16�01059 103�5403800 137 1974–2010 RIDKuchinarai E 54 16�2602900 104�02007 146 1990–2009 RIDPon Thong E 70 16�17015 104�00033 141 1990–2009 RIDMueang E 2A 15�47006 104�08036 127 2007–2008 RIDMaha Chana E 20A 15�31059 104�1502400 122 1976–2010 RID

RID: Royal Irrigation Department, TMD: Thai Meteorological Department.

Figure 2. Schematic representation of the methodology used in flood hazard map creation.



3 October 2016

data were used as a forcing to the hydrological and hydrau-lic model to generate flood inundation areas in the basin infuture.

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2.2. GCM, RCP scenarios and bias correction

GCMs with sufficient historical and future datasets ofthe representative concentration pathways (RCPs) familywere selected. The selected number allows sufficient assess-ment of uncertainty without exacerbating the computa-tional demands of the study. The GCM ensemble hereinincludes 3 out of the 60 GCMs in the World ClimateResearch Programme’s (WCRP’s) Coupled Model Inter-comparison Project phase5 (CMIP5) dataset, BCC-CSM1.1, CCSM4 and MIROC5 (Table 2). The simulationscover the period from 1975 to 2005 (historical run) and


from 2010 to 2099 with IPCC Representative Concentra-tion Pathways 4.5 (RCP4.5) and 8.5 (RCP8.5). TheRCP4.5 is a stabilization scenario where total radioactiveforcing is stabilized before 2100 by employing technologiesand strategies to reduce greenhouse gas emissions. TheRCP8.5 is a business as usual scenario and characterizedby increasing greenhouse gas emissions and high green-house gas concentration levels.

Delta change method was used to correct biasness inoutputs of GCMs. This method is preferable when RCMoutput is not available for rapid assessment of multiple cli-mate change scenario or when multi GCM experiments arerequired, as in this case. The delta method assumes thefuture model biases for both mean and variability will besame as that of present day simulations. Changes in the cli-mate are calculated from model results and applied to the



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Table 2A list of the climate models used in this study with a brief indication of their origin, resolution and the number of realizations available for each climatechange scenario.

Model’s name Resolution(�)

Grids (Long-Lat)

Scenarios Research center

Beijing Climate Center Climate System Model version 1.1 (BCC-CSM1.1)

2.81 � 2.81 128 � 64 RCP4.5RCP8.5

BCC, China MeteorologicalAdministration, China

The Community Climate System Model version 4 (CCSM4) 1.25 � 0.90 288 � 192 RCP4.5RCP8.5

National Center for AtmosphericResearch, USA

Model for Interdisciplinary Research on Climate-Earth System,version 5 (MIROC5)

1.40 � 1.40 256 � 128 RCP4.5RCP8.5

Atmosphere and Ocean ResearchInstitute, Japan

P = gross rainfall

Pa = net rainfall on land surface

ET0 = interception evaporation

ET = actual evapotranspiration

Sr max= maximum root zone capacity

Srz = root zone storage

SD = local saturation deficit

Suz = unsaturated zone storage

qv = groundwater recharge

qb = base flow

Table 3Hazard index for depth of flooding.

Depth of flooding (m) Degree of flood hazard Description HI

D > 3.50 Very high ‘‘Extreme danger: flood zone with deep fast flowing water” 41.00–3.5 High ‘‘Danger: flood zone with deep fast flowing water” 30.60–1.00 Moderate ‘‘Danger: flood zone with deep fast flowing water” 2D < 0.60 Low ‘‘Flood zone with shallow flowing water or deep standing water” 1



3 October 2016

baseline data (Rasmussen et al., 2012; Lettenmaier et al.,1999; Gellens and Roulin, 1998).

Outputs from 3 GCMs for the 1971–2000 (baseline),2010–2039 (2020s), 2040–2069 (2050s) and 2070–2099(2080s) were used to generate climate change scenarios.The 2020s, 2050s and 2080s correspond to near-, mid andfar future, respectively. Climate scenarios informationwas then transferred to the hydrological model and fre-quency analysis was performed on the simulated hydrolog-ical scenarios. The outputs of the hydrological modelingwere used as input for flood inundation analysis.


2.3. Hydrological modeling

The hydrological simulations were carried out usingBlock-wise use of TOPMODEL with Muskingum–Cungemethod (BTOPMC) model. It is an extension of the TOP-MODEL concepts (Beven et al., 1995), which is developedin order to overcome the limitations of using the TOPMO-DEL for large river basins. For large river basins, spatialheterogeneity and timing of flow to outlet are the impor-tant factors. For representing spatial variability inBTOPMC, a catchment is composed of grid cells, which



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Table 4Relative change in annual precipitation in future periods compared to baseline period of 1971–2000 in the YangRiver Basin.

Periods Scenarios Annual precipitation (mm)

Max Avg. Min

2020s RCP4.5 104.00 41.61 25.39RCP8.5 74.53 32.44 21.47

2050s RCP4.5 109.05 49.89 37.80RCP8.5 105.47 42.60 38.79

2080s RCP4.5 37.03 25.13 8.58RCP8.5 67.23 37.91 37.70

Table 5Percentage changes in annual average precipitation in future periods compared to baseline period (1971–2000) in the Yang River Basin.

GCMs Percentage change as compared to baseline

RCP4.5 RCP8.5

2020s 2050s 2080s 2020s 2050s 2080s

BCCCSM1.1 47.00 48.50 17.85 39.19 47.69 24.68CCSM4 26.46 30.14 6.90 21.9 25.11 36.81MIROC5 18.00 24.64 24.26 15.66 26.92 24.58



3 October 2016

can be divided into sub-catchments, where each sub-catchment is considered as a block or a unit. The effectiveprecipitation for any sub basin, i, and soil layer, k, andtime, t is calculated based on Eq. (1).

Reði; tÞ ¼ Roði; tÞ � SrmaxðiÞ � Epði; tÞ ð1Þwhere Re represents the effective rainfall, Ro indicates totalprecipitation, Srmax is the maximum storage capacity inthe root zone and Ep denotes evapotranspiration.BTOPMC integrates Shuttleworth–Wallace model to cal-culate the potential evapotranspiration. Fig. 3 gives thedetails of the runoff generation for each grid cells inBTOPMC model. Further details are available in Aoet al. (2006) and Ishidaira et al. (2005). BTOPMC modelhas been successfully applied in many basins worldwidewith satisfactory performance (Manandhar et al., 2013;Phan et al., 2010; Bao et al., 2010; Wan et al., 2009;Shrestha et al., 2007). In addition, it has also been appliedsuccessfully to Mekong River basin which validates themodel’s ability to represent the hydrology of the basin(Kiem et al., 2008; Hapuarachchi et al., 2008).

The model was calibrated by adjusting saturated transi-tivity, decay coefficient and rooting depth for the period of2002–2006. The calibrated parameters were used as inputfor the validation period of 1997–2001 in order to checkfor the best goodness-of-fit with the observed dischargeat Kuchinarai, Kalasin station (E54).

2.4. Frequency analysis and designed hydrographs

In order to analyze and design the frequency of probablemaximum stream flow, annual maximum runoffs of 1980–2009 were used and a distributed hydrological model wasused to simulate the annual maximum runoffs for the


future periods of 2020s, 2050s and 2080s. A Gumbel distri-bution method was applied to illustrate the extreme floodfrequency. For the flood inundation modeling under cli-mate change conditions, several return periods such as25, 50 and 100 years of stream flow are considered.

Designed synthetic hydrographs were developed byapplying two methods, the dimensionless hydrographmethod and the flood frequency analysis. The dimension-less hydrograph was derived from the extreme runoff pat-tern in 2007. For flood frequency analysis, Gumbel’sdistribution was selected to analyze the historical runoffdata recorded from 1980 to 2009. Moreover, the Gumbelprobability distribution was also used to predict future run-off simulated by the distributed hydrological model byusing the bias corrected future climate data. The frequencyanalysis provided the probable maximum runoff with 25,50 and 100 year return period. The dimensionless hydro-graphs are used to derive synthetic inflow hydrographsfor the above mentioned return periods.

2.5. Hydraulic modeling

Flood inundation areas were simulated using HEC-RAS, a 1-D hydraulic model innovated by the US ArmyCorps of Engineers, under unsteady flow conditions (U.S.Corps of Engineers, 2002). The topography of channeland floodplain information was derived from HEC-GeoRAS software which is used as an extension in Arc-GIS, for processing geospatial data for use with HEC-RAS.

Discharge and river water level were set as upstream anddownstream boundaries. The upstream boundary in thisstudy is at Kuchinarai station (E.54)—a stream-flow gaug-ing station. In order to take backwater effect into the Yang



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Figure 4. Changes in annual average Tmax (a, b & c) and Tmin (d, e & f) for the 2020s, 2050s and 2080s relative to the baseline period (1975–2005) underRCP4.5 and RCP8.5 in the Yang River Basin. The line in the shaded box shows the median value, and the bottom and top of the box show the 25th and75th percentiles, and the whiskers show the minimum and maximum values of temperatures.

Table 6Performance of BTOPMC in Yang River basin.

Period NSE (%) R2 Vr

Calibration (2002–2006) 62.80 0.63 0.97Validation (1997–2001) 66.45 0.68 1.12



3 October 2016

River model, the water level at the junction between YangRiver and Chi River was also calculated by HEC-RASmodel. Based on the availability of spatial extent of waterand flood level in the basin, A. Pon Thong (E.70) stationwas selected to setup the model. The model was calibrated

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by adjusting manning’s roughness coefficient (n) in order togive the best goodness-of-fit for the period from 1 July to30 November 2005. A period from 1 July to 30 November2007 was used for model validation.

The BTOPMC model performance was evaluated bycomputing Nash-Sutcliffe Efficiency (NSE) (Nash andSutcliffe, 1970), Coefficient of Determination (R2) Volumeratio (Vr). Similarly, HEC-RAS model performance wasevaluated by computing Coefficient of Determination(R2) Volume Bias (VB) and Relative Peak Error (RPE).

The Nash–Sutcliffe Efficiency (NSE) is defined as:



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1/1/2002 1/1/2003 1/1/2004 12/31/2004 12/31/2005

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Dis

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Simulated Discharge Observed Discharge Precipitation

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Pre

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m³/s

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Simulated Discharge Observed Discharge Precipitation

(a)

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Figure 5. Comparison of observed and simulated discharge at Kuchinarai, Kalasin (E54) (a) calibration period (1 Jan 2002 to 31 Dec 2006) and (b)validation period (1 Jan 1997 to 31 Dec 2001).



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NSE ¼ 1�Pn

i¼1ðXobs;i � X sim;iÞ2Pni¼1ðXobs;i � X sim;iÞ2

ð2Þ
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where Xobs and X sim are the observed and simulated vari-

ables, respectively, Xobs is the average observed dischargeand n is the number of time step.

The Coefficient of Determination (R2) is calculated as:
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R2 ¼Pn

i¼1ðXobs;i � XobsÞðX sim;i � X simÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1ðXobs;i � XobsÞ2

q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1ðX sim;i � X simÞ2

q0B@

1CA

2

ð3Þ


where Xobs and X sim are the observed and simulated climate

variables at time i, respectively, Xobs is the average observedclimate variables and n is the number of time step.

The Volume Bias (VB) is expressed as follows:

VB ¼Pn

i¼1ðXobs;i � X sim;iÞPni¼1Xobs;i

� �ð4Þ

where Xobs and X sim are the observed and simulated climatevariables at time i, respectively, and n is the number of timestep.

The Volume Ratio (Vr) indicates the relation betweensimulated discharges and observed discharges and calcu-lated as below:



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Table 7Probable peak discharge estimated for RCP 8.5 and RCP 4.5 of three GCMs at Kuchinarai station.

Time period Scenario GCM 25 years Relative change (%) 50 years Relative change (%) 100 years Relative change (%)

Baseline 552 612 670

2020s RCP 8.5 BCCCSM1.1 702 27 809 32 915 37CCSM4 692 25 790 29 870 30MIROC5 653 18 750 23 800 19

RCP 4.5 BCCCSM1.1 684 24 780 28 900 34CCSM4 663 20 760 24 880 31MIROC5 651 18 745 22 850 27





Table 8Performance of HEC-RAS model in Yang River.

Period R2 RPE VB

Calibration (2005) 0.96 0.71 �0.01Validation (2007) 0.92 0.94 �0.01

131132133134135136137138139140141142

30-Jun 20-Jul 9-Aug 29-Aug

Wat

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evel

(mas

l)

Year (2

Observed Simulated

131132133134135136137138139140141142

30-Jun 20-Jul 9-Aug 29-Aug

Wat

er L

evel

(mas

l)

Year (2

Observed Simulated

(a)

(b)

Figure 6. Comparison of observed (blue) and simulated (red) water level at P(2007) of HEC-RAS model.



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V r ¼Pn

i¼1X sim;iPni¼1Xobs;i

ð5Þ


18-Sep 8-Oct 28-Oct 17-Nov005)

18-Sep 8-Oct 28-Oct 17-Nov007)

on Thong station (E.70) during (a) calibration (2005) and (b) validation



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Table 9Flood inundation areas in different return period floods for baseline period.

Degree of flood hazard Simulated flood area in km2 (in %)

25-year 50-year 100-year

Low 25.44 (12.38) 28.01 (12.51) 28.79 (12.02)Moderate 19.25 (9.37) 17.32 (7.73) 17.73 (7.40)High 141.66 (68.95) 151.63 (67.71) 154.31 (64.42)Very high 19.10 (9.30) 26.99 (12.05) 38.69 (16.15)Total 205.44 223.94 239.51



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The Relative Peak Error (RPE) is applied to measurethe relative difference between observed and simulatedpeaks. It is calculated as:

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2.6. Flood frequency analysis

The objective of flood frequency analysis was to relatethe magnitude of floods to their frequency of occurrenceusing probability distribution. In order to commence thisobjective, the calculation of the statistical parameters ofthe proposed distribution was made by the method ofmoments from the given data. In this study the annualmaximum runoffs for four different periods (1990s, 2020s,2050s and 2080s) were calculated for different return periodfloods.

2.7. Flood hazard mapping

Flood hazard mapping is to determine areas with aprobability of a flooding event for a defined return period(Han, 2011). With the results of hydraulic calculations, theflood outline can be calculated. The degree of flood hazarddepends on several hydrological factors such as floodvelocity and inundated depths. Since this study appliedthe 1-D hydraulic model, the hazard index was assignedwith corresponding to different inundated depths. Thedegree of flood hazard was classified into four hazard cat-egories based on inundation depth classes corresponding tothree critical inundated depths 0.6, 1.0 and 3.5 m as sug-gested by Tu and Tingsanchali (2010) which is describedin Table 3.

3. Results and discussion

3.1. Changes in mean temperature and rainfall

Table 4 shows the absolute changes in annual rainfallfrom ensemble of three GCMs and Table 5 shows the per-centage change in rainfall for the 2020s, 2050s and 2080s in


comparison to baseline period (1971–2000). The annualrainfall is projected to increase in near and mid futureand comparatively higher under RCP 4.5 whereas the rain-fall is projected to increase in the lower magnitude in farfuture under both scenarios (Table 4). Similarly, percent-age change in annual rainfall differs among the GCMs,higher change is projected by BCCCSM1.1 and lower byMIROC5 under both RCP scenarios (Table 5).

As shown in Fig. 4, the minimum and maximum annualtemperature was projected to increase in all future periods.Considering the median values of ensembles, the highestincreases in temperature were predicted for the RCP8.5,while the lowest were predicted for RCP 4.5. The differ-ences between the predicted changes in temperature forthe different emissions scenario will also increase in thefuture. This sharp difference is because of the significantincrease in differences among the different emission scenar-ios themselves. The median values of the projections justifythis finding. During 2080s, for Tmax, the median value asprojected under the RCP8.5 scenario is 1.1 �C higher thanas projected under the RCP4.5 scenario shown in the fol-lowing box plot (Fig. 4). Similarly, during the same period,change in Tmin is 2.2 �C higher for RCP8.5 than RCP4.5as projected by median values.

3.1.1. Performance of the BTOPMC

During the calibration, BTOPMC uses observed rainfalldata of 2002–2006 as input into a systematic search formodel parameters which produce the best goodness-of-fitbetween the simulated discharge and observed dischargeat Kuchinarai, Kalasin (E54) station. Thereafter, 5 yearsfrom 1997 to 2001 were used for validation purposes whereboth calibration and validation periods cover extremeflood events in the study area. The performance of themodel during calibration and validation period is summa-rized in Table 6 and Fig. 5. The low flow periods are under-estimated during calibration whereas overestimated duringvalidation for the first three years followed by underestima-tion. In addition, it can also be noticed that the model isable to simulate the low peaks very well however it isunable to fetch the high peak flow during heavy rainfallevents during validation period. Nonetheless, the modelevaluation by NSE, R2 and Vr indicates that the model per-forms reasonably well in the basin.



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3.1.2. Performance of hydrological analysis and design

3.1.2.1. Flood frequency analysis. Table 7 illustrates theprobable peak discharge at Kuchinarai station underRCP 4.5 and RCP 8.5 scenarios. Maximum change in dis-charge is observed for 100 years return period for all thethree future periods under both scenarios. The highest rel-ative change was observed under RCP 8.5 in 2080s whereasthe lowest relative change was observed under RCP 4.5 in2020s.

Table 10Flood inundation depth generated by HEC-RAS – landuse relationship for th

Return period Low ModeraLand use D < 0.6 0.6 < D

(km2) (km2)

25-year Forest 0.88 0.56Croplands 21.69 16.52Grasslands 1.67 1.45Urban 1.19 0.72Water Bodies 1.64 2.21Total 25.44 19.25



Table 11Flood hazard areas estimated from various return periods under ensembles of

Time period Return period Scenarios LowD < 0.6(km2)

2020s 25 RCP 8.5 30RCP 4.5 16

50 RCP 8.5 38RCP 4.5 20

100 RCP 8.5 25RCP 4.5 17

2050s 25 RCP 8.5 27RCP 4.5 20

50 RCP 8.5 30RCP 4.5 21

100 RCP 8.5 32RCP 4.5 22

2080s 25 RCP 8.5 32RCP 4.5 20

50 RCP 8.5 36RCP 4.5 26

100 RCP 8.5 40RCP 4.5 30


3.1.2.2. Designed hydrographs. Synthetic hydrograph gener-ated for different return periods at Kuchinarai station forbaseline period suggests maximum flood is expected for100 year return period followed by 50 and 25 years. Inaddition, it is also observed that the time to peak for allthe return period floods follows same trend as that of theobserved hydrograph. Moreover, maximum variation inthe magnitude of the designed hydrographs is observed atthe peak.

e baseline period in 25, 50 and 100 year return period floods.

te High Very high Total area< 1.0 1.0 < D < 3.5 D > 3.5

(km2) (km2) (km2)

9.81 4.06 15.30114.47 6.78 159.4616.04 8.23 27.381.35 0.03 3.3038.96 11.48 54.29141.66 19.10 205.44

7.85 6.55 16.08125.86 10.68 175.5615.92 9.72 28.212.00 0.03 4.0835.17 17.41 54.84151.63 26.99 223.94

6.66 8.30 16.84130.20 18.63 188.4714.87 11.71 28.882.58 0.05 5.3229.89 23.84 55.23154.31 38.69 239.51

RCP 8.5 and RCP 4.5 in Yang River Basin.

Moderate High Very high Total area0.6 < D < 1.0 1.0 < D < 3.5 D > 3.5(km2) (km2) (km2) (km2)

18 150 45 24318 142 43 21818 147 74 27714 151 112 29718 143 140 32513 117 130 277

17 141 106 29118 132 102 27215 156 123 32413 146 106 28616 103 142 29314 92 91 219

18 142 93 28417 141 96 27417 141 95 28911 132 121 29011 131 122 30410 118 143 300



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3.1.3. Performance of HEC-RAS model

The calibration and validation statistics suggests themodel simulates the observed flood in good agreement(Table 8). Fig. 6 demonstrates the simulated water levelcompared to the observed water level at Pon Thing station(E.70). The validation results suggest the maximumobserved water surface level at 141 masl on 7 October,2007. The simulated water level is 139 masl on the sameday illustrating the model’s ability to fetch the time to peakat the same time.

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3.2. Projected changes in floods

3.2.1. Flood inundated areas for baseline period (1980–

2009)

Table 9 illustrates the areas of inundation under differ-ent degrees of flood hazard. Based on the assigned degreeof flood hazard, although all class of flood hazards showsan increase trend, yet the spatial coverage of high flooddepth is observed to be maximum under present climate.In case of moderate and high flood depth events highestspatial coverage is observed for 25 year return period. Inaddition, very high flood depth is noted to have maximumcoverage of 38.69 km2 which includes 16.15% of total areafor 100-year return period. It is also noteworthy that thetrend in the area of inundation for degree of flood hazardreduces from 25- to 100-years return period for low, mod-erate and high flood depth. However, for very high flooddepth, area of inundation is observed to follow increasingtrend for the corresponding return periods. For the totalarea of inundation, flood of 100-year return period hasthe maximum area of inundation about 239.51 km2 imply-ing the severity of extreme floods in the region under pre-sent climate.

The different landuse under flood inundation was alsoanalyzed and found that cropland areas are significantlyaffected. The area increases from 159.46 to 188.47 km2

for 25 to 100 year return period (Table 10). It is also evi-dent that, only change in inundation area for high and veryhigh flood depth influences the total area for croplandsindicating agricultural vulnerability. This implies thatfarming lands acts as retention areas for flood water inthe region. It can also be noticed that forests experienceshigh and very high flood depth of all return periods com-pared to low and moderate flood depth. In addition, thespatial extent of high flood depth declines with increasedreturn period.

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3.2.2. Changes in future flood inundation area

The simulation was carried out for the present andfuture scenarios of extreme rainfall events. Future floodinundation areas were simulated for 2020s, 2050s and2080s with respect to RCP 4.5 and RCP 8.5 scenarios(Table 11). It is noticed that under RCP 8.5 scenarios,the spatial coverage of flooded area increases noticeably


for all return periods and three future periods relative tothe baseline period. The total flooded areas ranges from243 to 291 km2 for 25 year return period, 277 to 324 km2

for 50 year return period and 293 to 325 km2 for 100 yearreturn period. Under RCP 4.5 scenarios the total areasunder floods are 218–274 km2 for 25 year return period,286–290 km2 under 50 year return period and 219 to300 km2 under 100 year return period.

Similar studies done in Mekong delta region of Vietnamsuggest under climate change high and very high floods areexpected to be more intense (Dinh et al., 2012). Moreover,studies on streamflow under climate change on Mekongriver basin suggests the frequency of peak discharge isexpected to change from 75 year return period to 25 yearfor future climate by 2045s (Lauri et al., 2012) whichimplies the results of the present research are in line withother studies done in the region.

4. Conclusions

In this study, climate change impacts on flood hazardpotential was examined using three GCMs and hydrologi-cal and hydraulic models in the Yang River Basin of Thai-land. The hydrological and hydraulic models werecalibrated and validated satisfactorily against runoff dataand future flows were generated. There was a considerableuncertainty in the results; however, several inferences canbe made based on this study. Generally, temperature andrainfall is expected to increase in future. Different GCMsprojected the increase in temperature and rainfall in differ-ent range. Area inundated by flood under future climatesuggests 100 year return period floods are most severe. Itshowed that approximately 300–325 km2 area will beflooded under future climate change scenarios of which sig-nificant areas are under croplands. In addition, for a partic-ular return period of flood, a shift from very high underbaseline period to moderate flood is expected for future cli-mate under RCP 8.5 scenario. Furthermore, 100 yearreturn period flood is expected to be 25 years in near future(2020s) for both climate scenarios which signifies the severethreat of flood in future under climate change. The outputof this study not only indicates the severity of flood in theregion but also indicates potential damage in food produc-tion and negative effects on the livelihoods of local people.Therefore, proper land use planning and risk-based designof hydraulic structures must be integral part of mitigationplan when addressing vulnerabilities to future flood dam-ages in the basin.

5. Uncited references

Bao (2006), Graiprab et al. (2010), Hoanh et al. (2010),Jones et al. (2004), Mainuddin et al. (2011, 2013), Tuan andChinvanno (2011), Ty et al. (2012), and Willmott (1984,1981).



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3 October 2016

Acknowledgements

The authors would like to appreciate Dr. Jun Magomeand the BTOPMC Development Team of YamanashiUniversity, Japan for providing the model to conduct thisstudy.

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