assessment of water conflict in mae chaem river basin, northern thailand

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Assessment of water conflict in MaeChaem River Basin, Northern ThailandChaiwat Ekkawatpanit a b , So Kazama a , Masaki Sawamoto a &Priyantha Ranjan ca Department of Civil Engineering , Tohoku University , Sendai,Japanb Department of Civil Engineering , King Mongkut's University ofTechnology Thonburi , Bangkok, Thailandc Department of Civil Engineering , Curtin University ofTechnology , Perth, AustraliaPublished online: 18 May 2009.

To cite this article: Chaiwat Ekkawatpanit , So Kazama , Masaki Sawamoto & Priyantha Ranjan(2009) Assessment of water conflict in Mae Chaem River Basin, Northern Thailand, WaterInternational, 34:2, 242-263, DOI: 10.1080/02508060902895892

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Water InternationalVol. 34, No. 2, June 2009, 242–263

ISSN 0250-8060 print/ISSN 1941-1707 online© 2009 International Water Resources AssociationDOI: 10.1080/02508060902895892http://www.informaworld.com

RWIN0250-80601941-1707Water International, Vol. 34, No. 2, Mar 2009: pp. 0–0Water InternationalAssessment of water conflict in Mae Chaem River Basin, Northern ThailandWater InternationalC. EkkawatpanitChaiwat Ekkawatpanita,b*, So Kazamaa, Masaki Sawamotoa and Priyantha Ranjanc

aDepartment of Civil Engineering, Tohoku University, Sendai, Japan; bDepartment of Civil Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand; cDepartment of Civil Engineering, Curtin University of Technology, Perth, Australia

(Received 4 March 2007; final version received 16 March 2009)

For this study, we conducted a quantitative water resources assessment of the Mae ChaemRiver Basin, Thailand, an area with dry season water scarcity and water use conflictsbetween upstream and downstream inhabitants. The block-wise TOPMODEL with theMuskingum–Cunge flow routing method (BTOPMC) was used to predict run-off in 21sub-basins and Geographic Information System (GIS) was employed to collect informationfor crop water demand evaluation. Four sub-basins exhibited critical water conditions in2000. The conversion of forestlands into agricultural lands during the past decade hasengendered water scarcity in the dry season and flooding in the wet season.

Keywords: BTOPMC; water demand; water conflict; GIS; northern Thailand

1. IntroductionIn recent years, water supply shortages have become an urgent issue in South-East Asiabecause water demand is increasing concomitant with rapid economic development andurbanization. Simultaneously, surface water supplies are menaced by land use change(Chuan 2003). Wilk et al. (2001) studied the Nam Pong catchment in northeastern Thailandduring 1957–1995, noting changes in rainfall patterns coincident with reductions in theforest cover of 27–80%. Results of their study showed increased run-off generation afterdeforestation, with the putative cause identified as the traditional practice of retaining treestumps amid cultivated plots. In northern Thailand, which consists of highlands, forestareas are decreasing as agricultural areas are increasing. Water demand is also increasingand water scarcity is occurring both upstream and downstream, which engenders conflictsbetween upstream and downstream inhabitants. This is often attributed to increased wateruse and water storage in upstream areas (Ekasingh et al. 2005). The literature shows thatwater use conflicts between upstream and downstream communities in northern Thailandhave exacerbated inter-regional tensions (Badaenonch 2005, Yamaguchi 2006).

This paper presents a methodology for evaluation of water conflict on a catchmentscale, based particularly on water supply and water demand during the dry season andpeak flood discharge during the wet season. We use the block-wise TOPMODEL with theMuskingum–Cunge flow routing method (BTOPMC), a physically based semi-distributedhydrological model, to predict surface run-off in sub-basins of the Mae Chaem River

*Corresponding author. Email: [email protected]

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mailto:[email protected]

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Water International 243

Basin in northern Thailand and for comparison to water demand using a crop water demandmodel. This study uses information extracted from various field surveys and geographicinformation system (GIS) data (e.g., digital elevation model [DEM], land use data); more-over, a GIS data analysis tool is used extensively to acquire the requisite data.

2. The study area: Mae Chaem River BasinThe Mae Chaem River Basin is located in northwestern Chiang Mai Province in northernThailand. The watershed covers an area of 3853 km2. Its catchment is long and narrow,approximately 100 km long and up to 40 km wide, as shown in Figure 1. The Mae ChaemRiver is a major upper sub-tributary of the Ping River, which in turn is the largest tributaryof the Chao Phraya River, Thailand’s most important river. The catchment extends fromlatitudes 18°12′N to 19°8′N and longitudes 98°8′E to 98°34′E. The area consists of high-mountainous areas with elevations ranging from approximately 650 m above mean sealevel (m.s.l.) near the city of Mae Chaem to more than 2500 m.s.l. on the slopes ofMt. Inthanon, Thailand’s highest peak. The lowest elevation of this basin is 282 m.s.l. (Kurajiet al. 2001). The average annual rainfall at the city of Mae Chaem is 973.3 mm; around 90% ofthe rainfall occurs during the southwest monsoon period, which is during May–October.For evaluation, the Mae Chaem River Basin was divided into 21 sub-basins.

The population of Mae Chaem River Basin comprises hill tribe populations (Karen,Lawa, Hmong and Lisu) and Northern Thai. The four hill tribe populations reside at thedifferent altitude zones in this area, with different native languages, cultures and agricultural

Figure 1. The Mae Chaem River Basin, Thailand.

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244 C. Ekkawatpanit et al.

practices. The Lawa migrated to this area around the middle of the eighth century. Theylive in a remote area in the southwest of the Mae Chaem River Basin with an agriculturalpractice of shifting cultivation. The Lawa were assimilated by Northern Thai and Karen.The Karen resettled in the middle of the eighth century from Myanmar. They have settled inthe middle altitude zone and practice a traditional rotational shifting cultivation system. TheKaren exchanged forest with Northern Thai to supplement their agricultural production.The Karen are the most populous group in the Mae Chaem River Basin (around 50% ofthe total population in this area). The Hmong migrated to this area after World War IIfrom Mae Hong Son Province (west of Mae Chaem) and Hot District (south of MaeChaem). They live in the highland and lowland altitude zones of the river basin. They used togrow opium in shifting cultivation but have changed to commercial highland cash cropsincluding cabbage and tropical fruit trees under the nationally ordered opium substitution plan-ning. The Lisu migrated to Mae Chaem after the Mae Chaem Watershed DevelopmentProject. There is only one Lisu village in this area. The Northern Thai are local Thai wholive in lowland areas and adopt a rice cultivation system supplemented with vegetablesand soybeans in irrigated areas (ICRAF 2001, Hares 2006). Agriculture is the main sourceof livelihood. Most farmers grow cash crops and many work as agricultural labour. Theaverage annual income was 12,191 baht per household, while the regional average annualincome of the poorest households was 40,850 baht (Hares 2006). A royal development projectstarted and has continued working in this area since the 1980s. There are many activitiesincluding research, afforestation and community development. H.M. Queen Sirikit started aproject in 1982 known as the Queen Sirikit Reforestration Project (Suan Pah Sirikit) aimed atpromoting water conservation to mitigate the effects of deforestation. This project, operated bythe Royal Forestry Department, set up many forest conservation groups to take care of forests inthe village. In addition, there are government organizations (GOs) and non-government organiza-tions (NGOs) such as The Tambon Administration Organization (TAO), Raks Thai Foundationand World Agroforestry Centre (ICRAF) in Mae Chaem River Basin to improve management ofagroforestry landscapes and community development (ICRAF 2001, Hares 2006).

3. MethodologyThis study is intended to obtain a time series of water availability and water demand in thewatershed using hydrological and non-hydrological information. The time series of wateravailability is based mainly on the hydrological cycle, which is shown as a mathematicalmodel because water in the river constitutes the whole volume of the available water in thestudy region. Time series of water demand is derived from the estimation of waterconsumption, which requires data other than those of hydrological interest. Agriculturalirrigation was considered to be the main water use in this study.

3.1. Hydrological modelThe semi-distributed hydrological model is based on the block-wise TOPMODEL withthe Muskingum-Cunge flow routing method (BTOPMC) (Ao et al. 1999, Takeuchi et al.1999, Nawarathna et al. 2002).

3.2. Model structure of BTOPMCThe BTOPMC was developed for large river basins. It is a mixed model based on subdivi-sion of the entire basin into sub-basins, each consisting of several grid cells (Ao et al.

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1999, Takeuchi et al. 1999, Nawarathna et al. 2002). Its main sub-models and parametersare introduced briefly as follows.

3.2.1. Topographic sub-model and parametersThe watershed is described by drainage networks extracted from a DEM, in which all pitsare filled with calculated small elevation increments (Ao et al. 2003). Using the resultingstream network data as input, the topographic index, , of any grid cell i (Beven andKirkby 1979) is calculated using Equation (1):

where ai is the drainage area per unit of contour length and tan bi denotes the slope of gridcell i.

The basin is subdivided into sub-basins; their average topographic indices l(k) arecalculated as

where k is the sub-basin code and is the total number of grid cells belonging tosub-basin k. A sub-basin is the area to which the run-off generation sub-model is applied.Sub-basins can be rectangular blocks or areas with natural boundaries, with the latterbeing classified according to the Pfafstetter coding system (Ao et al. 2002). Both and

are used for run-off calculation. Equations (1) and (2) constitute the topographicparameters in BTOPMC.

3.2.2. Run-off generation sub-model and its parametersRun-off calculation is carried out for each grid cell by applying the assumptions andconcepts of TOPMODEL. For a block, an average saturation deficit S(t + 1) is determinedusing Equation (3):

where S(t) is the previous time step average saturation deficit, Qv(t) is the input to ground-water from the unsaturated zone and Qb(t) is the groundwater discharge to the stream overall grids in the block.

The block average saturation deficit S(t) is distributed to the local saturation deficitS(i,t) at grid cell i according to the magnitude of the local soil topographical index relativeto its block average γ:

where m is the decay factor of T0 (lateral transmissivity with respect to the saturation deficit),and g is the block average of soil topographic index, which is given by .

li

lbii

i

a=

⎛⎝⎜

⎞⎠⎟

lntan

, (1)

ll

( ) ( ),( )

kN

ki

pi

N kp

==∑

1

(2)

N kp ( )

lil( )k

S t S t Q t Q tv b( ) ( ) ( ) ( )+ = − +1 (3)

S i t S t ma

T, ( ) ln

tan,( ) = + −

⎛⎝⎜

⎞⎠⎟

⎛

⎝⎜⎞

⎠⎟g

b0

(4)

ln( / tan )a T0 b

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The root zone first receives rainfall at the ith grid cell generated using the Thiessenpolygon method. The storage in the root zone Srz(i,t) changes over time as

where R(i,t) is precipitation approximated by the Thiessen method and E(i,t) isevapotranspiration.

The excess of root zone storage Srz(i,t) infiltrates to the unsaturated zone; its storageSuz(i,t) is expressible as

Overland flow from grid cell i, , can be given as

Groundwater discharge is considered to be semi-steady depending on the saturationdeficit. The hydraulic gradient is assumed to be parallel to the ground surface. Groundwaterdischarge from grid cell I ( ) is determined using Equation (8):

where T0 is the lateral transmissivity of soil profile when saturated to surface, m is thedecay factor of T0 and tan bi denotes the slope of grid cell i.

3.2.3. Flow routing sub-model and its parametersIn the BTOPMC model, the Muskingum–Cunge (M–C) method (Cunge 1969) is selectedas the flow routing technique. As a physically based flow routing method, rather than anempirical approach, this algorithm is equivalent to the convection diffusion model. Differ-ent techniques are useful to route flow to the outlet. The M–C method is useful for a largestream network because it can successively calculate the flow rate everywhere along astream, even if all stream nodes are disturbed by the time differences of flow wave arrivalsat each junction. In the flow routing calculation, the cross section of the river is assumedto be rectangular; its width, B(m), is approximated using Equation (9):

where the constant C = 10 and A denotes the drainage area (km2) (Lu et al. 1991).

3.3. Input dataInput data for BTOPMC fall into three main categories: (1) topography, (2) time series ofhydrological data and (3) land surface characterization.

3.3.1. TopographyThe DEM is used to describe the watershed topography. The topography of the MaeChaem Basin was acquired initially using a 30-m DEM (Figure 2(a)). To aggregate this

S i t S i t R i t E i trz rz( , ) ( , ) ( , ) ( , ),= − + −1 (5)

S i t S i t S i t S i tuz uz rz rz( , ) ( , ) ( , ) ( , ).max= − + −1 (6)

q i tof ( , )

q i t S i t S i tuzof ( , ) ( , ) ( , ).= − (7)

q i tb ( , )

q i t T ebS i t m

i( , ) tan ,( , ) /= −0 b (8)

B i C A i( ) ( )= (9)

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30-m DEM to a 250-m resolution, GIS techniques are used; the results are then adopted todelineate the catchment boundary, slope aspect, flow direction, flow accumulation, rivernetwork, etc. In the DEM structure, the catchment is a group of cells contributing to acommon pour point. The DEM-generated river network tracks flow paths along the steep-est slopes among eight neighbouring cells (O’Callaghan and Mark 1984).

For this study, the Mae Chaem Basin was divided into its 21 sub-basins usinghydrologic modelling in ArcView GIS. A map of sub-basins in Mae Chaem is presented inFigure 2(b). The water demand of each sub-basin was computed using a crop waterdemand model and streamflow in the river at the outlet of the sub-basin using theBTOPMC model.

3.3.2. Land surface characterizationThe first data set of the time series of land use is a historical data set acquired from theLand Development Department (LDD), Ministry of Agriculture and Cooperatives, Thai-land, for the 1989 crop year (May 1989–April 1990). These data were then generalizedinto five classes: paddy, agriculture, forest, urban and other. The second data set repre-sents the land use for the crop year 2000 (May 2000–April 2001). Most land used in thisstudy was for forests and agricultural land, constituting about 90 and 7–9% of the landarea, respectively. Comparison of 1989 and 2000 shows that some agricultural areas havebeen converted to forests by afforestation, which has been promoted in the study areasince 1994.

3.3.3. Time series of hydrological dataThe rainfall of this mountainous basin is characterized by large seasonal and annualvariations that are influenced by tropical monsoon patterns (Walker 2002). The oro-graphic effect imparts an altitudinal increase of the spatial rainfall distribution(Dairaku et al. 2000, Kuraji et al. 2001). To represent rainfall variation within this

Figure 2. Study area: Mae Chaem River Basin. (a) DEM, (b) Sub-basins, (c) River network, raingauges and stream gauge locations.

(a) (b) (c)

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catchment, we used daily rainfall data obtained from six stations, as shown in Figure 2(c):Hot, Mae Chaem and Doi Bo Kaeo stations belong to the Royal Irrigation Department (RID),whereas Khun Yuam, Samoeng and Mae La Noi belong to the Thai MeteorologicalDepartment (TMD).

The temperature in this study area fluctuates from 40°C at the highest to 8°C at thelowest. The mean annual temperature in Chiang Mai is 25.5°C (1993–2003) and the rainyseason is May–October. The hydrologic regime of the Mae Chaem River consists of a baseflow (dry season) from November to April and a high flow (wet season) during May–October,the latter contributing 90% of the total flow. For model calibration and verification, dailyaverage stream-flow measurements were conducted at the P.14 station as the basin outlet,which belongs to the RID; Huai Phung and Kong Kan stations in the basin belong to theDepartment of Water Resources (DWR), as shown in Figure 2(c).

3.4. Crop water demandThe crop water demand of each crop is calculated on the basis of meeting the evapotran-spiration rate of disease-free crops growing in large fields under optimal soil con-ditions including sufficient water and fertility and achieving full production potentialunder the given growing environment. This demand quantity depends mainly on the climate,growing season, crop development and agricultural and irrigation practices (Doorenbosand Pruitt 1977). Actually, is calculated as the product of a crop coefficient (Kc)and the reference crop evaporation . Factors affecting the value of Kc mainly includethe crop characteristics, crop planting, crop development rate, a growing season lengthand climate conditions (Maidment 1993).

The field water requirement is the water required to maintain the storage of the cropfield’s water at the desired level (Piper et al. 1989). The components of the field water bal-ance for wet-foot and dry-foot crops are shown schematically in Figure 3. The field waterstorage is classified into three levels: minimum (SMIN), normal (SNOR) and maximum(SMAX). For paddy fields, storage represents the depth of water stored within the ridges.

( )ETcrop

( )ETcrop

( )ETo

Figure 3. Schematic diagram of field water balance. Source: Piper et al. (1989).

SMAX

QIRR

R

ETcorp

OREQ

DRAIN

RIDGE

PERC

SNORSMIN

SMAX

QIRR

R OREQ

DRAIN

PERC

ETcorp

SNORSMIN

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In addition, SMIN is the minimum level to which the water can fall before irrigation isnecessary, SNOR is the normal or desired storage and SMAX is the maximum field storage,as determined by the ridge height. In fields with dry-foot crops, these field storage param-eters represent the soils’ storage characteristics. The water balance of a field is calculatedas follows for wet-foot and dry-foot crops (Piper et al. 1989):

where STt is the initial field water storage (mm), STt+1 is field water storage at the end ofthe time step (mm), PERC is deep percolation (mm) and OREQ represents other require-ments (mm) including land preparation and nursery requirements.

The irrigation requirement can be determined considering the value of STt+1 as (Piperet al. 1989):

where DRAIN is the drainage water (mm) caused by spillage over the top of the ridge andQIRR is the irrigation water requirement (mm).

In condition (iv), irrigation water should be supplied to the field until water stor-age satisfies normal storage levels. Consequently, irrigation wateris applied to bring the field water storage level up to SNOR. Field water storage lev-els are based on local cultivation practices and soil properties. For paddies, SMAXwas set at the typical effective ridge level of paddy fields in the region, SMIN wasset as the minimum paddy water level recommended by the RID and SNOR wastaken as the mean of these two values. The values used for this study are shown inTable 1.

3.5. Water sufficiency indexFor assessment of water sufficiency for a specific sub-basin i (i = 1, …, n), an index wasdeveloped based on the ratio of the difference of streamflow (available water) and crop

i) − excess water

ii) − no irrigation required

iii) − adequate water

− no irrigation required

iv) − insufficient water

ST ST R ET PERC OREQt t+ = + − − −1 crop , (10)

ST SMAX

DRAIN ST SMAXt

t

+

+

>= −

1

1

ST SMAX

QIRRt+ =

=1

0

SMAX ST SMINt> >+1

QIRR = 0

SMIN ST

QIRR SNOR STt

t

>= −

+

+

1

1

ST SNORt+ =( )1

Table 1. Field water storage (mm).

SMIN SNOR SMAX

Paddy 45.00 90.00 135.00Dry-foot crops −60.00 −15.00 0.00

Source: Piper et al. (1989).

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water demand (RO − WD) to the available water (RO). The water sufficiency index isdefined as

where RO is the streamflow at the outlet of sub-basin i (m3) and WD is the water demandof sub-basin i (m3).

This water sufficiency index (WSI), which is calculated on a monthly basis, is used asan indicator of the degree of water sufficiency at the basin level. A water shortage exists inthe sub-basin if the value of WSI is negative. It should be noted that the concept of WSI isfirst proposed in this paper. This also includes the concept of combined water sufficiencyindex (CWSI) and potential downstream flooding hazard (PDFH), which will be explainedlater in this paper.

4. Results and discussion4.1. Hydrological model4.1.1. Model calibration and simulationHydrological modelling consists of the basin representation and response simulation. TheTOP model parameters are intended to be physically interpretable. The BTOPMC modelwas modified as a semi-distributed model in this application. Using this model, calcula-tion is performed on a pixel-by-pixel basis. However, the present parameters wereobtained only on the basis of land use distribution. This was done using a 250-m resolu-tion of digital elevation and land use. Because no snow falls in the study area, the numberof model parameters can be reduced to four, including the saturated soil transmissivityT0 (m2 h−1), the decay factor m(m), the maximum storage capacity of the root zone becauseof vegetation (m) and the Manning’s roughness coefficient n (Nawarathna et al.2002). Four model parameters per block were calibrated by trial and error. Simulationswere carried out for 1989 and 2000. The model was calibrated at the P.14 station at theoutlet of Mae Chaem River Basin. The model performance was evaluated based on theindex of agreement (IA) (Willmott 1982). The IA considers the model errors for estimationof the mean or variance of the observed data sets. The indices are an improvement over thecoefficient of determination, but they are sensitive to outliers because they include thesquared error terms:

In this equation, is the observed value at the i th time, represents the simulatedvalue, N is the total number of observations or simulations and and respectivelyindicate the mean values of and .

Figure 4 displays comparisons of observed and simulation hydrographs for 1989 and 2000,the IA being 0.87 and 0.92 in 1989 and 2000. The overall performance of the simulation

WSIRO WD

ROii i

i=

−, (11)

Srz max

IA

O P

P O O O

i ii

N

i ii

N= −

−

− + −

=

=

∑

∑1 0

2

1

2

1

.

( )

( )(12)

Oi Pi

O POi Pi

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seems to be satisfactory. The overall efficiency of IA at the Huai Phung station was 0.83and 0.85 in 1989 and 2000, respectively, whereas at the Kong Kan station, the overallefficiency of IA was 0.87 and 0.90 in 1989 and 2000, respectively. Model parametersobtained for the calibration were used to predict the flow in 21 referenced sub-basins(See Figure 2(b)).

Figure 4. Comparison of observed and simulated hydrographs at P.14 station.

0

50

100

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1/5/

1989

15/5

/198

9

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/198

9

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/198

9

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/198

9

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/198

9

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/198

9

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1989

21/8

/198

9

4/9/

1989

18/9

/198

9

2/10

/198

9

16/1

0/19

89

30/1

0/19

89

13/1

1/19

89

27/1

1/19

89

11/1

2/19

89

25/1

2/19

89

8/1/

1990

22/1

/199

0

5/2/

1990

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/199

0

5/3/

1990

19/3

/199

0

2/4/

1990

16/4

/199

0

30/4

/199

0

Q (m

3 /s) Observation

Simulation

(a) 1989.

0

50

100

150

200

250

300

350

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450

500

1/5/

2000

15/5

/200

0

29/5

/200

0

12/6

/200

0

26/6

/200

0

10/7

/200

0

24/7

/200

0

7/8/

2000

21/8

/200

0

4/9/

2000

18/9

/200

0

2/10

/200

0

16/1

0/20

00

30/1

0/20

00

13/1

1/20

00

27/1

1/20

00

11/1

2/20

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/200

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/200

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/200

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/200

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/200

1

Q (m

3 /s )

Observation

Simulation

(b) 2000.

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4.2. Effects of afforestation on crop water demandAssessment of water use typically centers on agricultural irrigation demand, which is muchgreater than other demands for domestic and industrial uses. A crop model was applied tocrop irrigation in 1989 and 2000. The cropping pattern for each category of irrigationscheme can be selected from among a set of seven crops that are appropriate to the land useof the Mae Chaem River Basin. The model uses the FAO approach (Doorenbos and Pruitt1977) to calculate daily crop water requirements. The estimated monthly crop water use ofthe Mae Chaem River Basin is shown in Figure 5. The amounts of annual crop waterdemand were 11.37 × 107 m3 in 1989 and 7.48 × 107 m3 in 2000, indicating that the annualcrop water demand had decreased markedly during 1989–2000. Agricultural irrigationdemand in June 1989 constituted around 30% of the annual crop water demand of 1989. Thereason for this remarkable water demand in June is that most agriculture land in this area isdevoted to maize and rice cultivation: the crop calendar of maize is June–October, and thatof rice is June–November. A huge water demand exists at the initial stage of both crops.Regarding land use in the Mae Chaem River Basin, some agricultural areas were convertedto forest areas through afforestation, which has been promoted in the study area. Conse-quently, crop water demand decreased in 2000. Figure 5 clearly depicts this reduction.

4.3. Water sufficiency index in sub-basinsIn this approach, a water sufficiency index is introduced as the ratio of water withdrawals(for crop water demand) to surface run-off. The WSI is an indicator of the regional waterstatus. Tables 2 and 3 respectively present monthly WSI values for respective sub-basinsin 1989 and 2000. In addition, Scenario A was tested to evaluate WSI with assumed rain-fall scenario and land use of the Mae Chaem River Basin in 2000. Scenario A used rainfalldata period, with rainfall data adjusted to be 10% heavier than rainfall in 2000 and 10%longer drought period than rainfall in 2000. Land use in scenario A, which is based on land

Figure 5. Monthly crop water demand in the Mae Chaem River Basin: 1989 and 2000.

0

5

10

15

20

25

30

35

40

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar AprMonth

Cro

p w

ater

dem

and

(m3x

106 )

1989

2000

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Table 2. Water Sufficiency Index in 1989.

Sub-basin

WSI

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

1 0.94 0.83 0.96 0.96 0.98 0.98 0.88 0.93 0.95 1.00 1.00 1.002 1.00 0.86 1.00 1.00 1.00 0.99 0.95 0.95 0.98 1.00 1.00 1.003 0.96 0.72 0.98 0.98 0.98 0.99 0.93 0.95 0.97 1.00 1.00 1.004 0.94 0.57 0.98 0.96 0.97 0.98 0.90 0.94 0.96 1.00 1.00 1.005 0.94 −0.29 0.96 0.98 0.99 0.99 0.93 0.96 0.97 1.00 1.00 1.006 0.96 0.73 0.98 0.97 0.98 0.98 0.92 0.95 0.97 1.00 1.00 1.007 0.99 0.77 0.99 0.99 1.00 1.00 0.98 0.99 0.99 1.00 1.00 1.008 0.97 0.75 0.98 0.98 0.99 0.99 0.94 0.97 0.98 1.00 1.00 1.009 0.92 −0.84 0.98 0.97 0.97 0.98 0.91 0.95 0.96 1.00 1.00 1.0010 0.96 0.62 0.98 0.97 0.98 0.98 0.92 0.96 0.97 1.00 1.00 1.0011 0.82 0.79 0.95 0.90 0.92 0.93 0.71 0.83 0.88 1.00 1.00 1.0012 0.76 0.73 0.92 0.85 0.88 0.90 0.57 0.75 0.83 1.00 1.00 1.0013 0.83 0.63 0.96 0.93 0.93 0.95 0.78 0.87 0.90 1.00 1.00 1.0014 0.69 0.51 0.89 0.81 0.83 0.87 0.43 0.66 0.76 1.00 1.00 1.0015 0.96 0.74 0.98 0.98 0.98 0.98 0.93 0.96 0.97 1.00 1.00 1.0016 0.82 0.67 0.95 0.91 0.91 0.93 0.72 0.83 0.89 1.00 1.00 1.0017 0.76 0.65 0.92 0.86 0.88 0.91 0.61 0.77 0.84 1.00 1.00 1.0018 0.89 0.41 0.94 0.96 0.97 0.97 0.87 0.87 0.81 0.73 0.92 1.0019 1.00 0.81 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.0020 0.95 0.66 0.97 0.97 0.98 0.98 0.91 0.94 0.95 0.98 0.99 1.0021 0.92 0.64 0.96 0.95 0.96 0.97 0.87 0.87 0.81 0.72 0.93 1.00

Table 3. Water Sufficiency Index in 2000.

Sub-basin

WSI


1 0.99 0.95 0.96 1.00 1.00 0.99 0.97 0.96 0.94 1.00 1.00 1.002 0.99 0.94 0.99 1.00 1.00 0.98 0.98 0.98 0.99 1.00 1.00 1.003 1.00 0.98 0.98 1.00 1.00 1.00 0.98 0.98 0.98 1.00 1.00 1.004 0.98 0.93 0.89 0.95 1.00 0.93 0.93 0.90 0.89 1.00 1.00 1.005 0.96 0.66 0.55 0.98 1.00 0.93 0.92 0.67 −0.01 1.00 1.00 1.006 0.98 0.95 0.94 0.98 1.00 0.96 0.95 0.94 0.95 1.00 1.00 1.007 0.98 0.93 0.94 0.96 1.00 0.95 0.96 0.95 0.98 1.00 1.00 1.008 0.99 0.94 0.93 0.98 1.00 0.96 0.96 0.94 0.93 1.00 1.00 1.009 0.99 −0.14 −2.20 0.79 1.00 0.93 0.98 −0.64 −6.29 1.00 1.00 1.0010 0.99 0.78 0.38 0.95 1.00 0.97 0.92 0.64 −0.29 1.00 1.00 1.0011 0.93 0.88 0.74 0.84 1.00 0.71 0.70 0.61 0.87 1.00 1.00 1.0012 0.92 0.85 0.70 0.83 1.00 0.65 0.65 0.55 0.85 1.00 1.00 1.0013 1.00 −3.16 −5.64 0.11 1.00 0.93 0.94 0.91 0.97 1.00 1.00 1.0014 0.96 −1.74 −3.44 0.39 1.00 0.79 0.79 0.72 0.90 1.00 1.00 1.0015 0.98 0.86 0.82 0.96 1.00 0.94 0.94 0.91 0.87 0.99 1.00 1.0016 0.97 0.88 0.87 0.91 1.00 0.87 0.87 0.83 0.94 1.00 1.00 1.0017 0.94 0.86 0.80 0.88 1.00 0.76 0.76 0.70 0.90 1.00 1.00 1.0018 0.64 −1.18 −1.74 0.10 1.00 −0.13 −0.04 −0.56 −1.02 0.26 0.78 0.9819 0.98 0.99 0.75 1.00 1.00 0.95 0.96 0.63 −0.36 1.00 1.00 1.0020 1.00 0.98 0.96 1.00 1.00 1.00 1.00 0.98 0.92 1.00 1.00 1.0021 0.98 0.90 0.86 0.97 1.00 0.95 0.95 0.89 0.77 0.94 0.98 1.00

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use of the Mae Chaem River Basin in 2000, was compared to see climate variability. In addi-tion, due to many organizations working in this area, the future land use in the Mae ChaemRiver Basin is likely to be improve, as the WSI in Scenario A shows in Table 4. The simula-tion of Scenario A enables the ability for prediction of future situation after climate change,which seriously concerns local people. Those tables show that a water deficit occurred earlyin the wet season in 1989 in Sub-basin 5, but had recovered by 2000. Conversely, in Sub-basin 9, the result shows that a serious water deficit occurred in the early wet season and theearly dry season in 2000, but not during the dry season in 1989. A similar situation is visiblefor Sub-basins 13, 14 and 19. In Sub-basin 18, water deficits occurred in both the wet anddry seasons in 2000. A serious situation is apparent for Sub-basin 9 in both the dry and wetseasons and in Sub-basins 13 and 14 in the wet season of 2000. Water use conflict is moreintense in the dry season than in the wet season because sufficient rainfall occurs during thewet season. Considering the overall situation of the Mae Chaem catchment, the year 1989 isdistinguished by sustained high values of the WSI, which override effects of afforestation inthe basin. WSI in Scenario A showed a similar situation in 2000 generally, but Decemberand January (dry season) showed a more serious water deficit than in 2000 because ScenarioA has 10% longer drought period than rainfall in 2000. The dry season (June–July) showed alarger water deficit in Scenario A than in 2000 because Scenario A has 10% longer droughtperiod than in 2000.

4.3. Water conflict and land use change4.3.1. Dry season conflictWhen an upstream area has a water deficit, it adversely affects the downstream water sup-ply. Upstream inhabitants draw large amounts of water from streams, which engender a

Table 4. Water Sufficiency Index in Scenario A.

Sub-basin

WSI


1 0.99 0.96 0.97 1.00 1.00 0.99 0.96 0.96 0.94 1.00 1.00 1.002 0.99 0.95 0.99 1.00 1.00 0.98 0.98 0.98 0.99 1.00 1.00 1.003 1.00 0.98 0.99 1.00 1.00 1.00 0.98 0.98 0.97 1.00 1.00 1.004 0.99 0.95 0.90 0.96 1.00 0.93 0.93 0.89 0.89 1.00 1.00 1.005 0.97 0.73 0.65 0.98 1.00 0.92 0.90 0.62 −0.11 1.00 1.00 1.006 0.99 0.96 0.95 0.98 1.00 0.96 0.95 0.93 0.95 1.00 1.00 1.007 0.99 0.95 0.95 0.97 1.00 0.95 0.95 0.94 0.98 1.00 1.00 1.008 0.99 0.95 0.95 0.98 1.00 0.96 0.96 0.94 0.92 1.00 1.00 1.009 0.99 0.05 −1.87 0.83 1.00 0.93 0.98 −0.67 −6.35 1.00 1.00 1.0010 0.99 0.83 0.45 0.96 1.00 0.97 0.92 0.64 −0.30 1.00 1.00 1.0011 0.95 0.90 0.77 0.86 1.00 0.72 0.69 0.60 0.86 1.00 1.00 1.0012 0.94 0.88 0.74 0.86 1.00 0.67 0.64 0.55 0.85 1.00 1.00 1.0013 1.00 −2.46 −4.87 0.27 1.00 0.93 0.93 0.91 0.97 1.00 1.00 1.0014 0.97 −1.26 −2.90 0.50 1.00 0.79 0.78 0.71 0.90 1.00 1.00 1.0015 0.98 0.89 0.86 0.97 1.00 0.95 0.93 0.90 0.86 0.99 1.00 1.0016 0.97 0.91 0.89 0.92 1.00 0.87 0.86 0.82 0.94 1.00 1.00 1.0017 0.95 0.89 0.82 0.90 1.00 0.78 0.76 0.69 0.90 1.00 1.00 1.0018 0.70 −0.78 −1.29 0.26 1.00 0.03 0.01 −0.51 −0.97 0.27 0.79 0.9819 0.98 0.99 0.80 1.00 1.00 0.96 0.96 0.63 −0.36 1.00 1.00 1.0020 1.00 0.99 0.97 1.00 1.00 1.00 1.00 0.98 0.92 1.00 1.00 1.0021 0.99 0.92 0.89 0.97 1.00 0.95 0.94 0.88 0.76 0.94 0.98 1.00

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reduction of downstream flow when upstream areas have a water deficit. This reduction ofdownstream water creates problems for downstream inhabitants. The situation worsens ifthe downstream area also has a water deficit. Therefore, we defined a condition of a com-bined water sufficiency index (CWSI) to reflect the interaction between upstream anddownstream areas: CWSI is defined as a matrix form:

If CWSI has a minus value of both WSIup and WSIdown (−,−) (Area C in Figure 6), itindicates that both upstream and downstream areas have deficits, thereby creating waterconflicts in both upstream and downstream areas. Similarly, there is no water deficit eitherupstream or downstream if CWSI is (+, +) (Area B in Figure 6). Tables 3 and 4 show thatthe worst water conflict situation occurred in January 2001, with negative values of WSI in

CWSI WSI WSI= ( , )up down (13)

Figure 6. CWSI in January 1990 and 2001.

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several sub-basins. Figure 6 shows the CWSI for each node in January 1990 and January2001. It portrays the occurrence of water conflicts in Sub-basins 9 and 10 in 2001 becausewater deficits existed in both upstream and downstream areas. This situation had notoccurred in January 1990. Figure 6(a) shows a fair situation between upstream and down-stream in January 1990 because WSI value in upstream was almost equal to that in thedownstream, and all CWSI points are located in area B. According to Figure 6(b), sub-basins with both positive WSI (Area B) showed no water conflicts and showed a fair situ-ation upstream and downstream, except for nodes (20, 21). An unfair situation in nodes(20, 21) occurs because downstream water use is more than upstream water use, but noconflicts occur because water deficit in both upstream and downstream did not occur. Ifthese nodes were moved to Area C (−,−) or Area D (+, −), the future water demand andsupply might be vulnerable. Long-term evaluation using long-term data is highly recom-mended for that kind of estimation. Nodes in Area A of Figure 6(b) [e.g., (5, 7), (10, 15),(18, 20), (19, 20)] show that upstream areas have a water deficit, but downstream areas dis-played no water deficit in January 2001. This situation is not likely to create water conflictbetween upstream and downstream areas, but in these nodes, upstream sub-basins use alarger amount of water than available water, which is apparent in Sub-basins 5, 10, 18 and 19.

Figure 7 shows a similar situation of interaction between upstream and downstreamareas in Scenario A because the land use of Scenario A is same as the 2000 land use andrainfall data is different. It can be concluded that land use change is significant for waterdeficits. Regarding the relationship between water deficits and land use change, it isimportant to elucidate the effects of land use changes on water conflicts. The salient rea-son for water conflict in Sub-basin 9 is the high demand for cabbage cultivation. Deforest-ation occurred in Sub-basins 9 and 10 by changing the land use type to cabbage cultivationas a cash crop activity. Figure 8 shows that Sub-basins 9 and 10 show a higher percentageof land (17% in Sub-basin 9 and 15% in Sub-basin 10) used for cabbage cultivation. Inaddition, the worst water conflicts occur in Sub-basins 9 and 10. The forest area has beenincreasing and agricultural lands have decreased when we consider land use in Sub-basins5, 18 and 19. However, farmers have started to cultivate in both dry and wet seasonsinstead of only the wet season (e.g., soybean, shallot), which engenders water diversion

Figure 7. CWSI in Scenario A.

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during the dry season in Sub-basins 5, 18 and 19, even though no deforestation occurred inthose sub-basins. Demonstrably, land use plays an important role in water demand. Landuse is an important factor for planning and management of regional water resources.

Conflict between Sub-basins 9 and 10 has also been described in the literature(Badaenonch 2005). The Raks Thai Foundation also mentioned in their research that asevere water conflict pertains in Sub-basins 9 and 10. This foundation was established as aregistered non-profit organization in 1997 to develop the livelihood of the upland inhabit-ants and natural resources management in the Mae Chaem watershed. Our estimation alsoshowed that a severe water conflict exists, as evidenced by the CWSI. The forest area

Figure 8. Land use categories by sub-basins in 1989 and 2000.

100%

90%

80%

Are

a(%

)

70%

60%1 2 3 4 5 6 7 8 9 10 11 12

Sub-basin

13 14 15 16 17 18 19 20 21

other

Urban

Cabbage

Agriculture

Paddy

Forest

100%

90%

80%

70%

60%1 2 3 4 5 6 7 8 9 10 11 12

Sub-basin13 14 15 16 17 18 19 20 21

other

Urban

Cabbage

Agriculture

Paddy

Forest

Are

a(%

)

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amount throughout the basin was higher, but deforestation took place in some sub-basins,with transformation of those areas to agricultural use (e.g., for cabbage and shallots). TheCWSI further reveals that the nodes of Sub-basins 9 and 10 show a high negative value.The tension between upstream (Sub-basin 9) and downstream (Sub-basin 10) villages overwater use in the Mae Suk watershed was analysed based on the land use change in theupper watershed. During the dry season, a water shortage exists in streams that flowthrough downstream villages. Upland forest areas have been converted to agriculturalareas, thereby creating a higher water demand upstream. Sub-basin 9 (upstream of theMae Suk watershed), which was transformed to agricultural land, became a menace todownstream water security concomitant with intensified agricultural land use upstream.However, water demand in downstream areas has increased concurrently, principally withincreased dry season cropping. Issues of deforestation create changes in the quantity andtiming of water supplies and water demand, which are the main sources of tension amongupstream and downstream communities in the Mae Suk watershed.

4.3.2. Wet season conflictFurthermore, disastrous flooding occurs during the wet season. The water conflict in thewet season centres upon the issue of downstream flooding. Downstream inhabitants havecomplained that flooding occurs because of deforestation in the upper watershed. There-fore, we computed the peak discharge per unit area, ( ), for eachsub-basin. The catchment area is the easiest factor to relate hydrology to the difference ofthis ratio upstream and downstream. It facilitates the assessment of the PDFH, as shownby Equation (14):

In this equation, is the ratio of maximum discharge and catchment areaupstream and is the ratio of maximum discharge and catchment areadownstream.

A high value of PDFH indicates the likelihood of a peak flood discharge to downstreamareas if upstream land use changes. Tables 5 and 6, respectively, present the PDFH ineach sub-basin in 1989 and 2000. Results show that the maximum values of PDFH occurin 1989 in Sub-basins 1 and 3. That means that peak flow was discharged from Sub-basin 1to Sub-basin 3 in 1989. It recovered in 2000 because of the land use changes from agricul-tural areas to forest. In contrast, during 2000, the maximum values of PDFH were inSub-basins 12 and 15, implying peak flow discharges from Sub-basin 12 to Sub-basin 15.Considering land use, as shown in Figure 8, the results indicate a peak flow discharge inSub-basin 12 (Mae Uam), which is higher because of deforestation in that sub-basin.Although Figure 8 shows that deforestation occurs in Sub-basins 9 and 10, the PDFH is nothigher in Sub-basins 9 and 10. The DEM data show that the land slope of Sub-basin 9 is notsteep and does not lead to a high discharge rate. Sub-basin 12 includes the steep slopes ofMt. Inthanon, which engender a high flow rate. These results show that deforestation ofsteep slopes creates flooding in downstream sub-basins. Table 7 presents PDFH in scenarioA; the maximum value of PDFH shows the relationship between Sub-basins 12 and 15 inthe similar 2000 case. It can be confirmed that land use change and topography are important

( / )Q Apeak 103 3 2m m/

PDFHQ

A

Q

A=

⎛⎝⎜

⎞⎠⎟

−⎛⎝⎜

⎞⎠⎟

peak

up

peak

down

. (14)

( / )Q Apeak up( / )Q Apeak down

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Tabl

e 5.

PDFH

in 1

989.

Ups

tream

Subb

asin

12

34

56

78

910

1112

1314

1516

1718

1920

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nstre

am3

4.55

−0.0

76

2.06

−0.0

37

−1.9

68

0.08

0.29

10−3

.39

12−0

.15

14−0

.72

150.

350.

861.

750.

4917

−1.1

420

0.72

1.40

−0.8

91.

6821

−0.4

5

Tabl

e 6.

PDFH

in 2

000.

Ups

tream

Subb

asin

12

34

56

78

910

1112

1314

1516

1718

1920

Dow

nstre

am3

3.91

−0.1

76

2.54

4.49

7−5

.01

80.

11−1

.38

10−1

1.26

12−0

.45

14−3

.03

15-0

.16

5.25

7.86

6.21

17−3

.72

20−0

.15

4.65

−5.9

1−6

.45

21−0

.27

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Tabl

e 7.

PDFH

in sc

enar

io A

.

Ups

tream

Subb

asin

12

34

56

78

910

1112

1314

1516

1718

1920

Dow

nstre

am3

5.46

−0.1

36

−4.0

76.

227

−7.0

38

0.28

−2.0

910

−15.

9012

−0.4

614

−4.5

815

−0.2

27.

5910

.31

8.31

17−4

.63

20−0

.15

5.54

−7.8

9−9

.01

21−0

.23

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for high peak flow. In addition, this result shows that forests are important for reducingthe peak flow, which inhibits flooding. Upstream mountain areas with steep slopes areparticularly vulnerable to flooding because of deforestation, which increases the peakflood discharge and destroys downstream areas. However, the PDFH has negative val-ues in some sub-basins such as Sub-basins (2, 3), (3, 6) and (5, 7), because downstreamslopes are steeper than upstream slopes. This situation fosters soil erosion in down-stream areas.

4.3.3. Population and water conflictsBased on data from Thomas et al. (2004) and Trisophon and Punyawadee (2003), thepopulation density in Sub-basin 9 (Mae Suk watershed) is 31 person/km2 and that in Sub-basin 12 (Mae Uam watershed) is 65 person/km2. These data show that Sub-basin 9 has awater deficit but does not suffer from flooding when we consider Sub-basins 9 and 12.Sub-basin 12 has no deficit, but it experiences flooding. The population density is higherin Sub-basin 12, which indicates that population is concentrated in areas that have no dryseason water conflicts, even though they have floods. One reason for this change of popu-lation density might be that flooding does not occur frequently, although dry season waterscarcity occurs frequently. In addition, flooding is a disastrous event and dry season waterconflicts cannot be classified as a disaster.

5. ConclusionsWe assessed water resources in Thailand’s Mae Chaem River Basin, which is con-fronting problems such as dry season water scarcity and water use conflicts betweenupstream and downstream areas. To predict run-off in sub-basins, BTOPMC wasused; GIS was employed to collect the necessary information for agricultural irriga-tion demand evaluation. This study has presented an example of integrating availabletools and hydrological knowledge to analyse water scarcity problems. Comparison ofthe WSI indicates that Sub-basin 9 (Upper Mae Suk watershed) faces the worst situ-ation in both dry and wet season water deficits. Although no critical water conflictexists in the overall basin, Sub-basins 9, 10, 12 and 15 show critical water conflicts in2000. The CWSI is useful to evaluate dry season water use conflicts between upstreamand downstream areas. It indicates a water conflict between upstream and downstreamareas if CWSI shows negative values for both upstream and downstream areas. Fur-thermore, an indicator, PDFH, was introduced to reflect water conflicts during thewet season. A PDFH with a higher value indicates that downstream flooding willoccur, such as in Sub-basins 12 and 15.

This study has also showed that deforestation, which converts forest lands to agricul-tural land, creates water scarcity in the dry season and engenders flooding in the wet sea-son. Deforestation in upstream mountain areas engenders a higher peak discharge, whichresults in downstream flooding. With increasing demands for improved upstream water-shed management, a pressing need exists to implement sustainable land use strategies,which would serve the respective interests of upstream and downstream communities.However, the results of water resource assessments show that, in addition to increasedwater deficits and flooding, water scarcity is not apparent on the scale of the whole basin,but situations of regional-scale conflicts occur.

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AcknowledgementsThis study was made possible largely through the Grants-in-Aid for Scientific Research from theJapan Society for the Promotion of Science (JSPS) (So Kazama). We would like to acknowledge thegenerosity of those grants. We wish to acknowledge to The Royal Thai Government for financialsupport to the first author. We are also indebted to Royal Irrigation Department for providing mete-orological and run-off data. Finally, three anonymous reviewers are thanked for greatly improvingthe quality of this contribution.

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assessment of water conflict in mae chaem river basin, northern thailand

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