Optimal allocation of bulk water supplies to competing use sectors based on economic criterion – An application to the Chao Phraya River Basin, Thailand

Journal of Hydrology 401 (2011) 22–35

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Optimal allocation of bulk water supplies to competing use sectors based on economic criterion – An application to the Chao Phraya River Basin, Thailand L. Divakar a,⇑, M.S. Babel a, S.R. Perret a,b, A. Das Gupta a a b

Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Klong Luang, Pathumthani, Thailand CIRAD, UMR G-EAU Bp 5095, 34196 Montpellier cedex 5 - France

a r t i c l e

i n f o

Article history: Received 31 January 2011 Accepted 3 February 2011 Available online 25 February 2011 This manuscript was handled by G. Syme, Editor-in-Chief Keywords: Hydro-economic model Optimal water allocation Maximization of economic benefit Net economic return Chao Phraya River Basin Thailand

s u m m a r y The study develops a model for optimal bulk allocations of limited available water based on an economic criterion to competing use sectors such as agriculture, domestic, industry and hydropower. The model comprises a reservoir operation module (ROM) and a water allocation module (WAM). ROM determines the amount of water available for allocation, which is used as an input to WAM with an objective function to maximize the net economic benefits of bulk allocations to different use sectors. The total net benefit functions for agriculture and hydropower sectors and the marginal net benefit from domestic and industrial sectors are established and are categorically taken as fixed in the present study. The developed model is applied to the Chao Phraya basin in Thailand. The case study results indicate that the WAM can improve net economic returns compared to the current water allocation practices. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction The peril of water scarcity is mainly caused by enduring and extensive overexploitation, pollution and increasing demand of water for economic development. There are instances of demand exceeding water availability leading to conflict among and within water use sectors. This threat may be mitigated by improved water management practices such as efficient allocation of available water resources. Scientific and efficient water allocation based on economic efficiency helps in creating a balance between the demand and the supply of water resources. It can also form a basis for consensus of water sharing among different demand sectors. Water allocation problems are on the rise in many parts of the world, especially during dry season, as water supply is quite steady while water demands are increasing at a much faster rate. In order to ascertain the best water allocation plans under various constraints, mathematical modeling is often used to provide a basinwide representation of water availability and use. It also offers a transparent framework for analysis and debate of water resource sharing and development options. Pioneering works in the early seventies attempted to link up hydrology with economics to represent the responses to variations in supply and cost (Young and Bredehoeft, 1972). The use of ⇑ Corresponding author. Tel.: +66 2524 5790; fax: +66 2524 6425. E-mail addresses: [email protected] (L. Divakar), [email protected] (M.S. Babel). 0022-1694/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2011.02.003

marginal value of water for efficient allocation was recognized by Daubert and Young (1981). Noel and Howitt (1982) worked towards an optimal, spatial and intertemporal allocation with hydrologic and economic theories. Different economic approaches like assessing the potential of limited market institutions (Vaux and Howitt, 1984) and allocation with market transfers (Booker and Young, 1994) were developed to alleviate water scarcity. Further studies tried to better integrate allocation problems with economic, hydrologic, and social perspectives (McKinney and Cai, 1996). An optimization model to compare the economic benefits of the optimal versus historic plans (Ward and Lynch, 1996), a framework with benefits of water rights trading (Rosegrant et al., 2000), and designing and evaluating strategies to improve the physical and economic productivity (Rodgers and Zaafrano, 2002) are some examples of integrated economic-hydrologic approaches. Recent research has further emphasized integration, resorting to advanced modeling or optimization approaches, and with attempts towards direct support to policy- and decision-making. An aggregate, integrated economic-hydrologic model to analyze water allocation and use under alternate policy scenarios was applied to Mekong Basin in China and Dong-Nai Basin in Vietnam (Ringler, 2001; Ringler and Nguyen, 2004). Cai et al. (2003) reapplied the advanced hydrologic, agronomic, and economic components to explore both economic and environmental consequences of various policy choices. Babel et al. (2005) developed a simple interactive and integrated water allocation model to assist planners and decision makers in optimal allocation of limited water

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

from a reservoir to different user sectors. Ward et al. (2006) took the approach further and implemented institutional adjustments to limit drought damages. An extensive and comprehensive review of hydro-economic models and further research needs has been provided by Heinz et al. (2007) and Harou et al. (2009). Ward (2009) systematically integrated costs and benefits into an integrated physical, institutional and economic analysis for an expandable prototype river basin. The present study strengthens the idea that improved water management through efficient allocation is the key solution to combat the water crisis during the dry season. The main objective of the paper is to develop and demonstrate the applicability of a simple and user friendly water allocation model to allocate bulk water supplies to competing use sectors based on economic criterion. The research builds up on the optimal water allocation model developed by Babel et al. (2005) by applying it to a real case of Chao Phraya River Basin in Thailand with an objective of maximizing the economic benefits from different use sectors. Several scenarios are analyzed which can assist the planners and decision makers to develop suitable water allocation policies and plans for the study basin. Critical review and analysis of the existing water allocation practices in the Chao Phraya River Basin is carried out and the results are compared with the model results to demonstrate the versatility of the water allocation model. 2. Study area: the Chao Phraya River Basin The Chao Phraya River Basin, often called the lifeline of Thailand, with an area of 157,925 km2 covers almost one-third of country’s geographical area and is divided into upper, middle and lower basin (delta) as shown in Fig. 1. The river is about 365 km long and flows south from the highlands on the country’s northern border to the head of the Gulf of Thailand. The main tributaries – Ping, Wang, Yom, and Nan – unite at Nakhon Sawan (junction C.2) to form the main Chao Phraya River. The Chao Phraya splits to become the Tha Chin River just north of Chainat province. It supplies water and supports navigation, fisheries, and recreation. It is also a sink for wastewater within the watershed area. A diversion dam called the Chao Phraya, which is also known as Chainat dam, is constructed on the Chao Phraya River to divert water to irrigation areas. Sakae Krang, a small sub-basin in the north of Tha Chin, and Pasak, in the southeastern part of Chao Phraya, contribute their flow into the main river. Bhumipol on the Ping River and Sirikit on the Nan River are the two largest dams constructed in 1964 and 1974, with an effective storage capacity of 9662 and 6660 Mm3, respectively. They store runoff concentrated in the wet season and supply water during the dry season for agriculture, domestic use, hydropower generation, industrial use, navigational purposes and for prevention of seawater intrusion from the Gulf of Thailand. The groundwater in the basin is used mainly by the industrial sector with some unaccounted pumping for agricultural use. 2.1. Water availability and demand The water budget in the Chao Phraya basin has a surplus during the wet season and has a deficit during the dry season. The total runoff volume generated in the Chao Phraya River Basin is estimated at 33,187 Mm3. With a population of 24 million in the whole basin, the per capita water availability in 2006 was only 1378 m3/year, which is less than the 3242.6 m3/year (WRI, 2007) average for Thailand. Since the population is mainly concentrated in Bangkok and its vicinity, the per capita water availability of the Chao Phraya main stream is only 145 m3. This clearly indicates that the Chao Phraya basin is a water scarce basin and the limited available water must be managed efficiently for sustainable

23

economic development. The Royal Irrigation Department (RID) is responsible for irrigation development and management in Thailand. Irrigation development in the basin includes large, medium and small-scale projects spread over mainly in the Ping, Nan and Chao Phraya sub-basins. Four large and 34 medium and small irrigation projects are located between downstream of the Bhumipol and Nakon Sawan. Similarly, there are four large and 64 medium and small irrigation projects, including the Phitsanulok irrigation project between downstream of the Sirikit dam and Nakon Sawan. There exits 26 large irrigation projects known as the Greater Chao Phraya Irrigation Project (GCPIP) in the Chao Phraya Delta. The irrigation area and intensity in the basin are given in Table 1. The total irrigable area in the basin is about 1.45 Mha and of which 1.2 Mha is under GCPIP. Domestic and industrial activities in the Ping and Nan sub-basins comprise the water use in the upstream of Nakhon Sawan. Paddy is the main crop, as it is grown over the majority of the area in the wet and dry seasons with an overall cropping intensity of 91% and 39%, respectively, in the study area. At some places in the study area even three crops of paddy rice in a year or seven crops in two years are grown with additional irrigation needs met from the shallow groundwater, especially in the upper part of the lower Chao Phraya Basin. The average water demands in the wet and dry seasons of different sectors are presented in Table 2. The total average demand in the dry season for agriculture, domestic, environment (salinity control), and industry and recreation is 6051, 444, 635 and 387 Mm3, respectively. It is seen that the Chao Phraya sub-basin accounts for more than 85% of the total demand of which about 80% is for the agricultural sector. The demand for various sectors has increased rapidly in the recent years with significant rise in irrigation demand. A typical example of the total monthly normal demand from different sectors in the dry season and the combined release from the two reservoirs (Bhumipol and Sirikit) for the year 2004 is presented in Fig. 2. Total demand from January to June 2004 is 12,395 Mm3 compared to the total release from the two reservoirs of 6477 Mm3. The Electricity Generating Authority of Thailand (EGAT) manages the hydropower generation at the two major hydropower plants with installed capacity of 720.6 MW at Bhumipol and 500 MW at Sirikit. As the hydropower plants are online with the reservoir, the water for hydropower power generation is considered as non-consumptive and non-competing, especially as these reservoirs are operated for downstream demand and hydropower production is considered as a by-product. The study considers the demand from the Metropolitan Waterworks Authority (MWA) and the Provincial Waterworks Authority (PWA), the two main agencies responsible for providing potable and industrial water supply nationwide. The water supply to the residences and to the government office buildings is considered as the domestic water use in the study. The MWA pumping station on the Chao Phraya River is located at about 90 km upstream of the Gulf of Thailand and PWA has its pumping stations along the river. The MWA engages in production and distribution of potable water in the Bangkok metropolitan regions, consisting of three provinces namely Bangkok metropolis, Nonthaburi and Samutprakan. The PWA, on the other hand, is responsible for water source development, conveyance, pumping, and treatment, storage, and distribution facilities from all urban and rural communities in the provinces. The Nakhon Sawan region and Saraburi region as classified by PWA are considered in the study. The salinity intrusion from the Gulf of Thailand can extend up to 175 km upstream, which in turn affects a major area of irrigated agriculture and the water withdrawal by the MWA for domestic and industrial purposes. Hence, it is necessary to keep the seawater intrusion away in the Chao Phraya main river. As per the norm set

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

Yo m

Wa ng

24

Sirikit dam Bhumipol dam

Upper basin

Nan

g Pin

Middle basin

Sakae Krang Nakhon Sawan

Chao Phraya

Tha Chin

Pas ak

Lower basin

Fig. 1. Location map of the Chao Phraya River basin.

Table 1 Cropping area and intensity in the Chao Phraya River basin. Sub-basin

Area (ha)

Cropping intensity (%)

Irrigable

Wet

Dry

Wet

Dry

Total

Ping (D/S of Bhumipol) Nan (D/S of Sirikit) Chao Phraya

74,880 160,741 1218,024

57,504 235,621 1033,600

17,360 84,268 468,800

77 147 85

23 52 38

100 199 123

Total

1453,645

1326,725

570,428

91

39

131

by the Royal Irrigation Department (RID) a minimum flow of 350 Mm3 in the dry season (January to June) is required in the lower delta region of the Chao Phraya river to repel saline intrusion (Molle et al., 2001). However, a total of 181 Mm3 per month is fixed as the minimum amount to be released downstream from the Chao Phraya diversion dam into the river for salinity control, navigation and pumping by MWA for municipal water supply. 2.2. The existing water allocation practices As discussed above, the water demand exceeds the available water in the dry season in the two reservoirs. Currently, a commit-

tee comprising representatives from the various Ministries/Departments involved such as Ministry of Agriculture and Co-operatives (MOAC), Department of Agricultural Extension (DOAE), RID, EGAT, DEDE (Department of Alternative Energy Development and Efficiency) under Ministry of Energy (MOEN), representatives from MWA, PWA and other stakeholders decide on the water allocation in the Chao Phraya River Basin through a discussion process. The discussion process usually starts in October every year to determine water allocation in the next dry season (January to June) for different sectors. The decision on water allocation is made based on the knowledge of estimated active storage in the Bhumipol and Sirikit reservoirs on 1st January provided by EGAT

25

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35 Table 2 Average water demand in the Chao Phraya River basin. Source: Royal Irrigation Department and the World Bank (2001). Sector

Water demand (Mm3)

Season

Ping (D/S of Bhumipol)

Nan (D/S of Sirikit) Phitsanulok

Chao Phraya

Total

6900 5036 11,936

8070 6051 14,121

Lower Nan

Wet Dry Total

397 300 697

Domestic

Wet Dry Total

11 11 21

20 20 41

413 413 826

444 444 888

Industry and recreation

Wet Dry Total

2 2 4

3 3 6

383 383 765

387 387 775

Environment

Wet Dry Total

– – –

– – –

635 635 1269

635 635 12,69

Total

Wet Dry Total

409 312 721

797 738 1535

8330 6466 14,796

9536 7517 17,052

Demand or release (Mm3)

Agriculture

510 431 940

3000 2500 2000 1500 1000 500 0

Jan

Feb

Mar

Apr

May

Jun

Monthly demand Release from Bhumipol and Sirikit dams Fig. 2. Water demand and reservoir releases in the dry season of 2004.

and demand data given by above mentioned departments/technical offices and other stakeholders. The total storage in the Bhumipol and Sirikit dams on 1st of January is classified for allocation purposes according to years having total storage in the two reservoirs lower than 4000 (very dry year), 4000–6500 (dry year), 6500–12,000 (normal year) and higher than 12,000 million m3 (wet year) as given in Table 3. The values in parentheses are the revised allocation criteria being

264 284 548

followed from 2000. Once the volume of water allocated to agricultural sector is known, the corresponding irrigation area to be planted is calculated using the approximate water demand per unit area based on the experience. The regional offices of RID consults the Provincial agricultural services and prepares a crude prerepartition of the area by Province, with areas broken down according to crops and water status (irrigated/non irrigated). The Office of Agricultural Economics decides on the planting area according to the price of the crops. DEDP specifies the share of water, which can be pumped by the pumping stations along the river. These proposals are further approved and made official by the Dry Season Committee, chaired by the Minister of the MOAC. Before the dry season starts, a pre-seasonal water allocation plan is prepared by RID to match the cultivation areas with the amount of available water from the two reservoirs. A standard value (derived from past experiences) of 12,500 m3/ha/season for the paddy is assumed for the estimation of an irrigable area. This area is considered as the upper limit and the sum of areas of paddy fields within the command of each distributing canal is adjusted accordingly and a final target of irrigable area is determined. This information is then communicated to farmers by RID through the irrigation projects. The water is allocated with a priority to use sectors in the following order: domestic, industries, agriculture (for less water-using crops), salinity control, agriculture (dry season rice) and navigation.

Table 3 Criteria of water allocation in the dry season in the Chao Phraya basin. Source: Royal Irrigation Department and the World Bank (2001) and Ueda et al. (2005). Years

Very dry

Dry

Normal

Wet

Total storage on 1st January in Bhumipol and Sirikit dams (Mm3) Water Demands (Mm3) Water uses U/S of Nakorn Sawan Various activities in Ping and Nan sub-basins Phitsanulok Project Water use in the greater Chao Phraya irrigation project

<4000 300 0 900

4000 to 6500 300 200 2100

0 550 250

200 650 350

Total release from Bhumipol and Sirikit dams (Mm3)

2000

3800

6500 to 12,000 800 500 3200 (3300) 300 750 450 (350) 6000

Dry season paddy fields in the Phitsanulok and the Greater Chao Phraya Irrigation Projects (Mha)

0.16

0.24

>12,000 800 500 3700 (5500) 300 750 450 (350) 6500 (8500) 0.56 (0.66)

Navigation Metropolitan waterworks authority Salinity control

0.51 (0.5)

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L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

2.3. Water charge The agricultural sector uses the greatest volume of water and irrigation water in Thailand is supplied free of charge. Operation and maintenance costs including electricity costs for pumping irrigation water are covered by the government. Although the State Irrigation Act of BE 2485 (1942) authorizes a fee of 0.01 US$/m3 from the farmers, this has never been put into practice and introduction of a charge for irrigation water is a sensitive issue. Due to the dependence of rice prices on the world market, a water charge corresponding to an increase in production costs cannot be easily passed to the consumers. Hence, establishing a water fee for rice farmers in the current context is unlikely (Molle, 2001). MWA charges a rate of about 0.25 and 0.39 US$/m3 for domestic and industrial water respectively. The charges for bulk bottled water are about 139 US$/m3 and the average cost of treated water is considered to be 0.86 US$/ m3. 3. Water allocation model A conceptual framework and the components of the developed water allocation model is shown in Fig. 3. The model is adapted from the study carried out by Babel et al. (2005). It comprises a reservoir operation model (ROM) and a water allocation model (WAM). HEC-ResSim, developed by USACE (2003), is used as the ROM to determine the available water (AW) which is used as an input to the WAM. The methodology mainly deals with allocation of water based on the defined objective function. The objective function (OF) in WAM in the present study is designed to allocate water to maximize the net economic benefit from different water use sectors. Two types of water demand are defined in the model: normal demand (Dnor) and minimum demand (Dmin). Dnor is the demand of a sector, which may or may not be met whereas Dmin is the minimum amount of water required by a sector which must be allocated in the allocation process. Dnor is used as an input in the ROM and also used to calculate the net economic returns (NER) from individual sectors. Optimal water allocation to the competing sectors is carried out depending on available water (AW) with the objective of maximi-

zation of economic benefit. If the AW is more than the Dnor of all the sectors together, then there is no allocation required and all sectors receive water as per their demand. The model allocates water to maximize economic returns if the AW is less than the total Dnor but greater than Dmin of all the sectors together. However, in case of very scarce situations where AW is less than Dmin then water is not allocated on basis of economic principles. The allocation of water under such conditions will be either equity-, stressor priority-based. Equity-based allocation is primarily concerned with the fairness of allocation among economically desperate groups. In stress-based allocation the deficiency in water is equally shared among all the sectors concerned. If priority-based allocation is selected, when AW is less than Dmin, a particular sector may be given priority for allocation as agreed by the stakeholders. 3.1. Components of the model The model consists of two main components: a hydrologic component that deals with the water balances in the river system and an economic component that includes the estimation of net economic return to different water uses. 3.1.1. Hydrologic component Major hydrologic relations and processes, which are based on the water resources system, include: (1) inflows to the reservoir; (2) evaporation and other losses from the reservoir; (3) reservoir releases; (4) side flows (local flows contributed by small rivers and irrigation command areas to the main stream which may be non-existent in the dry season), if any to the river system; and (5) water demand from different sectors. ROM is used to represent the hydrologic component of the model to determine the available water to be allocated to competing sectors. 3.1.2. Economic component This component consists of two sub-components. First subcomponent is the estimation of net economic return to different water use sectors and the second sub-component is the allocation of water to different sectors based on the criterion of maximization of economic benefits. Different approaches are used to estimate the

River Basin Hydrologic system operation

AW ROM

Offstream Uses

Instream Uses

Domestic

Power Generation

Industrial

Environment

True

Allocated water = D n

AW > D n

Irrigation False

Equity based allocation True

Hydrologic Component

Priority based allocation

AW < D m

Stress based allocation

False

NER

Estimation of economic return

Maximization of net benefit

WAM Agriculture

Domestic

Environment

Hydropower

Industrial

Water allocation Economic Component Fig. 3. Conceptual framework and components of the water allocation model (adapted from Babel et al. (2005)).

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L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

net economic return to agriculture, domestic, industry, hydropower generation and salinity control and the details are presented and discussed in Section 3.3. The marginal net benefit functions have been established for all the water use sectors. Although the value of water is a function of water allocated and scarcity, the present study considers the total and the marginal net benefit as constant and does not vary with the amount of water allocated. 3.2. Objective function (OF) Maximization of net economic benefit is defined as the ratio of the total economic value (summation of the products of water allocated and the economic return of the sectors) to the maximum achievable total economic value (product of total available water and the maximum net economic return of a sector among the sectors considered). The objective function is represented as

2

n P

Si  NERi

3

7 6 i¼1 7 OF ¼ 6 4AWðtotalÞ  NERmax 5

ð1Þ

where OF = objective function of maximization of NER; Si = water supplied to sector i (m3); NERi = net economic returns per unit volume of water from sector i (US$/m3); AW = available water (m3); NERmax = maximum net economic return of a sector among the sectors considered (US$/m3)The constraints for the optimization are presented below. (i) Water availability constraint: n X

Si 6 AW

ð2Þ

i¼1

(ii) Water demand and supply constraint:

Dnori P Si P Dmin i

ð3Þ

where Dnori = Normal or estimated water demand by sector i; Dmini = Minimum demand by sector i. (iii) Non-negativity constraints:

Si P 0; Dnori P 0; and Dmin i P 0

ð4Þ

3.3. Net economic return to different water uses The net economic return to irrigation water in agricultural sector is computed by residual imputation method. The total cost of production is subtracted from the total benefit from the crop production and then divided by the monthly water use. This is then multiplied by the ratio of monthly water use to the total seasonal water use, in order to obtain the monthly net economic return per volume of water. The fertilizer, labor, machinery and other costs are considered constant in estimating the NER of water use in agriculture as the model is used to allocate water on monthly basis. This is expressed in Eq. (5).

NERagr

ha); M(agdm,cp) = machinery cost by demand site and crop (US$/ha); L(agdm,cp) = labor cost by demand site and crop (US$/ ha); O(agdm,cp) = other production cost by demand site and crop (US$/ha); P(cp) = crop price (US$/mt); ws = price of water for agriculture (US$/m3); MW(agdm,m) = monthly withdrawal for irrigation at off-take level (Mm3); n = number of crops; m = number of months. Net economic return to water use in domestic sector can be established using the demand function. Many empirical studies (as reported by Griffin, 2006, and Diaz and Brown, 2000) on industrial and residential water demand suggest a downward sloping demand curve concave to the origin. Power and exponential functions are options for portraying such a shape. An exponential function has the advantage of intersecting the price (or marginal net benefit) axis to represent the cost of a more expensive alternative if one exists (being the maximum willingness to pay or WTP). Positive alternatives may include, inter alia, private pumping of groundwater, water conveyance or trucking from other locations, and purchasing bottled water. The last alternative has been used in the present study in the case of domestic demand from the MWA and the PWA. Similarly, for the industrial or business sector, the alternatives may include groundwater pumping and/or water treatment. Treated water is used as the alternative for industrial and business supply in the present study. The analytical form of the marginal net benefit function for the domestic and industrial (business) sectors used here follows an exponential decay model

MNB ¼ a  exp Q =  b

ð6Þ

where MNB = marginal net benefit or price; Q = quantity of water used or demanded; a = maximum price or MNB when Q = 0. There is a notable and common lack of information related to MNB or utility change on account of water quantity supplied. Eq. (6) draws the inverse demand curve (MNB as a function of quantity used or demanded) which is hardly ever known entirely. However, the exponential inverse demand function may be estimated by knowing only two empirically-defined points along the curve. From a mathematical perspective, a straight line closely approximates a curve for small changes in the variables and therefore the use of a linear demand function is justified. One point being the current price of the piped water supply and the other being the maximum willingness to pay (WTP) for the next best alternative as discussed above. As indicated in Eq. (6), at maximum MNB (or WTP), quantity demanded (or supplied) falls to zero. WTP forms the price P1, where quantity Q1 used tends to be zero. Another point is considered at existing price P2 where the quantity used is Q2. The exponential reverse demand function or log demand function is drawn from Eq. (6) and shown as:

Q ¼ b½ln a  ln P2 

ð7Þ

where a = P1 and b = Q2/ln(P1/P2). The consumer surplus (CS) (the difference between the total amount that consumers are willing to pay for a good or service and the total amount that they actually pay) and the producer sur-

2 3 Pm  Pm n 6 X M þ L þ OÞagdm; cp  ws  1 MWðagdm;mÞ MWðm; cpÞ7 6 ðA  Y  PÞagdm; cp  A  ðF þP 7  n1 ¼ m 4 5 P 1 MWðagdm;mÞ 1 MWðm; cpÞ

ð5Þ

1

where NERagr = net benefit from agriculture (US$/ m3); cp = crop; agdm = agriculture demand site; A(agdm,cp) = area under agriculture for the crop across demand sites (ha); Yactual(agdm,cp) = actual yield from agriculture for the specific crop (mt/ha); F(agdm,cp) = fertilizer input cost by demand site and crop (US$/

plus (the difference between the price the producers are willing to supply a good for, and the price they actually receive) is typically considered to measure the total economic benefit of the water supply sector by integration of MNB functions between P2 and P1 (WTP). In this study the supply of water to residences and

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L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

government offices by the MWA is considered as the domestic sector for NER estimation. The producer surplus is considered negligible as water supply is normally a public utility and not aimed for profit making; hence the consumer surplus alone is considered as the net benefit. However, the limitation in particular using WTP may be the apparent difficulty in measuring willingness to pay and the conceptual problems associated with developing dollar estimates of economic value on the basis of how people respond to hypothetical questions. The value of bottled water is considered the next best alternative where the quantity demanded falls to zero. The demand for the domestic sector intersects the price axis at a very high price of 139 US$/m3, the price of the bottled water. In 2004 the consumers paid 0.25 US$/m3 and consumed 536.95 million m3. Computing Eq. (7) with these data affords access to a and b parameters, as follows:

a ¼ P1 ¼ 139 b ¼ Q 2 = lnðP1 =P2 Þ ¼ 85; 160; 554 Net economic return to water use in industrial (business) sector has been established using the similar approach followed in the domestic sector. In this study the supply of water to the industrial sector by the MWA is considered for estimation of NER and as discussed in the domestic sector the producer surplus is again considered negligible. The industrial sector only uses 4.1% of the water supplied by MWA, and the majority of water used by industries is taken from groundwater and the artesian wells owned by the industries’ as reported by Kumar and Young (1996). Treated water is considered as the next best alternative to the given choice and its price is used as the maximum WTP when the quantity demanded falls to zero. The demand for the industrial sector intersects the price axis at a price of 0.86 US$/m3 of treated water and this truncates the demand curve at an estimate where Q1 is zero. In 2004, consumers paid 0.39 US$/m3 and consumed an amount of 13.17 million m3. Computing Eq. (7) with these data affords access to a and b parameters, as follows:

a ¼ P1 ¼ 0:86 b ¼ Q 2 = lnðP1 =P2 Þ ¼ 16; 616; 152:68 The hydropower net benefit function is calculated by multiplying power production by the difference between the power selling price and the power cost. This, when divided by the water passing through the power plants, gives the net benefit per unit volume of water.

  NERðpowÞ ¼ powerðpwstÞ  P price ðpwstÞ  Pcos t ðpwstÞ =Q total

ð8Þ

where NERpow = net profit from power production (US$/m3); power(pwst) = power production at the production site (KW h); Pprice(pwst) = average power selling price (US$/KW h); Pcost(pwst) = average power production cost(US$/KW h); Qtotal = total discharge from the plant (m3) In the present study, the net economic return to environmental water use is only for salinity control and is estimated by using the replacement cost method. Although it does not provide the best measure of environmental economic value, yet it assumes the costs of avoiding damages or replacing services and constitutes a good proxy. The annual cost of restoring damage due to surface water salinity is unknown. A desalinization plan with an objective to desalinize the salt water entering into the basin from the gulf is considered and the desalinization cost is assumed as a proxy to measure the benefit of preventing salt-water intrusion. The economic return to unit water use in environmental sector (water allocation is mainly to control salt-water intrusion) is difficult to quantify. This is estimated indirectly by carrying out two sets of allocations. One set of allocations is made to different

sectors other than the environmental sector assuming no water is required for flushing of salinity. The other set of allocations is carried out by allocating water to all the sectors after diverting the known amount of water for flushing of salinity. The two sets of allocations are carried out to determine the cost at which the water used for flushing of salinity is to be desalinized (assuming no water is used for salinity control). The cost of desalinization depends on the use of water. The product of the change in volume of water allocated to each sector in both the allocations and the unit charge of desalinization gives the cost of desalinization for each sector. The total economic returns to both the allocations are estimated and the cost of desalinization is subtracted (where it replaces the damage caused by not allocating water for salinity control) from the total economic return of the first allocation. Both allocations are equated to the water available. The results are based on the assumption that if one incurs costs to avoid damages caused by salinity intrusion then these services must be worth at least what one pays to cover the cost of damage. 4. Results 4.1. Net economic returns (NER) The NER per unit volume of water from agriculture, domestic, industrial and hydropower generation estimated using Eqs. (5)–(8), respectively, are presented in Table 6. For the agricultural sector, since the total economic value or the total benefit of water use is divided by an overall average supply to the sector (MW(agdm,m)), NER is actually the marginal net benefit yielded at the quantity supplied. The residual imputation arises from the fact that representative farm crop budgets developed for a region are used to estimate the maximum revenue share of the water input. The total annual crop revenue less non-water input costs is a residual, the maximum amount the farmer could pay for water and still cover costs of production. It thus represents the on-site value of water. This monetary amount, divided by the total quantity of water used on the crop, determines a maximum average willingness to pay for water for that crop (WTP per unit of water per year). Depending on whether or not fixed costs are included, such values will be long-term or short-term average values respectively (Gibbons, 1986). NER as such would vary with the amount of water supplied as yield is a function of water; however, NER is taken fixed in this study due to non-availability of data on rice yield response to water for the study area. For the Chao Phraya basin the estimated NER to irrigation water in the agricultural sector during the dry season is 50 US$/1000 m3. The MNB is an integration of the net benefit throughout each and every additional unit of water used. Compared with the agricultural demand, the domestic and industrial demands are relatively very small and found to be inelastic, especially when the full demand is not met and the supply only satisfies the basic needs. However, near full satisfaction domestic and residential demands are much more elastic, i.e., demand reacts sharply to change in price. In other words, when the curve is flat (high quantities, low MNB) and elasticity of demand is high, minor changes in the quantity supplied do not affect the marginal net benefit to a considerable extent. Since the net benefit depends also on the amount of water supplied, the actual change in net benefit remains to be seen. The NER to water use in the domestic sector is estimated to be 21,738 US$/1000 m3. For simplification, the NER to water use by non-domestic sectors (commercial, government agencies, state enterprise and industries) supplied by the MWA is considered equal to the economic return per unit volume of water from the industrial sector due to the lack of detailed information. Although the industries in the study area use both surface water and

1142 1153 153 -889 525 -145 865 1226 -23 9642 8553 6653 2711 6525 6855 7865 9726 6477 600 500 450 350 350 350 350 350 350 400 300 300 0 300 300 300 300 300 750 740 695 626 640 642 643 647 645 8500 7400 6500 3600 6000 7000 7000 8500 6500 14,582 12,107 8200 3879 11,930 13,585 14,068 15,300 9250 1996 1997 1998 1999 2000 2001 2002 2003 2004

800 500 500 200 500 500 500 800 500

900 800 800 300 800 800 800 900 800

5050 4550 3700 2100 3300 4300 4300 5500 3950

6750 5850 5000 2600 4600 5600 5600 7200 5250

750 750 750 650 750 750 750 750 750

400 300 300 0 300 300 300 300 300

600 500 450 350 350 350 350 350 350

7866 6942 5133 1663 5148 5462 6462 8314 5107

MWA (Mm3) Total irrigation (Mm3) Total release (Mm3) Salinity (Mm3) Chao Phraya irrigation project (Mm3) Domestic industrial and other uses in Ping and Nan basin (Mm3) Phitsanulok irrigation project (Mm3)

Planned release

Total storage on 1st January in Bhumipol and Sirikit dams (Mm3) Year

Table 4 Planned and actual release of water in the dry season in the Chao Phraya River basin.

Total irrigation (Mm3)

MWA (Mm3)

Navigation (Mm3)

Actual release

Navigation (Mm3)

Salinity (Mm3)

Total release (Mm3)

(ActualPlanned) Release (Mm3)

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

29

groundwater, the present study considered only the surface water use by the industry based on the data obtained from MWA and PWA. The NER to water use in the industrial sector is calculated as 205 US$/1000 m3. The NER to water use in hydropower generation is estimated as 3 US$/1000 m3. The cost of production and the sale price of hydropower are assumed to be constant for the dry season of 6 months for the aggregate amount of flow passing through the turbines. When the two sets of allocations are applied to Chao Phraya River Basin the extra water (water used for flushing salinity in the second allocation) in the first allocation is served to agriculture due to very low demand in other sectors compared to the agriculture. This extra amount is considered to be desalinated, to be used for agriculture. Desalinization of water for agricultural use though not a common practice is assumed to determine how much has to be spent to upgrade the quality of water so that it can be used for agricultural purposes or in other words how beneficial will it be to allocate this water to the environment sector rather than diverting it to other sectors. The cost of desalinization of water to be used in agriculture sector is considered to be 0.5 USD/m3. The NER of water to environmental sector (salinity control) is estimated as 10 USD/1000 m3. However, the analysis is carried out considering only the agricultural, domestic and industrial sector and not for the ecosystem as whole. Though the cost of treatment for desalinization of agriculture water is way less than for domestic and industrial sectors but it is definitely not a cost effective approach due to high investment cost. 4.2. Analysis of current allocation The water demand and supply data indicate that the scarcity in the study basin is intensely experienced in years when the dry season begins with low reservoir stocks where this deficient amount of water is to be allocated among different users, including domestic, irrigation and hydropower and for the environment (salinity control). Local flows (flow contributed by watersheds below the reservoirs and along the river) being non-existent in the dry season, the water stored in the two dams is the only available source. However, the plentiful inflow to the reservoirs in the wet season satisfies the demand and does not require a technical criterion for allocation. The Chao Phraya basin is, therefore, considered as a closed basin in the dry season. The planned water allocation for different sectors depends mainly on the amount of available storage in the two dams on the 1st of January, based on past experience and the criteria provided in Table 3. Planned water releases, based on current practices (Table 3), and actual releases for different sectors in the dry season are given in Table 4. The actual release of water for industrial, domestic, irrigation and other activities (majority of which is considered as unaccounted abstractions) in the Ping and the Nan sub-basins and the requirement for navigation and salinity control in the Chao Phraya sub-basin are taken to be the same as planned due to lack of actual detailed field data. The difference between the actual and the planned releases is positive in most of the years (except in 1999, 2001 and 2004), where the actual release is more than the planned release and correspondingly the actual irrigation areas are larger than planned. About 80% or more of the total reservoir release, in most of the years except in 1998 and 1999, is diverted for irrigation. However, the drought experienced in 1998–1999 has contributed to the major difference in the planned and actual releases in 1999. The difference between the planned release and the actual release for the domestic sector may be due to high priority given to domestic sector in the allocation, which is planned higher than the actual use. In the very dry year of 1999 with only about 24% of

30

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

Table 5 Irrigation area and water use per unit area based on planned and actual releases of water in the dry season in the Chao Phraya River basin. Year

1996 1997 1998 1999 2000 2001 2002 2003 2004

Total storage on 1st January in Bhumipol and Sirikit dams (Mm3)

Planned release

Actual release

Total irrigation (Mm3)

Area in dry season (Mha)

Water use (m3/ha)

Total irrigation (Mm3)

Area in dry season (Mha)

Water use (m3/ha)

14,582 12,107 8200 3879 11,930 13,585 14,068 15,300 9250

6750 5850 5000 2600 4600 5600 5600 7200 5250

0.56 0.53 0.43 0.30 0.50 0.54 0.56 0.66 0.56

12,054 11,080 11,574 8553 9274 10,448 10,000 10,976 9375

7866 6942 5133 1663 5148 5462 6462 8314 5107

0.66 0.65 0.61 0.56 0.78 0.70 0.76 0.80 0.80

11,846 10,686 8464 2978 6566 7777 8556 10,434 6277

the active storage in the reservoir, the domestic and salinity control sectors are given priority over demands from other sectors. However, in this very dry year no allocation was planned for navigation. In the normal years such as 1998, 2001 and 2004, small differences are seen between the planned release and the actual withdrawals, however in the wet years the actual withdrawals are considerably more than the planned release to empty the reservoir for the following rainy season. The irrigation area and water use per unit area based on the planned and actual releases of water in the dry season are given in Table 5. As a larger area is irrigated by actual water withdrawals, the planned water use is considerably higher than the actual water use. It is interesting to note that in the dry year of 1999 the water use based on the planned release is 8553 m3/ha whereas the water use based on the actual releases 2978 m3/ha is only. The low water use per unit area based on the actual withdrawals may be due to the better water management practices adopted by the farmers, or that the assumed value of 12,500 m3/ha for

paddy rice used by the authorities is relatively high in the calculation of target irrigated area in the dry season. The actual total release for irrigation from the reservoirs in most of the years is more than the planned release due to uncontrolled planting and political intervention to release more water than is planned to satisfy the demand of increased cropped area. On the contrary, the possibility of farmers pumping groundwater to supplement surface water cannot be ruled out. It is interesting to note that water use in agriculture is as low as 2978 m3/ha in the very dry year of 1999 and as high as 11,846 m3/ha in the year 1996, almost a fourfold increase in the year with plentiful water storage in the reservoirs. One of the major reasons for the overuse of water in agriculture is that the irrigation water supplied from the two reservoirs and groundwater (mainly used to supplement surface water supplies and for land preparation in dry season and in drought years, for crop requirements in the early part of the wet season, and as a supplementary source of water for farms located at the tail end of distribution canals) is far more accessible. This

Table 6 Analysis of current allocation practices based on the actual releases in 2004 in the Chao Phraya River Basin. January

February

March

April

May

June

Total (average)

Agriculture

NER (US$/1000 m3) Actual withdrawal (Mm3) Demand (Mm3) Satisfaction (%) Total benefit (US$  106)

50 952 2209 43 48

50 1097 2376 46 55

50 1260 2422 52 63

50 1122 1728 65 56

50 544 706 77 27

50 133 1429 5 7

(50) 5107 10,870 (47) 255

Domestic

NER (US$/1000 m3) Actual withdrawal (Mm3) Demand (Mm3) Satisfaction (%) Total benefit (US$  106)

21,738 65 70 92 1406

21,738 65 71 92 1415

21,738 63 69 92 1374

21,738 68 74 93 1488

21,738 70 75 93 1517

21,738 70 75 93 1514

(21,738) 401 434 (92) 8714

Industry

NER (US$/1000 m3) Actual withdrawal (Mm3) Demand (Mm3) Satisfaction (%) Total return (US$  106)

205 52 58 89 11

205 52 58 90 11

205 51 56 90 10

205 55 61 90 11

205 54 60 90 11

205 56 62 91 12

(205) 319 355 (90) 66

Hydropower

NER (US$/1000 m3) Actual withdrawal (Mm3) Demand (Mm3) Satisfaction (%) Total return (US$  106)

3 1188 1188 100 4

3 1334 1334 100 4

3 1493 1493 100 5

3 1361 1361 100 4

3 783 783 100 2

3 317 317 100 1

(3) 6477 6477 (100) 20

Environment (salinity) and navigation

NER (US$/1000 m3) (SC*) Actual withdrawal (Mm3)

10 58 50 123 88 1

10 58 50 124 88 1

10 58 50 125 86 1

10 58 50 119 91 1

10 58 50 123 88 1

10 58 50 123 88 1

(10) 350 300 736 (88) 4

1469

1486

1453

1560

1558

1533

9059

SC* Navigation

Demand (Mm3) Satisfaction (%) Total benefit (US$  106) from SC* Total monthly economic return (US$  106) 1US$ = 40 THB. * Salinity control.

31

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

also suggests that higher water use efficiency is possible when water supplies are limited, such as in water-scarce and drought conditions. The analysis results of current allocation practices in terms of level of satisfaction of demand sectors (defined as the ratio of the water allocated to a sector to the normal demand of the sector) and total economic benefits based on the estimated NER are presented in Table 6. With the first priority given to domestic sector, the average satisfaction in meeting the domestic demand is highest (92%) followed by industrial (business) sector (90%), salinity control and navigation (88%) and the lowest (47%) in meeting the irrigation demand. Total economic benefit of about 9059 million US$ is achieved to which the domestic sector is the main contributor followed by agriculture, industrial, hydropower and salinity control sectors with a value of US$ 8714, 255, 66, 20 and 4 million, respectively (Table 6). The allocation for environmental (salinity control) and navigation sector is carried out separately but the NER to water use is only estimated for salinity control, hence the economic benefit only corresponds to allocation for salinity control. Since the hydropower plants are online with the reservoirs, the release from the reservoir (except the spill) passes through the turbines to generate electricity. Neglecting the losses incurred, hydropower is consid-

ered as a non-consumable and non-competing sector in this study and its water allocation is therefore equal to the total reservoir release in the dry season. 4.3. Model applications The developed water allocation model is applied to the Chao Phraya River Basin in Thailand, and five competing sectors are considered. As stated earlier, the basin is water scarce in the dry season and hence the developed model is applied for the dry season period from January to June. The input data to ROM includes physical and operational characteristics of the reservoirs, power plant related data, inflow to the reservoir, losses from the reservoir, downstream demand, channel characteristics and local flow along the reaches. These data were collected from the relevant government agencies. The normal demand of different economic sectors is determined for a period from 1985 to 2004. HEC-ResSim (ROM) was run on a daily basis and the simulation was carried out from 01 May 1985 to 31 April 2005. Fig. 4 shows the river and reservoir network of the Chao Phraya River Basin and the comparison of observed and ROM simulated flow. Simulation results match well the observed flow at junction C.2 (downstream of the two reservoirs, where the Ping and the Nan rivers join to

180.0 160.0

Volume (Mm3)

140.0 120.0 100.0 80.0 60.0 40.0 120.00

20.0 0.0 Jun-85 Jun-87 Jun-89 Jun-91 Jun-93 Jun-95 Jun-97 Jun-99 Jun-01 Jun-03

Volume (Mm3)

100.00 80.00

Simulated Observed

Month

60.00

500.0

40.00

450.0 20.00

Simulated Observed

Month

C.2

Volume (Mm3)

400.0

0.00 Jun-85 Jun-87 Jun-89 Jun-91 Jun-93 Jun-95 Jun-97 Jun-99 Jun-01 Jun-03

350.0 300.0 250.0 200.0 150.0 100.0 50.0

500.0

0.0 Jun-85 Jun-87 Jun-89 Jun-91 Jun-93 Jun-95 Jun-97 Jun-99 Jun-01 Jun-03

400.0 350.0 300.0

Simulated Observed

Month

250.0 500.0

200.0 150.0

C.13

100.0 50.0 0.0 Jun85

Jun87

Jun89

Jun91

Jun93

Jun95 Month

Jun97

Jun99

Jun01

Jun03 Simulated Observed

450.0

C.7

Volume (Mm3)

Volume (Mm3)

450.0

400.0 350.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0 Jun85

Jun87

Jun89

Jun91

Jun93

Jun95 Month

Fig. 4. Observed and ROM simulated flow in Chao Phraya River basin.

Jun97

Jun99

Jun01

Jun03 Simulated Observed

9754

303 9429 0 21 0*

9775

6478

56 100 0 100 0 6044 434 0 6478 0 248 9429 73 21 4*

9768

6478

46 100 100 100 100 4953 434 355 6478 736 265 9429 73 21 4*

9805

6478

49 100 100 100 70 5294 434 355 6478 515 281 9429 73 21 1*

9809

6478

52 100 100 100 10 5615 434 355 6478 74 285 9429 73 21 0*

6478 9059 6477 12,395 Total

SC = salinity control, Nav = navigation. * Only for salinity control.

52 100 100 100 0 5690 434 355 6478 0 255 8714 66 20 4 47 92 90 100 88 10,870 434 355 6478 736

5107 401 319 6477 650

Level of satisfaction (%) Allocated water (Mm3) Allocated water (Mm3) Level of satisfaction (%)

Total economic benefit (106) US$ Allocated water (Mm3)

Level of satisfaction (%)

Total economic benefit (106) US$

Allocated water (Mm3)

Level of satisfaction (%)

Total economic benefit (106) US$

Allocated water (Mm3)

Level of satisfaction (%)

Total economic benefit (106) US$

Allocated water (Mm3)

Level of satisfaction (%)

Total economic benefit (106) US$

Scenario 5 Scenario 4 Scenario 3 Scenario 2 Scenario 1 Current allocation practices

Agriculture Domestic Industry Hydropower Environment (SC & Nav)

4.3.1. Scenario1 With the objective of maximization of economic benefit the model first allocates water to the sector which has highest NER followed by the sector with next highest NER. The model results for Scenario 1 are presented in Table 7. The model allocates water first to the domestic sector which has highest economic return. After fulfilling the normal demand of the domestic sector, water is allo-

Normal demand (Mm3)

Scenario 1: Minimum demand as 0% of the normal demand for all sectors. Scenario 2: Minimum demand as 10% of the normal demand for all sectors. Scenario 3: Minimum demand as 70% and 80% of the normal demand for environmental and domestic sectors respectively. Scenario 4: Priority given to a single (environment) sector. Scenario 5: priority given to two (domestic and agriculture) sectors.

Sectors

form the Chao Phraya River), junction C.13 and junction C.7, which indicates satisfactory performance of ROM. The model calculated release from the Bhumipol and Sirikit reservoirs are considered as the available water (AW), which is then used as input in WAM. Hence the allocation also depends on the reservoir operation (ROM) which mainly depends on the downstream demand. During the dry season (January to June) the available water to be allocated is what is released from the two reservoirs as there is no side flow or local flow along the river. The AW in the dry season is less than the total water required to satisfy the demand from five sectors. In this study, the demands of different demand sites were pooled sector-wise and considered for water allocation. To demonstrate the applicability of the model a monthly aggregate of the limited available water of 1190, 1138, 1500, 1365, 770 and 315 million m3 in the dry season (January to June, 2004) is allocated for a monthly normal demand of 2460, 2628, 2672, 1982, 963, and 1689 million m3 respectively in the most optimal way satisfying the user and also considering the benefits from its uses. Water required for navigation is taken together with the flow needed to flush the salt water into the sea. However, as stated earlier, the total economic return to water use in environmental (salinity control) and navigation sector is based on the economic return from the environmental sector. The actual allocations/withdrawals under the existing allocation practices are used for comparison. In the existing situation, about 88% of the total demand is from agricultural sector and only 79% of the total water withdrawal is used for agriculture (Table 6). The hydropower sector is considered as a non-consuming sector and the water released from both the reservoirs passes through the turbine to generate hydropower and hence hydropower is a non-competing sector in this study. The level of satisfaction (which is calculated with respect to the normal demand of water by the sector) is an indication of the percentage of water demand fulfilled, the remainder of which denotes the stress level. Different scenarios are developed, by varying minimum demand (Dm) and by giving priority to single and multiple sectors, to evaluate the applicability of the water allocation model to the Chao Phraya River Basin. The scenarios developed give a wide spectrum of the situation to the local decision makers and allow them to further develop and analyze other scenarios which suit the situation and improve the water management in this water-short and closed basin. The proposed water allocation model associated with the economic criteria is expected to support decision making process for water allocation in the Chao Phraya River Basin. Hence an effort is made to clearly present a broad range of results corresponding to different scenarios of water allocation for the decision and policy-making process and is presented in Table 7. The scenarios are defined as follows:

Total economic benefit (106) US$

L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

Table 7 Comparison of current allocation with different scenarios of the model allocation.

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L. Divakar et al. / Journal of Hydrology 401 (2011) 22–35

cated to the industrial sector followed by the agricultural sector. However, there is no water left to satisfy the requirement for salinity control and navigation. Hence, no water is allocated to the environmental sector and the level of satisfaction in meeting its demand is zero. With the full satisfaction of domestic and industrial sectors, and a 5% increase in agricultural sector, Scenario 1 produces an economic benefit of US$ 9809 million which is US$ 750 million more than the total economic benefit estimated with current allocation practices. 4.3.2. Scenario 2 Scenario 2 considers giving a specified and equal percentage of normal demand as minimum demand to all sectors. In this scenario the model first allocates the specified minimum amount of water to each sector and then allocates the remaining water based on the allocation objective of maximization of economic return. The model results with 10% of normal demand as the minimum demand for all sectors are presented in Table 7. After satisfying the specified minimum demand, the model allocates water to the domestic and industrial sectors, which get fully satisfied. The remaining water is allocated to the agricultural sector which is supplied with a satisfaction level of about 52% of its demand. Along with 100% satisfaction level in domestic and industrial sector, 10% of the environmental demand is also satisfied. The total economic benefit from all sectors in Scenario 2 is US$ 9805 million which is US$ 746 million more than that of the current allocation. 4.3.3. Scenario 3 Similar to Scenario 2, a third scenario (Scenario 3) with varying percentages of normal demand as minimum demand for particular sectors is considered. Table 7 also presents the results when 80% and 70% of normal demand is considered as the minimum demand for the domestic and environment (salinity control and navigation) sectors respectively. The model first allocates water to satisfy 80% of the normal demand of domestic sector (which has a higher economic return), followed by 70% of the normal demand of environment (salinity control and navigation sectors) and the remaining water according to the objective of maximization of economic return. This results in satisfying the demands of domestic and industrial sectors fully, the environmental sector to a level of 70%, and the agricultural sector to 49%. With an increase in satisfaction of 8%, 10% and 2% in domestic, industrial and agricultural sector, respectively, and a decrease of 18% in the environmental sector, the economic benefit achieved in the scenario is 9768 million US$. Scenario3 thus has an additional benefit of US$ 709 million as compared to the benefit based on the current allocation. 4.3.4. Scenario 4 Based on the economic allocation criterion a sector with least net economic return is given the last priority. But, that sector may be important from social and/or environmental considerations. It may be interesting to give priority to a particular sector which has low economic returns. If the available water is sufficient to fulfill the normal demand of the prioritized sector, the model first satisfies the demand of the prioritized sectors and then the remaining water is allocated to other sectors according to the objective of allocation. Scenario 4 considers giving priority to the environment (salinity control and navigation sectors), and the model results are given in Table 7. In this scenario the environmental sector is fully satisfied unlike in Scenario 1, where the agricultural sector with its larger demand and higher economic benefit is supplied first, after which there is no water remaining to be allocated for salinity control and navigation. An additional economic benefit of US$ 716 million is achieved with 46% satisfaction level in the agricultural sector and full satisfaction in all other sectors.

33

4.3.5. Scenario 5 When the competing sectors are equally important to the decision makers and planners then equal priority can be given to two or more sectors. Similar to the previous scenario, the model tries to satisfy the multi-prioritized sectors first and then allocates water to the other sectors. In Scenario 5, agriculture and domestic sectors are considered as multi-prioritized sectors. The model results of this scenario are presented in Table 7. In spite of the equal priority given to the agriculture and the domestic sectors, the model first allocates water to the latter due to its higher economic return. Next, water is allocated to the agricultural sector. After allocation to the agricultural sector, there is no water left to be allocated to other sectors. Hence 100% level of satisfaction in meeting the demand is achieved in the domestic sector and 56% of total agricultural demand is satisfied. In this scenario an additional economic benefit of US$ 695 million is achieved compared to the current allocation practices. 5. Discussion The proposed model gives an insight into the economic and hydrologic interaction for water allocation to different water use sectors. The model structure can be expanded by incorporating parallel stems and can be applied to complex water resource networks or the model can be run separately for each reservoir in the network. In the present study however the releases from the reservoirs are aggregated for allocation which is also the current practice followed by the local decision makers as presented earlier. The irrigation demands immediately downstream of the reservoirs are met from the respective reservoirs. The water shortages in the dry season however begin further downstream in Chao Phraya Delta which receives water from both the reservoirs. The reservoir simulation and aggregated allocation calculation were carried out for a 20-year period. The allocation results are only shown for the dry season (6 months) of 2004 with the available water (AW) of 6477 Mm3 representing the normal (average) conditions as per the current allocation criteria given in Table 3. In the current allocation practice, the decision with respect to allocation of water is made in October for the following 6 months of dry period from January to June every year by the allocation committee as discussed earlier. The analysis compares the current allocation with the allocation of water based on five different scenarios to demonstrate the applicability of the model. The scenarios provide wide range of situations which the decision makers would like to consider to achieve maximum economic benefit or to strike a balance between the benefit and the satisfaction. The water allocation in Scenario 1 disqualifies navigation sector due to low economic benefit per unit volume of water but overall produces maximum benefit. Navigation in Chao Phraya is generally restricted to a smaller and declining number of commercial craft, yet the allocation must take into account the need to maintain sufficient flow for river transport. In that case implementing Scenario 1 will not be possible. Nevertheless the decision makers can support the decisions accounting for economic benefits by implementing a suitable allocation scenario. The economic dimension of water allocation based on consideration of economic return per cubic meter of water utilization addresses the economic crisis and helps to create a sound economic structure. Although it is not realistic to allocate water purely on economic criteria, the study provides insights with respect to possibility of improving water allocation and water management in the study basin. All the scenarios except Scenario 4 improve water allocation to agriculture sector compared to the current allocation, as indicated by the satisfaction level. The purpose of analyzing different scenarios was to demonstrate the applicability of the model so that a suitable scenario acceptable to stakeholders can be devel-

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oped, analyzed and implemented objectively to the water situation in the basin. Monsoon is the main source of water supply for rice grown in Thailand and only less than 20% of agriculture is irrigated. Due to the rise in paddy price farmers are willingly take risk for paddy cultivation even under uncertainty of water supply. The country’s GDP in the sixties was mainly contributed by agriculture sector but since mid seventies industrial sector surpassed agriculture and has increased gradually to its current leading position. However, the labor force in the industrial sector has not grown to equal as that of the agricultural labor force and this discrepancy creates an unbalanced condition, especially for the agricultural sector which results in higher cost of input resources. On the whole, as there is a big disparity in the elasticity of the different water allocations the agricultural sector in due course may be the one which must adapt to changes. Better policy-making by restricting the paddy area in the dry season can be brought about by using economic value of irrigated water or by setting up compensation for the farmer’s opportunity cost. Shifts towards low consumption crops, high-yielding varieties, on-farm storage, modifying cropping calendars, restricting uncontrolled planting or even setting a suitable pricing mechanism for irrigation water also entails wide differences in absolute water requirements for irrigation. Now the question is whether the farmers would limit the area of agriculture willingly, in favor of the alternatives offered to them, or whether they would be thrown into unemployment and poverty. An optimistic and realistic scenario in this situation would be diversification in the use of water; reduction in cropping areas with a sustained growth of non-agricultural sectors so that the drop in agriculture is of less economic significance. There are some limitations and challenges that need to be addressed in the water allocation model developed in this study through further research. NER to water use for salinity control and navigation should be determined using other suitable approaches and methods instead of the replacement cost method adopted here. The study assumes a fixed net benefit irrespective of amount of water allocated to each of the use sectors. This is justified as the monthly variation in the water allocated to different sectors is very small. Also, the economic benefit from agriculture sector is obtained over a growing season of several months. Moreover, establishing full marginal net benefit (MNB) curves is no easy task. Under such conditions, assuming one figure of marginal net benefit may yield acceptable results. However, using the full MNB curves and making the system fully dynamic and iterative would enable allocation entirely based on economic efficiency. In other words, if one user is allocated a given amount, then it has a certain net benefit depending on its MNB curve, such net benefit might then justify whether it needs more or less water, accordingly quantity allocated has to be adjusted, which in turn affects other users, and so on. Additionally, to address complex water resources network the two components (ROM and WAM) of the model should be combined and allocation be made at spatial locations in the river basin to take full advantage of an integrated hydro-economic model which runs with results of ROM fed into WAM and the results of WAM into the ROM until the optimized allocation of water with maximized economic benefit is achieved. 6. Conclusions The study develops a water allocation model which consists of a reservoir operation model (ROM) and a water allocation model (WAM). The ROM determines the available water for allocation in the dry season, which is used as an input to the WAM. The objective function maximizes the net economic returns to different use sectors. The maximization of economic benefits is achieved by moving water to higher-valued domestic and industrial uses and

therefore emphasizes adopting efficient irrigation practices, and investing in water conservation practices. Different techniques have been used to estimate the NER to water use in agricultural, domestic, industrial, hydropower, and salinity control sectors in the Chao Phraya River Basin. The NER is as low as USD 3 per thousand m3 for hydropower sector to as high as USD 21,738 per thousand m3 for domestic use. The estimated NER of water use in the agriculture sector is USD 50 per thousand m3. The environmental (salinity control) sector has a NER of USD 10 per thousand m3. The study reviews and analyzes current allocation practices in the Chao Phraya River Basin in Thailand, which are carried out by developing a pre-seasonal water allocation plan in October each year for the following dry season from January to June. The annual allocation plan is based on past experience and judgment to match the demand of various sectors with the estimated amount of water storage volume on 1st of January in the two reservoirs supplying water to the basin. The analysis of the current allocation practices in the basin indicates that the actual releases from the reservoir are more than the planned releases and the actual allocation to irrigation sector is more than the planned allocation as the actual areas under the irrigation are higher than the planned irrigation coverage. The optimal allocation of aggregate water supplies to different sectors using the economic criterion developed in this study has multiple benefits – it maximizes the economic return, it is flexible enough to deal with the sectoral preferences, and it often increases the level of satisfaction in meeting the demand of a particular sector. Five scenarios are considered to verify the applicability of the model and to analyze the effect of the allocation results on the level of satisfaction and the economic benefit from water use within the Chao Phraya River Basin. The model results show an additional benefit of about 700–750 million US$ per annum compared to the current allocation, with an optimum level of satisfaction. It is interesting to note that the level of satisfaction to agriculture sector is improved compared to the current allocation, except Scenario 4 in which reduction is about one percent. The developed water allocation model can be used as a tool to analyze several other scenarios and the model results can assist decision-makers in coming up with improved water allocation criteria and plans that are acceptable to stakeholders and increase economic efficiency of water use in the basin.

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