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โครงการหนังสืออิเล็กทรอนิกส์ด้านการเกษตร เฉลิมพระเกียรติพระบาทสมเด็จพระเจ้าอยู่หัว

Chapter 7

Water stress index assessment

In this chapter, data collection and assumptions used for assessing water stress index

(WSI) and results of WSI for the major river basins in Thailand are detailed as follows:

7.1 Data collection for assessing water stress index

To assess the water stress index (WSI) of a basin’s geographic location, the hydrological

information regarding the water supply, storage and water withdrawals need to be determined. In
the study, the “top-down approach” has been used for assessing the WSI indices for all the major

watersheds of Thailand. With this approach, the assessment is conducted based on the available
data regarding the water resources and water demands for the main sectors referred from several

governmental departments e.g. RID and HAII combined with the theoretical calculations. In this
data collection phase, the hydrological data regarding rainfall and runoff and the secondary data

regarding water withdrawal or water demand for various purposes are collected in order to
determine the “Withdrawal-to-Availability (WTA)” index following Equation (5).

7.1.1 Estimation of water withdrawal

To determine the withdrawal-to-availability (WTA), Table 7.1 shows the estimated water
withdrawal for different basins in Thailand obtained from the Royal Irrigation Department (RID,

2011). The withdrawals are classified into four sectors i.e. agriculture, domestic, industry, and
livestock. The total water withdrawal for a basin is the sum of estimated water uses for those four

sectors. Methodologies e.g. water intensity and assumptions used for assessing the water
demands for different sectors in Table 7.1 are referred from the work manual of RID (2011) and

are summarized in Sections 3.1.1 – 3.1.4.

Table 7.1 Water demands of 25 watersheds (RID, 2011)

3
Water demands (million m /year)
Watersheds Total
Domestic Agriculture Industry * Livestock

Salawin 30.34 817.91 4.70 3.29 856.24
Kok 160.69 162.17 11.41 10.16 344.43

Ping 70.02 2,458.70 32.62 13.17 2,574.51

Wang 160.69 575.50 21.41 4.13 761.73

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