Thailand currently ranks third among the most water-intensive countries in the world. The percentage shares of water demand in the country's agriculture, manufacturing, and service sectors, which are major economic sectors, are 75%, 3%, and 5%, respectively. With the continuous growth of the economy, the demand for water is steadily rising, while the expansion of water supply remains constrained by several factors and the water supply is also affected by climate change. This study uses the input-output model to examine the relationship between water usage and the economic system in Thailand in 2010. The constructed input-output model is the integration of the Leontief inverse matrix, the matrix of water usage, and the details of the gross domestic product (GDP). The model indicates the linkage between GDP expansion and water demand in both direct and indirect usage. The computation result obtained from the model indicates that the agricultural sector is the major water user, with its ratio of direct water use being the highest. The manufacturing sector records the highest ratio of indirect water use, which is influenced by its supply chain comprising the agriculture and service sectors. This model and its results may serve as the main foundation for the design of economic and environmental policies oriented toward optimizing water demand and supply. The model can also be extended and enriched with detailed mechanisms of economic behavior to allow further complex analyzes such as water pricing policies.

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Financial feasibility is usually a concern in water reclamation projects. Aside from internal benefits, water reclamation in industrial parks delivers health and environmental benefits not normally considered in cost–benefit analyses (CBA). This study investigated the influence of environmental benefits on the feasibility of water reclamation projects with flow rate scenarios in accordance with industrial parks in Chonburi, Thailand. CBAs of water reclamation plants for industrial water supply, consisting of ultrafiltration (UF) and reverse osmosis (RO), with flow rates of 5200, 10,000, 15,000, and 25,000 m3/day and discount rates of 3%, 5%, 7%, 9% and 11% were conducted. Considering only the direct costs and benefits, none of the projects were financially feasible. However, when the environmental benefits were included, the projects became profitable in all cases except those with a flow rate of 5200 m3/day and discount rates of 5%, 7%, 9%, and 11% and those with flow rates of 10,000 and 25,000 m3/day and an 11% discount rate. Further, CBAs of water reclamation projects in industrial parks for irrigation were conducted with post-treatment processes consisting of sand filtration and chlorine disinfection for flow rates of 240, 480, 2400, 3600, and 4800 m3/day. The projects are profitable, regardless of environmental benefits.

This paper presents a flexible approach that is real options to increase expected value in water infrastructure systems. Real options make an adaptable ability to respond the systems more effective to good opportunity and withdrawn unproductive situations from loss of investments in the future. The result of this approach is compared with traditional net present value in cases of with and without uncertainty to show expected values of investment of industrial water demand and supply schemes. It shows that real options in system can increase expected value of investment by reducing negative risks and increasing opportunities. An example of water infrastructure investment to support increasing industrial water demand demonstrates the use and results of this approach.

Remote sensing has emerged as a useful tool for monitoring spatial and temporal distribution of sediment fluxes in the water bodies. The large quantity of sediment transported by the river waters create a number of water resources and environmental problems and could shorten the useful life of many downstream reservoirs and dams. The objective of the present article is to quantify the distribution of suspended sediment by means of Advanced Land Observing Satellite/Advanced Visible and Near Infrared Radiometer type 2 (ALOS/AVNIR-2) and Systeme Pour l'Observation de la Terre (SPOT) satellite data. Multispectral remotely sensed algorithm was developed to investigate the distribution of suspended sediment in the Indus River, Pakistan, and Tarbela dam reservoir. Reflectance of ALOS/AVNIR-2 band 3 and band 4 and SPOT-2 satellite band 2 and band 3 was found to be the best predictor of suspended sediment concentration in the surface waters. To deal with the lack of in situ data, remotely sensed data are coupled with optical modeling and the desired parameter is derived by model inversion technique. The developed methodology is an effective and efficient tool for monitoring erosion, deposition areas, and sediment distribution in large rivers.