A METHODOLOGY TO ESTIMATE WATER DEMAND FOR THERMAL POWER PLANTS IN DATA-SCARCE REGIONS USING SATELLITE IMAGES

This technical note by the World Resources Institute proposes a three-step methodology to estimate water withdrawal and consumption for thermal power plants in data-scarce regions. The approach integrates the visual identification of cooling and fuel types via satellite imagery with empirical water-use factors. When tested against 200 U.S. power plants, the method demonstrated high precision in identifying technology and fuel types, though it faced more challenges in accurately estimating water demand for specific cooling systems.

• The proposed methodology uses a three-step process to estimate water use: first, identifying cooling and fuel types through satellite images; second, assigning water withdrawal and consumption factors based on those identifications; and third, calculating total water use using net generation data.
• In a test involving 200 power plants in the United States, the methodology achieved 90 percent precision in identifying cooling technology and 89 percent precision in identifying fuel types.
• The methodology's precision in estimating water demand was 69 percent when tested against U.S. power plants.
• The accuracy of water demand estimates varied significantly by cooling system; while 100 percent of dry and recirculating tower cooled plants' reported data fell within the estimated ranges, only 29 percent of once-through and 14 percent of recirculating pond systems did.
• The research identifies specific visual markers for different thermal power plant types: coal plants often have black coal fields and conveyor belts; nuclear plants feature large domed towers and lack flue gas stacks; and concentrated solar power plants are characterized by arrays of mirrors.
• The methodology has several limitations, including an inability to distinguish between different generation technologies within the same fuel type (such as subcritical versus supercritical plants) and a reliance on U.S.-based water-use factors that may not apply globally due to differences in climate and regulation.

APA

LUO, T., KRISHNASWAMI, A., & LI, X. (2018). A METHODOLOGY TO ESTIMATE WATER DEMAND FOR THERMAL POWER PLANTS IN DATA-SCARCE REGIONS USING SATELLITE IMAGES. World Resources Institute. https://wriorg.s3.amazonaws.com/s3fs-public/17_TECH_PowerPlants_V7.pdf

Chicago

LUO, TIANYI, ARJUN KRISHNASWAMI, and XINYUE LI. A METHODOLOGY TO ESTIMATE WATER DEMAND FOR THERMAL POWER PLANTS IN DATA-SCARCE REGIONS USING SATELLITE IMAGES. World Resources Institute, 2018. https://wriorg.s3.amazonaws.com/s3fs-public/17_TECH_PowerPlants_V7.pdf.

Wikipedia

{{cite report |last1=LUO |first1=TIANYI |last2=KRISHNASWAMI |first2=ARJUN |last3=LI |first3=XINYUE |title=A METHODOLOGY TO ESTIMATE WATER DEMAND FOR THERMAL POWER PLANTS IN DATA-SCARCE REGIONS USING SATELLITE IMAGES |publisher=World Resources Institute |date=January 2018 |url=https://wriorg.s3.amazonaws.com/s3fs-public/17_TECH_PowerPlants_V7.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{luo2018methodology, author = {LUO, TIANYI and KRISHNASWAMI, ARJUN and LI, XINYUE}, title = {{A METHODOLOGY TO ESTIMATE WATER DEMAND FOR THERMAL POWER PLANTS IN DATA-SCARCE REGIONS USING SATELLITE IMAGES}}, institution = {World Resources Institute}, year = {2018}, month = jan, url = {https://wriorg.s3.amazonaws.com/s3fs-public/17_TECH_PowerPlants_V7.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }