Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches Academic Article uri icon

abstract

  • © 2019 Elsevier Inc. Evaporation from open surface water is a critical and continuous process in the water cycle. Globally, evaporation losses from reservoirs are estimated to be greater than the combined consumption from industrial and domestic water uses. However, this large volume of water loss is only coarsely considered in modern water resources management practices due to the complexities involved with quantifying these losses. By fusing remote sensing and modeling approaches, this study developed a novel method to accurately estimate the evaporation losses from 721 reservoirs in the contiguous United States (CONUS). Reservoir surface areas were extracted and enhanced from the Landsat based Global Surface Water Dataset (GSWD) from March 1984 to October 2015. The evaporation rate was modeled using the Penman Equation in which the lake heat storage term was considered. Validation results using in situ observations suggest that this approach can significantly improve the accuracy of the simulated monthly reservoir evaporation rate. The evaporation losses were subsequently estimated as the product of the surface area and evaporation rate. This paper presents a first of its kind, comprehensively validated, locally practical, and continentally consistent reservoir evaporation dataset. The results suggest that the long term averaged annual evaporation volume from these 721 reservoirs is 33.73 × 10 9 m 3 , which is equivalent to 93% of the annual public water supply of the United States (in 2010). An increasing trend of the evaporation rate (0.0076 mm/d/year) and a slightly decreasing trend of the total surface area (−0.011 × 10 9 m 2 /year) were both detected during the study period. As a result, the total evaporation shows an insignificant trend, yet with significant spatial heterogeneity. This new reservoir evaporation dataset can help facilitate more efficient water management practices.

altmetric score

  • 9.25

author list (cited authors)

  • Zhao, G., & Gao, H.

citation count

  • 27

publication date

  • June 2019