A remote sensing method for estimating regional reservoir area and evaporative loss

Zhang, Hua; Gorelick, Steven M.; Zimba, Paul V.; Zhang, Xiaodong

doi:10.1016/j.jhydrol.2017.10.007

A remote sensing method for estimating regional reservoir area and evaporative loss

Journal Article · Sat Oct 07 00:00:00 EDT 2017 · Journal of Hydrology

DOI:https://doi.org/10.1016/j.jhydrol.2017.10.007· OSTI ID:1415402

^[1]; Gorelick, Steven M. ^[2]; Zimba, Paul V. ^[3]; Zhang, Xiaodong ^[4]

Texas A & M Univ., Corpus Christi, TX (United States). School of Engineering and Computing Sciences
Stanford Univ., CA (United States). Dept. of Earth System Science
Texas A & M Univ., Corpus Christi, TX (United States). Center for Coastal Studies
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Evaporation from the water surface of a reservoir can significantly affect its function of ensuring the availability and temporal stability of water supply. Current estimations of reservoir evaporative loss are dependent on water area derived from a reservoir storage-area curve. Such curves are unavailable if the reservoir is located in a data-sparse region or questionable if long-term sedimentation has changed the original elevation-area relationship. In this paper, we propose a remote sensing framework to estimate reservoir evaporative loss at the regional scale. This framework uses a multispectral water index to extract reservoir area from Landsat imagery and estimate monthly evaporation volume based on pan-derived evaporative rates. The optimal index threshold is determined based on local observations and extended to unobserved locations and periods. Built on the cloud computing capacity of the Google Earth Engine, this framework can efficiently analyze satellite images at large spatiotemporal scales, where such analysis is infeasible with a single computer. Our study involves 200 major reservoirs in Texas, captured in 17,811 Landsat images over a 32-year period. The results show that these reservoirs contribute to an annual evaporative loss of 8.0 billion cubic meters, equivalent to 20% of their total active storage or 53% of total annual water use in Texas. At five coastal basins, reservoir evaporative losses exceed the minimum freshwater inflows required to sustain ecosystem health and fishery productivity of the receiving estuaries. Reservoir evaporative loss can be significant enough to counterbalance the positive effects of impounding water and to offset the contribution of water conservation and reuse practices. Our results also reveal the spatially variable performance of the multispectral water index and indicate the limitation of using scene-level cloud cover to screen satellite images. Finally, this study demonstrates the advantage of combining satellite remote sensing and cloud computing to support regional water resources assessment.

View Accepted Manuscript (DOE)

Research Organization:: Texas A & M Univ., Corpus Christi, TX (United States); Stanford Univ., CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; Texas Comprehensive Research Fund (TCRF) (United States); National Science Foundation (NSF) (United States)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1415402

Report Number(s):: LA-UR--17-24804

Journal Information:: Journal of Hydrology, Journal Name: Journal of Hydrology Vol. 555; ISSN 0022-1694

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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