25 October 2016 The estimation the Great Lakes net basin supply: implications for water level fluctuations
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We computed Net Basin Supplies to the Great Lakes by estimated over-lake evaporation using the bulk aerodynamic method and derived over-lake precipitation from MERRA associated with the runoff data provided by GLERL. The long-term trend of NBS components that are over-lake evaporation rate, over-lake precipitation and runoff were also addressed. We also evaluated the relationship between NBS and change in water level for each of the Great Lakes. The simple correlation analysis was performed in order to determine the relationship between the climate teleconnection indices with NBS, over-lake precipitation, and over-lake evaporation. A brief review on the effect of climate teleconnection on the Great Lakes NBS, over-lake evaporation, and over-lake precipitation were given in the last section. The distribution of over-lake precipitation is spread throughout the year with two peaks in April and October. The monthly average of runoff entering to the Great Lakes was highest in April while the lowest monthly mean runoff was in September. The evaporation rate was highest in January for all Lakes but Lake Ontario, which was highest in early December. The evaporation rate then sharply drops in March the evaporation processes continue again in August. The calculations of NBS were limited by the availability of runoff data. The highest NBS was in April while the lowest monthly average NBS was varied in December, January, and February. The correlation analysis was based on various teleconnection indices and NBS, over-lake evaporation, and over-lake precipitation. NAO was positively correlated with NBS in all Lakes but Lake Superior while over-lake evaporation showed a negative correlation with PDO and Niño 3.4.
Conference Presentation
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Sitthisak Moukomla, Sitthisak Moukomla, Peter D. Blanken, Peter D. Blanken, } "The estimation the Great Lakes net basin supply: implications for water level fluctuations", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999815 (25 October 2016); doi: 10.1117/12.2240850; https://doi.org/10.1117/12.2240850

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