The applicability of remote sensing and geodetic methods for studying water bodies on the western White Sea coastстатья

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1. Frolova_te_al_2015_The_applicability_of_remote-sensing_an... Frolova_te_al_2015_The_applicability_of_remote-sensing_an... 1,7 МБ 9 декабря 2015 [e_d_krasnova]

[1] The applicability of remote sensing and geodetic methods for studying water bodies on the western white sea coast / N. Frolova, E. Krasnova, M. Fatkhi et al. // EARSeL eProceedings. — 2015. — Vol. 14, no. 1. — P. 71–80. Abstract The article presents the results of field studies carried out by the Department of Land Hydrology, Faculty of Geography, Lomonosov Moscow State University, in the area near the White Sea Biological Station, Moscow State University (Kandalaksha Bay, White Sea) in January, June, and September 2014 and January 2015. The objects of studies were the unique lakes on the White Sea coast at different stages of their isolation and separation from the sea. Geodetic methods were applied to determine the surface elevation of modern lakes. The degree of isolation of the studied water bodies was estimated and used to identify the following types of lakes: those permanently disconnected from the sea, those having such connection periodically, and those having it permanently in the form of daily tidal influence. The decrease in seawater inflow caused by ice cover was estimated with the use of a conductivity data logger. In winter, the study area is difficult to access and to work in; therefore, remote sensing products such as GlobSnow were used to determine water storage in the snow cover yet another that affects water level and water chemistry in lakes after snow melting. It was shown that large-scale satellite data could be used for assessing the total snow water equivalent in a small area along coast zone. However snow surveys are still in need to determine the local characteristics and spatial variability of snow cover on the ice cover of lakes and lake watersheds. The ground-based observations of snow storage in winters 2010, 2014, and 2015 were compared with remote sensing data obtained by the Finnish Meteorological Institute (the GlobSnow project). [ DOI ]

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