Spreading and transformation of freshwater discharge is one of the key factors influencing sea surface layer in the Arctic Ocean shelf. In this study, seasonal and interannual variability of river plumes formed by the major rivers of the Russian Arctic sector in is examined. The analysis is based on recently developed regional model for reconstruction sea surface salinity (SSS) from Soil Moisture Active Passive (SMAP) satellite data based on machine learning approaches. This model improves standard SMAP SSS product and provides high-accuracy surface salinity data for the Eurasian Arctic shelf, which is affected by river discharge. In particular, this data could be used for high-accuracy assessments of spreading area and internal structure of the Ob-Yenisei and Lena river plumes, which was not the case of standard SMAP SSS data.
Using the obtained SSS data, spreading of the Ob-Yenisei and Lena plumes from 2015 to 2024 is described. The most typical positions of plumes during ice-free season are distinguished and associated with external wind and river discharge forcing. Special attention is given to the anomalous spreading of the Ob-Yenisei plume in the Kara Sea in 2015 and the Lena plume in the East Siberian Sea in 2019. The observed synoptic variability of the plumes is compared with the Ekman theory of wind-induced ocean surface circulation. Finally, machine learning approaches are applied to predict motion of the plumes using wind forcing and ocean altimetry satellite data.

Sea surface salinity,River plume,Arctic Ocean,Machine Learning