Abstract:A large number of rivers are frozen annually, and the river ice cover has an influence on the geomorphological processes. These processes in cohesive sediment rivers are not fully understood. Therefore, this paper demonstrates the impact of river ice cover on sediment transport, i.e. turbidity, suspended sediment loads and erosion potential, compared with a river with ice-free flow conditions. The present sediment transportation conditions during the annual cycle are analysed, and the implications of climate change on wintertime geomorphological processes are estimated. A one-dimensional hydrodynamic model has been applied to the Kokemäenjoki River in Southwest Finland. The shear stress forces directed to the river bed are simulated with present and projected hydroclimatic conditions. The results of shear stress simulations indicate that a thermally formed smooth ice cover diminishes river bed erosion, compared with an ice-free river with similar discharges. Based on long-term field data, the river ice cover reduces turbidity statistically significantly. Furthermore, suspended sediment concentrations measured in ice-free and icecovered river water reveal a diminishing effect of ice cover on riverine sediment load. The hydrodynamic simulations suggest that the influence of rippled ice cover on shear stress is varying. Climate change is projected to increase the winter discharges by 27-77% on average by 2070-2099. Thus, the increasing winter discharges and possible diminishing ice cover periods both increase the erosion potential of the river bed. Hence, the wintertime sediment load of the river is expected to become larger in the future.

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Space‐borne remote sensing techniques enable near real‐time mapping of floods cost‐efficiently. Synthetic aperture radar (SAR) and optical sensors are the most suitable for flood detection. However, SAR has become more popular, due to the independence of sunlight and weather conditions, and the increasing data availability. Typical spring floods occurred in northern Finland during 2018. Various remote sensing sources were utilised for monitoring and damage estimation of the flooding. Floods were mapped with the SAR‐based Finnish Flood Centre's Flood Detection Algorithm (FC‐FloDA), a standard threshold‐based approach applied to Sentinel‐1, and a visual interpretation of Sentinel‐2 images. In addition, flood maps from the Copernicus Emergency Management Service (EMS) and aerial photographs from the city of Tornio were ordered. The flood products and interpretations were compared, and a deeper accuracy assessment was conducted on the FC‐FloDA maps. FC‐FloDA was, in general, the most successful in detecting floods within the test areas. The EMS product and the Sentinel‐1 interpretation worked well in open areas, but did not detect floods in forests. The superiority of Flood Centre's product is mainly based on the adaptation of the algorithm to northern boreal environments and the selection of an optimal polarisation for detecting floods also under tree canopies.