Despite ubiquitous warming, the lower Oder River typically freezes over almost every year. Ice jams may occur during freeze-up and ice cover breakup phases, particularly in the middle and lower reaches of the river, with weirs and piers. The slush ice and ice blocks may accumulate to form ice jams, leading to backwater effects and substantial water level rise. The small bottom slope of the lower Oder and the tidal backflow from the Baltic Sea enhance the formation of ice jams during cold weather conditions, jeopardizing the dikes. Therefore, development of an ice jam flood forecasting system for the Oder River is much needed. This commentary presents selected results from an international workshop that took place in Wrocław (Poland) on 26–27 November 2018 that brought together an international team of experts to explore the requirements and research opportunities in the field of ice jam flood forecasting and risk assessment for the Oder River section along the German–Polish border. The workshop launched a platform for collaboration amongst Canadian, German and Polish scientists, government officials and water managers to pave a way forward for joint research focused on achieving the long-term goal of forecasting, assessing and mitigating ice jam impacts along the lower Oder. German and Polish government agencies are in need of new tools to forecast ice jams and assess their subsequent consequences and risks to communities and ship navigation along a river. Addressing these issues will also help research and ice flood management in a Canadian context. A research program would aim to develop a modelling system by addressing fundamental issues that impede the prediction of ice jam events and their consequences in cold regions.

The main aim of the presented work was to assess Landsat 8 satellite imagery for the presence of cloud cover over the terminal zone of the Aavatsmark Glacier (NW Spitsbergen, Svalbard). The work used all downloadable Landsat 8 imagery taken from the start of the mission (early 2013) to the end of 2020 and covering the entire area of interest (AOI). There were a total of 868 satellite images. The degree of visibility of the AOI zone in each image was calculated using Quality Assessment Band image (QA), which is an integral part of the Landsat 8 dataset. The QA data were reclassified, grouped into specific visibility classes and presented on an annual and monthly basis. An analysis of the incidence of usable imagery, i.e. imagery with no more than 5% cloud cover, was also carried out. Of all the available imagery, over the years analysed, only 176 (approx. 20%) contained a fully visible area, while approx. 60% of the images had more than 95% cloud cover. These data were also compared with the results of cloud cover at the nearest weather station in Ny-Ålesund.

Soil erodibility is one of the crucial parameters for modelling soil erosion, expressed as the K-factor. The presented study tries to illustrate the spatial variance of K-factor on a local scale through the investigation of soil properties and descriptive spatial analysis utilising GIS tools at microscale in a young hummocky moraine landscape in Northern Poland. The results of the interpolation of K-factor values illustrate their changing from high values in eroded pedons on the tops of hummocks to low values in kettle holes. The middle position is occupied by slightly and non-eroded pedons. The mean weight results were very similar to data that were found on the scale of Europe and Poland. In landscapes with heterogeneous soil cover, there are significant differences in maps based on different approaches to data visualisation. There are advantages and disadvantages to both (1) referring to mean values of the K index for soil contours representing different soil types and (2) interpolating the values obtained from individual points (GIS tool). Interpolation can be used for a thoroughly examined area with a high number of input points, while a map based on mean K index values for soil contours would be more effective in homogeneous areas.