The assessment and mapping of riverine flood hazards and risks is recognized by many countries as an important tool for characterizing floods and developing flood management plans. Often, however, these management plans give attention primarily to open-water floods, with ice-jam floods being mostly an afterthought once these plans have been drafted. In some Nordic regions, ice-jam floods can be more severe than open-water floods, with floodwater levels of ice-jam floods often exceeding levels of open-water floods for the same return periods. Hence, it is imperative that flooding due to river ice processes be considered in flood management plans. This also pertains to European member states who are required to submit renewed flood management plans every six years to the European governance authorities. On 19 and 20 October 2022, a workshop entitled “Assessing and mitigating ice-jam flood hazard and risk” was hosted in Poznań, Poland to explore the necessity of incorporating ice-jam flood hazard and risk assessments in the European Union’s Flood Directive. The presentations given at the workshop provided a good overview of flood risk assessments in Europe and how they may change due to the climate in the future. Perspectives from Norway, Sweden, Finland, Germany, and Poland were presented. Mitigation measures, particularly the artificial breakage of river ice covers and ice-jam flood forecasting, were shared. Advances in ice processes were also presented at the workshop, including state-of-the-art developments in tracking ice-floe velocities using particle tracking velocimetry, characterizing hanging dam ice, designing new ice-control structures, detecting, and monitoring river ice covers using composite imagery from both radar and optical satellite sensors, and calculating ice-jam flood hazards using a stochastic modelling approach.

This paper presents a new approach for delineating river coastlines in RGB close-range nadir aerial imagery acquired by unmanned aerial vehicles (UAVs), aimed at facilitating waterline detection through the reduction of the dimensions of a colour space and the use of coarse grids rather than pixels. Since water has uniform brightness, expressed as the value (V) component in the hue, saturation, value (HSV) colour model, the reduction in question is attained by extracting V and investigating its histogram to identify areas where V does not vary considerably. A set of 30 nadir UAV-acquired photos, taken at five different locations in Poland, were used to validate the approach. For 67% of all analysed images (both wide and narrow rivers were photographed), the detection rate was above 50% (with the false hit rate ranged between 5.00% and 61.36%, mean 36.62%). When the analysis was limited to wide rivers, the percentage of images in which detection rate exceeded 50% increased to 80%, and the false hit rates remained similar. Apart from the river width, land cover in the vicinity of the river, as well as uniformity of water colour, were found to be factors which influence the waterline detection performance. Our contribution to the existing knowledge is a rough waterline detection approach based on limited information (only the V band, and grids rather than pixels).

In frame of the project no. 2020/38/E/ST10/00295 carried out within the Sonata BIS programme, financed by the National Science Centre of Poland, we consider different approaches to delineate the river coastline based only on close-range visible light aerial images (RGB) acquired by unmanned aerial vehicles (UAVs). Among the scrutinized methods, we use automatic mapping of extent of water bodies by means of image classification. It was found that the best results of reconstructing inundation extent (even 95%) were obtained using the supervised methods, in particular the maximum likelihood algorithm. The accuracy assessment of this classification, using the confusion matrix visualization, allowed us to notice that the areas incorrectly classified as "water underestimation" (surface where there is real inundation which was not indicated by the classifier) are located mainly on the borders of the water bodies.In most of the analyzed cases, the incorrectly classified "water underestimation" areas form a narrow zone around the inundated areas, which can be interpreted as the water-land interface zone. Therefore, it is possible to delineate, with high probability, the approximate water-land boundary line. In low-altitude aerial photographs or orthophotomaps with visible fragments of river channels, the designation of such "water underestimation" zone allowed us to delineate the approximate course of the river channel coastline. This approach was tested on several UAV images acquired over the middle Odra River channel in western Poland. We analyzed several images representing various terrain situations: (1) the river channel completely visible, without vegetation, where the visual determination of the reference coastline by the human expert was not difficult, (2) the river channel partially shaded, where significant classifier errors can be expected, (3) the river channel partially covered, for example by vegetation, where the course of the real coastline is uncertain. The obtained results confirm that the proposed approach allows to reconstruct some courses of the coastline for channels with a width of at least 100 m using the "water underestimation" areas.

Flood management is very important task in the context of rapid climate changes. Increasing the frequency of extreme weather and fluvial phenomena, such as droughts, water shortages or floods determines that detecting water bodies and boundaries between them and surrounding surface is an important and challenging issue. We elaborated a new approach for delineating river coastline based only on close-range RGB nadir images acquired by means of UAV (unmanned aerial vehicle), converted to HSV (hue, saturation, value) color space. We used spectral characteristics of water surface which has uniform V component, while another land cover types have heterogeneous V. Areas, where character of V changes considerably, are suspected to be river coastline. Every aerial image was divided into 250 x 250 px cells, within which we calculated some statistical values (kurtosis, concentration around mode) in order to characterize shape of empirical distribution. Among others, we focused on identifying multi-modal or leptokurtic histograms. Results show that the detection rate (also known as the producer accuracy) ranges from 22,22% to 92,00%, while the false hit rate (also known as error of commission) ranges from 5,00% to 82,76%. For 70% of all analyzed images, presenting both narrow (10 m) and wide (more than 100 m) rivers, the detection rate was above 50%. Considering the subset of photos presenting only wide rivers, detection rate above 50% occurred for 75% of these images. For these cases, 56% of images do not exceed the false hit rate above 40%. The research is supported by the National Science Centre, Poland, through the project no. 2020/38/E/ST10/00295.