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.

The role of evapotranspiration (ET) for meteorology, agriculture, and hydrology has been identified as key during centuries (Miralles et al., 2016). Our current understanding of this process arises two hundred years ago (e.g.,

<p>Water level information is highly sought-after by operational hydrologists and emergency managers to improve flood management in, for example, West Africa (Lienert et al 2020). A main constraint of large-scale flood forecasting systems is an inability to convert streamflow volumes to water level at specific locations. Accurately representing water levels – and hence potential impacts of peak flows on local scale – is possible through detailed field work and hydraulic simulations (e.g. Massazza et al 2020). However, large-scale implementation of such approaches is typically constrained by lack of detailed topographic data. In this study we therefore develop a pragmatic method to estimate water levels using rating curves created through a combination of ground-based (in-situ) hydrometric gauge observations, hydrological simulations, and satellite altimetry data. Specifically, rating curves are created based on simulated discharge from HYPE models and 305 in-situ discharge observations from 1980 to 2020, in addition to 42 in-situ and 558 virtual water level stations (i.e. locations where Sentinel-3 missions intersect large rivers) from 2018 to 2020. The rating curves were estimated by fitting a conventional power-law equation. For the in-situ data this could be done directly from the two variables. These were, however, very scarce. We therefore exploited the EO-based virtual stations to be able to predict water levels at many more locations. To this end, rating curves were estimated using simulated discharge together with EO-based water level data at the virtual station locations. The inverted rating curve equation was subsequently used to transform simulated discharge to water level. The water levels estimated from simulated discharge were finally compared with the in-situ and virtual altimetry stations using accuracy performance metrics such as Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE). Furthermore, we examine and compare the rating curve uncertainty obtained from different data sources (in-situ, modelled and satellite data). This pragmatic methodology can be used in operational hydrology, specifically flood forecasting, to render forecasts more relevant at local scale and hence enable better flood risk management.</p>

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models using the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.