The application of linear interpolation for handling missing hydrological data is unequivocal. On one hand, such an approach offers good reconstruction in the vicinity of last observation before a no-data gap and first measurement after the gap. On the other hand, it omits irregular variability of hydrological data. Such an irregularity can be described by time series models, such as for instance the autoregressive integrated moving average (ARIMA) model. Herein, we propose a method which combines linear interpolation with autoregressive integrated model (ARI, i.e. ARIMA without a moving average part), named LinAR (available at GitHub), as a tool for inputing hydrological data. Linear interpolation is combined with the ARI model through linear scaling the ARI-based prediction issued for the no-data gap. Such an approach contributes to the current state of art in gap-filling methods since it removes artificial jumps between last stochastic prediction and first known observation after the gap, also introducing some irregular variability in the first part of the no-data gap. The LinAR method is applied and evaluated on hourly water level data collected between 2016 and 2021 (52,608 hourly steps) from 28 gauges strategically located within the Odra/Oder River basin in southwestern and western Poland. The data was sourced from Institute of Meteorology and Water Management (Poland). Evaluating the performance with over 100 million assessments in the validation experiment, the study demonstrates that the LinAR approach outperforms the purely linear method, especially for short no-data gaps (up to 12 hourly steps) and for rivers of considerable size. Based on rigorous statistical analysis of root mean square error (RMSE) – expressed (1) absolutely, (2) as percentages and (3) using RMSE error bars – the percentage improvement, understood as percentage difference between RMSE of linear and LinAR interpolations, was found to reach up to 10%.

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.

Natural treatment systems for wastewater (NTSW) allow us to not only reduce environmental pollution with sewage, but also to facilitate the reuse of water. This study presents almost 2.5 years of operation of a NTSW pilot plant, where the purpose of which was to purify domestic sewage from the building of the Institute of Applied Ecology (with three permanent residents and up to five employees) to the quality of drinking water. The NTSW consists of a septic tank, compost beds, and denitrification, phosphorus, and active carbon beds. With an active area of 3 m² per person and a hydraulic residence time (HRT) of 6 days (excluding the HRT of the tank of 8 days), the NTSW allowed for a mean reduction of 99%, 95%, and 98% for the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solids (TSSs), respectively. The renewed water was characterized by average concentrations of 2.2 mg O₂/dm³, 17.8 mg O₂/dm³, 2.1 mg/dm³, 4.9 mg O₂/dm³, and 0.6 nephelometric turbidity units for BOD, COD, TSS, oxidation, and turbidity, respectively. Thus, it met Polish and European drinking water requirements in terms of oxidation and turbidity. This water can be reused for toilet flushing and irrigation.

For nearly three decades, satellite radar altimetry has provided measurements of the water surface elevation (WSE) of rivers. These observations can be used to calculate the water surface slope (WSS), which is an essential parameter for estimating flow velocity and river discharge. In this study, we calculate a non time‐varying high‐resolution WSS of 11 Polish rivers based on multi‐mission altimetry observations from 11 satellites in the period from 1994 to 2023. The proposed approach is based on a weighted least squares adjustment with an additional Laplace condition and an a priori gradient condition. The processing is divided into river sections not interrupted by dams and reservoirs. After proper determination of the WSE for each river kilometer (bin), the WSS between adjacent bins is calculated. To assess the accuracy of the estimated WSS, it is compared with slopes between gauge stations, which are referenced to a common vertical datum. Such gauge stations are available for 8 studied rivers. The root mean squared error (RMSE) ranges from 4 mm/km to 77 mm/km, with an average of 27 mm/km. However, the mean RMSE decreases to 11 mm/km when the 2 mountain rivers are excluded. The WSS accuracies are also compared with slope data sets based on digital elevation models, ICESat‐2 altimetry, and lidar. For 6 rivers the estimated WSS shows the highest accuracy. The improvement was particularly significant for mountain rivers. The proposed approach allows an accurate, non time‐varying high‐resolution WSS even for small and medium‐sized rivers and can be applied to almost any river worldwide.

This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in $$\varvec{\approx }$$≈ 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions.