Search Results (47)

Ice-jam formation during winter low-flow conditions represents a persistent hydrotechnical hazard in small and medium-sized rivers of Central Europe. Despite extensive monitoring efforts, preventive structural measures remain insufficiently developed and rarely evaluated under real geomorphological constraints. This study proposes and hydraulically verifies a low-profile riverbed sill designed to suppress the initiation and stabilization of frazil and anchor ice during critical winter discharges. The analysis integrates 20 years of hydrological and water-temperature data (2004–2024), 26 detailed cross-sectional surveys, a high-resolution longitudinal profile derived from DMR 3.0, and a newly formulated Ice-Jam Risk Index (Iice) combining flow velocity, depth-to-width ratio and thermal deficit. Application to the Torysa River (rkm 42.8–43.6) revealed a clearly defined high-risk zone (rkm 43.20–43.38), where hydraulic conditions frequently fall below the critical thresholds for ice accumulation (U < 0.35 m·s⁻¹; h/B < (h/B)_crit; ΔT > 0.5 °C), indicating shallow and laterally widened channel sections prone to anchor-ice stabilization. Model simulations demonstrated that the proposed sill increases mean velocity by 22–35% during Q₆₅–Q₈₅ conditions, reducing the local I(ice) by 61%, while preserving the conveyance capacity for discharges above Q₅₀ and avoiding measurable backwater impacts upstream. Field-based morphology, risk index interpolation and hydraulic modeling all confirm that the structure effectively disrupts the formation of stable anchor-ice nuclei, which have historically triggered severe ice-jam floods in this reach (2011/12, 2016/17, 2021/22). The results show that a properly dimensioned low-profile sill provides a passive, low-cost, and transferable engineering solution for winter flood risk mitigation, outperforming reactive ice-management techniques while maintaining ecological and hydraulic compatibility with small natural rivers. The methodology is replicable for other rivers where supercooling, low-flow hydraulics and channel morphology jointly control ice-jam initiation. Full article

The presence of ice cover significantly alters the hydraulic characteristics of river channels, and the evolutionary law of ice drift velocity is crucial for understanding the ice-jam floods (IJFs) formation mechanism during the spring IJFs breakup period. Based on miniature ice buoy locators and Sentinel-2 satellite remote sensing data, this study systematically analyzes the channel characteristics of the upper Heilongjiang River and the regulatory effect of channel morphology on ice drift velocity. The results show that the river width of the upper Heilongjiang River exhibits a widening trend, with a variation range of 212 to 1292 m, characterized by large longitudinal dispersion and significant spatial variability. During the 2024 spring IJFs breakup period, the ice drift velocity ranges from 0.57 to 3.48 m/s with an average of 1.92 m/s, and a significant decreasing trend is observed when the ice drift passes through the entrances/exits of meandering bends and the confluences of distributaries in braided channels. The longitudinal distribution law of ice drift velocity revealed in this study can provide key data support and scientific reference for the accurate prediction of IJFs and the prevention and control of IJFs. Full article

River ice is a common natural phenomenon in cold regions during winter, and it is also one of the key factors that must be considered in the development and utilization of water resources in these areas. In this paper, based on a two-dimensional hydrodynamic model and ice dynamics model coupled with a linear thermodynamic process, this study simulates and validates the formation, decay, transport, and accumulation of river ice at the Toudaoguai reach of the Yellow River in Inner Mongolia during the winters of 2019–2020 and 2020–2021. The influence of different parameters on backwater level variations caused by ice jams is further investigated using a modified Morris sensitivity analysis method. The results show that (1) the coupled thermal-dynamic model can accurately simulate the formation, transport, and accumulation process of river ice in natural river, as well as the freeze-up patterns and corresponding hydraulic characteristics. (2) Due to the influence of river topography, flow rate, and flow density, the freeze-up form is slightly different in different years, and the low discharge process favor a more stable freeze-up. (3) According to the modified Morris screening method, discharge (Q) and ice concentration (N) are the most sensitive to the change in the backwater water level after the ice jam, and the sensitivity is more than 50%. The next most sensitive factor is the ice-cover roughness (n_i), whereas ice porosity (e_f) exhibits a negative sensitivity to the water level after ice jam. Thus, this study provides effective tools to reproduce the process of river ice transport and accumulation in the reach of the Yellow River (Inner Mongolia section) and offers technical support and insights for ice-flood prevention and mitigation in this section. Full article

Mechanical breakup of river ice cover and associated ice jam flooding is a major concern for riverine communities in cold regions. The ability to forecast breakup ice jams is essential for river ice management. Numerous studies on forecasting breakup ice jams have been conducted. This study reviews existing breakup forecasting methods, including data-driven and machine learning techniques, and discusses their shortcomings and possible improvements in selecting input parameters. Since the weather during breakup time often changes rapidly, forecasting in a Nowcasting mode to assess the risk of mechanical breakup and ice jam flooding is necessary to issue flood warnings and support emergency operations. A physically based method for rapidly forecasting ice cover breakup and ice jam flooding is developed, which also provides information to improve existing forecasting methods. Full article

The Peace-Athabasca Delta (PAD) in northern Alberta, Canada, is one of the world’s largest inland freshwater deltas and is largely located within the Wood Buffalo National Park, a UNESCO World Heritage Site. Owing to its ecological and socioeconomic significance, the PAD has been designated a Ramsar wetland of international importance. A paucity of large-scale Peace River ice jam flooding and concurrent drying trend during the past five decades has motivated various studies on relevant processes and on possible remedial action. In turn, many of these studies are informed by a flood record that was compiled in 1995, based on Historical information and Traditional Knowledge (H-TK flood record). Later work has expressed occasional reservations regarding the accuracy of this record, while much more is now known about the physical and hydroclimatic controls of PAD ice jams. This paper examines the 20th century portion of the H-TK record in the light of recent scientific advances made since the 1990s and of a wealth of hydrometric and climatic indicators, along with eyewitness corroborations, that extend back to the early 1900s. Systematic observational data and monitoring reports that have become available since the 1990s have also provided valuable documentation of PAD flooding. It is concluded that the record of major ice-jam floods is reliable, while the possibility of “missed” events cannot be precluded. The record of minor ice jam floods, which is largely inferred from reversed tributary flows entering Lake Athabasca, may not be reliable because more than half of the reported events might not have occurred at all. The value of the H-TK record is primarily in the major events, which generate overland inundation and can amply recharge various ponds, lakes, and wetlands of the PAD. Implications of the results for pre- and post-regulation flood frequencies and for future park management are discussed. Full article

Ice disasters in the Yellow River’s Inner Mongolia reach exhibit sudden onset and high destructiveness, driven by climatic and channel constraints. The Shisifen Bend, within this reach, is particularly prone to initial ice jamming during freeze-up periods annually. This susceptibility arises from channel narrowing, increased upstream ice influx, and complex river morphology. To address persistent ice flood risks and mitigation challenges at Shisifen Bend, this study developed a coupled ice-transport numerical model. Utilizing MIKE21’s hydrodynamic and particle tracking modules alongside measured bathymetric and depth data, the model simulates ice movement under three distinct flow conditions: 2000, 2500, and 3000 m³/s. Analysis of ice trajectories and distribution patterns under varying flow conditions reveals key transport mechanisms for both ice and water. These findings provide critical insights for enhancing ice flood prevention and disaster reduction strategies along the Inner Mongolia Yellow River during freeze-up period. Full article

This study introduces a novel methodology for assessing ice-jam flood hazards along river channels. It employs empirical equations that relate non-dimensional ice-jam stage to discharge, enabling the generation of an ensemble of longitudinal profiles of ice-jam backwater levels through Monte-Carlo simulations. These simulations produce non-exceedance probability profiles, which indicate the likelihood of various flood levels occurring due to ice jams. The flood levels associated with specific return periods were validated using historical gauge records. The empirical equations require input parameters such as channel width, slope, and thalweg elevation, which were obtained from bathymetric surveys. This approach is applied to assess ice-jam flood hazards by extrapolating data from a gauged reach at Fort Simpson to an ungauged reach at Jean Marie River along the Mackenzie River in Canada’s Northwest Territories. The analysis further suggests that climate change is likely to increase the severity of ice-jam flood hazards in both reaches by the end of the century. This methodology is applicable to other cold-region rivers in Canada and northern Europe, provided similar fluvial geomorphological and hydro-meteorological data are available, making it a valuable tool for ice-jam flood risk assessment in other ungauged areas. Full article

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions. Full article

Ice-jam floods (IJFs) are a significant hydrological phenomenon in the upper reaches of the Heilongjiang River, posing substantial threats to public safety and property. This study employed various feature selection techniques, including the Pearson correlation coefficient (PCC), Grey Relational Analysis (GRA), mutual information (MI), and stepwise regression (SR), to identify key predictors of river ice break-up dates. Based on this, we constructed various machine learning models, including Extreme Gradient Boosting (XGBoost), Backpropagation Neural Network (BPNN), Random Forest (RF), and Support Vector Regression (SVR). The results indicate that the ice reserves in the Oupu to Heihe section have the most significant impact on the ice break-up date in the Heihe section. Additionally, the accumulated temperature during the break-up period and average temperature before river ice break-up are identified as features closely related to the river’s opening in all four feature selection methods. The choice of feature selection method notably impacts the performance of the machine learning models in predicting the river ice break-up dates. Among the models tested, XGBoost with PCC-based feature selection achieved the highest accuracy (RMSE = 2.074, MAE = 1.571, R² = 0.784, NSE = 0.756, TSS = 0.950). This study provides a more accurate and effective method for predicting river ice break-up dates, offering a scientific basis for preventing and managing IJF disasters. Full article

Ice jamming is the primary mechanism that can generate overland flooding and recharge the isolated basins of the Peace–Athabasca Delta (PAD), a valuable ecosystem of international importance and the ancient homeland of the Indigenous Peoples of the region. Focusing on the regulated Peace River and the Peace Sector of the delta, which has been experiencing a drying trend in between rare ice-jam floods over the last ~50 years, this study describes recent notable breakup events, associated observational data, and numerical applications to determine river discharge during the breakup events. Synthesis and interpretation of this material provide a new physical understanding that can inform the ongoing development of a protocol for strategic flow releases toward enhancing basin recharge in years when major ice jams are likely to form near the PAD. Additionally, several recommendations are made for future monitoring activities and improvements in proposed antecedent criteria for early identification of “promising” breakup events. Full article

This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling techniques that can better capture the complex interactions between ice cover, water levels, and channel geometry. (iii) Combining data from multiple sources such as satellite imagery, ground-based sensors, numerical models, and machine learning algorithms can significantly improve the accuracy and reliability of ice-jam flood forecasts. (iv) Effective decision-support tools are crucial for integrating ice-jam flood forecasts into emergency response and mitigation strategies. Full article

The interaction of water flow, ice, and structures is common in fluvial ice processes, particularly around Ice Control Structures (ICSs) that are used to manage and prevent ice jam floods. To evaluate the effectiveness of ICSs, it is essential to understand the complex interaction between water flow, ice and the structure. Numerical modeling is a valuable tool that can facilitate such understanding. Until now, classical Eulerian mesh-based methods have not been evaluated for the simulation of ice interaction with ICS. In this paper we evaluate the capability, accuracy, and efficiency of a coupled Computational Fluid Dynamic (CFD) and multi-body motion numerical model, based on the mesh-based FLOW-3D V.2023 R1 software for simulation of ice-structure interactions in several benchmark cases. The model’s performance was compared with results from meshless-based models (performed by others) for the same laboratory test cases that were used as a reference for the comparison. To this end, simulation results from a range of dam break laboratory experiments were analyzed, encompassing varying numbers of floating objects with distinct characteristics, both in the presence and absence of ICS, and under different downstream water levels. The results show that the overall accuracy of the FLOW-3D model under various experimental conditions resulted in a RMSE of 0.0534 as opposed to an overall RMSE of 0.0599 for the meshless methods. Instabilities were observed in the FLOW-3D model for more complex phenomena that involve open boundaries and a larger number of blocks. Although the FLOW-3D model exhibited a similar computational time to the GPU-accelerated meshless-based models, constraints on the processors speed and the number of cores available for use by the processors could limit the computational time. Full article

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. Full article