This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

Spaceborne synthetic aperture radar (SAR) is an important technology for ship detection applications. It can provide timely information on small boat locations, which is important for security and safety applications. This paper describes the advantages of using very-high-resolution TerraSAR-X data acquired in Staring Spotlight mode for detecting small boats. Coincident to the SAR image acquisitions, Electro-Optical satellite imagery was used, together with field photographs of boats. The results demonstrate the ability to distinguish SAR signatures of small wooden and fiberglass vessels with size of up to 4 m in length.

Spring-breakup ice-jam floods are a major hazard for many rivers in cold regions. They can cause severe damage to infrastructure, property, and ecosystems along riverbanks. To reduce the risk and impact of these events, it is essential to develop reliable and timely forecasting systems that can provide early warning and guidance for mitigation actions. In this paper, we highlight the elements and processes required for the successful development of a spring-breakup ice-jam flood forecasting system, using the lower Churchill River in Labrador, Canada as a case study. We review the existing forecasting methodologies and systems for spring-breakup ice-jam floods and discuss their strengths and limitations. We then describe the case study of the lower Churchill River, where a large ice-jam flood occurred in May 2017, triggering an independent review and a series of recommendations for improving the flood preparedness and response. We present the main components and features of the forecasting system that was developed for the lower Churchill River, based on the recommendations from the independent review. We also discuss the improvements that were made to the forecasting system, such as parallelization, adaptation, and determination of ice-jam prone areas. Finally, we provide some conclusions and recommendations for future research and development of spring-breakup ice-jam flood forecasting systems, focusing on the requirements for a technical framework that incorporates community engagement and special considerations for regulated rivers.

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.

Operating in the polar regions is growing increasingly important due to expanding research needs, emerging economic opportunities and efforts to protect national sovereignty.Ships operating in the Arctic and Antarctic face heightened risks and more severe consequences in the event of an accident due to sea ice, icebergs, harsh sea states, low visibility and extreme temperatures. These hazards are compounded by sparse infrastructures and remoteness, with immediate assistances not readily available during emergencies.While climate change is quickly reducing the amount of sea ice, this does not necessarily translate to a reduction in risk in the short-term. On the contrary, uncertainly due to changing sea ice conditions, and an increase in icebergs due to melting glaciers, can increase risk.The operational sea ice community provides information to lessen risk to life, property and the environment and to improve operational efficiency. That information is used by ships to avoid or navigate through sea ice, by ship operators in planning polar voyages, and by policy makers to assess the impact of climate change on future decisions regarding polar operations. Beyond safety, these services also increase operational efficiency by enabling optimized routing, reduced fuel consumption, and shorter transit times.The information provided by the operational sea ice community comes from in-situ measurements, satellite earth observation data, and sophisticated models of the atmosphere, oceans, sea ice, and icebergs. These data streams have historically been assembled and interpreted by highly trained human analysts. Given the rapid increase in the amount of data that needs to be analyzed and the constraints on workforces due to government fiscal reductions, their work is increasingly being assisted by artificial intelligence. Furthermore, because sea ice is highly dynamic and can change within a matter of hours, automation and AI are indispensable for providing real-time and forecast information.The operational sea ice workflows have many of the attributes of an Earth System Digital Twin:Utilization of detailed digital models of relevant earth systems (atmosphere, ocean, sea ice),Continuous incorporation of near-real-time data from in-situ sensors and earth observation satellites,Use of artificial intelligence and machine learning,Generation of predictive models and forecasts, andProvision of advance analytics and decision support tools to allow end-users to optimize their choices.In fact, much of the information both used and generated by the operational sea ice community is available in Destination Earth, the initiative of the European Commission to develop a digital twin of the Earth.This presentation will examine the provision of operational sea ice information as an example of the application of digital twins to provide actionable insights on climate adaptation and disaster risk reduction. It will present current capabilities, AI-enabled workflows, and lessons learned from operational implementation, with a focus on supporting safe and sustainable polar shipping.

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures.