Conference Agenda

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the use of remote sensing, including satellite imagery, aerial surveys, and near-surface observations, to monitor ice cover development in the Beauharnois Canal along the St. Lawrence River. 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. These findings demonstrate the value of remote sensing in river ice modelling and potential applications to support operational decision-making for regulated river systems.

When heavy snow or thinning ice allows seawater to intrude into the snow–ice interface, a saline slush layer forms, softening the surface and reducing traction. Because flooding is often invisible, travelers risk becoming stuck in remote areas, creating hazardous conditions. Saline slush also alters the snowpack’s physical and electromagnetic properties. Increased liquid water and salinity affect microwave signal interactions, complicating the estimation of ice properties using remote sensing. Depending on snow depth, temperature, and salinity, slush may refreeze or remain unfrozen, influencing ice thickness and heat transfer. Synthetic Aperture Radar (SAR) is widely used to monitor sea ice under all weather and light conditions. Its signal penetrates the dry snowpack and respond to changes at the snow base, making SAR suitable for detecting seawater flooding. However, SAR observations are sensitive to the target dielectric properties, surface roughness, frequency, incidence angle, and environmental variability. L-band coherence has shown sensitivity to flooding, but its behaviour on snow-covered ice remains poorly understood. This study examines the relationship between seawater flooding and SAR coherence using X- and L-band data collected alongside 2024–2025 field measurements in Qikiqtarjuaq, Nunavut. This research will show how SAR coherence can reveal flooded ice, supporting safer travel in northern communities.

River ice is a key variable in northern regions, with impacts on transportation, infrastructure and flood events. There is increasing emphasis on using remote sensing data to assist operational monitoring. This study investigates the use of synthetic aperture radar (SAR) data for this purpose. The main goal is to provide an open, accessible and scalable approach for accurate semantic segmentation of SAR data into ice and water classes.

Snow Water Equivalent (SWE) refers to the quantity of water contained within the snowpack, which is a critical component of the seasonal water cycle in cold regions, notably Canada. The Gravity Recovery and Climate Experiment (GRACE) mission primarily focuses on quantifying Terrestrial Water Storage Anomalies (TWSA),

which is the sum of anomalies in groundwater, soil moisture, surface water, and snow/ice. Separating the individual components with high precision is a challenging task due to the complex interactions of these parameters and their uncertainties involved. This study proposes an enhanced estimator which is modified based on the

spectral combination theory, to extract the SWE component from GRACE/GRACE-FO (Follow-On) TWS measurements. This estimator uses a hydrological model and its uncertainty to optimally extract the SWE component from the GRACE monthly models in spectral domain. The approach was applied in eight selected basins across

Canada, covering a diverse range of climatic and geographical conditions. Different winter seasons of each basin were considered, including the peak accumulation and ablation phases of the snowpack, from January 2003 to the end of 2022.

During the winters of 2023-2024 and 2024-2025, machine learning (ML) based models were implemented to predict ice thickness at eight sites on the Churchill River and Lake Melville, Labrador for one- and three-day horizons. The forecast ice thicknesses were fed into the Churchill River Flood Forecasting System (CRFFS) operated by the Newfoundland and Labrador (NL) provincial government’s Water Resources Management Division (WRMD). The models were trained on measured ice thickness data from 2017-2023, with the 2024-2025 models additionally trained with data from the 2023-2024 ice season. The 2023-2024 models were deep learning models that used Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), and the 2024-2025 models were ML models that used a simpler gradient boosting regression (GBR) algorithm. The LSTM (2023-2024) models used a running time-series of local meteorological observations as predictor variables to directly forecast ice thickness, and the GBR (2024-2025) models mainly used forecast surface energy balance variables to predict changes in ice thickness. The average performance of the models across the eight sites was comparable between the two ice seasons; however, the 2024-2025 season models improved performance at key sites on the Churchill River that are critical to ice jam flood forecasting. This paper describes the development of the models and their operation and comparative performance over the 2023-2025 ice seasons.

Accurate, reliable, and early-warning forecasts of river ice breakup are essential for flood risk mitigation and public safety, particularly in relation to river transportation and ice road operations. Synthetic Aperture Radar (SAR) satellite imagery has been widely utilized for monitoring river ice conditions due to its sensitivity to surface roughness and dielectric properties. This study advances traditional SAR applications and, to our knowledge, presents the first model that directly incorporates SAR data as input within a machine learning (ML) framework for river ice breakup prediction. The method leverages the correlation between SAR backscatter dynamics and the onset of surface melt. The model was evaluated using leave-one-out cross-validation, achieving an overall accuracy of 92%, an F1-score of 0.91, a Kappa coefficient of 0.84, and a mean absolute error (MAE) of less than 6 days for both the two- and three-week forecasts. The algorithm was also implemented in near-real-time operational settings, demonstrating strong performance with MAE values ranging from zero to four days across different river segments. The approach was further tested on an independent site, where it maintained robust predictive skill. The newly developed method shows strong potential for two- and three-week forecasting of river ice breakup, offering a scalable, cost-effective, and operationally viable tool for management and early warning applications.