In the higher latitudes of the northern hemisphere, ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Since ice-jam formation mechanism is stochastic and depends on numerous unpredictable hydraulic and river ice factors, ice-jam associated flood forecasting is a very challenging task. A stochastic modelling framework was developed to forecast real-time ice jam flood severity along the transborder (New Brunswick/Maine) Saint John River of North America during the spring breakup 2021. Modélisation environnementale communautaire—surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast. A Monte-Carlo analysis (MOCA) framework was applied to simulate hundreds of possible ice-jam scenarios for the model domain from Fort Kent to Grand Falls using a hydrodynamic river ice model, RIVICE. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.

Ice-jam floods pose a serious threat to many riverside communities in cold regions. Ice-jam-related flooding can cause loss of human life, millions of dollars in property damage, and adverse impacts on ecology. An effective flood management strategy is necessary to reduce the overall risk in flood-prone areas. Most of these strategies require a detailed risk-based management study to assess their effectiveness in reducing flood risk. Zoning regulation is a sustainable measure to reduce overall flood risk for a flood-prone area. Zoning regulation is a specified area in a floodplain where certain restrictions apply to different land uses (e.g., development or business). A stochastic framework was introduced to evaluate the effectiveness of a potential zoning regulation. A stochastic framework encompasses the impacts of all the possible expected floods instead of a more traditional approach where a single design flood is incorporated. The downtown area of Fort McMurray along the Athabasca River was selected to explore the impact of zoning regulation on reducing expected annual damages (EAD) from ice-jam flooding. The results show that a hypothetical zoning regulation for a certain area in the town of Fort McMurray (TFM) can be effective in substantially reducing the level of EAD. A global sensitivity analysis was also applied to understand the impacts of model inputs on ice-jam flood risk using a regional sensitivity method. The results show that model boundary conditions such as river discharge, the inflowing volume of ice and ice-jam toe locations are highly sensitive to ice-jam flood risk.

River ice is an important hydraulic and hydrological component of many rivers in the high northern latitudes of the world. It controls the hydraulic characteristics of streamflow, affects the geomorphology of channels, and can cause flooding due to ice-jam formation during ice-cover freeze-up and breakup periods. In recent decades, climate change has considerably altered ice regimes, affecting the severity of ice-jam flooding. Although many approaches have been developed to model river ice regimes and the severity of ice-jam flooding, appropriate methods that account for the impacts of future climate on ice-jam flooding have not been well established. Therefore, the main goals of this study are to review current knowledge regarding climate change impacts on river ice processes and to assess current modelling capabilities to determine the severity of ice jams under future climatic conditions. Finally, a conceptual river ice-jam modelling approach is presented for incorporating climate change impacts on ice jams.

Abstract Ice jam floods (IJF) are a major concern for many riverine communities, government and non-government authorities and companies in the higher latitudes of the northern hemisphere. Ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Ice jam flood forecasting is challenging as its formation mechanism is chaotic and depends on numerous unpredictable hydraulic and river ice factors. In this study, Modélisation environnementale communautaire – surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast, an important boundary condition for any modelling of river ice-jam flood forecasting. A stochastic modelling approach was then applied to simulate hundreds of possible ice-jam scenarios using the hydrodynamic river ice model RIVICE within a Monte-Carlo Analysis (MOCA) framework for the Saint John River from Fort Kent to Grand Falls. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.

Abstract Model parameters and boundary conditions characterizing flood domains in riverine flood modelling play an important role in the delineation of flood hazard along rivers. Since the digital elevation model (DEM) is an integral part of the delineation of flood hazard, it is necessary to determine the relative sensitivity of the DEM alongside the hydraulic model parameters and boundary conditions. This study provides a novel framework to examine the relative sensitivity of a river ice hydraulic model and various DEMs on ice-jam flood delineation. The Athabasca River at Fort McMurray in Canada is presented as a test site. The study found that ice-jam flood delineation is highly sensitive to DEMs. While flood hazard delineation is low to moderate sensitive to all the model parameters, it is highly sensitive to almost all the boundary conditions.

Though mitigation measures and research have increased over the last few decades, ice jams and associated flooding continue to be one of the most underestimated disasters in many northern countries. Operational ice jam flood forecasting systems are becoming one of the more prominent tools used in mitigating ice-related flood risk within Canada. Several forecasting systems have been adopted across the country and forecasters are constantly looking to improve the accuracy and consistency of their systems. The Lower Red River in Manitoba has been the subject in discussion of many ice jam related studies, and a data-driven ice-jam hazard forecasting system is currently in use at this site. This system differs from hydrologic model driven forecasting systems used for other ice jam prone rivers across Canada. This study focuses on identifying the methodology of the data driven ice jam flood forecasting system, along with the methodology of the forecasting procedures. Furthermore, the effectiveness of the data driven forecasting system is measured and assessed for the Lower Red River's 2020 breakup season.

Ice‐jam flood risk management requires new approaches to reduce flood damages. Although many structural and non‐structural measures are implemented to reduce the impacts of ice‐jam flooding, there are still many challenges in identifying appropriate strategies to reduce the ice‐jam flood risk along northern rivers. The main purpose of this study is to provide a novel methodological framework to assess the feasibility of various ice‐jam flood mitigation measures based on risk analysis. A total of three ice‐jam flood mitigation measures (artificial breakup, sediment dredging and dike installation) were examined using a stochastic modelling framework for the potential to reduce the ice‐jam flood risk along the Athabasca River at Fort McMurray. An ensemble of hundreds of backwater level profiles was used to construct ice‐jam flood hazard maps to estimate expected annual damages, using depth‐damage curves for structural and content damages, within the downtown area of Fort McMurray. The results show that, while sediment dredging may be able to reduce a certain level of expected annual damages in the town, and artificial breakup and a dike with a crest elevation of 250 m a.s.l. can be the most effective measures to reduce the amount of expected annual damages.

AbstractIn cold-region environments, ice-jam floods (IJFs) can result in high water levels in rivers to overtop levees, leading to devastating floods. Since climatic conditions play an important ro...

Flooding is one of the most frequent and most costly natural disasters that occur throughout Canada, and although there is ongoing work to update and improve flood hazard assessments and mapping of high flood risk rivers throughout the country, most studies only delve into open water flooding. However, many rivers in Canada experience higher peak water levels due to ice jamming, resulting in severe flooding of surrounding areas. Hence, there is an urgency to expand current flood hazard assessments to include ice jam flooding for better flood management practices. One area that is often plagued with ice jam flooding is the lowest reach of Manitoba’s Red River. The Lower Red River is a low-lying river with a terminus inland delta where water levels are governed by Lake Winnipeg. Ice jam floods often divert water into the lower Red River’s floodplain that is continually being encroached by development. RIVICE, Environment Canada’s one-dimensional ice hydraulic model, was set up within a Monte Carlo framework to simulate an envelope of backwater level profiles that result from ice jams within the study site. Non-exceedance probability profiles were created from the envelope of backwater level profiles to assess ice jam flood hazard.

In many northern rivers, ice-jam flooding can be more severe than open-water flooding, leading to human casualties, damages to property and infrastructure, and adverse impacts on the ecology. Consequently, ice jam related flooding is a major concern for many riverside communities, water authorities, insurance companies, and government agencies. Ice-jam flood hazard delineation and risk analysis are important measures for flood preparation, mitigation, and management strategies. Although methodologies and techniques for open-water flood hazard and risk assessment are well established, methodologies and techniques for ice-jam flood hazard and risk assessment are often unavailable or less developed. In addition to this, a considerable number of studies have been conducted in the context of flood management, but a very limited number of studies have been carried out in real-time flood risk analysis during operational flood forecasting. In this paper, the current status of ice-jam flood hazard delineation and risk analysis is discussed. A framework for real-time risk analysis for operational flood forecasting is also discussed. Finally, current limitations and future requirements for developing effective ice-jam flood hazard delineation and risk analysis methodologies are provided.

Abstract Forecasting ice jams and their consequential flooding is more challenging than predicting open water flood conditions. This is due to the chaotic nature of ice jam formation since slight changes in water and ice flows, location of the ice jam toe along the river and initial water levels at the time of jam formation can lead to marked differences in the outcome of backwater level elevations and flood severity. In this paper, we introduce a novel, operational real-time flood forecasting system that captures this stochastic nature of ice-jam floods and places the forecasts in a probabilistic context in the form of flood hazard maps (probability of flood extents and depths). This novel system was tested successfully for the ice-cover breakup period in the spring of 2018 along the Athabasca River at the Town of Fort McMurray, Canada.

Ice jams are critical components of the hydraulic regimes of rivers in cold regions. In addition to contributing to the maintenance of wetland ecology, including aquatic animals and waterfowl, ice jams provide essential moisture and nutrient replenishment to perched lakes and ponds in northern inland deltas. However, river ice-jam flooding can have detrimental impacts on in-stream aquatic ecosystems, cause damage to property and infrastructure, and present hazards to riverside communities. In order to maintain sustainable communities and ecosystems, ice-jam flooding must be both mitigated and promoted. This study reviews various flood management strategies used worldwide, and points to the knowledge gaps in these strategies. The main objective of the paper is to provide a framework for a sustainable ice-jam flood management strategy in order to better protect riverine socio-economic and socio-ecological systems. Sustainable flood management must be a carefully adopted and integrated strategy that includes both economic and ecological perspectives in order to mitigate ice-jam flooding in riverside socio-economic systems, while at the same time promoting ice-jam flooding of riverine socio-ecological systems such as inland deltas.

The accuracy of digital elevation models (DEMs) plays an important role in many terrain-related applications, particular in high northern latitudes where there is uncertainty in DEMs. Using the interferometric synthetic aperture radar techniques, this study examined how different RADARSAT-2 beam modes can be used to generate DEMs with high accuracy. Using a conventional interferometry method, the Spotlight DEM shows the highest accuracy among all studied DEM products, with the root-mean-square error (RMSE) ranging from 13.9 to 17.4 m, followed by the F0W3 DEM and U26W2 DEM. The error sources in DEM generation due to uncertainty in perpendicular baseline and atmospheric delay are likely more important than the random phase noise caused by volume scattering and environmental changes during synthetic aperture radar (SAR) acquisitions. The small baselines subset (SBAS) method did not significantly improve DEM quality due to the limitation of the number of SAR images in this study. The integration of both Spotlight conventional DEMs and SBAS DEM considerably improved results yielding high-quality DEMs for the study area, with an RMSE of 9.7 m. Further studies are necessary to quantitatively evaluate the effects of surface motion as well as the orbital and atmospheric errors on the DEM accuracy. The Slave River Delta in the Northwest Territories of Canada was used as a test case.