A Stochastic Modelling Approach to Forecast Real-time Ice Jam Flood Severity Along the Transborder (New Brunswick/Maine) Saint John River of North America

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.

The Lower Vistula and Its Ice Problems

In many countries of the northern hemisphere during winter period ice forms appear on various water bodies, which results in significant changes of physical, chemical and ecological conditions. These changes are different in rivers, channels, lakes or once-through reservoirs. On the terrain of Poland ice always caused considerable problems affecting intensive inland navigation and other river use. These problems appeared especially on the Vistula River, which in 17th and 18th century was one of the most navigable rivers in Europe. The Vistula is the largest Polish river, which flows from the south in the Carpathian Mountains to the Baltic Sea in the north. It is the second largest river, after Neva, of the Baltic Sea catchment. The length of the Vistula is 1047 km and its catchment amounts to 194 thousand km2. The predominant part of the Vistula river basin (87%) is now on Polish territory and the remaining (13%) catchment is in Belarus, Ukraine and Slovakia. The course of the Vistula can be divided into three distinctly different sections: upper, middle and lower. These river sections have appropriate catchments with their tributaries. There are hydraulic structures on the main river course and on its tributaries which serve navigation, hydroenergy, flood protection, water supply and recreation. All over the Vistula catchment there are frequent floods during spring and summer time resulting from excessive precipitation but in winter caused by ice phenomena. Numerous flow problems appear especially along the lower Vistula course because of ice phenomena and they result very often in severe flood problems. The Vistula has a very variable time and spatial discharge, because of existing climate conditions over its catchment. The aim of the paper is to present hydraulic and hydrologic characteristics of the Lower Vistula river with special emphasis on the management of this river section for navigation, hydroenergy, flood protection and water supply in view of ice phenomena appearing there. Information concerning changes of water characteristics due to various water temperatures are presented as well as on the formation of various forms of ice in flowing water. Numerous ice studies were carried out in Poland and especially on the Lower Vistula section as it was very ice prone and where many ice jams and ice-jam floods occurred. A special hydraulic situation existed at the mouth of the Vistula, which caused important floods in the 18th century and resulted in the construction of a special direct channel to the sea (Przekop Wisły) solving flood problems in this area. Information is presented on changes in open channel flow due to the appearance of ice cover and other ice forms. The paper includes ample information on the run, consequences and studies connected with a very important ice-jam-flood on the upper part of Włocławek reservoir in 1982.

Exploring the Potential of Zoning Regulation for Reducing Ice-Jam Flood Risk Using a Stochastic Modelling Framework

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.

Local scour around a bridge pier under ice-jammed flow condition – an experimental study

The appearance of an ice jam in a river crucially distorts local hydrodynamic conditions including water level, flow velocity, riverbed form and local scour processes. Laboratory experiments are used for the first time here to study ice-induced scour processes near a bridge pier. Results show that with an ice sheet cover the scour hole depth around a bridge is increased by about 10% compared to under equivalent open flow conditions. More dramatically, ice-jammed flows induce both greater scour depths and scour variability, with the maximum scour depth under an ice-jammed flow as much as 200% greater than under equivalent open flow conditions. Under an ice-jammed condition, both the maximum depth and length of scour holes around a bridge pier increase with the flow velocity while the maximum scour hole depth increases with ice-jam thickness. Also, quite naturally, the height of the resulting deposition dune downstream of a scour hole responds to flow velocity and ice jam thickness. Using the laboratory data under ice-jammed conditions, predictive relationships are derived between the flow’s Froude number and both the dimensionless maximum scour depth and the dimensionless maximum scour length.

The Impact of the Delta Change Approach on the Severity of Ice-jam Flooding Under Future Climate Scenarios

Projection of the impact of future climate on ice-jam flood intensity is an essential component of a flood mitigation strategy for many northern communities. General Circulation Model (GCM) outputs are used to derive hydrological conditions under future climate scenarios. Although GCMs are often downscaled to a point of interest, there can still be significant differences between modelled climate scenarios and historically observed climate scenarios. Therefore, the model-indicated changes between baseline and future values of climatic scenarios are applied to observed baseline values to estimate projected future values. This can be carried out by using the delta change method which is an approach for adjusting GCM output. This study evaluates the impact of the delta change method on the frequency and severity of ice-jam flooding under a future climate scenario. The Athabasca River at Fort McMurray is presented as the test site. Streamflow conditions were derived from a physically-based hydrological model, Modélisation Environnementale communautaire-Surface Hydrology (MESH), by forcing the Canadian Regional Climate Model (CRCM) driven by the Third Generation Coupled Climate Model (CGCM3) for both baseline (1971–2000) and future (2041–2070) periods. Streamflow under future climatic conditions was developed based on the delta change method for both absolute and relative changes. The adjusting streamflow was then used in a fully dynamic river ice hydraulic model, RIVICE, to project future ice-jam scenarios using a stochastic modelling framework. Finally, the impact of the delta changes on the frequency and severity of simulated ice-jam flooding was assessed by producing ice-jam stage-frequency distributions (SFDs) under future climatic conditions. The results indicate that there is a notable difference in the projected frequency and severity of ice-jam flooding between absolute and relative change approaches.

Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.