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Abstract The Chaudière River in Quebec, Canada, is well known for its frequent ice jam flooding events. As part of a larger watershed research program, an extensive field campaign has been carried out during the 2018–2019 and 2019–2020 winter seasons to quantify the spatiotemporal characteristics of the break-up processes along the Chaudière River. The results showed that mid-winter ice jams have formed in the Intermediate Chaudière and persisted until spring break-up. Spring break-ups were initiated in the Upper Chaudière, and then, almost simultaneously, in the Intermediate and Lower Chaudière reaches. The break-up in the Intermediate Chaudière usually lasts longer than the rest of the river since the slope is much milder, and the occurrence of mid-winter ice jams has been seen to delay the ice clearing. A reach-by-reach characterization of the cumulative degree day of thawing and discharge thresholds for the onset of break-up has been identified. During the field campaign, 51 ice jams were documented together with their location, length, date of formation, and the morphological feature triggering jam formation. Break-up patterns, hydrometeorological thresholds of ice mobilization, and ice jam sites identified in this study can serve as a basis for the implementation of an early warning system.
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Over the past decades, a variety of ice control structures (ICSs) have been designed and built, but to date, there has been no systematic evaluation of the effectiveness of these structures. To achieve this objective, first an understanding of the interaction between different ice processes and the ICSs must be established. For this purpose, a total of four ICSs located in the province of Québec were monitored during the 2021–2022 winter. The results showed that the ice jam holding time could vary from 1.5 to 68.5 h. The release of the jam was mechanically driven when the ratio of release to initiation Froude number was higher than one and was thermally driven when this ratio was lower than one, and the water temperature increased between initiation and release. Also, as the ratio of the total pier spacing to upstream river width increased, the holding time decreased.
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Ice control structures (ICSs) play a vital role in preventing ice jams and safeguarding communities by either stabilizing ice cover or relocating jams upstream. Understanding and modeling the interaction between ice floes and these structures is crucial for assessing their effectiveness and optimizing their designs. However, simulating these complex multi-physics systems poses challenges for numerical techniques. In this paper, we introduce and evaluate a fully-Lagrangian mesh-free continuum-discrete model based on the Smoothed Particles Hydrodynamics (SPH) method and Discrete Element Method (DEM) for three-dimensional (3D) simulation of ice interactions with control structures. To validate and parameterize the numerical model, we conduct two sets of experiments using real and artificial ice materials: (1) dam-break wave-ice-structure interaction and (2) ice-ICS interaction in an open channel. By comparing numerical and experimental results we demonstrate the capability and relative accuracy of our model. Our findings indicate that real ice generally exhibits faster jam evolution and ice passage through the ICS compared to artificial ice. Moreover, we identify the Froude number and ice material type as important factors influencing jam formation, evolution, and ICS effectiveness. Through sensitivity analysis of material properties, we highlight the significant impact of friction and restitution coefficients.
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Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.