Karem Chokmani

Ice jams represent a recurrent natural hazard in Quebec, where geomorphological and structural river characteristics play a key role in determining their spatial distribution. This study improves and evaluates the Ice Jam Predisposition Index (IJPI) using a dataset of 35 rivers across southern Quebec. The enhanced model integrates geomorphological parameters, including channel narrowing, sinuosity, and slope, along with structural (bridge) and hydrological (tributary) features to delineate reaches prone to ice-jam formation. Including rapids as an inverse indicator helps distinguish high-energy segments where jams are unlikely to occur. Model validation shows that over 90 % of observed ice jams occurred in reaches with medium to high predisposition, with an overall recall of 0.90, indicating promising performance as a screening tool. The results highlight the dominant influence of flat slopes, channel confinement, and curvature on jam formation, suggesting that the model can serve as a useful spatial tool for regional ice-jam risk management. These results demonstrate the potential of the proposed framework to support operational flood risk management by allowing proactive identification of vulnerable river sections. Decision-makers and emergency management agencies can use the model to prioritise monitoring efforts and improve preparation for ice-jam-related hazards.

Ice jams represent a recurrent natural hazard in Quebec, where geomorphological and structural river characteristics play a key role in determining their spatial distribution. This study improves and evaluates the Ice Jam Predisposition Index (IJPI) using a dataset of 35 rivers across southern Quebec. The enhanced model integrates geomorphological parameters, including channel narrowing, sinuosity, and slope, along with structural (bridge) and hydrological (tributary) features to delineate reaches prone to ice-jam formation. Including rapids as an inverse indicator helps distinguish high-energy segments where jams are unlikely to occur. Model validation shows that over 90 % of observed ice jams occurred in reaches with medium to high predisposition, with an overall recall of 0.90, indicating promising performance as a screening tool. The results highlight the dominant influence of flat slopes, channel confinement, and curvature on jam formation, suggesting that the model can serve as a useful spatial tool for regional ice-jam risk management. These results demonstrate the potential of the proposed framework to support operational flood risk management by allowing proactive identification of vulnerable river sections. Decision-makers and emergency management agencies can use the model to prioritise monitoring efforts and improve preparation for ice-jam-related hazards.

Ice jams represent a recurrent natural hazard in Quebec, where geomorphological and structural river characteristics play a key role in determining their spatial distribution. This study improves and evaluates the Ice Jam Predisposition Index (IJPI) using a dataset of 35 rivers across southern Quebec. The enhanced model integrates geomorphological parameters, including channel narrowing, sinuosity, and slope, along with structural (bridge) and hydrological (tributary) features to delineate reaches prone to ice-jam formation. Including rapids as an inverse indicator helps distinguish high-energy segments where jams are unlikely to occur. Model validation shows that over 90 % of observed ice jams occurred in reaches with medium to high predisposition, with an overall recall of 0.90, indicating promising performance as a screening tool. The results highlight the dominant influence of flat slopes, channel confinement, and curvature on jam formation, suggesting that the model can serve as a useful spatial tool for regional ice-jam risk management. These results demonstrate the potential of the proposed framework to support operational flood risk management by allowing proactive identification of vulnerable river sections. Decision-makers and emergency management agencies can use the model to prioritise monitoring efforts and improve preparation for ice-jam-related hazards.

Surface water monitoring often requires frequent and reliable sampling at locations that are difficult or unsafe to access using conventional methods. To address these constraints, unmanned aerial vehicles (UAVs) have increasingly been used as platforms for automated water collection. This paper presents a UAV-assisted water sampling system designed to provide a high degree of operational flexibility through control of sampling depth and collected volume. The proposed system can collect up to 3 L of water distributed across six individual containers, enabling a variety of sampling strategies, including discrete, composite, and multi-depth sampling. Sampling depths of up to 3.25 m can be achieved, supporting depth-resolved investigations such as stratified water-column analyses. Compared with existing UAV-based samplers, the proposed system combines configurable sampling depth, configurable sampling volume, and a multi-bottle architecture within a single platform. To support sample integrity, an automated cleaning sequence is executed before each collection step to reduce the risk of cross-contamination between samples. Experimental validation demonstrated repeatable depth deployment under controlled conditions and consistent volume-control performance, with relative errors generally within ±3% and a maximum observed deviation of 5%. In addition, a task-based analysis provided an initial characterization of the energy demand associated with the sampling process. Overall, the results indicate that the proposed architecture can support flexible and protocol-oriented water sampling operations while extending the sampling capabilities of existing UAV-based systems.

Ice jams represent a recurrent natural hazard in Quebec, where geomorphological and structural river characteristics play a key role in determining their spatial distribution. This study improves and evaluates the Ice Jam Predisposition Index (IJPI) using a dataset of 35 rivers across southern Quebec. The enhanced model integrates geomorphological parameters, including channel narrowing, sinuosity, and slope, along with structural (bridge) and hydrological (tributary) features to delineate reaches prone to ice-jam formation. Including rapids as an inverse indicator helps distinguish high-energy segments where jams are unlikely to occur. Model validation shows that over 90 % of observed ice jams occurred in reaches with medium to high predisposition, with an overall recall of 0.90, indicating promising performance as a screening tool. The results highlight the dominant influence of flat slopes, channel confinement, and curvature on jam formation, suggesting that the model can serve as a useful spatial tool for regional ice-jam risk management. These results demonstrate the potential of the proposed framework to support operational flood risk management by allowing proactive identification of vulnerable river sections. Decision-makers and emergency management agencies can use the model to prioritise monitoring efforts and improve preparation for ice-jam-related hazards.

Modeling river ice jam risks under present and future climate is challenging task due to the sudden nature of ice jams. In this study, we develop a hybrid machine-learning model to predict the daily probability of ice jam occurrence under current climate and use it to project ice jam risks under future climates. A random forest ensemble simulation with two components (temporal and spatial) was calibrated and validated using 108 historical (2005-2022) ice jam events recorded within 882 river reaches along the Chaudière and Beaurivage rivers in Eastern Canada, where ice jams cause frequent damages to nearby populations. The model integrates both hydrometeorological predictors known to influence ice resistance (e.g., freezing degree days as a proxy for ice thickness) and dislocation forces (melting degree days, rainfall, changes in streamflow), as well as channel morphological conditions prone to ice congestion (e.g., sinuosity, channel width, slope breaks). Predictor importance analysis shows that ice jam occurrence is preconditioned by cold, snowy winter conditions and rapidly rising streamflow in winter and/or spring. An increase in sinuosity and a reduction in the width of the river channels are the main causes of ice congestion. Daily times series from a bias-adjusted ensemble of dynamically downscaled climate simulations were then used to force the model into future climate under two representative concentration pathways (RCP4.5 and RCP8.5). Ice jam occurrence is projected to decrease overall in the future compared with the historical reference period, both for the RCP4.5 and RCP8.5 trajectories. A seasonal shift is also projected for the future, with ice jam events becoming more concentrated during February- March, rarer in April, and practically absent in December-January, especially for the 2080-2100 horizon. This research represents a significant advance in ice jam prediction, combining spatial and temporal approaches.

Flood hazard assessment has traditionally relied on hydraulic models that require extensive boundary conditions, detailed topographic data, and substantial computational resources. Their reliability, however, is often undermined by the scarcity of in-situ observations during extreme events, which increases uncertainty in flood extent delineation. Remote sensing has therefore emerged as a valuable alternative for flood mapping. Yet, most recent deep learning studies have focused on maximizing in-sample accuracy while providing limited evaluation of spatial transferability and operational readiness when applied outside the training domain.To address these constraints and move beyond the usual focus on in-sample accuracy in flood mapping, this study introduces an operationally oriented deep learning framework designed for spatial transferability. The approach combines two modality-specific U-Net models, one for Sentinel-1 (SAR) and one for Sentinel-2 (optical), trained on a globally sourced dataset (C2S-MS) of flood events. Beyond conventional model training, we incorporate key steps to support generalization: (i) a hydro-climatic and geomorphological characterization of the training dataset, combined with a multivariate similarity analysis to assess the environmental representativeness of the target region, (ii) an assessment of structural biases to orient model design, and (iii) a deployment-driven validation strategy to rigorously evaluate spatial transferability under operational constraints. On the C2S-MS test set, the S1 model achieved a global IoU of 85 % and F1-score of 92 %, while the S2 model reached 89 % and 94 %, respectively.By unifying environmental representativeness assessment, modality-specific deep learning architectures, and rigorous spatial validation, this work advances a transferable and operationally oriented framework for satellite-based flood mapping. Beyond this specific application, the proposed methodology provides generalizable insights into model design and evaluation for Earth observation studies confronted with domain shift and spatial extrapolation.

Management zones (MZs) are a key precision agriculture strategy for managing spatial variability in crops, but conventional delineation methods are costly, time-consuming, and rely on specialized equipment. Previous studies in potato production have primarily relied on single-year NDVI or proximal soil sensor data analyses, limiting their ability to capture temporal stability and variability across multiple fields. This study addresses this gap by applying multi-year, multi-source NDVI composites to characterize spatial and temporal patterns of agricultural potential across 17 commercial potato fields at McCain’s Farm of the Future, Florenceville-Bristol, New Brunswick. A total of 230 NDVI images from Sentinel-2 and Landsat 8 (2015–2023) were processed into composite metrics (mean, standard deviation, skewness) to delineate three agricultural potential (AP) MZs. Validation was conducted using 2023 potato tuber yield and soil physicochemical properties. The results showed statistically significant correlations between NDVI metrics and key soil nutrients (total carbon: |r| < 0.19; total nitrogen: |r| < 0.28), with tuber yield (|r| < 0.41). Spatial patterns of total carbon and nitrogen corresponded with delineated MZs, and tuber yield variability partially aligned with these zones. These findings demonstrate that multi-year NDVI composites provide a cost-effective and scalable approach for mapping agricultural potential, capturing both spatial and temporal variability, and supporting data-driven management decisions in potato production systems.

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts.