Modelling ice using the River1D ice model
Bibliographic record
Abstract
Ice formation on rivers is a dynamic process that eventually leads to the complete and continuous ice cover with which the people of northern, and not-so northern regions of the globe are familiar. As water temperatures decrease in the Fall and attain slightly subfreezing levels, different ice forms begin to appear in the water. Interacting with each other, and driven by the turbulent river flow, these ice forms evolve into accumulations that cause many socioeconomic and ecological problems, while sometimes having beneficial impacts. Transportation and energy generation are two sectors that are particularly affected by ice formation, while infrastructure, private property and human safety can be imperilled by extreme freeze up events.\nThe accelerating pace of climate change has pronounced effects on the cryosphere components of our planet, particularly in polar and subpolar regions. Among these, river ice dynamics play a crucial role in shaping hydrological systems, influencing infrastructure, and impacting ecosystems (Beltaos 2013).\nThe primary objective of this research is to comprehensively characterize the River1D ice model, developed by Alberta University, by elucidating its fundamental principles, mathematical foundations, and underlying assumptions. Through an extensive literature review, we contextualize the significance of understanding river ice dynamics in the face of global climate change and its consequential impact on water resources and infrastructure.\n This master's thesis delves into the realm of river ice modelling, focusing on the utilization and exploration of the River1D ice model.\nThe thesis employs a systematic approach, beginning with the validation and verification of the River1D ice model through a comparison of its simulations with observed field data from diverse case studies. By rigorously assessing the model's accuracy and reliability, we aim to establish its credibility as a tool for simulating and predicting river ice dynamics.\nThe research extends beyond the technical intricacies of the River1D ice model to explore its practical applications. Through case studies and simulations, we investigate the model's potential in predicting future river ice conditions, managing water resources, and informing decision-making processes for climate change adaptation.\nIn conclusion, this master's thesis offers a comprehensive investigation of the River1D ice model, shedding light on its capabilities, limitations, and potential contributions to the field of river ice modelling. By enhancing our understanding of the complex interplay between climate, hydrology, and river ice dynamics, this research seeks to provide valuable insights for researchers, policymakers, and practitioners engaged in the sustainable management of cold-region water resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".