Modeling of Ice-jam Flooding: Integrating SUMMA with River Ice Processes for Climate Change Impacts
Bibliographic record
Abstract
Ice-jam flooding linked with the interactions of hydrological and cryosphere processes is a serious threat to riverine communities in cold regions. This work uses the coupling of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrological model, which represents a wide range of hydrological processes, and the mizuRoute river routing model with that of a river ice model (i.e., RIVICE model) to project of ice-jam floods under changing climatic conditions. In fact, SUMMA and mizuRoute simulate streamflow, which is then passed to RIVICE to model ice formation and dynamics. The dynamics of streamflow simulated by SUMMA / mizuRoute include comprehensive representation of various hydrological processes, while the RIVICE model considers the processes of ice formation, frazil ice dynamics, and accumulation. This coupled modeling framework is applied to the Klondike River in Yukon, Canada, one of the regions historically affected by ice-jam flooding. This study uniquely integrates these models to enable projection of future ice-jam flood scenarios. The simulations are driven by climate projections from the CMIP6 datasets, enabling comprehensive assessments of future freeze-up events and associated flood risks at high spatial and temporal resolution. This work contributes to the increasing value of integrated hydrological and cryospheric modeling, improving flood risk assessments and informing adaptive strategies, such as improved forecasting systems and infrastructure design, for community protection in cold regions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".