The Impact of the Delta Change Approach on the Severity of Ice-jam Flooding Under Future Climate Scenarios
Bibliographic record
Abstract
Abstract 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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".