Impact of climate change on ice regime in a river regulated for hydropower
Bibliographic record
Abstract
The ice conditions in a regulated river will depend on the climatic changes as well as the changes to the hydropower operation strategies in the future. The existing literature shows that very few studies have been carried out to investigate the impact of climate change on the river ice regime, which is important for operation of hydropower in cold climates. In this study, a series of modelling tools have been used to transform the climate change signal in terms of precipitation and air temperature into cross-section based river ice assessment in a basin with a complicated hydropower system. The study is based on the EURO-CORDEX climate change data extracted from a regional climate model driven by a suite of five general circulation models with three representative concentration pathways. Hydrological model simulation results show that the winter and spring flow will be increased, which will have an impact on the river ice conditions towards the middle and end of this century. Reservoir-hydropower model simulation shows that the production flows in the winter will be increased in the future. River ice model simulation shows the number of days with freezing water temperature are reduced in the future climate, and correspondingly days with frazil ice are reduced at most of the locations in the study area. The future period with ice cover will also be shortened. The paper also demonstrates a general methodology and procedure to simulate future ice conditions in a regulated river combining multiple models and data sets.
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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 | 0.000 |
| 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".