Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties
Bibliographic record
Abstract
Abstract. This paper addresses two major challenges in climate change impact analysis on water resources systems: (i) incorporation of a large range of potential climate change scenarios and (ii) quantification of related modelling uncertainties. The methodology of climate change impact modelling is developed and illustrated through application to a hydropower plant in the Swiss Alps that uses the discharge of a highly glacierised catchment. The potential climate change impacts are analysed in terms of system performance for the control period (1961–1990) and for the future period (2070–2099) under a range of climate change scenarios. The system performance is simulated through a set of four model types, including the production of regional climate change scenarios based on global-mean warming scenarios, the corresponding discharge model, the model of glacier surface evolution and the hydropower management model. The modelling uncertainties inherent in each model type are characterised and quantified separately. The overall modelling uncertainty is simulated through Monte Carlo simulations of the system behaviour for the control and the future period. The results obtained for both periods lead to the conclusion that potential climate change has a statistically significant negative impact on the system performance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".