Opportunities for hydropower under climate change in snow-ice dominated landscapes: case of the Hálslón Catchment Kárahnjúkar Hydropower Plant in eastern Iceland 
Bibliographic record
Abstract
Northern landscapes like the Arctic regions of Northern Europe, Canada and Iceland, are especially susceptible to the effects of climate change, considering accelerated glacial melt due to increased temperatures. Glacier melt will inevitably change the runoff regimes of northern catchments, with increased streamflow in the near future. This increases flooding hazards but also bears economic opportunity with increased hydropower potential.This study examines the future hydrological dynamics of the Hálslón catchment in eastern Iceland, focusing on the impacts of climate change on streamflow and hydroelectric potential. 70% of the 1’615 km² catchment are covered by Vatnajökull, Europe’s largest glacier. The catchment drains into the Hálslón reservoir, the main lake of the Kárahnjúkar Hydropower Plant system, a 690 MW facility that produces nearly a quarter of Iceland's electricity.Using the semi-distributed HBV-Light hydrological model, we performed 10,000 automatic Monte-Carlo calibration runs with a multi-objective approach, optimizing both discharge and glacier mass balance. Future streamflow scenarios were simulated for 2015–2100 using 12 climate models, three Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0, SSP5-8.5), and the 10 best parameter sets derived from calibration to address uncertainties.Preliminary results indicate a potential doubling of annual inflow to the Hálslón reservoir by the end of the century, driven by intense glacier melt and changing precipitation patterns. This excess flow, currently unutilized and discharged via spillways, represents significant untapped hydroelectric potential. At present, excess flow accounts for up to 20% of yearly inflow but could rise to over 50% by century’s end, according to modeling projections. The substantial increase in streamflow underscores the need for adaptive management strategies to optimize Iceland's hydroelectric infrastructure, leveraging emerging opportunities for renewable energy production. This research demonstrates the integration of hydrological and climatic models to evaluate the impacts of environmental change on vital 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".