System Dynamics Approach for Assessing the Behaviour of the Lim Reservoir System (Serbia) under Changing Climate Conditions
Bibliographic record
Abstract
Investigating the impact of climate change on the management of a complex multipurpose water system is a critical issue. The presented study focuses on different steps of the climate change impact analysis process: (i) Use of three regional climate models (RCMs), (ii) use of four bias correction methods (BCMs), (iii) use of three concentration scenarios (CSs), (iv) use of two model averaging procedures, (v) use of the hydrological model and (vi) use of the system dynamics simulation model (SDSM). The analyses are performed for a future period, from 2006 to 2055 and the reference period, from 1971 to 2000. As a case study area, the Lim water system in Serbia (southeast Europe) is used. The Lim river system consists of four hydraulically connected reservoirs (Uvac, Kokin Brod, Radojnja, Potpec) with a primary purpose of hydropower generation. The results of the climate change impact analyses indicate change in the future hydropower generation at the annual level from −3.5% to +17.9%. The change has a seasonal variation with an increase for the winter season up to +20.3% and decrease for the summer season up to −33.6%. Furthermore, the study analyzes the uncertainty in the SDSM outputs introduced by different steps of the modelling process. The most dominant source of uncertainty in power production is the choice of BCMs (54%), followed by the selection of RCMs (41%). The least significant source of uncertainty is the choice of CSs (6%). The uncertainty in the inflows and outflows is equally dominated by the choice of BCM (49%) and RCM (45%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".