Climate Change Impacts on Hydrological Processes in a South-Eastern European Catchment
Bibliographic record
Abstract
The output extracted from CNRM, MPR, and ICHEC Global Circulation Models for RCP 4.5 and RCP 8.5 Representative Concentration Pathways has been used in conjunction with the SWAT model for evaluating the impacts of future climate changes on hydrological processes in a Romanian catchment (Neajlov, 3720 km2 area) in the short (2021–2050) and long term (2071–2100). During the growing season, precipitation will decrease by up to 7.5% and temperature will increase by up to 4.2 °C by 2100. For the long term (2071–2100), the decrease in soil water content (i.e., 14% under RCP 4.5 and 21.5% under RCP 8.5) and streamflow (i.e., 4.2% under RCP 4.5 and 9.7% under RCP 8.5) during the growing season will accentuate the water stress in an already water-deficient area. The snow amount will be reduced under RCP 8.5 by more than 40% for the long term, consequently impacting the streamflow temporal dynamics. In addition, our results suggest that hydrological processes in the lower portions of the catchment are more sensitive to climate change. This study is the first Romanian catchment-scale study of this nature, and its findings support the development of tailored climate adaptation strategies at local and regional scales in Romania or elsewhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".