Impact of reservoir evaporation on future water availability in north-eastern Brazil: a multi-scenario assessment
Bibliographic record
Abstract
The potential effects of climatic changes on water resources are crucial to be assessed, particularly in dry regions such as north-east Brazil (1 million km 2 ), where water supply is highly reliant on open-water reservoirs. This study analyses the impact of evaporation (by the Penman method) on water availability for four scenarios based on two regional climatic models (Eta-CanESM2 and Eta-MIROC5) using the Representative Concentration Pathways (RCPs) 4.5 and 8.5. We compared the water availability in the period of 2071–2100 with that of the historical period (1961–2005). The scenarios derived from the Eta-CanESM2 model indicate an increase in the dry-season evaporative rate (2 % and 6 %, respectively) by the end of the century. Unlike the above scenarios, the ones derived from the Eta-MIROC5 model both show a change in the dry-season evaporative rate of −2 %. Consequently, for a 90 % reliability level, the expected reservoir capacity to supply water with high reliability is reduced by 80 %. It is reasonable to state that both patterns of future evaporation in the reservoirs may prove to be plausible. Because model-based projections of climate impact on water resources can be quite divergent, it is necessary to develop adaptations that do not need quantitative projections of changes in hydrological variables but rather ranges of projected values. Our analysis shows how open-water reservoirs might be impacted by climate change in dry regions. These findings complement a body of knowledge on the estimation of water availability in a changing climate and provide new data on and insights into water management in reservoir-dependent drylands.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".