Drought intensification in Brazilian catchments: implications for water and land management
Bibliographic record
Abstract
Abstract Droughts exert widespread impacts on both natural and social systems, and there is accumulating evidence that this situation may worsen in the context of global warming. Despite the importance of assessing changes in droughts to understand their potential future impacts on society, studies are unevenly distributed worldwide. In this study, utilizing bias-corrected CMIP6 simulations and a standard precipitation-evaporation index based approach, we quantified expected changes in future drought properties across 735 Brazilian catchments under SSP2-4.5 and SSP5-8.5 scenarios. Beyond evaluating the statistical properties of future droughts, we assessed their occurrence under both land use and water demand perspectives and propose a new framework to better understand their link with changes in long- and short-term conditions of precipitation ( P ) and potential evapotranspiration ( PET ). Our results indicate that drought events are projected to become more frequent and severe in the future, with high CMIP6 model agreement. According to the SSP5-8.5 scenario, at least half of Brazilian cropland and pasture areas will experience an increase of over 30% in drought properties by the end of the century. Furthermore, among the 85% of catchments expected to experience more severe droughts, nearly 90% are also projected to exhibit increased water demand, which will likely exacerbate future water scarcity. The investigation of the relationship between droughts changes and climate variables suggests that catchments with augmented droughts in the future will likely exhibit increased long-term average PET and P -variability, but not necessarily long-term average P . For instance, over 50% of evaluated Brazilian catchments are expected to experience an intensification of drought properties even with increases in P mean . We believe this study may contribute (a) to improve Brazilian water resiliency by helping achieve the objectives of the National Water Security Plan and (b) to deepen our understanding of droughts in an uncertain future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".