The Impacts of Climate Change on Water Availability: A Case Study in the Tietê River Basin, Brazil
Bibliographic record
Abstract
The purpose of this study is to discuss how assessing climate change impacts on water availability can drive new adaption links between different water uses (e.g., irrigation, urban and industrial water supply, energy generation). This is a case study for the Tietê River basin, located in southeastern Brazil, the most populated region in the country. The study quantified how water availability behaves under two climate change scenarios (SSP-RCP 2-4.5 and SSP-RCP 3-7.0), in two time horizons (20:30 and 20:50) using the outputs of four global circulation models. All model data were bias-corrected, and the flow catchment was measured by a distributed hydrologic model. An in-depth analysis on the operation of hydropower plants, the main source of electricity generation in Brazil, was conducted. Promissão power plant’s operation was simulated using an optimizer model. Results shows that total mean yearly amount of water is expected to increase in 2030 and 2050 (11% and 8%, respectively, under SSP-RCP 2-4.5 scenario; and 16% and 14%, respectively, under SSP-RCP 3-7.0 scenario). However, dry and wet seasons tend to become more extreme, and water availability is more concentrated in the rainy season (up to 26% in February and 27% in March and April combined). Climate models also point to increases up to 93% in peak flows. In terms of power output, results point to an increase in energy fluctuation and in spilled flows and a higher risk of damage to nearby infrastructure, crop fields, and cities placed along the river.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".