Assessing Hydrological Response and Resilience of Watersheds as Strategy for Climatic Change Adaptation in Neotropical Region
Bibliographic record
Abstract
This study aimed to assess the hydrological response and resilience of watersheds in a neotropical region to identify regions sensitive to climate variations, enabling the development of adaptive strategies in response to global environmental changes. This study applied Budyko’s framework using Fuh’s hydrological model rewritten by Zhou to estimate hydrological response and Budyko’s metrics (deviation and elasticity) to estimate hydrological resilience to climatic changes in 26 watersheds in southeastern Brazil. The proposed modeling was able to capture the differences among the watersheds, with “m” values ranging from 1.79 to 3.63. It was possible to rank the hydrological resilience from low to high across watersheds using Budyko’s metrics, where the highest values of elasticity were found in watersheds with a higher percentage of forest cover. The sensitive analyses showed that watersheds with higher “m” values are more sensitive to changes in precipitation and potential evapotranspiration. The results also demonstrate that mean elevation and stream density were two key variables that influence the “m” value; these physiographic characteristics may alter the water and energy balance of the watershed affecting the water yield. A relationship between watershed’s hydrological response and resilience was proposed to identify critical areas for the stability of water yield in the watersheds, providing a guide for public policy and suggesting ways to help the management of water resources in watersheds.
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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 | 0.001 | 0.002 |
| 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".