Hydrological modeling of the Ribeirão das Posses – An assessment based on the Agricultural Ecosystem Services (AgES) watershed model
Bibliographic record
Abstract
Southeastern Brazil has recently experienced drought conditions that have impacted watershed conservation and the management of water quality and quantity for agricultural and urban demands. The Ribeirão das Posses watershed is being monitored as a headwater of the Jaguarí River, which is one of the contributing rivers of the Cantareira Reservoir Complex in the state of São Paulo. The landscape has changed over the last century from native forests to more homogeneous vegetation for pastures, crops and some forest plantations of eucalyptus, which have cumulative impacts on water yield and quality. Currently, the Projeto Conservador das Águas (Water Conservationist Project) has planted small areas with native species vegetation in order to recover degraded areas. The objective of this study was to evaluate the quantity of water in the Ribeirão das Posses Basin by both measurements and by simulating hydrological responses. The Agricultural Ecosystem Services (AgES) watershed model was applied to simulate water movement and storage among land areas. The simulation period was from 2009 to 2014, because the daily streamflow and meteorological data were available for model calibration and testing. We discuss data input requirements, model calibration to fit measured streamflow, and sensitivity to spatially variable rainfall inputs. The calibrated model may be used to estimate streamflow during periods of missing data, and in the future to estimate impacts of land use changes on stream water quantity and quality. Such information can be used in programs of payments for ecosystem services.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".