Evaluation of the SWAT Model for the Simulation of Flow and Water Balance Based on Orbital Data in a Poorly Monitored Basin in the Brazilian Amazon
Bibliographic record
Abstract
The Amazon basin, the world’s largest river basin, is a key global climate regulator. Due to the lack of an extensive network of gauging stations, this basin remains poorly monitored, hindering the management of its water resources. Due to the vast extension of the Amazon basin, hydrological modeling is the only viable approach to monitor its current status. Here, we used the Soil and Water Assessment Tool (SWAT), a process-based and time-continuous eco-hydrological model, to simulate streamflow and hydrologic water balance in an Amazonian watershed where only a few gauging stations (the Jari River Basin) are available. SWAT inputs consisted of reanalysis data based on orbital remote sensing. The calibration and validation of the SWAT model indicated a good agreement according to Nash-Sutcliffe (NS, 0.85 and 0.89), Standard Deviation Ratio (RSR, 0.39 and 0.33), and Percent Bias (PBIAS, −9.5 and −0.6) values. Overall, the model satisfactorily simulated water flow and balance characteristics, such as evapotranspiration, surface runoff, and groundwater. The SWAT model is suitable for tropical river basin management and scenario simulations of environmental changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".