Hydrological Modeling of the Tampaon River in the Context of Climate Change
Bibliographic record
Abstract
This work compares the hydrological modeling of the Tampaon River Basin (in east-central Mexico) with two hydrological models (SWAT and GR4J) and then evaluates the impact of climate change on the water balance of the basin. The calibration and validation of the models (over 14-year periods) show that both performed satisfactorily when simulating daily flows. The results indicate that SWAT more precisely reproduces observed mean monthly streamflow while GR4J overestimates it during the dry season and underestimates it during the rainy season. The analysis of the impact of climate change was performed by using climate ensemble simulations derived from the Canadian Global Climate Model (CGCM3) downscaled by the Canadian Regional Climate Model (CRCM). The climate simulations (after bias correction) were used as input data for both hydrological models for two periods: a reference period (1971 – 2000) and a future period (2041-2070). The results indicate a significant decrease in mean monthly streamflow in the Tampaon River Basin for the future period (-36 to -55%), as well as a decrease in maximum monthly streamflow (-34 to -60%) and minimum monthly streamflow (-36 to -49%). The results from this study provide an overall perspective of the potential impact of climate change on the hydrological response of the Tampaon River Basin.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".