Simulation of Extreme Hydrometeorological Events under Tropical Conditions Using a Distributed Hydrological Model
Why this work is in the frame
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Bibliographic record
Abstract
Change in climatic conditions worldwide has increased the frequency and severity of extreme hydrometeorological events (EHEs). Mexico is an example of this: the country has been affected by the occurrence of EHEs leading to important economic, social, and environmental losses. The objective of this investigation was to apply a Canadian Distributed Hydrological Model (DHM) to tropical conditions, and to evaluate its capacity to simulate flows in a basin in the central Gulf of Mexico. Additionally, we used this calibrated and validated DHM to predict streamflow before the occurence of an EHEs. The results of the DHM show satisfactory goodness-of-fit indicators between the observed and simulated flows in the calibration process (NSE=0.83, RSR=0.41 and BIAS=-4.3), as well as its validation (NSE=0.775, RSR=0.4735 and BIAS=2.45). The DHM showed its applicability to streamflow simulation and confirmed a reliable efficiency in the modeling of thirteen EHEs (NSE=0.78 ± 0.13, RSR=0.46 ± 0.14, and PBIAS=-0.48 ± 7.5). DHM can serve as a tool to identify vulnerabilities before floods and assist in devising more rational and sustainable management of water resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it