Sensitivity Analysis in Hydrological Modeling for the Gulf of México
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The progressive change in climatic conditions worldwide have caused an increase in the frequency and severity of extreme weather phenomena (EHP), an example is what happened in recent years (1990-2014) in southeastern Mexico, it has been affected by the presence of the EHP (floods and droughts), leaving substantial economic, social and environmental losses. An alternative to this problem is the use of hydrological simulation models for its possible operation at low cost, but these provide extrapolations or predictions that have some degree of uncertainty, which reduces the applicability and confidence in their results. Thus, the assessment of uncertainty in hydrologic modeling is important, especially when their results are used to support decision-making on the management of water resources. Therefore, the objective of this research is the evaluation of distributed hydrological modeling (HDM) to determine the sensitivity and uncertainty of the rainfall-runoff model using Monte Carlo tool toolbox (MCAT). The main conclusion of this work is the establishment of a strategy sensitivity analysis is needed to accelerate and optimize the calibration process, in the estimation of parameters and to understand the behavior of the model itself to the possible variation of the parameters more representative, which have an intrinsic error in its determination and define the dependencies of these parameters in the model solution.
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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