Sensitivity Analysis in Hydrological Modeling for the Gulf of México
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".