Investigating the Role of Hydrological Model Parameter Uncertainties in Future Streamflow Projections
Bibliographic record
Abstract
Calibrated hydrological models forced with the climate data from various climate models have been widely employed for future streamflow projection. But a major cause of concern in such an analysis has been the suite of uncertainties inherent in the modeling chain that begins from the climate models and ends with the hydrological models. The uncertainties contributed by the hydrological models have generally been given a lesser focus. In the present research, the contribution of the hydrological model parameter uncertainty has been investigated. The multiobjective evolutionary algorithm (MOEA) is employed for calibrating the hydrological model, the Soil and Water Assessment Tool (SWAT), developed for the Magpie River, located in Northern Ontario. The calibrated model was then forced with the data from an ensemble of six regional climate models for projecting the scenario streamflow and evaluating associated uncertainties. A significant variation in seasonal water availability is projected for the two scenario periods studied. The contribution of the hydrological model parameter uncertainty in the streamflow projection is found to be significant, lying in the range of 16%–83%, depending on the month.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".